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a/.doctrees/tutorials/robust-optimization/modeling/two-stage-robust.doctree b/.doctrees/tutorials/robust-optimization/modeling/two-stage-robust.doctree new file mode 100644 index 00000000..5ce19d78 Binary files /dev/null and b/.doctrees/tutorials/robust-optimization/modeling/two-stage-robust.doctree differ diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 00000000..e69de29b diff --git a/_sources/api/classes/branch-and-bound/BranchAndBound.rst.txt b/_sources/api/classes/branch-and-bound/BranchAndBound.rst.txt new file mode 100644 index 00000000..1b7b3d75 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/BranchAndBound.rst.txt @@ -0,0 +1,31 @@ +.. _api_BranchAndBound: + +BranchAndBound +============== + +This class is an optimizer factory which creates a new branch-and-bound algorithm. +It can be used to create customized branch-and-bound algorithms with a large degree of freedom. + +.. seealso:: + + If you are not familiar with optimizers and optimizer factories, please refer to :ref:`this page `. + +.. admonition:: Example + + Here, we create a simple branch-and-bound algorithm where branching is done on integer variables which are being + relaxed. Each node is solved by the external solver GLPK. Nodes are selected according to the "best-bound" + rule while variables are selected according to the "most-infeasible" branching rule. + + The created algorithm also incorporates sub-tree exploration of maximum depth 2. + + .. code-block:: + + model.use( + BranchAndBound() + .with_node_optimizer( GLPK::ContinuousRelaxation() ) + .with_branching_rule( MostInfeasible() ) + .with_node_selection_rule( BestBound() ) + .with_subtree_depth(2) + ); + +.. doxygenclass:: idol::BranchAndBound diff --git a/_sources/api/classes/branch-and-bound/BranchAndBoundCallback.rst.txt b/_sources/api/classes/branch-and-bound/BranchAndBoundCallback.rst.txt new file mode 100644 index 00000000..5432eb88 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/BranchAndBoundCallback.rst.txt @@ -0,0 +1,74 @@ +.. _api_BranchAndBoundCallback: + +BranchAndBoundCallback +====================== + +This class can be used to create callbacks to run on idol's :ref:`branch-and-bound implementation `. + +.. warning:: + + BranchAndBoundCallback-s is an *advanced* feature. + + Please, make sure that what you are trying do cannot be done with + a :ref:`solver-independent callback ` first, or with pre-existing callbacks, e.g., + :ref:`UserCutCallback ` or :ref:`LazyCutCallback `. + + The advantage of using a :ref:`BranchAndBoundCallback ` instead of a standard + solver-independent :ref:`Callback ` lies in the possibility to access specific information regarding + the execution of the branch-and-bound algorithm. For instance, accessing a node's information, or the current + relaxation model being solved. + +.. admonition:: Example + + Here is an example of callback which prints out the event triggering it and, when the event corresponds to `NodeLoaded`, + prints the node's model to be solved. + + .. code-block:: + + class MyCallback : public BranchAndBoundCallbackFactory { + public: + class Strategy : public BranchAndBoundCallback { + protected: + void operator()(CallbackEvent t_event) override { + + std::cout << "MyCallback is called for " << t_event << std::endl; + + if (t_event != NodeLoaded) { + return; + } + + std::cout << "The problem being solve at node " << node().id() << std::endl; + std::cout << relaxation() << std::endl; + + } + }; + + BranchAndBoundCallback *operator()() override { + return new Strategy(); + } + + [[nodiscard]] BranchAndBoundCallbackFactory *clone() const override { + return new MyCallback(*this); + } + }; + + Then, this callback can be used as follows. + + .. code-block:: + + model.use( + BranchAndBound() + .with_node_optimizer(GLPK::ContinuousRelaxation()) + .with_branching_rule(MostInfeasible()) + .with_node_selection_rule(DepthFirst()) + .with_callback(MyCallback()); + ); + +.. hint:: + + By default, most of the objects returned by BranchAndBoundCallback methods are const. If you wish to access non-const + versions (e.g., if you want to perform non-standard updates to the relaxed model or want to change the node's current + information manually), you should use the advanced interface obtained by calling ``BranchAndBoundCallback::advanced_interface``. + +.. doxygenclass:: idol::BranchAndBoundCallback + :protected-members: diff --git a/_sources/api/classes/branch-and-bound/BranchAndBoundCallbackFactory.rst.txt b/_sources/api/classes/branch-and-bound/BranchAndBoundCallbackFactory.rst.txt new file mode 100644 index 00000000..a94a06f9 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/BranchAndBoundCallbackFactory.rst.txt @@ -0,0 +1,5 @@ +BranchAndBoundCallbackFactory +============================= + +.. doxygenclass:: idol::BranchAndBoundCallbackFactory + :protected-members: diff --git a/_sources/api/classes/branch-and-bound/cuts/KnapsackCover.rst.txt b/_sources/api/classes/branch-and-bound/cuts/KnapsackCover.rst.txt new file mode 100644 index 00000000..44d2fb60 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/cuts/KnapsackCover.rst.txt @@ -0,0 +1,4 @@ +Cuts::KnapsackCover +=================== + +.. doxygenclass:: idol::Cuts::KnapsackCover diff --git a/_sources/api/classes/branch-and-bound/cuts/index.rst.txt b/_sources/api/classes/branch-and-bound/cuts/index.rst.txt new file mode 100644 index 00000000..4ff9d8fa --- /dev/null +++ b/_sources/api/classes/branch-and-bound/cuts/index.rst.txt @@ -0,0 +1,10 @@ +Cutting Planes +============== + +.. toctree:: + :maxdepth: 1 + :glob: + + * + + diff --git a/_sources/api/classes/branch-and-bound/heuristics/LocalBranching.rst.txt b/_sources/api/classes/branch-and-bound/heuristics/LocalBranching.rst.txt new file mode 100644 index 00000000..37c62452 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/heuristics/LocalBranching.rst.txt @@ -0,0 +1,4 @@ +Heuristics::LocalBranching +========================== + +.. doxygenclass:: idol::Heuristics::LocalBranching diff --git a/_sources/api/classes/branch-and-bound/heuristics/RENS.rst.txt b/_sources/api/classes/branch-and-bound/heuristics/RENS.rst.txt new file mode 100644 index 00000000..3b951da0 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/heuristics/RENS.rst.txt @@ -0,0 +1,4 @@ +Heuristics::RENS +================ + +.. doxygenclass:: idol::Heuristics::RENS diff --git a/_sources/api/classes/branch-and-bound/heuristics/SimpleRounding.rst.txt b/_sources/api/classes/branch-and-bound/heuristics/SimpleRounding.rst.txt new file mode 100644 index 00000000..0a0e0e74 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/heuristics/SimpleRounding.rst.txt @@ -0,0 +1,4 @@ +Heuristics::SimpleRounding +========================== + +.. doxygenclass:: idol::Heuristics::SimpleRounding diff --git a/_sources/api/classes/branch-and-bound/heuristics/index.rst.txt b/_sources/api/classes/branch-and-bound/heuristics/index.rst.txt new file mode 100644 index 00000000..d59a952f --- /dev/null +++ b/_sources/api/classes/branch-and-bound/heuristics/index.rst.txt @@ -0,0 +1,10 @@ +Heuristics +========== + +.. toctree:: + :maxdepth: 1 + :glob: + + * + + diff --git a/_sources/api/classes/branch-and-bound/index.rst.txt b/_sources/api/classes/branch-and-bound/index.rst.txt new file mode 100644 index 00000000..63035c91 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/index.rst.txt @@ -0,0 +1,12 @@ +Branch-and-Bound Algorithm +========================== + +.. toctree:: + :maxdepth: 2 + :glob: + + * + variable-selection/index + node-selection/index + heuristics/index + cuts/index diff --git a/_sources/api/classes/branch-and-bound/node-selection/BestBound.rst.txt b/_sources/api/classes/branch-and-bound/node-selection/BestBound.rst.txt new file mode 100644 index 00000000..466ba897 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/node-selection/BestBound.rst.txt @@ -0,0 +1,4 @@ +BestBound +========= + +.. doxygenclass:: idol::BestBound diff --git a/_sources/api/classes/branch-and-bound/node-selection/BestEstimate.rst.txt b/_sources/api/classes/branch-and-bound/node-selection/BestEstimate.rst.txt new file mode 100644 index 00000000..7242d970 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/node-selection/BestEstimate.rst.txt @@ -0,0 +1,6 @@ +BestEstimate +============ + +The "best estimate" node selection rule was introduced in *J. J. H. Forrest, J. P. H. Hirst, J. A. Tomlin, (1974) Practical Solution of Large Mixed Integer Programming Problems with Umpire. Management Science 20(5):736-773*. + +.. doxygenclass:: idol::BestEstimate diff --git a/_sources/api/classes/branch-and-bound/node-selection/BreadthFirst.rst.txt b/_sources/api/classes/branch-and-bound/node-selection/BreadthFirst.rst.txt new file mode 100644 index 00000000..f579e0ba --- /dev/null +++ b/_sources/api/classes/branch-and-bound/node-selection/BreadthFirst.rst.txt @@ -0,0 +1,4 @@ +BreadthFirst +============ + +.. doxygenclass:: idol::BreadthFirst diff --git a/_sources/api/classes/branch-and-bound/node-selection/DepthFirst.rst.txt b/_sources/api/classes/branch-and-bound/node-selection/DepthFirst.rst.txt new file mode 100644 index 00000000..31c0f920 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/node-selection/DepthFirst.rst.txt @@ -0,0 +1,4 @@ +DepthFirst +========== + +.. doxygenclass:: idol::DepthFirst diff --git a/_sources/api/classes/branch-and-bound/node-selection/WorstBound.rst.txt b/_sources/api/classes/branch-and-bound/node-selection/WorstBound.rst.txt new file mode 100644 index 00000000..3e009532 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/node-selection/WorstBound.rst.txt @@ -0,0 +1,4 @@ +WorstBound +========== + +.. doxygenclass:: idol::WorstBound diff --git a/_sources/api/classes/branch-and-bound/node-selection/index.rst.txt b/_sources/api/classes/branch-and-bound/node-selection/index.rst.txt new file mode 100644 index 00000000..09ffa958 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/node-selection/index.rst.txt @@ -0,0 +1,10 @@ +Node Selection Rules +==================== + +.. toctree:: + :maxdepth: 1 + :glob: + + * + + diff --git a/_sources/api/classes/branch-and-bound/variable-selection/FirstInfeasibleFound.rst.txt b/_sources/api/classes/branch-and-bound/variable-selection/FirstInfeasibleFound.rst.txt new file mode 100644 index 00000000..1723395c --- /dev/null +++ b/_sources/api/classes/branch-and-bound/variable-selection/FirstInfeasibleFound.rst.txt @@ -0,0 +1,4 @@ +FirstInfeasibleFound +==================== + +.. doxygenclass:: idol::FirstInfeasibleFound diff --git a/_sources/api/classes/branch-and-bound/variable-selection/LeastInfeasible.rst.txt b/_sources/api/classes/branch-and-bound/variable-selection/LeastInfeasible.rst.txt new file mode 100644 index 00000000..84dc1d37 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/variable-selection/LeastInfeasible.rst.txt @@ -0,0 +1,4 @@ +LeastInfeasible +=============== + +.. doxygenclass:: idol::LeastInfeasible diff --git a/_sources/api/classes/branch-and-bound/variable-selection/MostInfeasible.rst.txt b/_sources/api/classes/branch-and-bound/variable-selection/MostInfeasible.rst.txt new file mode 100644 index 00000000..e47c57c9 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/variable-selection/MostInfeasible.rst.txt @@ -0,0 +1,4 @@ +MostInfeasible +============== + +.. doxygenclass:: idol::MostInfeasible diff --git a/_sources/api/classes/branch-and-bound/variable-selection/PseudoCost.rst.txt b/_sources/api/classes/branch-and-bound/variable-selection/PseudoCost.rst.txt new file mode 100644 index 00000000..2ccb7772 --- /dev/null +++ b/_sources/api/classes/branch-and-bound/variable-selection/PseudoCost.rst.txt @@ -0,0 +1,4 @@ +PseudoCost +========== + +.. doxygenclass:: idol::PseudoCost diff --git a/_sources/api/classes/branch-and-bound/variable-selection/StrongBranching.rst.txt b/_sources/api/classes/branch-and-bound/variable-selection/StrongBranching.rst.txt new file mode 100644 index 00000000..8c96f18c --- /dev/null +++ b/_sources/api/classes/branch-and-bound/variable-selection/StrongBranching.rst.txt @@ -0,0 +1,4 @@ +StrongBranching +=============== + +.. doxygenclass:: idol::StrongBranching diff --git a/_sources/api/classes/branch-and-bound/variable-selection/UniformlyRandom.rst.txt b/_sources/api/classes/branch-and-bound/variable-selection/UniformlyRandom.rst.txt new file mode 100644 index 00000000..a34f4a6c --- /dev/null +++ b/_sources/api/classes/branch-and-bound/variable-selection/UniformlyRandom.rst.txt @@ -0,0 +1,4 @@ +UniformlyRandom +=============== + +.. doxygenclass:: idol::UniformlyRandom diff --git a/_sources/api/classes/branch-and-bound/variable-selection/index.rst.txt b/_sources/api/classes/branch-and-bound/variable-selection/index.rst.txt new file mode 100644 index 00000000..3d0a084c --- /dev/null +++ b/_sources/api/classes/branch-and-bound/variable-selection/index.rst.txt @@ -0,0 +1,10 @@ +Branching Rules +=============== + +.. toctree:: + :maxdepth: 1 + :glob: + + * + + diff --git a/_sources/api/classes/column-and-constraint-generation/ColumnAndConstraintGeneration.rst.txt b/_sources/api/classes/column-and-constraint-generation/ColumnAndConstraintGeneration.rst.txt new file mode 100644 index 00000000..afe09bfa --- /dev/null +++ b/_sources/api/classes/column-and-constraint-generation/ColumnAndConstraintGeneration.rst.txt @@ -0,0 +1,6 @@ +.. _api_Robust_ColumnAndConstraintGeneration_doxygen: + +Robust::ColumnAndConstraintGeneration +===================================== + +.. doxygenclass:: idol::Robust::ColumnAndConstraintGeneration diff --git a/_sources/api/classes/column-and-constraint-generation/Optimizers_ColumnAndConstraintGeneration.rst.txt b/_sources/api/classes/column-and-constraint-generation/Optimizers_ColumnAndConstraintGeneration.rst.txt new file mode 100644 index 00000000..b0b7d157 --- /dev/null +++ b/_sources/api/classes/column-and-constraint-generation/Optimizers_ColumnAndConstraintGeneration.rst.txt @@ -0,0 +1,4 @@ +Optimizers::Robust::ColumnAndConstraintGeneration +================================================= + +.. doxygenclass:: idol::Optimizers::Robust::ColumnAndConstraintGeneration diff --git a/_sources/api/classes/column-and-constraint-generation/index.rst.txt b/_sources/api/classes/column-and-constraint-generation/index.rst.txt new file mode 100644 index 00000000..3808c7a1 --- /dev/null +++ b/_sources/api/classes/column-and-constraint-generation/index.rst.txt @@ -0,0 +1,8 @@ +Column-and-Constraint Generation Algorithm +========================================== + +.. toctree:: + :maxdepth: 1 + :glob: + + * diff --git a/_sources/api/classes/column-generation/ArtificialCosts.rst.txt b/_sources/api/classes/column-generation/ArtificialCosts.rst.txt new file mode 100644 index 00000000..6fbeae5c --- /dev/null +++ b/_sources/api/classes/column-generation/ArtificialCosts.rst.txt @@ -0,0 +1,4 @@ +DantzigWolfe::ArtificialCosts +============================= + +.. doxygenclass:: idol::DantzigWolfe::ArtificialCosts diff --git a/_sources/api/classes/column-generation/DantzigWolfeDecomposition.rst.txt b/_sources/api/classes/column-generation/DantzigWolfeDecomposition.rst.txt new file mode 100644 index 00000000..16710386 --- /dev/null +++ b/_sources/api/classes/column-generation/DantzigWolfeDecomposition.rst.txt @@ -0,0 +1,9 @@ +DantzigWolfeDecomposition +========================= + +.. hint:: + + This page documents on the `DantzigWolfeDecomposition` optimizer factory. Note that there are also + :ref:`Dantzig-Wolfe Decomposition tutorials `. + +.. doxygenclass:: idol::DantzigWolfeDecomposition diff --git a/_sources/api/classes/column-generation/FarkasPricing.rst.txt b/_sources/api/classes/column-generation/FarkasPricing.rst.txt new file mode 100644 index 00000000..1de6106c --- /dev/null +++ b/_sources/api/classes/column-generation/FarkasPricing.rst.txt @@ -0,0 +1,4 @@ +DantzigWolfe::FarkasPricing +=========================== + +.. doxygenclass:: idol::DantzigWolfe::FarkasPricing diff --git a/_sources/api/classes/column-generation/IntegerMaster.rst.txt b/_sources/api/classes/column-generation/IntegerMaster.rst.txt new file mode 100644 index 00000000..caa0f997 --- /dev/null +++ b/_sources/api/classes/column-generation/IntegerMaster.rst.txt @@ -0,0 +1,9 @@ +Heuristics::IntegerMaster +========================= + +.. warning:: + + This heuristic can only be used with a :code:`BranchAndBound` optimizer in which nodes are solved by + :code:`DantzigWolfeDecomposition`. + +.. doxygenclass:: idol::Heuristics::IntegerMaster diff --git a/_sources/api/classes/column-generation/Optimizers_DantzigWolfeDecomposition.rst.txt b/_sources/api/classes/column-generation/Optimizers_DantzigWolfeDecomposition.rst.txt new file mode 100644 index 00000000..5fb77aee --- /dev/null +++ b/_sources/api/classes/column-generation/Optimizers_DantzigWolfeDecomposition.rst.txt @@ -0,0 +1,4 @@ +Optimizers::DantzigWolfeDecomposition +===================================== + +.. doxygenclass:: idol::Optimizers::DantzigWolfeDecomposition diff --git a/_sources/api/classes/column-generation/index.rst.txt b/_sources/api/classes/column-generation/index.rst.txt new file mode 100644 index 00000000..dca08d98 --- /dev/null +++ b/_sources/api/classes/column-generation/index.rst.txt @@ -0,0 +1,10 @@ +Column Generation Algorithm +=========================== + +.. toctree:: + :maxdepth: 1 + :glob: + + * + stabilization/index + diff --git a/_sources/api/classes/column-generation/stabilization/Neame.rst.txt b/_sources/api/classes/column-generation/stabilization/Neame.rst.txt new file mode 100644 index 00000000..ed2377a2 --- /dev/null +++ b/_sources/api/classes/column-generation/stabilization/Neame.rst.txt @@ -0,0 +1,4 @@ +DantzigWolfe::Neame +=================== + +.. doxygenclass:: idol::DantzigWolfe::Neame diff --git a/_sources/api/classes/column-generation/stabilization/Wentges.rst.txt b/_sources/api/classes/column-generation/stabilization/Wentges.rst.txt new file mode 100644 index 00000000..1303efa0 --- /dev/null +++ b/_sources/api/classes/column-generation/stabilization/Wentges.rst.txt @@ -0,0 +1,4 @@ +DantzigWolfe::Wentges +===================== + +.. doxygenclass:: idol::DantzigWolfe::Wentges diff --git a/_sources/api/classes/column-generation/stabilization/index.rst.txt b/_sources/api/classes/column-generation/stabilization/index.rst.txt new file mode 100644 index 00000000..25288e1e --- /dev/null +++ b/_sources/api/classes/column-generation/stabilization/index.rst.txt @@ -0,0 +1,8 @@ +Stabilization +============= + +.. toctree:: + :maxdepth: 1 + :glob: + + * diff --git a/_sources/api/classes/common/Annotation.rst.txt b/_sources/api/classes/common/Annotation.rst.txt new file mode 100644 index 00000000..a81f38fd --- /dev/null +++ b/_sources/api/classes/common/Annotation.rst.txt @@ -0,0 +1,27 @@ +Annotation +========== + +Annotations are additional information associated to an optimization object (e.g., a constraint or a variable). +The Annotation class takes two template arguments: an optimization object type and a value type. For instance, we +can create an annotation for constraints of type :code:`unsigned int` as follows. + +.. code:: cpp + + Env env; + + const unsigned int default_value = 0; + + Annotation annotation(env, "my_annotation", default_value); + +Note that annotations are global, i.e., they do not relate to a given optimization model and can, therefore, be accessed +anywhere in the code. Thus, given a constraint called :code:`constraint`, the value of the annotation can be accessed +by calling the :code:`Ctr::get` method. + +.. code:: cpp + + std::cout << "My annotation is " << constraint.get(annotation) << std::endl; // "0", i.e., the default_value value + +Annotations are, in particular, used to give decomposition instructions to idol when designing a Branch-and-Price algorithm. +To learn more, check our `Branch-and-Price tutorials `_. + +.. doxygenclass:: idol::Annotation diff --git a/_sources/api/classes/common/Column.rst.txt b/_sources/api/classes/common/Column.rst.txt new file mode 100644 index 00000000..4f16f907 --- /dev/null +++ b/_sources/api/classes/common/Column.rst.txt @@ -0,0 +1,6 @@ +.. _api_Column: + +Column +====== + +.. doxygenclass:: idol::Column diff --git a/_sources/api/classes/common/Constant.rst.txt b/_sources/api/classes/common/Constant.rst.txt new file mode 100644 index 00000000..910e997b --- /dev/null +++ b/_sources/api/classes/common/Constant.rst.txt @@ -0,0 +1,6 @@ +.. _api_Constant: + +Constant +======== + +.. doxygenclass:: idol::Constant diff --git a/_sources/api/classes/common/Ctr.rst.txt b/_sources/api/classes/common/Ctr.rst.txt new file mode 100644 index 00000000..483b7feb --- /dev/null +++ b/_sources/api/classes/common/Ctr.rst.txt @@ -0,0 +1,6 @@ +.. _api_Ctr: + +Ctr +=== + +.. doxygenclass:: idol::Ctr diff --git a/_sources/api/classes/common/Env.rst.txt b/_sources/api/classes/common/Env.rst.txt new file mode 100644 index 00000000..42199481 --- /dev/null +++ b/_sources/api/classes/common/Env.rst.txt @@ -0,0 +1,6 @@ +.. _api_Env: + +Env +=== + +.. doxygenclass:: idol::Env diff --git a/_sources/api/classes/common/Expr.rst.txt b/_sources/api/classes/common/Expr.rst.txt new file mode 100644 index 00000000..89fb9869 --- /dev/null +++ b/_sources/api/classes/common/Expr.rst.txt @@ -0,0 +1,6 @@ +.. _api_Expr: + +Expr +==== + +.. doxygenclass:: idol::Expr diff --git a/_sources/api/classes/common/LinExpr.rst.txt b/_sources/api/classes/common/LinExpr.rst.txt new file mode 100644 index 00000000..1b8d6163 --- /dev/null +++ b/_sources/api/classes/common/LinExpr.rst.txt @@ -0,0 +1,24 @@ +.. _api_LinExpr: + +LinExpr +======= + +This class is used to represent linear expressions in the modeling interface of idol. + +It consists in a set of linear terms which represent products of keys (typically, variables) and scalars. + +.. admonition:: Example + + In this example, we create a linear expression representing the following linear expression. + + .. math:: + + 3 x_0 + 2 x_1 + + .. code-block:: cpp + + auto x = model.add_vars(Dim<1>(2), 0, Inf, Continuous, "x"); + + LinExpr lin_expr = 3 * x[0] + 2 * x[1]; + +.. doxygenclass:: idol::LinExpr diff --git a/_sources/api/classes/common/Model.rst.txt b/_sources/api/classes/common/Model.rst.txt new file mode 100644 index 00000000..30d36814 --- /dev/null +++ b/_sources/api/classes/common/Model.rst.txt @@ -0,0 +1,37 @@ +.. _api_Model: + +Model +===== + +.. admonition:: Example: + + Here is a short example which builds and solves the following optimization problem. + + .. math:: + + \begin{array}{ll} + \textrm{minimize } & -x_0 - 1.5 x_1 + 3 \\ + \textrm{subject to } & x_0 + 2 x_1 \le 1.5 \\ + & 0 \le x_0 \le 1 \\ + & 0 \le x_1 \le 1 + \end{array} + + .. code-block:: cpp + + Env env; + + Model model(env); + + auto x = model.add_vars(Dim<1>(2), 0, 1, Continuous, "x"); + + auto c = model.add_ctr(x[0] + 2 * x[1] <= 1.5); + + model.set_obj_expr(-x[0] - 1.5 * x[1] + 3); + + model.use(Gurobi()); + + model.optimize(); + + std::cout << "Best obj = " << model.get_best_obj() << std::endl; + +.. doxygenclass:: idol::Model diff --git a/_sources/api/classes/common/Param.rst.txt b/_sources/api/classes/common/Param.rst.txt new file mode 100644 index 00000000..1684f2f3 --- /dev/null +++ b/_sources/api/classes/common/Param.rst.txt @@ -0,0 +1,6 @@ +.. _api_Param: + +Param +===== + +.. doxygenclass:: idol::Param diff --git a/_sources/api/classes/common/QuadExpr.rst.txt b/_sources/api/classes/common/QuadExpr.rst.txt new file mode 100644 index 00000000..0071954b --- /dev/null +++ b/_sources/api/classes/common/QuadExpr.rst.txt @@ -0,0 +1,6 @@ +.. _api_QuadExpr: + +QuadExpr +======== + +.. doxygenclass:: idol::QuadExpr diff --git a/_sources/api/classes/common/Row.rst.txt b/_sources/api/classes/common/Row.rst.txt new file mode 100644 index 00000000..c3f40773 --- /dev/null +++ b/_sources/api/classes/common/Row.rst.txt @@ -0,0 +1,6 @@ +.. _api_Row: + +Row +=== + +.. doxygenclass:: idol::Row diff --git a/_sources/api/classes/common/SolutionDual.rst.txt b/_sources/api/classes/common/SolutionDual.rst.txt new file mode 100644 index 00000000..b9b94f30 --- /dev/null +++ b/_sources/api/classes/common/SolutionDual.rst.txt @@ -0,0 +1,6 @@ +.. _api_Solution_Dual: + +Solution::Dual +============== + +.. doxygenclass:: idol::Solution::Dual diff --git a/_sources/api/classes/common/SolutionPrimal.rst.txt b/_sources/api/classes/common/SolutionPrimal.rst.txt new file mode 100644 index 00000000..76334ab7 --- /dev/null +++ b/_sources/api/classes/common/SolutionPrimal.rst.txt @@ -0,0 +1,6 @@ +.. _api_Solution_Primal: + +Solution::Primal +================ + +.. doxygenclass:: idol::Solution::Primal diff --git a/_sources/api/classes/common/TempCtr.rst.txt b/_sources/api/classes/common/TempCtr.rst.txt new file mode 100644 index 00000000..ee27bcce --- /dev/null +++ b/_sources/api/classes/common/TempCtr.rst.txt @@ -0,0 +1,6 @@ +.. _api_TempCtr: + +TempCtr +======= + +.. doxygenclass:: idol::TempCtr diff --git a/_sources/api/classes/common/TempVar.rst.txt b/_sources/api/classes/common/TempVar.rst.txt new file mode 100644 index 00000000..a7ed4792 --- /dev/null +++ b/_sources/api/classes/common/TempVar.rst.txt @@ -0,0 +1,6 @@ +.. _api_mip_variables_TempVar: + +TempVar +======= + +.. doxygenclass:: idol::TempVar diff --git a/_sources/api/classes/common/Var.rst.txt b/_sources/api/classes/common/Var.rst.txt new file mode 100644 index 00000000..fbc83472 --- /dev/null +++ b/_sources/api/classes/common/Var.rst.txt @@ -0,0 +1,6 @@ +.. _api_Var: + +Var +=== + +.. doxygenclass:: idol::Var diff --git a/_sources/api/classes/common/index.rst.txt b/_sources/api/classes/common/index.rst.txt new file mode 100644 index 00000000..2940c6bf --- /dev/null +++ b/_sources/api/classes/common/index.rst.txt @@ -0,0 +1,12 @@ +.. _classes_common: + +Common +====== + +.. toctree:: + :maxdepth: 1 + :glob: + + * + + diff --git a/_sources/api/classes/external-bilevel/MibS.rst.txt b/_sources/api/classes/external-bilevel/MibS.rst.txt new file mode 100644 index 00000000..8e17661d --- /dev/null +++ b/_sources/api/classes/external-bilevel/MibS.rst.txt @@ -0,0 +1,4 @@ +Bilevel::Mibs +============= + +.. doxygenclass:: idol::Bilevel::MibS diff --git a/_sources/api/classes/external-bilevel/Optimizers_MibS.rst.txt b/_sources/api/classes/external-bilevel/Optimizers_MibS.rst.txt new file mode 100644 index 00000000..286a87fe --- /dev/null +++ b/_sources/api/classes/external-bilevel/Optimizers_MibS.rst.txt @@ -0,0 +1,4 @@ +Optimizers::Bilevel::Mibs +========================= + +.. doxygenclass:: idol::Optimizers::Bilevel::MibS diff --git a/_sources/api/classes/external-bilevel/index.rst.txt b/_sources/api/classes/external-bilevel/index.rst.txt new file mode 100644 index 00000000..b2e2114f --- /dev/null +++ b/_sources/api/classes/external-bilevel/index.rst.txt @@ -0,0 +1,8 @@ +External Bilevel Solvers +======================== + +.. toctree:: + :maxdepth: 1 + :glob: + + * diff --git a/_sources/api/classes/external-mip/GLPK.rst.txt b/_sources/api/classes/external-mip/GLPK.rst.txt new file mode 100644 index 00000000..393b1340 --- /dev/null +++ b/_sources/api/classes/external-mip/GLPK.rst.txt @@ -0,0 +1,6 @@ +.. _api_GLPK: + +GLPK +==== + +.. doxygenclass:: idol::GLPK diff --git a/_sources/api/classes/external-mip/Gurobi.rst.txt b/_sources/api/classes/external-mip/Gurobi.rst.txt new file mode 100644 index 00000000..e6db74d7 --- /dev/null +++ b/_sources/api/classes/external-mip/Gurobi.rst.txt @@ -0,0 +1,6 @@ +.. _api_Gurobi: + +Gurobi +====== + +.. doxygenclass:: idol::Gurobi diff --git a/_sources/api/classes/external-mip/HiGHS.rst.txt b/_sources/api/classes/external-mip/HiGHS.rst.txt new file mode 100644 index 00000000..630ba8b7 --- /dev/null +++ b/_sources/api/classes/external-mip/HiGHS.rst.txt @@ -0,0 +1,4 @@ +HiGHS +===== + +.. doxygenclass:: idol::HiGHS diff --git a/_sources/api/classes/external-mip/Mosek.rst.txt b/_sources/api/classes/external-mip/Mosek.rst.txt new file mode 100644 index 00000000..ce11678d --- /dev/null +++ b/_sources/api/classes/external-mip/Mosek.rst.txt @@ -0,0 +1,4 @@ +Mosek +===== + +.. doxygenclass:: idol::Mosek diff --git a/_sources/api/classes/external-mip/Optimizers_GLPK.rst.txt b/_sources/api/classes/external-mip/Optimizers_GLPK.rst.txt new file mode 100644 index 00000000..9f29e263 --- /dev/null +++ b/_sources/api/classes/external-mip/Optimizers_GLPK.rst.txt @@ -0,0 +1,6 @@ +.. _api_Optimizers_GLPK: + +Optimizers::GLPK +================ + +.. doxygenclass:: idol::Optimizers::GLPK diff --git a/_sources/api/classes/external-mip/Optimizers_Gurobi.rst.txt b/_sources/api/classes/external-mip/Optimizers_Gurobi.rst.txt new file mode 100644 index 00000000..85ba65fa --- /dev/null +++ b/_sources/api/classes/external-mip/Optimizers_Gurobi.rst.txt @@ -0,0 +1,4 @@ +Optimizers::Gurobi +================== + +.. doxygenclass:: idol::Optimizers::Gurobi diff --git a/_sources/api/classes/external-mip/Optimizers_HiGHS.rst.txt b/_sources/api/classes/external-mip/Optimizers_HiGHS.rst.txt new file mode 100644 index 00000000..155705fa --- /dev/null +++ b/_sources/api/classes/external-mip/Optimizers_HiGHS.rst.txt @@ -0,0 +1,4 @@ +Optimizers::HiGHS +================= + +.. doxygenclass:: idol::Optimizers::HiGHS diff --git a/_sources/api/classes/external-mip/Optimizers_Mosek.rst.txt b/_sources/api/classes/external-mip/Optimizers_Mosek.rst.txt new file mode 100644 index 00000000..3dc87036 --- /dev/null +++ b/_sources/api/classes/external-mip/Optimizers_Mosek.rst.txt @@ -0,0 +1,4 @@ +Optimizers::Mosek +================= + +.. doxygenclass:: idol::Optimizers::Mosek diff --git a/_sources/api/classes/external-mip/Osi/OsiCbc.rst.txt b/_sources/api/classes/external-mip/Osi/OsiCbc.rst.txt new file mode 100644 index 00000000..8a98a4c5 --- /dev/null +++ b/_sources/api/classes/external-mip/Osi/OsiCbc.rst.txt @@ -0,0 +1,4 @@ +OsiCbc +====== + +.. doxygenclass:: idol::OsiCbc diff --git a/_sources/api/classes/external-mip/Osi/OsiClp.rst.txt b/_sources/api/classes/external-mip/Osi/OsiClp.rst.txt new file mode 100644 index 00000000..8b924606 --- /dev/null +++ b/_sources/api/classes/external-mip/Osi/OsiClp.rst.txt @@ -0,0 +1,4 @@ +OsiClp +====== + +.. doxygenclass:: idol::OsiClp diff --git a/_sources/api/classes/external-mip/Osi/OsiCplex.rst.txt b/_sources/api/classes/external-mip/Osi/OsiCplex.rst.txt new file mode 100644 index 00000000..927ddf8e --- /dev/null +++ b/_sources/api/classes/external-mip/Osi/OsiCplex.rst.txt @@ -0,0 +1,4 @@ +OsiCplex +======== + +.. doxygenclass:: idol::OsiCplex diff --git a/_sources/api/classes/external-mip/Osi/OsiSymphony.rst.txt b/_sources/api/classes/external-mip/Osi/OsiSymphony.rst.txt new file mode 100644 index 00000000..928f06a5 --- /dev/null +++ b/_sources/api/classes/external-mip/Osi/OsiSymphony.rst.txt @@ -0,0 +1,4 @@ +OsiSymphony +=========== + +.. doxygenclass:: idol::OsiSymphony diff --git a/_sources/api/classes/external-mip/Osi/index.rst.txt b/_sources/api/classes/external-mip/Osi/index.rst.txt new file mode 100644 index 00000000..a4bb869a --- /dev/null +++ b/_sources/api/classes/external-mip/Osi/index.rst.txt @@ -0,0 +1,34 @@ +COIN-OR/Osi (Cplex, Xpress, Cbc, ...) +===================================== + +Idol can be linked with the `coin-or/Osi `_ library (*Open Solver Interface*) to use any solver having an osi interface. + +.. hint:: + + At the moment, only Cplex has been properly tested and can be used as follows. + + .. code-block:: + + model.use(OsiCplex()); + +.. hint:: + + Note that Osi can also be used with *any* Osi object by directly passing the interface as an argument. + + .. code-block:: + + model.use(Osi(OsiCpxSolverInterface())); // same as: model.use(OsiCplex()); + +.. warning:: + + Though idol has dedicated optimizers for Symphony, Cbc and Clp, it seems that the osi interface for these solvers do not + fully fulfill the interface requirement to have a stable usage. + +.. toctree:: + :maxdepth: 1 + :glob: + + * + +.. doxygenclass:: idol::Osi + diff --git a/_sources/api/classes/external-mip/index.rst.txt b/_sources/api/classes/external-mip/index.rst.txt new file mode 100644 index 00000000..6fb86245 --- /dev/null +++ b/_sources/api/classes/external-mip/index.rst.txt @@ -0,0 +1,9 @@ +External MIP solvers +==================== + +.. toctree:: + :maxdepth: 2 + :glob: + + * + Osi/index diff --git a/_sources/api/classes/index.rst.txt b/_sources/api/classes/index.rst.txt new file mode 100644 index 00000000..45ca55f1 --- /dev/null +++ b/_sources/api/classes/index.rst.txt @@ -0,0 +1,36 @@ +.. _classes: + +Class List +========== + +Mixed-Integer Programming +^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 2 + :glob: + + common/index + external-mip/index + universal-callbacks/index + branch-and-bound/index + column-generation/index + +Bilevel Optimization +^^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 2 + :glob: + + external-bilevel/index + +Robust Optimization +^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 2 + :glob: + + column-and-constraint-generation/index + diff --git a/_sources/api/classes/universal-callbacks/Callback.rst.txt b/_sources/api/classes/universal-callbacks/Callback.rst.txt new file mode 100644 index 00000000..63c00d3a --- /dev/null +++ b/_sources/api/classes/universal-callbacks/Callback.rst.txt @@ -0,0 +1,85 @@ +.. _api_Callback: + +Callback +======== + +Callbacks are user-defined functions which are invoked during the execution of branch-and-bound algorithms at certain +key steps of the procedure. These key points are called :ref:`events ` in idol. +Callback functions can be used to monitor the algorithm's progress, control its behavior, or perform additional processing. + +UserCutCallback and LazyCutCallback +----------------------------------- + +A common use of callbacks is to implement custom cut generation procedures at each node of the branch-and-bound tree. Note that +idol offers out-of-the-box callback routines for cut separation. Namely, :ref:`UserCutCallback ` +and :ref:`LazyCutCallback `. Yet, it is totally possible to define your own cut generation procedure +by using the ``Callback::add_user_cut`` and ``Callback::add_lazy_cut`` methods inside your callback. + +In a nutshell, :ref:`UserCutCallback ` (or, similarly, ``Callback::add_user_cut``) are intended for +cuts which have a potential to improve the quality of the relaxation but whose presence in the model is not necessary for the +branch-and-bound to converge to an optimal solution. :ref:`UserCutCallback ` callbacks are called when +a non-valid solution has been found. + +:ref:`LazyCutCallback ` (or, similarly, ``Callback::add_lazy_cut``), +instead, are intended to generate constraints which should be part of the model but which were omitted in the model for some reason. +A typical reason for omitting constraints in a model's definition is the large number of such constraints. +:ref:`LazyCutCallback ` callbacks are called when a valid solution has been found. + +Creating your own callback +-------------------------- + +If you want to create your own callback, you should first create a new class which inherits the ``Callback`` class and +overrides the ``operator()(CallbackEvent)`` method. + +Note that callbacks cannot be given "as-is" to an optimizer but must be passed through a ``CallbackFactory``. A callback +factory is a class whose role is to create a new callback object whenever it is needed. Every callback factories must be +a child (or little child) of the ``CallbackFactory`` class. + +.. admonition:: Example + + We now give an example of callback which prints out every valid solution found. + + .. code-block:: + + class MyCallback : public CallbackFactory { + public: + + class Strategy { // Real callback implementation + protected: + void operator()(CallbackEvent t_event) { + + if (t_event != IncumbentSolution) { + return; + } + + std::cout << primal_solution() << std::endl; + } + } + + Callback* operator()() { // Creates a new callback object + return new Strategy(); + } + + CallbackFactory* clone() const { // Creates a copy of the callback factory + return new MyCallback(*this); + } + + } + + Later, it is then possible to use this callback in compliant optimizers like :ref:`Gurobi ` or :ref:`BranchAndBound `. + + .. code-block:: + + model.use( + Gurobi().with_callback(MyCallback(); + ); + + model.optimize(); + +.. hint:: + + Note that there also exists more advanced callbacks which are specific to idol's branch-and-bound implementation. + For these advanced feature, see :ref:`BranchAndBoundCallback `. + +.. doxygenclass:: idol::Callback + :protected-members: diff --git a/_sources/api/classes/universal-callbacks/CallbackFactory.rst.txt b/_sources/api/classes/universal-callbacks/CallbackFactory.rst.txt new file mode 100644 index 00000000..e795fb1d --- /dev/null +++ b/_sources/api/classes/universal-callbacks/CallbackFactory.rst.txt @@ -0,0 +1,7 @@ +.. _api_CallbackFactory: + +CallbackFactory +=============== + +.. doxygenclass:: idol::CallbackFactory + :protected-members: diff --git a/_sources/api/classes/universal-callbacks/LazyCutCallback.rst.txt b/_sources/api/classes/universal-callbacks/LazyCutCallback.rst.txt new file mode 100644 index 00000000..ab484275 --- /dev/null +++ b/_sources/api/classes/universal-callbacks/LazyCutCallback.rst.txt @@ -0,0 +1,60 @@ +.. _api_LazyCutCallback: + +LazyCutCallback +=============== + +Lazy cuts are essentially constraints which are part of an optimization model but which have been omitted in the current +definition of the model. Lazy cuts typically arise in models with a large number of constraints. Instead of enumerating +then all, it may be judicious to omit some of them to get a smaller optimization model. Then, the returned solution is +checked for feasibility against the whole set of constraints. If a violated constraint is identified, we add the +constraint to the model and resolve. If not, then all the constraints are satisfied and the solution is optimal for the +original model in which all constraints materializes. + +The LazyCutCallback can be used to implement lazy cut constraint generation on the fly, during the optimization process. + +Consider the following optimization problem: + +.. math:: + + \begin{align} + \min_x \ & c^\top x \\ + \text{s.t.} \ & x\in X, \\ + & \xi^\top x \le \xi_0 \quad (\xi_0,\xi) \in \Xi, + \end{align} + +in which assume that :math:`|\Xi|` is large (potentially infinite). The idea is to start by solving the following +relaxed master problem in which constraints associated to :math:`(\xi_0,\xi)` have been omitted. + +.. math:: + + \begin{align} + \min_x \ & c^\top x \\ + \text{s.t.} \ & x\in X. + \end{align} + +If we assume that this problem is feasible and bounded, we can denote by :math:`x^*` a solution of this problem. Then, +we search for a violated constraint ":math:`\xi^\top x \le \xi_0`" for some :math:`(\xi_0,\xi)\in\Xi`. Observe that +a constraint is violated if, and only if, the following holds: + +.. math:: + + \left(\exists(\xi_0,\xi)\in\Xi, \ \xi^\top x^* > \xi_0\right) + \Leftrightarrow + \left(\max_{(\xi_0,\xi)\in\Xi} \xi_0 - \xi^\top x^* < 0\right). + +Thus, the LazyCutCallback automatically solve the optimization problem on the right handside and checks for its value. +A new constraint is added if, and only if, + +.. math:: + \max_{\xi\in\Xi} \xi_0 - \xi^\top x^* < -\varepsilon, + +with :math:`\varepsilon` a given tolerance (by default, :code:`Tolerance::Feasibility`). + + +.. hint:: + + You may also be interested by a tutorial showing how you can implement a simple Benders Decomposition using lazy + cuts. :ref:`See Benders Decomposition tutorial `. + + +.. doxygenclass:: idol::LazyCutCallback \ No newline at end of file diff --git a/_sources/api/classes/universal-callbacks/UserCutCallback.rst.txt b/_sources/api/classes/universal-callbacks/UserCutCallback.rst.txt new file mode 100644 index 00000000..cf10e6e1 --- /dev/null +++ b/_sources/api/classes/universal-callbacks/UserCutCallback.rst.txt @@ -0,0 +1,6 @@ +.. _api_UserCutCallback: + +UserCutCallback +=============== + +.. doxygenclass:: idol::UserCutCallback \ No newline at end of file diff --git a/_sources/api/classes/universal-callbacks/index.rst.txt b/_sources/api/classes/universal-callbacks/index.rst.txt new file mode 100644 index 00000000..e97ddd7e --- /dev/null +++ b/_sources/api/classes/universal-callbacks/index.rst.txt @@ -0,0 +1,8 @@ +Universal Callbacks +=================== + +.. toctree:: + :maxdepth: 1 + :glob: + + * diff --git a/_sources/api/constants/index.rst.txt b/_sources/api/constants/index.rst.txt new file mode 100644 index 00000000..b949e0b4 --- /dev/null +++ b/_sources/api/constants/index.rst.txt @@ -0,0 +1,15 @@ +.. _constants: + +Constants +========= + +Common +^^^^^^ + +.. toctree:: + :maxdepth: 1 + :glob: + + tolerances + + diff --git a/_sources/api/constants/tolerances.rst.txt b/_sources/api/constants/tolerances.rst.txt new file mode 100644 index 00000000..d25b2cef --- /dev/null +++ b/_sources/api/constants/tolerances.rst.txt @@ -0,0 +1,6 @@ +.. _api_tolerances: + +Tolerances and Numerics +======================= + +.. doxygenfile:: numericals.h \ No newline at end of file diff --git a/_sources/api/index.rst.txt b/_sources/api/index.rst.txt new file mode 100644 index 00000000..8676a6db --- /dev/null +++ b/_sources/api/index.rst.txt @@ -0,0 +1,12 @@ +.. _api: + +API +=== + +.. toctree:: + :maxdepth: 1 + :glob: + + classes/index + types/index + constants/index diff --git a/_sources/api/types/common/CtrType.rst.txt b/_sources/api/types/common/CtrType.rst.txt new file mode 100644 index 00000000..ab331e89 --- /dev/null +++ b/_sources/api/types/common/CtrType.rst.txt @@ -0,0 +1,6 @@ +.. _api_CtrType: + +CtrType +======= + +.. doxygenenum:: idol::CtrType diff --git a/_sources/api/types/common/VarType.rst.txt b/_sources/api/types/common/VarType.rst.txt new file mode 100644 index 00000000..75c7d75e --- /dev/null +++ b/_sources/api/types/common/VarType.rst.txt @@ -0,0 +1,6 @@ +.. _api_variables_VarType: + +VarType +======= + +.. doxygenenum:: idol::VarType diff --git a/_sources/api/types/common/index.rst.txt b/_sources/api/types/common/index.rst.txt new file mode 100644 index 00000000..8e7da97b --- /dev/null +++ b/_sources/api/types/common/index.rst.txt @@ -0,0 +1,9 @@ +Common +====== + +.. toctree:: + :maxdepth: 1 + :glob: + + * + diff --git a/_sources/api/types/index.rst.txt b/_sources/api/types/index.rst.txt new file mode 100644 index 00000000..64cc0da1 --- /dev/null +++ b/_sources/api/types/index.rst.txt @@ -0,0 +1,16 @@ +.. _types: + +Type List +========= + +Mixed-Integer Programming +^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 2 + :glob: + + common/index + universal-callbacks/index + + diff --git a/_sources/api/types/universal-callbacks/CallbackEvent.rst.txt b/_sources/api/types/universal-callbacks/CallbackEvent.rst.txt new file mode 100644 index 00000000..823233be --- /dev/null +++ b/_sources/api/types/universal-callbacks/CallbackEvent.rst.txt @@ -0,0 +1,6 @@ +.. _api_CallbackEvent: + +Events +====== + +.. doxygenenum:: idol::CallbackEvent \ No newline at end of file diff --git a/_sources/api/types/universal-callbacks/index.rst.txt b/_sources/api/types/universal-callbacks/index.rst.txt new file mode 100644 index 00000000..f04d73b4 --- /dev/null +++ b/_sources/api/types/universal-callbacks/index.rst.txt @@ -0,0 +1,9 @@ +Universal Callbacks +=================== + +.. toctree:: + :maxdepth: 1 + :glob: + + * + diff --git a/_sources/benchmark/index.rst.txt b/_sources/benchmark/index.rst.txt new file mode 100644 index 00000000..827cf506 --- /dev/null +++ b/_sources/benchmark/index.rst.txt @@ -0,0 +1,13 @@ +.. _benchmark: + +Benchmark +========= + + +* A benchmark for the **Branch-and-Price** implementation is available for the `Generalized Assignment Problem `_. +* A benchmark for the **Branch-and-Bound** implementation is available for the `Knapsack Problem `_ + +.. image:: https://raw.githubusercontent.com/hlefebvr/idol-benchmark-gap/gh-pages/profile.png + +This is a performance profile computed according to :cite:`Dolan2002`. + diff --git a/_sources/developer_guide/build-the-doc.rst.txt b/_sources/developer_guide/build-the-doc.rst.txt new file mode 100644 index 00000000..4d331aef --- /dev/null +++ b/_sources/developer_guide/build-the-doc.rst.txt @@ -0,0 +1,61 @@ +.. highlight:: sh +.. _dev_build_the_doc: + +Build the doc! +============== + +This page explains how to build the documentation on a local machine. + +Dependencies +------------ + +Description +^^^^^^^^^^^ + +The documentation is built using `doxygen`_ and a bunch of other tools, in particular: + +* `doxygen`_ the C++ documentation generator; +* `Sphinx`_ the python documentation generator; +* `sphinx-rtd-theme `_ a Sphinx theme used to render the + HTML version of the documentation; +* `sphinx-sitemap `_ a Sphinx extension + to generate multi-version and multi-language sitemaps.org compliant sitemaps for the HTML version + of a Sphinx documentation; +* `sphinx-copybutton `_ a Sphinx extension which + automatically adds buttons on code-blocks environments to copy its content; +* `Breathe`_ the Sphinx plugin for integrating doxygen outputs into Sphinx. + +For a detailed description on how these tools interact, please refer to `this article `_. + +Installation guide (Ubuntu) +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +:: + + sudo apt-get install doxygen # installs the last version of doxygen + + sudo apt-get install python3-sphinx # installs the last version of Sphinx + + sudo apt-get install python3-breathe # installs the last version of Breathe + + pip3 install sphinx-sitemap sphinx-rtd-theme sphinx-copybutton # installs theme and extensions + +Build instructions +------------------ + +By default, CMake automatically creates a ``doc`` target. All you have to do to build the documentation website +is to build this target. For instance, inside a ``build`` directory, the following command can be used. + +:: + + cmake .. && make doc + +Note that you do not need to use ``make`` and use the following generic command. + +:: + + cmake --build . --target doc + +.. _doxygen: https://doxygen.nl/ +.. _Sphinx: https://www.sphinx-doc.org/en/master/ +.. _Breathe: https://github.com/breathe-doc/breathe \ No newline at end of file diff --git a/_sources/developer_guide/index.rst.txt b/_sources/developer_guide/index.rst.txt new file mode 100644 index 00000000..e222b3ee --- /dev/null +++ b/_sources/developer_guide/index.rst.txt @@ -0,0 +1,11 @@ +.. _dev: + +Developer guide +=============== + +.. toctree:: + :maxdepth: 2 + :glob: + + build-the-doc + tests \ No newline at end of file diff --git a/_sources/developer_guide/tests.rst.txt b/_sources/developer_guide/tests.rst.txt new file mode 100644 index 00000000..bd9cfb10 --- /dev/null +++ b/_sources/developer_guide/tests.rst.txt @@ -0,0 +1,91 @@ +.. _tests: + +Unit and integration tests +========================== + +This page explains how to build the unit and integration tests on a local machine. + +Dependencies +------------ + +Tests are managed by the C++ unit testing framework `Catch2 `_. + +When CMake is told to create test targets, it will automatically look for an installation of Catch2 on your +machine. If it cannot be found, CMake will attempt to download Catch2 from its GitHub repository. + + +Installation guide (Ubuntu) +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: shell + + git clone https://github.com/catchorg/Catch2.git # Clone Catch2 repository + + cd Catch2 # go inside the new directory + + cmake -Bbuild -H. -DBUILD_TESTING=OFF # Create install target + + sudo cmake --build build/ --target install # Build and install + + +Build instructions +------------------ + +By default, CMake will not create test targets nor look for Catch2. To tell CMake to generate them, you +need to set the CMake option ``BUILD_TESTS`` to ``YES``. + + +.. admonition:: Example + + The following will tell CMake to create targets for tests. + + .. code-block:: + + cmake -DBUILD_TESTS=YES .. + + For instance, we can now build all the existing tests + + .. code-block:: + + make tests + + and run them. + + .. code-block:: + + ./tests/tests + +Code coverage options +--------------------- + +Code coverage is done via ``gcov``. + +If you want to active code coverage options, you shall set the CMake option ``WITH_TEST_COVERAGE`` to ``YES``. +Note that this only has an effect when ``BUILD_TESTS`` is also set to ``YES``. + +.. admonition:: Example + + The following command will tell CMake to generate targets for tests with code coverage options. + + .. code-block:: + + cmake -DBUILD_TESTS=YES -DWITH_CODE_COVERAGE=YES .. + + Then, you should build and run the tests. + + .. code-block:: + + make tests && ./tests/tests + + Once you are there, you may generate a report to a file called ``coverage.info`` by using ``gcov``. + + .. code-block:: + + lcov --directory . --capture --output-file coverage.info + +Also note that this forces ``g++`` to be used for compiling. + +.. tip:: + + Code coverage reports are also automatically generated at every push action on idol's GitHub repository and + are available on `codecov `_. diff --git a/_sources/examples/bilevel-kkt.rst.txt b/_sources/examples/bilevel-kkt.rst.txt new file mode 100644 index 00000000..81e9426b --- /dev/null +++ b/_sources/examples/bilevel-kkt.rst.txt @@ -0,0 +1,35 @@ +.. _example_bilevel_kkt: + +Bilevel LP-LP (KKT Reformulation) +================================= + +Problem Definition +------------------ + +This example is taken from :cite:`Kleinert2021` and is an LP-LP bilevel problem. + +The problem is formulated as follows: + +.. math:: + + \begin{align} + \min_{x, y} \ & x + 6 y \\ + \text{s.t.} \ & -x + 5y \le 12.5 \\ + & x \ge 0 \\ + & y\in + \begin{array}[t]{l} + \displaystyle \underset{y}{\text{arg min}} \ & -y \\ + \text{s.t.} \ & 2 x - y \ge 0, \\ + & -x - y \ge -6, \\ + & -x + 6 y \ge -3, \\ + & x + 3 y \ge 3. + \end{array} + \end{align} + +KKT Reformulation with idol +--------------------------- + +In this example, we show how to model this LP-LP bilevel problem and how to derive its KKT reformulation. + +.. literalinclude:: ../../examples/bilevel-optimization/kkt.example.cpp + :language: cpp diff --git a/_sources/examples/bilevel-mibs.rst.txt b/_sources/examples/bilevel-mibs.rst.txt new file mode 100644 index 00000000..231c796a --- /dev/null +++ b/_sources/examples/bilevel-mibs.rst.txt @@ -0,0 +1,36 @@ +.. _example_mibs: + +Bilevel MILP-MILP (MibS) +======================== + +Problem Definition +------------------ + +This example is taken from :cite:`Moore1990` and is a bilevel problem where the upper level is a mixed-integer linear program (MILP) and the lower level is a mixed-integer linear program (MILP). + +The problem is formulated as follows: + +.. math:: + + \begin{align} + \min_{x, y} \ & -x + -10 y \\ + \text{s.t.} \ & x \in \mathbb Z_+ \\ + & y\in + \begin{array}[t]{l} + \displaystyle \underset{y}{\text{arg min}} \ & y \\ + \text{s.t.} \ & -25 x + 20 y \leq 30, \\ + & x + 2 y \leq 10, \\ + & 2 x - y \leq 15, \\ + & 2 x + 10 y \geq 15, \\ + & y \geq 0, \\ + & y \in \mathbb Z_+. + \end{array} + \end{align} + +Implementation with idol +------------------------ + +In this example, we show how to model this MILP-MILP bilevel problem and how to solve it using the MibS solver. + +.. literalinclude:: ../../examples/bilevel-optimization/mibs.example.cpp + :language: cpp diff --git a/_sources/examples/facility-location-problem.rst.txt b/_sources/examples/facility-location-problem.rst.txt new file mode 100644 index 00000000..c81328bc --- /dev/null +++ b/_sources/examples/facility-location-problem.rst.txt @@ -0,0 +1,37 @@ +.. _example_flp: + +Facility Location Problem (Branch-and-Bound) +============================================ + +Problem Definition +------------------ + +We consider the capacitated Facility Location Problem (FLP). +Given a set of potential facility locations :math:`V_1` and a set of customers :math:`V_2`, the goal is to select +a subset of facility location to activate in order to serve all customers' demand, while minimizing the total cost. + +Each facility :math:`i\in V_1` has an opening cost :math:`f_i` and a maximum capacity :math:`q_i`. +Each customer :math:`j\in V_2` has a demand :math:`d_j`. +The unitary cost for serving customer :math:`j\in V_2` from facility :math:`i\in V_1` is :math:`t_{ij}`. + +We model the capacitated FLP with the MILP + +.. math:: + + \begin{align*} + \min_{x,y} \ & \sum_{i\in V_1} f_i x_i + \sum_{i\in V_1} \sum_{j\in V_2} t_{ij} y_{ij} \\ + \text{s.t.} & \sum_{j\in V_2} d_j y_{ij} \le q_i && i\in V_1 \\ + & \sum_{i\in V_1} y_{ij} = 1 && j\in V_2 \\ + & y_{ij} \le x_i && i\in V_1, j\in V_2 \\ + & x_i \in \{0,1\} && i\in V_1 \\ + & y_{ij} \in \{0,1\} && i\in V_1, j\in V_2. + \end{align*} + +Implementation with idol +------------------------ + +In this example, we show how to model the capacitated FLP with idol and how to solve it using a "hand-crafted" +branch-and-bound algorithm. + +.. literalinclude:: ../../examples/mixed-integer-optimization/facility.example.cpp + :language: cpp diff --git a/_sources/examples/generalized-assignment-problem-bap.rst.txt b/_sources/examples/generalized-assignment-problem-bap.rst.txt new file mode 100644 index 00000000..ab30e54e --- /dev/null +++ b/_sources/examples/generalized-assignment-problem-bap.rst.txt @@ -0,0 +1,43 @@ +.. _example_gap_bap: + +Generalized Assignment Problem (Branch-and-Price) +================================================= + +Problem Definition +------------------ + +We consider the Generalized Assignment Problem (GAP). +Given a set of :math:`m` agents and :math:`n` jobs, the goal is to assign each job to exactly one agent in such a +way that the total cost is minimized, while respecting the capacity constraints of each agent. + +Each agent :math:`i\in\{1,\dotsc,m\}` has a capacity :math:`C_i`. +Each job :math:`j\in\{1,\dotsc,n\}` has a resource consumption :math:`r_{ij}` and a cost :math:`c_{ij}` when assigned to agent :math:`i`. + +We model the GAP with the following binary linear program: + +.. math:: + + \begin{align*} + \min_{x} \ & \sum_{i=1}^m \sum_{j=1}^n c_{ij} x_{ij} \\ + \text{s.t.} & \sum_{j=1}^n r_{ij} x_{ij} \le C_i && i=1,\dotsc,m \\ + & \sum_{i=1}^m x_{ij} = 1 && j=1,\dotsc,n \\ + & x_{ij} \in \{0,1\} && i=1,\dotsc,m, j=1,\dotsc,n. + \end{align*} + +Decomposition +------------- + +In this example, we use Dantzig-Wolfe decomposition to break down the problem into a master problem and subproblems. The master problem coordinates the assignment of jobs to agents, while the subproblems handle the capacity constraints for each agent individually. + +1. **Master Problem:** The master problem is responsible for ensuring that each job is assigned to exactly one agent. It maintains the overall objective of minimizing the total cost. + +2. **Subproblems:** Each subproblem corresponds to an agent and ensures that the agent's capacity constraints are respected. The subproblems are solved independently and their solutions are used to update the master problem. + +Implementation with idol +------------------------ + +In this example, we show how to model the Generalized Assignment Problem with idol and how to solve it using a +Dantzig-Wolfe decomposition within a branch-and-bound framework, i.e., a branch-and-price algorithm. + +.. literalinclude:: ../../examples/mixed-integer-optimization/assignment-bap.example.cpp + :language: cpp diff --git a/_sources/examples/generalized-assignment-problem-penalty-bap.rst.txt b/_sources/examples/generalized-assignment-problem-penalty-bap.rst.txt new file mode 100644 index 00000000..7f48201a --- /dev/null +++ b/_sources/examples/generalized-assignment-problem-penalty-bap.rst.txt @@ -0,0 +1,45 @@ +Generalized Assignment Problem (Column Generation + Penalty Method) +=================================================================== + +Problem Definition +------------------ + +We consider the Generalized Assignment Problem (GAP) already studied in the :ref:`Branch-and-Price example `. + +In this example, we solve the continuous relaxation of the GAP using a penalty method and column generation. + +First, we consider the continuous relaxation of the GAP: + +.. math:: + + \begin{align*} + \min_{x} \ & \sum_{i=1}^m \sum_{j=1}^n c_{ij} x_{ij} \\ + \text{s.t.} & \sum_{j=1}^n r_{ij} x_{ij} \le C_i && i=1,\dotsc,m \\ + & \sum_{i=1}^m x_{ij} = 1 && j=1,\dotsc,n \\ + & x_{ij} \in [0,1] && i=1,\dotsc,m, j=1,\dotsc,n. + \end{align*} + +Then, we introduce penalty parameters :math:`\rho_j` to penalize the constraints :math:`\sum_{i=1}^m x_{ij} = 1` in the objective function. + +.. math:: + + \begin{align*} + \min_{x} \ & \sum_{i=1}^m \sum_{j=1}^n c_{ij} x_{ij} + \sum_{j=1}^n \rho_j \left| \sum_{i=1}^m x_{ij} - 1 \right| \\ + \text{s.t.} & \sum_{j=1}^n r_{ij} x_{ij} \le C_i && i=1,\dotsc,m \\ + & x_{ij} \in [0,1] && i=1,\dotsc,m, j=1,\dotsc,n. + \end{align*} + +Throughout the optimization process, we update the penalty parameters :math:`\rho_j` iteratively to enforce the feasibility of the solution. + +Finally, we solve the continuous relaxation of the GAP using column generation to generate new columns (variables) and improve the objective function. +That is, we iteratively solve the master problem and the subproblems to generate new columns and update the master problem. + + +Implementation with idol +------------------------ + +In this example, we show how to model the Generalized Assignment Problem with idol and how to solve it using a +Dantzig-Wolfe decomposition within a branch-and-bound framework, i.e., a branch-and-price algorithm. + +.. literalinclude:: ../../examples/mixed-integer-optimization/assignment-penalty-bap.example.cpp + :language: cpp diff --git a/_sources/examples/index.rst.txt b/_sources/examples/index.rst.txt new file mode 100644 index 00000000..edc1c345 --- /dev/null +++ b/_sources/examples/index.rst.txt @@ -0,0 +1,50 @@ +.. _examples: + +Examples +======== + +Mixed-Integer Programming +^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + + knapsack-problem + +Branch-and-Bound +^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + + facility-location-problem + +Column Generation and Branch-and-Price +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + + generalized-assignment-problem-bap + generalized-assignment-problem-penalty-bap + +Bilevel Optimization +^^^^^^^^^^^^^^^^^^^^ + + +.. toctree:: + :maxdepth: 1 + + bilevel-kkt + bilevel-mibs + + +Robust Optimization +^^^^^^^^^^^^^^^^^^^ + + +.. toctree:: + :maxdepth: 1 + + two-stage-robust-facility-location-problem + diff --git a/_sources/examples/knapsack-problem.rst.txt b/_sources/examples/knapsack-problem.rst.txt new file mode 100644 index 00000000..bc54d339 --- /dev/null +++ b/_sources/examples/knapsack-problem.rst.txt @@ -0,0 +1,30 @@ +Knapsack Problem (HiGHS) +========================= + +Problem Definition +------------------ + +We consider the Knapsack Problem (KP). +Given a set of :math:`n` items, each of which having a weight and a profit, the goal is to +select of subset of items such that the total weight does not exceed a given capacity and the total profit is maximized. + +For each item :math:`j\in\{1,\dotsc,n\}`, we denote its weight by :math:`w_j` and its profit by :math:`p_j`. +The maximum capacity of the knapsack is :math:`C`. + +We model the KP with the following binary linear program: + +.. math:: + + \begin{align*} + \max_{x} \ & \sum_{j=1}^n p_j x_j \\ + \text{s.t.} & \sum_{j=1}^n w_j x_j \le C \\ + & x_j \in \{0,1\} && j=1,\dotsc,n. + \end{align*} + +Implementation with idol +------------------------ + +In this example, we show how to model the Knapsack Problem with idol and how to solve it using the HiGHS solver. + +.. literalinclude:: ../../examples/mixed-integer-optimization/knapsack.example.cpp + :language: cpp diff --git a/_sources/examples/two-stage-robust-facility-location-problem.rst.txt b/_sources/examples/two-stage-robust-facility-location-problem.rst.txt new file mode 100644 index 00000000..508ee29d --- /dev/null +++ b/_sources/examples/two-stage-robust-facility-location-problem.rst.txt @@ -0,0 +1,52 @@ +.. _example_robust_flp_ccg: + +Two-Stage Robust Facility Location Problem (CCG) +================================================ + +Problem Description +------------------- + +We consider a robust version of the capacitated Facility Location Problem (FLP). +Given a set of potential facility locations :math:`V_1` and a set of customers :math:`V_2`, the goal is to select a subset of facility locations +to activate in order to serve all customers' demand, while minimizing the total cost. +This version introduces uncertainty in the customers' demands. + +Note that there is also an example for the :ref:`deterministic version of the FLP using Column Generation `. + +Each facility :math:`i\in V_1` has an opening cost :math:`f_i` and a maximum capacity :math:`q_i`. +Each customer :math:`j\in V_2` has a demand :math:`d_j`. +The unitary cost for serving customer :math:`j\in V_2` from facility :math:`i\in V_1` is :math:`t_{ij}`. +The uncertainty in customer demands is controlled by a parameter :math:`\Gamma`. + +In this robust variant, we consider that the demands are uncertain and can be expressed as :math:`d_j(\xi) = d_j(1 + p\xi_j)` +with :math:`p` being the maximum increase in demand and :math:`\xi` being an unknown vector taken in the uncertainty set + +.. math:: + + \Xi := \left\{ \xi\in[ 0, 1 ]^{|V_2|} : \sum_{j\in V_2} \xi_j \le \Gamma \right\}. + +We model the two-stage robust FLP as + +.. math:: + + \min_{x\in \{0,1\}^{|V_1|}} \ \left\{ \sum_{i\in V_1} f_i x_i + \max_{\xi\in \Xi} \ \min_{y\in Y(x,\xi)} \ \sum_{i\in V_1} \sum_{j\in V_2} t_{ij} y_{ij} \right\} + +where :math:`Y(x,\xi)` is the set of feasible solutions for the second stage problem, given the first stage solution :math:`x` and the realization :math:`\xi` of the uncertain demand vector. +It is defined as the set of vectors :math:`y\in \mathbb{R}^{|V_1|\times|V_2|}` that satisfy the following constraints + +.. math:: + + \begin{align*} + & \sum_{i\in V_1} y_{ij} = d_j(\xi) && j\in V_2, \\ + & \sum_{j\in V_2} y_{ij} \le q_i x_i && i\in V_1, \\ + & y_{ij} \ge 0 && i\in V_1, j\in V_2. + \end{align*} + +Implementation with idol +------------------------ + +We now show how to implement the two-stage robust FLP with idol and how to solve it using a CCG algorithm. +Here, the "adversarial problem" is solved by calling the bilevel solver MibS. + +.. literalinclude:: ../../examples/robust-optimization/robust_ccg.example.cpp + :language: cpp diff --git a/_sources/faq/index.rst.txt b/_sources/faq/index.rst.txt new file mode 100644 index 00000000..2c92ff37 --- /dev/null +++ b/_sources/faq/index.rst.txt @@ -0,0 +1,10 @@ +.. _faq: + +FAQ +--- + +.. toctree:: + :maxdepth: 1 + :glob: + + * diff --git a/_sources/faq/mosek-and-eigen.rst.txt b/_sources/faq/mosek-and-eigen.rst.txt new file mode 100644 index 00000000..f54b1354 --- /dev/null +++ b/_sources/faq/mosek-and-eigen.rst.txt @@ -0,0 +1,83 @@ +.. _mosek_and_socp: + +Why is Eigen necessary to solve QPs and QCQPs with Mosek? +========================================================= + +In Short +-------- + +The interface of idol is based on quadratic expressions like + +.. math:: + + \sum_{j=1}^n a_{ij}x_j + \sum_{j=1}^n\sum_{k=1}^n q_{jk}^ix_jx_k \le b_i. + +The C++ interface of Mosek, instead, is based on "conic expressions" like + +.. math:: + + (x_0, \textbf{Fx}) \in \mathcal Q^n, + +where :math:`\mathcal Q^n` denotes the second-order cone and :math:`F` is some matrix related to :math:`Q^i`. + +In order to make the conversion between the Mosek interface and +the idol interface (for instance, computing the matrix :math:`F`), +one needs to compute an eigen value decomposition. +This is automatically done by idol using `Eigen `_. +This is why Eigen is necessary if one wants to use Mosek with idol to solve QPs or QCQPs. + +Detailed Answer +--------------- + +Consider the quadratic expression + +.. math:: + + \sum_{j=1}^n a_{ij}x_j + \sum_{j=1}^n\sum_{k=1}^n q_{jk}^ix_jx_k \le b_i. + +It can be written as + +.. math:: + + a_{(i)}^\top x + x^\top Q^i x \le b_i. + +The first task is to compute an eigen value decomposition of :math:`Q^i`, i.e., +to find matrices :math:`L` and :math:`D` such that :math:`Q^i = L D L^\top` +and :math:`D` is a diagonal matrix containing the eigen values of :math:`Q^i`. + +If there are more than two negative eigen values, the constraint is not convex and an exeception is thrown. + +Otherwise, we compute :math:`F` and :math:`N` so that :math:`x^\top Q^i x = \lVert Fx \rVert_2^2 + x^\top N x`. +This is done by setting :math:`F = \sqrt{D^+} L^\top` where :math:`D^+` is :math:`D` with the negative eigen values replaced by zero, +and :math:`N = L D^- L^\top`. + +Hence, we have that the constraint is expressed as + +.. math:: + + \lVert Fx \rVert_2^2 + x^\top N x \le b_i - a_{(i)}^\top x. + +If :math:`N = 0`, it can be written as + +.. math:: + + \begin{align} + & \lVert Fx \rVert_2^2 \le b_i - a_{(i)}^\top x \\ + \iff & (.5, b_i - a_{(i)}^\top x, Fx) \in\mathcal Q^{n+2}_r, + \end{align} + +where :math:`\mathcal Q^{n+2}_r` denotes the rotated second-order cone. + +Otherwise, if :math:`N` has one non-zero entry, say :math:`n_{ij}`. +Then, it must be that :math:`a_{(i)} = 0` and :math:`b_i \le 0` for the constraint to be +converted by idol. Under this assumption, the constraint can be written as + +.. math:: + + \begin{align} + & \lVert Fx \rVert_2^2 + \sqrt{-b_i}^2 \le n_{ij} x_i x_j \\ + \iff & (.5 n_{i,j} x_i, x_j, Fx, \sqrt{-b_i}) \in\mathcal Q^{n+1}_r, + \end{align} + +where it is assumed that :math:`n_{i,j} x_{i}x_{j} \ge 0` holds +(for now, a warning is printed to enlight this expectation). \ No newline at end of file diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 00000000..4e34fbb0 --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,181 @@ +.. raw:: html + +
+ +

A C++ framework for optimization

+ +

+ Idol is a powerful and flexible library designed to help you build new mathematical optimization algorithms and solve more and more challenging problems efficiently. +

+ + + Get started now +   + To installation guidelines + + +
+ +What Is Idol? +------------- + +Idol is a C++ library for **mathematical optimization** and complex decision making. It is designed to help you build new +algorithms easily for solving more and more challenging problems. It is a versatile and powerful tool that can be +used to solve a wide range of optimization problems, including mixed-integer linear programming (MILP), quadratically constrained +problems (MIQCQP and MIQP), bilevel problems (BO), robust optimization problems (RO and ARO) and many more. + +It is designed to be easy to use and to integrate with other software, so that you can use it +to build new algorithms and solve new problems without having to start from scratch. For instance, here is a piece of +code which create a Branch-and-Price algorithm. + +.. code:: + + const auto branch_and_price = branch_and_bound + column_generation; + +Here, `branch_and_bound` and `column_generation` are two objects which are +combined to create a new algorithm, `branch_and_price`, which can be instantiated and executed. + +Getting Started With Idol +------------------------- + +If you are new to idol, be sure to have a look at our :ref:`tutorials `. If you want to get a fast hands-on +start, have a look at our :ref:`local installation guideline `. It is the easiest installation process +one could think of since it automatically downloads the latest version of idol, and installs it locally in a sub-folder. +It's really a mater of seconds before you can start using idol. + +.. admonition:: Example + + Idol has a user-friendly interface which should look natural to people working in optimization. For instance, + here is how one solves a Knapsack Problem using the `Gurobi `_ solver. + + .. code:: cpp + + using namespace idol; + + const unsigned int n_items = 5; + const double[] profit = { 40., 50., 100., 95., 30., }; + const double[] weight = { 2., 3.14, 1.98, 5., 3., }; + const double capacity = 10.; + + Env env; + + Model model(env); + + const auto x = model.add_vars(Dim<1>(n_items), 0., 1., Binary, "x"); + + model.add_ctr(idol_Sum(j, Range(n_items), weight[j] * x[j]) <= capacity); + + model.set_obj_expr(idol_Sum(j, Range(n_items), -profit[j] * x[j]); + + model.use(Gurobi()); + + model.optimize(); + +Is This a MIP Solver? +--------------------- + +The idol library is not a MIP solver in itself. Indeed, it typically needs to call external +solvers (e.g., `GLPK `_, `Gurobi `_ or many others) +as a sub-routine of more complex algorithmic schemes (e.g., within a Branch-and-Price scheme). + +The idea is to work hand in hand with existing fine-tuned and well-engineered optimization +software to enhance their possibilities. By doing so, we aim at solving larger and more complex problems than what is currently possible, +or even problems for which theoretical evidence indicate that no MIP of reasonable size can model them completely (e.g., :math:`\Sigma_i^P`-hard problems). + +Even though idol is not a MIP solver, it can be used to solve standalone LPs, QPs, MILPs, MIQPs, and MIQCQPs through the +many external solvers it interfaces with. +A clear advantage of using idol to solve these problems is that it offers a unified and common interface to every solver. +Thus, you can write your code once and test it with different solvers! +The following solvers are currently supported by idol + +* `Gurobi `_ +* `Mosek `_ +* `GLPK `_ +* `HiGHS `_ +* `COIN-OR/Osi `_ which then gives you access to CPLEX, Symphony or any Osi compatible solver. + +Current Features +---------------- + +Mixed-Integer Optimization +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Interfacing External Optimization Solvers +""""""""""""""""""""""""""""""""""""""""" + +The idol library can be used to interface external solvers like `GLPK `_ +or `Gurobi `_ to solve standalone LPs, QPs, MILPs, MIQPs, and MIQCQPs. + +It also provides classical callbacks one would need to implement when solving hard problems like generating user cuts +or lazy constraints. Simply give idol the separation model and let it handle the rest. + +Branch-and-Bound Algorithms +""""""""""""""""""""""""""" + +Idol can be used to create a custom branch-and-bound scheme very easily in which virtually anything can be tweaked. + +* **Node-agnostic:** The B&B algorithm can work with any node type as long as the user properly + defines how nodes must be updated. A default node type is already implemented for classical variable branching. This + node type can also be inherited, e.g., to enrich the information carried out from each parent node to its children. +* **Customizable branching rules:** The default branching rules include: most infeasible, least infeasible, first + infeasible found, uniformly random, strong branching (with phases) and pseudo-cost branching. The user can create its + own branching rule if needed. +* **Customizable node selection rules:** The default node selection rules include: best estimate, best bound, worst bound, + depth first, breadth first. The user can create its own node selection rule if needed. +* **Callbacks:** The user can specify its own callback to locally or globally modify a node's problem, submit heuristic + solutions, or influence the execution of the overall tree search. +* **Sub-tree exploration**. The B&B algorithm supports sub-trees exploration to reach valid solutions to the original + problem as quick as possible. + +Column Generation and Dantzig-Wolfe Decomposition +""""""""""""""""""""""""""""""""""""""""""""""""" + +Idol can be used to build and custom CG algorithms with the following features. + +* **Infeasible master problem procedures:** Idol can handle infeasible master problems by resorting to Farkas pricing or + by adding artificial variables to the original problem (if this fails, idol will automatically switch to pure phase I + so as to exactly prove infeasibility). +* **Automatic pool clean up:** If the master problem contains too many variables, they can be automatically removed. + This behavior is controlled by user parameters. +* **Stabilization via dual price smoothing:** The CG procedure can be stabilized using dual price smoothing controlled + by user inputs. Default implementation include `Wentges (1997) `_ and + `Neame (2000) `_. +* **Parallel pricing:** Each pricing problem can be solved in parallel. +* **Nested column generation:** A key idea of idol is that every optimizer is seen as a black-box to solve a given optimization model. + As such, a CG algorithm can easily be "plugged in" any algorithmic phase. For instance, solving the pricing of + a CG algorithm can be done by yet another CG algorithm. + +Bilevel Optimization +^^^^^^^^^^^^^^^^^^^^ + +Interfacing External Optimization Solvers +""""""""""""""""""""""""""""""""""""""""" + +Currently, idol can interface with the mixed-integer bilevel solver `coin-or/MibS `_. + +Robust Optimization +^^^^^^^^^^^^^^^^^^^^ + +Column-and-Constraint Generation +"""""""""""""""""""""""""""""""" + +Idol can be used to build and custom CCG algorithms with the following features. + +* **Separation Sub-Routine**: Different separation strategies are implemented in idol to solve the separation problem in + the CCG algorithm. Most importantly, any bilevel solver can be used to solve the max-min separation problem. If needed, the user can also easily implement its own separation strategy. +* **Trust Region Stabilization**: The CCG algorithm can be stabilized using trust region if the first-stage decisions are binary. + +Table of Contents +----------------- + +.. toctree:: + :maxdepth: 1 + + installation/index + tutorials/index + examples/index + api/index + faq/index + benchmark/index + developer_guide/index + references diff --git a/_sources/installation/global-installation/index.rst.txt b/_sources/installation/global-installation/index.rst.txt new file mode 100644 index 00000000..6c84c6cf --- /dev/null +++ b/_sources/installation/global-installation/index.rst.txt @@ -0,0 +1,97 @@ +.. _basics_installation: + +.. role:: bash(code) + :language: bash + +Global Installation +=================== + +This page is dedicated to the local installation of idol. It is thought for more advanced users who want to install +idol globally on their computer (e.g., in `/usr/lib`). +If you simply want to "try out" idol, we advise you to opt for a :ref:`local installation `. +The installation process is managed by the CMake build system. You will therefore need to have it +installed on your computer. + +The installation process follows the classical CMake build process "configure, build, install". + +Configure +--------- + +The first step is to create a :bash:`build` directory. +This is where the library will be compiled before being it is installed (i.e., copied) on your system. + +.. code-block:: bash + + mkdir build + cd build + +Then, you should call CMake to configure the build process. +It is at this time that we let CMake look for the necessary dependencies such as, e.g., external optimization solvers. +By default, running CMake without options will cause idol to be built without any external solver. Note that options +should be specified as follows: + +.. code-block:: bash + + cmake -D= .. + +A list of **all possible options** can be found on :ref:`this page `. + +.. admonition:: Example for Gurobi + + Here is an example of a possible CMake command which will create the necessary targets for configuring idol to interface Gurobi. + + .. code-block:: bash + + cmake -DUSE_GUROBI=YES .. + + Note that this example assumes that your environment variable :bash:`GUROBI_HOME` has been correctly configured + (see `this official Gurobi page `_). + + If you want to specify the path to the Gurobi installation folder, you can use the :bash:`GUROBI_DIR` option. + + .. code-block:: bash + + cmake -DUSE_GUROBI=YES -DGUROBI_DIR=/path/to/gurobi/install/dir .. + + +Build +----- + +Now that the build process has been configured, we can build idol by running the following command. + +.. code-block:: bash + + make idol + +This will compile the idol C++ library, and make it ready to be installed (i.e., copied) on your computer. + +If you encounter any error at this stage, please, do not hesitate to contact us on our `GitHub page `_. + +Install +------- + +To install idol on your computer, run the following command. + +.. code-block:: bash + + sudo make install + +.. hint:: + + Here, you need superuser rights to install idol globally. If you want to install idol on a specific folder, + you may also use the :bash:`CMAKE_INSTALL_PREFIX` CMake option to change the destination folder. + + .. code-block:: bash + + cmake -CMAKE_INSTALL_PREFIX=/my/custom/installation/folder .. + make install + + +Table of Contents +----------------- + +.. toctree:: + :maxdepth: 1 + :glob: + + * diff --git a/_sources/installation/global-installation/linking.rst.txt b/_sources/installation/global-installation/linking.rst.txt new file mode 100644 index 00000000..fffca91f --- /dev/null +++ b/_sources/installation/global-installation/linking.rst.txt @@ -0,0 +1,53 @@ +.. _linking: + +.. role:: bash(code) + :language: bash + +Linking Your C++ Project +======================== + +If you have installed idol globally, you can link your C++ project with idol using CMake. +Note that if idol was locally installed, you should refer to the :ref:`local installation guideline `, +this page is **only for global installation**. + +Idol is built with and for CMake (even though it is still possible to link using :bash:`Makefile` or pure :bash:`gcc`, :bash:`clang`, ...). + +Here is a minimal :bash:`CMakeLists.txt` which creates a new executable target linked with idol. + +.. code-block:: cmake + + cmake_minimum_required(VERSION 3.22) + project(my_project) + + set(CMAKE_CXX_STANDARD 17) + + add_executable(my_target main.cpp) + + find_package(idol REQUIRED) ## Here, we search for the installed library idol + + target_link_library(my_target PUBLIC idol) ## Here, we ask CMake to link with idol + +Note that you do not need to link with any external solver if idol was already linked with one. + +The following :bash:`main.cpp` is then a minimal example. + +.. code-block:: cpp + + #include + #include + + int main(int t_argc, const char** t_argv) { + + using namespace idol; + + Env env; + + Model model(env); + + const auto x = model.add_var(0., 1., Binary, "x"); + + // ... + + return 0; + } + diff --git a/_sources/installation/index.rst.txt b/_sources/installation/index.rst.txt new file mode 100644 index 00000000..6b6e2717 --- /dev/null +++ b/_sources/installation/index.rst.txt @@ -0,0 +1,21 @@ +.. _installation: + +Installation +============ + +You are here on the installation guidelines. This page will guide through the installation process of idol. + +There are two ways to install idol: + +* :ref:`A local installation ` lets CMake automatically download and install idol a sub-folder of your + CMake project. This is the easiest way to get started with idol. +* :ref:`A global installation ` installs idol globally on your computer (e.g., in `/usr/lib`). + +.. toctree:: + :hidden: + :maxdepth: 1 + :glob: + + local_installation + global-installation/index + options diff --git a/_sources/installation/local_installation.rst.txt b/_sources/installation/local_installation.rst.txt new file mode 100644 index 00000000..3cb98df8 --- /dev/null +++ b/_sources/installation/local_installation.rst.txt @@ -0,0 +1,81 @@ +.. _installation_local: + +.. role:: bash(code) + :language: bash + +Local Installation +================== + +This page is dedicated to the local installation of idol. This is the easiest way to get your project working with +idol without installing it globally on your computer. +If you want to install idol globally, please read our :ref:`global installation guide `. + +The idea behind local installation is to let CMake download and manage the installation of idol in a sub-folder of your +project. This is done by using the CMake :bash:`FetchContent_*` directives. + +We provide here a minimal :bash:`CMakeLists.txt`. + +.. code-block:: cmake + + cmake_minimum_required(VERSION 3.23) + project(my_project) + + set(CMAKE_CXX_STANDARD 17) + + include(FetchContent) + + # Define your idol CMake options here + set(USE_GUROBI YES) # For instance, here, we specify that Gurobi will be used by idol + + # Tell CMake which version of idol you desire + FetchContent_Declare( + idol + GIT_REPOSITORY https://github.com/hlefebvr/idol.git + GIT_TAG origin/main + ) + + # Ask CMake to download idol and install it to a sub-folder + FetchContent_MakeAvailable(idol) + + # Normal executable definition + add_executable(my_target main.cpp) + + # Link your CMake target with idol as classically done + target_link_libraries(my_target PUBLIC idol) + +By default, CMake will download the latest version of idol. However, you can also explicitly specify the desired version +of idol you want to use by setting the :bash:`GIT_TAG` variable in the :bash:`FetchContent_Declare` function to the specific +version, e.g., :bash:`v0.2.4-alpha`. + +.. hint:: + + As you can see in the above example, some options must be set to tell CMake to look for external solvers. Here, we + specify that Gurobi will be used by idol. You can, of course, add other solvers such as Mosek or GLPK. + + This is done with the :bash:`set` CMake function. For instance, to set the option :bash:`MY_OPTION` to the + value :bash:`MY_VALUE`. You should add :code:`set(MY_OPTION MY_VALUE)` to your :bash:`CMakeLists.txt` file. + + A list of **all possible options** can be found on :ref:`this page `. + +Then, here is a starting :bash:`main.cpp`. + +.. code-block:: cpp + + #include + #include + + int main(int t_argc, const char** t_argv) { + + using namespace idol; + + Env env; + + Model model(env); + + const auto x = model.add_var(0., 1., Binary, "x"); + + // ... + + return 0; + } + diff --git a/_sources/installation/options.rst.txt b/_sources/installation/options.rst.txt new file mode 100644 index 00000000..83689086 --- /dev/null +++ b/_sources/installation/options.rst.txt @@ -0,0 +1,202 @@ +.. _cmake_options: + +.. role:: bash(code) + :language: bash + +List of CMake Options +===================== + +This page contains a list of all CMake options that can be used to configure idol. In particular, this is useful to +link idol with external solvers. + +.. contents:: Table of Contents + :local: + :depth: 2 + +Linking with External Solvers +----------------------------- + +Gurobi (Commercial Solver) +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To link with Gurobi, you will need to pass the :bash:`USE_GUROBI` CMake option with value :bash:`YES`. + +By default, CMake will look for Gurobi inside the folder indicated by the environment variable :bash:`GUROBI_HOME` +(see `this official Gurobi page `_). +Note that it is also possible to pass the CMake option :bash:`GUROBI_DIR=/path/to/gurobi/install/dir` in order to specify +another directory to look for Gurobi. + +.. admonition:: Example + + The following will create an :bash:`install` target which will install idol with Gurobi. + + .. code-block:: bash + + cmake -DUSE_GUROBI=YES -DGUROBI_DIR=/path/to/gurobi/install/dir .. + + If you are doing a *local installation*, then the same can be achieved as follows. + + .. code-block:: bash + + set(USE_GUROBI YES) + set(GUROBI_DIR /path/to/gurobi/install/dir) + + +Mosek (Commercial Solver) +^^^^^^^^^^^^^^^^^^^^^^^^^ + +To link with Mosek, you will need to pass the :bash:`USE_MOSEK` CMake option with value :bash:`YES`. + +By default, CMake will look for Mosek inside the folder indicated by the environment variable :bash:`MOSEK_HOME`. +Note that it is also possible to pass the CMake option :bash:`MOSEK_DIR=/path/to/mosek/install/dir` in order to specify +another directory to look for Mosek. + +Note that :bash:`MOSEK_HOME` (or equivalently :bash:`MOSEK_DIR`) should point to the Mosek installation folder where +folders :bash:`h` and :bash:`bin` can be found. For instance, :bash:`MOSEK_HOME=/home//mosek/10.0/tools/platform/linux64x86`. + +.. admonition:: Example + + The following will create an :bash:`install` target which will install idol with Mosek. + + .. code-block:: bash + + cmake -DUSE_MOSEK=YES -DMOSEK_DIR=/path/to/mosek/install/dir .. + + If you are doing a *local installation*, then the same can be achieved as follows. + + .. code-block:: bash + + set(USE_MOSEK YES) + set(MOSEK_DIR /path/to/mosek/install/dir) + +.. attention:: + + If you intend to use Mosek for solving QPs or SOCPs, please :ref:`read this `. + +GLPK (Open-Source Solver) +^^^^^^^^^^^^^^^^^^^^^^^^^ + +To link with GLPK, you will need to pass the :bash:`USE_GLPK` CMake option with value :bash:`YES`. + +By default, CMake will look for GLPK inside the folder indicated by the environment variable :bash:`GLPK_HOME` as well +as in the default installation folders :bash:`/usr/include` and :bash:`/usr/lib`. +Note that it is also possible to pass the CMake option :bash:`GLPK_DIR=/path/to/glpk/install/dir` in order to specify +another directory to look for GLPK. + +.. admonition:: Example + + The following will create an :bash:`install` target which will install idol with GLPK. + + .. code-block:: bash + + cmake -DUSE_GLPK=YES -DGLPK_DIR=/path/to/glpk/install/dir .. + + If you are doing a *local installation*, then the same can be achieved as follows. + + .. code-block:: bash + + set(USE_GLPK YES) + set(GLPK_DIR /path/to/glpk/install/dir) + + +HiGHS (Open-Source Solver) +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To link with GLPK, you will need to pass the :bash:`USE_HIGHS` CMake option with value :bash:`YES`. + +By default, CMake will look for HIGHS inside the default installation folders :bash:`/usr/include` and :bash:`/usr/lib`. +Note that it is also possible to pass the CMake option :bash:`HIGHS_DIR=/path/to/highs/install/dir` in order to specify +another directory to look for GLPK. + +.. admonition:: Example + + The following will create an :bash:`install` target which will install idol with GLPK. + + .. code-block:: bash + + cmake -DUSE_HIGHS=YES -DHIGHS_DIR=/path/to/glpk/install/dir .. + + If you are doing a *local installation*, then the same can be achieved as follows. + + .. code-block:: bash + + set(USE_HIGHS YES) + set(HIGHS_DIR /path/to/glpk/install/dir) + + +Building Examples +----------------- + +:ref:`Examples :` can be found at the root level of the idol repository, inside the directory ``examples``. + +By default, CMake will not generate targets to build these example. To tell CMake to generate these, you +need to set the CMake option ``BUILD_EXAMPLE`` to ``YES``. + +.. admonition:: Example + + The following will tell CMake to create targets for examples. + + .. code-block:: + + cmake -DBUILD_EXAMPLES=YES .. + + For instance, we can now build the Knapsack Problem example, + + .. code-block:: + + make example_knapsack + + and run it. + + .. code-block:: + + cd examples && ./example_knapsack + +Other optional dependencies +--------------------------- + +Using martinus/robin-hood-hashing hash map +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Internally, idol uses unordered maps (also called hash maps). +Unfortunately, the default :code:`std` implementation is not the best choice when +it comes to performance. To avoid this, if found relevant, users can tell idol to use the hash-map implementation of +`martinus/robin-hood-hashing `_. It's actually a header-only library, so +all you have to do is to tell CMake where the :code:`robin_hood.hpp` file is. This is done through the CMake option +:code:`ROBINHOOD_DIR=/path/to/robin_hood/folder` together with :code:`USE_ROBINHOOD=YES`. + +Note that, if :code:`ROBINHOOD_DIR` is not specified, :code:`robin_hood.hpp` is searched for in :code:`/usr/include`, +:code:`/usr/include/robin_hood/`, :code:`/usr/local/include/`, :code:`/usr/local/include/robin_hood/` and in the path +stored in the environment variable :code:`ROBINHOOD_HOME`. + +.. admonition:: Example + + The following will create an :bash:`install` target which will install idol with martinus's robin_hood hash map + implementation. + + .. code-block:: + + cmake -DUSE_ROBINHOOD=YES -DROBINHOOD_DIR=/path/to/robin_hood/folder .. + +Using Eigen +^^^^^^^^^^^ + +Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms. +It is internally used by idol for some of its functionalities. In particular, this is *necessary* for solving QPs and SOCPs +with the Mosek solver. + +Since Eigen is a header-only library, all you have to do is to tell CMake where the :code:`Eigen` folder is. This is done +through the CMake option :code:`EIGEN_DIR=/path/to/eigen/folder` together with :code:`USE_EIGEN=YES`. + +.. admonition:: Example + + The following will create an :bash:`install` target which will install idol with Eigen. + + .. code-block:: + + cmake -DUSE_EIGEN=YES -DEIGEN_DIR=/path/to/eigen/folder .. + +Building Unit and Integration Tests +----------------------------------- + +To build tests for idol, please refer to this :ref:`developer page `. \ No newline at end of file diff --git a/_sources/references.rst.txt b/_sources/references.rst.txt new file mode 100644 index 00000000..c8779726 --- /dev/null +++ b/_sources/references.rst.txt @@ -0,0 +1,7 @@ +References +========== + +The implementation of idol is based on a lot of scientific publications. Here is a selected subset. + +.. bibliography:: + :all: diff --git a/_sources/tutorials/bilevel-optimization/index.rst.txt b/_sources/tutorials/bilevel-optimization/index.rst.txt new file mode 100644 index 00000000..056044e6 --- /dev/null +++ b/_sources/tutorials/bilevel-optimization/index.rst.txt @@ -0,0 +1,11 @@ +.. _bilevel: + +Bilevel Optimization +==================== + +.. toctree:: + :maxdepth: 3 + :glob: + + modeling/index + wrappers/index diff --git a/_sources/tutorials/bilevel-optimization/modeling/index.rst.txt b/_sources/tutorials/bilevel-optimization/modeling/index.rst.txt new file mode 100644 index 00000000..a64d9ab9 --- /dev/null +++ b/_sources/tutorials/bilevel-optimization/modeling/index.rst.txt @@ -0,0 +1,8 @@ +Modeling +======== + +.. toctree:: + :maxdepth: 1 + :glob: + + optimistic diff --git a/_sources/tutorials/bilevel-optimization/modeling/optimistic.rst.txt b/_sources/tutorials/bilevel-optimization/modeling/optimistic.rst.txt new file mode 100644 index 00000000..84ae2138 --- /dev/null +++ b/_sources/tutorials/bilevel-optimization/modeling/optimistic.rst.txt @@ -0,0 +1,139 @@ +.. _tutorial_optimistic_bilevel: + +Modeling an Optimistic Bilevel Problem +====================================== + +In this tutorial, we will see how to model an optimistic bilevel problem in idol. + +To follow this tutorial, you should be familiar with bilevel optimization and modeling optimization problems in idol. +If this is not the case, we recommend you to read the tutorial on :ref:`MIP modeling `. + +.. contents:: Table of Contents + :local: + :depth: 2 + +Problem Definition and Main Steps +---------------------------------- + +We consider the optimistic bilevel problem + +.. math:: + + \begin{align} + \min_{x, y} \ & -x + -10 y \\ + \text{s.t.} \ & x \in \mathbb Z_+ \\ + & y\in + \begin{array}[t]{rl} + \displaystyle \underset{y}{\text{arg min}} \ & y \\ + \text{s.t.} \ & -25 x + 20 y \leq 30, \\ + & x + 2 y \leq 10, \\ + & 2 x - y \leq 15, \\ + & 2 x + 10 y \geq 15, \\ + & y \geq 0, \\ + & y \in \mathbb Z_+. + \end{array} + \end{align} + +This is an ILP-ILP bilevel problem which is taken from :cite:`Moore1990` (Example 1). + +To model this problem in idol, there are three main steps: + +1. Define the high-point relaxation (HPR) model, i.e., in our example, + + .. math:: + + \begin{align} + \min_{x, y} \ & -x + -10 y \\ + \text{s.t.} \ & x \in \mathbb Z_+ \\ + & -25 x + 20 y \leq 30, \\ + & x + 2 y \leq 10, \\ + & 2 x - y \leq 15, \\ + & 2 x + 10 y \geq 15, \\ + & y \geq 0, \\ + & y \in \mathbb Z_+. + \end{align} + + This model defines all constraints and variables of the bilevel problem. + The feasible region is classically called the shared-constraint set. + +2. Describe which variables and constraints are part of the lower-level problem. + +3. Define the lower-level objective function. + +Modeling the High-Point Relaxation +---------------------------------- + +The HPR can be modeled in the same way as a classical optimization problem. +If you are not familiar with modeling optimization problems in idol, +we recommend you to read the tutorial on :ref:`MIP modeling `. + +Here is the code to model the HPR of the bilevel problem. + +.. code:: + + Env env; + Model high_point_relaxation(env); + + auto x = high_point_relaxation.add_var(0, Inf, Integer, "x"); + auto y = high_point_relaxation.add_var(0, Inf, Integer, "y"); + + high_point_relaxation.set_obj_expr(-x - 10 * y); + auto follower_c1 = high_point_relaxation.add_ctr(-25 * x + 20 * y <= 30); + auto follower_c2 = high_point_relaxation.add_ctr(x + 2 * y <= 10); + auto follower_c3 = high_point_relaxation.add_ctr(2 * x - y <= 15); + auto follower_c4 = high_point_relaxation.add_ctr(2 * x + 10 * y >= 15); + +Describing the Lower-Level Problem +---------------------------------- + +To describe the lower-level problem, we need to specify which variables and constraints are part of the lower-level problem. +This done by creating an object of type :code:`Bilevel::LowerLevelDescription` and calling the methods :code:`set_follower_var` +and :code:`set_follower_ctr`. + +.. code:: + + Bilevel::LowerLevelDescription description(env); + description.set_follower_var(y); + description.set_follower_ctr(follower_c1); + description.set_follower_ctr(follower_c2); + description.set_follower_ctr(follower_c3); + description.set_follower_ctr(follower_c4); + +Note that this does nothing more but to create two new :code:`Annotation` to indicate variables and constraints that are part of the lower-level problem. +These annotations are used by the bilevel solver to identify the lower-level problem. +In particular, all variables and constraints that are not annotated as follower variables or constraints are considered as leader variables and constraints +and have an annotation which is equal to :code:`MasterId`. + +Also note that it is possible to create and use your own annotation. For instance, the following code is equivalent to the previous one. + + +.. code:: + + Annotation follower_vars(env, MasterId, "follower_variables"); + y.set(follower_vars, 0); + + Annotation follower_ctrs(env, MasterId, "follower_constraints"); + follower_c1.set(follower_ctrs, 0); + follower_c2.set(follower_ctrs, 0); + follower_c3.set(follower_ctrs, 0); + follower_c4.set(follower_ctrs, 0); + + + Bilevel::LowerLevelDescription description(follower_vars, follower_ctrs); + + +Defining the Lower-Level Objective Function +------------------------------------------- + +Finally, we need to define the lower-level objective function. +This is done by calling the method :code:`set_follower_obj_expr` on the object of type :code:`Bilevel::LowerLevelDescription`. +An :code:`Expr` object is passed as argument to this method. + +.. code:: + + description.set_follower_obj_expr(y); + +Complete Example +---------------- + +A complete example is available :ref:`here `. diff --git a/_sources/tutorials/bilevel-optimization/wrappers/index.rst.txt b/_sources/tutorials/bilevel-optimization/wrappers/index.rst.txt new file mode 100644 index 00000000..8aff8053 --- /dev/null +++ b/_sources/tutorials/bilevel-optimization/wrappers/index.rst.txt @@ -0,0 +1,8 @@ +External Solvers +================ + +.. toctree:: + :maxdepth: 1 + :glob: + + mibs diff --git a/_sources/tutorials/bilevel-optimization/wrappers/mibs.rst.txt b/_sources/tutorials/bilevel-optimization/wrappers/mibs.rst.txt new file mode 100644 index 00000000..0e8eb260 --- /dev/null +++ b/_sources/tutorials/bilevel-optimization/wrappers/mibs.rst.txt @@ -0,0 +1,35 @@ +Solving Mixed-Integer Bilevel Problems with MibS +================================================ + +MibS is an optimization solver for mixed-integer bilevel problems. +Idol seemlessly integrates with MibS to solve bilevel problems. + +We will see that solving bilevel problems with MibS is very similar to solving bilevel problems with any optimizer in idol. + +We will assume that you have your bilevel problem modeled already in idol. In particular, we consider that you have +two variables: + +1. :code:`high_point_relaxation` which is a :code:`Model` representing the high-point relaxation of your bilevel problem. + +2. :code:`description` which is a :code:`Bilevel:LowerLevelDescription` object representing the bilevel problem. If you do not know what this is or how to create it, please refer to the :ref:`previous tutorial `. + +Then, you can solve your bilevel problem with MibS as follows: + +.. code:: + + high_point_relaxation.use(Bilevel::MibS(description)); + + high_point_relaxation.optimize(); + + std::cout << save_primal(high_point_relaxation) << std::endl; + +Notice how the MibS solver is attached to the high-point relaxation model. + +The rest of the code is the same as with any other solver. + +.. warning:: + + To use MibS, you need to have the MibS library installed on your system and idol linked to the executable. + You can download MibS from `here `_. + + Then, idol should be compiled with the options :code:`USE_MIBS=YES`, :code:`USE_CLP=YES`. diff --git a/_sources/tutorials/index.rst.txt b/_sources/tutorials/index.rst.txt new file mode 100644 index 00000000..605c422c --- /dev/null +++ b/_sources/tutorials/index.rst.txt @@ -0,0 +1,35 @@ +.. _tutorials: + +Tutorials +========= + +Mixed-Integer Programming +------------------------- + +.. toctree:: + :maxdepth: 3 + :glob: + :titlesonly: + + mixed-integer-programming/index + +Bilevel Optimization +-------------------- + +.. toctree:: + :maxdepth: 3 + :glob: + :titlesonly: + + bilevel-optimization/index + +Robust Optimization +------------------- + +.. toctree:: + :maxdepth: 3 + :glob: + :titlesonly: + + robust-optimization/index + diff --git a/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-branching-rule.rst.txt b/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-branching-rule.rst.txt new file mode 100644 index 00000000..c0573535 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-branching-rule.rst.txt @@ -0,0 +1,6 @@ +Creating Your Own Branching Rule (Advanced) [TODO] +================================================== + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-callback.rst.txt b/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-callback.rst.txt new file mode 100644 index 00000000..aec7ac3d --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-callback.rst.txt @@ -0,0 +1,8 @@ +.. _tutorial_create_bb_callback: + +Create Your Own Callback (Advanced) [TODO] +========================================== + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-node-selection-rule.rst.txt b/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-node-selection-rule.rst.txt new file mode 100644 index 00000000..e0c4c853 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-node-selection-rule.rst.txt @@ -0,0 +1,6 @@ +Creating Your Own Node Selection Rule (Advanced) [TODO] +======================================================= + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-node.rst.txt b/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-node.rst.txt new file mode 100644 index 00000000..2a76e7d3 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/branch-and-bound/create-node.rst.txt @@ -0,0 +1,6 @@ +Creating Your Own Node Class (Advanced) [TODO] +============================================== + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/branch-and-bound/index.rst.txt b/_sources/tutorials/mixed-integer-programming/branch-and-bound/index.rst.txt new file mode 100644 index 00000000..8ab23168 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/branch-and-bound/index.rst.txt @@ -0,0 +1,17 @@ +Branch-and-Bound Algorithm +========================== + +.. warning:: + + This is a work in progress. Thank you for your understanding. + +.. toctree:: + :maxdepth: 1 + :glob: + + write-branch-and-bound + user-cuts-lazy-constraints + create-callback + create-node + create-branching-rule + create-node-selection-rule diff --git a/_sources/tutorials/mixed-integer-programming/branch-and-bound/user-cuts-lazy-constraints.rst.txt b/_sources/tutorials/mixed-integer-programming/branch-and-bound/user-cuts-lazy-constraints.rst.txt new file mode 100644 index 00000000..8bca1539 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/branch-and-bound/user-cuts-lazy-constraints.rst.txt @@ -0,0 +1,6 @@ +Adding User Cuts and Lazy Constraints [TODO] +============================================ + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/branch-and-bound/write-branch-and-bound.rst.txt b/_sources/tutorials/mixed-integer-programming/branch-and-bound/write-branch-and-bound.rst.txt new file mode 100644 index 00000000..c031523b --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/branch-and-bound/write-branch-and-bound.rst.txt @@ -0,0 +1,6 @@ +Writing an LP-based Branch-and-Bound [TODO] +=========================================== + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/dantzig-wolfe.rst.txt b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/dantzig-wolfe.rst.txt new file mode 100644 index 00000000..cd4142a3 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/dantzig-wolfe.rst.txt @@ -0,0 +1,8 @@ +.. _tutorial_column_generation: + +Modeling and Solving A Dantzig-Wolfe Reformulation [TODO] +========================================================= + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/in-out-stabilization.rst.txt b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/in-out-stabilization.rst.txt new file mode 100644 index 00000000..0c900feb --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/in-out-stabilization.rst.txt @@ -0,0 +1,6 @@ +Using In-Out Stabilization [TODO] +================================= + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/index.rst.txt b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/index.rst.txt new file mode 100644 index 00000000..0aba8b1d --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/index.rst.txt @@ -0,0 +1,19 @@ +.. _tutorial_DantzigWolfe: + +Column Generation and Branch-and-Price Algorithm +================================================ + +.. warning:: + + This is a work in progress. Thank you for your understanding. + +.. toctree:: + :maxdepth: 1 + :glob: + + dantzig-wolfe + in-out-stabilization + writing-branch-and-price + integer-master + strong-branching + writing-branch-and-cut-and-price diff --git a/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/integer-master.rst.txt b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/integer-master.rst.txt new file mode 100644 index 00000000..d0fbaf72 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/integer-master.rst.txt @@ -0,0 +1,6 @@ +The Integer Master Heuristic [TODO] +=================================== + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/old/dantzig_wolfe.rst.txt b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/old/dantzig_wolfe.rst.txt new file mode 100644 index 00000000..1e4264f7 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/old/dantzig_wolfe.rst.txt @@ -0,0 +1,298 @@ +.. _tutorials_dantzig_wolfe_gap: + +.. role:: cpp(code) + :language: cpp + +Branch-and-Price 101 +==================== + +In this section, we will show how to use the Branch-and-Price solver to solve the *Generalized Assignment Problem* (GAP) +using an external solver to solve each sub-problem. + +.. hint:: + + This tutorial regards the `advanced topic` of Column Generation and Dantzig-Wolfe decomposition. + Rudimentary notions in the following subjects are recommended: + + - `Column Generation and Branch-and-Price algorithms `_ + - `Dantzig-Wolfe decomposition `_ + - `Generalized Assignment Problem `_. + +.. seealso:: + + A `Benchmark on Generalized Assignment Problem `_ is + available. + +Mathematical Models +------------------- + +In this section, we assume that the reader is familiar with GAP. +Let :math:`m` be the number of agents and let :math:`n` be the number of tasks to be performed. + +Let :math:`c_{ij}` be the cost for assigning task :math:`j` to agent :math:`i`, :math:`w_{ij}` be the resource +consumption of task :math:`j` when performed by agent :math:`i` and let :math:`t_i` be the resource capacity of agent +:math:`i`. + +Original Formulation +^^^^^^^^^^^^^^^^^^^^ + +The Generalized Assignment Problem (GAP) can be modeled as + +.. math:: + + \min_x \quad & \sum_{i=1}^m\sum_{j=1}^n c_{ij} x_{ij} \\ + \textrm{s.t.} \quad & \sum_{j=1}^n w_{ij} x_{ij} \le t_i & i=1,...,m, \\ + & \sum_{i=1}^m x_{ij} = 1 & j = 1,...,n, \\ + & x_{ij}\in\{0,1\} & i=1,...,m, j=1,...,n. + +Here, variable :math:`x_{ij}` encodes the assignment decision and equals 1 if and only if task :math:`j` is assigned to +agent :math:`i`. + +Dantzig-Wolfe Reformulation +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Let us enumerate the list of all feasible assignments, i.e., let + +.. math:: + + \{\bar x^e_{ij} \}_{e\in E} = \left\{ x \in \{ 0,1 \}^{mn} : \sum_{j=1}^n w_{ij}x_{ij} \le t_i \quad i=1,...,m \right\}, + +in which :math:`E` denotes a list for their indices. The Dantzig-Wolfe reformulation of GAP reads + +.. math:: + + \min_{\lambda} \quad & \sum_{e\in E} \lambda_e\left( \sum_{i=1}^m\sum_{j=1}^n c_{ij}\bar x_{ij}^e \right) \\ + \textrm{s.t.} \quad & \sum_{e\in E} \lambda_e \left( \sum_{i=1}^m \bar x_{ij}^e \right) = 1 & j=1,...,n, \\ + & \sum_{e\in E} \lambda_e = 1, \\ + & \lambda_e \in \{ 0, 1 \} & \forall e\in E. + +Though this model contains an exponential number of variables (i.e., columns) it can be solved efficiently using +Column Generation and Branch-and-price. In such a case, the pricing problem is a Knapsack Problem. + +Automatic Reformulation +----------------------- + +The simplest way to solve a problem using Column Generation and idol is through its automatic reformulation feature. +To use this, one simply needs to give the *original space formulation* of the problem +and to indicate which constraints should be moved to the pricing problem (here, the knapsack constraints). + +The Original Formulation +^^^^^^^^^^^^^^^^^^^^^^^^ + +To read an instance for GAP, we first need to include the header file located in :code:`"idol/problems/generalized-assignment-problem/GAP_Instance.h"`. +This will allow us to use idol's instance parser for GAP. +Assuming that the instance file is named :code:`instance.txt`, it can be loaded as follows. + +.. code-block:: cpp + + const auto instance = Problems::GAP::read_instance("instance.txt"); + + const unsigned int n_agents = instance.n_agents(); + const unsigned int n_jobs = instance.n_jobs(); + +We are now ready to model our problem (for more details, refer to :ref:`this tutorial on modeling `) + +.. code-block:: cpp + + // Create optimization environment + Env env; + + // Create model + Model model(env); + + // Create assignment variables (x_ij binaries) + auto x = model.add_vars(Dim<2>(n_agents, n_jobs), 0., 1., Binary, "x"); + + // Create knapsack constraints (i.e., capacity constraints) + for (unsigned int i = 0 ; i < n_agents ; ++i) { + model.add_ctr(idol_Sum(j, Range(n_jobs), instance.resource_consumption(i, j) * x[i][j]) <= instance.capacity(i), "capacity_" + std::to_string(i)); + } + + // Create assignment constraints + for (unsigned int j = 0 ; j < n_jobs ; ++j) { + model.add(idol_Sum(i, Range(n_agents), x[i][j]) == 1, "assignment_" + std::to_string(j)); + } + + // Set the objective function + model.set_obj_expr(idol_Sum(i, Range(n_agents), idol_Sum(j, Range(n_jobs), instance.cost(i, j) * x[i][j]))); + +Giving Decomposition Instructions +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +We are now at the crucial step of indicating which constraint should be moved to the pricing problem. In idol, this is done by using +*annotations*. Annotations are additional information associated to an optimization object (e.g., a constraint or a variable). +Note that annotations are global, i.e., they do not relate to a given optimization model. + +Every annotation is formed with two template arguments: an optimization object type and a value type. Here, we want to add +annotations to constraints, thus, the optimization object type must be :code:`Ctr`. The value type can typically be any desired +type. Here, however, the Dantzig-Wolfe decomposition needs an annotation corresponding to an :code:`unsigned int` which corresponds +to the sub-problem index to which the constraint shall be moved to. Thus, we create the annotation as follows. + +.. code-block:: cpp + + Annotation decomposition(env, "decomposition", MasterId); + +Here, we pass three arguments to the constructor of :code:`Annotation`. First, we pass the optimization +environment which will store the annotation. Then, a name (mandatory) is given to the annotation, here, "decomposition". +Finally, a default value is given and equals "MasterId". This will tell idol that constraints which have not been annotated +should remain in the master problem. + +Now, observe how the annotation is applied to the capacity constraints. + +.. code:: cpp + + for (unsigned int i = 0 ; i < n_agents ; ++i) { + Ctr capacity = model.add_ctr(idol_Sum(j, Range(n_jobs), instance.resource_consumption(i, j) * x[i][j]) <= instance.capacity(i), "capacity_" + std::to_string(i)); + capacity.set(decomposition, i); // <-- Annotating the capacity constraint + } + +Here, the first capacity constraint is moved to the first pricing problem (id: 0), +the second constraint to the second pricing problem (id: 1), and so on. + +Note that another decomposition would be materialized as follows. + +.. code:: cpp + + for (unsigned int i = 0 ; i < n_agents ; ++i) { + Ctr capacity = model.add_ctr(idol_Sum(j, Range(n_jobs), instance.resource_consumption(i, j) * x[i][j]) <= instance.capacity(i), "capacity_" + std::to_string(i)); + capacity.set(decomposition, 0); // <-- Annotating the capacity constraint + } + +Here, all the knapsack constraints would be moved to the same pricing problem (id: 0). + +Creating the Branch-and-Price Algorithm +--------------------------------------- + +Now that the desired decomposition has been specified, we can specify the desired optimizer to solve our model. +Here, we want to solve our problem using a Branch-and-Price algorithm, i.e., a Branch-and-Bound algorithm where each node +in the Branch-and-Bound tree is solved by a Dantzig-Wolfe decomposition. + +To begin with, we need to give some specification about how each sub-problem will be solved. In other words, we need +to specify the optimizer(s) used for pricing during the column generation process. This is done by first creating a +:code:`DantzigWolfe::SubProblem` object. + +.. code:: cpp + + const auto sub_problem_specifications = DantzigWolfe::SubProblem() + .add_optimizer(Gurobi()); + +Then, we can create our Column Generation algorithm (factory) in the following way. + +.. code:: cpp + + const auto column_generation = DantzigWolfeDecomposition(decomposition) + .with_master_optimizer(Gurobi::ContinuousRelaxation()) + .with_default_sub_problem_spec(sub_problem_specifications); + +Here, we are solving the relaxed master problem using Gurobi. We also define our default sub-problem specifications to +be the one we just defined. + +Our remaining task is to embed our Column Generation routine inside of a Branch-and-Bound algorithm. +This can be done as follows. + +.. code:: cpp + + const auto branch_and_bound = BranchAndBound() + /* Each node is solved by Column Generation */ + .with_node_optimizer(column_generation) + + /* Variables are selected for branching using the most-infeasible rule */ + .with_branching_rule(MostInfeasible()) + + /* Nodes are selected using the best-bound rule */ + .with_node_selection_rule(BestBound()) + + /* The algorithm will run with a time limit of 3600 */ + .with_time_limit(3600) + + ); + +Then, we can tell idol to use this algorithm for solving our model by using the :code:`Model::use` method. + +.. code:: cpp + + model.use(branch_and_bound); + +Finally, one can simply call the :code:`Model::optimize` method as follows. + +.. code:: cpp + + model.optimize(); + + +That's it! The problem is being solved by column generation, and possibly branching on fractional variables. + +.. hint:: + + Note that it is possible to obtain logs using the `with_log_level` method on the desired optimizer. + For instance, one may want to have + logs for the branch-and-bound optimizer. Then, one should do as follows. + + .. code:: cpp + + model.use( + BranchAndBound() + + /* ... omitting identical details */ + + .with_log_level(Info, Blue) + ); + +The rest remains unchanged and one can use :code:`Model::optimize` to solve the problem and retrieve the solution +through methods like :code:`Model::get_status` and :code:`Model::get_var_primal`. + +.. admonition:: Example + + Here, we can solve our model using a Dantzig-Wolfe decomposition. + + .. code-block:: + + model.optimize(); + + std::cout << save_primal(model) << std::endl; + + This will produce the following output (e.g.). + + .. code-block:: text + + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [branch-and-bound] + [2023-04-07 13:45:44] [info] [branch-and-bound] + [2023-04-07 13:45:44] [info] [branch-and-bound] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + [2023-04-07 13:45:44] [info] [dantzig-wolfe] + + +----------------------- + | Status: Optimal + | Reason: Proved + | ObjVal: -233.00 + | Values: + | x_1_7 = 1.00 + | x_2_0 = 1.00 + | x_2_1 = 1.00 + | x_1_4 = 1.00 + | x_1_5 = 1.00 + | x_0_6 = 1.00 + | x_0_2 = 1.00 + | x_0_3 = 1.00 + +----------------------- diff --git a/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/old/heuristic.rst.txt b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/old/heuristic.rst.txt new file mode 100644 index 00000000..d8817e27 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/old/heuristic.rst.txt @@ -0,0 +1,15 @@ +.. _tutorials_dantzig_wolfe_heuristic: + +.. role:: cpp(code) + :language: cpp + +Heuristic Pricing (to come) +=========================== + +Hey, you've implemented your first Branch-and-Price algorithm and are curious about how to use your own heuristic for +pricing during the Column Generation algorithm? Then you are at the right place! If not, make sure to check the +`Generalized Assignment Problem tutorial <_tutorials_dantzig_wolfe>`_. + +.. warning:: + + This tutorial is still under construction. diff --git a/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/old/stabilization.rst.txt b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/old/stabilization.rst.txt new file mode 100644 index 00000000..8e2eaafa --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/old/stabilization.rst.txt @@ -0,0 +1,15 @@ +.. _tutorials_dantzig_wolfe_stabilization: + +.. role:: cpp(code) + :language: cpp + +In-Out Stabilization (to come) +============================== + +Hey, you've implemented your first Branch-and-Price algorithm and are curious about how to stabilize your Column +Generation algorithm? Then you are at the right place! If not, make sure to check the +`Generalized Assignment Problem tutorial <_tutorials_dantzig_wolfe>`_. + +.. warning:: + + This tutorial is still under construction. diff --git a/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/strong-branching.rst.txt b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/strong-branching.rst.txt new file mode 100644 index 00000000..cbd0bfd3 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/strong-branching.rst.txt @@ -0,0 +1,198 @@ +Using Strong Branching +====================== + +.. contents:: Table of Contents + :local: + :depth: 2 + +Basics +------ + +Strong Branching is a technique that falls into the category of *variable selection rules*, a crucial aspect of +Branch-and-Bound algorithms. + +More specifically, the task is to decide which variable to branch on at each node of the Branch-and-Bound tree, i.e, +among a set of branching candidates whose value must be integer, one must +decide which variable with fractional value in the current solution of the relaxation to choose for creating +child nodes. + +The most common rule is the so-called *Most-Infeasible* rule, which selects a variable whose fractional +part is closest to `0.5`. Unfortunately, this rule performs badly in practice. Most importantly, if solving a node is +computationally hard, it makes sense to spend some time in carefully choosing the variable to branch on. This is typically +the case when nodes are solved using Column Generation. + +.. hint:: + + Clearly, Strong Branching is not only used in the context of column generation. It is a general technique that can be + used in any context where solving a node is computationally expensive. Thus, this tutorial is not specific to the + context of column generation, though we will use it as an example. + +The idea of Strong Branching is to evaluate the effect of branching on a variable before branching actually happens. + +To be more clear, let :math:`C` be a set of indices for branching candidates at a given node, i.e., for each :math:`j\in C`, +:math:`x_j` is an integer variable in the original problem but with :math:`x_j^*\notin\mathbb Z` at the current node. +Strong Branching tries to evaluate the effect of applying branching constraints :math:`x_j \le \lfloor x_j^* \rfloor` and +:math:`x_j \ge \lceil x_j^* \rceil` to the current node. To do so, it solves, before branching happens, and for each :math:`j\in C`, +both the left and right child node. Let :math:`z_i^\le` and :math:`z_i^\ge` denote the (optimal) value of the +left and right nodes if branching is performed on :math:`x_j`. The "effect of branching on :math:`x_j`" is then estimated by +computing a score, noted :math:`\text{Score}(j)`, based on :math:`z_j^\le` and :math:`z_j^\ge`. Then Strong Branching +selects the variable with an index :math:`j^*` such that (for minimization problems) + +.. math:: + + j^* \in \text{argmax}\{ \text{Score}(j) : j\in C \}. + +Empirically, Strong Branching is known to produce substantially smaller Branch-and-Bound trees compared to other +branching rules. Unfortunately, computing :math:`\text{Score}(j)` typically requires a lot of time. To avoid this, +several techniques have been designed such as *Restricted Strong Branching* and *Strong Branching with Phases* (see below). + +For more details, please refer to :cite:`ACHTERBERG200542`. + +Scoring Functions +^^^^^^^^^^^^^^^^^ + +Two common scoring functions :math:`\text{Score}(j)` are found in the literature. The *linear* formula :cite:`Linderoth1999` + +.. math:: + + \text{LinearScore}(j) := (1 - \mu) \min(\Delta_j^\le, \Delta_j^\ge) + \mu \max(\Delta_j^\le, \Delta_j^\ge), + +and the *product* formula + +.. math:: + + \text{ProductScore}(j) := \max(\Delta_j^\le, \varepsilon) \max(\Delta_j^\ge, \varepsilon), + +in which :math:`\Delta_j^\le := z_j^\le - z^*` and :math:`\Delta_j^\ge := z_j^\ge - z^*` with :math:`z^*` denoting the +(optimal) value of the current node. Parameters :math:`\mu\in[0,1]` and :math:`\varepsilon > 0` are given. +In idol, :math:`\mu = 1/6` and :math:`\varepsilon = 10^{-6}`. + +Variants +^^^^^^^^ + +There are several variants of Strong Branching. The most common ones are: + +* **Full Strong Branching** denotes the standard Strong Branching rule which solves all :math:`2|C|` nodes at each branching + decision. The drawback of this approach is that it may take a lot of time to solve all these sub-problems before branching + actually happens. +* **Restricted Strong Branching** is an attempt to reduce the computational burden of Full Strong Branching. The idea is to + consider only a maximum of :math:`K` branching candidates at each branching decision instead of the whole set :math:`C`. + Thus, :math:`C` is replaced by a smaller set :math:`R\subseteq C` such that :math:`|R| = K` with :math:`K` fixed. + The "restricted branching candidate set" :math:`R` is created by taking the :math:`K` first variables selected by, yet + another, branching rule, e.g., the most-infeasible rule. +* **Strong Branching with Look Ahead** is similar to *Restricted Strong Branching* yet differs from it by not specifying a + fixed size for the "restricted branching candidate set" :math:`R`. Instead, it considers a look ahead parameter, noted + :math:`L`, and applies the Full Strong Branching rule. However, if the branching candidate does not change after :math:`L` + iterations, the algorithm stops and the current branching candidate is returned. +* **Strong Branching with Phases** is a combination of the above three approaches which applies different schemes depending + on the level of the current node in the Branch-and-Bound tree. Additionally, it allows to solve each node only approximately + by, e.g., imposing a maximum number of iterations for the underlying Column Generation algorithm. + +Implementation +-------------- + +This section explains how to use the Strong Branching rule in idol. +It is based on the Generalized Assignment Problem example from the :ref:`Column Generation `. +More specifically, we will assume that you have a variable +:code:`model` of type :code:`Model` which has a decomposable structure specified by the annotation :code:`(Annotation) decomposition`. + +Full Strong Branching +^^^^^^^^^^^^^^^^^^^^^ + +Recall that the Branch-and-Price algorithm is created by the following code. + +.. code:: cpp + + const auto column_generation = + DantzigWolfeDecomposition(decomposition) + .with_master_optimizer(Gurobi::ContinuousRelaxation()) + .with_default_sub_problem_spec( + DantzigWolfe::SubProblem() + .add_optimizer(Gurobi()) + ); + +Now, we will show how to use Strong Branching as a branching rule. This is done while creating our Branch-and-Bound algorithm. In +particular, we will use the :code:`StrongBranching` class to define our branching rule. We can, for instance, simply +declare + +.. code:: cpp + + const auto branching_rule = + StrongBranching(); + +which will create a new Full Strong Branching rule. Just like any other branching rule, it can be used by calling the +:code:`BranchAndBound::with_branching_rule` method. + +.. code:: cpp + + const auto branch_and_bound = + BranchAndBound() + .with_branching_rule(branching_rule) + .with_node_selection_strategy(BestBound()); + +Then, we can write a Branch-and-Price algorithm and solve our problem as follows. + +.. code:: cpp + + const auto branch_and_price = branch_and_bound + column_generation; + + model.use(branch_and_price); + + model.optimize(); + +Beware that here, we only implemented Full Strong Branching which, as we saw, is not computationally convenient... +Let's see how to implemented Restricted Strong Branching. + +Restricted Strong Branching +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To implement Restricted Branching, one simply needs to call the :code:`StrongBranching::with_max_n_variables` method. +This is done as follows. + +.. code:: cpp + + const auto branching_rule = + StrongBranching() + .with_max_n_variables(50); + +Here, we set the maximum number of considered variables equal to :math:`K = 50`. + +Phases +^^^^^^ + +In this section, we will discuss how to implement phases with the strong branching rule. This is done by using the +:code:`StrongBranching::add_phase` method. This method takes three arguments: a phase type, which is used to indicate +how each node should be solved, e.g., with some iteration limit, a maximum number of variables to consider, for restricted +strong branching, and a maximum depth, used to trigger the phase based on the level of the current node in the Branch-and-Bound +tree. + +Here is an instance of strong branching with phases which, for nodes whose level is below or equal to 3, applies Full +Strong Branching, then switches to Restricted Strong Branching with :math:`K = 30` and which solves nodes with an iteration +limit of 20. + +.. code:: cpp + + const auto branching_rule = + StrongBranching() + .add_phase(StrongBranchingPhases::WithNodeOptimizer(), std::numeric_limits::max(), 3) + .add_phase(StrongBranchingPhases::WithIterationLimit(20), 30, std::numeric_limits::max()); + +Observe how we used :code:`std::numeric_limits::max()` to remove restrictions on the number of +considered variables and on the maximum depth for the final phase. Note that, by default, if no phase is triggered for a +given depth, e.g., because it was not specified, Full Strong Branching is applied. Here, however, we make sure that the +second phase is always triggered. + +Changing the Scoring Function +----------------------------- + +The scoring function can be changed by calling the :code:`StrongBranching::with_scoring_function` method. This method +takes a scoring function as an argument. The scoring function is a sub-class of :code:`NodeScoreFunction` and can be +:code:`Linear` or :code:`Product`. + +By default, idol uses the product scoring function. To change it to the linear scoring function, one can simply write + +.. code:: + + const auto branching_rule = + StrongBranching() + .with_scoring_function(NodeScoreFunctions::Linear()); diff --git a/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-cut-and-price.rst.txt b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-cut-and-price.rst.txt new file mode 100644 index 00000000..8d6500ff --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-cut-and-price.rst.txt @@ -0,0 +1,6 @@ +Writing A Branch-and-Cut-and-Price Algorithm [TODO] +=================================================== + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-price.rst.txt b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-price.rst.txt new file mode 100644 index 00000000..04ac0519 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-price.rst.txt @@ -0,0 +1,6 @@ +Writing A Branch-and-Price Algorithm [TODO] +=========================================== + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/index.rst.txt b/_sources/tutorials/mixed-integer-programming/index.rst.txt new file mode 100644 index 00000000..d50a1833 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/index.rst.txt @@ -0,0 +1,13 @@ +.. _mip: + +Mixed-Integer Programming +========================= + +.. toctree:: + :maxdepth: 2 + :glob: + + modeling/index + optimizers/index + branch-and-bound/index + dantzig-wolfe/index diff --git a/_sources/tutorials/mixed-integer-programming/modeling/constraints.rst.txt b/_sources/tutorials/mixed-integer-programming/modeling/constraints.rst.txt new file mode 100644 index 00000000..aa159efc --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/modeling/constraints.rst.txt @@ -0,0 +1,106 @@ +.. _api_constraints: + +Constraints +----------- + +.. contents:: Table of Contents + :local: + :depth: 2 + +Creating Constraints +^^^^^^^^^^^^^^^^^^^^ + +Similarly to variables, constraints are easily created and added to a given ``Model``. + +Constraints can be created by calling the constructor of the :ref:`Ctr ` class and added to a model by means of +:cpp:`Model::add` or by calling the :cpp:`Model::add_ctr` method. See for instance. + +.. code:: + + Env env; + Model model(env); + + Var x_0(env, 0., Inf, Continuous, "x_0"); + Var x_1(env, 0., Inf, Continuous, "x_1"); + Ctr constraint(env, x_0 + x_1 >= 1); + + model.add(x_0); + model.add(x_1); + model.add(constraint); + +A more compact version of this code is obtained by making use of the ``Model::add_vars`` and ``Model::add_ctr`` methods. + +.. code-block:: cpp + + Env env; + Model model(env); + + const auto x = model.add_vars(Dim<1>(2), 0., Inf, Continuous, "x"); + const auto constraint = model.add_ctr(x[0] + x[1] >= 1); + +As you can see, a constraint is created using the pattern :code:`{expression} {sign} {expression}` where + +* :code:`{sign}` is one of :code:`<=`, :code:`>=` and :code:`==`; +* :code:`{expression}` is an expression, i.e., an instance of :code:`Expr`. + +Actually, the pattern :code:`{expression} {sign} {expression}` only creates a "temporary" constraint, i.e., a constraint +which is not associated to any model. This is why we need to add it to a model by calling the :code:`Model::add_ctr` method. +Temporary constraints are objects of the class :ref:`TempCtr `. An equivalent code would be. + +.. code:: cpp + + Env env; + Model model(env); + + const auto x = model.add_vars(Dim<1>(2), 0., Inf, Continuous, "x"); + const auto temporary_constraint = TempCtr(Row(x[0] + x[1], 1), GreaterThan); + + model.add_ctr(temporary_constraint); + +Here, we used the class :ref:`Row ` to create the row associated to the constraint. The sign of the constraint is +specified by the second argument of the constructor of the :ref:`TempCtr ` class, and can take values +:code:`LessThan`, :code:`GreaterThan` and :code:`EqualTo`. + +Accessing Constraints +^^^^^^^^^^^^^^^^^^^^^ + +Information about a given constraint in a model can be accessed by calling the corresponding methods of the model. +The type of a constraint can be accessed by calling the :cpp:`Model::get_ctr_type` method. The row of a constraint can be +accessed by calling the :cpp:`Model::get_ctr_row` method. + +If a given model has been solved and feasibility could be proved (or better, optimality), the dual value of a constraint +can be accessed by calling the :cpp:`Model::get_ctr_dual` method. +For infeasible models, a Farkas certificate (dual ray) can be accessed by calling the :cpp:`Model::get_ctr_farkas` method. + +The :cpp:`Model::has` method can be used to check if a given constraint is in the model. + +The current index of a constraint in the model can be accessed by calling the :cpp:`Model::get_ctr_index` method. +Beware, however, that the index may change if the model is modified. + +For more details, see the :ref:`Model ` class. + +Modifying Constraints +^^^^^^^^^^^^^^^^^^^^^ + +The type of a constraint can be modified by calling the :cpp:`Ctr::set_ctr_type` method. +The value of the right-hand side of a constraint can be modified by calling the :cpp:`Ctr::set_rhs` method. + +The row of a constraint in the model can be modified by calling the :cpp:`Ctr::set_row` method. +For instance. + +.. code:: cpp + + Row row; + row.set_rhs(2); + row.linear().set(x, coefficient_for_x_in_constraint); + row.linear().set(y, coefficient_for_y_in_constraint); + + constraint.set_ctr_row(constraint, row); + +For more details, see the :ref:`Model ` class. + +Removing Constraints +^^^^^^^^^^^^^^^^^^^^ + +A constraint can be removed from a model by calling the :cpp:`Model::remove` method. + diff --git a/_sources/tutorials/mixed-integer-programming/modeling/environment.rst.txt b/_sources/tutorials/mixed-integer-programming/modeling/environment.rst.txt new file mode 100644 index 00000000..7017cfbf --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/modeling/environment.rst.txt @@ -0,0 +1,14 @@ +The Environment +--------------- + +Every optimization object (such as variables and constraints) are managed by an "optimization environment". +It is the environment that controls the death and life of such objects. It is also through the environment that idol +manages the different versions each optimization object may have during the execution of your program. + +Typically, only one environment should be used by your code. Yet, it is not forbidden to have several environments at the same time (not advised). + +Environments are objects of the :ref:`Env ` class, and can be created as follows. + +.. code-block:: + + Env env; // Creates a new optimization environment. diff --git a/_sources/tutorials/mixed-integer-programming/modeling/expressions.rst.txt b/_sources/tutorials/mixed-integer-programming/modeling/expressions.rst.txt new file mode 100644 index 00000000..b1b55c88 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/modeling/expressions.rst.txt @@ -0,0 +1,80 @@ +Expressions +----------- + +An expression generically refers to any mathematical expression involving variables or constraints. In idol, expressions +are represented by the :ref:`Expr ` class. + +An expression can be created by adding, subtracting or multiplying variables with constants or other expressions. For instance, +the following code creates the mathematical expression :math:`1 + 3 x_0 + x_1 + x_0 + 2 x_0 x_1`. + +.. code:: cpp + + const Expr expr = 1 + 3 * x[0] + x[1] + x[0] + 2 * x[0] * x[1]; + + std::cout << expr << std::endl; // "1 + 4 * x[0] + 1 * x[1] + 2 * x[0] * x[1]" + +Expressions are composed of three parts: + +* A constant (or offset) which is an instance of the :ref:`Constant ` class; +* A linear part which is an instance of the :ref:`LinExpr ` class; +* A quadratic part which is an instance of the :ref:`QuadExpr ` class. + +Each of these parts can be accessed using the methods :cpp:`Expr::constant`, :cpp:`Expr::linear` and :cpp:`Expr::quadratic`, respectively. +For instance, consider the following code. + +.. code-block:: cpp + + for (const auto& [var, coefficient] : expr.linear()) { + std::cout << var << " is multiplied by " << coefficient << std::endl; + } + +This code iterates over the linear part of the expression and prints the variables and their coefficients. + + +.. admonition:: About constants in expressions + + Without going into too much detail, we should here precise that each constant multiplying a variable in an :cpp:`Expr`, + as well as the offset constant, can actually be composite. For instance, this is the case when a variable is multiplied by + a parameter which is considered fixed in the current model but cannot be evaluated at the time the expression is created. + + Creating such "parameters" can be done by prepending a variable with the ``!`` symbol. This will automatically + create an instance of the :ref:`Param ` class. + For instance, the following code creates + a constant which involves the variables :math:`\xi_0` and :math:`\xi_1`, viewed as parameters. + + .. code-block:: + + Env env; + const Model model(env); + const auto x = model.add_vars(Dim<1>(2), 0., Inf, Continuous, "x"); + + const Model model2(env); + const auto xi = model2.add_vars(Dim<1>(2), 0., 1., Continuous, "xi"); + + const Expr expr = (1 + 2 * !xi[0]) * x[0] + 3 * !xi[1] * x[1]; + + Here, ``1 + 2 * !xi_0`` is an instance of the ``Constant`` object and can be used as follows. + + .. code-block:: + + Constant constant = 1 + 2 * !xi_0; + + std::cout << constant.numerical() << std::endl; // output: 1 + + for (const auto& [param, coeff] : constant) { + std::cout << coeff << " * " << param << std::endl; // output: 2 * !xi_0 + } + + Note that a parameter can be turned back into a variable by calling the :cpp:`Param::as` template method. + See, for instance. + + .. code-block:: cpp + + const auto param = !xi[0]; + + if (param.is()) { + const auto var = param.as(); + // do somthing with the variable + } + + Parameters can be variables or constraints. diff --git a/_sources/tutorials/mixed-integer-programming/modeling/index.rst.txt b/_sources/tutorials/mixed-integer-programming/modeling/index.rst.txt new file mode 100644 index 00000000..ebd207c1 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/modeling/index.rst.txt @@ -0,0 +1,30 @@ +.. _mip_modeling: + +.. role:: cpp(code) + :language: cpp + +Modeling +======== + +This page introduces the basic concepts of modeling optimization problems in idol. +We will discuss how to create optimization models and add variables, constraints and objective functions to that model. +We will also see how to access the different components of an existing model. + + +All classes which are used for modeling (standard) optimization problems can be accessed by including :code:`#include `. + +To simplify things, we will use the :code:`namespace idol` in the following examples. This dispenses us from prefixing +every class with :code:`idol::`. For instance, we will use :code:`Model` instead of :code:`idol::Model`. + +Let's get started. + +.. toctree:: + :maxdepth: 1 + :glob: + + environment + models + variables + expressions + constraints + objective-function diff --git a/_sources/tutorials/mixed-integer-programming/modeling/models.rst.txt b/_sources/tutorials/mixed-integer-programming/modeling/models.rst.txt new file mode 100644 index 00000000..fd8ee2a6 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/modeling/models.rst.txt @@ -0,0 +1,86 @@ +Models +------ + +The :ref:`Model ` class is used to represent a (standard) optimization model, i.e., a mathematical program of the form + +.. math:: + + \begin{align} + \min_x \ & c^\top x + x^\top D x + c_0 \\ + \text{s.t.} \ & a_{(i)}^\top x + x^\top Q^i x \le b_i \quad i=1,\dotsc,m \\ + & x_j \in \mathbb Z \quad j=1,\dotsc,p + \end{align} + +Here, :math:`x_j` are the decision variables of this optimization problem while matrices :math:`c, D, A` and :math:`Q^i` are given +input parameters for this model. Additionally, variables :math:`x_j` having :math:`j\in\{ 1,\dotsc,p \}` have to be integer-valued. + + +.. contents:: Table of Contents + :local: + :depth: 2 + +Creating a Model +^^^^^^^^^^^^^^^^ + +A Model is created by calling the constructor of the :cpp:`Model` class. Note that it is necessary to pass an environment +to the constructor. + +The following code creates a new optimization model. + +.. code-block:: cpp + + Env env; + Model model(env); + +We can now define our decision variables and constraints, and add an objective function to our model. +This is detailed in the :ref:`Variables `, :ref:`Constraints ` and :ref:`Objective ` sections. + +Reading a Model from a File +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A Model can also be created by reading a model from a file (typically, an MPS or an LP file). +To do this, idol relies on an external solver. The following code reads a model from a file using Gurobi. + +.. code-block:: cpp + + Env env; + auto model = Gurobi::read_from_file(env, "/path/to/model.mps"); + // auto model = GLPK::read_from_file(env, "/path/to/model.mps"); + +Writing a Model to a File +^^^^^^^^^^^^^^^^^^^^^^^^^ + +To write an optimization model to a file, the easiest way is again to rely on an external solver. +This is done by attaching a solver to a model, then by calling the :code:`Model::write` method. + +.. code:: + + Env env; + Model model = some_function_creating_my_model(env); // this is assumed to create the desired optimization model + + model.use(Gurobi()); // We will use Gurobi as the optimizer + + model.write("instance.lp"); // Creates a new file "instance.lp" storing the model + +Iterating over the Variables and Constraints +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The :ref:`Model ` class provides methods to iterate over the variables and constraints of the model. +See for instance, the following example. + +.. code-block:: cpp + + for (const auto& var : model.vars()) { + std::cout << "Variable " << var.name() << " belongs to the model." << std::endl; + } + + for (const auto& ctr : model.ctrs()) { + std::cout << "Constraint " << ctr.name() << " belongs to the model." << std::endl; + } + +The number of variables and constraints can be obtained by doing the following. + +.. code:: cpp + + std::cout << "Nb. of variables: " << model.vars().size() << std::endl; + std::cout << "Nb. of constraints: " << model.ctrs().size() << std::endl; diff --git a/_sources/tutorials/mixed-integer-programming/modeling/objective-function.rst.txt b/_sources/tutorials/mixed-integer-programming/modeling/objective-function.rst.txt new file mode 100644 index 00000000..b697101d --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/modeling/objective-function.rst.txt @@ -0,0 +1,45 @@ +.. _api_objective_functions: + +Objective Functions +=================== + +.. contents:: Table of Contents + :local: + :depth: 2 + +Adding an Objective Function +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The objective function of an optimization model can specified by the ``Model::set_obj_expr`` method. The objective function is a linear expression of the decision variables. + +Here is an example which sets the objective function to :math:`-x_0 + 2 x_1`; + +.. code-block:: + + model.set_obj_expr(-x_0 + 2 * x_1); + +Accessing the Objective Function +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The objective function can be accessed using the ``Model::get_obj_expr`` method. +The objective sense can be accessed using the ``Model::get_obj_sense`` method. + +If a given model has been solved, the best objective function value can be accessed using the ``Model::get_best_obj`` method. +The best bound can be accessed using the ``Model::get_best_bound`` method. + +Changing the Optimization Sense +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To change the optimization sense, use the ``Model::set_obj_sense`` method. +The optimization sense can be either ``Minimize`` or ``Maximize``. For instance, + +.. code:: cpp + + model.set_obj_sense(Maximize); + +Alternatively, one can use the constructor of the ``Model`` class to set the optimization sense. + +.. code:: cpp + + Env env; + Model model(env, Maximize); // Creates a model for maximization diff --git a/_sources/tutorials/mixed-integer-programming/modeling/variables.rst.txt b/_sources/tutorials/mixed-integer-programming/modeling/variables.rst.txt new file mode 100644 index 00000000..edd3085f --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/modeling/variables.rst.txt @@ -0,0 +1,149 @@ +.. _api_variables: + +Variables +--------- + +Variables are the decision variables of an optimization problem. They are the objects that we want to find the best +values for, with respect to some objective function given the specified constraints. + +.. contents:: Table of Contents + :local: + :depth: 2 + +Creating Variables +^^^^^^^^^^^^^^^^^^^ + +There are mainly two ways to create variables in idol. The first one is to use the :ref:`Var ` class directly, and the +second one is to use the :cpp:`Model::add_var` method. + +Using the :cpp:`Var` class directly creates a variable that is not associated to any model. We will there need to add it +explicitly to a model. This is done by calling the :cpp:`Model::add` method. See for instance the following code which +creates a continuous variable :math:`x`, with bounds :math:`[0,\infty)`, and adds it to the model. + +.. code:: cpp + + Var x(env, 0., Inf, Continuous, "x"); + + model.add(x); // Variable x is added to the model + +Note that a variable may belong to several models at the same time. Each version of the variable in each model is then independent. +For instance, one could do the following. + +.. code:: cpp + + Var x(env, 0., Inf, Continuous, "x"); + + model.add(x); + + Model model2(env); + model2.add(x); + model2.set_var_ub(x, 10.); + +In this example, the variable :math:`x` is added to two different models. The upper bound of :math:`x` in the second model +is then set to 10. The upper bound of :math:`x` in the first model is not affected by this change. + +A more straightforward way to create variables is to call the ``add_var`` method of the ``Model`` class. +For instance, one can do: + +.. code-block:: cpp + + const auto x = model.add_var(0, Inf, Continuous, "x"); + +Here, we do not need to repeat the environment since the model's environment is used by default. Moreover, we do not need to +explicitly add the variable to the model since it is done automatically by the ``add_var`` method. + +Again, a variable may belong to several models at the same time. And we can do as follows. + +.. code:: cpp + + const auto x = model.add_var(0, Inf, Continuous, "x"); + + Model model2(env); + model2.add(x); + model2.set_var_ub(x, 10.); + +Note that an even more compact version of the previous code is obtained by using the ``Model::add`` method with additional +parameters. For instance, the following code is equivalent to the previous one. + +.. code:: cpp + + const auto x = model.add(0, Inf, Continuous, "x"); + + Model model2(env); + model2.add(x, TempVar(0, 10, Continuous)); + +Here, we used the class :ref:`TempVar ` to create a temporary variable. A temporary variable is a variable +which has not been instantiate yet. It is only a "template" of a variable specifying its bounds and type. Here, we therefore +add the variable :math:`x` to the second model and directly set its attributes (bounds and type) accordingly. + +Creating Several Variables at Once +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Sometimes, you will find it convenient to create several variables at once. This can be done by calling the ``Var::make_vector`` +function, or the ``Model::add_vars`` method. These functions require +an extra parameter specifying the dimension of the new variable. + +For instance, the following code creates variables :math:`y_{ij}` with :math:`i=1,...,K` and :math:`j=1,...,T`. + +.. code:: cpp + + const auto y = Var::make_vector(env, Dim<2>(K, T), 0., Inf, Continuous, "y"); + + model.add_vector(y); + + std::cout << y[0][0] << std::endl; // "y_0_0" + +Similarly, one could use the more direct ``Model::add_vars`` method. + +.. code:: cpp + + const auto y = model.add_vars(Dim<2>(K, T), 0., Inf, Continuous, "y"); + + std::cout << y[0][0] << std::endl; // "y_0_0" + +Integer and binary variables can be created by setting the type of the variable to :cpp:`Integer` and :cpp:`Binary` respectively. + +Accessing Variables +^^^^^^^^^^^^^^^^^^^ + +Information about a given variable in a model can be accessed by calling the corresponding methods of the model. For instance, +one can access the lower bound of a variable by calling the :cpp:`Model::get_var_lb` method. Similarly, the upper bound of a +variable can be accessed by calling the :cpp:`Model::get_var_ub` method. The type of a variable can be accessed by calling the +:cpp:`Model::get_var_type` method. The column of a variable in the model can be accessed by calling the :cpp:`Model::get_var_column` +method. + +If a given model has been solved and feasibility could be proved (or better, optimality), the value of a variable +can be accessed by calling the :cpp:`Model::get_var_primal` method. +When applicable, its reduced cost can be accessed by calling the :cpp:`Model::get_var_reduced_cost` method. +Similarly, for unbounded models, a primal ray can be accessed by calling the :cpp:`Model::get_var_ray` method. + +The :cpp:`Model::has` method can be used to check if a given variable is in the model. + +The current index of a variable in the model can be accessed by calling the :cpp:`Model::get_var_index` method. +Beware, however, that the index may change if the model is modified. + +For more details, see the :ref:`Model ` class. + +Modifying Variables +^^^^^^^^^^^^^^^^^^^ + +The bounds of a variable can be modified by calling the :cpp:`Model::set_var_lb` and :cpp:`Model::set_var_ub` methods. +The type of a variable can be modified by calling the :cpp:`Model::set_var_type` method. + +The column of a variable in the model can be modified by calling the :cpp:`Model::set_var_column` method. +For instance. + +.. code:: cpp + + Column column(objective_coefficient_for_x); + column.linear().set(constraint1, constraint1_coefficient_for_x); + column.linear().set(constraint2, constraint2_coefficient_for_x); + + model.set_var_column(x, column); + +For more details, see the :ref:`Model ` class. + +Removing Variables +^^^^^^^^^^^^^^^^^^ + +A variable can be removed from a model by calling the :cpp:`Model::remove` method. diff --git a/_sources/tutorials/mixed-integer-programming/optimizers/access-optimizer.rst.txt b/_sources/tutorials/mixed-integer-programming/optimizers/access-optimizer.rst.txt new file mode 100644 index 00000000..bc018a79 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/optimizers/access-optimizer.rst.txt @@ -0,0 +1,32 @@ +Getting Access to The Underlying Optimizer +========================================== + +In some situations, you may want to access the underlying optimizer. +For instance, this could be the case if you want to get optimizer-specific information which are not exposed by the :code:`Model` interface. + +In this case, you can use the :code:`Model::optimizer` method. Note that this method +will return a reference of type :code:`Optimizer&`. + +To access optimizer-specific routines, you will need to cast the reference to the appropriate optimizer type. +This is made easier by the :code:`Optimizer::as` and :code:`Optimizer::is` methods, which allow you to cast the optimizer to a specific type or check if it is of a specific type. + +For instance, the following code shows how to access the underlying Gurobi optimizer of model. + +.. code:: + + Env env; + Model model(env); + const auto x = model.add_var(0.0, 1.0, Continuous, Column(-1), "x"); + + model.use(Gurobi()); + model.optimize(); + + auto& gurobi_optimizer = model.optimizer().as(); // Cast the optimizer to "Optimizers::Gurobi" + + GRBVar& var = gurobi_optimizer[x]; // Access the optimizer-specific routines + +.. warning:: + + A common mistake is to try to cast the optimizer to the wrong type. In particular, to cast optimizer to its + optimizer factory type (e.g., :code:`Gurobi` instead of :code:`Optimizers::Gurobi`). + This will result in a runtime error. diff --git a/_sources/tutorials/mixed-integer-programming/optimizers/create-optimizer.rst.txt b/_sources/tutorials/mixed-integer-programming/optimizers/create-optimizer.rst.txt new file mode 100644 index 00000000..f5f2e67b --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/optimizers/create-optimizer.rst.txt @@ -0,0 +1,6 @@ +Creating Your Own Optimizer (Advanced) [TODO] +============================================= + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/mixed-integer-programming/optimizers/create-universal-callback.rst.txt b/_sources/tutorials/mixed-integer-programming/optimizers/create-universal-callback.rst.txt new file mode 100644 index 00000000..e85540f7 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/optimizers/create-universal-callback.rst.txt @@ -0,0 +1,214 @@ +.. _tutorial_create_callback: + +Creating Your Own Universal Callback +==================================== + +In this page, we will show you how to create your own universal callback. +Universal callbacks are callbacks which are not specific to a particular solver. In that sense, they are generic and can be +used with any solver. + +Note that there are also solver-specific callbacks, which are specific to a particular solver. +If you are looking for this, you can refer to :ref:`this page `. + +Creating your own callback can useful if you want to create a callback that is not included in the +default set of callbacks implemented in idol. +If you are looking for quick and easy-to-use callbacks for separating user cuts or lazy constraints, +you can refer to :ref:`this page `. + +.. contents:: Table of Contents + :local: + :depth: 2 + +Basics +^^^^^^ + +Creating your callback is done by creating a sub-class of the :ref:`Callback ` class and overriding the +:code:`Callack::operator()` method. It is this method that will be called by the solver at each iteration. + +Note, however, that callbacks cannot be given "as-is" to an optimizer but must be passed through a :ref:`CallbackFactory `. A callback +factory is a class whose role is to create a new callback object whenever it is needed. Every callback factories must be +a child (or little child) of the :ref:`CallbackFactory ` class. + +The following code shows how to create a callback factory and a callback that prints the value of the primal solution at each +iteration. + +.. code:: cpp + + class MyCallback : public CallbackFactory { + public: + + class Strategy { // Real callback implementation + protected: + void operator()(CallbackEvent t_event) { + + if (t_event != IncumbentSolution) { + return; + } + + std::cout << primal_solution() << std::endl; + } + } + + Callback* operator()() { // Creates a new callback object + return new Strategy(); + } + + CallbackFactory* clone() const { // Creates a copy of the callback factory + return new MyCallback(*this); + } + + } + +As you can see, the callback factory has two important methods: :code:`operator()` and :code:`clone()`. The :code:`operator()` +method is used to create a new callback object, while the :code:`clone()` method is used to create a copy of the callback factory. + +The nested class :code:`Strategy` is the actual callback implementation. It is a sub-class of the :ref:`Callback ` class and +overrides the :code:`operator()` method. In this example, the callback prints the value of the primal solution whenever the event +triggering the callback is :code:`IncumbentSolution`. In other words, this callback will print out all incumbent solutions found by the solver. + +As for the :ref:`UserCutCallback ` and :ref:`LazyConstraintCallback ` classes, +our new callback can be added to an optimizer as follows. + +.. code:: cpp + + model.use( + Gurobi().add_callback(MyCallback()) + ); + + model.optimize(); + +A Simple Example: Knapsack Cover Cuts +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. hint:: + + This section is dedicated to the "advanced topic" of knapsack cover inequalities. + Rudimentary notions on `Knapsack problems `_ and + `Cover inequalities `_ are recommended. + + +In this example, we will show how to create a callback that separates knapsack cover cuts. A knapsack cover cut is a valid inequality +for the knapsack problem. It is defined as follows: + +.. math:: + + \sum_{i \in C} x_i \leq |C| - 1 + + +where :math:`C` defines a cover of the knapsack, i.e., a set of items such that the sum of their weights is greater than the capacity of the knapsack. + +Given a solution :math:`\hat x` to the continuous relaxation of the knapsack problem, we can check whether it violates a cover inequality. +This is done by solving the following separation problem. + +.. math:: + + \begin{align} + \max_{z} \ & (1 - \hat x)^\top z & \ge 1 \\ + \text{s.t.} \ & w^\top z \ge W + 1, \\ + & z\in\{0,1\}^n. + \end{align} + +A cover inequality is violated if and only if the optimal objective value of this problem is strictly less than 1. +In such a case, a new cut should be added. + +We will write a callback that separates knapsack cover cuts. + +To this end, we first create our knapsack problem model. This is done as follows. + +.. code:: cpp + + Env env; + Model knapsack(env, Maximize); + + const auto x = knapsack.add_vars(Dim<1>(n), 0, 1, Binary, "x"); + + knapsack.add_ctr(idol_Sum(i, Range(n_items), w[i] * x[i]) <= W); + knapsack.set_obj_expr(idol_Sum(i, Range(n_items), p[i] * x[i])); + +Then, we create our callback factory. It is this factory that will be used to create a new callback object when needed. +Since we need to pass some parameters to the callback, we will use the constructor of the callback factory to pass these parameters. +This is done as follows. + +.. code:: cpp + + + class KnapsackCover : public CallbackFactory { + const std::vector m_x; + const std::vector m_weights; + const std::vector m_profits; + const double m_capacity; + public: + KnapsackCover(const std::vector& t_x, + const std::vector& t_weights, + const std::vector& t_profits, + double t_capacity) + : m_x(t_x), m_weights(t_weights), m_profits(t_profits), m_capacity(t_capacity) {} + + class Strategy; + + Callback* operator()() { // Creates a new callback object + return new Strategy(m_x, m_weights, m_profits, m_capacity); + } + + CallbackFactory* clone() const { // Creates a copy of the callback factory + return new MyCallback(*this); + } + + } + +The real implementation of the callback is done in the nested class :code:`Strategy`. +This class is a sub-class of the :ref:`Callback ` class and is defined as follows. + +.. code:: cpp + + class KnapsackCover::Strategy { // Real callback implementation + const std::vector m_x; + const std::vector m_weights; + const std::vector m_profits; + const double m_capacity; + protected: + Strategy(const std::vector& t_x, + const std::vector& t_weights, + const std::vector& t_profits, + double t_capacity) + : m_x(t_x), m_weights(t_weights), m_profits(t_profits), m_capacity(t_capacity) {} + + void operator()(CallbackEvent t_event) { + + if (t_event != InvalidSolution) { + return; + } + + auto& env = parent().env(); + const auto fractional_point = primal_solution(); + + Model separation(env, Maximize); + + const auto z = separation.add_vars(Dim<1>(m_x.size()), 0, 1, Binary, "z"); + separation.add_ctr(idol_Sum(i, Range(m_x.size()), m_weights[i] * z[i]) >= m_capacity + 1); + separation.set_obj_expr(idol_Sum(i, Range(m_x.size()), (1 - fractional_point[i]) * z[i])); + + separation.use(Gurobi()); + + separation.optimize(); + + if (separation.get_best_obj() < 1) { + return; + } + + const auto cut = idol_Sum(i, Range(m_x.size()), separation.get_var_primal(z[i]) * (1 - x[i])) >= 1; + + add_user_cut(cut); + + } + } + +Finally, we can add our callback to the optimizer as follows. + +.. code:: + + knapsack.use( + Gurobi::Continuous().add_callback(KnapsackCover(x, w, p, W)) + ); + + knapsack.optimize(); diff --git a/_sources/tutorials/mixed-integer-programming/optimizers/index.rst.txt b/_sources/tutorials/mixed-integer-programming/optimizers/index.rst.txt new file mode 100644 index 00000000..ee6368a7 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/optimizers/index.rst.txt @@ -0,0 +1,120 @@ +.. _mip_optimizers: + +.. role:: cpp(code) + :language: cpp + +Optimizers +========== + +This page discusses the concept of optimizers and optimizer factories in idol. +We will discuss how to set up an optimizer for solving a model and how to access the solution of the model. + +In idol, an optimizer is a class that is responsible for solving a given optimization problem. +It is created by an optimizer factory, which is a class that is responsible for assembling and creating optimizers. + +We start with an example. Consider the following code, which models a simple knapsack problem: + +.. code:: cpp + + Env env; + Model model(env, Maximize); + const auto x = model.add_vars(Dim<1>(3), 0, 1, Binary, "x"); + model.add_ctr(x[0] + 2 * x[1] + 3 * x[2] <= 5); + model.set_obj_expr(x[0] + x[1] + x[2]); + +The code creates a model with three binary variables, and adds a constraint and an objective function to it. +Currently, the model does not have an optimizer, so it cannot be solved. +Calling :code:`model.optimize()` will result in an exception being thrown. + +To set up an optimizer, we need to call the :code:`Model::use` method, which takes an optimizer factory as an argument. +For instance, let us use the :ref:`GLPK ` optimizer factory, which creates an optimizer using GLPK to solve our model: + +.. code:: cpp + + model.use(GLPK()); + +Here, we assume that idol was linked with the GLPK library during installation. +Now, calling :code:`model.optimize()` will cause the optimizer factory to create an optimizer, which will solve the model. +Since we are using the :ref:`GLPK ` optimizer factory, an instance of :ref:`Optimizers::GLPK ` +will be created and used to solve the model. + +Note that we can modify the model after setting up the optimizer. The optimizer will be updated accordingly. For instance. + +.. code:: + + model.add_ctr(x[0] + x[1] <= 1); // Add a new constraint + model.optimize(); // The optimizer will solve the model with the new constraint + +Idol provides several optimizer factories, each of which creates an optimizer using a different solvers or implementing different algorithms. +For instance, the :ref:`Gurobi ` optimizer factory creates an optimizer using the Gurobi solver, +while the :ref:`BranchAndBound ` optimizer factory creates an optimizer implementing a branch-and-bound algorithm. + +Typically, users only work with optimizer factories, and do not need to create optimizers directly. +Passing arguments to an optimizer is always possible at creation time through the optimizer factory. +For instance, here is a small example of how to pass a time limit to the GLPK optimizer: + +.. code:: + + model.use(GLPK().with_time_limit(10)); // add time limit of 10 seconds + +As it can be seen, arguments are given by calling the :code:`with_` method of the optimizer factory, followed by the name of the argument. + + +In some cases, it is useful to nest optimizers to, e.g., to use an optimizer to solve a sub-problem of another optimization problem. +The process of nesting optimizers is straightforward and follows the same pattern as setting up an optimizer for a model. + +For instance, consider the following code, which implements a simple branch-and-bound algorithm to solve our knapsack problem: + +.. code:: cpp + + model.use( + /* The overall algorithm is a branch-and-bound */ + BranchAndBound() + + /* Each node is solved by the continuous relaxation of the problem, with GLPK */ + .with_node_solver(GLPK::ContinuousRelaxation()) + + /* Variables are selected for branching using the most-infeasible rule */ + .with_branching_rule(MostInfeasible()) + + /* Nodes are selected using the best-bound rule */ + .with_node_selection_rule(BestBound()) + + /* Turn on logs */ + .with_logs(true) + + /* The algorithm will run with a time limit of 3600 */ + .with_time_limit(3600) + ) + ); + +In this example, we create a branch-and-bound algorithm to solve the model, and we use GLPK to solve the continuous relaxation of the problem at each node. +Also note that we can nest optimizers as many times as we want with no restrictions. + +To simplify the writing of complex optimizer factories, it may be useful to use temporary variables to store intermediate results. +For instance. + +.. code:: cpp + + const auto continuous_relaxation = GLPK::ContinuousRelaxation().with_logs(true); + const auto branching_rule = MostInfeasible(); + const auto node_selection_rule = BestBound(); + + const auto branch_and_bound = BranchAndBound() + .with_node_solver(continuous_relaxation) + .with_branching_rule(branching_rule) + .with_node_selection_rule(node_selection_rule) + .with_logs(true) + .with_time_limit(3600); + + model.use(branch_and_bound); + +.. toctree:: + :maxdepth: 1 + :glob: + + solving + access-optimizer + universal-callbacks + create-universal-callback + create-optimizer diff --git a/_sources/tutorials/mixed-integer-programming/optimizers/solving.rst.txt b/_sources/tutorials/mixed-integer-programming/optimizers/solving.rst.txt new file mode 100644 index 00000000..70750f5e --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/optimizers/solving.rst.txt @@ -0,0 +1,139 @@ +Solving a Model and Accessing the Solution +========================================== + +.. contents:: Table of Contents + :local: + :depth: 2 + +Solving a Model +^^^^^^^^^^^^^^^ + +This page shoes how to solve a model and access the solution. +The reader should be familiar with the concept of optimizer and optimizer factories. If this is not the case, +please refer to the page on :ref:`Optimizers `. + +Let us consider the following code. + +.. code:: cpp + + using namespace idol; + + const unsigned int n_items = 5; + const double[] profit = { 40., 50., 100., 95., 30., }; + const double[] weight = { 2., 3.14, 1.98, 5., 3., }; + const double capacity = 10.; + + Env env; + Model model(env, Maximize); + + const auto x = model.add_vars(Dim<1>(n_items), 0., 1., Binary, "x"); + model.add(idol_Sum(j, Range(n_items), weight[j] * x[j] ) <= capacity); + model.set_obj_expr(idol_Sum(j, Range(n_items), profit[i] * x[i]); + +This code creates a model for the knapsack problem. As described in the page on :ref:`this page `, +we now set up an optimizer and solve the model. We use GLPK. + +.. code:: cpp + + model.use(GLPK()); + +Solving the model is done by a single class to the :code:`optimize` method. + +.. code:: cpp + + model.optimize(); + +Then, idol provides several methods to access the solution. + +First, :code:`get_status` and :code:`get_reason` return the status and the reason of the solution. +The status can be, for instance, :code:`Optimal` or :code:`Infeasible`. The reason provides more details about the status. +For instance, if the status is :code:`Infeasible`, the reason can be that the original problem is infeasible (:code:`Proved`), +or that the solver reached a time limit before finding a feasible solution (:code:`TimeLimit`). + +The best objective value found can be accessed using :code:`get_best_obj`. This is the best objective value among feasible solutions considered during the execution of the algorithm. +The best objective value bound can be accessed using :code:`get_best_bound`. For instance, this can be a dual bound. +The methods :code:`get_relative_gap` and :code:`get_absolute_gap` return the relative and absolute optimality gaps, respectively. +See :ref:`this page ` for more details about gaps and tolerances. + +The methods :code:`get_var_primal` and :code:`get_var_ray` return the primal value and the (primal) ray value of a given variable, respectively. +Note that primal values are only accessible if the model has status :code:`Feasible`, :code:`SubOptimal` or :code:`Optimal`. +Similarly, a primal ray is only accessible if the model has status :code:`Unbounded`. + +The methods :code:`get_ctr_dual` and :code:`get_ctr_farkas` return the dual value and the Farkas certificate value of a given constraint, respectively. +Note that dual values are only accessible if the model is continuous and has status :code:`Optimal` or :code:`Feasible`. +Similarly, a Farkas certificate is only accessible if the model has status :code:`Infeasible`. + +Saving a Solution +^^^^^^^^^^^^^^^^^ + +Sometimes, you will find it useful to save a solution to access it later. idol provides the following functions to do so: +:code:`save_primal`, :code:`save_ray`, :code:`save_dual` and :code:`save_farkas`. +Each of these functions takes a model as argument and returns an object of the class :ref:`Solution::Primal ` or :ref:`Solution::Primal ` depending on the function. +The returned object stores the results of corresponding calls to :code:`get_var_primal`, :code:`get_var_ray`, :code:`get_ctr_dual` or :code:`get_ctr_farkas` methods. + +.. admonition:: Example + + This example shows how to solve a model using HiGHS and retrieves some piece of information about its solution. + + .. code-block:: + + model.use(HiGHS()); + + model.optimize(); + + const auto status = model.get_status(); + + if (status == Optimal) { + + std::cout << "Optimal solution found!" << std::endl; + + const auto primal_values = save_primal(model); + + std::cout << primal_values << std::endl; + + } else { + + std::cout << "An optimal solution could not be found." << std::endl; + + std::cout << "HiGHS returned status " << status << std::endl; + + std::cout << "The reason for this status is " << model.get_reason() << std::endl; + + if (status == Feasible) { + + std::cout << "The optimality gap is " << model.get_relative_gap() * 100 << " %" << std::endl; + + } else if (status == Unbounded) { + + std::cout << "An unbounded ray is" << std::endl; + + const auto primal_ray = save_ray(model); + + std::cout << primal_ray << std::endl; + + } else if (status == Infeasible) { + + std::cout << "A Farkas certificate is" << std::endl; + + const auto farkas = save_farkas(model); + + std::cout << farkas << std::endl; + + } + + } + +Saving a Projected Solution +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In a more advanced solution scheme, you may deal with extended formulations of an original model, and may want to save +the projected solution on the original problem space. +In such a case, you can use the functions :code:`save_*` with an additional argument to specify the original model. +For instance. + +.. code:: cpp + + const auto primal_values = save_primal(original_model, higher_dimensional_model); + +This code will return an object of the class :ref:`Solution::Primal ` storing the results of corresponding calls to :code:`get_var_primal` methods on the higher dimensional model +for the original model variables. diff --git a/_sources/tutorials/mixed-integer-programming/optimizers/universal-callbacks.rst.txt b/_sources/tutorials/mixed-integer-programming/optimizers/universal-callbacks.rst.txt new file mode 100644 index 00000000..ccc6a329 --- /dev/null +++ b/_sources/tutorials/mixed-integer-programming/optimizers/universal-callbacks.rst.txt @@ -0,0 +1,259 @@ +.. _tutorial_user_lazy_cuts: + +Adding User Cuts and Lazy Constraints +===================================== + +.. contents:: Table of Contents + :local: + :depth: 2 + +Introduction +^^^^^^^^^^^^ + +When solving a mixed-integer program (MIP), the underlying optimizers typically make use of a branch-and-cut algorithm. +This algorithm iteratively solves a(n LP) relaxation of the problem, and then adds cuts to the problem to strengthen the relaxation. +Typically, these cuts are generated by the solver itself. However, the user can also add his or her own cuts to the problem. +This is useful when the user has domain-specific knowledge that can be used to strengthen the relaxation. + +Classically, there are two types of cuts that can be added to a MIP: user cuts and lazy constraints. + +* **User cuts** are cuts which are not necessary to define the feasible region of the problem, but which can be used to strengthen the relaxation. As a matter of fact, *User cuts never cut off feasible solutions to the original problem*. +* **Lazy constraints** are constraints which are necessary to define the feasible region of the problem but are either unlikely to be violated or impractical to generate in advance. Lazy constraints are only added to the problem if they are violated by a current solution. + +In both cases, user cuts and lazy constraints are added to the problem within a callback function, given to the optimizer. +Then, the optimizer will call this callback function at each node of the branch-and-cut tree, allowing to look for violated cuts and constraints that can be added to the problem. + +Though it is possible for the user to create his or her own callback functions, +the idol library provides a simple way to add user cuts and lazy constraints to a MIP, using the :ref:`UserCutCallback ` and :ref:`LazyCutCallback ` classes. +In case you are interested in creating your own callback functions, you can refer to :ref:`this page `. + +Now, both classes are derived from the :ref:`CallbackFactory ` class, and are used to create callback objects that can be passed to the optimizer. +They both have a similar interface: First, the user defines a model for the separation problem (i.e., the problem that will be solved to generate the cuts or constraints), +and the corresponding cut to be added to the original problem. +Then, the user creates a callback factory object, and passes the model and the cut to the factory. +The user also specifies which optimizer should be used to solve the separation problem. +Finally, the user passes the callback factory to the optimizer, which will manage the execution of the callback. + +The main difference between the two classes is that the :ref:`UserCutCallback ` class is called +whenever an invalid solution is found, e.g., a point which violates integer requirements, while the :ref:`LazyCutCallback ` class is called +whenever a new valid solution is found to check that it satisfies all the lazy constraints. + +In the next section, we will show how to implement + +* a simple separation procedure for knapsack cover inequalities using the :ref:`UserCutCallback `, and +* a straightforward Bender's decomposition algorithm using the :ref:`LazyCutCallback ` class. + +.. contents:: Table of Contents + :local: + :depth: 2 + +An Example of User Cuts: Knapsack Cover Inequalities +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. hint:: + + This section is dedicated to the "advanced topic" of knapsack cover inequalities. + Rudimentary notions on `Knapsack problems `_ and + `Cover inequalities `_ are recommended. + +Consider the following knapsack problem. + +.. math:: + + \begin{align} + \max_{x} \ & p^\top x \\ + \text{s.t.} \ & w^\top x \le W, \\ + & x\in\{0,1\}^n. + \end{align} + +Here, :math:`x` is a binary vector, :math:`p` is a vector of profits, :math:`w` is a vector of weights, and :math:`W` is the capacity of the knapsack. + +It is well-known that the knapsack problem can be strengthened by adding cover inequalities. A cover inequality is a constraint of the form + +.. math:: + + \sum_{i\in C} x_i \le |C| - 1, + +where :math:`C` defines a cover of the knapsack, i.e., a set of items such that the sum of their weights is greater than the capacity of the knapsack. + +Given a solution :math:`\hat x` to the continuous relaxation of the knapsack problem, we can check whether it violates a cover inequality. +This is done by solving the following separation problem. + +.. math:: + + \begin{align} + \max_{z} \ & (1 - \hat x)^\top z & \ge 1 \\ + \text{s.t.} \ & w^\top z \ge W + 1, \\ + & z\in\{0,1\}^n. + \end{align} + +A cover inequality is violated if and only if the optimal objective value of this problem is strictly less than 1. +In such a case, a new cut should be added. + +As anticipated, we need to define three different things: + +- the original problem, i.e., the problem to be solved by the branch-and-cut algorithm; +- the feasible region of the separation problem, i.e., the set of all cover inequalities; +- the shape of the cuts to be added. + +Defining the original problem is straightforward and can be done as follows. + +.. code:: cpp + + Env env; + Model knapsack(env, Maximize); + + const auto x = knapsack.add_vars(Dim<1>(n), 0, 1, Binary, "x"); + + knapsack.add_ctr(idol_Sum(i, Range(n_items), w[i] * x[i]) <= W); + knapsack.set_obj_expr(idol_Sum(i, Range(n_items), p[i] * x[i])); + +Similarly, the feasible region of the separation problem can be defined as follows. + +.. code:: cpp + + Model cover(env); + + const auto z = cover.add_vars(Dim<1>(n), 0, 1, Binary, "z"); + + cover.add_ctr(idol_Sum(i, Range(n_items), w[i] * z[i]) >= W + 1); + +Finally, we need to define the cuts to be added to the original problem for a given cover inequality :math:`C`. +**Cuts are always expressed as if they were part of the original problem**. What we mean by this is that, here, :math:`x` +should be seen as a *variable* while :math:`z` should be seen as a *constant*. + +We therefore have, for a given :math:`z`, + +.. code:: cpp + + const auto cover_cut = idol_Sum(i, Range(n_items), !z[i] * x[i]) <= idol_Sum(i, Range(n_items), 1 - !z[i]); + +See how the z variables are "turned into" constants by prepending them with an "!" symbol. + +We are now ready to create the callback factory and pass it to the optimizer. This is done as follows. + +.. code:: cpp + + knapsack.use( + Gurobi::Continuous() + .add_callback( + UserCutCallback(cover, cover_cut) + .with_separation_optimizer(Gurobi()) + ) + ); + + knapsack.optimize(); + +Here, we solve the continuous relaxation of the knapsack problem using the Gurobi optimizer, and add the cover inequalities using the :ref:`UserCutCallback `. + +An Example of Lazy Cut Constraints: Benders Optimality Cuts +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. hint:: + + This section is dedicated to the "advanced topic" of Benders Decomposition. + Rudimentary notions on `Linear Programming duality `_ and + `Benders Decomposition `_ the following subjects are recommended. + +We will base our example on the following model taken from `Blanco, V., (2016), Benders Decomposition, MINLP School: Theory +and Applications `_. + +.. math:: + + \begin{align} + \min_{x,y} \ & 2 x_0 + 3x_1 + 2y \\ + \text{s.t.} \ & x_0 + 2x_1 + y \ge 3, \\ + & 2x_0 - x_1 + 3y \ge 4, \\ + & x\ge 0, y\in \mathbb N. + \end{align} + +The Benders reformulation of this problem, by considering :math:`y` as the complicating variable, leads to + +.. math:: + + \begin{align} + \min_{y,z} \ & 2y + z \\ + \text{s.t.} \ & z \ge \lambda_1 ( 3 - y ) + \lambda_2(4 - 3y) \qquad \forall \lambda \in \Lambda, \\ + & z \ge 0, y\in\mathbb N, + \end{align} + +with :math:`\Lambda` defined as the set of all dual feasible points, i.e., those :math:`\lambda\in\mathbb R^2_+` such that + +.. math:: + + \begin{align} + & \lambda_0 + 2 \lambda_1 \le 2, \\ + & 2\lambda_0 - \lambda_1 \le 3. + \end{align} + +Now, we will show how to implement the Benders decomposition algorithm using the :ref:`LazyCutCallback ` class. +This will be done so that constraints :math:`z \ge \hat \lambda_1 ( 3 - y ) + \hat \lambda_2(4 - 3y)` are added to the master problem +whenever a violated Benders cut is found. + +As anticipated, we need to define three different things: + +- the master problem, i.e., the problem to be solved at each node of the branch-and-cut tree; +- the dual space :math:`\Lambda`, i.e., the feasible region of the separation problem; +- the shape of the cuts to be added. + +Defining the master problem is straightforward and can be done as follows. + +.. code:: cpp + + + Env env; + + Model master(env); + + const auto y = master.add_var(0, Inf, Integer, "y"); + const auto z = master.add_var(0, Inf, Continuous, "z"); + + master.set_obj_expr(2 * y + z); + +Similarly, the dual space :math:`\Lambda` can be defined as follows. + + +.. code:: + + Model dual_space(env); + + const auto lambda = dual_space.add_vars(Dim<1>(2), 0, Inf, Continuous, "lambda"); + + dual_space.add_ctr(lambda[0] + 2 + lambda[1] <= 2); + dual_space.add_ctr(2 * lambda[0] - lambda[1] <= 3); + + +Finally, we need to define the cuts to be added to the master problem for a given dual variable :math:`\lambda`. +**Cuts are always expressed as if they were part of the master problem**. What we mean by this is that, here, :math:`y` +should be seen as a *variable* while :math:`\lambda` should be seen as a *constant*. + +We therefore have, for a given :math:`\lambda`, + +.. code:: + + const auto benders_cut = z >= !lambda[0] * (3 - y) + !lambda[1] * (4 - 3 * y); + +See how the lambda variables are "turned into" constants by prepending them with an "!" symbol. + +We are now ready to create the callback factory and pass it to the optimizer. This is done as follows. + +.. code:: + + master.use( + Gurobi() + .add_callback( + LazyCutCallback(dual_space, benders_cut) + .with_separation_optimizer(Gurobi()) + ) + .with_lazy_cut(true) + ); + + master.optimize(); + +That's it! The optimizer will now call the callback function at each node of the branch-and-cut tree, and add the cuts to the master problem whenever a violated constraint is found. + +.. hint:: + + Here, we added a call to :code:`Gurobi::with_lazy_cut`. This is because the Gurobi optimizer does not support lazy cuts by default + and one needs to explicitly enable them. + diff --git a/_sources/tutorials/robust-optimization/column-and-constraint-generation/index.rst.txt b/_sources/tutorials/robust-optimization/column-and-constraint-generation/index.rst.txt new file mode 100644 index 00000000..1c7f8bca --- /dev/null +++ b/_sources/tutorials/robust-optimization/column-and-constraint-generation/index.rst.txt @@ -0,0 +1,12 @@ +Column-and-Constraint-Generation Algorithm +========================================== + + +.. toctree:: + :maxdepth: 1 + :glob: + + introduction + write-ccg + trust-region-stabilization + writing-separator diff --git a/_sources/tutorials/robust-optimization/column-and-constraint-generation/introduction.rst.txt b/_sources/tutorials/robust-optimization/column-and-constraint-generation/introduction.rst.txt new file mode 100644 index 00000000..e8f84a02 --- /dev/null +++ b/_sources/tutorials/robust-optimization/column-and-constraint-generation/introduction.rst.txt @@ -0,0 +1,165 @@ +Introduction +============ + +The Column-and-Constraint Generation (CCG) algorithm is a classical method for solving two-stage robust optimization problems. +It was originally introduced by :cite:`Zeng2013`. + +In idol, the implementation of CCG alows to solve a class of problems which is slightly more general than classical +two-stage robust optimization problems. This class of problems is defined in the following section. + +.. contents:: Table of Contents + :local: + :depth: 2 + +Problem Definition and Notation +------------------------------- + +The CCG algorithm can solve optimization problems of the form + +.. math:: + :label: eq:original-problem + + \begin{align} + \min_x \quad & f(x) \\ + \text{s.t.} \quad & x\in X, \\ + & \forall \xi\in\Xi, \ \exists y\in Y(x,\xi), \ G(x,y,\xi) \le 0. + \end{align} + +in which :math:`f:\mathbb R^{n_x}\rightarrow\mathbb R` is a given function, +:math:`X\subseteq\mathbb R^{n_x}` is a given set, called the *first-stage feasible set*, +:math:`\Xi\subseteq\mathbb R^{n_\xi}` is a given set, called the *uncertainty set*, typically compact, +:math:`Y(x,\xi) \subseteq \mathbb R^{n_y}` is a set, defined for all :math:`x\in X` and :math:`\xi\in\Xi`, called the *second-stage feasible set*, and +:math:`G:\mathbb R^{n_x+n_y+n_\xi}\rightarrow\mathbb R^\ell` is a given vector of functions, defining the so-called *coupling constraints* between the uncertainty, the first- and the second-stage decisions. + +.. admonition:: Regarding Coupling Constraints + + It is clear that coupling constraints are redundant since they can be incorporated into the definition of the second-stage feasible set. + However, we will see that explicitly defining *coupling constraints* may lead to different implementations of the CCG algorithm. + We will dive into this aspect after introducing the concept of *separators*. + +.. hint:: + + Consider the Min-Max-Min problem + + .. math:: + + \min_{x\in X} \ \max_{\xi\in\Xi} \ \min_{y\in Y(x,\xi)} \ \psi(x,y,\xi). + + Clearly, this is a special case of :math:numref:`eq:original-problem` since it can be written as + + .. math:: + + \begin{align} + \min_{x_0,x} \quad & x_0 \\ + \text{s.t.} \quad & (x_0,x) \in\mathbb R\times X, \\ + & \forall \xi\in\Xi, \ \exists y\in Y(x,\xi), \ \psi(x,y,\xi) \le x_0. + \end{align} + + Here, there is only one coupling constraint, which is :math:`\psi(x,y,\xi) \le x_0`. + + +The Algorithm +------------- + +Basic Idea +^^^^^^^^^^ + +The idea of the CCG algorithm is to consider a finite subset of points in :math:`\Xi`, say :math:`\{ \xi^1, \dotsc, \xi^k \}`, +and to solve the following problem instead of :math:numref:`eq:original-problem`: + +.. math:: + :label: eq:master-problem + + \begin{align} + \min_{x_0,x,y^1,\dotsc,y^K} \quad & f(x) \\ + \text{s.t.} \quad & x\in X, \\ + & G(x,y^t,\xi^t) \le 0 \qquad k=1,\dotsc,K, \\ + & y^t\in Y(x,\xi^t) \qquad k=1,\dotsc,K. + \end{align} + +Here, a new variable :math:`y^t` has been introduced for each :math:`t=1,...,k`, enforcing that, indeed, +there exists :math:`y^t\in Y(x,\xi^t)` such that :math:`G(x,y^t,\xi^t) \le 0`. + +Note that Problem :math:numref:`eq:master-problem` is a relaxation of Problem :math:numref:`eq:original-problem` since +any feasible point of :math:numref:`eq:original-problem` is also feasible for :math:numref:`eq:master-problem` (for some :math:`y^1,\dotsc,y^K`). + +Now, given a solution :math:`\hat x\in X` to the relaxed problem :math:numref:`eq:master-problem`, +one needs to check whether :math:`\hat x` is feasible for Problem :math:numref:`eq:original-problem`. +Thus, one seeks a scenario :math:`\xi^*\in\Xi` such that, either :math:`Y(\hat x, \xi^*)` is empty, or :math:`G(\hat x,y,\xi^*) > 0` for all :math:`y\in Y(\hat x, \xi^*)`. +If no such scenario exists, then :math:`\hat x` is feasible for :math:numref:`eq:original-problem`. Otherwise, the new scenario :math:`\xi^*` is added to the set of considered scenarios and the process is repeated. + +Identifying a missing scenario is called *separation*, and can be done by solving the following problem: + +.. math:: + :name: eq:separation-problem + + \max_{\xi\in \Xi} \ \max_{\ell=1,...,L} \left\{ \ \min_{ y\in Y(\hat x,\xi) } \ G_\ell(\hat x,y,\xi) \right\}. + +If the optimal value of the separation problem is non-positive, then :math:`\hat x` is feasible for :math:numref:`eq:original-problem`. +Otherwise, the solution to the separation problem gives a new scenario :math:`\xi^*` to be added to the set of scenarios. + +Note that we use the convention :math:`\max \emptyset = -\infty` and :math:`\min \emptyset = +\infty`. + +Separators +^^^^^^^^^^ + +Clearly, the separation problem :math:numref:`eq:separation-problem` can be solved in many different ways. In idol, +it is therefore possible to give a user-defined functor, called a *separator*, which solves the separation problem. +Note that the most common ways to solve the separation problem are already implemented in idol. +Yet, if you wish to implement your own separator, you should refer to :ref:`this tutorial `. + +Shortly put, the separator solves problems of the form + +.. math:: + :label: eq:single-separation-problem + + \max_{\xi\in \Xi} \ \min_{ y\in Y(\hat x,\xi) } \ G_\ell(\hat x,y,\xi), + +for a given :math:`G_\ell` (:math:`\ell\in\{1,...,L\}`). + +Note that it is ensured that the separator always solves a problem which is feasible. +Indeed, in case Problem :math:numref:`eq:separation-problem` +is not known to satisfy the *complete recourse assumption* (i.e., it is not known whether :math:`\forall x\in X, \forall\xi\in\Xi, \exists y\in Y(x,\xi)` holds), +the CCG algorithm will first solve a feasibility version of the separation problem to check whether +:math:`\hat x` is such that for all :math:`\xi\in\Xi` there exists :math:`y\in Y(\hat x,\xi)`. +Fortunately, it is also possible to specify that the complete recourse assumption holds, in which case the feasibility version of the separation problem is not solved. + +Let :math:`\xi^{\ell}` denote the solution to the separation problem :math:numref:`eq:single-separation-problem` for a given :math:`\ell\in\{1,...,L\}`. +Then, a scenario :math:`\xi^{\ell^*}` is added to Problem :math:numref:`eq:master-problem` if and only if + +.. math:: + + \ell^* \in \underset{\ell=1,...,L}{\text{argmax}} \ \min_{ y\in Y(\hat x,\xi^\ell) } \ G_\ell(\hat x,y,\xi^\ell) > \varepsilon_\text{feas}. + +See :ref:`the dedicated page ` for more details. + +On the Impact of Coupling Constraints +------------------------------------- + +We now discuss the impact of the definition of the coupling constraints :math:`G` on the implementation of the CCG algorithm. +Clearly, one obtains an equivalent problem to :math:numref:`eq:original-problem` by defining the second-stage feasible set as + +.. math:: + + \tilde Y(x,\xi) = \{ y\in Y(x,\xi) \ | \ G(x,y,\xi) \le 0 \}, + +and by considering the problem + +.. math:: + + \begin{align} + \min_x \quad & f(x) \\ + \text{s.t.} \quad & x\in X, \\ + & \forall \xi\in\Xi, \ \exists y\in \tilde Y(\hat x,\xi). + \end{align} + +In this case, the separation problem becomes + +.. math:: + + \max_{\xi\in\Xi} \ \min_{ y\in \tilde Y(\hat x,\xi) } \ 0, + +which is a feasibility problem. + +Though the two approaches are equivalent, in the sense that they will both lead to a solution to :math:numref:`eq:original-problem`, +they may lead to different computational performances. An interested reader may refer to, e.g., :cite:`Ayoub2016` for more details. diff --git a/_sources/tutorials/robust-optimization/column-and-constraint-generation/trust-region-stabilization.rst.txt b/_sources/tutorials/robust-optimization/column-and-constraint-generation/trust-region-stabilization.rst.txt new file mode 100644 index 00000000..d38ce803 --- /dev/null +++ b/_sources/tutorials/robust-optimization/column-and-constraint-generation/trust-region-stabilization.rst.txt @@ -0,0 +1,132 @@ +Using Trust Region Stabilization +================================ + +This tutorial describes how to use the trust region stabilization in the CCG algorithm. +This stabilization technique is inspired by the work of :cite:`Baena2020` on Benders decomposition. + +.. admonition:: Assumption + + This feature is only applicable if the first-stage decisions are binary, i.e., :math:`X \subseteq \{0,1\}^{n_x}`. + +.. contents:: Table of Contents + :local: + :depth: 2 + +Introduction +------------ + +In this section, we discuss stabilization of the CCG algorithm if the first-stage decisions are binary, i.e., :math:`X \subseteq \{0,1\}^{n_x}`. + +Let :math:`\bar x` be a given stability center, i.e., a point that is thought to be a "good" solution to the problem. +The following constraint is called a *local branching constraint*: + +.. math:: + + \Delta(x,\bar x) := \sum_{i:\bar x_i = 1} (1 - x_i) + \sum_{i:\bar x_i = 0} x_i \le \rho, + +with :math:`\rho` a given radius. Additionally, we call *reversed local branching constraint* the following constraint: + +.. math:: + + \Delta(x,\bar x) \ge \rho + 1. + +At each iteration, the stabilized CCG algorithm solves the following problem instead of :math:numref:`eq:master-problem`: + +.. math:: + + \begin{align} + \min_{x_0,x} \quad & f(x) \\ + \text{s.t.} \quad & x\in X, \\ + & G(x,y^t,\xi^t) \le 0 \quad t=1,\dotsc,k, \\ + & y^t\in Y(x,\xi^t) \quad t=1,\dotsc,k, \\ + & \Delta(x,\bar x) \le \rho, \\ + & \Delta(x,\bar x') \ge \rho' + 1 \quad \forall (\bar x', \rho') \in R, + \end{align} + +for some set :math:`R\subseteq X\times\mathbb N` of reversed local branching constraints. + +The complete stabilized CCG algorithm is described with the following pseudocode. + +.. raw:: html + + 
+        \begin{algorithm}
+        \caption{Stabilized CCG Algorithm}
+        \begin{algorithmic}
+        \REQUIRE An initial radius \( \rho\in\{ 1,\dotsc, n_x \} \) and an initial point \( \bar x\in X \).
+        \STATE Initialize \( k \gets 0 \), \( UB \gets +\infty \), \( LB \gets -\infty \), \( R \gets \emptyset \)
+        \STATE Solve the restricted master problem (RMP)
+        \IF{the RMP is infeasible}
+            \IF{ \( \rho \ge n_x \) }
+                \STATE STOP, \( UB \) is the optimal value.
+            \ENDIF
+            \STATE Add a reversed local branching constraint, \( R \gets R \cup (\bar x, \rho) \)
+            \STATE Increase \( \rho \)
+        \ELSE
+            \STATE Let \( x^k \) be the solution of the RMP and \( v^k \) be its value
+            \STATE Solve the separation problem, let \( \xi^k \) be the solution and \( s^k \) be its value
+            \IF{ \( s^k \le \varepsilon_\text{feas} \) }
+                \STATE \( UB \gets \min\{ UB, v^k \} \)
+                \STATE Solve the RMP without the stabilization constraints, let $\underline v^k$ be its value, set \( LB \gets \underline v^k \)
+                \IF{ \( UB - LB \le \varepsilon \) }
+                    \STATE STOP, \( UB \) is the optimal value.
+                \ENDIF
+                \STATE Add a reversed local branching constraint, \( R \gets R \cup (x^k, \rho) \)
+                \STATE Update the stability center \( \bar x \gets x^k \)
+                \STATE Optionally, reset \( \rho \gets 1 \)
+            \ENDIF
+            \STATE \( k \gets k + 1 \)
+            \STATE Go back to step 2
+        \ENDIF
+        \end{algorithmic}
+        \end{algorithm}
+
+ + +Note that if :math:`\rho \ge n_x`, the stabilized CCG is exactly the CCG algorithm. + +Implementation in idol +---------------------- + +Activating the trust region stabilization in idol is done through the method :code:`with_stabilization` of the :code:`idol::Robust::ColumnAndConstraintGeneration` class. +An object of the class :code:`Robust::CCGStabilizers::TrustRegion` must be passed as an argument to the method. + +The following code shows how to use the trust region stabilization in idol. + +.. code:: + + model.use( + Robust::ColumnAndConstraintGeneration(stages, uncertainty_set) + .with_master_optimizer(Gurobi()) + .with_separator(Robust::CCGSeparators::Bilevel()) + .with_stabilization(Robust::CCGStabilizers::TrustRegion()) + .with_logs(true) + ); + + model.optimize(); + +Note that the radius :math:`\rho` is set to + +.. math:: + + \rho \gets \lceil \mu_i n_x \rceil + +where :math:`\mu_i` is a parameter controlling the size of the trust region and :math:`n_x` is the number of (binary) first-stage variables. By default, :math:`\mu_i` takes value in +::math:`\lbrace .01, .02, .5 \rbrace`. +You can set the values of :math:`\mu_i` by calling the method :code:`with_trust_factors` of the class :code:`Robust::CCGStabilizers::TrustRegion`. +For instance, the following code sets the trust factors to :math:`\lbrace .02, .5 \rbrace`. + +.. code:: + + model.use( + Robust::ColumnAndConstraintGeneration(stages, uncertainty_set) + .with_master_optimizer(Gurobi()) + .with_separator(Robust::CCGSeparators::Bilevel()) + .with_stabilization( + Robust::CCGStabilizers::TrustRegion() + .with_trust_factors({.02, .5}) + ) + .with_logs(true) + ); diff --git a/_sources/tutorials/robust-optimization/column-and-constraint-generation/write-ccg.rst.txt b/_sources/tutorials/robust-optimization/column-and-constraint-generation/write-ccg.rst.txt new file mode 100644 index 00000000..0907e718 --- /dev/null +++ b/_sources/tutorials/robust-optimization/column-and-constraint-generation/write-ccg.rst.txt @@ -0,0 +1,49 @@ +.. _tutorial_ccg: + +Writing a Column-and-Constraint-Generation Algorithm +==================================================== + +In this tutorial, we will see how to write a column-and-constraint-generation to solve a two-stage robust problem. + +To this end, we will assume that you have your two-stage robust problem modeled already in idol. In particular, +we consider that you have + +1. :code:`(idol::Model) model` which is the deterministic model of the problem in which the uncertain data are seen as parameters. + +2. :code:`(idol::Model) uncertainty_set` which is the uncertainty set of the robust problem. + +3. :code:`(idol::Robust::StageDescription) stages` which stores the assignments of variables and constraints to each stage. + +If you do not know what these are, please refer to the tutorial on :ref:`how to model a two-stage robust problem `. + +Then, you can solve this two-stage robust problem using a column-and-constraint-generation algorithm as follows: + +.. code:: + + model.use( + Robust::ColumnAndConstraintGeneration(stages, uncertainty_set) + .with_master_optimizer(Gurobi()) + .with_separator(Robust::CCGSeparators::Bilevel()) + .with_logs(true) + ); + + model.optimize(); + + std::cout << save_primal(model) << std::endl; + +Notice that the optimizer is attached to the deterministic model and that both the uncertainty set and the stages are passed as arguments. +An optimizer to solve the master problem is necessary and will be called when needed. Here, we use the Gurobi optimizer. + +Most importantly, it is also necessary to specify a separator. The separator is a sub-routine of the CCG algorithm +which solves the separation problem, i.e., it finds the worst-case scenario for a given solution to the master problem. +In this example, we use the Bilevel separator which calls the MibS bilevel optimization solver at each iteration. + +Finally, the method :code:`optimize` is called to solve the problem and the solution is printed. + +.. admonition:: + + Here, we have assumed that the problem does not satisfy the complete recourse assumption, i.e., it is not known if + :math:`\forall x\in X, \forall\xi\in\Xi, \exists y\in Y(x,\xi)`. If, to the contrary, this assumption holds, + it can be communicated to the solver by calling the method :code:`with_complete_recourse(true)`. By doing this, + the CCG algorithms will not solve the feasibility separation problem which is most likely to result in a faster convergence. + diff --git a/_sources/tutorials/robust-optimization/column-and-constraint-generation/writing-separator.rst.txt b/_sources/tutorials/robust-optimization/column-and-constraint-generation/writing-separator.rst.txt new file mode 100644 index 00000000..5cc62d57 --- /dev/null +++ b/_sources/tutorials/robust-optimization/column-and-constraint-generation/writing-separator.rst.txt @@ -0,0 +1,8 @@ +.. _tutorial_write_ccg_separator: + +Writing Your Own Separator (Advanced) [TODO] +============================================ + +.. warning:: + + This is a work in progress. Thank you for your understanding. diff --git a/_sources/tutorials/robust-optimization/index.rst.txt b/_sources/tutorials/robust-optimization/index.rst.txt new file mode 100644 index 00000000..2be1b7a5 --- /dev/null +++ b/_sources/tutorials/robust-optimization/index.rst.txt @@ -0,0 +1,11 @@ +.. _robust: + +Robust Optimization +=================== + +.. toctree:: + :maxdepth: 3 + :glob: + + modeling/index + column-and-constraint-generation/index diff --git a/_sources/tutorials/robust-optimization/modeling/index.rst.txt b/_sources/tutorials/robust-optimization/modeling/index.rst.txt new file mode 100644 index 00000000..0881dccb --- /dev/null +++ b/_sources/tutorials/robust-optimization/modeling/index.rst.txt @@ -0,0 +1,8 @@ +Modeling +======== + +.. toctree:: + :maxdepth: 1 + :glob: + + two-stage-robust diff --git a/_sources/tutorials/robust-optimization/modeling/two-stage-robust.rst.txt b/_sources/tutorials/robust-optimization/modeling/two-stage-robust.rst.txt new file mode 100644 index 00000000..55e58d6d --- /dev/null +++ b/_sources/tutorials/robust-optimization/modeling/two-stage-robust.rst.txt @@ -0,0 +1,219 @@ +.. _modeling_two_stage_robust_problem: + +Modeling a Two-Stage Robust Problem +=================================== + +In this tutorial, we will see how to model a two-stage robust problem in idol. + +To follow this tutorial, you should be familiar with two-stage robust optimization and modeling optimization problems in idol. +If this is not the case, we recommend you to read the tutorial on :ref:`MIP modeling `. + +.. contents:: Table of Contents + :local: + :depth: 2 + +A Two-Stage Robust Facility Location Problem +-------------------------------------------- + +We consider a two-stage robust facility location problem (FLP) where demands are uncertain. + +Given a set of potential facility locations :math:`V_1` and a set of customers :math:`V_2`, the goal is to select a subset of facility locations +to activate in order to serve all customers' demand, while minimizing the total cost. +This version introduces uncertainty in the customers' demands. + +Note that there is also an example for the :ref:`deterministic version of the FLP using Column Generation `. + +Each facility :math:`i\in V_1` has an opening cost :math:`f_i` and a maximum capacity :math:`q_i`. +Each customer :math:`j\in V_2` has a demand :math:`d_j`. +The unitary cost for serving customer :math:`j\in V_2` from facility :math:`i\in V_1` is :math:`t_{ij}`. +The uncertainty in customer demands is controlled by a parameter :math:`\Gamma`. + +In this robust variant, we consider that the demands are uncertain and can be expressed as :math:`d_j(\xi) = d_j(1 + p\xi_j)` +with :math:`p` being the maximum increase in demand and :math:`\xi` being an unknown vector taken in the uncertainty set + +.. math:: + + \Xi := \left\{ \xi\in[ 0, 1 ]^{|V_2|} : \sum_{j\in V_2} \xi_j \le \Gamma \right\}. + +We model the two-stage robust FLP as + +.. math:: + + \min_{x\in \{0,1\}^{|V_1|}} \ \left\{ \sum_{i\in V_1} f_i x_i + \max_{\xi\in \Xi} \ \min_{y\in Y(x,\xi)} \ \sum_{i\in V_1} \sum_{j\in V_2} t_{ij} y_{ij} \right\} + +where :math:`Y(x,\xi)` is the set of feasible solutions for the second stage problem, given the first stage solution :math:`x` and the realization :math:`\xi` of the uncertain demand vector. +It is defined as the set of vectors :math:`y\in \mathbb{R}^{|V_1|\times|V_2|}` that satisfy the following constraints + +.. math:: + + \begin{align*} + & \sum_{i\in V_1} y_{ij} = d_j(\xi) && j\in V_2, \\ + & \sum_{j\in V_2} y_{ij} \le q_i x_i && i\in V_1, \\ + & y_{ij} \ge 0 && i\in V_1, j\in V_2. + \end{align*} + +In what follows, we will assume that we have a variable :code:`instance` of type :code:`idol::Problems::FLP::Instance` +that contains the data of the problem. Most typically, you will want to read the instance from a file. This is done as follows. + +.. code:: + + // Read instance + const auto instance = Problems::FLP::read_instance_1991_Cornuejols_eal("robust_ccg.data.txt"); + +Additionally, we define an optimization environment :code:`env` and some parameters. + +.. code:: + + Env env; + + const double Gamma = 3; + const double percentage_increase = .2; + +Modeling Steps +-------------- + +To model a two-stage robust problem, one needs to perform the following steps: + +1. Define an uncertainty set :math:`\Xi`. +2. Define the deterministic model in which :math:`\xi` is a parameter. +3. Assign a stage to each variable and constraint. + +Let's see how these steps are done in idol. + +Defining the Uncertainty Set +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Modeling the uncertainty set :math:`\Xi` is done in the same way as modeling any classical optimization problem. +For instance, one can do as follows. + +.. code:: + + const unsigned int n_customers = instance.n_customers(); + + Model uncertainty_set(env); + + auto xi = uncertainty_set.add_vars(Dim<1>(n_customers), 0., 1, Binary, "xi"); + uncertainty_set.add_ctr(idol_Sum(j, Range(n_customers), xi[j]) <= Gamma); + +Note that defining an objective function is not necessary since the uncertainty set is not optimized over. +If an objective function is defined, it will be ignored by the optimizer. + +Defining the Deterministic Model +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The deterministic model underlying the two-stage robust FLP is the same as the classical FLP, except that the demand is seen as a parameter. + +Recall that a variable, e.g., :code:`xi[0]`, can be turned into a parameter by prepending it with :code:`!`. + +Hence, +we can define the deterministic model as follows. + +.. code:: + + const unsigned int n_facilities = instance.n_facilities(); + + Model model(env); + + const auto x = model.add_vars(Dim<1>(n_facilities), 0., 1., Binary, "x"); + const auto y = model.add_vars(Dim<2>(n_facilities, n_customers), 0., Inf, Continuous, "y"); + + // Capacity constraints + for (unsigned int i = 0 ; i < n_facilities ; ++i) { + model.add_ctr(idol_Sum(j, Range(n_customers), y[i][j]) <= instance.capacity(i) * x[i]); + } + + // Demand satisfaction constraints + for (unsigned int j = 0 ; j < n_customers ; ++j) { + // IMPORTANT: here we use the parameter "!xi[j]" instead of the variable "xi[j]" + model.add_ctr(idol_Sum(i, Range(n_facilities), y[i][j]) == instance.demand(j) * (1 + percentage_increase * !xi[j])); + } + + // Objective function + model.seobj_expr(idol_Sum(i, Range(n_facilities), + instance.fixed_cost(i) * x[i] + + idol_Sum(j, Range(n_customers), + instance.per_unit_transportation_cost(i, j) * y[i][j] + ) + ) + ); + +Assigning Stages +^^^^^^^^^^^^^^^^ + +The last step is to assign a stage to each variable and constraint. Here, variables :math:`x` are first-stage variables +and variables :math:`y` are second-stage variables, i.e., they depend on the realization of the uncertain demand. +Similarly, all constraints are second-stage constraints since they are part of the second-stage feasible region. + +Assigning stages is done by creating a new object of type :code:`idol::Robust::StageDescription`. +Under the hood, this object does nothing more but defining new annotations for variables and constraints storing +the assigned stage of each variable and constraint. It is created as follows. + +.. code:: + + Robust::StageDescription stages(env); + +By default, all variables and constraints are assigned to the first stage. +To assign a variable or constraint to the second stage, one can use the method :code:`set_stage` of the object :code:`stages`. +For instance, one can do as follows. + +.. code:: + + for (const auto& var : model.vars()) { + if (var.name().front() != 'x') { + stages.set_stage(var, 2); + } + } + +Similarly, since all constraints are second-stage constraints, one can do as follows. + +.. code:: + + for (const auto& ctr : model.ctrs()) { + stages.set_stage(ctr, 2); + } + +.. admonition:: About stage annotations + + Note that it is also possible to define your own annotations to assign variables and constraints to stages. + This is a rather advanced feature and it is your responsability to ensure that the annotations are consistent with the model. + + The annotations are based on the following conventions: all first-stage variables and constraints have the annotation evaluating to :code:`MasterId`. + All second-stage variables and constraints have the annotation evaluating to :code:`0`. + + For instance, the following code is equivalent to the previous one. + + .. code:: + + Annotation stage_vars(model, "stage_vars", MasterId); // By default, all variables are first-stage variables + Annotation stage_ctrs(model, "stage_ctrs", MasterId); // By default, all constraints are first-stage constraints + + for (const auto& var : model.vars()) { + if (var.name().front() != 'x') { + var.set(stage_vars, 0); // Assign variable to the second stage + } + } + + for (const auto& ctr : model.ctrs()) { + ctr.set(stage_ctrs, 0); // Assign constraint to the second stage + } + + idol::Robust::StageDescription stages(stage_vars, stage_ctrs); + + By doing so, a call to :code:`stages.stage(var)` will return "1" for all first-stage variables and "2" for all second-stage variables. + The underlying annotation can be obtained using + + .. code:: + + Annotation stage_vars = stages.stage_vars() + + Finally, also note the method :code:`stages.stage_index(var)` that will return "0" for all first-stage variables and "1" for all second-stage variables. + + +That's it! We have now modeled a two-stage robust FLP in idol. Note that you will now need +to attach an optimizer to the model to solve it. +To this end, be sure to check the tutorials on optimizers for two-stage robust problems, e.g., :ref:`the column-and-constraint generation tutorial `. + +Complete Example +---------------- + +A complete example is given :ref:`here ` \ No newline at end of file diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 00000000..81415803 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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+} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + + +/* -- other body styles ----------------------------------------------------- */ + +ol.arabic { + list-style: decimal; +} + +ol.loweralpha { + list-style: lower-alpha; +} + +ol.upperalpha { + list-style: upper-alpha; +} + +ol.lowerroman { + list-style: lower-roman; +} + +ol.upperroman { + list-style: upper-roman; +} + +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/_static/check-solid.svg b/_static/check-solid.svg new file mode 100644 index 00000000..92fad4b5 --- /dev/null +++ b/_static/check-solid.svg @@ -0,0 +1,4 @@ + + + + diff --git a/_static/clipboard.min.js b/_static/clipboard.min.js new file mode 100644 index 00000000..54b3c463 --- /dev/null +++ b/_static/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.8 + * https://clipboardjs.com/ + * + * Licensed MIT © Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1 + + + + diff --git a/_static/copybutton.css b/_static/copybutton.css new file mode 100644 index 00000000..f1916ec7 --- /dev/null +++ b/_static/copybutton.css @@ -0,0 +1,94 @@ +/* Copy buttons */ +button.copybtn { + position: absolute; + display: flex; + top: .3em; + right: .3em; + width: 1.7em; + height: 1.7em; + opacity: 0; + transition: opacity 0.3s, border .3s, background-color .3s; + user-select: none; + padding: 0; + border: none; + outline: none; + border-radius: 0.4em; + /* The colors that GitHub uses */ + border: #1b1f2426 1px solid; + background-color: #f6f8fa; + color: #57606a; +} + +button.copybtn.success { + border-color: #22863a; + color: #22863a; +} + +button.copybtn svg { + stroke: currentColor; + width: 1.5em; + height: 1.5em; + padding: 0.1em; +} + +div.highlight { + position: relative; +} + +/* Show the copybutton */ +.highlight:hover button.copybtn, button.copybtn.success { + opacity: 1; +} + +.highlight button.copybtn:hover { + background-color: rgb(235, 235, 235); +} + +.highlight button.copybtn:active { + background-color: rgb(187, 187, 187); +} + +/** + * A minimal CSS-only tooltip copied from: + * https://codepen.io/mildrenben/pen/rVBrpK + * + * To use, write HTML like the following: + * + *

Short

+ */ + .o-tooltip--left { + position: relative; + } + + .o-tooltip--left:after { + opacity: 0; + visibility: hidden; + position: absolute; + content: attr(data-tooltip); + padding: .2em; + font-size: .8em; + left: -.2em; + background: grey; + color: white; + white-space: nowrap; + z-index: 2; + border-radius: 2px; + transform: translateX(-102%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); +} + +.o-tooltip--left:hover:after { + display: block; + opacity: 1; + visibility: visible; + transform: translateX(-100%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); + transition-delay: .5s; +} + +/* By default the copy button shouldn't show up when printing a page */ +@media print { + button.copybtn { + display: none; + } +} diff --git a/_static/copybutton.js b/_static/copybutton.js new file mode 100644 index 00000000..2ea7ff3e --- /dev/null +++ b/_static/copybutton.js @@ -0,0 +1,248 @@ +// Localization support +const messages = { + 'en': { + 'copy': 'Copy', + 'copy_to_clipboard': 'Copy to clipboard', + 'copy_success': 'Copied!', + 'copy_failure': 'Failed to copy', + }, + 'es' : { + 'copy': 'Copiar', + 'copy_to_clipboard': 'Copiar al portapapeles', + 'copy_success': '¡Copiado!', + 'copy_failure': 'Error al copiar', + }, + 'de' : { + 'copy': 'Kopieren', + 'copy_to_clipboard': 'In die Zwischenablage kopieren', + 'copy_success': 'Kopiert!', + 'copy_failure': 'Fehler beim Kopieren', + }, + 'fr' : { + 'copy': 'Copier', + 'copy_to_clipboard': 'Copier dans le presse-papier', + 'copy_success': 'Copié !', + 'copy_failure': 'Échec de la copie', + }, + 'ru': { + 'copy': 'Скопировать', + 'copy_to_clipboard': 'Скопировать в буфер', + 'copy_success': 'Скопировано!', + 'copy_failure': 'Не удалось скопировать', + }, + 'zh-CN': { + 'copy': '复制', + 'copy_to_clipboard': '复制到剪贴板', + 'copy_success': '复制成功!', + 'copy_failure': '复制失败', + }, + 'it' : { + 'copy': 'Copiare', + 'copy_to_clipboard': 'Copiato negli appunti', + 'copy_success': 'Copiato!', + 'copy_failure': 'Errore durante la copia', + } +} + +let locale = 'en' +if( document.documentElement.lang !== undefined + && messages[document.documentElement.lang] !== undefined ) { + locale = document.documentElement.lang +} + +let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; +if (doc_url_root == '#') { + doc_url_root = ''; +} + +/** + * SVG files for our copy buttons + */ +let iconCheck = ` + ${messages[locale]['copy_success']} + + +` + +// If the user specified their own SVG use that, otherwise use the default +let iconCopy = ``; +if (!iconCopy) { + iconCopy = ` + ${messages[locale]['copy_to_clipboard']} + + + +` +} + +/** + * Set up copy/paste for code blocks + */ + +const runWhenDOMLoaded = cb => { + if (document.readyState != 'loading') { + cb() + } else if (document.addEventListener) { + document.addEventListener('DOMContentLoaded', cb) + } else { + document.attachEvent('onreadystatechange', function() { + if (document.readyState == 'complete') cb() + }) + } +} + +const codeCellId = index => `codecell${index}` + +// Clears selected text since ClipboardJS will select the text when copying +const clearSelection = () => { + if (window.getSelection) { + window.getSelection().removeAllRanges() + } else if (document.selection) { + document.selection.empty() + } +} + +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + +const temporarilyChangeTooltip = (el, oldText, newText) => { + el.setAttribute('data-tooltip', newText) + el.classList.add('success') + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) +} + +// Changes the copy button icon for two seconds, then changes it back +const temporarilyChangeIcon = (el) => { + el.innerHTML = iconCheck; + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) +} + +const addCopyButtonToCodeCells = () => { + // If ClipboardJS hasn't loaded, wait a bit and try again. This + // happens because we load ClipboardJS asynchronously. + if (window.ClipboardJS === undefined) { + setTimeout(addCopyButtonToCodeCells, 250) + return + } + + // Add copybuttons to all of our code cells + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) + codeCells.forEach((codeCell, index) => { + const id = codeCellId(index) + codeCell.setAttribute('id', id) + + const clipboardButton = id => + `` + codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) + }) + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} + + +var copyTargetText = (trigger) => { + var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') +} + + // Initialize with a callback so we can modify the text before copy + const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) + + // Update UI with error/success messages + clipboard.on('success', event => { + clearSelection() + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) + temporarilyChangeIcon(event.trigger) + }) + + clipboard.on('error', event => { + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) + }) +} + +runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_static/copybutton_funcs.js b/_static/copybutton_funcs.js new file mode 100644 index 00000000..dbe1aaad --- /dev/null +++ b/_static/copybutton_funcs.js @@ -0,0 +1,73 @@ +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} diff --git a/_static/css/badge_only.css b/_static/css/badge_only.css new file mode 100644 index 00000000..88ba55b9 --- /dev/null +++ b/_static/css/badge_only.css @@ -0,0 +1 @@ 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origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + 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r=function(e){this._input=e;this._remain=e;this._pos=0;this._nextAtom=this._currentAtom=null;this._next()};r.prototype.accept=function(e,t){if(this._nextAtom.type===e&&this._matchText(t)){this._next();return this._currentAtom.text}return null};r.prototype.expect=function(e,t){var n=this._nextAtom;if(n.type!==e){throw new u(`Expected an atom of ${e} but received ${n.type}`,this._pos,this._input)}if(!this._matchText(t)){throw new u(`Expected \`${t}\` but received \`${n.text}\``,this._pos,this._input)}this._next();return this._currentAtom.text};r.prototype.get=function(){return this._currentAtom};var o={exec:function(e){var t=[{start:"$",end:"$"},{start:"\\(",end:"\\)"}];var n=e.length;for(var i=0;i0&&a[l-1]==="\\"){var h=l+o.length;a=a.slice(h);s+=h;continue}var p=[e.slice(0,s+l+o.length),e.slice(r.length,s+l)];return p}}return null}};var p={special:/^(\\\\|\\{|\\}|\\\$|\\&|\\#|\\%|\\_)/,math:o,func:/^\\([a-zA-Z]+)/,open:/^\{/,close:/^\}/,quote:/^(`|``|'|'')/,ordinary:/^[^\\{}$&#%_\s]+/};var c=/^%.*/;var f=/^\s+/;r.prototype._skip=function(e){this._pos+=e;this._remain=this._remain.slice(e)};r.prototype._next=function(){var e=false;while(1){var t=f.exec(this._remain);if(t){e=true;var n=t[0].length;this._skip(n)}var i=c.exec(this._remain);if(!i)break;var r=i[0].length;this._skip(r)}this._currentAtom=this._nextAtom;if(this._remain===""){this._nextAtom={type:"EOF",text:null,whitespace:false};return false}for(var o in p){var s=p[o];var a=s.exec(this._remain);if(!a)continue;var l=a[0];var h=a[1]?a[1]:l;this._nextAtom={type:o,text:h,whitespace:e};this._pos+=l.length;this._remain=this._remain.slice(a[0].length);return true}throw new u("Unrecoganizable atom",this._pos,this._input)};r.prototype._matchText=function(e){if(e===null||e===undefined)return true;if(i.isString(e))return e.toLowerCase()===this._nextAtom.text.toLowerCase();else return e.some(e=>e.toLowerCase()===this._nextAtom.text.toLowerCase())};t.exports=r},{"./ParseError":3,"./utils":6}],3:[function(e,t,n){function i(e,t,n){var i=`Error: ${e}`;if(t!==undefined&&n!==undefined){i+=` at position ${t}: \``;n=`${n.slice(0,t)}\u21B1${n.slice(t)}`;var r=Math.max(0,t-15);var o=t+15;i+=`${n.slice(r,o)}\``}this.message=i}i.prototype=Object.create(Error.prototype);i.prototype.constructor=i;t.exports=i},{}],4:[function(e,t,n){var s=e("./utils");var r=e("./ParseError");var a=function(e,t){this.type=e;this.value=t;this.children=[]};a.prototype.toString=function(e){if(!e)e=0;var t="";for(var n=0;n`;if(this.value)i+=` (${s.toString(this.value)})`;i+="\n";if(this.children){for(var r=0;r0){e.addChild(t);continue}break}return e};i.prototype._parseCaption=function(){var e=this._lexer;if(!e.accept("func","caption"))return null;var t=new a("caption");e.expect("open");t.addChild(this._parseCloseText());e.expect("close");return t};i.prototype._parseBlock=function(){var e=new a("block");while(true){var t=this._parseControl();if(t){e.addChild(t);continue}var n=this._parseFunction();if(n){e.addChild(n);continue}var i=this._parseStatement(h);if(i){e.addChild(i);continue}var r=this._parseCommand(p);if(r){e.addChild(r);continue}var o=this._parseComment();if(o){e.addChild(o);continue}break}return e};i.prototype._parseControl=function(){var e;if(e=this._parseIf())return e;if(e=this._parseLoop())return e;if(e=this._parseRepeat())return e;if(e=this._parseUpon())return e};i.prototype._parseFunction=function(){var e=this._lexer;if(!e.accept("func",["function","procedure"]))return null;var t=this._lexer.get().text;e.expect("open");var n=e.expect("ordinary");e.expect("close");e.expect("open");var i=this._parseCloseText();e.expect("close");var r=this._parseBlock();e.expect("func",`end${t}`);var o=new a("function",{type:t,name:n});o.addChild(i);o.addChild(r);return o};i.prototype._parseIf=function(){if(!this._lexer.accept("func","if"))return null;var e=new a("if");this._lexer.expect("open");e.addChild(this._parseCond());this._lexer.expect("close");e.addChild(this._parseBlock());var t=0;while(this._lexer.accept("func",["elif","elsif","elseif"])){this._lexer.expect("open");e.addChild(this._parseCond());this._lexer.expect("close");e.addChild(this._parseBlock());t++}var n=false;if(this._lexer.accept("func","else")){n=true;e.addChild(this._parseBlock())}this._lexer.expect("func","endif");e.value={numElif:t,hasElse:n};return e};i.prototype._parseLoop=function(){if(!this._lexer.accept("func",["FOR","FORALL","WHILE"]))return null;var e=this._lexer.get().text.toLowerCase();var t=new a("loop",e);this._lexer.expect("open");t.addChild(this._parseCond());this._lexer.expect("close");t.addChild(this._parseBlock());var n=e!=="forall"?`end${e}`:"endfor";this._lexer.expect("func",n);return t};i.prototype._parseRepeat=function(){if(!this._lexer.accept("func",["REPEAT"]))return null;var e=this._lexer.get().text.toLowerCase();var t=new a("repeat",e);t.addChild(this._parseBlock());this._lexer.expect("func","until");this._lexer.expect("open");t.addChild(this._parseCond());this._lexer.expect("close");return t};i.prototype._parseUpon=function(){if(!this._lexer.accept("func","upon"))return null;var e=new a("upon");this._lexer.expect("open");e.addChild(this._parseCond());this._lexer.expect("close");e.addChild(this._parseBlock());this._lexer.expect("func","endupon");return e};var l=["ensure","require","input","output"];var h=["state","print","return"];i.prototype._parseStatement=function(e){if(!this._lexer.accept("func",e))return null;var t=this._lexer.get().text.toLowerCase();var n=new a("statement",t);n.addChild(this._parseOpenText());return n};var p=["break","continue"];i.prototype._parseCommand=function(e){if(!this._lexer.accept("func",e))return null;var t=this._lexer.get().text.toLowerCase();var n=new a("command",t);return n};i.prototype._parseComment=function(){if(!this._lexer.accept("func","comment"))return null;var e=new a("comment");this._lexer.expect("open");e.addChild(this._parseCloseText());this._lexer.expect("close");return e};i.prototype._parseCall=function(){var e=this._lexer;if(!e.accept("func","call"))return null;var t=e.get().whitespace;e.expect("open");var n=e.expect("ordinary");e.expect("close");var i=new a("call");i.whitespace=t;i.value=n;e.expect("open");var r=this._parseCloseText();i.addChild(r);e.expect("close");return i};i.prototype._parseCond=i.prototype._parseCloseText=function(){return this._parseText("close")};i.prototype._parseOpenText=function(){return this._parseText("open")};i.prototype._parseText=function(e){var t=new a(`${e}-text`);var n=false;var i;while(true){i=this._parseAtom()||this._parseCall();if(i){if(n)i.whitespace|=n;t.addChild(i);continue}if(this._lexer.accept("open")){i=this._parseCloseText();n=this._lexer.get().whitespace;i.whitespace=n;t.addChild(i);this._lexer.expect("close");n=this._lexer.get().whitespace;continue}break}return t};var u={ordinary:{tokenType:"ordinary"},math:{tokenType:"math"},special:{tokenType:"special"},"cond-symbol":{tokenType:"func",tokenValues:["and","or","not","true","false","to","downto"]},"quote-symbol":{tokenType:"quote"},"sizing-dclr":{tokenType:"func",tokenValues:["tiny","scriptsize","footnotesize","small","normalsize","large","Large","LARGE","huge","Huge"]},"font-dclr":{tokenType:"func",tokenValues:["normalfont","rmfamily","sffamily","ttfamily","upshape","itshape","slshape","scshape","bfseries","mdseries","lfseries"]},"font-cmd":{tokenType:"func",tokenValues:["textnormal","textrm","textsf","texttt","textup","textit","textsl","textsc","uppercase","lowercase","textbf","textmd","textlf"]},"text-symbol":{tokenType:"func",tokenValues:["textbackslash"]}};i.prototype._parseAtom=function(){for(var e in u){var t=u[e];var n=this._lexer.accept(t.tokenType,t.tokenValues);if(n===null)continue;var i=this._lexer.get().whitespace;if(e!=="ordinary"&&e!=="math")n=n.toLowerCase();return new o(e,n,i)}return null};t.exports=i},{"./ParseError":3,"./utils":6}],5:[function(n,e,t){var a=n("./utils");function A(e){this._css={};this._fontSize=this._outerFontSize=e!==undefined?e:1}A.prototype.outerFontSize=function(e){if(e!==undefined)this._outerFontSize=e;return this._outerFontSize};A.prototype.fontSize=function(){return this._fontSize};A.prototype._fontCommandTable={normalfont:{"font-family":"KaTeX_Main"},rmfamily:{"font-family":"KaTeX_Main"},sffamily:{"font-family":"KaTeX_SansSerif"},ttfamily:{"font-family":"KaTeX_Typewriter"},bfseries:{"font-weight":"bold"},mdseries:{"font-weight":"medium"},lfseries:{"font-weight":"lighter"},upshape:{"font-style":"normal","font-variant":"normal"},itshape:{"font-style":"italic","font-variant":"normal"},scshape:{"font-style":"normal","font-variant":"small-caps"},slshape:{"font-style":"oblique","font-variant":"normal"},textnormal:{"font-family":"KaTeX_Main"},textrm:{"font-family":"KaTeX_Main"},textsf:{"font-family":"KaTeX_SansSerif"},texttt:{"font-family":"KaTeX_Typewriter"},textbf:{"font-weight":"bold"},textmd:{"font-weight":"medium"},textlf:{"font-weight":"lighter"},textup:{"font-style":"normal","font-variant":"normal"},textit:{"font-style":"italic","font-variant":"normal"},textsc:{"font-style":"normal","font-variant":"small-caps"},textsl:{"font-style":"oblique","font-variant":"normal"},uppercase:{"text-transform":"uppercase"},lowercase:{"text-transform":"lowercase"}};A.prototype._sizingScalesTable={tiny:.68,scriptsize:.8,footnotesize:.85,small:.92,normalsize:1,large:1.17,Large:1.41,LARGE:1.58,huge:1.9,Huge:2.28};A.prototype.updateByCommand=function(e){var t=this._fontCommandTable[e];if(t!==undefined){for(var n in t)this._css[n]=t[n];return}var i=this._sizingScalesTable[e];if(i!==undefined){this._outerFontSize=this._fontSize;this._fontSize=i;return}throw new ParserError("Unrecognized `text-style` command")};A.prototype.toCSS=function(){var e="";for(var t in this._css){var n=this._css[t];if(n===undefined)continue;e+=`${t}:${n};`}if(this._fontSize!==this._outerFontSize)e+=`font-size:${this._fontSize/this._outerFontSize}em;`;return e};function B(e,t){this._nodes=e;this._textStyle=t}B.prototype._renderCloseText=function(e,t){var n=new A(this._textStyle.fontSize());var i=new B(e.children,n);if(e.whitespace)this._html.putText(" ");this._html.putHTML(i.renderToHTML(t))};B.prototype.renderToHTML=function(e){this._html=new _;var t;while((t=this._nodes.shift())!==undefined){var n=t.type;var i=t.value;if(t.whitespace)this._html.putText(" ");switch(n){case"ordinary":this._html.putText(i);break;case"math":if(typeof e==="undefined"){throw EvalError("No math backend found. Please setup KaTeX or MathJax.")}else if(e.name==="katex"){this._html.putHTML(e.driver.renderToString(i))}else if(e.name==="mathjax"){if(e.version<3){this._html.putText(`$${i}$`)}else{this._html.putHTML(e.driver.tex2chtml(i,{display:false}).outerHTML)}}else{throw new EvalError(`Unknown math backend ${e}`)}break;case"cond-symbol":this._html.beginSpan("ps-keyword").putText(i.toLowerCase()).endSpan();break;case"special":if(i==="\\\\"){this._html.putHTML("
");break}var r={"\\{":"{","\\}":"}","\\$":"$","\\&":"&","\\#":"#","\\%":"%","\\_":"_"};var o=r[i];this._html.putText(o);break;case"text-symbol":var s={textbackslash:"\\"};var a=s[i];this._html.putText(a);break;case"quote-symbol":var l={"`":"\u2018","``":"\u201c","'":"\u2019","''":"\u201d"};var h=l[i];this._html.putText(h);break;case"call":this._html.beginSpan("ps-funcname").putText(i).endSpan();this._html.write("(");var p=t.children[0];this._renderCloseText(p,e);this._html.write(")");break;case"close-text":this._renderCloseText(t,e);break;case"font-dclr":case"sizing-dclr":this._textStyle.updateByCommand(i);this._html.beginSpan(null,this._textStyle.toCSS());var u=new B(this._nodes,this._textStyle);this._html.putHTML(u.renderToHTML(e));this._html.endSpan();break;case"font-cmd":var c=this._nodes[0];if(c.type!=="close-text")continue;var f=new A(this._textStyle.fontSize());f.updateByCommand(i);this._html.beginSpan(null,f.toCSS());var d=new B(c.children,f);this._html.putHTML(d.renderToHTML(e));this._html.endSpan();break;default:throw new ParseError(`Unexpected ParseNode of type ${t.type}`)}}return this._html.toMarkup()};function _(){this._body=[];this._textBuf=[]}_.prototype.beginDiv=function(e,t,n){this._beginTag("div",e,t,n);this._body.push("\n");return this};_.prototype.endDiv=function(){this._endTag("div");this._body.push("\n");return this};_.prototype.beginP=function(e,t,n){this._beginTag("p",e,t,n);this._body.push("\n");return this};_.prototype.endP=function(){this._flushText();this._endTag("p");this._body.push("\n");return this};_.prototype.beginSpan=function(e,t,n){this._flushText();return this._beginTag("span",e,t,n)};_.prototype.endSpan=function(){this._flushText();return this._endTag("span")};_.prototype.putHTML=function(e){this._flushText();this._body.push(e);return this};_.prototype.putText=function(e){this._textBuf.push(e);return this};_.prototype.write=function(e){this._body.push(e)};_.prototype.toMarkup=function(){this._flushText();var e=this._body.join("");return e.trim()};_.prototype.toDOM=function(){var e=this.toMarkup();var t=document.createElement("div");t.innerHTML=e;return t.firstChild};_.prototype._flushText=function(){if(this._textBuf.length===0)return;var e=this._textBuf.join("");this._body.push(this._escapeHtml(e));this._textBuf=[]};_.prototype._beginTag=function(e,t,n,i){var r=`<${e}`;if(t)r+=` class="${t}"`;if(n){var o;if(a.isString(n)){o=n}else{o="";for(var s in n){attrVal=n[s];o+=`${s}:${attrVal};`}}if(i)o+=i;r+=` style="${o}"`}r+=">";this._body.push(r);return this};_.prototype._endTag=function(e){this._body.push(``);return this};var i={"&":"&","<":"<",">":">",'"':""","'":"'","/":"/"};_.prototype._escapeHtml=function(e){return String(e).replace(/[&<>"'/]/g,e=>i[e])};function r(e){e=e||{};this.indentSize=e.indentSize?this._parseEmVal(e.indentSize):1.2;this.commentDelimiter=e.commentDelimiter!==undefined?e.commentDelimiter:" // ";this.lineNumberPunc=e.lineNumberPunc!==undefined?e.lineNumberPunc:":";this.lineNumber=e.lineNumber!==undefined?e.lineNumber:false;this.noEnd=e.noEnd!==undefined?e.noEnd:false;this.scopeLines=e.scopeLines!==undefined?e.scopeLines:false;if(e.captionCount!==undefined)F.captionCount=e.captionCount;this.titlePrefix=e.titlePrefix!==undefined?e.titlePrefix:"Algorithm"}r.prototype._parseEmVal=function(e){e=e.trim();if(e.indexOf("em")!==e.length-2)throw new TypeError("Unit error; expected `em` suffix");return Number(e.substring(0,e.length-2))};function F(e,t){this._root=e.parse();this._options=new r(t);this._openLine=false;this._blockLevel=0;this._textLevel=-1;this._globalTextStyle=new A;this.backend=undefined;try{if(typeof katex==="undefined")katex=n("katex")}catch(e){}try{if(typeof MathJax==="undefined")MathJax=n("mathjax")}catch(e){}if(typeof katex!=="undefined"){this.backend={name:"katex",driver:katex}}else if(typeof MathJax!=="undefined"){this.backend={name:"mathjax",version:parseInt(MathJax.version.split(".")[0]),driver:MathJax}}}F.captionCount=0;F.prototype.toMarkup=function(){var e=this._html=new _;this._buildTree(this._root);delete this._html;return e.toMarkup()};F.prototype.toDOM=function(){var e=this.toMarkup();var t=document.createElement("div");t.innerHTML=e;return t.firstChild};F.prototype._beginGroup=function(e,t,n){this._closeLineIfAny();this._html.beginDiv(`ps-${e}${t?` ${t}`:""}`,n)};F.prototype._endGroup=function(e){this._closeLineIfAny();this._html.endDiv()};F.prototype._beginBlock=function(){var e=this._options.lineNumber&&this._blockLevel===0?.6:0;var t=this._options.indentSize+e;if(this._options.scopeLines)t/=2;this._beginGroup("block",null,{"margin-left":`${t}em`});this._blockLevel++};F.prototype._endBlock=function(){this._closeLineIfAny();this._endGroup();this._blockLevel--};F.prototype._newLine=function(){this._closeLineIfAny();this._openLine=true;this._globalTextStyle.outerFontSize(1);var e=this._options.indentSize;if(this._blockLevel>0){this._numLOC++;this._html.beginP("ps-line ps-code",this._globalTextStyle.toCSS());var t=this._options.lineNumber?e*1.25:0;t+=this._options.scopeLines?e*.1:0;if(this._options.lineNumber){this._html.beginSpan("ps-linenum",{left:`${-((this._blockLevel-1)*t)}em`}).putText(this._numLOC+this._options.lineNumberPunc).endSpan()}}else{this._html.beginP("ps-line",{"text-indent":`${-e}em`,"padding-left":`${e}em`},this._globalTextStyle.toCSS())}};F.prototype._closeLineIfAny=function(){if(!this._openLine)return;this._html.endP();this._openLine=false};F.prototype._typeKeyword=function(e){this._html.beginSpan("ps-keyword").putText(e).endSpan()};F.prototype._typeFuncName=function(e){this._html.beginSpan("ps-funcname").putText(e).endSpan()};F.prototype._typeText=function(e){this._html.write(e)};F.prototype._buildTreeForAllChildren=function(e){var t=e.children;for(var n=0;n0&&t[0].type==="comment"){var n=t.shift();this._buildTree(n)}};F.prototype._buildTree=function(e){var t;var n;var i;switch(e.type){case"root":this._beginGroup("root");this._buildTreeForAllChildren(e);this._endGroup();break;case"algorithm":var r;for(t=0;t { + const [docname, title, anchor, descr, score, filename, kind] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename, kind] = item; + + let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.setAttribute("role", "list"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename, kind]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score + boost, + filenames[file], + SearchResultKind.title, + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + SearchResultKind.index, + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + SearchResultKind.object, + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + SearchResultKind.text, + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/api/classes/branch-and-bound/BranchAndBound.html b/api/classes/branch-and-bound/BranchAndBound.html new file mode 100644 index 00000000..38a57e44 --- /dev/null +++ b/api/classes/branch-and-bound/BranchAndBound.html @@ -0,0 +1,717 @@ + + + + + + + + + BranchAndBound — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

BranchAndBound

+

This class is an optimizer factory which creates a new branch-and-bound algorithm. +It can be used to create customized branch-and-bound algorithms with a large degree of freedom.

+
+

See also

+

If you are not familiar with optimizers and optimizer factories, please refer to this page.

+
+
+

Example

+

Here, we create a simple branch-and-bound algorithm where branching is done on integer variables which are being +relaxed. Each node is solved by the external solver GLPK. Nodes are selected according to the “best-bound” +rule while variables are selected according to the “most-infeasible” branching rule.

+

The created algorithm also incorporates sub-tree exploration of maximum depth 2.

+
model.use(
+    BranchAndBound()
+        .with_node_optimizer( GLPK::ContinuousRelaxation() )
+        .with_branching_rule( MostInfeasible() )
+        .with_node_selection_rule( BestBound() )
+        .with_subtree_depth(2)
+);
+
+
+
+
+
+template<class NodeT = idol::DefaultNodeInfo>
class BranchAndBound : public idol::OptimizerFactoryWithDefaultParameters<BranchAndBound<idol::DefaultNodeInfo>>
+
+
Template Parameters:
+

NodeT – the class used to store nodes information. It is strongly advised to inherit from NodeVarInfo in order to create your own node type.

+
+
+
+

Public Types

+
+
+template<class ReturnT, class T>
using only_if_has_Strategy = typename std::pair<typename T::template Strategy<NodeT>, ReturnT>::second_type
+

This type is used to exploit SFINAE in order to identify classes having a sub-class named Strategy<NodeInfoT>. This is used to make calls like .with_node_selection_rule(DepthFirst()); which will actually call .with_node_selection_rule(DepthFirst::Strategy<NodeInfoT>()).

+
+ +
+
+

Public Functions

+
+
+BranchAndBound() = default
+

Creates a new branch-and-bound algorithm.

+

Example: 

model.use( BranchAndBound() );
+
+
+

+
+ +
+
+BranchAndBound(const BranchAndBound &t_rhs)
+

Copy constructor

+
+
Parameters:
+

t_rhs – the object to copy

+
+
+
+ +
+
+BranchAndBound<NodeT> &with_node_optimizer(const OptimizerFactory &t_node_optimizer)
+

Sets the optimizer for solving each of the branch-and-bound tree nodes

+

Example: 

auto algorithm = BranchAndBound()
+                     .with_node_optimizer(GLPK::ContinuousRelaxation());
+
+
+

+
+
Parameters:
+

t_node_optimizer – the optimizer factory the node problems

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+void set_node_optimizer(const OptimizerFactory &t_node_optimizer)
+
+ +
+
+BranchAndBound<NodeT> &operator+=(const OptimizerFactory &t_node_optimizer)
+
+ +
+
+BranchAndBound<NodeT> &with_branching_rule(const BranchingRuleFactory<NodeT> &t_branching_rule)
+

Sets the branching rule used to create child nodes

+

Example: 

auto algorithm = BranchAndBound<NodeVarInfo>()
+                     .with_branching_rule(MostInfeasible::Strategy<NodeVarInfo>());
+
+
+

+
+
Parameters:
+

t_branching_rule – the branching rule

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class BranchingRuleFactoryT>
only_if_has_Strategy<BranchAndBound<NodeT>&, BranchingRuleFactoryT> with_branching_rule(const BranchingRuleFactoryT &t_branching_rule)
+

Sets the branching rule used to create child nodes.

+

Here, the function is called only when BranchingRuleFactoryT has a nested template class named Strategy<NodeInfoT>. In such a case, the branching rule is created by calling BranchingRuleFactoryT::Strategy<NodeInfoT>(t_branching_rule). This is used to avoid the user repeating the node type NodeInfoT being used.

+

Example: 

auto algorithm = BranchAndBound<NodeVarInfo>()
+                     .with_branching_rule(MostInfeasible());
+
+
+

+
+
Template Parameters:
+

BranchingRuleFactoryT – the class containing a nested template class named Strategy

+
+
Parameters:
+

t_branching_rule – the branching rule

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<NodeT> &with_node_selection_rule(const NodeSelectionRuleFactory<NodeT> &t_node_selection)
+

Sets the node selection rule to explore the branch and bound tree.

+

auto algorithm = BranchAndBound<NodeVarInfo>()
+                     .with_node_selection(BestBound::Strategy<NodeVarInfo>());
+
+
+

+
+
Parameters:
+

t_node_selection – the node selection rule

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class NodeSelectionRuleFactoryT>
only_if_has_Strategy<BranchAndBound<NodeT>&, NodeSelectionRuleFactoryT> with_node_selection_rule(const NodeSelectionRuleFactoryT &t_node_selection_rule)
+

Sets the node selection rule to explore the branch and bound tree.

+

Here, the function is called only when NodeSelectionRuleFactoryT has a nested template class named Strategy<NodeInfoT>. In such a case, the node selection rule is created by calling NodeSelectionRuleFactoryT::Strategy<NodeInfoT>(t_node_selection_rule). This is used to avoid the user repeating the node type NodeInfoT being used.

+

Example: 

auto algorithm = BranchAndBound<NodeVarInfo>()
+                     .with_node_selection_rule(BestBound());
+
+
+

+
+
Template Parameters:
+

NodeSelectionRuleFactoryT – the class containing a nested template class named Strategy

+
+
Parameters:
+

t_node_selection_rule – the node selection rule

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound(BranchAndBound&&) noexcept = default
+
+ +
+
+BranchAndBound &operator=(const BranchAndBound&) = delete
+
+ +
+
+BranchAndBound &operator=(BranchAndBound&&) noexcept = delete
+
+ +
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+virtual OptimizerFactory *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+BranchAndBound<NodeT> &with_subtree_depth(unsigned int t_depth)
+

Sets the depth for sub-tree exploration.

+

When a node is selected for branching, each of its children is explored. This exploration takes the form of a sub-tree exploration. When this parameter is set to 0, only the root node of this sub-tree is solved. Thus every child node are solved and the branch-and-bound algorithm is continued.

+

For strictly greater values of this parameter, the sub-tree is explored with a maximum depth equal to the value of this parameter.

+

For example, with a value of 1, each child node is solved along with its child nodes.

+

Example: 

auto algorithm = BranchAndBound()
+                     .with_subtree_depth(1);
+
+
+

+
+
Parameters:
+

t_depth – the maximum sub-tree exploration depth

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<NodeT> &with_logger(const Logs::BranchAndBound::Factory<NodeT> &t_log_factory)
+
+ +
+
+BranchAndBound<NodeT> &with_scaling(bool t_value)
+
+ +
+
+BranchAndBound<NodeT> &add_callback(const BranchAndBoundCallbackFactory<NodeT> &t_callback)
+

Adds a callback which will be called by the optimizer.

+

Note that this method can be called multiple times so that multiple callbacks can be added.

+

auto algorithm = BranchAndBound()
+                     .with_callback(IntegerMaster.rst());
+
+
+

+
+
Parameters:
+

t_callback – the callback factory

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<NodeT> &add_callback(const CallbackFactory &t_callback)
+

Adds a (solver independent) callback which will be called by the optimizer.

+

Note that this method can be called multiple times so that multiple callbacks can be added.

+

Here, the Callback is automatically converted into a BranchAndBoundCallback<NodeInfoT>.

+

auto algorithm = BranchAndBound()
+                     .with_callback(UserCutCallback(separation_model, my_cut));
+
+
+

+
+
Parameters:
+

t_callback – the callback factory

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class NodeSelectionRuleFactoryT>
idol::BranchAndBound<NodeT>::template only_if_has_Strategy<idol::BranchAndBound<NodeT>&, NodeSelectionRuleFactoryT> with_node_selection_rule(const NodeSelectionRuleFactoryT &t_node_selection_rule)
+
+ +
+
+template<class BranchingRuleFactoryT>
idol::BranchAndBound<NodeT>::template only_if_has_Strategy<idol::BranchAndBound<NodeT>&, BranchingRuleFactoryT> with_branching_rule(const BranchingRuleFactoryT &t_branching_rule)
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &conditional(bool t_conditional_value, const std::function<void(BranchAndBound<idol::DefaultNodeInfo>&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+BranchAndBound<idol::DefaultNodeInfo> &conditional(bool t_conditional_value, const std::function<void(BranchAndBound<idol::DefaultNodeInfo>&)> &t_if, const std::function<void(BranchAndBound<idol::DefaultNodeInfo>&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/BranchAndBoundCallback.html b/api/classes/branch-and-bound/BranchAndBoundCallback.html new file mode 100644 index 00000000..96bf3c33 --- /dev/null +++ b/api/classes/branch-and-bound/BranchAndBoundCallback.html @@ -0,0 +1,361 @@ + + + + + + + + + BranchAndBoundCallback — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

BranchAndBoundCallback

+

This class can be used to create callbacks to run on idol’s branch-and-bound implementation.

+
+

Warning

+

BranchAndBoundCallback-s is an advanced feature.

+

Please, make sure that what you are trying do cannot be done with +a solver-independent callback first, or with pre-existing callbacks, e.g., +UserCutCallback or LazyCutCallback.

+

The advantage of using a BranchAndBoundCallback instead of a standard +solver-independent Callback lies in the possibility to access specific information regarding +the execution of the branch-and-bound algorithm. For instance, accessing a node’s information, or the current +relaxation model being solved.

+
+
+

Example

+

Here is an example of callback which prints out the event triggering it and, when the event corresponds to NodeLoaded, +prints the node’s model to be solved.

+
class MyCallback : public BranchAndBoundCallbackFactory<NodeInfo> {
+public:
+    class Strategy : public BranchAndBoundCallback<NodeInfo> {
+    protected:
+        void operator()(CallbackEvent t_event) override {
+
+            std::cout << "MyCallback is called for " << t_event << std::endl;
+
+            if (t_event != NodeLoaded) {
+                return;
+            }
+
+            std::cout << "The problem being solve at node " << node().id() << std::endl;
+            std::cout << relaxation() << std::endl;
+
+        }
+    };
+
+    BranchAndBoundCallback<NodeInfo> *operator()() override {
+        return new Strategy();
+    }
+
+    [[nodiscard]] BranchAndBoundCallbackFactory<NodeInfo> *clone() const override {
+        return new MyCallback(*this);
+    }
+};
+
+
+

Then, this callback can be used as follows.

+
model.use(
+    BranchAndBound()
+        .with_node_optimizer(GLPK::ContinuousRelaxation())
+        .with_branching_rule(MostInfeasible())
+        .with_node_selection_rule(DepthFirst())
+        .with_callback(MyCallback());
+);
+
+
+
+
+

Hint

+

By default, most of the objects returned by BranchAndBoundCallback methods are const. If you wish to access non-const +versions (e.g., if you want to perform non-standard updates to the relaxed model or want to change the node’s current +information manually), you should use the advanced interface obtained by calling BranchAndBoundCallback::advanced_interface.

+
+
+
+template<class NodeInfoT>
class BranchAndBoundCallback
+

Subclassed by idol::CallbackAsBranchAndBoundCallback< NodeInfoT >::Strategy, idol::Cuts::KnapsackCover< NodeInfoT >::Strategy, idol::Heuristics::IntegerMaster< NodeInfoT >::Strategy, idol::Utils::ExportBranchAndBoundTreeToCSV< NodeInfoT >::Strategy

+
+

Public Functions

+
+
+virtual ~BranchAndBoundCallback() = default
+
+ +
+
+

Protected Functions

+
+
+inline virtual void initialize()
+

This method is called at the very beginning of the Branch-and-Bound algorithm

+
+ +
+
+virtual void operator()(CallbackEvent t_event) = 0
+

This method is left for the user to write and consists in the main execution block of the callback.

+
+
Parameters:
+

t_event – The event triggering the callback

+
+
+
+ +
+
+inline virtual void log_after_termination()
+
+ +
+
+void add_user_cut(const TempCtr &t_cut)
+

Adds a user cut to the relaxation

+
+
Parameters:
+

t_cut – the cut to be added

+
+
+
+ +
+
+void add_lazy_cut(const TempCtr &t_cut)
+

Adds a lazy cut to the relaxation

+
+
Parameters:
+

t_cut – the cut to be added

+
+
+
+ +
+
+const Node<NodeInfoT> &node() const
+

Returns the node which is currently explored

+
+
Returns:
+

the node which is currently explored

+
+
+
+ +
+
+const Model &relaxation() const
+

Returns the current node’s model being solved

+
+
Returns:
+

the current node’s model being solved

+
+
+
+ +
+
+const Model &original_model() const
+

Returns the original model from which the branch-and-bound algorithm started (i.e., the original non-relaxed model)

+
+
Returns:
+

the original model

+
+
+
+ +
+
+void submit_heuristic_solution(NodeInfoT *t_info)
+

Submits a new solution to the branch-and-bound tree algorithm.

+

The solution is checked for validity according to the branch-and-bound tree branching rule and is set as incumbent if and only if it is valid and improves the current best objective.

+
+
Parameters:
+

t_info – a node information storing the solution

+
+
+
+ +
+
+void submit_bound(double t_bound)
+

Submits a new proven bound.

+

The given bound is set as best bound if and only if it improves the current best bound.

+
+
Parameters:
+

t_bound – a proven bound

+
+
+
+ +
+
+const SideEffectRegistry &side_effect_registry() const
+

Returns the side effect registry

+
+
Returns:
+

the side effect registry

+
+
+
+ +
+
+const Timer &time() const
+
+ +
+
+double best_bound() const
+
+ +
+
+double best_obj() const
+
+ +
+
+void terminate()
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/BranchAndBoundCallbackFactory.html b/api/classes/branch-and-bound/BranchAndBoundCallbackFactory.html new file mode 100644 index 00000000..93ada0cf --- /dev/null +++ b/api/classes/branch-and-bound/BranchAndBoundCallbackFactory.html @@ -0,0 +1,175 @@ + + + + + + + + + BranchAndBoundCallbackFactory — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

BranchAndBoundCallbackFactory

+
+
+template<class NodeInfoT>
class BranchAndBoundCallbackFactory
+

Subclassed by idol::CallbackAsBranchAndBoundCallback< NodeInfoT >

+
+

Public Functions

+
+
+virtual ~BranchAndBoundCallbackFactory() = default
+
+ +
+
+virtual BranchAndBoundCallback<NodeInfoT> *operator()() = 0
+
+ +
+
+virtual BranchAndBoundCallbackFactory<NodeInfoT> *clone() const = 0
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/cuts/KnapsackCover.html b/api/classes/branch-and-bound/cuts/KnapsackCover.html new file mode 100644 index 00000000..77f0a93c --- /dev/null +++ b/api/classes/branch-and-bound/cuts/KnapsackCover.html @@ -0,0 +1,206 @@ + + + + + + + + + Cuts::KnapsackCover — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Cuts::KnapsackCover

+
+
+template<class NodeInfoT = idol::DefaultNodeInfo>
class KnapsackCover : public idol::BranchAndBoundCallbackFactory<idol::DefaultNodeInfo>
+
+

Public Functions

+
+
+virtual BranchAndBoundCallback<NodeInfoT> *operator()() override
+
+ +
+
+virtual BranchAndBoundCallbackFactory<NodeInfoT> *clone() const override
+
+ +
+
+KnapsackCover &with_lifting(bool t_value)
+
+ +
+
+KnapsackCover &with_tree_node_cuts(bool t_value)
+
+ +
+
+KnapsackCover &with_max_pass_root_node(unsigned int t_value)
+
+ +
+
+KnapsackCover &with_max_cuts_factor(double t_value)
+
+ +
+
+
+class Strategy : public idol::BranchAndBoundCallback<NodeInfoT>
+
+

Public Functions

+
+
+Strategy(bool t_use_lifting, bool t_apply_to_tree_nodes, unsigned int t_max_pass_root_node, double t_max_cuts_factor)
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/cuts/index.html b/api/classes/branch-and-bound/cuts/index.html new file mode 100644 index 00000000..7e715d1c --- /dev/null +++ b/api/classes/branch-and-bound/cuts/index.html @@ -0,0 +1,159 @@ + + + + + + + + + Cutting Planes — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Cutting Planes

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/heuristics/LocalBranching.html b/api/classes/branch-and-bound/heuristics/LocalBranching.html new file mode 100644 index 00000000..d860f363 --- /dev/null +++ b/api/classes/branch-and-bound/heuristics/LocalBranching.html @@ -0,0 +1,198 @@ + + + + + + + + + Heuristics::LocalBranching — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Heuristics::LocalBranching

+
+
+class LocalBranching : public idol::CallbackFactory
+
+

Public Functions

+
+
+LocalBranching() = default
+
+ +
+
+inline virtual Callback *operator()() override
+
+ +
+
+inline virtual CallbackFactory *clone() const override
+
+ +
+
+LocalBranching &with_optimizer(const OptimizerFactory &t_optimizer_factory)
+
+ +
+
+
+class Strategy : public idol::Callback
+
+

Public Functions

+
+
+Strategy(unsigned int t_neighbourhood_size, OptimizerFactory *t_optimizer_factory)
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/heuristics/RENS.html b/api/classes/branch-and-bound/heuristics/RENS.html new file mode 100644 index 00000000..ee852139 --- /dev/null +++ b/api/classes/branch-and-bound/heuristics/RENS.html @@ -0,0 +1,198 @@ + + + + + + + + + Heuristics::RENS — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Heuristics::RENS

+
+
+class RENS : public idol::CallbackFactory
+
+

Public Functions

+
+
+RENS() = default
+
+ +
+
+inline virtual Callback *operator()() override
+
+ +
+
+inline virtual CallbackFactory *clone() const override
+
+ +
+
+RENS &with_optimizer(const OptimizerFactory &t_optimizer_factory)
+
+ +
+
+
+class Strategy : public idol::Callback
+
+

Public Functions

+
+
+Strategy(double t_minimum_ratio_of_integer_variables_to_fix, double t_minimum_ratio_of_variables_to_fix, OptimizerFactory *t_optimizer_factory)
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/heuristics/SimpleRounding.html b/api/classes/branch-and-bound/heuristics/SimpleRounding.html new file mode 100644 index 00000000..1df00d29 --- /dev/null +++ b/api/classes/branch-and-bound/heuristics/SimpleRounding.html @@ -0,0 +1,185 @@ + + + + + + + + + Heuristics::SimpleRounding — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Heuristics::SimpleRounding

+
+
+class SimpleRounding : public idol::CallbackFactory
+
+

Public Functions

+
+
+SimpleRounding() = default
+
+ +
+
+inline virtual Callback *operator()() override
+
+ +
+
+inline virtual CallbackFactory *clone() const override
+
+ +
+
+
+class Strategy : public idol::Callback
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/heuristics/index.html b/api/classes/branch-and-bound/heuristics/index.html new file mode 100644 index 00000000..a9bd6bdd --- /dev/null +++ b/api/classes/branch-and-bound/heuristics/index.html @@ -0,0 +1,163 @@ + + + + + + + + + Heuristics — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/index.html b/api/classes/branch-and-bound/index.html new file mode 100644 index 00000000..1a5ffe0f --- /dev/null +++ b/api/classes/branch-and-bound/index.html @@ -0,0 +1,193 @@ + + + + + + + + + Branch-and-Bound Algorithm — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/node-selection/BestBound.html b/api/classes/branch-and-bound/node-selection/BestBound.html new file mode 100644 index 00000000..c13498c3 --- /dev/null +++ b/api/classes/branch-and-bound/node-selection/BestBound.html @@ -0,0 +1,187 @@ + + + + + + + + + BestBound — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

BestBound

+
+
+class BestBound
+
+
+template<class NodeT>
class Strategy : public idol::NodeSelectionRuleFactory<NodeT>
+
+

Public Functions

+
+
+inline explicit Strategy(const BestBound &t_parent)
+
+ +
+
+inline virtual NodeSelectionRules::BestBound<NodeT> *operator()(Optimizers::BranchAndBound<NodeT> &t_parent) const override
+
+ +
+
+inline virtual Strategy *clone() const override
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/node-selection/BestEstimate.html b/api/classes/branch-and-bound/node-selection/BestEstimate.html new file mode 100644 index 00000000..2fb78a24 --- /dev/null +++ b/api/classes/branch-and-bound/node-selection/BestEstimate.html @@ -0,0 +1,188 @@ + + + + + + + + + BestEstimate — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

BestEstimate

+

The “best estimate” node selection rule was introduced in J. J. H. Forrest, J. P. H. Hirst, J. A. Tomlin, (1974) Practical Solution of Large Mixed Integer Programming Problems with Umpire. Management Science 20(5):736-773.

+
+
+class BestEstimate
+
+
+template<class NodeT>
class Strategy : public idol::NodeSelectionRuleFactory<NodeT>
+
+

Public Functions

+
+
+inline explicit Strategy(const BestEstimate &t_parent)
+
+ +
+
+inline virtual NodeSelectionRules::BestEstimate<NodeT> *operator()(Optimizers::BranchAndBound<NodeT> &t_parent) const override
+
+ +
+
+inline virtual Strategy *clone() const override
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/node-selection/BreadthFirst.html b/api/classes/branch-and-bound/node-selection/BreadthFirst.html new file mode 100644 index 00000000..1d588fff --- /dev/null +++ b/api/classes/branch-and-bound/node-selection/BreadthFirst.html @@ -0,0 +1,187 @@ + + + + + + + + + BreadthFirst — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

BreadthFirst

+
+
+class BreadthFirst
+
+
+template<class NodeT>
class Strategy : public idol::NodeSelectionRuleFactory<NodeT>
+
+

Public Functions

+
+
+inline explicit Strategy(const BreadthFirst &t_parent)
+
+ +
+
+inline virtual NodeSelectionRules::BreadthFirst<NodeT> *operator()(Optimizers::BranchAndBound<NodeT> &t_parent) const override
+
+ +
+
+inline virtual Strategy *clone() const override
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/node-selection/DepthFirst.html b/api/classes/branch-and-bound/node-selection/DepthFirst.html new file mode 100644 index 00000000..49c9d28b --- /dev/null +++ b/api/classes/branch-and-bound/node-selection/DepthFirst.html @@ -0,0 +1,187 @@ + + + + + + + + + DepthFirst — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

DepthFirst

+
+
+class DepthFirst
+
+
+template<class NodeT>
class Strategy : public idol::NodeSelectionRuleFactory<NodeT>
+
+

Public Functions

+
+
+inline explicit Strategy(const DepthFirst &t_parent)
+
+ +
+
+inline virtual NodeSelectionRules::DepthFirst<NodeT> *operator()(Optimizers::BranchAndBound<NodeT> &t_parent) const override
+
+ +
+
+inline virtual Strategy *clone() const override
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/node-selection/WorstBound.html b/api/classes/branch-and-bound/node-selection/WorstBound.html new file mode 100644 index 00000000..15247ecd --- /dev/null +++ b/api/classes/branch-and-bound/node-selection/WorstBound.html @@ -0,0 +1,187 @@ + + + + + + + + + WorstBound — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

WorstBound

+
+
+class WorstBound
+
+
+template<class NodeT>
class Strategy : public idol::NodeSelectionRuleFactory<NodeT>
+
+

Public Functions

+
+
+inline explicit Strategy(const WorstBound &t_parent)
+
+ +
+
+inline virtual NodeSelectionRules::WorstBound<NodeT> *operator()(Optimizers::BranchAndBound<NodeT> &t_parent) const override
+
+ +
+
+inline virtual Strategy *clone() const override
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/node-selection/index.html b/api/classes/branch-and-bound/node-selection/index.html new file mode 100644 index 00000000..2625e552 --- /dev/null +++ b/api/classes/branch-and-bound/node-selection/index.html @@ -0,0 +1,167 @@ + + + + + + + + + Node Selection Rules — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Node Selection Rules

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/variable-selection/FirstInfeasibleFound.html b/api/classes/branch-and-bound/variable-selection/FirstInfeasibleFound.html new file mode 100644 index 00000000..af05e488 --- /dev/null +++ b/api/classes/branch-and-bound/variable-selection/FirstInfeasibleFound.html @@ -0,0 +1,211 @@ + + + + + + + + + FirstInfeasibleFound — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

FirstInfeasibleFound

+
+
+class FirstInfeasibleFound : public idol::VariableBranching
+
+

Public Functions

+
+
+FirstInfeasibleFound() = default
+
+ +
+
+template<class IteratorT>
inline FirstInfeasibleFound(IteratorT t_begin, IteratorT t_end)
+
+ +
+
+
+template<class NodeInfoT>
class Strategy : public idol::VariableBranching::Strategy<NodeInfoT>
+
+

Public Functions

+
+
+Strategy() = default
+
+ +
+
+inline explicit Strategy(const FirstInfeasibleFound &t_parent)
+
+ +
+
+inline BranchingRules::VariableBranching<NodeInfoT> *operator()(const Optimizers::BranchAndBound<NodeInfoT> &t_parent) const override
+
+ +
+
+inline virtual VariableBranching::Strategy<NodeInfoT> *clone() const override
+
+ +
+
+virtual BranchingRule<NodeT> *operator()(const Optimizers::BranchAndBound<NodeT> &t_parent) const = 0
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/variable-selection/LeastInfeasible.html b/api/classes/branch-and-bound/variable-selection/LeastInfeasible.html new file mode 100644 index 00000000..8b0a3407 --- /dev/null +++ b/api/classes/branch-and-bound/variable-selection/LeastInfeasible.html @@ -0,0 +1,211 @@ + + + + + + + + + LeastInfeasible — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

LeastInfeasible

+
+
+class LeastInfeasible : public idol::VariableBranching
+
+

Public Functions

+
+
+LeastInfeasible() = default
+
+ +
+
+template<class IteratorT>
inline LeastInfeasible(IteratorT t_begin, IteratorT t_end)
+
+ +
+
+
+template<class NodeInfoT>
class Strategy : public idol::VariableBranching::Strategy<NodeInfoT>
+
+

Public Functions

+
+
+Strategy() = default
+
+ +
+
+inline explicit Strategy(const LeastInfeasible &t_parent)
+
+ +
+
+inline BranchingRules::VariableBranching<NodeInfoT> *operator()(const Optimizers::BranchAndBound<NodeInfoT> &t_parent) const override
+
+ +
+
+inline virtual VariableBranching::Strategy<NodeInfoT> *clone() const override
+
+ +
+
+virtual BranchingRule<NodeT> *operator()(const Optimizers::BranchAndBound<NodeT> &t_parent) const = 0
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/variable-selection/MostInfeasible.html b/api/classes/branch-and-bound/variable-selection/MostInfeasible.html new file mode 100644 index 00000000..c8b07375 --- /dev/null +++ b/api/classes/branch-and-bound/variable-selection/MostInfeasible.html @@ -0,0 +1,211 @@ + + + + + + + + + MostInfeasible — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

MostInfeasible

+
+
+class MostInfeasible : public idol::VariableBranching
+
+

Public Functions

+
+
+MostInfeasible() = default
+
+ +
+
+template<class IteratorT>
inline MostInfeasible(IteratorT t_begin, IteratorT t_end)
+
+ +
+
+
+template<class NodeInfoT>
class Strategy : public idol::VariableBranching::Strategy<NodeInfoT>
+
+

Public Functions

+
+
+Strategy() = default
+
+ +
+
+inline explicit Strategy(const MostInfeasible &t_parent)
+
+ +
+
+inline BranchingRules::VariableBranching<NodeInfoT> *operator()(const Optimizers::BranchAndBound<NodeInfoT> &t_parent) const override
+
+ +
+
+inline virtual VariableBranching::Strategy<NodeInfoT> *clone() const override
+
+ +
+
+virtual BranchingRule<NodeT> *operator()(const Optimizers::BranchAndBound<NodeT> &t_parent) const = 0
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/variable-selection/PseudoCost.html b/api/classes/branch-and-bound/variable-selection/PseudoCost.html new file mode 100644 index 00000000..91f25c17 --- /dev/null +++ b/api/classes/branch-and-bound/variable-selection/PseudoCost.html @@ -0,0 +1,211 @@ + + + + + + + + + PseudoCost — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

PseudoCost

+
+
+class PseudoCost : public idol::VariableBranching
+
+

Public Functions

+
+
+PseudoCost() = default
+
+ +
+
+template<class IteratorT>
inline PseudoCost(IteratorT t_begin, IteratorT t_end)
+
+ +
+
+
+template<class NodeInfoT>
class Strategy : public idol::VariableBranching::Strategy<NodeInfoT>
+
+

Public Functions

+
+
+Strategy() = default
+
+ +
+
+inline explicit Strategy(const PseudoCost &t_parent)
+
+ +
+
+inline BranchingRules::VariableBranching<NodeInfoT> *operator()(const Optimizers::BranchAndBound<NodeInfoT> &t_parent) const override
+
+ +
+
+inline virtual VariableBranching::Strategy<NodeInfoT> *clone() const override
+
+ +
+
+virtual BranchingRule<NodeT> *operator()(const Optimizers::BranchAndBound<NodeT> &t_parent) const = 0
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/variable-selection/StrongBranching.html b/api/classes/branch-and-bound/variable-selection/StrongBranching.html new file mode 100644 index 00000000..62a59235 --- /dev/null +++ b/api/classes/branch-and-bound/variable-selection/StrongBranching.html @@ -0,0 +1,231 @@ + + + + + + + + + StrongBranching — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

StrongBranching

+
+
+class StrongBranching : public idol::VariableBranching
+
+

Public Functions

+
+
+StrongBranching() = default
+
+ +
+
+template<class IteratorT>
inline StrongBranching(IteratorT t_begin, IteratorT t_end)
+
+ +
+
+StrongBranching &with_max_n_variables(unsigned int t_n_variables)
+
+ +
+
+StrongBranching &with_node_scoring_function(const NodeScoreFunction &t_score_function)
+
+ +
+
+StrongBranching &add_phase(const StrongBranchingPhaseType &t_phase, unsigned int t_max_n_variables, unsigned int t_max_depth)
+
+ +
+
+
+template<class NodeInfoT>
class Strategy : public idol::VariableBranching::Strategy<NodeInfoT>
+
+

Public Functions

+
+
+Strategy() = default
+
+ +
+
+Strategy(const Strategy<NodeInfoT> &t_src)
+
+ +
+
+explicit Strategy(const StrongBranching &t_parent)
+
+ +
+
+inline BranchingRules::VariableBranching<NodeInfoT> *operator()(const Optimizers::BranchAndBound<NodeInfoT> &t_parent) const override
+
+ +
+
+inline virtual VariableBranching::Strategy<NodeInfoT> *clone() const override
+
+ +
+
+virtual BranchingRule<NodeT> *operator()(const Optimizers::BranchAndBound<NodeT> &t_parent) const = 0
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/variable-selection/UniformlyRandom.html b/api/classes/branch-and-bound/variable-selection/UniformlyRandom.html new file mode 100644 index 00000000..084e7a97 --- /dev/null +++ b/api/classes/branch-and-bound/variable-selection/UniformlyRandom.html @@ -0,0 +1,216 @@ + + + + + + + + + UniformlyRandom — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

UniformlyRandom

+
+
+class UniformlyRandom : public idol::VariableBranching
+
+

Public Functions

+
+
+UniformlyRandom() = default
+
+ +
+
+template<class IteratorT>
inline UniformlyRandom(IteratorT t_begin, IteratorT t_end)
+
+ +
+
+inline UniformlyRandom &with_seed(unsigned int t_seed)
+
+ +
+
+
+template<class NodeInfoT>
class Strategy : public idol::VariableBranching::Strategy<NodeInfoT>
+
+

Public Functions

+
+
+Strategy() = default
+
+ +
+
+inline explicit Strategy(const UniformlyRandom &t_parent)
+
+ +
+
+inline BranchingRules::VariableBranching<NodeInfoT> *operator()(const Optimizers::BranchAndBound<NodeInfoT> &t_parent) const override
+
+ +
+
+inline virtual VariableBranching::Strategy<NodeInfoT> *clone() const override
+
+ +
+
+virtual BranchingRule<NodeT> *operator()(const Optimizers::BranchAndBound<NodeT> &t_parent) const = 0
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/branch-and-bound/variable-selection/index.html b/api/classes/branch-and-bound/variable-selection/index.html new file mode 100644 index 00000000..16ea20c2 --- /dev/null +++ b/api/classes/branch-and-bound/variable-selection/index.html @@ -0,0 +1,169 @@ + + + + + + + + + Branching Rules — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-and-constraint-generation/ColumnAndConstraintGeneration.html b/api/classes/column-and-constraint-generation/ColumnAndConstraintGeneration.html new file mode 100644 index 00000000..8a6c7d50 --- /dev/null +++ b/api/classes/column-and-constraint-generation/ColumnAndConstraintGeneration.html @@ -0,0 +1,489 @@ + + + + + + + + + Robust::ColumnAndConstraintGeneration — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Robust::ColumnAndConstraintGeneration

+
+
+class ColumnAndConstraintGeneration : public idol::OptimizerFactoryWithDefaultParameters<ColumnAndConstraintGeneration>
+
+

Public Functions

+
+
+ColumnAndConstraintGeneration(Robust::StageDescription t_stage_description, const Model &t_uncertainty_set)
+
+ +
+
+ColumnAndConstraintGeneration(const ColumnAndConstraintGeneration &t_src)
+
+ +
+
+ColumnAndConstraintGeneration(ColumnAndConstraintGeneration&&) noexcept = default
+
+ +
+
+ColumnAndConstraintGeneration &operator=(const ColumnAndConstraintGeneration&) = delete
+
+ +
+
+ColumnAndConstraintGeneration &operator=(ColumnAndConstraintGeneration&&) noexcept = default
+
+ +
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+virtual ColumnAndConstraintGeneration *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+ColumnAndConstraintGeneration &with_master_optimizer(const OptimizerFactory &t_optimizer)
+
+ +
+
+ColumnAndConstraintGeneration &with_separator(const CCGSeparator &t_separator)
+
+ +
+
+ColumnAndConstraintGeneration &with_stabilization(const CCGStabilizer &t_stabilizer)
+
+ +
+
+ColumnAndConstraintGeneration &with_complete_recourse(bool t_value)
+
+ +
+
+ColumnAndConstraintGeneration &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+ColumnAndConstraintGeneration &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+ColumnAndConstraintGeneration &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+ColumnAndConstraintGeneration &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+ColumnAndConstraintGeneration &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+ColumnAndConstraintGeneration &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+ColumnAndConstraintGeneration &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+ColumnAndConstraintGeneration &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+ColumnAndConstraintGeneration &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+ColumnAndConstraintGeneration &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+ColumnAndConstraintGeneration &conditional(bool t_conditional_value, const std::function<void(ColumnAndConstraintGeneration&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+ColumnAndConstraintGeneration &conditional(bool t_conditional_value, const std::function<void(ColumnAndConstraintGeneration&)> &t_if, const std::function<void(ColumnAndConstraintGeneration&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-and-constraint-generation/Optimizers_ColumnAndConstraintGeneration.html b/api/classes/column-and-constraint-generation/Optimizers_ColumnAndConstraintGeneration.html new file mode 100644 index 00000000..5a1c4b48 --- /dev/null +++ b/api/classes/column-and-constraint-generation/Optimizers_ColumnAndConstraintGeneration.html @@ -0,0 +1,424 @@ + + + + + + + + + Optimizers::Robust::ColumnAndConstraintGeneration — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Optimizers::Robust::ColumnAndConstraintGeneration

+
+
+class ColumnAndConstraintGeneration : public idol::Algorithm
+
+

Public Functions

+
+
+ColumnAndConstraintGeneration(const Model &t_parent, const Model &t_uncertainty_set, const OptimizerFactory &t_master_optimizer, const idol::Robust::CCGSeparator &t_separator, const idol::Robust::CCGStabilizer &t_stabilizer, idol::Robust::StageDescription t_stage_description, bool t_complete_recourse)
+
+ +
+
+virtual std::string name() const override
+
+ +
+
+virtual double get_var_primal(const Var &t_var) const override
+
+ +
+
+virtual double get_var_reduced_cost(const Var &t_var) const override
+
+ +
+
+virtual double get_var_ray(const Var &t_var) const override
+
+ +
+
+virtual double get_ctr_dual(const Ctr &t_ctr) const override
+
+ +
+
+virtual double get_ctr_farkas(const Ctr &t_ctr) const override
+
+ +
+
+virtual unsigned int get_n_solutions() const override
+
+ +
+
+virtual unsigned int get_solution_index() const override
+
+ +
+
+inline const Model &master_problem() const
+
+ +
+
+inline const Model &uncertainty_set() const
+
+ +
+
+inline const idol::Robust::StageDescription stage_description() const
+
+ +
+
+inline bool complete_recourse() const
+
+ +
+
+inline unsigned int n_iterations() const
+
+ +
+
+virtual SolutionStatus get_status() const override
+
+ +
+
+virtual SolutionReason get_reason() const override
+
+ +
+
+virtual double get_best_obj() const override
+
+ +
+
+virtual double get_best_bound() const override
+
+ +
+
+virtual double get_relative_gap() const override
+
+ +
+
+virtual double get_absolute_gap() const override
+
+ +
+
+inline virtual const ::idol::Model &parent() const
+
+ +
+
+inline bool get_param_logs() const
+
+ +
+
+inline virtual void set_param_logs(bool t_value)
+
+ +
+
+inline double get_param_time_limit() const
+
+ +
+
+inline virtual void set_param_time_limit(double t_time_limit)
+
+ +
+
+inline unsigned int get_param_thread_limit() const
+
+ +
+
+inline virtual void set_param_threads(unsigned int t_threads)
+
+ +
+
+double get_param_best_obj_stop() const
+
+ +
+
+inline virtual void set_param_best_obj_stop(double t_best_obj_stop)
+
+ +
+
+double get_param_best_bound_stop() const
+
+ +
+
+inline virtual void set_param_best_bound_stop(double t_best_bound_stop)
+
+ +
+
+inline double get_tol_mip_relative_gap() const
+
+ +
+
+inline virtual void set_tol_mip_relative_gap(double t_tol_mip_relative_gap)
+
+ +
+
+inline double get_tol_mip_absolute_gap() const
+
+ +
+
+inline virtual void set_tol_mip_absolute_gap(double t_mip_tol_absolute_gap)
+
+ +
+
+inline double get_tol_feasibility() const
+
+ +
+
+inline virtual void set_tol_feasibility(double t_tol_feasibility)
+
+ +
+
+inline double get_tol_optimality() const
+
+ +
+
+inline virtual void set_tol_optimality(double t_tol_optimality)
+
+ +
+
+inline double get_tol_integer() const
+
+ +
+
+inline virtual void set_tol_integer(double t_tol_integer)
+
+ +
+
+inline unsigned int get_param_iteration_limit() const
+
+ +
+
+inline void set_param_iteration_limit(unsigned int t_iteration_limit)
+
+ +
+
+inline bool get_param_presolve() const
+
+ +
+
+inline virtual void set_param_presolve(bool t_value)
+
+ +
+
+inline bool get_param_infeasible_or_unbounded_info() const
+
+ +
+
+inline virtual void set_param_infeasible_or_unbounded_info(bool t_value)
+
+ +
+
+inline const Timer &time() const
+
+ +
+
+double get_remaining_time() const
+
+ +
+
+inline bool is_terminated() const
+
+ +
+
+virtual void terminate()
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-and-constraint-generation/index.html b/api/classes/column-and-constraint-generation/index.html new file mode 100644 index 00000000..9fe9e5e4 --- /dev/null +++ b/api/classes/column-and-constraint-generation/index.html @@ -0,0 +1,151 @@ + + + + + + + + + Column-and-Constraint Generation Algorithm — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Column-and-Constraint Generation Algorithm

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-generation/ArtificialCosts.html b/api/classes/column-generation/ArtificialCosts.html new file mode 100644 index 00000000..5eefd8ae --- /dev/null +++ b/api/classes/column-generation/ArtificialCosts.html @@ -0,0 +1,226 @@ + + + + + + + + + DantzigWolfe::ArtificialCosts — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

DantzigWolfe::ArtificialCosts

+
+
+class ArtificialCosts : public idol::DantzigWolfe::InfeasibilityStrategyFactory
+
+

Public Functions

+
+
+inline virtual InfeasibilityStrategyFactory::Strategy *operator()() const override
+
+ +
+
+inline virtual ArtificialCosts *clone() const override
+
+ +
+
+ArtificialCosts &with_max_updates_before_phase_I(unsigned int t_value)
+
+ +
+
+ArtificialCosts &with_update_factor(double t_value)
+
+ +
+
+ArtificialCosts &with_initial_costs(double t_value)
+
+ +
+
+
+class Strategy : public idol::DantzigWolfe::InfeasibilityStrategyFactory::Strategy
+
+

Public Functions

+
+
+Strategy(double t_initial_costs, double t_update_factor, unsigned int t_max_updates_before_phase_I)
+
+ +
+
+virtual void execute(Optimizers::DantzigWolfeDecomposition &t_parent) override
+
+ +
+
+inline SolutionStatus status() const
+
+ +
+
+inline SolutionReason reason() const
+
+ +
+
+inline double best_obj() const
+
+ +
+
+inline double best_bound() const
+
+ +
+
+const Solution::Primal &primal_solution() const
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-generation/DantzigWolfeDecomposition.html b/api/classes/column-generation/DantzigWolfeDecomposition.html new file mode 100644 index 00000000..61eaf73b --- /dev/null +++ b/api/classes/column-generation/DantzigWolfeDecomposition.html @@ -0,0 +1,523 @@ + + + + + + + + + DantzigWolfeDecomposition — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

DantzigWolfeDecomposition

+
+

Hint

+

This page documents on the DantzigWolfeDecomposition optimizer factory. Note that there are also +Dantzig-Wolfe Decomposition tutorials.

+
+
+
+class DantzigWolfeDecomposition : public idol::OptimizerFactoryWithDefaultParameters<DantzigWolfeDecomposition>
+
+

Public Functions

+
+
+DantzigWolfeDecomposition() = default
+
+ +
+
+explicit DantzigWolfeDecomposition(Annotation<Ctr, unsigned int> t_decomposition)
+
+ +
+
+DantzigWolfeDecomposition(Annotation<Ctr, unsigned int> t_ctr_decomposition, Annotation<Var, unsigned int> t_var_decomposition)
+
+ +
+
+DantzigWolfeDecomposition(const DantzigWolfeDecomposition &t_src)
+
+ +
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+virtual DantzigWolfeDecomposition *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+DantzigWolfeDecomposition &with_master_optimizer(const OptimizerFactory &t_optimizer_factory)
+
+ +
+
+DantzigWolfeDecomposition &with_default_sub_problem_spec(DantzigWolfe::SubProblem t_sub_problem)
+
+ +
+
+DantzigWolfeDecomposition &with_sub_problem_spec(unsigned int t_id, DantzigWolfe::SubProblem t_sub_problem)
+
+ +
+
+DantzigWolfeDecomposition &with_infeasibility_strategy(const DantzigWolfe::InfeasibilityStrategyFactory &t_strategy)
+
+ +
+
+DantzigWolfeDecomposition &with_hard_branching(bool t_value)
+
+ +
+
+DantzigWolfeDecomposition &with_infeasible_columns_removal(bool t_value)
+
+ +
+
+DantzigWolfeDecomposition &with_max_parallel_sub_problems(unsigned int t_n_sub_problems)
+
+ +
+
+DantzigWolfeDecomposition &with_logger(const Logs::DantzigWolfe::Factory &t_logger)
+
+ +
+
+DantzigWolfeDecomposition &with_dual_price_smoothing_stabilization(const DantzigWolfe::DualPriceSmoothingStabilization &t_stabilization)
+
+ +
+
+const DantzigWolfe::SubProblem &get_sub_problem_spec(unsigned int t_id) const
+
+ +
+
+DantzigWolfeDecomposition &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+DantzigWolfeDecomposition &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+DantzigWolfeDecomposition &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+DantzigWolfeDecomposition &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+DantzigWolfeDecomposition &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+DantzigWolfeDecomposition &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+DantzigWolfeDecomposition &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+DantzigWolfeDecomposition &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+DantzigWolfeDecomposition &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+DantzigWolfeDecomposition &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+DantzigWolfeDecomposition &conditional(bool t_conditional_value, const std::function<void(DantzigWolfeDecomposition&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+DantzigWolfeDecomposition &conditional(bool t_conditional_value, const std::function<void(DantzigWolfeDecomposition&)> &t_if, const std::function<void(DantzigWolfeDecomposition&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-generation/FarkasPricing.html b/api/classes/column-generation/FarkasPricing.html new file mode 100644 index 00000000..60084618 --- /dev/null +++ b/api/classes/column-generation/FarkasPricing.html @@ -0,0 +1,206 @@ + + + + + + + + + DantzigWolfe::FarkasPricing — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

DantzigWolfe::FarkasPricing

+
+
+class FarkasPricing : public idol::DantzigWolfe::InfeasibilityStrategyFactory
+
+

Public Functions

+
+
+inline virtual InfeasibilityStrategyFactory::Strategy *operator()() const override
+
+ +
+
+inline virtual FarkasPricing *clone() const override
+
+ +
+
+
+class Strategy : public idol::DantzigWolfe::InfeasibilityStrategyFactory::Strategy
+
+

Public Functions

+
+
+virtual void execute(Optimizers::DantzigWolfeDecomposition &t_parent) override
+
+ +
+
+inline SolutionStatus status() const
+
+ +
+
+inline SolutionReason reason() const
+
+ +
+
+inline double best_obj() const
+
+ +
+
+inline double best_bound() const
+
+ +
+
+const Solution::Primal &primal_solution() const
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-generation/IntegerMaster.html b/api/classes/column-generation/IntegerMaster.html new file mode 100644 index 00000000..0c7a118b --- /dev/null +++ b/api/classes/column-generation/IntegerMaster.html @@ -0,0 +1,266 @@ + + + + + + + + + Heuristics::IntegerMaster — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Heuristics::IntegerMaster

+
+

Warning

+

This heuristic can only be used with a BranchAndBound<NodeVarInfo> optimizer in which nodes are solved by +DantzigWolfeDecomposition.

+
+
+
+template<class NodeInfoT = idol::DefaultNodeInfo>
class IntegerMaster : public idol::BranchAndBoundCallbackFactory<idol::DefaultNodeInfo>
+
+

Public Functions

+
+
+IntegerMaster() = default
+
+ +
+
+IntegerMaster(IntegerMaster&&) noexcept = default
+
+ +
+
+IntegerMaster &operator=(const IntegerMaster&) = delete
+
+ +
+
+IntegerMaster &operator=(IntegerMaster&&) noexcept = default
+
+ +
+
+virtual BranchAndBoundCallback<NodeInfoT> *operator()() override
+
+ +
+
+virtual BranchAndBoundCallbackFactory<NodeInfoT> *clone() const override
+
+ +
+
+IntegerMaster &with_optimizer(const OptimizerFactory &t_optimizer)
+
+ +
+
+IntegerMaster &with_integer_columns(bool t_value)
+
+ +
+
+IntegerMaster &with_time_limit(double t_time_limit)
+
+ +
+
+IntegerMaster &with_iteration_limit(unsigned int t_iteration_limit)
+
+ +
+
+IntegerMaster &with_max_depth(unsigned int t_max_depth)
+
+ +
+
+IntegerMaster &with_frequency(unsigned int t_frequency)
+
+ +
+
+
+class Strategy : public idol::BranchAndBoundCallback<NodeInfoT>
+
+

Public Functions

+
+
+explicit Strategy(const OptimizerFactory &t_optimizer)
+
+ +
+
+inline bool with_integer_columns() const
+
+ +
+
+inline void set_integer_columns(bool t_value)
+
+ +
+
+inline void set_time_limit(double t_time_limit)
+
+ +
+
+inline void set_iteration_limit(unsigned int t_iteration_limit)
+
+ +
+
+inline void set_max_depth(unsigned int t_max_depth)
+
+ +
+
+inline void set_frequency(unsigned int t_frequency)
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-generation/Optimizers_DantzigWolfeDecomposition.html b/api/classes/column-generation/Optimizers_DantzigWolfeDecomposition.html new file mode 100644 index 00000000..6cfd1c66 --- /dev/null +++ b/api/classes/column-generation/Optimizers_DantzigWolfeDecomposition.html @@ -0,0 +1,436 @@ + + + + + + + + + Optimizers::DantzigWolfeDecomposition — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Optimizers::DantzigWolfeDecomposition

+
+
+class DantzigWolfeDecomposition : public idol::Algorithm
+
+

Public Functions

+
+
+DantzigWolfeDecomposition(const Model &t_model, idol::DantzigWolfe::Formulation &&t_formulation, const OptimizerFactory &t_master_optimizer_factory, const DantzigWolfe::DualPriceSmoothingStabilization &t_stabilization, unsigned int t_max_parallel_pricing, bool t_use_hard_branching, bool t_remove_infeasible_columns, std::vector<DantzigWolfe::SubProblem> &&t_sub_problem_specifications, std::optional<DantzigWolfe::SubProblem> t_default_sub_problem_spec, const DantzigWolfe::InfeasibilityStrategyFactory &t_strategy, const Logs::DantzigWolfe::Factory &t_logger_factory)
+
+ +
+
+virtual std::string name() const override
+
+ +
+
+inline const DantzigWolfe::Formulation &formulation() const
+
+ +
+
+inline DantzigWolfe::Formulation &formulation()
+
+ +
+
+virtual SolutionStatus get_status() const override
+
+ +
+
+virtual SolutionReason get_reason() const override
+
+ +
+
+virtual double get_best_obj() const override
+
+ +
+
+virtual double get_best_bound() const override
+
+ +
+
+virtual double get_relative_gap() const override
+
+ +
+
+virtual double get_absolute_gap() const override
+
+ +
+
+inline virtual const ::idol::Model &parent() const
+
+ +
+
+inline bool get_param_logs() const
+
+ +
+
+inline virtual void set_param_logs(bool t_value)
+
+ +
+
+inline double get_param_time_limit() const
+
+ +
+
+inline virtual void set_param_time_limit(double t_time_limit)
+
+ +
+
+inline unsigned int get_param_thread_limit() const
+
+ +
+
+inline virtual void set_param_threads(unsigned int t_threads)
+
+ +
+
+double get_param_best_obj_stop() const
+
+ +
+
+inline virtual void set_param_best_obj_stop(double t_best_obj_stop)
+
+ +
+
+double get_param_best_bound_stop() const
+
+ +
+
+inline virtual void set_param_best_bound_stop(double t_best_bound_stop)
+
+ +
+
+inline double get_tol_mip_relative_gap() const
+
+ +
+
+inline virtual void set_tol_mip_relative_gap(double t_tol_mip_relative_gap)
+
+ +
+
+inline double get_tol_mip_absolute_gap() const
+
+ +
+
+inline virtual void set_tol_mip_absolute_gap(double t_mip_tol_absolute_gap)
+
+ +
+
+inline double get_tol_feasibility() const
+
+ +
+
+inline virtual void set_tol_feasibility(double t_tol_feasibility)
+
+ +
+
+inline double get_tol_optimality() const
+
+ +
+
+inline virtual void set_tol_optimality(double t_tol_optimality)
+
+ +
+
+inline double get_tol_integer() const
+
+ +
+
+inline virtual void set_tol_integer(double t_tol_integer)
+
+ +
+
+inline unsigned int get_param_iteration_limit() const
+
+ +
+
+inline void set_param_iteration_limit(unsigned int t_iteration_limit)
+
+ +
+
+inline bool get_param_presolve() const
+
+ +
+
+inline virtual void set_param_presolve(bool t_value)
+
+ +
+
+inline bool get_param_infeasible_or_unbounded_info() const
+
+ +
+
+inline virtual void set_param_infeasible_or_unbounded_info(bool t_value)
+
+ +
+
+inline const Timer &time() const
+
+ +
+
+double get_remaining_time() const
+
+ +
+
+inline bool is_terminated() const
+
+ +
+
+virtual void terminate()
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+
+class ColumnGeneration
+
+

Public Functions

+
+
+ColumnGeneration(DantzigWolfeDecomposition &t_parent, bool t_use_farkas_for_infeasibility, double t_best_bound_stop)
+
+ +
+
+inline const DantzigWolfeDecomposition &parent() const
+
+ +
+
+inline DantzigWolfeDecomposition &parent()
+
+ +
+
+inline SolutionStatus status() const
+
+ +
+
+inline SolutionReason reason() const
+
+ +
+
+inline double best_obj() const
+
+ +
+
+inline double best_bound() const
+
+ +
+
+inline const Solution::Primal &primal_solution() const
+
+ +
+
+inline void set_best_bound_stop(double t_best_bound_stop)
+
+ +
+
+void execute()
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-generation/index.html b/api/classes/column-generation/index.html new file mode 100644 index 00000000..18aced50 --- /dev/null +++ b/api/classes/column-generation/index.html @@ -0,0 +1,159 @@ + + + + + + + + + Column Generation Algorithm — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-generation/stabilization/Neame.html b/api/classes/column-generation/stabilization/Neame.html new file mode 100644 index 00000000..40533ab9 --- /dev/null +++ b/api/classes/column-generation/stabilization/Neame.html @@ -0,0 +1,206 @@ + + + + + + + + + DantzigWolfe::Neame — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

DantzigWolfe::Neame

+
+
+class Neame : public idol::DantzigWolfe::DualPriceSmoothingStabilization
+
+

Public Functions

+
+
+inline explicit Neame(double t_initial_factor)
+
+ +
+
+inline virtual DualPriceSmoothingStabilization::Strategy *operator()() const override
+
+ +
+
+inline virtual DualPriceSmoothingStabilization *clone() const override
+
+ +
+
+
+class Strategy : public idol::DantzigWolfe::DualPriceSmoothingStabilization::Strategy
+
+

Public Functions

+
+
+inline explicit Strategy(double t_initial_factor)
+
+ +
+
+inline virtual void initialize() override
+
+ +
+
+inline virtual void update_stability_center(const Solution::Dual &t_master_dual) override
+
+ +
+
+inline virtual Solution::Dual compute_smoothed_dual_solution(const Solution::Dual &t_master_dual) override
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-generation/stabilization/Wentges.html b/api/classes/column-generation/stabilization/Wentges.html new file mode 100644 index 00000000..54feee75 --- /dev/null +++ b/api/classes/column-generation/stabilization/Wentges.html @@ -0,0 +1,206 @@ + + + + + + + + + DantzigWolfe::Wentges — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

DantzigWolfe::Wentges

+
+
+class Wentges : public idol::DantzigWolfe::DualPriceSmoothingStabilization
+
+

Public Functions

+
+
+inline explicit Wentges(double t_initial_factor)
+
+ +
+
+inline virtual DualPriceSmoothingStabilization::Strategy *operator()() const override
+
+ +
+
+inline virtual DualPriceSmoothingStabilization *clone() const override
+
+ +
+
+
+class Strategy : public idol::DantzigWolfe::DualPriceSmoothingStabilization::Strategy
+
+

Public Functions

+
+
+inline explicit Strategy(double t_initial_factor)
+
+ +
+
+inline virtual void initialize() override
+
+ +
+
+inline virtual void update_stability_center(const Solution::Dual &t_master_dual) override
+
+ +
+
+inline virtual Solution::Dual compute_smoothed_dual_solution(const Solution::Dual &t_master_dual) override
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/column-generation/stabilization/index.html b/api/classes/column-generation/stabilization/index.html new file mode 100644 index 00000000..dde111bd --- /dev/null +++ b/api/classes/column-generation/stabilization/index.html @@ -0,0 +1,160 @@ + + + + + + + + + Stabilization — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Stabilization

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/Annotation.html b/api/classes/common/Annotation.html new file mode 100644 index 00000000..dcf142a5 --- /dev/null +++ b/api/classes/common/Annotation.html @@ -0,0 +1,328 @@ + + + + + + + + + Annotation — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Annotation

+

Annotations are additional information associated to an optimization object (e.g., a constraint or a variable). +The Annotation class takes two template arguments: an optimization object type and a value type. For instance, we +can create an annotation for constraints of type unsigned int as follows.

+
Env env;
+
+const unsigned int default_value = 0;
+
+Annotation<Ctr, unsigned int> annotation(env, "my_annotation", default_value);
+
+
+

Note that annotations are global, i.e., they do not relate to a given optimization model and can, therefore, be accessed +anywhere in the code. Thus, given a constraint called constraint, the value of the annotation can be accessed +by calling the Ctr::get method.

+
std::cout << "My annotation is " << constraint.get(annotation) << std::endl; // "0", i.e., the default_value value
+
+
+

Annotations are, in particular, used to give decomposition instructions to idol when designing a Branch-and-Price algorithm. +To learn more, check our Branch-and-Price tutorials.

+
+
+template<class ObjectT, class ValueT = unsigned int>
class Annotation : public idol::impl::Annotation
+
+

Public Functions

+
+
+inline Annotation(Env &t_env, std::string t_name)
+

Constructor.

+

Creates a new annotation stored in t_env with name t_name. No default value is given.

+
+
Parameters:
+
    +

  • t_env – The environment which will store the annotation

    • +

  • t_name – The given name to the annotation

    • +
+
+
+
+ +
+
+template<class ...ArgsT>
inline Annotation(Env &t_env, std::string t_name, ArgsT&&... t_args)
+

Constructor.

+

Creates a new annotation stored in t_env with name t_name and a default value. The default value is built “in place” by calling the constructor of ValueT with the arguments given after t_name.

+
+
Template Parameters:
+

ArgsT – Parameter pack template types for constructing the default value of the annotation

+
+
Parameters:
+
    +

  • t_env – The environment which will store the annotation

    • +

  • t_name – The given name to the annotation

    • +

  • t_args – Parameter pack arguments for constructing the default value of the annotation

    • +
+
+
+
+ +
+
+inline virtual bool is_var_annotation() const override
+

Returns true if the annotation is for variables, false otherwise.

+
+
Returns:
+

true if the annotation is for variables, false otherwise

+
+
+
+ +
+
+inline virtual bool is_ctr_annotation() const override
+

Returns true if the annotation is for constraints, false otherwise.

+
+
Returns:
+

true if the annotation is for constraints, false otherwise

+
+
+
+ +
+
+inline const ValueT &default_value() const
+

Returns the default value of the annotation.

+
+
Returns:
+

The default value of the annotation

+
+
+
+ +
+
+inline unsigned int id() const
+

Returns the id of the annotation.

+

The id is unique within the environment.

+
+
Returns:
+

The id of the annotation

+
+
+
+ +
+
+inline const std::string &name() const
+

Returns the given name of the annotation.

+
+
Returns:
+

The given name of the annotation

+
+
+
+ +
+
+inline bool has_default() const
+

Returns true if the annotation has a default value, false otherwise.

+
+
Returns:
+

true if the annotation has a default value, false otherwise

+
+
+
+ +
+
+void free()
+

Frees the memory kept for the annotation in the environment.

+
+ +
+
+inline Env &env() const
+

Returns the underlying environment of the annotation.

+
+
Returns:
+

The underlying environment of the annotation.

+
+
+
+ +
+
+

Public Static Functions

+
+
+template<class ...ArgsT>
static inline Annotation<ObjectT, ValueT> make_with_default_value(Env &t_env, std::string t_name, ArgsT&&... t_args)
+

Creates a new annotation stored in t_env with name t_name and a default value. The default value is built “in place” by calling the constructor of ValueT with the arguments given after t_name.

+
+
Template Parameters:
+

ArgsT – Parameter pack template types for constructing the default value of the annotation

+
+
Parameters:
+
    +

  • t_env – The environment which will store the annotation

    • +

  • t_name – The given name to the annotation

    • +

  • t_args – Parameter pack arguments for constructing the default value of the annotation

    • +
+
+
Returns:
+

The created annotation

+
+
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/Column.html b/api/classes/common/Column.html new file mode 100644 index 00000000..e8412678 --- /dev/null +++ b/api/classes/common/Column.html @@ -0,0 +1,477 @@ + + + + + + + + + Column — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Column

+
+
+class Column : public idol::impl::Column
+

Column modeling-old object.

+

This class is used to represent an optimization model’s column. It is made of an objective coefficient and a set of { Ctr, Constant } pairs representing the coefficient (Constant) of the column in each row (Ctr). Such pairs are called components.

+
+

Public Functions

+
+
+Column() = default
+

Default constructor.

+

Creates an empty column.

+
+ +
+
+inline Column(Constant &&t_obj)
+

Constructor.

+

Creates a new column with no components and on objective coefficient equal to t_obj

+
+
Parameters:
+

t_obj – The objective coefficient

+
+
+
+ +
+
+inline Column(const Constant &t_obj)
+

Constructor.

+

Creates a new column with no components and on objective coefficient equal to t_obj

+
+
Parameters:
+

t_obj – The objective coefficient

+
+
+
+ +
+
+Column(const Column &t_src) = default
+

Copy constructor.

+
+
Parameters:
+

t_src – The object to be copied

+
+
+
+ +
+
+Column(Column &&t_src) noexcept = default
+

Move constructor.

+
+
Parameters:
+

t_src – The object to be moved

+
+
+
+ +
+
+Column &operator=(const Column &t_src) = default
+

Copy-assignment operator

+
+
Parameters:
+

t_src – The object to copy

+
+
Returns:
+

*this

+
+
+
+ +
+
+Column &operator=(Column &&t_src) noexcept = default
+

Move-assignment operator

+
+
Parameters:
+

t_src – The object to move

+
+
Returns:
+

*this

+
+
+
+ +
+
+Column fix(const Solution::Primal &t_primals) const
+

Creates a new column in which all Param in each Constant are replaced by their corresponding values in t_primals

+
+
Parameters:
+

t_primals – the primal values for the parameters.

+
+
Returns:
+

the new column.

+
+
+
+ +
+
+inline Constant &obj()
+

Returns the (linear) objective coefficient of the column.

+

For instance, a model’s variable x with column whose objective is 2 implies that the model has a term in the objective function which is 2 * x.

+
+
Returns:
+

The (linear) objective coefficient of the column

+
+
+
+ +
+
+inline const Constant &obj() const
+

Returns the (linear) objective coefficient of the column. For instance, a model’s variable x with column whose objective is 2 implies that the model has a term in the objective function which is 2 * x.

+
+
Returns:
+

The (linear) objective coefficient of the column

+
+
+
+ +
+
+inline ::idol::LinExpr<Var> &obj_quadratic()
+

Returns the (quadratic) objective coefficient of the column.

+

For instance, a model’s variable x with column whose objective is 2 * y implies that the model has a term in the objective function which is 2 * y * x.

+
+
Returns:
+

The (quadratic) objective coefficient of the column

+
+
+
+ +
+
+inline const ::idol::LinExpr<Var> &obj_quadratic() const
+

Returns the (quadratic) objective coefficient of the column.

+

For instance, a model’s variable x with column whose objective is 2 * y implies that the model has a term in the objective function which is 2 * y * x.

+
+
Returns:
+

The (quadratic) objective coefficient of the column

+
+
+
+ +
+
+inline LinExpr<Ctr> &linear()
+

Returns the (linear) components of the column.

+

For instance, a model’s variable x with a column having a component { constraint, 2 } implies that the model has a constraint constraint with a term 2 * x.

+
+
Returns:
+

The (linear) components of the column.

+
+
+
+ +
+
+inline const LinExpr<Ctr> &linear() const
+

Returns the (linear) components of the column.

+

For instance, a model’s variable x with a column having a component { constraint, 2 } implies that the model has a constraint constraint with a term 2 * x.

+
+
Returns:
+

The (linear) components of the column.

+
+
+
+ +
+
+inline QuadExpr<Ctr, Var> &quadratic()
+

Returns the (quadratic) components of the column.

+

For instance, a model’s variable x with a column having a component { constraint, 2 * y } implies that the model has a constraint constraint with a term 2 * y * x.

+
+
Returns:
+

The (quadratic) components of the column.

+
+
+
+ +
+
+inline const QuadExpr<Ctr, Var> &quadratic() const
+

Returns the (quadratic) components of the column.

+

For instance, a model’s variable x with a column having a component { constraint, 2 * y } implies that the model has a constraint constraint with a term 2 * y * x.

+
+
Returns:
+

The (quadratic) components of the column.

+
+
+
+ +
+
+inline void set_linear(LinExpr<Ctr> &&t_lin_expr)
+

Sets the (linear) components of the column.

+
+
Parameters:
+

t_lin_expr – The (linear) components of the column

+
+
+
+ +
+
+inline void set_linear(const LinExpr<Ctr> &t_lin_expr)
+

Sets the (linear) components of the column.

+
+
Parameters:
+

t_lin_expr – The (linear) components of the column

+
+
+
+ +
+
+inline void set_quadratic(QuadExpr<Ctr, Var> &&t_quad_expr)
+

Sets the (quadratic) components of the column.

+
+
Parameters:
+

t_lin_expr – The (quadratic) components of the column

+
+
+
+ +
+
+inline void set_quadratic(const QuadExpr<Ctr, Var> &t_quad_expr)
+

Sets the (quadratic) components of the column.

+
+
Parameters:
+

t_lin_expr – The (quadratic) components of the column

+
+
+
+ +
+
+inline void set_obj(Constant &&t_obj)
+

Sets the (linear) objective coefficient.

+
+
Parameters:
+

t_obj – The (linear) objective coefficient

+
+
+
+ +
+
+inline void set_obj(const Constant &t_obj)
+

Sets the (linear) objective coefficient.

+
+
Parameters:
+

t_obj – The (linear) objective coefficient

+
+
+
+ +
+
+inline Column &operator+=(const Column &t_rhs)
+

Adds the objective and components of t_rhs to the objective and components of the column.

+
+
Parameters:
+

t_rhs – the right hand-side

+
+
Returns:
+

*this

+
+
+
+ +
+
+inline Column &operator-=(const Column &t_rhs)
+

Subtracts the objective and components of t_rhs to the objective and components of the column.

+
+
Parameters:
+

t_rhs – the right hand-side

+
+
Returns:
+

*this

+
+
+
+ +
+
+inline Column &operator*=(double t_rhs)
+

Multiplies the objective and components of the column by a given factor.

+
+
Parameters:
+

t_rhs – the right hand-side

+
+
Returns:
+

*this

+
+
+
+ +
+
+

Public Static Attributes

+
+
+static const Column EmptyColumn
+

Represents an empty column

+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/Constant.html b/api/classes/common/Constant.html new file mode 100644 index 00000000..9ffb5b5d --- /dev/null +++ b/api/classes/common/Constant.html @@ -0,0 +1,546 @@ + + + + + + + + + Constant — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Constant

+
+
+class Constant
+

Constant term modeling-old object.

+

This class is used to build constant terms which will appear in models.

+

Typically, they represent a real number. However, they can also be an affine combination of Param. This can be useful to define specific decomposition schemes such as Dantzig-Wolfe reformulations where decision variables from a model A are seen as constant terms in a model B.

+

It is composed of a numerical term and a set of { Param, numerical } pairs representing the following mathematical expression.

+

\( \text{numerical}_0 + \sum_{j=1}^n \text{numerical}_j \times \text{Param}_j \)

+

Constant terms can be created by calling the appropriate constructor, or by using overloaded operators such as + and *. For instance, 2 * xi where xi is a Param will yield a Constant.

+
+

Public Functions

+
+
+Constant() = default
+

Create a new constant term equal to zero.

+
+ +
+
+Constant(const Param &t_param, double t_value = 1.)
+

Constructor.

+

Creates a new constant term representing t_value * t_param.

+
+
Parameters:
+
    +

  • t_param – The parameter.

    • +

  • t_value – The coefficient for the parameter.

    • +
+
+
+
+ +
+
+Constant(const Param &t_param_1, const Param &t_param_2, double t_value = 1.)
+

Constructor.

+

Creates a new constant term representing t_value * t_param_1 * t_param_2.

+
+
Parameters:
+
    +

  • t_param_1 – The first parameter.

    • +

  • t_param_2 – The second parameter.

    • +

  • t_value – The coefficient for the parameters.

    • +
+
+
+
+ +
+
+Constant(double t_constant)
+

Constructor.

+

Creates a new constant term equal to the constant given as argument.

+
+
Parameters:
+

t_constant – The constant.

+
+
+
+ +
+
+Constant(const Constant &t_src)
+

Copy constructor.

+
+ +
+
+Constant(Constant&&) noexcept = default
+

Move constructor.

+
+ +
+
+Constant &operator=(const Constant &t_rhs)
+

Copy-assignment operator.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Constant &operator=(Constant&&) noexcept = default
+

Move-assignment operator.

+
+
Returns:
+

*this

+
+
+
+ +
+
+void set(const Param &t_param, double t_value)
+

Sets the coefficient for a given parameter in the constant term.

+

If t_coefficient is zero, the entry for t_param is removed (i.e., not stored). The tolerance Tolerance::Sparsity is used to compare with zero.

+
+
Parameters:
+
    +

  • t_param – The parameter.

    • +

  • t_value – The coefficient associated to the parameter.

    • +
+
+
+
+ +
+
+void set(const Param &t_param_1, const Param &t_param_2, double t_value)
+

Sets the coefficient for a given pair of parameters in the constant term.

+

If t_coefficient is zero, the entry for t_param is removed (i.e., not stored). The tolerance Tolerance::Sparsity is used to compare with zero.

+
+
Parameters:
+
    +

  • t_param_1 – The first parameter.

    • +

  • t_param_2 – The second parameter.

    • +

  • t_value – The coefficient associated to the pair of parameters.

    • +
+
+
+
+ +
+
+double get(const Param &t_param) const
+

Returns the coefficient of the parameter given as argument.

+

If no coefficient is found, zero is returned.

+
+
Parameters:
+

t_param – The queried parameter.

+
+
+
+ +
+
+double get(const Param &t_param_1, const Param &t_param_2) const
+

Returns the coefficient of the pair of parameters given as arguments.

+
+
Parameters:
+
    +

  • t_param_1 – The first parameter.

    • +

  • t_param_2 – The second parameter.

    • +
+
+
Returns:
+

The queried parameters.

+
+
+
+ +
+
+inline void set_numerical(double t_constant)
+

Sets the numerical term equal to the constant given as parameter.

+
+
Parameters:
+

t_constant – The numerical term.

+
+
+
+ +
+
+inline double numerical() const
+

Returns the numerical term of the constant.

+
+ +
+
+double as_numerical() const
+

Returns the numerical term of the constant, and throws an exception if the Constant also contains non-numerical terms, i.e., the Constant is a parametrized constant.

+
+
Returns:
+

The numerical term of the constant

+
+
+
+ +
+
+bool is_zero() const
+

Returns true if the constant is equal to zero (i.e., if its numerical term is zero and no { Param, numerical } pair can be found). The tolerance Tolerance::Sparsity is used to compare with zero.

+
+ +
+
+inline unsigned int size() const
+

Returns the number of { Param, numerical } pairs stored inside the Constant.

+
+ +
+
+bool is_numerical() const
+

Returns true if the Constant is a pure numerical, i.e., the Constant is not a parametrized constant, false otherwise.

+
+ +
+
+inline auto linear()
+

Returns the linear part of the parametrized expression.

+
+
Returns:
+

The linear part of the parametrized expression.

+
+
+
+ +
+
+inline auto linear() const
+

Returns the linear part of the parametrized expression.

+
+
Returns:
+

The linear part of the parametrized expression.

+
+
+
+ +
+
+inline auto quadratic()
+

Returns the quadratic part of the parametrized expression.

+
+
Returns:
+

The quadratic part of the parametrized expression.

+
+
+
+ +
+
+inline auto quadratic() const
+

Returns the quadratic part of the parametrized expression.

+
+
Returns:
+

The quadratic part of the parametrized expression.

+
+
+
+ +
+
+Constant &operator*=(double t_coefficient)
+

Multiplies the Constant by t_coefficient (i.e., every { Param, numerical } pairs and the numerical term are multiplied).

+
+
Parameters:
+

t_coefficient – The coefficient for multiplication.

+
+
+
+ +
+
+Constant &operator/=(double t_coefficient)
+
+ +
+
+Constant &operator+=(double t_term)
+

Adds up a numerical term to the current numerical term.

+
+
Parameters:
+

t_term – The numerical term to add.

+
+
+
+ +
+
+Constant &operator+=(const Param &t_term)
+

Adds a parameter to the constant (equivalent to adding Constant(t_param, 1)).

+

Resulting zero entries are removed. The tolerance Tolerance::Sparsity is used to compare with zero.

+
+
Parameters:
+

t_term – The parameter to add.

+
+
+
+ +
+
+Constant &operator+=(const Constant &t_term)
+

Adds up another constant term (i.e., both the numerical terms and every { Param, numerical } pairs are added up).

+

Resulting zero-entries are removed. The tolerance Tolerance::Sparsity is used to compare with zero.

+
+
Parameters:
+

t_term –

+
+
+
+ +
+
+Constant &operator-=(const Constant &t_term)
+

Subtracts another constant term (i.e., both the numerical terms and every { Param, numerical } pairs are substracted).

+

Resulting zero-entries are removed. The tolerance Tolerance::Sparsity is used to compare with zero.

+
+
Parameters:
+

t_term – The constant to subtract.

+
+
+
+ +
+
+Constant &operator-=(double t_term)
+

Subtracts a numerical term to the current numerical term.

+
+
Parameters:
+

t_term – The numerical term to subtract.

+
+
+
+ +
+
+Constant &operator-=(Param t_term)
+

Subtracts parameter to the constant (equivalent to subtracting Constant(t_param, 1)).

+

Resulting zero entries are removed. The tolerance Tolerance::Sparsity is used to compare with zero.

+
+
Parameters:
+

t_term – The parameter to subtract.

+
+
+
+ +
+
+double fix(const Solution::Primal &t_primals) const
+

Computes the resulting numerical by replacing each Param by their corresponding values in t_primals.

+
+
Parameters:
+

t_primals – Primal values associated to the stored Param

+
+
Returns:
+

the resulting numerical

+
+
+
+ +
+
+double fix(const Solution::Dual &t_duals) const
+

Computes the resulting numerical by replacing each Param by their corresponding values in t_duals.

+
+
Parameters:
+

t_primals – Dual values associated to the stored Param

+
+
Returns:
+

the resulting numerical

+
+
+
+ +
+
+void round()
+
+ +
+
+Constant &multiply_with_precision(double t_factor, unsigned int t_n_digits)
+
+ +
+
+Constant &multiply_with_precision_by_power_of_10(unsigned int t_exponent, unsigned int t_n_digits)
+
+ +
+
+

Public Static Attributes

+
+
+static Constant Zero
+

Represents an empty constant.

+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/Ctr.html b/api/classes/common/Ctr.html new file mode 100644 index 00000000..d0c61534 --- /dev/null +++ b/api/classes/common/Ctr.html @@ -0,0 +1,366 @@ + + + + + + + + + Ctr — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Ctr

+
+
+class Ctr : public idol::Object<CtrVersion, Ctr>
+

Constraint modeling-old object.

+

The class is used to represent a constraint in an optimization model.

+

Constraints can be created by calling a constructor of Ctr, passing the optimization environment which will store the constraint’s versions as argument. The constraint can then be added to a model using the Model::add method.

+

Another approach is to directly use the Model::add_ctr method.

+

Note that constraints are created naturally by using intermediate temporary constraints TempCtr using operators such as <=, == and >=. For instance, one may do

+
model.add_ctr(x + y >= 2.);
+
+
+
+

Public Functions

+
+
+Ctr(Env &t_env, TempCtr &&t_temp_ctr, std::string t_name = "")
+

Constructor.

+

Creates a new constraint in the optimization environment.

+

If no name is given, a unique name in the environment is given.

+
+
Parameters:
+
    +

  • t_env – The optimization environment.

    • +

  • t_temp_ctr – The temporary constraint used to create the default version of the constraint.

    • +

  • t_name – The given name for the constraint.

    • +
+
+
+
+ +
+
+Ctr(Env &t_env, const TempCtr &t_temp_ctr, std::string t_name = "")
+

Constructor.

+

Creates a new constraint in the optimization environment.

+

If no name is given, a unique name in the environment is given.

+
+
Parameters:
+
    +

  • t_env – The optimization environment.

    • +

  • t_temp_ctr – The temporary constraint used to create the default version of the constraint.

    • +

  • t_name – The given name for the constraint.

    • +
+
+
+
+ +
+
+Ctr(Env &t_env, CtrType t_type, Constant &&t_constant, std::string t_name = "")
+

Constructor.

+

Creates a new constraint in the optimization environment.

+

If no name is given, a unique name in the environment is given.

+
+
Parameters:
+
    +

  • t_env – The optimization environment.

    • +

  • t_type – The type of the constraint in its default version.

    • +

  • t_constant – The right hand-side of the constraint in its default version.

    • +

  • t_name – The given name for the constraint.

    • +
+
+
+
+ +
+
+Ctr(Env &t_env, CtrType t_type, const Constant &t_constant, std::string t_name = "")
+

Constructor.

+

Creates a new constraint in the optimization environment.

+

If no name is given, a unique name in the environment is given.

+
+
Parameters:
+
    +

  • t_env – The optimization environment.

    • +

  • t_type – The type of the constraint in its default version.

    • +

  • t_constant – The right hand-side of the constraint in its default version.

    • +

  • t_name – The given name for the constraint.

    • +
+
+
+
+ +
+
+inline const std::string &name() const
+

Returns the name of the optimization object.

+
+
Returns:
+

The name of the optimization object.

+
+
+
+ +
+
+inline unsigned int id() const
+

Returns the id of the optimization object.

+
+
Returns:
+

The id of the optimization object.

+
+
+
+ +
+
+inline bool is_in(const Model &t_model) const
+

Returns true if the optimization object is part of the model t_model, false otherwise.

+
+
Parameters:
+

t_model – The model.

+
+
Returns:
+

True if the optimization object is part of the model t_model, false otherwise.

+
+
+
+ +
+
+inline const ValueT &get(const Annotation<Ctr, ValueT> &t_annotation) const
+

Returns the value of the given annotation t_annotation associated to the object.

+

If no value is found, the default value of the annotation is returned. If no default value was set, an exception is thrown.

+
+
Template Parameters:
+

ValueT – The value type of the annotation.

+
+
Parameters:
+

t_annotation – The annotation.

+
+
Returns:
+

The value of the annotation.

+
+
+
+ +
+
+inline void set(const Annotation<Ctr, ValueT> &t_annotation, ArgsT&&... t_args) const
+

Sets the value of the given annotation t_annotation associated to the object.

+
+
Template Parameters:
+
    +

  • ValueT – The value type of the annotation.

    • +

  • ArgsT – The parameter pack types for constructing the value of the annotation.

    • +
+
+
Parameters:
+
    +

  • t_annotation – The annotation.

    • +

  • t_args – The parameter pack arguments used to construct “in place” the value of the annotation.

    • +
+
+
+
+ +
+
+

Public Static Functions

+
+
+template<unsigned int N = 1, unsigned int I = 0>
static inline Vector<Ctr, N - I> make_vector(Env &t_env, const Dim<N> &t_dim, CtrType t_type, const Constant &t_constant, const std::string &t_name = "")
+

Creates a (nested) vector of constraints.

+

Example:

+

Env env;
+
+auto constraint = Ctr::make_vector(env, Dim<2>(m, n), LessOrEqual, 0., "constraint");
+
+std::cout << constraint[1][4].name() << std::endl; // "constraint_1_4"
+
+
+

+
+
Template Parameters:
+
    +

  • N – The dimension of the (nested) vector.

    • +

  • I – Used for recursion.

    • +
+
+
Parameters:
+
    +

  • t_env – The environment to store the constraints.

    • +

  • t_dim – The number of elements in each dimension.

    • +

  • t_type – The type of the constraints.

    • +

  • t_constant – The right hand-side of the constraints.

    • +

  • t_name – The given name of the constraints.

    • +
+
+
Returns:
+

A (nested) vector of constraints.

+
+
+
+ +
+
+

Friends

+
+
+friend class impl::Env
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/Env.html b/api/classes/common/Env.html new file mode 100644 index 00000000..b01bd351 --- /dev/null +++ b/api/classes/common/Env.html @@ -0,0 +1,221 @@ + + + + + + + + + Env — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Env

+
+
+class Env : public idol::impl::Env
+

Environment class.

+

This class stores and manages every optimization objects and annotations in idol.

+

Essentially, it is the environment that controls the death and lives of such objects. It is through the environment that idol manages the different versions each optimization object may have during the execution of your program.

+

Important: If an optimization environment is destroyed, all of its objects are also destroyed. Trying to access them will lead to undefined behavior and, eventually, segmentation fault.

+

Typically, only one environment should be used by your code, though it is possible to instantiate many environment (not advised).

+

Environments are objects of the Env class and can be created as follows.

+
Env env; // Creates a new optimization environment.
+
+
+
+

Public Functions

+
+
+Env() = default
+

Constructor.

+

Creates a new optimization environment.

+
+ +
+
+template<class T>
inline const auto &operator[](const T &t_object) const
+

Returns the default version of an optimization object.

+
+
Template Parameters:
+

T – The type of object queried.

+
+
Parameters:
+

t_object – The object. @returnthe The default version of an optimization object.

+
+
+
+ +
+
+

Friends

+
+
+friend class Model
+
+ +
+
+friend class Var
+
+ +
+
+friend class Ctr
+
+ +
+
+friend class impl::Annotation
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/Expr.html b/api/classes/common/Expr.html new file mode 100644 index 00000000..3da13c17 --- /dev/null +++ b/api/classes/common/Expr.html @@ -0,0 +1,338 @@ + + + + + + + + + Expr — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Expr

+
+
+template<class Key1 = idol::Var, class Key2 = Key1>
class Expr : public idol::impl::Expr<idol::Var, idol::Var>
+
+

Public Functions

+
+
+Expr() = default
+
+ +
+
+inline Expr(double t_num)
+
+ +
+
+inline Expr(const Param &t_param)
+
+ +
+
+inline Expr(Constant &&t_expr)
+
+ +
+
+inline Expr(const Constant &t_expr)
+
+ +
+
+inline Expr(const Key1 &t_var)
+
+ +
+
+inline Expr(LinExpr<Key1> &&t_expr)
+
+ +
+
+inline Expr(const LinExpr<Key1> &t_expr)
+
+ +
+
+inline Expr(QuadExpr<Key1> &&t_expr)
+
+ +
+
+inline Expr(const QuadExpr<Key1> &t_expr)
+
+ +
+
+inline Expr(LinExpr<Key1> &&t_lin_expr, QuadExpr<Key1, Key2> &&t_quad_expr, Constant &&t_constant)
+
+ +
+
+inline Expr(const LinExpr<Key1> &t_lin_expr, const QuadExpr<Key1, Key2> &t_quad_expr, const Constant &t_constant)
+
+ +
+
+Expr(const Expr &t_src) = default
+
+ +
+
+Expr(Expr&&) noexcept = default
+
+ +
+
+Expr &operator=(const Expr &t_rhs) = default
+
+ +
+
+Expr &operator=(Expr&&) noexcept = default
+
+ +
+
+Expr fix(const Solution::Primal &t_primals) const
+
+ +
+
+Expr<idol::Var, idol::Var> &operator+=(const Expr<idol::Var, idol::Var> &t_rhs)
+
+ +
+
+Expr<idol::Var, idol::Var> &operator-=(const Expr<idol::Var, idol::Var> &t_rhs)
+
+ +
+
+Expr<idol::Var, idol::Var> &operator*=(double t_rhs)
+
+ +
+
+Expr<idol::Var, idol::Var> &operator/=(double t_rhs)
+
+ +
+
+inline LinExpr<idol::Var> &linear()
+
+ +
+
+inline const LinExpr<idol::Var> &linear() const
+
+ +
+
+inline QuadExpr<idol::Var, idol::Var> &quadratic()
+
+ +
+
+inline const QuadExpr<idol::Var, idol::Var> &quadratic() const
+
+ +
+
+inline Constant &constant()
+
+ +
+
+inline const Constant &constant() const
+
+ +
+
+inline bool is_zero() const
+
+ +
+
+void round()
+
+ +
+
+double gcd() const
+
+ +
+
+double scale_to_integers(unsigned int t_n_significant_digits)
+
+ +
+
+Expr &multiply_with_precision(double t_factor, unsigned int t_n_digits)
+
+ +
+
+Expr &multiply_with_precision_by_power_of_10(unsigned int t_exponent, unsigned int t_n_digits)
+
+ +
+
+inline void clear()
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/LinExpr.html b/api/classes/common/LinExpr.html new file mode 100644 index 00000000..d66757da --- /dev/null +++ b/api/classes/common/LinExpr.html @@ -0,0 +1,404 @@ + + + + + + + + + LinExpr — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

LinExpr

+

This class is used to represent linear expressions in the modeling interface of idol.

+

It consists in a set of linear terms which represent products of keys (typically, variables) and scalars.

+
+

Example

+

In this example, we create a linear expression representing the following linear expression.

+
+\[3 x_0 + 2 x_1\]
+
auto x = model.add_vars(Dim<1>(2), 0, Inf, Continuous, "x");
+
+LinExpr lin_expr = 3 * x[0] + 2 * x[1];
+
+
+
+
+
+template<class Key = idol::Var>
class LinExpr : public idol::AbstractExpr<idol::Var, LinTerm<idol::Var>>
+
+
Template Parameters:
+

Key – the class representing keys

+
+
+
+

Public Functions

+
+
+LinExpr() = default
+
+ +
+
+LinExpr(const Key &t_key)
+
+ +
+
+LinExpr(Constant &&t_factor, const Key &t_key)
+
+ +
+
+LinExpr(const Constant &t_factor, const Key &t_key)
+
+ +
+
+void set(const Key &t_key, const Constant &t_coefficient)
+

Sets the value of a key’s coefficient

+

The constant is copied.

+

Example: 

LinExpr lin_expr;
+lin_expr.set(x[0], -1);
+
+
+

+
+
Parameters:
+
    +

  • t_key – the key

    • +

  • t_coefficient – the key’s coefficient

    • +
+
+
+
+ +
+
+virtual void set(const Key &t_key, Constant &&t_coefficient) override
+

Sets the value of a key’s coefficient

+

The constant is moved.

+

Example: 

LinExpr lin_expr;
+lin_expr.set(x[0], -1);
+
+
+

+
+
Parameters:
+
    +

  • t_key – the key

    • +

  • t_coefficient – the key’s coefficient

    • +
+
+
+
+ +
+
+virtual const Constant &get(const Key &t_key) const override
+

Returns a key’s coefficient

+

Example: 

LinExpr lin_expr = 2 * x[0] + 3 * x[1];
+
+std::cout << lin_expr.get(x[0]) << std::endl; // output: 2
+
+
+

+
+
Parameters:
+

t_key – the key

+
+
Returns:
+

the key’s coefficient

+
+
+
+ +
+
+virtual void remove(const Key &t_key) override
+

Sets the coefficient of a key to zero

+

This is equivalent to calling LinExpr<Key>::set with a zero coefficient.

+
+
Parameters:
+

t_key – the key

+
+
+
+ +
+
+LinExpr fix(const Solution::Primal &t_primals) const
+
+ +
+
+AbstractExpr<Key, IteratorOutputT, Hash, EqualTo> &operator+=(const AbstractExpr<Key, IteratorOutputT, Hash, EqualTo> &t_rhs)
+

Adds each term of t_rhs to the expression.

+

Zero entries are removed from the resulting expression.

+
+
Parameters:
+

t_rhs – the expression whose terms are to be added up.

+
+
Returns:
+

The object itself.

+
+
+
+ +
+
+AbstractExpr<Key, IteratorOutputT, Hash, EqualTo> &operator-=(const AbstractExpr<Key, IteratorOutputT, Hash, EqualTo> &t_rhs)
+

Subtracts each term of t_rhs to the expression.

+

Zero entries are removed from the resulting expression.

+
+
Parameters:
+

t_rhs – The object whose terms are to be subtracted.

+
+
Returns:
+

The object itself.

+
+
+
+ +
+
+AbstractExpr<Key, IteratorOutputT, Hash, EqualTo> &operator*=(double t_factor)
+

Multiplies every term of the expression by t_factor.

+

If t_factor equals zero with tolerance Tolerance::Sparsity, then the expression is emptied.

+
+
Parameters:
+

t_factor – The multiplying factor.

+
+
Returns:
+

The object itself.

+
+
+
+ +
+
+AbstractExpr<Key, IteratorOutputT, Hash, EqualTo> &operator/=(double t_factor)
+
+ +
+
+inline unsigned int size() const
+

Returns the size of the expression (i.e., number of non-zero entries which are currently stored).

+
+
Returns:
+

the size of the expression

+
+
+
+ +
+
+inline bool empty() const
+

Returns true if and only if the size is zero.

+
+
Returns:
+

true if and only if the size is zero

+
+
+
+ +
+
+inline const_iterator begin() const
+

Returns an iterator pointing to the first term of the expression.

+
+
Returns:
+

an iterator pointing to the first term of the expression

+
+
+
+ +
+
+inline const_iterator end() const
+

Returns an iterator pointing to the end of the expression.

+
+
Returns:
+

an iterator pointing to the end the expression

+
+
+
+ +
+
+inline void clear()
+

Removes all entries from the expression.

+
+ +
+
+void round()
+
+ +
+
+AbstractExpr multiply_with_precision(double t_factor, unsigned int t_n_digits)
+
+ +
+
+AbstractExpr multiply_with_precision_by_power_of_10(unsigned int t_exponent, unsigned int t_n_digits)
+
+ +
+
+long gcd() const
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/Model.html b/api/classes/common/Model.html new file mode 100644 index 00000000..a9447127 --- /dev/null +++ b/api/classes/common/Model.html @@ -0,0 +1,1756 @@ + + + + + + + + + Model — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Model

+
+

Example:

+

Here is a short example which builds and solves the following optimization problem.

+
+\[\begin{split}\begin{array}{ll} + \textrm{minimize } & -x_0 - 1.5 x_1 + 3 \\ + \textrm{subject to } & x_0 + 2 x_1 \le 1.5 \\ + & 0 \le x_0 \le 1 \\ + & 0 \le x_1 \le 1 +\end{array}\end{split}\]
+
Env env;
+
+Model model(env);
+
+auto x = model.add_vars(Dim<1>(2), 0, 1, Continuous, "x");
+
+auto c = model.add_ctr(x[0] + 2 * x[1] <= 1.5);
+
+model.set_obj_expr(-x[0] - 1.5 * x[1] + 3);
+
+model.use(Gurobi());
+
+model.optimize();
+
+std::cout << "Best obj = " << model.get_best_obj() << std::endl;
+
+
+
+
+
+class Model : public idol::Matrix
+

This class is used to represent a mathematical optimization model.

+
+

Public Functions

+
+
+explicit Model(Env &t_env)
+

Creates a new model for a mathematical optimization problem.

+

Example: 

Env env;
+Model model(env);
+
+
+

+
+
Parameters:
+

t_env – the optimization environment which will store the model

+
+
+
+ +
+
+Model(Env &t_env, ObjectiveSense t_sense)
+
+ +
+
+Model(Model&&) noexcept
+
+ +
+
+Model &operator=(const Model&) = delete
+
+ +
+
+Model &operator=(Model&&) noexcept = delete
+
+ +
+
+~Model() override
+
+ +
+
+Var add_var(double t_lb, double t_ub, VarType t_type, std::string t_name = "")
+

Creates a new decision variable in the model and returns it.

+

Example: 

model.add_var(0, 1, Binary, "x");
+
+
+

+
+
Parameters:
+
    +

  • t_lb – the lower bound in the model

    • +

  • t_ub – the upper bound in the model

    • +

  • t_type – the type in the model

    • +

  • t_name – the name of the variable

    • +
+
+
Returns:
+

the created variable

+
+
+
+ +
+
+Var add_var(double t_lb, double t_ub, VarType t_type, Column t_column, std::string t_name = "")
+

Creates a new decision variable in the model and returns it.

+

Example: 

model.add_var(0, 1, Binary, Column(-2), "x"); // Adds an objective coefficient of -2
+
+
+

+

Attention: every constraint involved in t_column must already be part of the model.

+
+
Parameters:
+
    +

  • t_lb – the lower bound in the model

    • +

  • t_ub – the upper bound in the model

    • +

  • t_type – the type in the model

    • +

  • t_column – the column in the model

    • +

  • t_name – the name of the variable

    • +
+
+
Returns:
+

the created variable

+
+
+
+ +
+
+template<unsigned int N>
Vector<Var, N> add_vars(Dim<N> t_dim, double t_lb, double t_ub, VarType t_type, const std::string &t_name = "")
+

Creates multiple decision variables in the model and returns them.

+

Example: 

auto x = model.add_vars(Dim<2>(n, m), 0, 1, Binary, "x");
+
+for (unsigned int i = 0 ; i < n ; ++i) {
+     for (unsigned int j = 0 ; j < m ; ++j) {
+         std::cout << x[i][j].name() << std::endl;
+     }
+}
+
+
+

+
+
Template Parameters:
+

N – the number of dimensions for the indices (e.g., N = 2 will create variables with 2 indices, \( x_{ij} \))

+
+
Parameters:
+
    +

  • t_dim – the dimensions for the indices

    • +

  • t_lb – the lower bound for the variables in the model

    • +

  • t_ub – the upper bound for the variables in the model

    • +

  • t_type – the type for the variables in the model

    • +

  • t_name – the base name for the variables (variables are then named by a combination of this name and indices)

    • +
+
+
Returns:
+

the create variables

+
+
+
+ +
+
+void add(const Var &t_var)
+

Adds an existing variable to the model.

+

Example: 

Env env;
+
+Model model1(env);
+auto x = model1.add_var(0, Inf, Continuous, "x");
+
+Model model2(env);
+model2.add(x);
+
+
+

+

Note that the default version of the variable is added to the model (i.e., the variable is added identical to its first version as defined at creation time).

+
+
Parameters:
+

t_var – the variable to add

+
+
+
+ +
+
+void add(const Var &t_var, TempVar t_temp_var)
+

Adds an existing variable to the model with different attributes than the default version (i.e., the variable is added to the model and has the attributes defined in t_temp_var).

+

Example: 

Env env;
+
+Model model1(env);
+auto x = model1.add_var(0, Inf, Continuous, Column(-2), "x"); // x has an objective cost of -2 in model1
+
+Model model2(env);
+model2.add(x, TempVar(0, Inf, Continuous, Column(0))); // x has an objective cost of 0 in model2
+
+
+

+

Attention: every constraint involved in t_temp_var must already be part of the model.

+
+
Parameters:
+
    +

  • t_var – the variable to add

    • +

  • t_temp_var – the attributes of the variable in the model

    • +
+
+
+
+ +
+
+bool has(const Var &t_var) const
+

Returns true if and only if the variable is part of the model

+

Example: 

auto x = model.add_var(-Inf, Inf, Continuous, "x");
+
+std::cout << model.has(x) << std::endl; // output: 1
+
+
+

+
+
Parameters:
+

t_var – the variable

+
+
Returns:
+

true if and only if the variable is part of the model

+
+
+
+ +
+
+void remove(const Var &t_var)
+

Removes a variable from the model.

+

Example: 

auto x = model.add_var(0, 1, Binary, "x");
+
+std::cout << model.has(x) << std::endl; // output: 1
+
+model.remove(x);
+
+std::cout << model.has(x) << std::endl; // output: 0
+
+
+

+

This also removes every reference to the variable in the objective and within the constraints.

+
+
Parameters:
+

t_var –

+
+
+
+ +
+
+inline ConstIteratorForward<std::vector<Var>> vars() const
+

Returns an object able to construct iterators over the set of variables of the model.

+

Example:

+

for (const Var& var : model.vars()) {
+     std::cout << var.name() << std::endl;
+}
+
+
+

+
+
Returns:
+

an object able to construct iterators over the set of variables of the model.

+
+
+
+ +
+
+Ctr add_ctr(TempCtr t_temp_ctr, std::string t_name = "")
+

Creates a new constraint in the model and returns it.

+

Example: 

auto constraint = model.add_ctr(x[0] + 2 * x[1] <= 1.5, "constraint");
+
+
+

+

Attention: every variable involved in t_temp_ctr must already be part of the model.

+
+
Parameters:
+
    +

  • t_temp_ctr – the constraint attributes in the model

    • +

  • t_name – the name of the constraint

    • +
+
+
Returns:
+

+
+
+
+ +
+
+Ctr add_ctr(Row &&t_row, CtrType t_type, std::string t_name = "")
+

Creates a new constraint in the model and returns it.

+

Example: 

auto constraint = model.add_ctr(Row(x[0] + 2 * x[1], 1.5), LessOrEqual);
+
+
+

+
+
Parameters:
+
    +

  • t_row – the row of the constraint in the model

    • +

  • t_type – the type of the constraint in the model

    • +

  • t_name – the name of the constraint

    • +
+
+
Returns:
+

the created constraint

+
+
+
+ +
+
+template<unsigned int N>
Vector<Ctr, N> add_ctrs(Dim<N> t_dim, CtrType t_type, const Constant &t_constant, const std::string &t_name = "")
+

Creates multiple constraints in the model and returns them.

+

Example: 

auto constraints = model.add_ctrs(Dim<2>(m, n), EqualTo, 1, "constraint");
+
+for (unsigned int i = 0 ; i < n ; ++i) {
+     for (unsigned int j = 0 ; j < m ; ++j) {
+         std::cout << constraints[i][j].name() << std::endl;
+     }
+}
+
+
+

+
+
Template Parameters:
+

N – the number of dimensions for the indices

+
+
Parameters:
+
    +

  • t_dim – the dimensions for the indices

    • +

  • t_type – the type of the constraints in the model

    • +

  • t_constant – the right hand-side of the constraints in the model

    • +

  • t_name – the base name for the constraints (constraints are then named by a combination of this name and indices)

    • +
+
+
Returns:
+

the created constraints

+
+
+
+ +
+
+void add(const Ctr &t_ctr)
+

Adds an existing variable to the model.

+

Example: 

Env env;
+
+Model model1(env);
+auto x = model.add_vars(Dim<1>(2), 0, Inf, Continuous, "x");
+auto constraint = model1.add_ctr(x[0] + 2 * x[1] <= 1.5);
+
+Model model2(env)
+model2.add(x[0]);
+model2.add(x[1]);
+model2.add(constraint);
+
+
+

+

Note that the default version of the constraint is added to the model (i.e., the constraint is added identical to its first version as defined at creation time).

+

Attention: every variable involved in the default version must already be part of the model.

+
+
Parameters:
+

t_ctr –

+
+
+
+ +
+
+void add(const Ctr &t_ctr, TempCtr t_temp_ctr)
+

Adds an existing constraint to the model with different attributes than the default version (i.e., the constraint is added to the model and has the attributes defined in t_temp_ctr).

+

Example: 

Env env;
+
+Model model1(env);
+auto x = model.add_vars(Dim<1>(2), 0, Inf, Continuous, "x");
+auto constraint = model1.add_ctr(x[0] + 2 * x[1] <= 1.5);
+
+Model model2(env)
+model2.add(x[0]);
+model2.add(x[1]);
+model2.add(constraint, x[0] - x[1] >= 0);
+
+
+

+

Attention: every variable involved in t_temp_var must already be part of the model.

+
+
Parameters:
+
    +

  • t_ctr – the constraint to add

    • +

  • t_temp_ctr – the attributes for the constraint in the model

    • +
+
+
+
+ +
+
+bool has(const Ctr &t_ctr) const
+

Returns true if and only if the constraint is part of the model.

+

Example: 

auto c = model.add_ctr(x[0] + 1.5 * x[1] <= 2);
+
+std::cout << model.has(c) << std::endl; // output: 1
+
+
+

+
+
Parameters:
+

t_ctr – the constraint

+
+
Returns:
+

true if and only if the constraint is part of the model

+
+
+
+ +
+
+void remove(const Ctr &t_ctr)
+

Removes a constraint from the model.

+

Example: 

auto c = model.add_ctr(x[0] + 1.5 * x[1] <= 2);
+
+std::cout << model.has(c) << std::endl; // output: 1
+
+model.remove(c);
+
+std::cout << model.has(c) << std::endl; // output: 0
+
+
+

+
+
Parameters:
+

t_ctr –

+
+
+
+ +
+
+inline ConstIteratorForward<std::vector<Ctr>> ctrs() const
+

Returns an object able to construct iterators over the set of constraints of the model.

+

Example: 

for (const Ctr& ctr : model.ctrs()) {
+     std::cout << ctr.name() << std::endl;
+}
+
+
+

+
+
Returns:
+

an object able to construct iterators over the set of constraints of the model.

+
+
+
+ +
+
+inline unsigned int id() const
+

Returns the unique id (within a given environment) of the model.

+

Example:

+

std::cout << model.id() << std::endl;
+
+
+

+
+
Returns:
+

+
+
+
+ +
+
+Model *clone() const
+

Creates a new identical model.

+

Example: 

Model* copy = model.clone();
+// ...
+delete copy;
+
+
+

+
+
Returns:
+

a pointer to the new copy

+
+
+
+ +
+
+Model copy() const
+
+ +
+
+void reserve_vars(unsigned int t_size)
+
+ +
+
+void reserve_ctrs(unsigned int t_size)
+
+ +
+
+inline Env &env() const
+

Returns the optimization environment of the model

+

Example:

+

Env& env = model.env();
+Var x(env, 0, 1, Binary, "x");
+
+
+

+
+
Returns:
+

the optimization environment

+
+
+
+ +
+
+void use(const OptimizerFactory &t_optimizer_factory)
+

Sets the optimizer for the model.

+

The optimizer is directly created using t_optimizer_factory. If an optimizer was already created, it is replaced by the new one.

+

Example: 

model.use(Gurobi());
+
+
+

+
+
Parameters:
+

t_optimizer_factory –

+
+
+
+ +
+
+bool has_optimizer() const
+

Returns true if and only if the model currently has an optimizer.

+

Example: 

model.use(Gurobi());
+
+std::cout << model.has_optimizer() << std::endl; // output: 1
+
+
+

+
+
Returns:
+

true if and only if the model currently has an optimizer.

+
+
+
+ +
+
+inline bool has_optimizer_factory() const
+
+ +
+
+inline const OptimizerFactory &optimizer_factory() const
+
+ +
+
+void unuse()
+

Removes an existing optimizer.

+

The model is therefore left without any optimizer.

+

Example: 

model.use(Gurobi());
+
+std::cout << model.has_optimizer() << std::endl; // output: 1
+
+model.unuse();
+
+std::cout << model.has_optimizer() << std::endl; // output: 0
+
+
+

+
+ +
+
+template<class T, unsigned int N>
void add_vector(const Vector<T, N> &t_vector)
+

Adds a vector of optimization objects to the model (i.e., variables or constraints).

+

Example: 

std::vector<std::vector<Var>> x = Var::make_vector(Dim<2>(n, m), 0, 1, Binary, "x");
+
+model.add_vector<Var, 2>(x);
+
+
+

+
+
Template Parameters:
+
    +

  • T – the type of optimization objects to add

    • +

  • N – the number of dimensions

    • +
+
+
Parameters:
+

t_vector – the vector of objects

+
+
+
+ +
+
+inline Optimizer &optimizer()
+

Returns a reference to the model’s optimizer.

+

Example: 

std::cout << model.optimizer().as<Optimizers::BranchAndBound>().n_solved_nodes() << std::endl;
+
+
+

+
+
Returns:
+

a reference to the model’s optimizer

+
+
+
+ +
+
+inline const Optimizer &optimizer() const
+

Returns a const reference to the model’s optimizer.

+

Example: 

std::cout << model.optimizer().as<Optimizers::BranchAndBound>().n_solved_nodes() << std::endl;
+
+
+

+
+
Returns:
+

a const reference to the model’s optimizer

+
+
+
+ +
+
+void optimize()
+

Solves the model using the model’s optimizer

+

Example: 

model.use(Gurobi());
+
+model.optimize();
+
+
+ Attention: an optimizer must have been set beforehand.

+
+ +
+
+void write(const std::string &t_name)
+

Asks the model’s optimizer to write the model (or any useful piece of information) to a file.

+

Example: 

model.use(Gurobi());
+
+model.write("model.lp");
+
+
+

+

Attention: an optimizer must have been set beforehand.

+
+
Parameters:
+

t_name – the destination file

+
+
+
+ +
+
+void update()
+

Asks the model’s optimizer to update his current version of the model.

+

Note that you typically do not need to call this method since updates are automatically done. While some external-mip perform “lazy updates” and actually update their representation of the model only when necessary (e.g., before solving or writing to file), even in this case, updates are automatically done so that the user should not need to use this method.

+

Example: 

model.add(x);
+
+model.update();
+
+
+

+

Attention: an optimizer must have been set beforehand.

+
+ +
+
+ObjectiveSense get_obj_sense() const
+

Returns the objective sense of the model (Minimize or Maximize).

+

Example: 

ObjectiveSense sense = model.get_obj_sense();
+
+if (sense == Minimize) {
+     std::cout << "This is a minimization problem" << std::endl;
+}
+
+
+

+
+
Returns:
+

the objective sense of the model

+
+
+
+ +
+
+const Expr<Var, Var> &get_obj_expr() const
+

Returns the objective function of the model.

+

Example: 

const Expr& objective = model.get_obj_expr();
+
+std::cout << "The objective function is " << objective << std::endl;
+
+
+

+
+
Returns:
+

the objective function of the model

+
+
+
+ +
+
+const LinExpr<Ctr> &get_rhs_expr() const
+

Returns the right hand-side of the model.

+

Example: 

const LinExpr<Ctr> rhs = model.get_rhs_expr();
+
+std::cout << "The model RHS is " << rhs << std::endl;
+
+
+

+
+
Returns:
+

the right hand-side of the model

+
+
+
+ +
+
+const Constant &get_mat_coeff(const Ctr &t_ctr, const Var &t_var) const
+

Returns a specific coefficient in the model’s matrix.

+

Example: 

const Constant& coeff = model.get_mat_coeff(c1, x[0]);
+std::cout << "The coefficient of x_0 in constraint c1 is " << coeff << std::endl;
+
+
+

+
+
Parameters:
+
    +

  • t_ctr – the constraint corresponding to the row

    • +

  • t_var – the column corresponding to the column

    • +
+
+
Returns:
+

the matrix coefficient

+
+
+
+ +
+
+SolutionStatus get_status() const
+

Returns the current status of the optimizer.

+

Example: 

model.optimize();
+
+if (model.get_status() == Optimal) {
+     std::cout << "Optimal solution found!" << std::endl;
+}
+
+
+

+

Attention: an optimizer must have been set beforehand.

+
+
Returns:
+

+
+
+
+ +
+
+SolutionReason get_reason() const
+

Returns the reason for the current status of the optimizer.

+

Example: 

model.optimize();
+
+if (get_status() == Fail) {
+     std::cout << "The optimizer failed because of " << get_reason() << std::endl; // e.g. output: Numeric
+}
+
+
+

+

Attention: an optimizer must have been set beforehand.

+
+
Returns:
+

+
+
+
+ +
+
+double get_best_obj() const
+

Returns the best objective value currently known by the optimizer (i.e., feasible solution objective value)

+

Example: 

model.optimize();
+
+if (model.get_status() == Optimal) {
+    std::cout << "Optimum = " << model.get_best_obj() << std::endl;
+}
+
+
+

+

Attention: an optimizer must have been set beforehand.

+
+
Returns:
+

the best objective value currently known by the optimizer

+
+
+
+ +
+
+double get_best_bound() const
+

Returns the best bound currently known by the optimizer (e.g., dual bound).

+

Example: 

model.optimize();
+
+if (get_status() == Feasible) {
+     const double best_obj = model.get_best_obj();
+     const double best_bound = model.get_best_bound();
+     std::cout << "Gap: " << relative_gap(best_bound, best_obj) * 100 << " % << std::endl;
+}
+
+
+

+
+
Returns:
+

the best bound currently known by the optimizer

+
+
+
+ +
+
+void set_obj_sense(ObjectiveSense t_value)
+

Sets the objective sense of the model.

+

Example: 

model.set_obj_sense(Maximize);
+
+
+

+
+
Parameters:
+

t_value – the new objective sense

+
+
+
+ +
+
+void set_obj_expr(const Expr<Var, Var> &t_objective)
+

Sets the objective expression of the model.

+

The expression is copied.

+

Example: 

Expr objective = - x[0] - 2 * x[2];
+model.set_obj_expr(objective);
+
+
+

+

Attention: every variable involved in t_objective must already be part of the model.

+
+
Parameters:
+

t_objective –

+
+
+
+ +
+
+void set_obj_expr(Expr<Var, Var> &&t_objective)
+

Sets the objective expression of the model.

+

The expression is moved.

+

Example: 

model.set_obj_expr(- x[0] - 2 * x[2]);
+
+
+

+

Attention: every variable involved in t_objective must already be part of the model.

+
+
Parameters:
+

t_objective –

+
+
+
+ +
+
+void set_rhs_expr(const LinExpr<Ctr> &t_rhs)
+

Sets the right hand-side of the model

+

The expression is copied.

+

Example: 

Lin<Ctr> rhs = 2 * c1 + 3 * c2;
+
+model.set_rhs_expr(rhs); // RHS is then 2 for constraint c1, 2 for constraint c2, 0 for others
+
+
+

+

Attention: every constraint involved in t_rhs must already be part of the model.

+
+
Parameters:
+

t_rhs – the right hand-side

+
+
+
+ +
+
+void set_rhs_expr(LinExpr<Ctr> &&t_rhs)
+

Sets the right hand-side of the model

+

The expression is moved.

+

Example: 

Lin<Ctr> rhs = 2 * c1 + 3 * c2;
+
+model.set_rhs_expr(std::move(rhs)); // RHS is then 2 for constraint c1, 2 for constraint c2, 0 for others
+
+
+

+

Attention: every constraint involved in t_rhs must already be part of the model.

+
+
Parameters:
+

t_rhs – the right hand-side

+
+
+
+ +
+
+void set_obj_const(const Constant &t_constant)
+

Sets the objective constant.

+

The constant is copied.

+

Example: 

Constant constant = 10;
+model.set_obj_const(constant);
+
+
+

+
+
Parameters:
+

t_constant – the constant

+
+
+
+ +
+
+void set_obj_const(Constant &&t_constant)
+

Sets the objective constant.

+

The constant is moved.

+

Example: 

model.set_obj_const(10);
+
+
+

+
+
Parameters:
+

t_constant – the constant

+
+
+
+ +
+
+void set_mat_coeff(const Ctr &t_ctr, const Var &t_var, const Constant &t_coeff)
+

Sets a specific matrix coefficient.

+

The coefficient is copied.

+

Example: 

Constant coeff = 3;
+model.set_mat_coeff(c1, x[0], coeff); // The coefficient of x_0 in constraint c1 is 3
+
+
+

+
+
Parameters:
+
    +

  • t_ctr – the constraint corresponding to the row

    • +

  • t_var – the variable corresponding to the column

    • +

  • t_coeff – the coefficient

    • +
+
+
+
+ +
+
+void set_mat_coeff(const Ctr &t_ctr, const Var &t_var, Constant &&t_coeff)
+

Sets a specific matrix coefficient.

+

The coefficient is moved.

+

Example: 

model.set_mat_coeff(c1, x[0], 3); // The coefficient of x_0 in constraint c1 is 3
+
+
+

+
+
Parameters:
+
    +

  • t_ctr – the constraint corresponding to the row

    • +

  • t_var – the variable corresponding to the column

    • +

  • t_coeff – the coefficient

    • +
+
+
+
+ +
+
+Ctr get_ctr_by_index(unsigned int t_index) const
+
+ +
+
+Var get_var_by_index(unsigned int t_index) const
+
+ +
+
+unsigned int get_ctr_index(const Ctr &t_ctr) const
+

Returns the current index of the constraint in the model.

+

Example: 

unsigned int index = model.get_ctr_index(c1);
+
+std::cout << "The current index of c1 is " << index << std::endl;
+
+
+

+

Note that the index of the constraint may change over time (in particular, when objects are removed from the model)

+
+
Parameters:
+

t_ctr – the constraint

+
+
Returns:
+

the constraint’s index

+
+
+
+ +
+
+CtrType get_ctr_type(const Ctr &t_ctr) const
+

Returns the type of the constraint in the model

+

Example: 

CtrType.rst.rst type = model.get_ctr_type(c1);
+
+if (type == LessOrEqual) {
+     std::cout << "c1 is a <= constraint" << std::endl;
+}
+
+
+

+
+
Parameters:
+

t_ctr – the constraint

+
+
Returns:
+

the type of the constraint

+
+
+
+ +
+
+const Row &get_ctr_row(const Ctr &t_ctr) const
+

Returns the row of the constraint in the model.

+

Example: 

const Row& row = model.get_ctr_row(c1);
+
+std::cout << "The linear part of c1 is " << row.linear() << std::endl;
+std::cout << "The quadratic part of c1 is " << row.quadratic() << std::endl;
+std::cout << "The RHS of c1 is " << row.rhs() << std::endl;
+
+
+

+
+
Parameters:
+

t_ctr – the constraint

+
+
Returns:
+

the constraint’s row

+
+
+
+ +
+
+double get_ctr_dual(const Ctr &t_ctr) const
+

Returns the dual value of a constraint

+

Example: 

model.optimize();
+
+const double dual = model.get_ctr_dual(c1);
+
+
+

+
+
Parameters:
+

t_ctr – the constraint

+
+
Returns:
+

the constraint’s dual value

+
+
+
+ +
+
+double get_ctr_farkas(const Ctr &t_ctr) const
+

Returns the Farkas certificate of the constraint

+

Example: 

model.optimize();
+
+if (model.get_status() == Infeasible) {
+     std::cout << "The Farkas certificate for c1 is " << model.get_ctr_farkas(c1) << std::endl;
+}
+
+
+

+
+
Parameters:
+

t_ctr – the constraint

+
+
Returns:
+

the farkas certificate for the constraint

+
+
+
+ +
+
+void set_ctr_rhs(const Ctr &t_ctr, const Constant &t_rhs)
+

Sets a constraint’s right hand-side

+

The constant is copied.

+

Example: 

Constant rhs = 10;
+model.set_ctr_rhs(rhs);
+
+
+

+
+
Parameters:
+
    +

  • t_ctr – the constraint

    • +

  • t_rhs – the right hand-side

    • +
+
+
+
+ +
+
+void set_ctr_rhs(const Ctr &t_ctr, Constant &&t_rhs)
+

Sets a constraint’s right hand-side

+

The constant is moved.

+

Example: 

model.set_ctr_rhs(10);
+
+
+

+
+
Parameters:
+
    +

  • t_ctr – the constraint

    • +

  • t_rhs – the constraint’s right hand-side

    • +
+
+
+
+ +
+
+void set_ctr_type(const Ctr &t_ctr, CtrType t_type)
+

Sets a constraint’s type.

+

Example: 

model.set_ctr_type(LessOrEqual);
+
+
+

+
+
Parameters:
+
    +

  • t_ctr – the constraint

    • +

  • t_type – the constraint’s type

    • +
+
+
+
+ +
+
+void set_ctr_row(const Ctr &t_ctr, const Row &t_row)
+

Sets a constraint’s row.

+

The row is copied.

+

Example: 

Row row(x[0] + 2 * x[1], 1.5);
+model.set_ctr_row(row);
+
+
+

+

Attention: every variable involved in t_row must already be part of the model.

+
+
Parameters:
+
    +

  • t_ctr – the constraint

    • +

  • t_row – the constraint’s row

    • +
+
+
+
+ +
+
+void set_ctr_row(const Ctr &t_ctr, Row &&t_row)
+

Sets a constraint’s row.

+

The row is moved.

+

Example: 

Row row(x[0] + 2 * x[1], 1.5);
+model.set_ctr_row(std::move(row));
+
+
+

+

Attention: every variable involved in t_row must already be part of the model.

+
+
Parameters:
+
    +

  • t_ctr – the constraint

    • +

  • t_row – the constraint’s row

    • +
+
+
+
+ +
+
+unsigned int get_var_index(const Var &t_var) const
+

Returns the current index of the variable in the model.

+

Example: 

unsigned int index = model.get_ctr_index(x[0]);
+
+std::cout << "The current index of x_0 is " << index << std::endl;
+
+
+

+

Note that the index of the variable may change over time (in particular, when objects are removed from the model)

+
+
Parameters:
+

t_var – the variable

+
+
Returns:
+

the variable’s index

+
+
+
+ +
+
+VarType get_var_type(const Var &t_var) const
+

Returns the variable’s type in the model

+

Example: 

VarType type = model.get_var_type(x[0]);
+
+if (type == Continuous) {
+     std::cout << "x_0 is a continuous variable" << std::endl;
+}
+
+
+

+
+
Parameters:
+

t_var – the variable

+
+
Returns:
+

the variable’s type

+
+
+
+ +
+
+double get_var_lb(const Var &t_var) const
+

Returns the lower bound of a variable in the model

+

Example: 

const double lb = model.get_var_lb(x[0]);
+
+std::cout << "x_0 >= " << lb << " is part of the model" << std::endl;
+
+
+

+
+
Parameters:
+

t_var – the variable

+
+
Returns:
+

the variable’s lower bound

+
+
+
+ +
+
+double get_var_ub(const Var &t_var) const
+

Returns the upper bound of a variable in the model

+

Example: 

const double ub = model.get_var_ub(x[0]);
+
+std::cout << "x_0 <= " << ub << " is part of the model" << std::endl;
+
+
+

+
+
Parameters:
+

t_var – the variable

+
+
Returns:
+

the variable’s upper bound

+
+
+
+ +
+
+double get_var_primal(const Var &t_var) const
+

Returns the primal value of a variable in the model

+

Example: 

model.optimize();
+
+if (model.get_status() == Optimal) {
+     std::cout << "Primal value of x_0 is " << model.get_var_primal(x[0]) << std::endl;
+}
+
+
+

+
+
Parameters:
+

t_var – the variable

+
+
Returns:
+

the primal value

+
+
+
+ +
+
+double get_var_reduced_cost(const Var &t_var) const
+

Returns the reduced cost of a variable in the model

+

Example: 

model.optimize();
+
+if (model.get_status() == Optimal) {
+     std::cout << "Reduced cost of x_0 is " << model.get_var_reduced_cost(x[0]) << std::endl;
+}
+
+
+

+
+
Parameters:
+

t_var – the variable

+
+
Returns:
+

the reduced cost

+
+
+
+ +
+
+double get_var_ray(const Var &t_var) const
+

When the model is unbounded, returns the ray value of a variable of the model

+

Example: 

model.optimize();
+
+if (model.get_status() == Unbounded) {
+     std::cout << "Ray value of x_0 is " << model.get_var_ray(x[0]) << std::endl;
+}
+
+
+

+
+
Parameters:
+

t_var – the variable

+
+
Returns:
+

the ray value

+
+
+
+ +
+
+const Column &get_var_column(const Var &t_var) const
+

Returns the column of a variable in the model.

+

Example: 

const Column& column = model.get_var_column(t_var);
+
+std::cout << "The objective coefficient of x_0 is " << column.obj() << std::endl;
+
+
+

+
+
Parameters:
+

t_var – the variable

+
+
Returns:
+

the variable’s column

+
+
+
+ +
+
+void set_var_type(const Var &t_var, VarType t_type)
+

Sets a variable’s type in the model.

+

Example: 

model.set_var_type(x[0], Binary); // x_0 is now binary
+
+
+

+
+
Parameters:
+
    +

  • t_var – the variable

    • +

  • t_type – the variable’s type

    • +
+
+
+
+ +
+
+void set_var_lb(const Var &t_var, double t_lb)
+

Sets a variable’s lower bound in the model

+

Example: 

model.set_var_lb(x[0], 1); // Set the restriction x_0 >= 1
+
+
+

+
+
Parameters:
+
    +

  • t_var – the variable

    • +

  • t_lb – the variable’s lower bound

    • +
+
+
+
+ +
+
+void set_var_ub(const Var &t_var, double t_ub)
+

Sets a variable’s upper bound in the model

+

Example: 

model.set_var_ub(x[0], 0); // Set the restriction x_0 <= 1
+
+
+

+
+
Parameters:
+
    +

  • t_var – the variable

    • +

  • t_lb – the variable’s upper bound

    • +
+
+
+
+ +
+
+void set_var_obj(const Var &t_var, const Constant &t_obj)
+

Sets a variable’s objective coefficient in the model.

+

The constant is copied.

+

Example: 

Constant obj = 10;
+model.set_var_obj(x[0], obj);
+
+
+

+
+
Parameters:
+
    +

  • t_var – the variable

    • +

  • t_obj – the variable’s objective coefficient

    • +
+
+
+
+ +
+
+void set_var_obj(const Var &t_var, Constant &&t_obj)
+

Sets a variable’s objective coefficient in the model.

+

The constant is moved.

+

Example: 

model.set_var_obj(x[0], 10);
+
+
+

+
+
Parameters:
+
    +

  • t_var – the variable

    • +

  • t_obj – the variable’s objective coefficient

    • +
+
+
+
+ +
+
+void set_var_column(const Var &t_var, const Column &t_column)
+

Sets a variable’s column in the model.

+

The column is copied.

+

Example: 

Column column(10);
+model.set_var_column(x[0], column);
+
+
+

+

Attention: every constraint involved in t_column must already be part of the model.

+
+
Parameters:
+
    +

  • t_var – the variable

    • +

  • t_column – the variable’s column

    • +
+
+
+
+ +
+
+void set_var_column(const Var &t_var, Column &&t_column)
+

Sets a variable’s column in the model.

+

The column is moved.

+

Example: 

Column column(10);
+model.set_var_column(x[0], std::move(column));
+
+
+

+

Attention: every constraint involved in t_column must already be part of the model.

+
+
Parameters:
+
    +

  • t_var – the variable

    • +

  • t_column – the variable’s column

    • +
+
+
+
+ +
+
+unsigned int get_n_solutions() const
+

Returns the number of available primal solutions.

+

Some external-mip can find several optimal or sub-optimal solutions (e.g., in branch-and-bound algorithms with solution pools). This function can be used to query the number of available solutions. The solution is selected with Model::set_solution_index and retrieved via get_var_primal and get_best_obj.

+

Example: 

model.optimize();
+
+std::cout << n_solutions << " solution(s) have been found" << std::endl;
+
+
+

+
+
Returns:
+

the number of available solution primal solutions

+
+
+
+ +
+
+unsigned int get_solution_index() const
+

Returns the current solution index.

+

Some external-mip can find several optimal or sub-optimal solutions (e.g., in branch-and-bound algorithms with solution pools). This function return the current solution index which is being returned by the model.

+

Example: 

model.optimize();
+
+std::cout << model.get_solution_index() << std::endl; // output: 0
+
+
+

+
+
Returns:
+

+
+
+
+ +
+
+void set_solution_index(unsigned int t_index)
+

Sets the index of the solution returned by get_var_primal, get_status and get_reason.

+

Some external-mip can find several optimal or sub-optimal solutions (e.g., in branch-and-bound algorithms with solution pools). This method can be used to change the solution index which is being returned by the model.

+

Example: 

model.optimize();
+
+const unsigned int n_solutions = model.get_n_solutions();
+
+std::cout << n_solutions << " solution(s) have been found" << std::endl;
+
+for (unsigned int i = 0 ; i < n_solutions ; ++i) {
+     model.set_solution_index(i);
+     std::cout << save_primal(model) << std::endl;
+}
+
+
+

+
+
Returns:
+

the number of available solution primal solutions

+
+
+
+ +
+
+void scale_to_integers(unsigned int t_n_digits)
+
+ +
+
+Model fix(const Solution::Primal &t_primals) const
+
+ +
+
+template<unsigned int N>
idol::Vector<idol::Var, N> add_vars(Dim<N> t_dim, double t_lb, double t_ub, VarType t_type, const std::string &t_name)
+
+ +
+
+template<unsigned int N>
idol::Vector<idol::Ctr, N> add_ctrs(Dim<N> t_dim, CtrType t_type, const Constant &t_constant, const std::string &t_name)
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/Param.html b/api/classes/common/Param.html new file mode 100644 index 00000000..63809a06 --- /dev/null +++ b/api/classes/common/Param.html @@ -0,0 +1,305 @@ + + + + + + + + + Param — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Param

+
+
+class Param
+

Parameter modeling-old object.

+

This class is used to model parameters for optimization models. More precisely, it represents a variable, or a constraint, from a model A which is considered constant in another model B.

+

Parameters are created using the Param constructors or by prepending a variable, or a constraint, by the ! symbol.

+

Example: 

auto parametrized_expression = 2 * !x * y; // Here, x is viewed as a constant
+
+
+

+
+

Public Functions

+
+
+inline explicit Param(const Var &t_var)
+

Constructor.

+

Creates a new parameter associated to a variable.

+
+
Parameters:
+

t_var – The variable.

+
+
+
+ +
+
+inline explicit Param(const Ctr &t_ctr)
+

Constructor.

+

Creates a new parameter associated to a constraint.

+
+
Parameters:
+

t_var – The constraint.

+
+
+
+ +
+
+Param(const Param &t_param) = default
+

Copy constructor.

+
+
Parameters:
+

t_param – The parameter to copy.

+
+
+
+ +
+
+Param(Param &&t_param) noexcept = default
+

Move constructor.

+
+
Parameters:
+

t_param – The parameter to move.

+
+
+
+ +
+
+Param &operator=(const Param &t_param) = default
+

Copy-assignment operator.

+
+
Parameters:
+

t_param – The parameter to copy.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Param &operator=(Param &&t_param) noexcept = default
+

Move-assignment operator.

+
+
Parameters:
+

t_param – The parameter to move.

+
+
Returns:
+

*this

+
+
+
+ +
+
+template<class T>
inline bool is() const
+

Returns true if the parameter represents on object of the class T, false otherwise.

+
+
Template Parameters:
+

T – The type to be tested.

+
+
Returns:
+

True if the parameter represents on object of the class T, false otherwise.

+
+
+
+ +
+
+template<class T>
inline T as() const
+

Returns the optimization object behind the parameter.

+

Example: 

Var x = model.add_var(0., 1., Continuous, "x");
+Param param = !x;
+Var var = param.as<Var>(); // "x"
+
+
+

+
+
Template Parameters:
+

T – The type of the underlying object.

+
+
Returns:
+

The optimization object behind the parameter.

+
+
+
+ +
+
+inline unsigned int id() const
+

Returns the id of the object behind the parameter.

+
+
Returns:
+

The id of the object behind the parameter.

+
+
+
+ +
+
+inline const std::string &name() const
+

Returns the name of the object behind the parameter.

+
+
Returns:
+

The name of the object behind the parameter.

+
+
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/QuadExpr.html b/api/classes/common/QuadExpr.html new file mode 100644 index 00000000..fcfe0ff3 --- /dev/null +++ b/api/classes/common/QuadExpr.html @@ -0,0 +1,228 @@ + + + + + + + + + QuadExpr — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

QuadExpr

+
+
+template<class Key1 = idol::Var, class Key2 = Key1, class Hash = std::conditional_t<std::is_same_v<Key1, Key2>, idol::impl::symmetric_pair_hash, std::hash<idol::Pair<Key1, Key2>>>, class EqualTo = std::conditional_t<std::is_same_v<Key1, Key2>, idol::impl::symmetric_pair_equal_to, std::equal_to<idol::Pair<Key1, Key2>>>>
class QuadExpr : public AbstractExpr<idol::Pair<idol::Var, idol::Var>, QuadTerm<idol::Var, idol::Var>, std::conditional_t<std::is_same_v<idol::Var, idol::Var>, idol::impl::symmetric_pair_hash, std::hash<idol::Pair<idol::Var, idol::Var>>>, std::conditional_t<std::is_same_v<idol::Var, idol::Var>, idol::impl::symmetric_pair_equal_to, std::equal_to<idol::Pair<idol::Var, idol::Var>>>>
+
+

Public Functions

+
+
+QuadExpr() = default
+
+ +
+
+QuadExpr(const Key1 &t_a, const Key2 &t_b)
+
+ +
+
+QuadExpr(Constant &&t_factor, const Key1 &t_a, const Key2 &t_b)
+
+ +
+
+QuadExpr(const Constant &t_factor, const Key1 &t_a, const Key2 &t_b)
+
+ +
+
+QuadExpr(const QuadExpr&) = default
+
+ +
+
+QuadExpr(QuadExpr&&) noexcept = default
+
+ +
+
+QuadExpr &operator=(const QuadExpr&) = default
+
+ +
+
+QuadExpr &operator=(QuadExpr&&) noexcept = default
+
+ +
+
+virtual ~QuadExpr() = default
+
+ +
+
+void set(const Key1 &t_a, const Key2 &t_b, Constant t_coefficient)
+
+ +
+
+const Constant &get(const Key1 &t_a, const Key2 &t_b) const
+
+ +
+
+QuadExpr fix(const Solution::Primal &t_primals) const
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/Row.html b/api/classes/common/Row.html new file mode 100644 index 00000000..e0c1dd3d --- /dev/null +++ b/api/classes/common/Row.html @@ -0,0 +1,555 @@ + + + + + + + + + Row — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Row

+
+
+class Row : public idol::impl::Row
+

Row modeling-old object.

+

This class is used to represent an optimization model’s row. It is made of a right hand-side and a set of { Var, Constant } pairs representing the coefficient (Constant) of the row in each column (Var). Such pairs are called left hand-side terms.

+

The whole left hand-side is stored as an Expr<Var, Var> while the whole right hand-side is stored as a Constant.

+

Example: 

auto constraint = model.add_ctr(x + y <= 1.);
+const auto& row = model.get_ctr_row(constraint); // Row(x + y, 1.)
+
+
+

+
+

Public Functions

+
+
+Row() = default
+
+ +
+
+inline Row(Expr<Var, Var> &&t_lhs, Expr<Var, Var> &&t_rhs)
+

Constructor.

+

Creates a new row with a left hand-side t_lhs and a right hand-side t_rhs.

+
+
Parameters:
+
    +

  • t_lhs – The left hand-side.

    • +

  • t_rhs – The right hand-side.

    • +
+
+
+
+ +
+
+inline Row(Expr<Var, Var> &&t_lhs, const Expr<Var, Var> &t_rhs)
+

Constructor.

+

Creates a new row with a left hand-side t_lhs and a right hand-side t_rhs.

+
+
Parameters:
+
    +

  • t_lhs – The left hand-side.

    • +

  • t_rhs – The right hand-side.

    • +
+
+
+
+ +
+
+inline Row(const Expr<Var, Var> &t_lhs, Expr<Var, Var> &&t_rhs)
+

Constructor.

+

Creates a new row with a left hand-side t_lhs and a right hand-side t_rhs.

+
+
Parameters:
+
    +

  • t_lhs – The left hand-side.

    • +

  • t_rhs – The right hand-side.

    • +
+
+
+
+ +
+
+inline Row(const Expr<Var, Var> &t_lhs, const Expr<Var, Var> &t_rhs)
+

Constructor.

+

Creates a new row with a left hand-side t_lhs and a right hand-side t_rhs.

+
+
Parameters:
+
    +

  • t_lhs – The left hand-side.

    • +

  • t_rhs – The right hand-side.

    • +
+
+
+
+ +
+
+Row(const Row &t_src) = default
+

Copy constructor.

+
+
Parameters:
+

t_src – The row to copy.

+
+
+
+ +
+
+Row(Row &&t_src) noexcept = default
+

Move constructor.

+
+
Parameters:
+

t_src – The row to move.

+
+
+
+ +
+
+Row &operator=(const Row &t_src) = default
+

Copy-assignment operator.

+
+
Parameters:
+

t_src – The row to copy.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Row &operator=(Row &&t_src) noexcept = default
+

Move-assignment operator.

+
+
Parameters:
+

t_src – The row to move.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Row fix(const Solution::Primal &t_primals) const
+

Creates a new row in which all Param in each Constant are replaced by their corresponding values in t_primals

+
+
Parameters:
+

t_primals – the primal values for the parameters.

+
+
Returns:
+

the new row.

+
+
+
+ +
+
+Row fix(const Solution::Dual &t_duals) const
+

Creates a new row in which all Param in each Constant are replaced by their corresponding values in t_duals

+
+
Parameters:
+

t_primals – the dual values for the parameters.

+
+
Returns:
+

the new row.

+
+
+
+ +
+
+double value(const Solution::Primal &t_primals) const
+

Returns the value of the row if the value of each variable is taken in t_primals, i.e., the difference between the left and the right hand-side.

+

If parameters are encountered, an exception is thrown.

+
+
Parameters:
+

t_primals – The primal values.

+
+
Returns:
+

The value of the row.

+
+
+
+ +
+
+bool is_violated(const Solution::Primal &t_primals, CtrType t_type, double t_tolerance = Tolerance::Feasibility) const
+

Returns true if the point stored in t_primals violates the constraint formed by the row and a constraint type t_type with tolerance t_tolerance.

+
+
Parameters:
+
    +

  • t_primals – The primal values.

    • +

  • t_type – The type of the constraint.

    • +

  • t_tolerance – The tolerance for feasibility.

    • +
+
+
Returns:
+

True if the given point violates the row.

+
+
+
+ +
+
+inline Constant &rhs()
+

Returns the right hand-side of the row.

+
+
Returns:
+

The right hand-side of the row.

+
+
+
+ +
+
+inline const Constant &rhs() const
+

Returns the right hand-side of the row.

+
+
Returns:
+

The right hand-side of the row.

+
+
+
+ +
+
+inline LinExpr<Var> &linear()
+

Returns the linear part of the left hand-side of the row.

+
+
Returns:
+

The linear part of the left hand-side of the row.

+
+
+
+ +
+
+inline const LinExpr<Var> &linear() const
+

Returns the linear part of the left hand-side of the row.

+
+
Returns:
+

The linear part of the left hand-side of the row.

+
+
+
+ +
+
+inline QuadExpr<Var, Var> &quadratic()
+

Returns the quadratic part of the left hand-side of the row.

+
+
Returns:
+

The quadratic part of the left hand-side of the row.

+
+
+
+ +
+
+inline const QuadExpr<Var, Var> &quadratic() const
+

Returns the quadratic part of the left hand-side of the row.

+
+
Returns:
+

The quadratic part of the left hand-side of the row.

+
+
+
+ +
+
+inline void set_linear(LinExpr<Var> &&t_lin_expr)
+

Sets the linear part of the left hand-side of the row.

+
+
Parameters:
+

t_lin_expr – The new linear part of the right hand-side.

+
+
+
+ +
+
+inline void set_linear(const LinExpr<Var> &t_lin_expr)
+

Sets the linear part of the left hand-side of the row.

+
+
Parameters:
+

t_lin_expr – The new linear part of the right hand-side.

+
+
+
+ +
+
+inline void set_quadratic(QuadExpr<Var, Var> &&t_quad_expr)
+

Sets the quadratic part of the left hand-side of the row.

+
+
Parameters:
+

t_lin_expr – The new quadratic part of the right hand-side.

+
+
+
+ +
+
+inline void set_quadratic(const QuadExpr<Var, Var> &t_quad_expr)
+

Sets the quadratic part of the left hand-side of the row.

+
+
Parameters:
+

t_lin_expr – The new quadratic part of the right hand-side.

+
+
+
+ +
+
+inline void set_rhs(Constant &&t_rhs)
+

Sets the right hand-side of the row.

+
+
Parameters:
+

t_rhs – The new right hand-side of the row.

+
+
+
+ +
+
+inline void set_rhs(const Constant &t_rhs)
+

Sets the right hand-side of the row.

+
+
Parameters:
+

t_rhs – The new right hand-side of the row.

+
+
+
+ +
+
+inline Row &operator+=(const Row &t_rhs)
+

Adds another row to the row (both the left and right hand-sides are added).

+
+
Parameters:
+

t_rhs – The row to add.

+
+
Returns:
+

*this

+
+
+
+ +
+
+inline Row &operator-=(const Row &t_rhs)
+

Subtracts another row to the row (both the left and right hand-sides are subtracted).

+
+
Parameters:
+

t_rhs – The row to subtarct.

+
+
Returns:
+

*this

+
+
+
+ +
+
+inline Row &operator*=(double t_rhs)
+

Multiplies all the entries of the row by a given factor (both the left and right hand-sides are multiplied).

+
+
Parameters:
+

t_rhs – The factor used for multiplication.

+
+
Returns:
+

*this

+
+
+
+ +
+
+inline Row &operator/=(double t_rhs)
+
+ +
+
+double scale_to_integers(unsigned int t_n_significant_digits)
+
+ +
+
+void multiply_with_precision_by_power_of_10(unsigned int t_exponent, unsigned int t_n_significant_digits)
+
+ +
+
+double gcd() const
+
+ +
+
+

Public Static Attributes

+
+
+static const Row EmptyRow
+

Represents an empty row.

+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/SolutionDual.html b/api/classes/common/SolutionDual.html new file mode 100644 index 00000000..f3e9cce3 --- /dev/null +++ b/api/classes/common/SolutionDual.html @@ -0,0 +1,439 @@ + + + + + + + + + Solution::Dual — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Solution::Dual

+
+
+class Dual : public idol::AbstractSolution<Ctr, Dual>
+

Dual solution class.

+

This class is used to store a dual solution to an optimization problem.

+

Typically, it is obtained by calling the save_dual function, or by creating a new solution from scratch.

+

Example 1: 

model.optimize();
+if (model.get_status() == Optimal) {
+     Solution::Dual solution = save_dual(model);
+     std::cout << solution << std::endl;
+}
+
+
+

+

Example 2: 

Solution::Dual solution;
+
+solution.set_status(Feasible);
+solution.set_objective_value(13.);
+solution.set(constraint_1, -100.);
+
+
+

+
+

Public Types

+
+
+using const_iterator = typename Map<Ctr, double>::const_iterator
+
+ +
+
+

Public Functions

+
+
+inline void set_status(SolutionStatus t_status)
+

Sets the solution status.

+
+
Parameters:
+

t_status – The desired solution status.

+
+
+
+ +
+
+inline SolutionStatus status() const
+

Returns the stored solution status.

+
+ +
+
+inline void set_reason(SolutionReason t_reason)
+

Sets the reason for the solution status.

+

Example: 

solution.set_status(Feasible);
+solution.set_reason(IterLimit); // We only found a feasible solution because of iteration limit reached
+
+
+

+
+
Parameters:
+

t_reason – The desired reason.

+
+
+
+ +
+
+inline SolutionReason reason() const
+

Returns the reason for the solution status.

+
+
Returns:
+

The reason for the solution status.

+
+
+
+ +
+
+inline void set_objective_value(double t_value)
+

Sets the objective value of the solution.

+
+
Parameters:
+

t_value – The desired objective value.

+
+
+
+ +
+
+inline double objective_value() const
+

Returns the objective value of the solution.

+
+ +
+
+inline bool has_objective_value() const
+

Returns true if the solution has an objective value, false otherwise.

+
+
Returns:
+

True if the solution has an objective value, false otherwise.

+
+
+
+ +
+
+inline void reset_objective_value()
+

Resets the stored objective value.

+

Trying to access the objective value after calling this method will throw an exception.

+
+ +
+
+void set(const Ctr &t_key, double t_value)
+

Sets the value associated to the object t_key given as argument.

+
+
Parameters:
+
    +

  • t_key – The key for which the entry should be set.

    • +

  • t_value – The desired value associated to the key.

    • +
+
+
+
+ +
+
+double get(const Ctr &t_key) const
+

Returns the value associated to the object t_key given as argument.

+

If no value is stored, zero is returned.

+
+
Parameters:
+

t_key – The queried key.

+
+
+
+ +
+
+inline unsigned int size() const
+

Returns the number of non-zero entries in the solution.

+
+
Returns:
+

The number of non-zero entries in the solution

+
+
+
+ +
+
+inline const_iterator begin() const
+

Returns an iterator on the values stored in the solution.

+
+
Returns:
+

An iterator on the values stored in the solution.

+
+
+
+ +
+
+inline const_iterator end() const
+

Returns an iterator on the values stored in the solution.

+
+
Returns:
+

An iterator on the values stored in the solution.

+
+
+
+ +
+
+inline const_iterator cbegin() const
+

Returns an iterator on the values stored in the solution.

+
+
Returns:
+

An iterator on the values stored in the solution.

+
+
+
+ +
+
+inline const_iterator cend() const
+

Returns an iterator on the values stored in the solution.

+
+
Returns:
+

An iterator on the values stored in the solution.

+
+
+
+ +
+
+double norm(double t_p = 2.) const
+

Returns the \( l_p \)-norm of the solution.

+

Note that Inf is a possible value for t_p, in which case, the infinity norm is computed.

+
+
Parameters:
+

t_p – The \( p \) parameter in the \( l_p \)-norm.

+
+
+
+ +
+
+Dual &merge_without_conflict(Dual t_rhs)
+

Merges the solution with another solution, explicitly requiring that no conflict arises (i.e., that no entry from the solution t_rhs is already stored in the solution). If a conflict is detected, an exception is thrown.

+
+
Parameters:
+

t_rhs – The solution to merge.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Dual &normalize(double t_p = 2.)
+

Normalizes the solution (i.e., divides every entry by the norm of the solution) with respect to a given \( l_p \)-norm.

+
+
Parameters:
+

t_p – The parameter \( p \) for the norm.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Dual &round(unsigned int t_n_digits = 0)
+

Rounds all the entries of the solution to the closest double with t_n_digits.

+

Using t_n_digits = 0 leads to the usual rounding, i.e., closest integer.

+
+
Parameters:
+

t_n_digits – The number of digits for the closest double.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Dual &operator+=(const Dual &t_rhs)
+

Adds the solution to another solution t_rhs.

+
+
Parameters:
+

t_rhs – The solution to add.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Dual &operator*=(double t_factor)
+

Multiplies every entries in the solution by t_factor.

+
+
Parameters:
+

t_factor – The factor for multiplication.

+
+
Returns:
+

*this

+
+
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/SolutionPrimal.html b/api/classes/common/SolutionPrimal.html new file mode 100644 index 00000000..36eced86 --- /dev/null +++ b/api/classes/common/SolutionPrimal.html @@ -0,0 +1,439 @@ + + + + + + + + + Solution::Primal — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Solution::Primal

+
+
+class Primal : public idol::AbstractSolution<Var, Primal>
+

Primal solution class.

+

This class is used to store a primal solution to an optimization problem.

+

Typically, it is obtained by calling the save_primal function, or by creating a new solution from scratch.

+

Example 1: 

model.optimize();
+if (model.get_status() == Optimal) {
+     Solution::Primal solution = save_primal(model);
+     std::cout << solution << std::endl;
+}
+
+
+

+

Example 2: 

Solution::Primal solution;
+
+solution.set_status(Feasible);
+solution.set_objective_value(13.);
+solution.set(x, 1.);
+
+
+

+
+

Public Types

+
+
+using const_iterator = typename Map<Var, double>::const_iterator
+
+ +
+
+

Public Functions

+
+
+inline void set_status(SolutionStatus t_status)
+

Sets the solution status.

+
+
Parameters:
+

t_status – The desired solution status.

+
+
+
+ +
+
+inline SolutionStatus status() const
+

Returns the stored solution status.

+
+ +
+
+inline void set_reason(SolutionReason t_reason)
+

Sets the reason for the solution status.

+

Example: 

solution.set_status(Feasible);
+solution.set_reason(IterLimit); // We only found a feasible solution because of iteration limit reached
+
+
+

+
+
Parameters:
+

t_reason – The desired reason.

+
+
+
+ +
+
+inline SolutionReason reason() const
+

Returns the reason for the solution status.

+
+
Returns:
+

The reason for the solution status.

+
+
+
+ +
+
+inline void set_objective_value(double t_value)
+

Sets the objective value of the solution.

+
+
Parameters:
+

t_value – The desired objective value.

+
+
+
+ +
+
+inline double objective_value() const
+

Returns the objective value of the solution.

+
+ +
+
+inline bool has_objective_value() const
+

Returns true if the solution has an objective value, false otherwise.

+
+
Returns:
+

True if the solution has an objective value, false otherwise.

+
+
+
+ +
+
+inline void reset_objective_value()
+

Resets the stored objective value.

+

Trying to access the objective value after calling this method will throw an exception.

+
+ +
+
+void set(const Var &t_key, double t_value)
+

Sets the value associated to the object t_key given as argument.

+
+
Parameters:
+
    +

  • t_key – The key for which the entry should be set.

    • +

  • t_value – The desired value associated to the key.

    • +
+
+
+
+ +
+
+double get(const Var &t_key) const
+

Returns the value associated to the object t_key given as argument.

+

If no value is stored, zero is returned.

+
+
Parameters:
+

t_key – The queried key.

+
+
+
+ +
+
+inline unsigned int size() const
+

Returns the number of non-zero entries in the solution.

+
+
Returns:
+

The number of non-zero entries in the solution

+
+
+
+ +
+
+inline const_iterator begin() const
+

Returns an iterator on the values stored in the solution.

+
+
Returns:
+

An iterator on the values stored in the solution.

+
+
+
+ +
+
+inline const_iterator end() const
+

Returns an iterator on the values stored in the solution.

+
+
Returns:
+

An iterator on the values stored in the solution.

+
+
+
+ +
+
+inline const_iterator cbegin() const
+

Returns an iterator on the values stored in the solution.

+
+
Returns:
+

An iterator on the values stored in the solution.

+
+
+
+ +
+
+inline const_iterator cend() const
+

Returns an iterator on the values stored in the solution.

+
+
Returns:
+

An iterator on the values stored in the solution.

+
+
+
+ +
+
+double norm(double t_p = 2.) const
+

Returns the \( l_p \)-norm of the solution.

+

Note that Inf is a possible value for t_p, in which case, the infinity norm is computed.

+
+
Parameters:
+

t_p – The \( p \) parameter in the \( l_p \)-norm.

+
+
+
+ +
+
+Primal &merge_without_conflict(Primal t_rhs)
+

Merges the solution with another solution, explicitly requiring that no conflict arises (i.e., that no entry from the solution t_rhs is already stored in the solution). If a conflict is detected, an exception is thrown.

+
+
Parameters:
+

t_rhs – The solution to merge.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Primal &normalize(double t_p = 2.)
+

Normalizes the solution (i.e., divides every entry by the norm of the solution) with respect to a given \( l_p \)-norm.

+
+
Parameters:
+

t_p – The parameter \( p \) for the norm.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Primal &round(unsigned int t_n_digits = 0)
+

Rounds all the entries of the solution to the closest double with t_n_digits.

+

Using t_n_digits = 0 leads to the usual rounding, i.e., closest integer.

+
+
Parameters:
+

t_n_digits – The number of digits for the closest double.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Primal &operator+=(const Primal &t_rhs)
+

Adds the solution to another solution t_rhs.

+
+
Parameters:
+

t_rhs – The solution to add.

+
+
Returns:
+

*this

+
+
+
+ +
+
+Primal &operator*=(double t_factor)
+

Multiplies every entries in the solution by t_factor.

+
+
Parameters:
+

t_factor – The factor for multiplication.

+
+
Returns:
+

*this

+
+
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/TempCtr.html b/api/classes/common/TempCtr.html new file mode 100644 index 00000000..1e645bef --- /dev/null +++ b/api/classes/common/TempCtr.html @@ -0,0 +1,302 @@ + + + + + + + + + TempCtr — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

TempCtr

+
+
+class TempCtr
+

Temporary constraint class.

+

This class represents a constraint which has not been built yet by a Model. It contains all the arguments needed to create a real constraint as instantiated by the Ctr class. It is made of a row (see Row) and a type (see CtrType.rst.rst).

+

Typically, one creates a TempCtr by using the overloaded operators <=, >= and == between variable expressions (i.e., Expr) and constant terms (i.e., Constant).

+

Example 1: 

...
+model.add_ctr(x + y <= 1.);
+
+
+ Example 2 (equivalent to Example 1): 
...
+model.add_ctr(TempCtr(Row(x + y, 1.), LessOrEqual));
+
+
+

+

Subclassed by idol::CtrVersion

+
+

Public Functions

+
+
+TempCtr() = default
+

Default constructor.

+

Creates an empty temporary constraint. The default type is LessOrEqual.

+
+ +
+
+inline TempCtr(Row &&t_row, CtrType t_type)
+

Constructor.

+

Creates a new temporary constraint.

+
+
Parameters:
+
    +

  • t_row – The desired row.

    • +

  • t_type – The desired constraint type.

    • +
+
+
+
+ +
+
+TempCtr(const TempCtr &t_src) = default
+

Copy constructor.

+
+
Parameters:
+

t_src – The object to copy.

+
+
+
+ +
+
+TempCtr(TempCtr &&t_src) noexcept = default
+

Move constructor.

+
+
Parameters:
+

t_src – The object to move.

+
+
+
+ +
+
+TempCtr &operator=(const TempCtr &t_src) = default
+

Copy-assignment operator.

+
+
Parameters:
+

t_src – The object to copy.

+
+
+
+ +
+
+TempCtr &operator=(TempCtr &&t_src) noexcept = default
+

Move-assignment operator.

+
+
Parameters:
+

t_src – The object to move.

+
+
+
+ +
+
+inline const Row &row() const
+

Returns the row of the temporary constraint (see Row).

+
+
Returns:
+

The row of the temporary constraint.

+
+
+
+ +
+
+inline Row &row()
+

Returns the row of the temporary constraint (see Row).

+
+
Returns:
+

The row of the temporary constraint.

+
+
+
+ +
+
+inline CtrType type() const
+

Returns the temporary constraint type.

+
+
Returns:
+

The temporary constraint type.

+
+
+
+ +
+
+inline void set_type(CtrType t_type)
+

Sets the type of the temporary constraint.

+
+
Parameters:
+

t_type – the desired type.

+
+
+
+ +
+
+bool is_violated(const Solution::Primal &t_solution) const
+

Returns true if the temporary constraint is violated by the given solution, false otherwise.

+
+
Parameters:
+

t_solution – The solution to check.

+
+
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/TempVar.html b/api/classes/common/TempVar.html new file mode 100644 index 00000000..a981ef1f --- /dev/null +++ b/api/classes/common/TempVar.html @@ -0,0 +1,333 @@ + + + + + + + + + TempVar — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

TempVar

+
+
+class TempVar
+

Temporary variable class.

+

This class represents a variable which has not been built yet by a Model. It contains all the arguments needed to create a real variable as instantiated by the Var class. It is made of a lower and an upper bound, a type (see VarType), and a column (see Column).

+

Example 1: 

model.add_var(0., 1., Binary, 2.);
+// is the same as
+model.add_var(TempVar(0., 1., Binary, 2.));
+
+
+

+

Subclassed by idol::VarVersion

+
+

Public Functions

+
+
+TempVar() = default
+

Default constructor.

+

Creates a new temporary variable. The default lower and upper bounds are 0 and Inf and the type is Continuous.

+
+ +
+
+inline TempVar(double t_lb, double t_ub, VarType t_type, Column &&t_column)
+

Constructor.

+

Creates a new temporary variable.

+
+
Parameters:
+
    +

  • t_lb – The desired lower bound.

    • +

  • t_ub – The desired upper bound.

    • +

  • t_type – The desired variable type.

    • +

  • t_column – The desired column.

    • +
+
+
+
+ +
+
+TempVar(TempVar &&t_src) = default
+

Copy constructor.

+
+
Parameters:
+

t_src – The object to copy.

+
+
+
+ +
+
+TempVar(const TempVar &t_src) = default
+

Move constructor.

+
+
Parameters:
+

t_src – The object to move.

+
+
+
+ +
+
+TempVar &operator=(TempVar &&t_src) = default
+

Copy-assignment operator.

+
+
Parameters:
+

t_src – The object to copy.

+
+
+
+ +
+
+TempVar &operator=(const TempVar &t_src) = default
+

Move-assignment operator.

+
+
Parameters:
+

t_src – The object to move.

+
+
+
+ +
+
+inline const Column &column() const
+

Returns the column of the temporary variable (see Column).

+
+
Returns:
+

The column of the temporary variable.

+
+
+
+ +
+
+inline Column &column()
+

Returns the column of the temporary variable (see Column).

+
+
Returns:
+

The column of the temporary variable.

+
+
+
+ +
+
+inline double lb() const
+

Returns the lower bound of the temporary variable.

+
+
Returns:
+

The lower bound of the temporary variable.

+
+
+
+ +
+
+inline void set_lb(double t_lb)
+

Sets the lower bound of the temporary variable.

+
+
Parameters:
+

t_lb – The desired lower bound of the temporary variable.

+
+
+
+ +
+
+inline double ub() const
+

Returns the upper bound of the temporary variable.

+
+
Returns:
+

The upper bound of the temporary variable.

+
+
+
+ +
+
+inline void set_ub(double t_ub)
+

Sets the upper bound of the temporary variable.

+
+
Parameters:
+

t_ub – The desired upper bound of the temporary variable.

+
+
+
+ +
+
+inline VarType type() const
+

Returns the type of the temporary variable.

+
+
Returns:
+

The type of the temporary variable.

+
+
+
+ +
+
+inline void set_type(VarType t_type)
+

Sets the type of the temporary variable.

+
+
Parameters:
+

t_type – The desired type of the temporary variable.

+
+
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/Var.html b/api/classes/common/Var.html new file mode 100644 index 00000000..3cf62954 --- /dev/null +++ b/api/classes/common/Var.html @@ -0,0 +1,375 @@ + + + + + + + + + Var — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Var

+
+
+class Var : public idol::Object<VarVersion, Var>
+

Variable modeling-old object.

+

The class is used to represent a variable in an optimization model.

+

Variables can be created by calling a constructor of Var, passing the optimization environment which will store the variable’s versions as argument. The variable can then be added to a model using the Model::add method.

+

Another approach is to directly use the Model::add_var method.

+
Var x = model.add_var(0, 1, Continuous, "x");
+
+
+
+

Public Functions

+
+
+Var(Env &t_env, double t_lb, double t_ub, VarType t_type, std::string t_name = "")
+

Constructor.

+

Creates a new variable in the optimization environment.

+

If no name is given, a unique name in the environment is given.

+
+
Parameters:
+
    +

  • t_env – The optimization environment.

    • +

  • t_lb – The lower bound (possibly -Inf) for the default version of the variable.

    • +

  • t_ub – The upper bound (possibly +Inf) for the default version of the variable.

    • +

  • t_type – The type for the default version of the variable.

    • +

  • t_name – The given name of the variable.

    • +
+
+
+
+ +
+
+Var(Env &t_env, double t_lb, double t_ub, VarType t_type, Column &&t_column, std::string t_name = "")
+

Constructor.

+

Creates a new variable in the optimization environment.

+

If no name is given, a unique name in the environment is given.

+
+
Parameters:
+
    +

  • t_env – The optimization environment.

    • +

  • t_lb – The lower bound (possibly -Inf) for the default version of the variable.

    • +

  • t_ub – The upper bound (possibly +Inf) for the default version of the variable.

    • +

  • t_type – The type for the default version of the variable.

    • +

  • t_column – The column for the default version of the variable.

    • +

  • t_name – The given name of the variable.

    • +
+
+
+
+ +
+
+Var(Env &t_env, double t_lb, double t_ub, VarType t_type, const Column &t_column, std::string t_name = "")
+

Constructor.

+

Creates a new variable in the optimization environment.

+

If no name is given, a unique name in the environment is given.

+
+
Parameters:
+
    +

  • t_env – The optimization environment.

    • +

  • t_lb – The lower bound (possibly -Inf) for the default version of the variable.

    • +

  • t_ub – The upper bound (possibly +Inf) for the default version of the variable.

    • +

  • t_type – The type for the default version of the variable.

    • +

  • t_column – The column for the default version of the variable.

    • +

  • t_name – The given name of the variable.

    • +
+
+
+
+ +
+
+Var(const Var&) = default
+
+ +
+
+Var(Var&&) = default
+
+ +
+
+Var &operator=(const Var&) = default
+
+ +
+
+Var &operator=(Var&&) = default
+
+ +
+
+inline const std::string &name() const
+

Returns the name of the optimization object.

+
+
Returns:
+

The name of the optimization object.

+
+
+
+ +
+
+inline unsigned int id() const
+

Returns the id of the optimization object.

+
+
Returns:
+

The id of the optimization object.

+
+
+
+ +
+
+inline bool is_in(const Model &t_model) const
+

Returns true if the optimization object is part of the model t_model, false otherwise.

+
+
Parameters:
+

t_model – The model.

+
+
Returns:
+

True if the optimization object is part of the model t_model, false otherwise.

+
+
+
+ +
+
+inline const ValueT &get(const Annotation<Var, ValueT> &t_annotation) const
+

Returns the value of the given annotation t_annotation associated to the object.

+

If no value is found, the default value of the annotation is returned. If no default value was set, an exception is thrown.

+
+
Template Parameters:
+

ValueT – The value type of the annotation.

+
+
Parameters:
+

t_annotation – The annotation.

+
+
Returns:
+

The value of the annotation.

+
+
+
+ +
+
+inline void set(const Annotation<Var, ValueT> &t_annotation, ArgsT&&... t_args) const
+

Sets the value of the given annotation t_annotation associated to the object.

+
+
Template Parameters:
+
    +

  • ValueT – The value type of the annotation.

    • +

  • ArgsT – The parameter pack types for constructing the value of the annotation.

    • +
+
+
Parameters:
+
    +

  • t_annotation – The annotation.

    • +

  • t_args – The parameter pack arguments used to construct “in place” the value of the annotation.

    • +
+
+
+
+ +
+
+

Public Static Functions

+
+
+template<unsigned int N = 1, unsigned int I = 0>
static inline Vector<Var, N - I> make_vector(Env &t_env, const Dim<N> &t_dim, double t_lb, double t_ub, VarType t_type, const std::string &t_name = "")
+

Creates a (nested) vector of variables.

+

Example:

+

Env env;
+
+auto x = Var::make_vector(env, Dim<2>(m, n), 0., 1., Continuous, "x");
+
+std::cout << x[1][4].name() << std::endl; // "x_1_4"
+
+
+

+
+
Template Parameters:
+
    +

  • N – The dimension of the (nested) vector.

    • +

  • I – Used for recursion.

    • +
+
+
Parameters:
+
    +

  • t_env – The environment to store the variables.

    • +

  • t_dim – The number of elements in each dimension.

    • +

  • t_lb – The lower bound.

    • +

  • t_ub – The upper bound.

    • +

  • t_type – The type of the variables.

    • +

  • t_name – The given name of the variables.

    • +
+
+
Returns:
+

A (nested) vector of variables.

+
+
+
+ +
+
+

Friends

+
+
+friend class impl::Env
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/common/index.html b/api/classes/common/index.html new file mode 100644 index 00000000..ae01fa60 --- /dev/null +++ b/api/classes/common/index.html @@ -0,0 +1,180 @@ + + + + + + + + + Common — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-bilevel/MibS.html b/api/classes/external-bilevel/MibS.html new file mode 100644 index 00000000..1576cdca --- /dev/null +++ b/api/classes/external-bilevel/MibS.html @@ -0,0 +1,474 @@ + + + + + + + + + Bilevel::Mibs — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Bilevel::Mibs

+
+
+class MibS : public idol::OptimizerFactoryWithDefaultParameters<MibS>
+
+

Public Functions

+
+
+MibS(Bilevel::LowerLevelDescription t_description)
+
+ +
+
+MibS(const MibS &t_src)
+
+ +
+
+MibS(MibS&&) noexcept = delete
+
+ +
+
+MibS &operator=(const MibS&) = delete
+
+ +
+
+MibS &operator=(MibS&&) noexcept = delete
+
+ +
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+MibS &with_osi_interface(const OsiSolverInterface &t_osi_optimizer)
+
+ +
+
+virtual MibS *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+MibS &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+MibS &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+MibS &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+MibS &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+MibS &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+MibS &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+MibS &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+MibS &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+MibS &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+MibS &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+MibS &conditional(bool t_conditional_value, const std::function<void(MibS&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+MibS &conditional(bool t_conditional_value, const std::function<void(MibS&)> &t_if, const std::function<void(MibS&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-bilevel/Optimizers_MibS.html b/api/classes/external-bilevel/Optimizers_MibS.html new file mode 100644 index 00000000..00340e6b --- /dev/null +++ b/api/classes/external-bilevel/Optimizers_MibS.html @@ -0,0 +1,150 @@ + + + + + + + + + Optimizers::Bilevel::Mibs — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Optimizers::Bilevel::Mibs

+
+

Warning

+

doxygenclass: Cannot find class “idol::Optimizers::Bilevel::MibS” in doxygen xml output for project “idol” from directory: _build/xml/

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-bilevel/index.html b/api/classes/external-bilevel/index.html new file mode 100644 index 00000000..204a177f --- /dev/null +++ b/api/classes/external-bilevel/index.html @@ -0,0 +1,151 @@ + + + + + + + + + External Bilevel Solvers — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

External Bilevel Solvers

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/GLPK.html b/api/classes/external-mip/GLPK.html new file mode 100644 index 00000000..08b5a0bc --- /dev/null +++ b/api/classes/external-mip/GLPK.html @@ -0,0 +1,489 @@ + + + + + + + + + GLPK — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

GLPK

+
+
+class GLPK : public idol::OptimizerFactoryWithDefaultParameters<GLPK>
+
+

Public Functions

+
+
+GLPK() = default
+
+ +
+
+GLPK(const GLPK&) = default
+
+ +
+
+GLPK(GLPK&&) noexcept = default
+
+ +
+
+GLPK &operator=(const GLPK&) = delete
+
+ +
+
+GLPK &operator=(GLPK&&) noexcept = delete
+
+ +
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+virtual GLPK *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+GLPK &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+GLPK &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+GLPK &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+GLPK &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+GLPK &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+GLPK &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+GLPK &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+GLPK &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+GLPK &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+GLPK &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+GLPK &conditional(bool t_conditional_value, const std::function<void(GLPK&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+GLPK &conditional(bool t_conditional_value, const std::function<void(GLPK&)> &t_if, const std::function<void(GLPK&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+

Public Static Functions

+
+
+static GLPK ContinuousRelaxation()
+
+ +
+
+static Model read_from_file(Env &t_env, const std::string &t_filename)
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/Gurobi.html b/api/classes/external-mip/Gurobi.html new file mode 100644 index 00000000..d57920f7 --- /dev/null +++ b/api/classes/external-mip/Gurobi.html @@ -0,0 +1,519 @@ + + + + + + + + + Gurobi — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Gurobi

+
+
+class Gurobi : public idol::OptimizerFactoryWithDefaultParameters<Gurobi>
+
+

Public Functions

+
+
+Gurobi() = default
+
+ +
+
+Gurobi(const Gurobi &t_src)
+
+ +
+
+Gurobi(Gurobi&&) noexcept = default
+
+ +
+
+Gurobi &operator=(const Gurobi&) = delete
+
+ +
+
+Gurobi &operator=(Gurobi&&) noexcept = default
+
+ +
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+Gurobi &add_callback(const CallbackFactory &t_cb)
+
+ +
+
+Gurobi &with_lazy_cut(bool t_value)
+
+ +
+
+Gurobi &with_max_n_solution_in_pool(unsigned int t_value)
+
+ +
+
+Gurobi &with_continuous_relaxation_only(bool t_value)
+
+ +
+
+Gurobi &with_external_param(GRB_IntParam t_param, int t_value)
+
+ +
+
+Gurobi &with_external_param(GRB_DoubleParam t_param, double t_value)
+
+ +
+
+virtual Gurobi *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+Gurobi &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Gurobi &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Gurobi &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Gurobi &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Gurobi &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Gurobi &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Gurobi &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Gurobi &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Gurobi &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Gurobi &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Gurobi &conditional(bool t_conditional_value, const std::function<void(Gurobi&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Gurobi &conditional(bool t_conditional_value, const std::function<void(Gurobi&)> &t_if, const std::function<void(Gurobi&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+

Public Static Functions

+
+
+static Gurobi ContinuousRelaxation()
+
+ +
+
+static Model read_from_file(Env &t_env, const std::string &t_filename)
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/HiGHS.html b/api/classes/external-mip/HiGHS.html new file mode 100644 index 00000000..ad0384c1 --- /dev/null +++ b/api/classes/external-mip/HiGHS.html @@ -0,0 +1,489 @@ + + + + + + + + + HiGHS — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

HiGHS

+
+
+class HiGHS : public idol::OptimizerFactoryWithDefaultParameters<HiGHS>
+
+

Public Functions

+
+
+HiGHS() = default
+
+ +
+
+HiGHS(const HiGHS&) = default
+
+ +
+
+HiGHS(HiGHS&&) noexcept = default
+
+ +
+
+HiGHS &operator=(const HiGHS&) = delete
+
+ +
+
+HiGHS &operator=(HiGHS&&) noexcept = delete
+
+ +
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+virtual HiGHS *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+HiGHS &with_continuous_relaxation_only(bool t_value)
+
+ +
+
+HiGHS &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+HiGHS &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+HiGHS &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+HiGHS &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+HiGHS &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+HiGHS &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+HiGHS &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+HiGHS &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+HiGHS &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+HiGHS &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+HiGHS &conditional(bool t_conditional_value, const std::function<void(HiGHS&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+HiGHS &conditional(bool t_conditional_value, const std::function<void(HiGHS&)> &t_if, const std::function<void(HiGHS&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+

Public Static Functions

+
+
+static HiGHS ContinuousRelaxation()
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/Mosek.html b/api/classes/external-mip/Mosek.html new file mode 100644 index 00000000..27c45d4b --- /dev/null +++ b/api/classes/external-mip/Mosek.html @@ -0,0 +1,509 @@ + + + + + + + + + Mosek — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

Mosek

+
+
+class Mosek : public idol::OptimizerFactoryWithDefaultParameters<Mosek>
+
+

Public Functions

+
+
+Mosek() = default
+
+ +
+
+Mosek(const Mosek&)
+
+ +
+
+Mosek(Mosek&&) noexcept = default
+
+ +
+
+Mosek &operator=(const Mosek&) = delete
+
+ +
+
+Mosek &operator=(Mosek&&) noexcept = delete
+
+ +
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+virtual Mosek *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+Mosek &add_callback(const CallbackFactory &t_cb)
+
+ +
+
+Mosek &with_continuous_relaxation_only(bool t_value)
+
+ +
+
+Mosek &with_external_parameter(const std::string &t_param, double t_value)
+
+ +
+
+Mosek &with_external_parameter(const std::string &t_param, int t_value)
+
+ +
+
+Mosek &with_external_parameter(const std::string &t_param, std::string t_value)
+
+ +
+
+Mosek &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Mosek &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Mosek &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Mosek &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Mosek &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Mosek &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Mosek &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Mosek &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Mosek &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Mosek &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Mosek &conditional(bool t_conditional_value, const std::function<void(Mosek&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Mosek &conditional(bool t_conditional_value, const std::function<void(Mosek&)> &t_if, const std::function<void(Mosek&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+

Public Static Functions

+
+
+static Mosek ContinuousRelaxation()
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/Optimizers_GLPK.html b/api/classes/external-mip/Optimizers_GLPK.html new file mode 100644 index 00000000..c535f0a5 --- /dev/null +++ b/api/classes/external-mip/Optimizers_GLPK.html @@ -0,0 +1,369 @@ + + + + + + + + + Optimizers::GLPK — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Optimizers::GLPK

+
+
+class GLPK : public idol::OptimizerWithLazyUpdates<int, int>
+
+

Public Functions

+
+
+explicit GLPK(const Model &t_model, bool t_continuous_relaxation)
+
+ +
+
+inline virtual std::string name() const override
+
+ +
+
+virtual void set_param_time_limit(double t_time_limit) override
+
+ +
+
+virtual void set_param_best_obj_stop(double t_best_obj_stop) override
+
+ +
+
+virtual void set_param_best_bound_stop(double t_best_bound_stop) override
+
+ +
+
+virtual void set_param_presolve(bool t_value) override
+
+ +
+
+virtual void set_param_logs(bool t_value) override
+
+ +
+
+inline int &operator[](const Var &t_var)
+
+ +
+
+inline const int &operator[](const Var &t_var) const
+
+ +
+
+inline int &operator[](const Ctr &t_ctr)
+
+ +
+
+inline const int &operator[](const Ctr &t_ctr) const
+
+ +
+
+inline virtual const ::idol::Model &parent() const
+
+ +
+
+inline bool get_param_logs() const
+
+ +
+
+inline double get_param_time_limit() const
+
+ +
+
+inline unsigned int get_param_thread_limit() const
+
+ +
+
+inline virtual void set_param_threads(unsigned int t_threads)
+
+ +
+
+double get_param_best_obj_stop() const
+
+ +
+
+double get_param_best_bound_stop() const
+
+ +
+
+inline double get_tol_mip_relative_gap() const
+
+ +
+
+inline virtual void set_tol_mip_relative_gap(double t_tol_mip_relative_gap)
+
+ +
+
+inline double get_tol_mip_absolute_gap() const
+
+ +
+
+inline virtual void set_tol_mip_absolute_gap(double t_mip_tol_absolute_gap)
+
+ +
+
+inline double get_tol_feasibility() const
+
+ +
+
+inline virtual void set_tol_feasibility(double t_tol_feasibility)
+
+ +
+
+inline double get_tol_optimality() const
+
+ +
+
+inline virtual void set_tol_optimality(double t_tol_optimality)
+
+ +
+
+inline double get_tol_integer() const
+
+ +
+
+inline virtual void set_tol_integer(double t_tol_integer)
+
+ +
+
+inline unsigned int get_param_iteration_limit() const
+
+ +
+
+inline void set_param_iteration_limit(unsigned int t_iteration_limit)
+
+ +
+
+inline bool get_param_presolve() const
+
+ +
+
+inline bool get_param_infeasible_or_unbounded_info() const
+
+ +
+
+inline virtual void set_param_infeasible_or_unbounded_info(bool t_value)
+
+ +
+
+inline const Timer &time() const
+
+ +
+
+double get_remaining_time() const
+
+ +
+
+inline bool is_terminated() const
+
+ +
+
+virtual void terminate()
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+

Public Static Functions

+
+
+static Model read_from_file(Env &t_env, const std::string &t_filename)
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/Optimizers_Gurobi.html b/api/classes/external-mip/Optimizers_Gurobi.html new file mode 100644 index 00000000..1b3bced6 --- /dev/null +++ b/api/classes/external-mip/Optimizers_Gurobi.html @@ -0,0 +1,427 @@ + + + + + + + + + Optimizers::Gurobi — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Optimizers::Gurobi

+
+
+class Gurobi : public idol::OptimizerWithLazyUpdates<GRBVar, std::variant<GRBConstr, GRBQConstr>>
+
+

Public Functions

+
+
+Gurobi(const Model &t_model, bool t_continuous_relaxation, GRBEnv &t_env)
+
+ +
+
+inline explicit Gurobi(const Model &t_model, bool t_continuous_relaxation)
+
+ +
+
+inline GRBEnv &env()
+
+ +
+
+inline const GRBEnv &env() const
+
+ +
+
+inline GRBModel &model()
+
+ +
+
+inline const GRBModel &model() const
+
+ +
+
+inline virtual std::string name() const override
+
+ +
+
+virtual void set_param_time_limit(double t_time_limit) override
+
+ +
+
+virtual void set_param_threads(unsigned int t_thread_limit) override
+
+ +
+
+virtual void set_param_best_obj_stop(double t_best_obj_stop) override
+
+ +
+
+virtual void set_param_best_bound_stop(double t_best_bound_stop) override
+
+ +
+
+virtual void set_param_presolve(bool t_value) override
+
+ +
+
+virtual void set_param_infeasible_or_unbounded_info(bool t_value) override
+
+ +
+
+void add_callback(Callback *t_ptr_to_callback)
+
+ +
+
+void set_lazy_cut(bool t_value)
+
+ +
+
+void set_max_n_solution_in_pool(unsigned int t_value)
+
+ +
+
+virtual void set_param_logs(bool t_value) override
+
+ +
+
+void set_param(GRB_IntParam t_param, int t_value)
+
+ +
+
+void set_param(GRB_DoubleParam t_param, double t_value)
+
+ +
+
+virtual void set_tol_mip_relative_gap(double t_relative_gap_tolerance) override
+
+ +
+
+virtual void set_tol_mip_absolute_gap(double t_absolute_gap_tolerance) override
+
+ +
+
+virtual void set_tol_feasibility(double t_tol_feasibility) override
+
+ +
+
+virtual void set_tol_optimality(double t_tol_optimality) override
+
+ +
+
+virtual void set_tol_integer(double t_tol_integer) override
+
+ +
+
+inline GRBVar &operator[](const Var &t_var)
+
+ +
+
+inline const GRBVar &operator[](const Var &t_var) const
+
+ +
+
+inline std::variant<GRBConstr, GRBQConstr> &operator[](const Ctr &t_ctr)
+
+ +
+
+inline const std::variant<GRBConstr, GRBQConstr> &operator[](const Ctr &t_ctr) const
+
+ +
+
+inline virtual const ::idol::Model &parent() const
+
+ +
+
+inline bool get_param_logs() const
+
+ +
+
+inline double get_param_time_limit() const
+
+ +
+
+inline unsigned int get_param_thread_limit() const
+
+ +
+
+double get_param_best_obj_stop() const
+
+ +
+
+double get_param_best_bound_stop() const
+
+ +
+
+inline double get_tol_mip_relative_gap() const
+
+ +
+
+inline double get_tol_mip_absolute_gap() const
+
+ +
+
+inline double get_tol_feasibility() const
+
+ +
+
+inline double get_tol_optimality() const
+
+ +
+
+inline double get_tol_integer() const
+
+ +
+
+inline unsigned int get_param_iteration_limit() const
+
+ +
+
+inline void set_param_iteration_limit(unsigned int t_iteration_limit)
+
+ +
+
+inline bool get_param_presolve() const
+
+ +
+
+inline bool get_param_infeasible_or_unbounded_info() const
+
+ +
+
+inline const Timer &time() const
+
+ +
+
+double get_remaining_time() const
+
+ +
+
+inline bool is_terminated() const
+
+ +
+
+virtual void terminate()
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+

Public Static Functions

+
+
+static Model read_from_file(Env &t_env, const std::string &t_filename)
+
+ +
+
+

Friends

+
+
+friend class ::idol::GurobiCallbackI
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/Optimizers_HiGHS.html b/api/classes/external-mip/Optimizers_HiGHS.html new file mode 100644 index 00000000..78a7772b --- /dev/null +++ b/api/classes/external-mip/Optimizers_HiGHS.html @@ -0,0 +1,157 @@ + + + + + + + + + Optimizers::HiGHS — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Optimizers::HiGHS

+
+

Warning

+

doxygenclass: Cannot find class “idol::Optimizers::HiGHS” in doxygen xml output for project “idol” from directory: _build/xml/

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/Optimizers_Mosek.html b/api/classes/external-mip/Optimizers_Mosek.html new file mode 100644 index 00000000..0b53044e --- /dev/null +++ b/api/classes/external-mip/Optimizers_Mosek.html @@ -0,0 +1,157 @@ + + + + + + + + + Optimizers::Mosek — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Optimizers::Mosek

+
+

Warning

+

doxygenclass: Cannot find class “idol::Optimizers::Mosek” in doxygen xml output for project “idol” from directory: _build/xml/

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/Osi/OsiCbc.html b/api/classes/external-mip/Osi/OsiCbc.html new file mode 100644 index 00000000..82724b62 --- /dev/null +++ b/api/classes/external-mip/Osi/OsiCbc.html @@ -0,0 +1,471 @@ + + + + + + + + + OsiCbc — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

OsiCbc

+
+
+class OsiCbc : public idol::Osi
+
+

Public Functions

+
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+OsiSolverInterface *create_osi_solver_interface() const
+
+ +
+
+virtual Osi *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+Osi &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &conditional(bool t_conditional_value, const std::function<void(Osi&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &conditional(bool t_conditional_value, const std::function<void(Osi&)> &t_if, const std::function<void(Osi&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+

Public Static Functions

+
+
+static Osi ContinuousRelaxation(const OsiSolverInterface &t_solver_interface)
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/Osi/OsiClp.html b/api/classes/external-mip/Osi/OsiClp.html new file mode 100644 index 00000000..54b8dcb4 --- /dev/null +++ b/api/classes/external-mip/Osi/OsiClp.html @@ -0,0 +1,471 @@ + + + + + + + + + OsiClp — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

OsiClp

+
+
+class OsiClp : public idol::Osi
+
+

Public Functions

+
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+OsiSolverInterface *create_osi_solver_interface() const
+
+ +
+
+virtual Osi *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+Osi &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &conditional(bool t_conditional_value, const std::function<void(Osi&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &conditional(bool t_conditional_value, const std::function<void(Osi&)> &t_if, const std::function<void(Osi&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+

Public Static Functions

+
+
+static Osi ContinuousRelaxation(const OsiSolverInterface &t_solver_interface)
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/Osi/OsiCplex.html b/api/classes/external-mip/Osi/OsiCplex.html new file mode 100644 index 00000000..62ef2b85 --- /dev/null +++ b/api/classes/external-mip/Osi/OsiCplex.html @@ -0,0 +1,471 @@ + + + + + + + + + OsiCplex — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

OsiCplex

+
+
+class OsiCplex : public idol::Osi
+
+

Public Functions

+
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+OsiSolverInterface *create_osi_solver_interface() const
+
+ +
+
+virtual Osi *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+Osi &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &conditional(bool t_conditional_value, const std::function<void(Osi&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &conditional(bool t_conditional_value, const std::function<void(Osi&)> &t_if, const std::function<void(Osi&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+

Public Static Functions

+
+
+static Osi ContinuousRelaxation(const OsiSolverInterface &t_solver_interface)
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/Osi/OsiSymphony.html b/api/classes/external-mip/Osi/OsiSymphony.html new file mode 100644 index 00000000..10ef32c8 --- /dev/null +++ b/api/classes/external-mip/Osi/OsiSymphony.html @@ -0,0 +1,471 @@ + + + + + + + + + OsiSymphony — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

OsiSymphony

+
+
+class OsiSymphony : public idol::Osi
+
+

Public Functions

+
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+OsiSolverInterface *create_osi_solver_interface() const
+
+ +
+
+virtual Osi *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+Osi &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &conditional(bool t_conditional_value, const std::function<void(Osi&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &conditional(bool t_conditional_value, const std::function<void(Osi&)> &t_if, const std::function<void(Osi&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+

Public Static Functions

+
+
+static Osi ContinuousRelaxation(const OsiSolverInterface &t_solver_interface)
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/Osi/index.html b/api/classes/external-mip/Osi/index.html new file mode 100644 index 00000000..8e829a5f --- /dev/null +++ b/api/classes/external-mip/Osi/index.html @@ -0,0 +1,524 @@ + + + + + + + + + COIN-OR/Osi (Cplex, Xpress, Cbc, …) — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

COIN-OR/Osi (Cplex, Xpress, Cbc, …)

+

Idol can be linked with the coin-or/Osi library (Open Solver Interface) to use any solver having an osi interface.

+
+

Hint

+

At the moment, only Cplex has been properly tested and can be used as follows.

+
model.use(OsiCplex());
+
+
+
+
+

Hint

+

Note that Osi can also be used with any Osi object by directly passing the interface as an argument.

+
model.use(Osi(OsiCpxSolverInterface())); // same as: model.use(OsiCplex());
+
+
+
+
+

Warning

+

Though idol has dedicated optimizers for Symphony, Cbc and Clp, it seems that the osi interface for these solvers do not +fully fulfill the interface requirement to have a stable usage.

+
+
+ +
+
+
+class Osi : public idol::OptimizerFactoryWithDefaultParameters<Osi>
+

Subclassed by idol::OsiCbc, idol::OsiClp, idol::OsiCplex, idol::OsiSymphony

+
+

Public Functions

+
+
+Osi(const OsiSolverInterface &t_solver_interface)
+
+ +
+
+Osi(const Osi &t_src)
+
+ +
+
+Osi(Osi&&) noexcept = default
+
+ +
+
+Osi &operator=(const Osi&) = delete
+
+ +
+
+Osi &operator=(Osi&&) noexcept = delete
+
+ +
+
+virtual Optimizer *operator()(const Model &t_model) const override
+

Creates and returns a new optimizer to solve the model given as parameter.

+
+
Parameters:
+

t_model – The model which the optimizer will solve

+
+
Returns:
+

A new optimizer for the model

+
+
+
+ +
+
+OsiSolverInterface *create_osi_solver_interface() const
+
+ +
+
+virtual Osi *clone() const override
+

Creates and return a copy of the optimizer factory. This is used for polymorphism.

+
+
Returns:
+

A copied object of the current object (i.e., *this)

+
+
+
+ +
+
+Osi &with_logs(bool t_value)
+

Sets the log_master level and color for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_logs(true);
+
+
+

+
+
Parameters:
+
    +

  • t_log_level – the log_master level

    • +

  • t_log_color – the output color

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_time_limit(double t_time_limit)
+

Sets the time limit for the optimizer

+

Example: 

auto algorithm = GLPK()
+                     .with_time_limit(3600);
+
+
+

+
+
Parameters:
+

t_time_limit – the time limit (in seconds)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_thread_limit(unsigned int t_max_n_threads)
+

Sets the maximum number of threads which the optimizer can use

+

Example: 

auto algorithm = GLPK()
+                     .with_thread_limit(5);
+
+
+

+
+
Parameters:
+

t_max_n_threads – the number of threads which can be used

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_iteration_limit(unsigned int t_iteration_count_limit)
+

Sets the maximum number of iterations which the optimizer go through

+

Example: 

auto algorithm = GLPK()
+                     .with_iteration_limit(200);
+
+
+

+
+
Parameters:
+

t_iteration_count_limit – the maximum number of iterations

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_best_bound_stop(double t_best_bound_stop)
+

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_obj = 0.;
+auto algorithm = GLPK()
+                     .with_best_bound_stop(my_known_best_obj);
+
+
+

+
+
Parameters:
+

t_best_bound_stop – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_best_obj_stop(double t_user_best_obj)
+

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

+

Example: 

const double my_known_best_bound = 0;
+auto algorithm = GLPK()
+                     .with_best_obj_stop(my_known_best_bound);
+
+
+

+
+
Parameters:
+

t_user_best_obj – the threshold

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_relative_gap_tolerance(double t_relative_gap_tolerance)
+

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%
+
+
+

+
+
Parameters:
+

t_relative_gap_tolerance – the relative gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_absolute_gap_tolerance(double t_absolute_gap_tolerance)
+

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

+

Example: 

auto algorithm = GLPK()
+                     .with_absolute_gap_tolerance(1e-4);
+
+
+

+
+
Parameters:
+

t_absolute_gap_tolerance – the absolute gap tolerance

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_presolve(bool t_value)
+

Sets the get_param_presolve activation for the optimizer.

+

Example: 

auto algorithm = GLPK()
+                     .with_presolve(false); // turns off get_param_presolve phase
+
+
+

+
+
Parameters:
+

t_value – the activation level for the optimizer’s get_param_presolve (0 for disabling, 1 for enabling)

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &with_infeasible_or_unbounded_info(bool t_value)
+

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

+

Example: 

auto algorithm = GLPK()
+                     .with_infeasible_or_unbounded_info(true);
+
+
+

+
+
Parameters:
+

t_value – the activation level

+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &conditional(bool t_conditional_value, const std::function<void(Osi&)> &t_if)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, nothing happens.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+Osi &conditional(bool t_conditional_value, const std::function<void(Osi&)> &t_if, const std::function<void(Osi&)> &t_else)
+

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

+

Example: 

for (const bool use_presolve : {true, false}) {
+     auto algorithm = GLPK()
+                         .conditional(use_presolve,
+                                         [](auto& x){ x.with_presolve(true); },
+                                         [](auto& x){ x.with_presolve(false); })
+      model.use(algorithm);
+      model.optimize();
+}
+
+
+

+
+
Parameters:
+
    +

  • t_conditional_value – if true, the t_if lambda function is executed, if false, the t_else lambda function is.

    • +

  • t_if – lambda function to execute in case t_conditional_value is true

    • +

  • t_else – lambda function to execute in case t_conditional_value is false

    • +
+
+
Returns:
+

the optimizer factory itself

+
+
+
+ +
+
+template<class T>
inline T &as()
+
+ +
+
+template<class T>
inline const T &as() const
+
+ +
+
+template<class T>
inline bool is() const
+
+ +
+
+

Public Static Functions

+
+
+static Osi ContinuousRelaxation(const OsiSolverInterface &t_solver_interface)
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/external-mip/index.html b/api/classes/external-mip/index.html new file mode 100644 index 00000000..89e7b52a --- /dev/null +++ b/api/classes/external-mip/index.html @@ -0,0 +1,190 @@ + + + + + + + + + External MIP solvers — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/index.html b/api/classes/index.html new file mode 100644 index 00000000..54b0468b --- /dev/null +++ b/api/classes/index.html @@ -0,0 +1,229 @@ + + + + + + + + + Class List — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Class List

+
+

Mixed-Integer Programming

+
+ +
+
+
+

Bilevel Optimization

+
+ +
+
+
+

Robust Optimization

+
+ +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/universal-callbacks/Callback.html b/api/classes/universal-callbacks/Callback.html new file mode 100644 index 00000000..47b48c2f --- /dev/null +++ b/api/classes/universal-callbacks/Callback.html @@ -0,0 +1,337 @@ + + + + + + + + + Callback — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Callback

+

Callbacks are user-defined functions which are invoked during the execution of branch-and-bound algorithms at certain +key steps of the procedure. These key points are called events in idol. +Callback functions can be used to monitor the algorithm’s progress, control its behavior, or perform additional processing.

+
+

UserCutCallback and LazyCutCallback

+

A common use of callbacks is to implement custom cut generation procedures at each node of the branch-and-bound tree. Note that +idol offers out-of-the-box callback routines for cut separation. Namely, UserCutCallback +and LazyCutCallback. Yet, it is totally possible to define your own cut generation procedure +by using the Callback::add_user_cut and Callback::add_lazy_cut methods inside your callback.

+

In a nutshell, UserCutCallback (or, similarly, Callback::add_user_cut) are intended for +cuts which have a potential to improve the quality of the relaxation but whose presence in the model is not necessary for the +branch-and-bound to converge to an optimal solution. UserCutCallback callbacks are called when +a non-valid solution has been found.

+

LazyCutCallback (or, similarly, Callback::add_lazy_cut), +instead, are intended to generate constraints which should be part of the model but which were omitted in the model for some reason. +A typical reason for omitting constraints in a model’s definition is the large number of such constraints. +LazyCutCallback callbacks are called when a valid solution has been found.

+
+
+

Creating your own callback

+

If you want to create your own callback, you should first create a new class which inherits the Callback class and +overrides the operator()(CallbackEvent) method.

+

Note that callbacks cannot be given “as-is” to an optimizer but must be passed through a CallbackFactory. A callback +factory is a class whose role is to create a new callback object whenever it is needed. Every callback factories must be +a child (or little child) of the CallbackFactory class.

+
+

Example

+

We now give an example of callback which prints out every valid solution found.

+
class MyCallback : public CallbackFactory {
+public:
+
+    class Strategy { // Real callback implementation
+    protected:
+        void operator()(CallbackEvent t_event) {
+
+               if (t_event != IncumbentSolution) {
+                    return;
+               }
+
+               std::cout << primal_solution() << std::endl;
+        }
+    }
+
+    Callback* operator()() { // Creates a new callback object
+        return new Strategy();
+    }
+
+    CallbackFactory* clone() const { // Creates a copy of the callback factory
+        return new MyCallback(*this);
+    }
+
+}
+
+
+

Later, it is then possible to use this callback in compliant optimizers like Gurobi or BranchAndBound.

+
model.use(
+    Gurobi().with_callback(MyCallback();
+);
+
+model.optimize();
+
+
+
+
+

Hint

+

Note that there also exists more advanced callbacks which are specific to idol’s branch-and-bound implementation. +For these advanced feature, see BranchAndBoundCallback.

+
+
+
+class Callback
+

Subclassed by idol::Heuristics::LocalBranching::Strategy, idol::Heuristics::RENS::Strategy, idol::Heuristics::SimpleRounding::Strategy, idol::impl::CutSeparation

+
+

Public Functions

+
+
+virtual ~Callback() = default
+
+ +
+
+

Protected Functions

+
+
+virtual const Model &original_model() const
+

Accesses the original model given to the solver

+
+ +
+
+virtual void submit_heuristic_solution(const Solution::Primal &t_solution)
+

Submits a new heuristic solution

+
+ +
+
+virtual void add_user_cut(const TempCtr &t_cut)
+

Adds a user cut to the relaxation

+
+
Parameters:
+

t_cut – the cut to be added

+
+
+
+ +
+
+virtual void add_lazy_cut(const TempCtr &t_cut)
+

Adds a lazy cut to the relaxation

+
+
Parameters:
+

t_cut – the cut to be added

+
+
+
+ +
+
+virtual Solution::Primal primal_solution() const
+

Returns the solution of the node which is currently being explored (when available)

+
+
Returns:
+

the solution of the current node

+
+
+
+ +
+
+const Timer &time() const
+

Returns the current time

+
+
Returns:
+

the optimizer’s time

+
+
+
+ +
+
+double best_obj() const
+

Returns the best objective value found so far

+
+
Returns:
+

the best objective value

+
+
+
+ +
+
+double best_bound() const
+

Returns the best bound found so far

+
+
Returns:
+

the best bound

+
+
+
+ +
+
+void terminate()
+

Asks the optimizer to terminate the optimization process as soon as possible

+
+ +
+
+virtual void operator()(CallbackEvent t_event) = 0
+

This method is left for the user to write and consists in the main execution block of the callback.

+
+
Parameters:
+

t_event – the event which triggered the call

+
+
+
+ +
+
+

Friends

+
+
+friend class ::idol::CallbackI
+
+ +
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/universal-callbacks/CallbackFactory.html b/api/classes/universal-callbacks/CallbackFactory.html new file mode 100644 index 00000000..6dd43f75 --- /dev/null +++ b/api/classes/universal-callbacks/CallbackFactory.html @@ -0,0 +1,172 @@ + + + + + + + + + CallbackFactory — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

CallbackFactory

+
+
+class CallbackFactory
+

Subclassed by idol::Heuristics::LocalBranching, idol::Heuristics::RENS, idol::Heuristics::SimpleRounding, idol::LazyCutCallback, idol::UserCutCallback

+
+

Public Functions

+
+
+virtual ~CallbackFactory() = default
+
+ +
+
+virtual Callback *operator()() = 0
+
+ +
+
+virtual CallbackFactory *clone() const = 0
+
+ +
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/universal-callbacks/LazyCutCallback.html b/api/classes/universal-callbacks/LazyCutCallback.html new file mode 100644 index 00000000..82bc90be --- /dev/null +++ b/api/classes/universal-callbacks/LazyCutCallback.html @@ -0,0 +1,228 @@ + + + + + + + + + LazyCutCallback — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

LazyCutCallback

+

Lazy cuts are essentially constraints which are part of an optimization model but which have been omitted in the current +definition of the model. Lazy cuts typically arise in models with a large number of constraints. Instead of enumerating +then all, it may be judicious to omit some of them to get a smaller optimization model. Then, the returned solution is +checked for feasibility against the whole set of constraints. If a violated constraint is identified, we add the +constraint to the model and resolve. If not, then all the constraints are satisfied and the solution is optimal for the +original model in which all constraints materializes.

+

The LazyCutCallback can be used to implement lazy cut constraint generation on the fly, during the optimization process.

+

Consider the following optimization problem:

+
+\[\begin{split}\begin{align} + \min_x \ & c^\top x \\ + \text{s.t.} \ & x\in X, \\ + & \xi^\top x \le \xi_0 \quad (\xi_0,\xi) \in \Xi, +\end{align}\end{split}\]
+

in which assume that \(|\Xi|\) is large (potentially infinite). The idea is to start by solving the following +relaxed master problem in which constraints associated to \((\xi_0,\xi)\) have been omitted.

+
+\[\begin{split}\begin{align} + \min_x \ & c^\top x \\ + \text{s.t.} \ & x\in X. +\end{align}\end{split}\]
+

If we assume that this problem is feasible and bounded, we can denote by \(x^*\) a solution of this problem. Then, +we search for a violated constraint “\(\xi^\top x \le \xi_0\)” for some \((\xi_0,\xi)\in\Xi\). Observe that +a constraint is violated if, and only if, the following holds:

+
+\[\left(\exists(\xi_0,\xi)\in\Xi, \ \xi^\top x^* > \xi_0\right) +\Leftrightarrow +\left(\max_{(\xi_0,\xi)\in\Xi} \xi_0 - \xi^\top x^* < 0\right).\]
+

Thus, the LazyCutCallback automatically solve the optimization problem on the right handside and checks for its value. +A new constraint is added if, and only if,

+
+\[\max_{\xi\in\Xi} \xi_0 - \xi^\top x^* < -\varepsilon,\]
+

with \(\varepsilon\) a given tolerance (by default, Tolerance::Feasibility).

+
+

Hint

+

You may also be interested by a tutorial showing how you can implement a simple Benders Decomposition using lazy +cuts. See Benders Decomposition tutorial.

+
+
+
+class LazyCutCallback : public idol::CallbackFactory
+
+

Public Functions

+
+
+inline LazyCutCallback(const Model &t_model, TempCtr t_cut)
+
+ +
+
+inline virtual Callback *operator()() override
+
+ +
+
+inline virtual CallbackFactory *clone() const override
+
+ +
+
+inline LazyCutCallback &with_separation_optimizer(const OptimizerFactory &t_optimizer_factory)
+
+ +
+
+
+class Strategy : public idol::impl::CutSeparation
+
+

Public Functions

+
+
+inline explicit Strategy(Model *t_separation_problem, TempCtr t_cut)
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/universal-callbacks/UserCutCallback.html b/api/classes/universal-callbacks/UserCutCallback.html new file mode 100644 index 00000000..a09802d2 --- /dev/null +++ b/api/classes/universal-callbacks/UserCutCallback.html @@ -0,0 +1,189 @@ + + + + + + + + + UserCutCallback — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

UserCutCallback

+
+
+class UserCutCallback : public idol::CallbackFactory
+
+

Public Functions

+
+
+inline UserCutCallback(const Model &t_model, TempCtr t_cut)
+
+ +
+
+inline virtual Callback *operator()() override
+
+ +
+
+inline virtual CallbackFactory *clone() const override
+
+ +
+
+inline UserCutCallback &with_separation_optimizer(const OptimizerFactory &t_optimizer_factory)
+
+ +
+
+
+class Strategy : public idol::impl::CutSeparation
+
+

Public Functions

+
+
+inline explicit Strategy(Model *t_separation_problem, TempCtr t_cut)
+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/classes/universal-callbacks/index.html b/api/classes/universal-callbacks/index.html new file mode 100644 index 00000000..e56be58c --- /dev/null +++ b/api/classes/universal-callbacks/index.html @@ -0,0 +1,156 @@ + + + + + + + + + Universal Callbacks — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Universal Callbacks

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/constants/index.html b/api/constants/index.html new file mode 100644 index 00000000..ed73a18a --- /dev/null +++ b/api/constants/index.html @@ -0,0 +1,143 @@ + + + + + + + + + Constants — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Constants

+
+

Common

+
+ +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/constants/tolerances.html b/api/constants/tolerances.html new file mode 100644 index 00000000..dc3bb96c --- /dev/null +++ b/api/constants/tolerances.html @@ -0,0 +1,271 @@ + + + + + + + + + Tolerances and Numerics — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Tolerances and Numerics

+
+
+namespace idol
+
+

Functions

+
+
+static bool is_pos_inf(double t_value)
+
+ +
+
+static bool is_neg_inf(double t_value)
+
+ +
+
+static bool is_inf(double t_value)
+
+ +
+
+static bool equals(double t_a, double t_b, double t_tolerance)
+
+ +
+
+static double relative_gap(double t_lb, double t_ub)
+
+ +
+
+static double absolute_gap(double t_lb, double t_ub)
+
+ +
+
+static bool is_integer(double t_value, double t_tolerance)
+
+ +
+
+static double round(double t_value, unsigned int t_n_digits = 0)
+
+ +
+
+static double multiply_with_precision(double t_a, double t_b, unsigned int t_n_digits)
+
+ +
+
+static double multiply_with_precision_by_power_of_10(double t_x, unsigned int t_exponent, unsigned int t_n_digits)
+
+ +
+
+

Variables

+
+
+static constexpr double Inf = 1e20
+
+ +
+
+
+namespace Tolerance
+

Stores the default high-level tolerances used in idol.

+

It is possible for external-mip to have additional tolerance parameters, yet, the tolerances defined in this namespace should always be taken into account by the optimizer. Apart from the Sparsity tolerance, users can also change tolerance values at a local level (i.e., at an optimizer level) rather than at a global level.

+
+

Variables

+
+
+static double Sparsity = 1e-8
+

Default: \( 10^{-8} \)

+

Recommended range: \( [ 10^{-10}, 10^{-5} ] \)

+

This tolerance is used when data is saved in a sparse manner. For instance, when a value close to zero should be stored or ignored when saving a primal point.

+
+ +
+
+static unsigned int Digits = 8
+
+ +
+
+static double MIPRelativeGap = 1e-4
+

Default: \( 10^{-4} \)

+

Recommended range: \( [ 0, +\infty ] \)

+

Used to declare optimality of a MIP solution by comparing with the current relative gap.

+

The relative gap is computed as follows:

+\[ RelativeGap := \frac{ |UB - LB| }{ 10^{-10} + |UB| }. \]
+

+
+ +
+
+static double MIPAbsoluteGap = 1e-5
+

Default: \( 10^{-5} \)

+

Recommended range: \( [ 0, \infty ] \)

+

Used to declare optimality of a MIP solution by comparing with the current absolute gap.

+

The absolute gap is computed as follows:

+\[ AbsoluteGap := |UB - LB| \]
+

+
+ +
+
+static double Integer = 10e-5
+

Default: \( 10^{-5} \)

+

Recommended range: \( [ 10^{-9}, 10^{-1} ] \)

+

Used to recognized integer values, i.e., a given value is considered integer when the closest integer point is closer than this tolerance.

+
+ +
+
+static double Feasibility = 10e-6
+

Default: \( 10^{-6} \)

+

Recommended range: \( [ 10^{-9}, 10^{-2} ] \)

+

Used to characterized constraint satisfaction, i.e., a constraint is satisfied if it is not violated by a larger amount than this tolerance.

+
+ +
+
+static double Optimality = 10e-6
+

Default: \( 10^{-6} \)

+

Recommended range: \( [ 10^{-9}, 10^{-2} ] \)

+

Used to characterize optimality, i.e., all reduced costs must be smaller than this tolerance.

+
+ +
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/index.html b/api/index.html new file mode 100644 index 00000000..d2c8178a --- /dev/null +++ b/api/index.html @@ -0,0 +1,138 @@ + + + + + + + + + API — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

API

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/types/common/CtrType.html b/api/types/common/CtrType.html new file mode 100644 index 00000000..fc9ec53a --- /dev/null +++ b/api/types/common/CtrType.html @@ -0,0 +1,162 @@ + + + + + + + + + CtrType — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

CtrType

+
+
+enum idol::CtrType
+

Values:

+
+
+enumerator LessOrEqual
+
+ +
+
+enumerator GreaterOrEqual
+
+ +
+
+enumerator Equal
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/types/common/VarType.html b/api/types/common/VarType.html new file mode 100644 index 00000000..aab0d9d7 --- /dev/null +++ b/api/types/common/VarType.html @@ -0,0 +1,162 @@ + + + + + + + + + VarType — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

VarType

+
+
+enum idol::VarType
+

Values:

+
+
+enumerator Continuous
+
+ +
+
+enumerator Integer
+
+ +
+
+enumerator Binary
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/types/common/index.html b/api/types/common/index.html new file mode 100644 index 00000000..0300b946 --- /dev/null +++ b/api/types/common/index.html @@ -0,0 +1,146 @@ + + + + + + + + + Common — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Common

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/types/index.html b/api/types/index.html new file mode 100644 index 00000000..4f30546c --- /dev/null +++ b/api/types/index.html @@ -0,0 +1,151 @@ + + + + + + + + + Type List — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Type List

+
+

Mixed-Integer Programming

+
+ +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/types/universal-callbacks/CallbackEvent.html b/api/types/universal-callbacks/CallbackEvent.html new file mode 100644 index 00000000..f9b90c2d --- /dev/null +++ b/api/types/universal-callbacks/CallbackEvent.html @@ -0,0 +1,171 @@ + + + + + + + + + Events — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Events

+
+
+enum idol::CallbackEvent
+

CallbackEvent is an enumerated type used to indicate events during a branch-and-bound tree execution.

+

It is typically used inside of a Callback to influence or modify the behaviour of the algorithm at execution time.

+

Values:

+
+
+enumerator NodeLoaded
+

Occurs when a node is about to be solved

+
+ +
+
+enumerator IncumbentSolution
+

Occurs when an incumbent solution has been found

+
+ +
+
+enumerator InvalidSolution
+

Occurs when a solution of the relaxation is not valid (e.g., not integer)

+
+ +
+
+enumerator PrunedSolution
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api/types/universal-callbacks/index.html b/api/types/universal-callbacks/index.html new file mode 100644 index 00000000..ac7387b8 --- /dev/null +++ b/api/types/universal-callbacks/index.html @@ -0,0 +1,144 @@ + + + + + + + + + Universal Callbacks — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Universal Callbacks

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/benchmark/index.html b/benchmark/index.html new file mode 100644 index 00000000..90a5088f --- /dev/null +++ b/benchmark/index.html @@ -0,0 +1,131 @@ + + + + + + + + + Benchmark — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Benchmark

+ +https://raw.githubusercontent.com/hlefebvr/idol-benchmark-gap/gh-pages/profile.png +

This is a performance profile computed according to [6].

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/developer_guide/build-the-doc.html b/developer_guide/build-the-doc.html new file mode 100644 index 00000000..e0d06805 --- /dev/null +++ b/developer_guide/build-the-doc.html @@ -0,0 +1,175 @@ + + + + + + + + + Build the doc! — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Build the doc!

+

This page explains how to build the documentation on a local machine.

+
+

Dependencies

+
+

Description

+

The documentation is built using doxygen and a bunch of other tools, in particular:

+
    +

  • doxygen the C++ documentation generator;

    • +

  • Sphinx the python documentation generator;

    • +

  • sphinx-rtd-theme a Sphinx theme used to render the +HTML version of the documentation;

    • +

  • sphinx-sitemap a Sphinx extension +to generate multi-version and multi-language sitemaps.org compliant sitemaps for the HTML version +of a Sphinx documentation;

    • +

  • sphinx-copybutton a Sphinx extension which +automatically adds buttons on code-blocks environments to copy its content;

    • +

  • Breathe the Sphinx plugin for integrating doxygen outputs into Sphinx.

    • +
+

For a detailed description on how these tools interact, please refer to this article.

+
+
+

Installation guide (Ubuntu)

+
sudo apt-get install doxygen # installs the last version of doxygen
+
+sudo apt-get install python3-sphinx # installs the last version of Sphinx
+
+sudo apt-get install python3-breathe # installs the last version of Breathe
+
+pip3 install sphinx-sitemap sphinx-rtd-theme sphinx-copybutton # installs theme and extensions
+
+
+
+
+
+

Build instructions

+

By default, CMake automatically creates a doc target. All you have to do to build the documentation website +is to build this target. For instance, inside a build directory, the following command can be used.

+
cmake .. && make doc
+
+
+

Note that you do not need to use make and use the following generic command.

+
cmake --build . --target doc
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/developer_guide/index.html b/developer_guide/index.html new file mode 100644 index 00000000..4e25f57a --- /dev/null +++ b/developer_guide/index.html @@ -0,0 +1,144 @@ + + + + + + + + + Developer guide — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/developer_guide/tests.html b/developer_guide/tests.html new file mode 100644 index 00000000..904ad303 --- /dev/null +++ b/developer_guide/tests.html @@ -0,0 +1,196 @@ + + + + + + + + + Unit and integration tests — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Unit and integration tests

+

This page explains how to build the unit and integration tests on a local machine.

+
+

Dependencies

+

Tests are managed by the C++ unit testing framework Catch2.

+

When CMake is told to create test targets, it will automatically look for an installation of Catch2 on your +machine. If it cannot be found, CMake will attempt to download Catch2 from its GitHub repository.

+
+

Installation guide (Ubuntu)

+
git clone https://github.com/catchorg/Catch2.git # Clone Catch2 repository
+
+cd Catch2 # go inside the new directory
+
+cmake -Bbuild -H. -DBUILD_TESTING=OFF # Create install target
+
+sudo cmake --build build/ --target install # Build and install
+
+
+
+
+
+

Build instructions

+

By default, CMake will not create test targets nor look for Catch2. To tell CMake to generate them, you +need to set the CMake option BUILD_TESTS to YES.

+
+

Example

+

The following will tell CMake to create targets for tests.

+
cmake -DBUILD_TESTS=YES ..
+
+
+

For instance, we can now build all the existing tests

+
make tests
+
+
+

and run them.

+
./tests/tests
+
+
+
+
+
+

Code coverage options

+

Code coverage is done via gcov.

+

If you want to active code coverage options, you shall set the CMake option WITH_TEST_COVERAGE to YES. +Note that this only has an effect when BUILD_TESTS is also set to YES.

+
+

Example

+

The following command will tell CMake to generate targets for tests with code coverage options.

+
cmake -DBUILD_TESTS=YES -DWITH_CODE_COVERAGE=YES ..
+
+
+

Then, you should build and run the tests.

+
make tests && ./tests/tests
+
+
+

Once you are there, you may generate a report to a file called coverage.info by using gcov.

+
lcov --directory . --capture --output-file coverage.info
+
+
+
+

Also note that this forces g++ to be used for compiling.

+
+

Tip

+

Code coverage reports are also automatically generated at every push action on idol’s GitHub repository and +are available on codecov.

+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/examples/bilevel-kkt.html b/examples/bilevel-kkt.html new file mode 100644 index 00000000..75adfcf5 --- /dev/null +++ b/examples/bilevel-kkt.html @@ -0,0 +1,234 @@ + + + + + + + + + Bilevel LP-LP (KKT Reformulation) — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Bilevel LP-LP (KKT Reformulation)

+
+

Problem Definition

+

This example is taken from [8] and is an LP-LP bilevel problem.

+

The problem is formulated as follows:

+
+\[\begin{split}\begin{align} + \min_{x, y} \ & x + 6 y \\ + \text{s.t.} \ & -x + 5y \le 12.5 \\ + & x \ge 0 \\ + & y\in + \begin{array}[t]{l} + \displaystyle \underset{y}{\text{arg min}} \ & -y \\ + \text{s.t.} \ & 2 x - y \ge 0, \\ + & -x - y \ge -6, \\ + & -x + 6 y \ge -3, \\ + & x + 3 y \ge 3. + \end{array} +\end{align}\end{split}\]
+
+
+

KKT Reformulation with idol

+

In this example, we show how to model this LP-LP bilevel problem and how to derive its KKT reformulation.

+
//
+// Created by henri on 30.08.24.
+//
+#include <iostream>
+#include <Research/idol/lib/include/idol/modeling.h>
+#include "idol/optimizers/bilevel-optimization/wrappers/MibS/MibS.h"
+#include "idol/modeling/bilevel-optimization/LowerLevelDescription.h"
+#include "idol/optimizers/mixed-integer-optimization/wrappers/Gurobi/Gurobi.h"
+#include "idol/modeling/bilevel-optimization/read_from_file.h"
+#include "idol/modeling/models/KKT.h"
+
+using namespace idol;
+
+int main(int t_argc, const char** t_argv) {
+
+    /*
+        This example is taken from Kleinert, T. (2021). “Algorithms for Mixed-Integer Bilevel Problems with Convex Followers.” PhD thesis.
+        min x + 6 y
+        s.t.
+
+        -x + 5 y <= 12.5,
+
+        y in argmin { -y :
+            2 x - y >= 0,
+            -x - y >= -6,
+            -x + 6 y >= -3,
+            x + 3 y >= 3.
+        }
+
+        x >= 0.
+
+     */
+
+    Env env;
+
+    // Define High Point Relaxation
+    Model high_point_relaxation(env);
+
+    auto x = high_point_relaxation.add_var(0, Inf, Continuous, "x");
+    auto y = high_point_relaxation.add_var(-Inf, Inf, Continuous, "y");
+
+    high_point_relaxation.set_obj_expr(x + 6 * y);
+    auto follower_c1 = high_point_relaxation.add_ctr(2 * x - y >= 0, "f1");
+    auto follower_c2 = high_point_relaxation.add_ctr(-x - y >= -6, "f2");
+    auto follower_c3 = high_point_relaxation.add_ctr(-x + 6 * y >= -3, "f3");
+    auto follower_c4 = high_point_relaxation.add_ctr(x + 3 * y >= 3, "f4");
+    high_point_relaxation.add_ctr(-x + 5 * y <= 12.5);
+
+    // Prepare bilevel description
+    Bilevel::LowerLevelDescription description(env);
+    description.set_follower_obj_expr(- y);
+    description.make_follower_var(y);
+    description.make_follower_ctr(follower_c1);
+    description.make_follower_ctr(follower_c2);
+    description.make_follower_ctr(follower_c3);
+    description.make_follower_ctr(follower_c4);
+
+    Reformulators::KKT reformulator(high_point_relaxation, description);
+
+    Model single_level(env);
+    reformulator.add_kkt_reformulation(single_level);
+
+    std::cout << high_point_relaxation << std::endl;
+    std::cout << single_level << std::endl;
+
+    single_level.use(Gurobi());
+
+    single_level.optimize();
+
+    std::cout << save_primal(single_level) << std::endl;
+
+    return 0;
+}
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/examples/bilevel-mibs.html b/examples/bilevel-mibs.html new file mode 100644 index 00000000..ecc2db30 --- /dev/null +++ b/examples/bilevel-mibs.html @@ -0,0 +1,231 @@ + + + + + + + + + Bilevel MILP-MILP (MibS) — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Bilevel MILP-MILP (MibS)

+
+

Problem Definition

+

This example is taken from [10] and is a bilevel problem where the upper level is a mixed-integer linear program (MILP) and the lower level is a mixed-integer linear program (MILP).

+

The problem is formulated as follows:

+
+\[\begin{split}\begin{align} + \min_{x, y} \ & -x + -10 y \\ + \text{s.t.} \ & x \in \mathbb Z_+ \\ + & y\in + \begin{array}[t]{l} + \displaystyle \underset{y}{\text{arg min}} \ & y \\ + \text{s.t.} \ & -25 x + 20 y \leq 30, \\ + & x + 2 y \leq 10, \\ + & 2 x - y \leq 15, \\ + & 2 x + 10 y \geq 15, \\ + & y \geq 0, \\ + & y \in \mathbb Z_+. + \end{array} +\end{align}\end{split}\]
+
+
+

Implementation with idol

+

In this example, we show how to model this MILP-MILP bilevel problem and how to solve it using the MibS solver.

+
//
+// Created by henri on 07.02.24.
+//
+#include <iostream>
+#include <Research/idol/lib/include/idol/modeling.h>
+#include <OsiClpSolverInterface.hpp>
+#include "idol/optimizers/bilevel-optimization/wrappers/MibS/MibS.h"
+#include "idol/modeling/bilevel-optimization/LowerLevelDescription.h"
+#include "idol/optimizers/mixed-integer-optimization/wrappers/Gurobi/Gurobi.h"
+#include "idol/modeling/bilevel-optimization/read_from_file.h"
+
+using namespace idol;
+
+int main(int t_argc, const char** t_argv) {
+
+    /*
+     This example is taken from "The Mixed Integer Linear Bilevel Programming Problem" (Moore and Bard, 1990).
+
+        min -1 x + -10 y
+        s.t.
+
+        y in argmin { y :
+            -25 x + 20 y <= 30,
+            1 x + 2 y <= 10,
+            2 x + -1 y <= 15,
+            2 x + 10 y >= 15,
+            y >= 0 and integer.
+        }
+        x >= 0 and integer.
+
+     */
+
+    Env env;
+
+    // Define High Point Relaxation
+    Model high_point_relaxation(env);
+
+    auto x = high_point_relaxation.add_var(0, Inf, Integer, "x");
+    auto y = high_point_relaxation.add_var(0, Inf, Integer, "y");
+
+    high_point_relaxation.set_obj_expr(-x - 10 * y);
+    auto follower_c1 = high_point_relaxation.add_ctr(-25 * x + 20 * y <= 30);
+    auto follower_c2 = high_point_relaxation.add_ctr(x + 2 * y <= 10);
+    auto follower_c3 = high_point_relaxation.add_ctr(2 * x - y <= 15);
+    auto follower_c4 = high_point_relaxation.add_ctr(2 * x + 10 * y >= 15);
+
+    // Prepare bilevel description
+    Bilevel::LowerLevelDescription description(env);
+    description.set_follower_obj_expr(y);
+    description.make_follower_var(y);
+    description.make_follower_ctr(follower_c1);
+    description.make_follower_ctr(follower_c2);
+    description.make_follower_ctr(follower_c3);
+    description.make_follower_ctr(follower_c4);
+
+    // Use coin-or/MibS as external solver
+    high_point_relaxation.use(
+                Bilevel::MibS(description)
+                    .with_osi_interface(OsiClpSolverInterface())
+                    .with_logs(true)
+    );
+
+    // Optimize and print solution
+    high_point_relaxation.optimize();
+
+    std::cout << save_primal(high_point_relaxation) << std::endl;
+
+    return 0;
+}
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/examples/facility-location-problem.html b/examples/facility-location-problem.html new file mode 100644 index 00000000..66fd47af --- /dev/null +++ b/examples/facility-location-problem.html @@ -0,0 +1,233 @@ + + + + + + + + + Facility Location Problem (Branch-and-Bound) — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Facility Location Problem (Branch-and-Bound)

+
+

Problem Definition

+

We consider the capacitated Facility Location Problem (FLP). +Given a set of potential facility locations \(V_1\) and a set of customers \(V_2\), the goal is to select +a subset of facility location to activate in order to serve all customers’ demand, while minimizing the total cost.

+

Each facility \(i\in V_1\) has an opening cost \(f_i\) and a maximum capacity \(q_i\). +Each customer \(j\in V_2\) has a demand \(d_j\). +The unitary cost for serving customer \(j\in V_2\) from facility \(i\in V_1\) is \(t_{ij}\).

+

We model the capacitated FLP with the MILP

+
+\[\begin{split}\begin{align*} + \min_{x,y} \ & \sum_{i\in V_1} f_i x_i + \sum_{i\in V_1} \sum_{j\in V_2} t_{ij} y_{ij} \\ + \text{s.t.} & \sum_{j\in V_2} d_j y_{ij} \le q_i && i\in V_1 \\ + & \sum_{i\in V_1} y_{ij} = 1 && j\in V_2 \\ + & y_{ij} \le x_i && i\in V_1, j\in V_2 \\ + & x_i \in \{0,1\} && i\in V_1 \\ + & y_{ij} \in \{0,1\} && i\in V_1, j\in V_2. +\end{align*}\end{split}\]
+
+
+

Implementation with idol

+

In this example, we show how to model the capacitated FLP with idol and how to solve it using a “hand-crafted” +branch-and-bound algorithm.

+
//
+// Created by henri on 06/04/23.
+//
+#include <iostream>
+#include "idol/modeling.h"
+#include "idol/problems/facility-location-problem/FLP_Instance.h"
+#include "idol/optimizers/mixed-integer-optimization/branch-and-bound/BranchAndBound.h"
+#include "idol/optimizers/mixed-integer-optimization/branch-and-bound/branching-rules/factories/PseudoCost.h"
+#include "idol/optimizers/mixed-integer-optimization/branch-and-bound/node-selection-rules/factories/BestEstimate.h"
+#include "idol/optimizers/mixed-integer-optimization/wrappers/HiGHS/HiGHS.h"
+#include "idol/optimizers/mixed-integer-optimization/callbacks/cutting-planes/KnapsackCover.h"
+#include "idol/optimizers/mixed-integer-optimization/wrappers/GLPK/GLPK.h"
+#include "idol/optimizers/mixed-integer-optimization/wrappers/Gurobi/Gurobi.h"
+
+using namespace idol;
+
+int main(int t_argc, const char** t_argv) {
+
+    Env env;
+
+    // Read instance
+    const auto instance = Problems::FLP::read_instance_1991_Cornuejols_et_al("robust_ccg.data.txt");
+    const unsigned int n_customers = instance.n_customers();
+    const unsigned int n_facilities = instance.n_facilities();
+
+    // Make model
+
+    Model model(env);
+
+    auto x = model.add_vars(Dim<1>(n_facilities), 0., 1., Binary, "x");
+    auto y = model.add_vars(Dim<2>(n_facilities, n_customers), 0., 1., Binary, "y");
+
+    for (unsigned int i = 0 ; i < n_facilities ; ++i) {
+        model.add_ctr(idol_Sum(j, Range(n_customers), instance.demand(j) * y[i][j]) <= instance.capacity(i));
+    }
+
+    for (unsigned int j = 0 ; j < n_customers ; ++j) {
+        model.add_ctr(idol_Sum(i, Range(n_facilities), y[i][j]) == 1);
+    }
+
+    for (unsigned int i = 0 ; i < n_facilities ; ++i) {
+        for (unsigned int j = 0 ; j < n_customers ; ++j) {
+            model.add_ctr(y[i][j] <= x[i]);
+        }
+    }
+
+    model.set_obj_expr(idol_Sum(i, Range(n_facilities),
+                                instance.fixed_cost(i) * x[i]
+                                + idol_Sum(j, Range(n_customers),
+                                             instance.per_unit_transportation_cost(i, j) *
+                                             instance.demand(j) *
+                                             y[i][j]
+                                         )
+                                 )
+                         );
+
+    // Set backend options
+    model.use(
+            BranchAndBound()
+                    .with_node_optimizer(Gurobi::ContinuousRelaxation())
+                    .add_callback(Cuts::KnapsackCover())
+                    .with_branching_rule(PseudoCost())
+                    .with_node_selection_rule(BestEstimate())
+    );
+
+    model.optimize();
+
+    std::cout << save_primal(model) << std::endl;
+
+    return 0;
+}
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/examples/generalized-assignment-problem-bap.html b/examples/generalized-assignment-problem-bap.html new file mode 100644 index 00000000..47ec60ed --- /dev/null +++ b/examples/generalized-assignment-problem-bap.html @@ -0,0 +1,253 @@ + + + + + + + + + Generalized Assignment Problem (Branch-and-Price) — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Generalized Assignment Problem (Branch-and-Price)

+
+

Problem Definition

+

We consider the Generalized Assignment Problem (GAP). +Given a set of \(m\) agents and \(n\) jobs, the goal is to assign each job to exactly one agent in such a +way that the total cost is minimized, while respecting the capacity constraints of each agent.

+

Each agent \(i\in\{1,\dotsc,m\}\) has a capacity \(C_i\). +Each job \(j\in\{1,\dotsc,n\}\) has a resource consumption \(r_{ij}\) and a cost \(c_{ij}\) when assigned to agent \(i\).

+

We model the GAP with the following binary linear program:

+
+\[\begin{split}\begin{align*} + \min_{x} \ & \sum_{i=1}^m \sum_{j=1}^n c_{ij} x_{ij} \\ + \text{s.t.} & \sum_{j=1}^n r_{ij} x_{ij} \le C_i && i=1,\dotsc,m \\ + & \sum_{i=1}^m x_{ij} = 1 && j=1,\dotsc,n \\ + & x_{ij} \in \{0,1\} && i=1,\dotsc,m, j=1,\dotsc,n. +\end{align*}\end{split}\]
+
+
+

Decomposition

+

In this example, we use Dantzig-Wolfe decomposition to break down the problem into a master problem and subproblems. The master problem coordinates the assignment of jobs to agents, while the subproblems handle the capacity constraints for each agent individually.

+
    +

  1. Master Problem: The master problem is responsible for ensuring that each job is assigned to exactly one agent. It maintains the overall objective of minimizing the total cost.

    2. +

  2. Subproblems: Each subproblem corresponds to an agent and ensures that the agent’s capacity constraints are respected. The subproblems are solved independently and their solutions are used to update the master problem.

    3. +
+
+
+

Implementation with idol

+

In this example, we show how to model the Generalized Assignment Problem with idol and how to solve it using a +Dantzig-Wolfe decomposition within a branch-and-bound framework, i.e., a branch-and-price algorithm.

+
//
+// Created by henri on 13/03/23.
+//
+#include <iostream>
+#include "idol/modeling.h"
+#include "idol/problems/generalized-assignment-problem/GAP_Instance.h"
+#include "idol/optimizers/mixed-integer-optimization/branch-and-bound/node-selection-rules/factories/WorstBound.h"
+#include "idol/optimizers/mixed-integer-optimization/branch-and-bound/BranchAndBound.h"
+#include "idol/optimizers/mixed-integer-optimization/callbacks/heuristics/IntegerMaster.h"
+#include "idol/optimizers/mixed-integer-optimization/branch-and-bound/branching-rules/factories/MostInfeasible.h"
+#include "idol/optimizers/mixed-integer-optimization/dantzig-wolfe/DantzigWolfeDecomposition.h"
+#include "idol/optimizers/mixed-integer-optimization/dantzig-wolfe/infeasibility-strategies/FarkasPricing.h"
+#include "idol/optimizers/mixed-integer-optimization/dantzig-wolfe/stabilization/Neame.h"
+#include "idol/optimizers/mixed-integer-optimization/dantzig-wolfe/logs/Info.h"
+#include "idol/optimizers/mixed-integer-optimization/wrappers/GLPK/GLPK.h"
+
+using namespace idol;
+
+int main(int t_argc, const char** t_argv) {
+
+    const auto instance = Problems::GAP::read_instance("assignment-bap.data.txt");
+
+    const unsigned int n_agents = instance.n_agents();
+    const unsigned int n_jobs = instance.n_jobs();
+
+    // Create optimization environment
+    Env env;
+
+    // Create model
+    Model model(env);
+
+    // Create decomposition annotation
+    Annotation<Ctr> decomposition(env, "decomposition", MasterId);
+
+    // Create assignment variables (x_ij binaries)
+    auto x = model.add_vars(Dim<2>(n_agents, n_jobs), 0., 1., Binary, "x");
+
+    // Create knapsack constraints (i.e., capacity constraints)
+    for (unsigned int i = 0 ; i < n_agents ; ++i) {
+        auto capacity = model.add_ctr(idol_Sum(j, Range(n_jobs), instance.resource_consumption(i, j) * x[i][j]) <= instance.capacity(i), "capacity_" + std::to_string(i));
+
+        capacity.set(decomposition, i); // Assign constraint to i-th subproblem
+    }
+
+    // Create assignment constraints
+    for (unsigned int j = 0 ; j < n_jobs ; ++j) {
+        model.add_ctr(idol_Sum(i, Range(n_agents), x[i][j]) == 1, "assignment_" + std::to_string(j));
+    }
+
+    // Set the objective function
+    model.set_obj_expr(idol_Sum(i, Range(n_agents), idol_Sum(j, Range(n_jobs), instance.cost(i, j) * x[i][j])));
+
+    // Build algorithms
+    const auto column_generation = DantzigWolfeDecomposition(decomposition)
+            .with_master_optimizer(GLPK::ContinuousRelaxation().with_logs(false))
+            .with_default_sub_problem_spec(
+                    DantzigWolfe::SubProblem()
+                            .add_optimizer(GLPK().with_logs(false))
+                            .with_column_pool_clean_up(1500, .75)
+            )
+            .with_logger(Logs::DantzigWolfe::Info().with_frequency_in_seconds(.00000001))
+            .with_dual_price_smoothing_stabilization(DantzigWolfe::Neame(.3))
+            .with_infeasibility_strategy(DantzigWolfe::FarkasPricing())
+            .with_hard_branching(true)
+            .with_logs(true);
+
+    const auto branch_and_bound = BranchAndBound()
+            .with_subtree_depth(0)
+            .with_branching_rule(MostInfeasible())
+            .with_node_selection_rule(WorstBound())
+            .add_callback(Heuristics::IntegerMaster().with_optimizer(GLPK().with_logs(false)))
+            .with_logs(true);
+
+    const auto branch_and_price = branch_and_bound + column_generation;
+
+    // Set optimizer
+    model.use(branch_and_price);
+
+    // Solve
+    model.optimize();
+
+    // Print solution
+    std::cout << save_primal(model) << std::endl;
+
+    return 0;
+}
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/examples/generalized-assignment-problem-penalty-bap.html b/examples/generalized-assignment-problem-penalty-bap.html new file mode 100644 index 00000000..840b9472 --- /dev/null +++ b/examples/generalized-assignment-problem-penalty-bap.html @@ -0,0 +1,261 @@ + + + + + + + + + Generalized Assignment Problem (Column Generation + Penalty Method) — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Generalized Assignment Problem (Column Generation + Penalty Method)

+
+

Problem Definition

+

We consider the Generalized Assignment Problem (GAP) already studied in the Branch-and-Price example.

+

In this example, we solve the continuous relaxation of the GAP using a penalty method and column generation.

+

First, we consider the continuous relaxation of the GAP:

+
+\[\begin{split}\begin{align*} + \min_{x} \ & \sum_{i=1}^m \sum_{j=1}^n c_{ij} x_{ij} \\ + \text{s.t.} & \sum_{j=1}^n r_{ij} x_{ij} \le C_i && i=1,\dotsc,m \\ + & \sum_{i=1}^m x_{ij} = 1 && j=1,\dotsc,n \\ + & x_{ij} \in [0,1] && i=1,\dotsc,m, j=1,\dotsc,n. +\end{align*}\end{split}\]
+

Then, we introduce penalty parameters \(\rho_j\) to penalize the constraints \(\sum_{i=1}^m x_{ij} = 1\) in the objective function.

+
+\[\begin{split}\begin{align*} + \min_{x} \ & \sum_{i=1}^m \sum_{j=1}^n c_{ij} x_{ij} + \sum_{j=1}^n \rho_j \left| \sum_{i=1}^m x_{ij} - 1 \right| \\ + \text{s.t.} & \sum_{j=1}^n r_{ij} x_{ij} \le C_i && i=1,\dotsc,m \\ + & x_{ij} \in [0,1] && i=1,\dotsc,m, j=1,\dotsc,n. +\end{align*}\end{split}\]
+

Throughout the optimization process, we update the penalty parameters \(\rho_j\) iteratively to enforce the feasibility of the solution.

+

Finally, we solve the continuous relaxation of the GAP using column generation to generate new columns (variables) and improve the objective function. +That is, we iteratively solve the master problem and the subproblems to generate new columns and update the master problem.

+
+
+

Implementation with idol

+

In this example, we show how to model the Generalized Assignment Problem with idol and how to solve it using a +Dantzig-Wolfe decomposition within a branch-and-bound framework, i.e., a branch-and-price algorithm.

+
//
+// Created by henri on 13/03/23.
+//
+#include <iostream>
+#include "idol/modeling.h"
+#include "idol/problems/generalized-assignment-problem/GAP_Instance.h"
+#include "idol/optimizers/mixed-integer-optimization/branch-and-bound/node-selection-rules/factories/WorstBound.h"
+#include "idol/optimizers/mixed-integer-optimization/branch-and-bound/BranchAndBound.h"
+#include "idol/optimizers/mixed-integer-optimization/callbacks/heuristics/IntegerMaster.h"
+#include "idol/optimizers/mixed-integer-optimization/branch-and-bound/branching-rules/factories/MostInfeasible.h"
+#include "idol/optimizers/mixed-integer-optimization/dantzig-wolfe/DantzigWolfeDecomposition.h"
+#include "idol/optimizers/mixed-integer-optimization/dantzig-wolfe/infeasibility-strategies/FarkasPricing.h"
+#include "idol/optimizers/mixed-integer-optimization/dantzig-wolfe/stabilization/Neame.h"
+#include "idol/optimizers/mixed-integer-optimization/dantzig-wolfe/logs/Info.h"
+#include "idol/optimizers/mixed-integer-optimization/padm/PenaltyMethod.h"
+#include "idol/optimizers/mixed-integer-optimization/wrappers/GLPK/GLPK.h"
+
+using namespace idol;
+
+int main(int t_argc, const char** t_argv) {
+
+    const auto instance = Problems::GAP::read_instance("assignment-penalty-bap.data.txt");
+
+    const unsigned int n_agents = instance.n_agents();
+    const unsigned int n_jobs = instance.n_jobs();
+
+    // Create optimization environment
+    Env env;
+
+    // Create model
+    Model model(env);
+
+    // Create decomposition annotation
+    Annotation<Ctr> decomposition(env, "decomposition", MasterId);
+
+    // Create penalized constraints annotation
+    Annotation<Ctr, bool> penalized_constraints(env, "penalized_constraints", false);
+
+    // Create assignment variables (x_ij binaries)
+    auto x = model.add_vars(Dim<2>(n_agents, n_jobs), 0., 1., Binary, "x");
+
+    // Create knapsack constraints (i.e., capacity constraints)
+    for (unsigned int i = 0 ; i < n_agents ; ++i) {
+        auto capacity = model.add_ctr(idol_Sum(j, Range(n_jobs), instance.resource_consumption(i, j) * x[i][j]) <= instance.capacity(i), "capacity_" + std::to_string(i));
+
+        capacity.set(decomposition, i); // Assign constraint to i-th subproblem
+    }
+
+    // Create assignment constraints
+    for (unsigned int j = 0 ; j < n_jobs ; ++j) {
+        auto assignment = model.add_ctr(idol_Sum(i, Range(n_agents), x[i][j]) == 1, "assignment_" + std::to_string(j));
+
+        assignment.set(penalized_constraints, true); // Penalize this constraint
+    }
+
+    // Set the objective function
+    model.set_obj_expr(idol_Sum(i, Range(n_agents), idol_Sum(j, Range(n_jobs), instance.cost(i, j) * x[i][j])));
+
+    const auto column_generation = DantzigWolfeDecomposition(decomposition)
+            .with_master_optimizer(GLPK::ContinuousRelaxation().with_logs(false))
+            .with_default_sub_problem_spec(
+                    DantzigWolfe::SubProblem()
+                            .add_optimizer(GLPK().with_logs(false))
+                            .with_column_pool_clean_up(1500, .75)
+            )
+            .with_logger(Logs::DantzigWolfe::Info().with_frequency_in_seconds(.00000001))
+            .with_dual_price_smoothing_stabilization(DantzigWolfe::Neame(.3))
+            .with_infeasibility_strategy(DantzigWolfe::FarkasPricing())
+            .with_hard_branching(true)
+            .with_logs(true);
+
+
+    const auto penalty_method = PenaltyMethod(penalized_constraints)
+            .with_penalty_update(PenaltyUpdates::Multiplicative(2))
+            .with_rescaling(true, 1e3)
+            .with_feasible_solution_status(Optimal);
+
+    const auto branch_and_bound = BranchAndBound()
+            .with_subtree_depth(0)
+            .with_branching_rule(MostInfeasible())
+            .with_node_selection_rule(WorstBound())
+            //.add_callback(Heuristics::IntegerMaster().with_osi_interface(HiGHS().with_logs(false)))
+            .with_logs(true);
+
+    // Set optimizer
+    model.use(branch_and_bound + (penalty_method + column_generation));
+
+    // Solve
+    model.optimize();
+
+    // Print solution
+    std::cout << save_primal(model) << std::endl;
+
+    return 0;
+}
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/examples/index.html b/examples/index.html new file mode 100644 index 00000000..fca191e9 --- /dev/null +++ b/examples/index.html @@ -0,0 +1,177 @@ + + + + + + + + + Examples — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Examples

+
+

Mixed-Integer Programming

+
+ +
+
+
+

Branch-and-Bound

+
+ +
+
+
+

Column Generation and Branch-and-Price

+
+ +
+
+
+

Bilevel Optimization

+
+ +
+
+
+

Robust Optimization

+
+ +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/examples/knapsack-problem.html b/examples/knapsack-problem.html new file mode 100644 index 00000000..dc6062d7 --- /dev/null +++ b/examples/knapsack-problem.html @@ -0,0 +1,196 @@ + + + + + + + + + Knapsack Problem (HiGHS) — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Knapsack Problem (HiGHS)

+
+

Problem Definition

+

We consider the Knapsack Problem (KP). +Given a set of \(n\) items, each of which having a weight and a profit, the goal is to +select of subset of items such that the total weight does not exceed a given capacity and the total profit is maximized.

+

For each item \(j\in\{1,\dotsc,n\}\), we denote its weight by \(w_j\) and its profit by \(p_j\). +The maximum capacity of the knapsack is \(C\).

+

We model the KP with the following binary linear program:

+
+\[\begin{split}\begin{align*} + \max_{x} \ & \sum_{j=1}^n p_j x_j \\ + \text{s.t.} & \sum_{j=1}^n w_j x_j \le C \\ + & x_j \in \{0,1\} && j=1,\dotsc,n. +\end{align*}\end{split}\]
+
+
+

Implementation with idol

+

In this example, we show how to model the Knapsack Problem with idol and how to solve it using the HiGHS solver.

+
//
+// Created by henri on 06/04/23.
+//
+#include <iostream>
+#include "idol/problems/knapsack-problem/KP_Instance.h"
+#include "idol/modeling.h"
+#include "idol/optimizers/mixed-integer-optimization/wrappers/HiGHS/HiGHS.h"
+
+using namespace idol;
+
+int main(int t_argc, const char** t_argv) {
+
+    const auto instance = Problems::KP::read_instance("knapsack.data.txt");
+
+    const auto n_items = instance.n_items();
+
+    Env env;
+
+    // Create model
+    Model model(env);
+
+    auto x = model.add_vars(Dim<1>(n_items), 0, 1, Binary, "x");
+
+    model.add_ctr(idol_Sum(j, Range(n_items), instance.weight(j) * x[j]) <= instance.capacity());
+
+    model.set_obj_expr(idol_Sum(j, Range(n_items), -instance.profit(j) * x[j]));
+
+    // Set optimizer
+    model.use(HiGHS());
+
+    // Solve
+    model.optimize();
+
+    std::cout << "Objective value = " << model.get_best_obj() << std::endl;
+
+    std::cout << save_primal(model) << std::endl;
+
+    return 0;
+}
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/examples/two-stage-robust-facility-location-problem.html b/examples/two-stage-robust-facility-location-problem.html new file mode 100644 index 00000000..1eedbf3b --- /dev/null +++ b/examples/two-stage-robust-facility-location-problem.html @@ -0,0 +1,297 @@ + + + + + + + + + Two-Stage Robust Facility Location Problem (CCG) — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Two-Stage Robust Facility Location Problem (CCG)

+
+

Problem Description

+

We consider a robust version of the capacitated Facility Location Problem (FLP). +Given a set of potential facility locations \(V_1\) and a set of customers \(V_2\), the goal is to select a subset of facility locations +to activate in order to serve all customers’ demand, while minimizing the total cost. +This version introduces uncertainty in the customers’ demands.

+

Note that there is also an example for the deterministic version of the FLP using Column Generation.

+

Each facility \(i\in V_1\) has an opening cost \(f_i\) and a maximum capacity \(q_i\). +Each customer \(j\in V_2\) has a demand \(d_j\). +The unitary cost for serving customer \(j\in V_2\) from facility \(i\in V_1\) is \(t_{ij}\). +The uncertainty in customer demands is controlled by a parameter \(\Gamma\).

+

In this robust variant, we consider that the demands are uncertain and can be expressed as \(d_j(\xi) = d_j(1 + p\xi_j)\) +with \(p\) being the maximum increase in demand and \(\xi\) being an unknown vector taken in the uncertainty set

+
+\[\Xi := \left\{ \xi\in[ 0, 1 ]^{|V_2|} : \sum_{j\in V_2} \xi_j \le \Gamma \right\}.\]
+

We model the two-stage robust FLP as

+
+\[\min_{x\in \{0,1\}^{|V_1|}} \ \left\{ \sum_{i\in V_1} f_i x_i + \max_{\xi\in \Xi} \ \min_{y\in Y(x,\xi)} \ \sum_{i\in V_1} \sum_{j\in V_2} t_{ij} y_{ij} \right\}\]
+

where \(Y(x,\xi)\) is the set of feasible solutions for the second stage problem, given the first stage solution \(x\) and the realization \(\xi\) of the uncertain demand vector. +It is defined as the set of vectors \(y\in \mathbb{R}^{|V_1|\times|V_2|}\) that satisfy the following constraints

+
+\[\begin{split}\begin{align*} + & \sum_{i\in V_1} y_{ij} = d_j(\xi) && j\in V_2, \\ + & \sum_{j\in V_2} y_{ij} \le q_i x_i && i\in V_1, \\ + & y_{ij} \ge 0 && i\in V_1, j\in V_2. +\end{align*}\end{split}\]
+
+
+

Implementation with idol

+

We now show how to implement the two-stage robust FLP with idol and how to solve it using a CCG algorithm. +Here, the “adversarial problem” is solved by calling the bilevel solver MibS.

+
//
+// Created by henri on 20.06.24.
+//
+#include <iostream>
+#include <Research/idol/lib/include/idol/modeling.h>
+#include "idol/optimizers/bilevel-optimization/wrappers/MibS/MibS.h"
+#include "idol/modeling/bilevel-optimization/LowerLevelDescription.h"
+#include "idol/optimizers/mixed-integer-optimization/wrappers/Gurobi/Gurobi.h"
+#include "idol/modeling/bilevel-optimization/read_from_file.h"
+#include "idol/problems/facility-location-problem/FLP_Instance.h"
+#include "idol/optimizers/robust-optimization/column-and-constraint-generation/ColumnAndConstraintGeneration.h"
+#include "idol/optimizers/robust-optimization/column-and-constraint-generation/separators/Bilevel.h"
+#include "idol/modeling/robust-optimization/StageDescription.h"
+#include "idol/optimizers/robust-optimization/column-and-constraint-generation/stabilizers/TrustRegion.h"
+
+using namespace idol;
+
+std::pair<Model, Vector<Var, 1>>
+create_uncertainty_set(Env& t_env,
+                       const Problems::FLP::Instance& t_instance,
+                       double t_Gamma);
+
+Model create_deterministic_model(Env& t_env,
+                                 const Problems::FLP::Instance& t_instance,
+                                 const Vector<Var, 1>& t_xi,
+                                 double t_percentage_increase);
+
+Robust::StageDescription create_stage_description(const Model& t_model);
+
+int main(int t_argc, const char** t_argv) {
+
+    Env env;
+
+    // Read instance
+    const auto instance = Problems::FLP::read_instance_1991_Cornuejols_et_al("robust_ccg.data.txt");
+    const double Gamma = 3;
+    const double percentage_increase = .2;
+
+    // Create models
+    auto [uncertainty_set, xi] = create_uncertainty_set(env, instance, Gamma);
+    auto model = create_deterministic_model(env, instance, xi, percentage_increase);
+    auto stages = create_stage_description(model);
+
+    model.use(
+            Robust::ColumnAndConstraintGeneration(stages, uncertainty_set)
+                    .with_master_optimizer(Gurobi())
+                    .with_separator(
+                            Robust::CCGSeparators::Bilevel()
+                    )
+                    //.with_complete_recourse(true)
+                    //.with_stabilization(Robust::CCGStabilizers::TrustRegion().with_trust_factors())
+                    .with_logs(true)
+    );
+
+    model.optimize();
+
+    std::cout << save_primal(model) << std::endl;
+
+    return 0;
+}
+
+
+std::pair<Model, Vector<Var, 1>>
+create_uncertainty_set(Env& t_env, const Problems::FLP::Instance& t_instance, double t_Gamma) {
+
+    const unsigned int n_customers = t_instance.n_customers();
+
+    Model uncertainty_set(t_env);
+
+    auto xi = uncertainty_set.add_vars(Dim<1>(n_customers), 0., 1, Binary, "xi");
+    uncertainty_set.add_ctr(idol_Sum(j, Range(n_customers), xi[j]) <= t_Gamma);
+
+    return {
+            std::move(uncertainty_set),
+            std::move(xi)
+    };
+}
+
+Model
+create_deterministic_model(Env& t_env, const Problems::FLP::Instance& t_instance, const Vector<Var, 1>& t_xi, double t_percentage_increase) {
+
+    const unsigned int n_customers = t_instance.n_customers();
+    const unsigned int n_facilities = t_instance.n_facilities();
+
+    Model result(t_env);
+
+    const auto x = result.add_vars(Dim<1>(n_facilities), 0., 1., Binary, "x");
+    const auto y = result.add_vars(Dim<2>(n_facilities, n_customers), 0., Inf, Continuous, "y");
+
+    // Capacity constraints
+    for (unsigned int i = 0 ; i < n_facilities ; ++i) {
+        result.add_ctr(idol_Sum(j, Range(n_customers), y[i][j]) <= t_instance.capacity(i) * x[i]);
+    }
+
+    // Demand satisfaction constraints
+    for (unsigned int j = 0 ; j < n_customers ; ++j) {
+        result.add_ctr(idol_Sum(i, Range(n_facilities), y[i][j]) == t_instance.demand(j) * (1 + t_percentage_increase * !t_xi[j]));
+    }
+
+    // Objective function
+    result.set_obj_expr(idol_Sum(i, Range(n_facilities),
+                                t_instance.fixed_cost(i) * x[i]
+                                + idol_Sum(j, Range(n_customers),
+                                           t_instance.per_unit_transportation_cost(i, j) * y[i][j]
+                                )
+                       )
+    );
+
+    return std::move(result);
+}
+
+Robust::StageDescription create_stage_description(const Model& t_model) {
+
+    Robust::StageDescription result(t_model.env());
+
+    for (const auto& var : t_model.vars()) {
+        if (var.name().front() != 'x') {
+            result.set_stage(var, 2);
+        }
+    }
+
+    for (const auto& ctr : t_model.ctrs()) {
+        result.set_stage(ctr, 2);
+    }
+
+    return result;
+}
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/faq/index.html b/faq/index.html new file mode 100644 index 00000000..e3bc8e91 --- /dev/null +++ b/faq/index.html @@ -0,0 +1,134 @@ + + + + + + + + + FAQ — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/faq/mosek-and-eigen.html b/faq/mosek-and-eigen.html new file mode 100644 index 00000000..5abb25c1 --- /dev/null +++ b/faq/mosek-and-eigen.html @@ -0,0 +1,181 @@ + + + + + + + + + Why is Eigen necessary to solve QPs and QCQPs with Mosek? — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Why is Eigen necessary to solve QPs and QCQPs with Mosek?

+
+

In Short

+

The interface of idol is based on quadratic expressions like

+
+\[\sum_{j=1}^n a_{ij}x_j + \sum_{j=1}^n\sum_{k=1}^n q_{jk}^ix_jx_k \le b_i.\]
+

The C++ interface of Mosek, instead, is based on “conic expressions” like

+
+\[(x_0, \textbf{Fx}) \in \mathcal Q^n,\]
+

where \(\mathcal Q^n\) denotes the second-order cone and \(F\) is some matrix related to \(Q^i\).

+

In order to make the conversion between the Mosek interface and +the idol interface (for instance, computing the matrix \(F\)), +one needs to compute an eigen value decomposition. +This is automatically done by idol using Eigen. +This is why Eigen is necessary if one wants to use Mosek with idol to solve QPs or QCQPs.

+
+
+

Detailed Answer

+

Consider the quadratic expression

+
+\[\sum_{j=1}^n a_{ij}x_j + \sum_{j=1}^n\sum_{k=1}^n q_{jk}^ix_jx_k \le b_i.\]
+

It can be written as

+
+\[a_{(i)}^\top x + x^\top Q^i x \le b_i.\]
+

The first task is to compute an eigen value decomposition of \(Q^i\), i.e., +to find matrices \(L\) and \(D\) such that \(Q^i = L D L^\top\) +and \(D\) is a diagonal matrix containing the eigen values of \(Q^i\).

+

If there are more than two negative eigen values, the constraint is not convex and an exeception is thrown.

+

Otherwise, we compute \(F\) and \(N\) so that \(x^\top Q^i x = \lVert Fx \rVert_2^2 + x^\top N x\). +This is done by setting \(F = \sqrt{D^+} L^\top\) where \(D^+\) is \(D\) with the negative eigen values replaced by zero, +and \(N = L D^- L^\top\).

+

Hence, we have that the constraint is expressed as

+
+\[\lVert Fx \rVert_2^2 + x^\top N x \le b_i - a_{(i)}^\top x.\]
+

If \(N = 0\), it can be written as

+
+\[\begin{split}\begin{align} + & \lVert Fx \rVert_2^2 \le b_i - a_{(i)}^\top x \\ + \iff & (.5, b_i - a_{(i)}^\top x, Fx) \in\mathcal Q^{n+2}_r, +\end{align}\end{split}\]
+

where \(\mathcal Q^{n+2}_r\) denotes the rotated second-order cone.

+

Otherwise, if \(N\) has one non-zero entry, say \(n_{ij}\). +Then, it must be that \(a_{(i)} = 0\) and \(b_i \le 0\) for the constraint to be +converted by idol. Under this assumption, the constraint can be written as

+
+\[\begin{split}\begin{align} + & \lVert Fx \rVert_2^2 + \sqrt{-b_i}^2 \le n_{ij} x_i x_j \\ + \iff & (.5 n_{i,j} x_i, x_j, Fx, \sqrt{-b_i}) \in\mathcal Q^{n+1}_r, +\end{align}\end{split}\]
+

where it is assumed that \(n_{i,j} x_{i}x_{j} \ge 0\) holds +(for now, a warning is printed to enlight this expectation).

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 00000000..36146b0a --- /dev/null +++ b/genindex.html @@ -0,0 +1,2078 @@ + + + + + + + + Index — idol + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

+ +
+ I + +
+

I

+ + + +
+ + + +
+
+
+ +
+ +
+

© Copyright 2023, Henri Lefebvre.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..40e11e73 --- /dev/null +++ b/index.html @@ -0,0 +1,288 @@ + + + + + + + + + What Is Idol? — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+ +

A C++ framework for optimization

+ +

+ Idol is a powerful and flexible library designed to help you build new mathematical optimization algorithms and solve more and more challenging problems efficiently. +

+ + + Get started now +   + To installation guidelines + + +
+

What Is Idol?

+

Idol is a C++ library for mathematical optimization and complex decision making. It is designed to help you build new +algorithms easily for solving more and more challenging problems. It is a versatile and powerful tool that can be +used to solve a wide range of optimization problems, including mixed-integer linear programming (MILP), quadratically constrained +problems (MIQCQP and MIQP), bilevel problems (BO), robust optimization problems (RO and ARO) and many more.

+

It is designed to be easy to use and to integrate with other software, so that you can use it +to build new algorithms and solve new problems without having to start from scratch. For instance, here is a piece of +code which create a Branch-and-Price algorithm.

+
const auto branch_and_price = branch_and_bound + column_generation;
+
+
+

Here, branch_and_bound and column_generation are two objects which are +combined to create a new algorithm, branch_and_price, which can be instantiated and executed.

+
+
+

Getting Started With Idol

+

If you are new to idol, be sure to have a look at our tutorials. If you want to get a fast hands-on +start, have a look at our local installation guideline. It is the easiest installation process +one could think of since it automatically downloads the latest version of idol, and installs it locally in a sub-folder. +It’s really a mater of seconds before you can start using idol.

+
+

Example

+

Idol has a user-friendly interface which should look natural to people working in optimization. For instance, +here is how one solves a Knapsack Problem using the Gurobi solver.

+
using namespace idol;
+
+const unsigned int n_items = 5;
+const double[] profit = { 40., 50., 100., 95., 30., };
+const double[] weight = { 2., 3.14, 1.98, 5., 3., };
+const double capacity = 10.;
+
+Env env;
+
+Model model(env);
+
+const auto x = model.add_vars(Dim<1>(n_items), 0., 1., Binary, "x");
+
+model.add_ctr(idol_Sum(j, Range(n_items), weight[j] * x[j]) <= capacity);
+
+model.set_obj_expr(idol_Sum(j, Range(n_items), -profit[j] * x[j]);
+
+model.use(Gurobi());
+
+model.optimize();
+
+
+
+
+
+

Is This a MIP Solver?

+

The idol library is not a MIP solver in itself. Indeed, it typically needs to call external +solvers (e.g., GLPK, Gurobi or many others) +as a sub-routine of more complex algorithmic schemes (e.g., within a Branch-and-Price scheme).

+

The idea is to work hand in hand with existing fine-tuned and well-engineered optimization +software to enhance their possibilities. By doing so, we aim at solving larger and more complex problems than what is currently possible, +or even problems for which theoretical evidence indicate that no MIP of reasonable size can model them completely (e.g., \(\Sigma_i^P\)-hard problems).

+

Even though idol is not a MIP solver, it can be used to solve standalone LPs, QPs, MILPs, MIQPs, and MIQCQPs through the +many external solvers it interfaces with. +A clear advantage of using idol to solve these problems is that it offers a unified and common interface to every solver. +Thus, you can write your code once and test it with different solvers! +The following solvers are currently supported by idol

+ +
+
+

Current Features

+
+

Mixed-Integer Optimization

+
+

Interfacing External Optimization Solvers

+

The idol library can be used to interface external solvers like GLPK +or Gurobi to solve standalone LPs, QPs, MILPs, MIQPs, and MIQCQPs.

+

It also provides classical callbacks one would need to implement when solving hard problems like generating user cuts +or lazy constraints. Simply give idol the separation model and let it handle the rest.

+
+
+

Branch-and-Bound Algorithms

+

Idol can be used to create a custom branch-and-bound scheme very easily in which virtually anything can be tweaked.

+
    +

  • Node-agnostic: The B&B algorithm can work with any node type as long as the user properly +defines how nodes must be updated. A default node type is already implemented for classical variable branching. This +node type can also be inherited, e.g., to enrich the information carried out from each parent node to its children.

    • +

  • Customizable branching rules: The default branching rules include: most infeasible, least infeasible, first +infeasible found, uniformly random, strong branching (with phases) and pseudo-cost branching. The user can create its +own branching rule if needed.

    • +

  • Customizable node selection rules: The default node selection rules include: best estimate, best bound, worst bound, +depth first, breadth first. The user can create its own node selection rule if needed.

    • +

  • Callbacks: The user can specify its own callback to locally or globally modify a node’s problem, submit heuristic +solutions, or influence the execution of the overall tree search.

    • +

  • Sub-tree exploration. The B&B algorithm supports sub-trees exploration to reach valid solutions to the original +problem as quick as possible.

    • +
+
+
+

Column Generation and Dantzig-Wolfe Decomposition

+

Idol can be used to build and custom CG algorithms with the following features.

+
    +

  • Infeasible master problem procedures: Idol can handle infeasible master problems by resorting to Farkas pricing or +by adding artificial variables to the original problem (if this fails, idol will automatically switch to pure phase I +so as to exactly prove infeasibility).

    • +

  • Automatic pool clean up: If the master problem contains too many variables, they can be automatically removed. +This behavior is controlled by user parameters.

    • +

  • Stabilization via dual price smoothing: The CG procedure can be stabilized using dual price smoothing controlled +by user inputs. Default implementation include Wentges (1997) and +Neame (2000).

    • +

  • Parallel pricing: Each pricing problem can be solved in parallel.

    • +

  • Nested column generation: A key idea of idol is that every optimizer is seen as a black-box to solve a given optimization model. +As such, a CG algorithm can easily be “plugged in” any algorithmic phase. For instance, solving the pricing of +a CG algorithm can be done by yet another CG algorithm.

    • +
+
+
+
+

Bilevel Optimization

+
+

Interfacing External Optimization Solvers

+

Currently, idol can interface with the mixed-integer bilevel solver coin-or/MibS.

+
+
+
+

Robust Optimization

+
+

Column-and-Constraint Generation

+

Idol can be used to build and custom CCG algorithms with the following features.

+
    +

  • Separation Sub-Routine: Different separation strategies are implemented in idol to solve the separation problem in +the CCG algorithm. Most importantly, any bilevel solver can be used to solve the max-min separation problem. If needed, the user can also easily implement its own separation strategy.

    • +

  • Trust Region Stabilization: The CCG algorithm can be stabilized using trust region if the first-stage decisions are binary.

    • +
+
+
+
+
+

Table of Contents

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/installation/global-installation/index.html b/installation/global-installation/index.html new file mode 100644 index 00000000..7d18674d --- /dev/null +++ b/installation/global-installation/index.html @@ -0,0 +1,203 @@ + + + + + + + + + Global Installation — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Global Installation

+

This page is dedicated to the local installation of idol. It is thought for more advanced users who want to install +idol globally on their computer (e.g., in /usr/lib). +If you simply want to “try out” idol, we advise you to opt for a local installation. +The installation process is managed by the CMake build system. You will therefore need to have it +installed on your computer.

+

The installation process follows the classical CMake build process “configure, build, install”.

+
+

Configure

+

The first step is to create a build directory. +This is where the library will be compiled before being it is installed (i.e., copied) on your system.

+
mkdir build
+cd build
+
+
+

Then, you should call CMake to configure the build process. +It is at this time that we let CMake look for the necessary dependencies such as, e.g., external optimization solvers. +By default, running CMake without options will cause idol to be built without any external solver. Note that options +should be specified as follows:

+
cmake -D<YOUR_CMAKE_OPTION>=<YOUR_VALUE> ..
+
+
+

A list of all possible options can be found on this page.

+
+

Example for Gurobi

+

Here is an example of a possible CMake command which will create the necessary targets for configuring idol to interface Gurobi.

+
cmake -DUSE_GUROBI=YES ..
+
+
+

Note that this example assumes that your environment variable GUROBI_HOME has been correctly configured +(see this official Gurobi page).

+

If you want to specify the path to the Gurobi installation folder, you can use the GUROBI_DIR option.

+
cmake -DUSE_GUROBI=YES -DGUROBI_DIR=/path/to/gurobi/install/dir ..
+
+
+
+
+
+

Build

+

Now that the build process has been configured, we can build idol by running the following command.

+
make idol
+
+
+

This will compile the idol C++ library, and make it ready to be installed (i.e., copied) on your computer.

+

If you encounter any error at this stage, please, do not hesitate to contact us on our GitHub page.

+
+
+

Install

+

To install idol on your computer, run the following command.

+
sudo make install
+
+
+
+

Hint

+

Here, you need superuser rights to install idol globally. If you want to install idol on a specific folder, +you may also use the CMAKE_INSTALL_PREFIX CMake option to change the destination folder.

+
cmake -CMAKE_INSTALL_PREFIX=/my/custom/installation/folder <YOUR_OTHER_CMAKE_OPTIONS...> ..
+make install
+
+
+
+
+
+

Table of Contents

+
+ +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/installation/global-installation/linking.html b/installation/global-installation/linking.html new file mode 100644 index 00000000..7edd46b5 --- /dev/null +++ b/installation/global-installation/linking.html @@ -0,0 +1,173 @@ + + + + + + + + + Linking Your C++ Project — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Linking Your C++ Project

+

If you have installed idol globally, you can link your C++ project with idol using CMake. +Note that if idol was locally installed, you should refer to the local installation guideline, +this page is only for global installation.

+

Idol is built with and for CMake (even though it is still possible to link using Makefile or pure gcc, clang, …).

+

Here is a minimal CMakeLists.txt which creates a new executable target linked with idol.

+
cmake_minimum_required(VERSION 3.22)
+project(my_project)
+
+set(CMAKE_CXX_STANDARD 17)
+
+add_executable(my_target main.cpp)
+
+find_package(idol REQUIRED) ## Here, we search for the installed library idol
+
+target_link_library(my_target PUBLIC idol) ## Here, we ask CMake to link with idol
+
+
+

Note that you do not need to link with any external solver if idol was already linked with one.

+

The following main.cpp is then a minimal example.

+
#include <iostream>
+#include <idol/modeling.h>
+
+int main(int t_argc, const char** t_argv) {
+
+    using namespace idol;
+
+    Env env;
+
+    Model model(env);
+
+    const auto x = model.add_var(0., 1., Binary, "x");
+
+    // ...
+
+    return 0;
+}
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/installation/index.html b/installation/index.html new file mode 100644 index 00000000..4def0fa2 --- /dev/null +++ b/installation/index.html @@ -0,0 +1,139 @@ + + + + + + + + + Installation — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
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+ +
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+ +
+

Installation

+

You are here on the installation guidelines. This page will guide through the installation process of idol.

+

There are two ways to install idol:

+
    +

  • A local installation lets CMake automatically download and install idol a sub-folder of your +CMake project. This is the easiest way to get started with idol.

    • +

  • A global installation installs idol globally on your computer (e.g., in /usr/lib).

    • +
+
+
+
+ + +
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+ +
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+ + + + \ No newline at end of file diff --git a/installation/local_installation.html b/installation/local_installation.html new file mode 100644 index 00000000..5fed6a3a --- /dev/null +++ b/installation/local_installation.html @@ -0,0 +1,195 @@ + + + + + + + + + Local Installation — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
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+ +
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+ +
+

Local Installation

+

This page is dedicated to the local installation of idol. This is the easiest way to get your project working with +idol without installing it globally on your computer. +If you want to install idol globally, please read our global installation guide.

+

The idea behind local installation is to let CMake download and manage the installation of idol in a sub-folder of your +project. This is done by using the CMake FetchContent_* directives.

+

We provide here a minimal CMakeLists.txt.

+
cmake_minimum_required(VERSION 3.23)
+project(my_project)
+
+set(CMAKE_CXX_STANDARD 17)
+
+include(FetchContent)
+
+# Define your idol CMake options here
+set(USE_GUROBI YES) # For instance, here, we specify that Gurobi will be used by idol
+
+# Tell CMake which version of idol you desire
+FetchContent_Declare(
+        idol
+        GIT_REPOSITORY https://github.com/hlefebvr/idol.git
+        GIT_TAG        origin/main
+)
+
+# Ask CMake to download idol and install it to a sub-folder
+FetchContent_MakeAvailable(idol)
+
+# Normal executable definition
+add_executable(my_target main.cpp)
+
+# Link your CMake target with idol as classically done
+target_link_libraries(my_target PUBLIC idol)
+
+
+

By default, CMake will download the latest version of idol. However, you can also explicitly specify the desired version +of idol you want to use by setting the GIT_TAG variable in the FetchContent_Declare function to the specific +version, e.g., v0.2.4-alpha.

+
+

Hint

+

As you can see in the above example, some options must be set to tell CMake to look for external solvers. Here, we +specify that Gurobi will be used by idol. You can, of course, add other solvers such as Mosek or GLPK.

+

This is done with the set CMake function. For instance, to set the option MY_OPTION to the +value MY_VALUE. You should add set(MY_OPTION MY_VALUE) to your CMakeLists.txt file.

+

A list of all possible options can be found on this page.

+
+

Then, here is a starting main.cpp.

+
#include <iostream>
+#include <idol/modeling.h>
+
+int main(int t_argc, const char** t_argv) {
+
+    using namespace idol;
+
+    Env env;
+
+    Model model(env);
+
+    const auto x = model.add_var(0., 1., Binary, "x");
+
+    // ...
+
+    return 0;
+}
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/installation/options.html b/installation/options.html new file mode 100644 index 00000000..c8d05881 --- /dev/null +++ b/installation/options.html @@ -0,0 +1,304 @@ + + + + + + + + + List of CMake Options — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

List of CMake Options

+

This page contains a list of all CMake options that can be used to configure idol. In particular, this is useful to +link idol with external solvers.

+ +
+

Linking with External Solvers

+
+

Gurobi (Commercial Solver)

+

To link with Gurobi, you will need to pass the USE_GUROBI CMake option with value YES.

+

By default, CMake will look for Gurobi inside the folder indicated by the environment variable GUROBI_HOME +(see this official Gurobi page). +Note that it is also possible to pass the CMake option GUROBI_DIR=/path/to/gurobi/install/dir in order to specify +another directory to look for Gurobi.

+
+

Example

+

The following will create an install target which will install idol with Gurobi.

+
cmake -DUSE_GUROBI=YES -DGUROBI_DIR=/path/to/gurobi/install/dir ..
+
+
+

If you are doing a local installation, then the same can be achieved as follows.

+
set(USE_GUROBI YES)
+set(GUROBI_DIR /path/to/gurobi/install/dir)
+
+
+
+
+
+

Mosek (Commercial Solver)

+

To link with Mosek, you will need to pass the USE_MOSEK CMake option with value YES.

+

By default, CMake will look for Mosek inside the folder indicated by the environment variable MOSEK_HOME. +Note that it is also possible to pass the CMake option MOSEK_DIR=/path/to/mosek/install/dir in order to specify +another directory to look for Mosek.

+

Note that MOSEK_HOME (or equivalently MOSEK_DIR) should point to the Mosek installation folder where +folders h and bin can be found. For instance, MOSEK_HOME=/home/<MY_USERNAME>/mosek/10.0/tools/platform/linux64x86.

+
+

Example

+

The following will create an install target which will install idol with Mosek.

+
cmake -DUSE_MOSEK=YES -DMOSEK_DIR=/path/to/mosek/install/dir ..
+
+
+

If you are doing a local installation, then the same can be achieved as follows.

+
set(USE_MOSEK YES)
+set(MOSEK_DIR /path/to/mosek/install/dir)
+
+
+
+
+

Attention

+

If you intend to use Mosek for solving QPs or SOCPs, please read this.

+
+
+
+

GLPK (Open-Source Solver)

+

To link with GLPK, you will need to pass the USE_GLPK CMake option with value YES.

+

By default, CMake will look for GLPK inside the folder indicated by the environment variable GLPK_HOME as well +as in the default installation folders /usr/include and /usr/lib. +Note that it is also possible to pass the CMake option GLPK_DIR=/path/to/glpk/install/dir in order to specify +another directory to look for GLPK.

+
+

Example

+

The following will create an install target which will install idol with GLPK.

+
cmake -DUSE_GLPK=YES -DGLPK_DIR=/path/to/glpk/install/dir ..
+
+
+

If you are doing a local installation, then the same can be achieved as follows.

+
set(USE_GLPK YES)
+set(GLPK_DIR /path/to/glpk/install/dir)
+
+
+
+
+
+

HiGHS (Open-Source Solver)

+

To link with GLPK, you will need to pass the USE_HIGHS CMake option with value YES.

+

By default, CMake will look for HIGHS inside the default installation folders /usr/include and /usr/lib. +Note that it is also possible to pass the CMake option HIGHS_DIR=/path/to/highs/install/dir in order to specify +another directory to look for GLPK.

+
+

Example

+

The following will create an install target which will install idol with GLPK.

+
cmake -DUSE_HIGHS=YES -DHIGHS_DIR=/path/to/glpk/install/dir ..
+
+
+

If you are doing a local installation, then the same can be achieved as follows.

+
set(USE_HIGHS YES)
+set(HIGHS_DIR /path/to/glpk/install/dir)
+
+
+
+
+
+
+

Building Examples

+

Examples : can be found at the root level of the idol repository, inside the directory examples.

+

By default, CMake will not generate targets to build these example. To tell CMake to generate these, you +need to set the CMake option BUILD_EXAMPLE to YES.

+
+

Example

+

The following will tell CMake to create targets for examples.

+
cmake -DBUILD_EXAMPLES=YES ..
+
+
+

For instance, we can now build the Knapsack Problem example,

+
make example_knapsack
+
+
+

and run it.

+
cd examples && ./example_knapsack
+
+
+
+
+
+

Other optional dependencies

+
+

Using martinus/robin-hood-hashing hash map

+

Internally, idol uses unordered maps (also called hash maps). +Unfortunately, the default std implementation is not the best choice when +it comes to performance. To avoid this, if found relevant, users can tell idol to use the hash-map implementation of +martinus/robin-hood-hashing. It’s actually a header-only library, so +all you have to do is to tell CMake where the robin_hood.hpp file is. This is done through the CMake option +ROBINHOOD_DIR=/path/to/robin_hood/folder together with USE_ROBINHOOD=YES.

+

Note that, if ROBINHOOD_DIR is not specified, robin_hood.hpp is searched for in /usr/include, +/usr/include/robin_hood/, /usr/local/include/, /usr/local/include/robin_hood/ and in the path +stored in the environment variable ROBINHOOD_HOME.

+
+

Example

+

The following will create an install target which will install idol with martinus’s robin_hood hash map +implementation.

+
cmake -DUSE_ROBINHOOD=YES -DROBINHOOD_DIR=/path/to/robin_hood/folder ..
+
+
+
+
+
+

Using Eigen

+

Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms. +It is internally used by idol for some of its functionalities. In particular, this is necessary for solving QPs and SOCPs +with the Mosek solver.

+

Since Eigen is a header-only library, all you have to do is to tell CMake where the Eigen folder is. This is done +through the CMake option EIGEN_DIR=/path/to/eigen/folder together with USE_EIGEN=YES.

+
+

Example

+

The following will create an install target which will install idol with Eigen.

+
cmake -DUSE_EIGEN=YES -DEIGEN_DIR=/path/to/eigen/folder ..
+
+
+
+
+
+
+

Building Unit and Integration Tests

+

To build tests for idol, please refer to this developer page.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 00000000..beb89a2d Binary files /dev/null and b/objects.inv differ diff --git a/references.html b/references.html new file mode 100644 index 00000000..24a39dc1 --- /dev/null +++ b/references.html @@ -0,0 +1,184 @@ + + + + + + + + + References — idol + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

References

+

The implementation of idol is based on a lot of scientific publications. Here is a selected subset.

+
+
+
+[1] +

Tobias Achterberg, Thorsten Koch, and Alexander Martin. Branching rules revisited. Operations Research Letters, 33(1):42–54, 2005. doi:https://doi.org/10.1016/j.orl.2004.04.002.

+
+
+[2] +

Josette Ayoub and Michael Poss. Decomposition for adjustable robust linear optimization subject to uncertainty polytope. Computational Management Science, 13(2):219–239, February 2016. URL: http://dx.doi.org/10.1007/s10287-016-0249-2, doi:10.1007/s10287-016-0249-2.

+
+
+[3] +

Daniel Baena, Jordi Castro, and Antonio Frangioni. Stabilized benders methods for large-scale combinatorial optimization, with application to data privacy. Management Science, 66(7):3051–3068, July 2020. URL: http://dx.doi.org/10.1287/mnsc.2019.3341, doi:10.1287/mnsc.2019.3341.

+
+
+[4] +

Timo Berthold. Primal heuristics for mixed integer programs. Master's thesis, Technische Universität Berlin, 2006.

+
+
+[5] +

George B. Dantzig and Philip Wolfe. The decomposition algorithm for linear programs. Econometrica, 29(4):767, October 1961. doi:10.2307/1911818.

+
+
+[6] +

Elizabeth D. Dolan and Jorge J. Moré. Benchmarking optimization software with performance profiles. Mathematical Programming, 91(2):201–213, Jan 2002. doi:10.1007/s101070100263.

+
+
+[7] +

J. J. H. Forrest, J. P. H. Hirst, and J. A. Tomlin. Practical solution of large mixed integer programming problems with umpire. Management Science, 20(5):736–773, January 1974. doi:10.1287/mnsc.20.5.736.

+
+
+[8] +

missing school in Kleinert2021

+
+
+[9] +

J. T. Linderoth and M. W. P. Savelsbergh. A computational study of search strategies for mixed integer programming. INFORMS Journal on Computing, 11(2):173–187, May 1999. doi:10.1287/ijoc.11.2.173.

+
+
+[10] +

James T. Moore and Jonathan F. Bard. The mixed integer linear bilevel programming problem. Operations Research, 38(5):911–921, October 1990. URL: http://dx.doi.org/10.1287/opre.38.5.911, doi:10.1287/opre.38.5.911.

+
+
+[11] +

A. Pessoa, R. Sadykov, E. Uchoa, and F. Vanderbeck. Automation and combination of linear-programming based stabilization techniques in column generation. INFORMS Journal on Computing, 30(2):339–360, May 2018. doi:10.1287/ijoc.2017.0784.

+
+
+[12] +

François Vanderbeck. Implementing Mixed Integer Column Generation, pages 331–358. Springer US, Boston, MA, 2005. doi:10.1007/0-387-25486-2_12.

+
+
+[13] +

Laurence Wolsey. Integer Programming. Wiley, September 2020. doi:10.1002/9781119606475.

+
+
+[14] +

Bo Zeng and Long Zhao. Solving two-stage robust optimization problems using a column-and-constraint generation method. Operations Research Letters, 41(5):457–461, 2013. doi:10.1016/j.orl.2013.05.003.

+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/search.html b/search.html new file mode 100644 index 00000000..5b208138 --- /dev/null +++ b/search.html @@ -0,0 +1,136 @@ + + + + + + + + Search — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
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  • Search
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© Copyright 2023, Henri Lefebvre.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 00000000..a501dae3 --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles": {"A Simple Example: Knapsack Cover Cuts": [[138, "a-simple-example-knapsack-cover-cuts"]], "A Two-Stage Robust Facility Location Problem": [[149, "a-two-stage-robust-facility-location-problem"]], "API": [[77, null]], "About constants in expressions": [[131, null]], "About stage annotations": [[149, null]], "Accessing Constraints": [[129, "accessing-constraints"]], "Accessing Variables": [[135, "accessing-variables"]], "Accessing the Objective Function": [[134, "accessing-the-objective-function"]], "Adding User Cuts and Lazy Constraints": [[141, null]], "Adding User Cuts and Lazy Constraints [TODO]": [[116, null]], "Adding an Objective Function": [[134, "adding-an-objective-function"]], "An Example of Lazy Cut Constraints: Benders Optimality Cuts": [[141, "an-example-of-lazy-cut-constraints-benders-optimality-cuts"]], "An Example of User Cuts: Knapsack Cover Inequalities": [[141, "an-example-of-user-cuts-knapsack-cover-inequalities"]], "Annotation": [[35, null]], "Assigning Stages": [[149, "assigning-stages"]], "Assumption": [[144, null]], "Automatic Reformulation": [[122, "automatic-reformulation"]], "Basic Idea": [[143, "basic-idea"]], "Basics": [[125, "basics"], [138, "basics"]], "Benchmark": [[84, null]], "BestBound": [[10, null]], "BestEstimate": [[11, null]], "Bilevel LP-LP (KKT Reformulation)": [[88, null]], "Bilevel MILP-MILP (MibS)": [[89, null]], "Bilevel Optimization": [[69, "bilevel-optimization"], [93, "bilevel-optimization"], [98, "bilevel-optimization"], [105, null], [110, "bilevel-optimization"]], "Bilevel::Mibs": [[52, null]], "Branch-and-Bound": [[93, "branch-and-bound"]], "Branch-and-Bound Algorithm": [[9, null], [115, null]], "Branch-and-Bound Algorithms": [[98, "branch-and-bound-algorithms"]], "Branch-and-Price 101": [[122, null]], "BranchAndBound": [[0, null]], "BranchAndBoundCallback": [[1, null]], "BranchAndBoundCallbackFactory": [[2, null]], "Branching Rules": [[22, null]], "BreadthFirst": [[12, null]], "Build": [[99, "build"]], "Build instructions": [[85, "build-instructions"], [87, "build-instructions"]], "Build the doc!": [[85, null]], "Building Examples": [[103, "building-examples"]], "Building Unit and Integration Tests": [[103, "building-unit-and-integration-tests"]], "COIN-OR/Osi (Cplex, Xpress, Cbc, \u2026)": [[67, null]], "Callback": [[70, null]], "CallbackFactory": [[71, null]], "Changing the Optimization Sense": [[134, "changing-the-optimization-sense"]], "Changing the Scoring Function": [[125, "changing-the-scoring-function"]], "Class List": [[69, null]], "Code coverage options": [[87, "code-coverage-options"]], "Column": [[36, null]], "Column Generation Algorithm": [[31, null]], "Column Generation and Branch-and-Price": [[93, "column-generation-and-branch-and-price"]], "Column Generation and Branch-and-Price Algorithm": [[120, null]], "Column Generation and Dantzig-Wolfe Decomposition": [[98, "column-generation-and-dantzig-wolfe-decomposition"]], "Column-and-Constraint Generation": [[98, "column-and-constraint-generation"]], "Column-and-Constraint Generation Algorithm": [[25, null]], "Column-and-Constraint-Generation Algorithm": [[142, null]], "Common": [[51, null], [75, "common"], [80, null]], "Complete Example": [[107, "complete-example"], [149, "complete-example"]], "Configure": [[99, "configure"]], "Constant": [[37, null]], "Constants": [[75, null]], "Constraints": [[129, null]], "Create Your Own Callback (Advanced) [TODO]": [[112, null]], "Creating Constraints": [[129, "creating-constraints"]], "Creating Several Variables at Once": [[135, "creating-several-variables-at-once"]], "Creating Variables": [[135, "creating-variables"]], "Creating Your Own Branching Rule (Advanced) [TODO]": [[111, null]], "Creating Your Own Node Class (Advanced) [TODO]": [[113, null]], "Creating Your Own Node Selection Rule (Advanced) [TODO]": [[114, null]], "Creating Your Own Optimizer (Advanced) [TODO]": [[137, null]], "Creating Your Own Universal Callback": [[138, null]], "Creating a Model": [[133, "creating-a-model"]], "Creating the Branch-and-Price Algorithm": [[122, "creating-the-branch-and-price-algorithm"]], "Creating your own callback": [[70, "creating-your-own-callback"]], "Ctr": [[38, null]], "CtrType": [[78, null]], "Current Features": [[98, "current-features"]], "Cuts::KnapsackCover": [[3, null]], "Cutting Planes": [[4, null]], "Dantzig-Wolfe Reformulation": [[122, "dantzig-wolfe-reformulation"]], "DantzigWolfe::ArtificialCosts": [[26, null]], "DantzigWolfe::FarkasPricing": [[28, null]], "DantzigWolfe::Neame": [[32, null]], "DantzigWolfe::Wentges": [[33, null]], "DantzigWolfeDecomposition": [[27, null]], "Decomposition": [[91, "decomposition"]], "Defining the Deterministic Model": [[149, "defining-the-deterministic-model"]], "Defining the Lower-Level Objective Function": [[107, "defining-the-lower-level-objective-function"]], "Defining the Uncertainty Set": [[149, "defining-the-uncertainty-set"]], "Dependencies": [[85, "dependencies"], [87, "dependencies"]], "DepthFirst": [[13, null]], "Describing the Lower-Level Problem": [[107, "describing-the-lower-level-problem"]], "Description": [[85, "description"]], "Detailed Answer": [[97, "detailed-answer"]], "Developer guide": [[86, null]], "Env": [[39, null]], "Events": [[82, null]], "Example": [[0, null], [1, null], [41, null], [70, null], [87, null], [87, null], [98, null], [103, null], [103, null], [103, null], [103, null], [103, null], [103, null], [103, null], [122, null], [140, null]], "Example for Gurobi": [[99, null]], "Example:": [[42, null]], "Examples": [[93, null]], "Expr": [[40, null]], "Expressions": [[131, null]], "External Bilevel Solvers": [[54, null]], "External MIP solvers": [[68, null]], "External Solvers": [[108, null]], "FAQ": [[96, null]], 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41, 42, 43, 45, 49, 70, 72, 87, 91, 95, 97, 98, 101, 103, 107, 122, 123, 124, 125, 129, 130, 131, 132, 133, 135, 136, 138, 139, 140, 141, 143, 144, 145, 149], "arg": [88, 89, 107], "argmax": [125, 143], "argmin": [88, 89], "argst": [35, 38, 50], "argument": [0, 23, 27, 35, 37, 38, 46, 47, 48, 49, 50, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67, 107, 122, 125, 129, 139, 140, 144, 145], "aris": [46, 47, 72], "aro": 98, "arrai": [42, 88, 89, 107], "articl": 85, "artifici": 98, "artificialcost": [31, 69], "as_numer": 37, "ask": [42, 70, 100, 102], "aspect": [125, 143], "assembl": 139, "assign": [36, 37, 43, 45, 48, 49, 84, 93, 122, 123, 124, 125, 145], "assignment_": [91, 92, 122], "associ": [35, 37, 38, 43, 46, 47, 50, 72, 122, 129, 135], "assum": [72, 97, 99, 109, 122, 125, 133, 139, 145, 149], "assumpt": [97, 143], "attach": [109, 133, 145, 149], "attempt": [87, 125], "attent": 42, "attribut": [36, 37, 42, 45, 135], "auto": [0, 23, 27, 37, 38, 39, 41, 42, 43, 45, 50, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67, 88, 89, 90, 91, 92, 94, 95, 98, 100, 102, 107, 122, 125, 129, 131, 133, 135, 136, 138, 139, 140, 141, 149], "autom": 104, "automat": [0, 42, 72, 85, 87, 97, 98, 101, 131, 135], "avail": [42, 70, 84, 87, 107, 122], "avoid": [0, 103, 125], "ayoub": 104, "b": [37, 43, 98, 104], "b_i": [97, 133], "back": [131, 144], "backend": 90, "badli": 125, "baena": 104, "bap": [91, 92], "bar": [122, 144], "bard": [89, 104], "base": [42, 97, 104, 110, 115, 125, 128, 141, 149], "basic": 132, "bbuild": 87, "becaus": [42, 46, 47, 125, 141], "becom": 143, "been": [42, 48, 49, 67, 70, 72, 82, 99, 122, 125, 129, 134, 135, 143], "befor": [42, 98, 99, 125, 140], "beforehand": 42, "begin": [1, 41, 42, 46, 47, 72, 88, 89, 90, 91, 92, 94, 95, 97, 107, 122, 133, 138, 141, 143, 144, 149], "behavior": [39, 70, 98], "behaviour": [0, 23, 27, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67, 82], "behind": [43, 102], "being": [0, 1, 42, 70, 95, 99, 122, 139, 149], "belong": [133, 135], "below": 125, "benchmark": 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125, 128, 134, 135, 139, 140], "box": [70, 98], "branch": [0, 1, 35, 42, 69, 70, 82, 84, 92, 104, 110, 123, 124, 128, 139, 141, 144], "branch_and_bound": [91, 92, 98, 122, 125, 139], "branch_and_pric": [91, 98, 125], "branchandbound": [1, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 29, 42, 69, 70, 90, 91, 92, 122, 125, 139], "branchandboundcallback": [0, 2, 3, 9, 29, 69, 70], "branchandboundcallbackfactori": [0, 1, 3, 9, 29, 69], "branching_rul": [125, 139], "branchingrul": [16, 17, 18, 19, 20, 21], "branchingrulefactori": 0, "branchingrulefactoryt": 0, "breadth": 98, "breadthfirst": [9, 15], "break": 91, "breath": 85, "build": [0, 23, 27, 37, 42, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67, 86, 91, 98], "build_exampl": 103, "build_test": 87, "built": [35, 48, 49, 85, 99, 100], "bunch": 85, "burden": 125, "button": 85, "c": [42, 72, 85, 87, 94, 97, 98, 99, 103, 125, 133, 138, 141], "c1": 42, "c2": 42, "c_": [91, 92, 122], "c_0": 133, "c_i": [91, 92], "call": [0, 1, 35, 36, 37, 38, 41, 42, 45, 46, 47, 50, 70, 87, 95, 98, 99, 103, 107, 122, 125, 129, 131, 133, 135, 138, 139, 140, 141, 143, 144, 145, 149], "callack": 138, "callback": [0, 1, 5, 6, 7, 60, 69, 71, 72, 73, 81, 82, 90, 91, 92, 98, 110, 115, 128, 139, 141], "callbackasbranchandboundcallback": [1, 2], "callbackev": [1, 70, 82, 138], "callbackfactori": [0, 5, 6, 7, 56, 58, 69, 70, 72, 73, 74, 138, 141], "callbacki": 70, "can": [0, 1, 23, 27, 29, 35, 37, 38, 39, 42, 50, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67, 70, 72, 76, 85, 87, 95, 97, 98, 99, 100, 102, 103, 107, 109, 122, 125, 129, 130, 131, 132, 133, 134, 135, 136, 138, 139, 140, 141, 143, 144, 145, 149], "candid": 125, "cannot": [1, 53, 61, 62, 70, 87, 131, 138, 139], "capac": [90, 91, 92, 94, 95, 98, 122, 138, 140, 141, 149], "capacit": [90, 95], "capacity_": [91, 92, 122], "caption": 144, "captur": 87, "carefulli": 125, "carri": 98, "case": [0, 23, 27, 42, 46, 47, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67, 107, 122, 125, 131, 136, 138, 139, 140, 141, 143, 145, 149], "cast": 136, "castro": 104, "catch2": 87, "catchorg": 87, "categori": 125, "caus": [99, 139], "cbc": [68, 69], "cbegin": [46, 47], "ccg": [93, 98, 143, 144, 145], "ccgsepar": [23, 24, 95, 144, 145], "ccgstabil": [23, 24, 95, 144], "cd": [87, 99, 103], "cend": [46, 47], "center": 144, "certain": 70, "certif": [0, 23, 27, 42, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67, 129, 140], "cg": 98, "challeng": 98, "chang": [1, 42, 76, 99, 129, 135], "char": [88, 89, 90, 91, 92, 94, 95, 100, 102], "character": 76, "check": [1, 35, 48, 72, 123, 124, 129, 135, 136, 138, 141, 143, 149], "child": [0, 70, 125, 138], "children": [0, 98], "choic": 103, "choos": 125, "clang": 100, "class": [0, 1, 2, 3, 5, 6, 7, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 30, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 77, 110, 115, 125, 128, 129, 130, 131, 132, 133, 134, 135, 138, 139, 140, 141, 143, 144], "classic": [98, 99, 102, 107, 141, 143, 149], "clean": 98, "clear": [40, 41, 98, 125, 143], "clearli": [125, 143], "clone": [0, 1, 2, 3, 5, 6, 7, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 23, 26, 27, 28, 29, 32, 33, 42, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67, 70, 71, 72, 73, 87, 138], "close": 76, "closer": 76, "closest": [46, 47, 76, 125], "clp": 67, "cmake": [85, 87, 99, 100, 101, 102], "cmake_cxx_standard": [100, 102], "cmake_install_prefix": 99, "cmake_minimum_requir": [100, 102], "cmakelist": [100, 102], "code": [35, 39, 85, 86, 98, 107, 109, 125, 129, 130, 131, 133, 135, 136, 138, 139, 140, 144, 149], "codecov": 87, "coeff": [42, 131], "coeffici": [36, 37, 41, 42, 45, 131], "coefficient_for_x_in_constraint": 129, "coefficient_for_y_in_constraint": 129, "coin": [68, 69, 89, 98], "color": [0, 23, 27, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67], "column": [42, 45, 49, 50, 51, 69, 95, 104, 110, 122, 123, 124, 125, 128, 135, 136, 143, 147, 149], "column_gener": [91, 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16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 30, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 55, 56, 57, 58, 59, 60, 63, 64, 65, 66, 67, 70, 71, 72, 73, 88, 89, 90, 91, 92, 94, 95, 98, 100, 102, 122, 125, 129, 131, 133, 135, 136, 138, 139, 140, 141, 149], "const_iter": [41, 46, 47], "constant": [36, 38, 40, 41, 42, 43, 44, 45, 48, 51, 69, 77, 141], "constexpr": 76, "constiteratorforward": 42, "constrain": 98, "constraint": [35, 36, 38, 42, 43, 45, 48, 69, 70, 72, 76, 91, 92, 95, 97, 104, 107, 110, 115, 122, 125, 128, 130, 131, 132, 135, 138, 139, 140, 144, 147, 149], "constraint1": 135, "constraint1_coefficient_for_x": 135, "constraint2": 135, "constraint2_coefficient_for_x": 135, "constraint_1": 46, "constraint_1_4": 38, "construct": [35, 38, 42, 50, 123, 124], "constructor": [0, 35, 36, 37, 38, 39, 43, 45, 48, 49, 50, 122, 129, 133, 134, 138], "consumpt": [91, 122], "contact": 99, "contain": [0, 37, 48, 49, 97, 98, 103, 122, 149], "content": 85, 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122, 130, 132, 138], "evid": 98, "exactli": [91, 98, 144], "exampl": [23, 27, 38, 43, 45, 46, 47, 48, 49, 50, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67, 88, 89, 90, 91, 92, 94, 95, 100, 102, 105, 125, 132, 133, 134, 135, 139, 145, 147], "example_knapsack": 103, "exce": 94, "except": [37, 38, 45, 46, 47, 50, 139, 149], "execept": 97, "execut": [0, 1, 23, 26, 27, 28, 30, 39, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67, 70, 82, 98, 100, 102, 109, 130, 140, 141], "exist": [1, 42, 70, 72, 87, 98, 132, 143], "expect": 97, "expens": 125, "explain": [85, 87, 125], "explicit": [10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 27, 29, 32, 33, 42, 43, 59, 60, 72, 73], "explicitli": [46, 47, 102, 135, 141, 143], "exploit": 0, "explor": [0, 1, 70, 98], "exponenti": 122, "exportbranchandboundtreetocsv": 1, "expos": 136, "expr": [42, 45, 48, 51, 69, 107, 129, 131], "express": [37, 41, 42, 48, 95, 97, 110, 128, 129, 132, 134, 141, 149], "extend": 140, "extens": 85, "extern": [0, 23, 27, 42, 52, 55, 56, 57, 58, 63, 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115, 120, 125, 128, 138, 139, 142, 147], "written": [97, 143], "wrong": 136, "x": [0, 23, 27, 36, 38, 41, 42, 43, 45, 47, 48, 50, 52, 55, 56, 57, 58, 63, 64, 65, 66, 67, 72, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 102, 107, 122, 129, 131, 133, 135, 136, 138, 139, 140, 141, 143, 144, 149], "x_": [42, 91, 92, 97, 122], "x_0": [41, 42, 97, 129, 131, 134, 141, 143, 144], "x_0_2": 122, "x_0_3": 122, "x_0_6": 122, "x_1": [41, 42, 129, 131, 134, 141], "x_1_4": [50, 122], "x_1_5": 122, "x_1_7": 122, "x_2_0": 122, "x_2_1": 122, "x_i": [90, 95, 97, 138, 141, 144, 149], "x_ij": [91, 92, 122], "x_j": [94, 97, 125, 133], "xi": [37, 72, 95, 131, 143, 144, 149], "xi_0": [72, 131], "xi_1": 131, "xi_j": [95, 149], "xml": [53, 61, 62], "xpress": [68, 69], "y": [36, 38, 43, 45, 48, 88, 89, 90, 95, 107, 129, 135, 141, 143, 144, 149], "y_": [90, 95, 135, 149], "y_0_0": 135, "ye": [87, 99, 102, 103, 109], "yet": [48, 49, 70, 76, 98, 125, 130, 135, 143], "yield": 37, "you": [0, 1, 42, 70, 72, 85, 87, 98, 99, 100, 101, 102, 103, 107, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 123, 124, 125, 126, 127, 129, 135, 136, 137, 138, 140, 141, 143, 144, 145, 146, 149], "your": [0, 39, 87, 98, 99, 101, 102, 107, 109, 110, 115, 116, 117, 118, 119, 120, 121, 123, 124, 126, 127, 128, 130, 139, 141, 142, 143, 145, 147, 149], "your_cmake_opt": 99, "your_other_cmake_opt": 99, "your_valu": 99, "z": [125, 133, 138, 141], "z_": [89, 107], "z_i": 125, "z_j": 125, "zeng": 104, "zero": [37, 41, 46, 47, 76, 97], "zhao": 104, "\u00e4": 104, "\u00e7": 104, "\u00e9": 104}, "titles": ["BranchAndBound", "BranchAndBoundCallback", "BranchAndBoundCallbackFactory", "Cuts::KnapsackCover", "Cutting Planes", "Heuristics::LocalBranching", "Heuristics::RENS", "Heuristics::SimpleRounding", "Heuristics", "Branch-and-Bound Algorithm", "BestBound", "BestEstimate", "BreadthFirst", "DepthFirst", "WorstBound", "Node Selection Rules", "FirstInfeasibleFound", "LeastInfeasible", "MostInfeasible", "PseudoCost", 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"LazyCutCallback", "UserCutCallback", "Universal Callbacks", "Constants", "Tolerances and Numerics", "API", "CtrType", "VarType", "Common", "Type List", "Events", "Universal Callbacks", "Benchmark", "Build the doc!", "Developer guide", "Unit and integration tests", "Bilevel LP-LP (KKT Reformulation)", "Bilevel MILP-MILP (MibS)", "Facility Location Problem (Branch-and-Bound)", "Generalized Assignment Problem (Branch-and-Price)", "Generalized Assignment Problem (Column Generation + Penalty Method)", "Examples", "Knapsack Problem (HiGHS)", "Two-Stage Robust Facility Location Problem (CCG)", "FAQ", "Why is Eigen necessary to solve QPs and QCQPs with Mosek?", "What Is Idol?", "Global Installation", "Linking Your C++ Project", "Installation", "Local Installation", "List of CMake Options", "References", "Bilevel Optimization", "Modeling", "Modeling an Optimistic Bilevel Problem", "External Solvers", "Solving Mixed-Integer Bilevel Problems with MibS", "Tutorials", "Creating Your Own Branching Rule (Advanced) [TODO]", "Create Your Own Callback (Advanced) [TODO]", "Creating Your Own Node Class (Advanced) [TODO]", "Creating Your Own Node Selection Rule (Advanced) [TODO]", "Branch-and-Bound Algorithm", "Adding User Cuts and Lazy Constraints [TODO]", "Writing an LP-based Branch-and-Bound [TODO]", "Modeling and Solving A Dantzig-Wolfe Reformulation [TODO]", "Using In-Out Stabilization [TODO]", "Column Generation and Branch-and-Price Algorithm", "The Integer Master Heuristic [TODO]", "Branch-and-Price 101", "Heuristic Pricing (to come)", "In-Out Stabilization (to come)", "Using Strong Branching", "Writing A Branch-and-Cut-and-Price Algorithm [TODO]", "Writing A Branch-and-Price Algorithm [TODO]", "Mixed-Integer Programming", "Constraints", "The Environment", "Expressions", "Modeling", "Models", "Objective Functions", "Variables", "Getting Access to The Underlying Optimizer", "Creating Your Own Optimizer (Advanced) [TODO]", "Creating Your Own Universal Callback", "Optimizers", 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\ No newline at end of file diff --git a/tutorials/bilevel-optimization/index.html b/tutorials/bilevel-optimization/index.html new file mode 100644 index 00000000..70e1fd7a --- /dev/null +++ b/tutorials/bilevel-optimization/index.html @@ -0,0 +1,155 @@ + + + + + + + + + Bilevel Optimization — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Modeling

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+ + +
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+ +
+

Modeling an Optimistic Bilevel Problem

+

In this tutorial, we will see how to model an optimistic bilevel problem in idol.

+

To follow this tutorial, you should be familiar with bilevel optimization and modeling optimization problems in idol. +If this is not the case, we recommend you to read the tutorial on MIP modeling.

+ +
+

Problem Definition and Main Steps

+

We consider the optimistic bilevel problem

+
+\[\begin{split}\begin{align} + \min_{x, y} \ & -x + -10 y \\ + \text{s.t.} \ & x \in \mathbb Z_+ \\ + & y\in + \begin{array}[t]{rl} + \displaystyle \underset{y}{\text{arg min}} \ & y \\ + \text{s.t.} \ & -25 x + 20 y \leq 30, \\ + & x + 2 y \leq 10, \\ + & 2 x - y \leq 15, \\ + & 2 x + 10 y \geq 15, \\ + & y \geq 0, \\ + & y \in \mathbb Z_+. + \end{array} +\end{align}\end{split}\]
+

This is an ILP-ILP bilevel problem which is taken from [10] (Example 1).

+

To model this problem in idol, there are three main steps:

+
    +

  1. Define the high-point relaxation (HPR) model, i.e., in our example,

    +
    +
    +\[\begin{split}\begin{align} + \min_{x, y} \ & -x + -10 y \\ + \text{s.t.} \ & x \in \mathbb Z_+ \\ + & -25 x + 20 y \leq 30, \\ + & x + 2 y \leq 10, \\ + & 2 x - y \leq 15, \\ + & 2 x + 10 y \geq 15, \\ + & y \geq 0, \\ + & y \in \mathbb Z_+. +\end{align}\end{split}\]
    +

    This model defines all constraints and variables of the bilevel problem. +The feasible region is classically called the shared-constraint set.

    +
    +
    2. +

  2. Describe which variables and constraints are part of the lower-level problem.

    3. +

  3. Define the lower-level objective function.

    4. +
+
+
+

Modeling the High-Point Relaxation

+

The HPR can be modeled in the same way as a classical optimization problem. +If you are not familiar with modeling optimization problems in idol, +we recommend you to read the tutorial on MIP modeling.

+

Here is the code to model the HPR of the bilevel problem.

+
Env env;
+Model high_point_relaxation(env);
+
+auto x = high_point_relaxation.add_var(0, Inf, Integer, "x");
+auto y = high_point_relaxation.add_var(0, Inf, Integer, "y");
+
+high_point_relaxation.set_obj_expr(-x - 10 * y);
+auto follower_c1 = high_point_relaxation.add_ctr(-25 * x + 20 * y <= 30);
+auto follower_c2 = high_point_relaxation.add_ctr(x + 2 * y <= 10);
+auto follower_c3 = high_point_relaxation.add_ctr(2 * x - y <= 15);
+auto follower_c4 = high_point_relaxation.add_ctr(2 * x + 10 * y >= 15);
+
+
+
+
+

Describing the Lower-Level Problem

+

To describe the lower-level problem, we need to specify which variables and constraints are part of the lower-level problem. +This done by creating an object of type Bilevel::LowerLevelDescription and calling the methods set_follower_var +and set_follower_ctr.

+
Bilevel::LowerLevelDescription description(env);
+description.set_follower_var(y);
+description.set_follower_ctr(follower_c1);
+description.set_follower_ctr(follower_c2);
+description.set_follower_ctr(follower_c3);
+description.set_follower_ctr(follower_c4);
+
+
+

Note that this does nothing more but to create two new Annotation<Var|Ctr> to indicate variables and constraints that are part of the lower-level problem. +These annotations are used by the bilevel solver to identify the lower-level problem. +In particular, all variables and constraints that are not annotated as follower variables or constraints are considered as leader variables and constraints +and have an annotation which is equal to MasterId.

+

Also note that it is possible to create and use your own annotation. For instance, the following code is equivalent to the previous one.

+
Annotation<Var> follower_vars(env, MasterId, "follower_variables");
+y.set(follower_vars, 0);
+
+Annotation<Ctr> follower_ctrs(env, MasterId, "follower_constraints");
+follower_c1.set(follower_ctrs, 0);
+follower_c2.set(follower_ctrs, 0);
+follower_c3.set(follower_ctrs, 0);
+follower_c4.set(follower_ctrs, 0);
+
+
+Bilevel::LowerLevelDescription description(follower_vars, follower_ctrs);
+
+
+
+
+

Defining the Lower-Level Objective Function

+

Finally, we need to define the lower-level objective function. +This is done by calling the method set_follower_obj_expr on the object of type Bilevel::LowerLevelDescription. +An Expr object is passed as argument to this method.

+
description.set_follower_obj_expr(y);
+
+
+
+
+

Complete Example

+

A complete example is available here.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/bilevel-optimization/wrappers/index.html b/tutorials/bilevel-optimization/wrappers/index.html new file mode 100644 index 00000000..be80b717 --- /dev/null +++ b/tutorials/bilevel-optimization/wrappers/index.html @@ -0,0 +1,144 @@ + + + + + + + + + External Solvers — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

External Solvers

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/bilevel-optimization/wrappers/mibs.html b/tutorials/bilevel-optimization/wrappers/mibs.html new file mode 100644 index 00000000..d97d8f7f --- /dev/null +++ b/tutorials/bilevel-optimization/wrappers/mibs.html @@ -0,0 +1,165 @@ + + + + + + + + + Solving Mixed-Integer Bilevel Problems with MibS — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Solving Mixed-Integer Bilevel Problems with MibS

+

MibS is an optimization solver for mixed-integer bilevel problems. +Idol seemlessly integrates with MibS to solve bilevel problems.

+

We will see that solving bilevel problems with MibS is very similar to solving bilevel problems with any optimizer in idol.

+

We will assume that you have your bilevel problem modeled already in idol. In particular, we consider that you have +two variables:

+
    +

  1. high_point_relaxation which is a Model representing the high-point relaxation of your bilevel problem.

    2. +

  2. description which is a Bilevel:LowerLevelDescription object representing the bilevel problem. If you do not know what this is or how to create it, please refer to the previous tutorial.

    3. +
+

Then, you can solve your bilevel problem with MibS as follows:

+
high_point_relaxation.use(Bilevel::MibS(description));
+
+high_point_relaxation.optimize();
+
+std::cout << save_primal(high_point_relaxation) << std::endl;
+
+
+

Notice how the MibS solver is attached to the high-point relaxation model.

+

The rest of the code is the same as with any other solver.

+
+

Warning

+

To use MibS, you need to have the MibS library installed on your system and idol linked to the executable. +You can download MibS from here.

+

Then, idol should be compiled with the options USE_MIBS=YES, USE_CLP=YES.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/index.html b/tutorials/index.html new file mode 100644 index 00000000..a79031f2 --- /dev/null +++ b/tutorials/index.html @@ -0,0 +1,215 @@ + + + + + + + + + Tutorials — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Tutorials

+
+

Mixed-Integer Programming

+
+ +
+
+
+

Bilevel Optimization

+
+ +
+
+
+

Robust Optimization

+
+ +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/branch-and-bound/create-branching-rule.html b/tutorials/mixed-integer-programming/branch-and-bound/create-branching-rule.html new file mode 100644 index 00000000..62b2a68d --- /dev/null +++ b/tutorials/mixed-integer-programming/branch-and-bound/create-branching-rule.html @@ -0,0 +1,151 @@ + + + + + + + + + Creating Your Own Branching Rule (Advanced) [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Creating Your Own Branching Rule (Advanced) [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/branch-and-bound/create-callback.html b/tutorials/mixed-integer-programming/branch-and-bound/create-callback.html new file mode 100644 index 00000000..837e5563 --- /dev/null +++ b/tutorials/mixed-integer-programming/branch-and-bound/create-callback.html @@ -0,0 +1,151 @@ + + + + + + + + + Create Your Own Callback (Advanced) [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Create Your Own Callback (Advanced) [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/branch-and-bound/create-node-selection-rule.html b/tutorials/mixed-integer-programming/branch-and-bound/create-node-selection-rule.html new file mode 100644 index 00000000..940806c2 --- /dev/null +++ b/tutorials/mixed-integer-programming/branch-and-bound/create-node-selection-rule.html @@ -0,0 +1,151 @@ + + + + + + + + + Creating Your Own Node Selection Rule (Advanced) [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Creating Your Own Node Selection Rule (Advanced) [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/branch-and-bound/create-node.html b/tutorials/mixed-integer-programming/branch-and-bound/create-node.html new file mode 100644 index 00000000..b0ad1ca4 --- /dev/null +++ b/tutorials/mixed-integer-programming/branch-and-bound/create-node.html @@ -0,0 +1,151 @@ + + + + + + + + + Creating Your Own Node Class (Advanced) [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Creating Your Own Node Class (Advanced) [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/branch-and-bound/index.html b/tutorials/mixed-integer-programming/branch-and-bound/index.html new file mode 100644 index 00000000..8bfb5299 --- /dev/null +++ b/tutorials/mixed-integer-programming/branch-and-bound/index.html @@ -0,0 +1,160 @@ + + + + + + + + + Branch-and-Bound Algorithm — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/branch-and-bound/user-cuts-lazy-constraints.html b/tutorials/mixed-integer-programming/branch-and-bound/user-cuts-lazy-constraints.html new file mode 100644 index 00000000..938d08a7 --- /dev/null +++ b/tutorials/mixed-integer-programming/branch-and-bound/user-cuts-lazy-constraints.html @@ -0,0 +1,151 @@ + + + + + + + + + Adding User Cuts and Lazy Constraints [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Adding User Cuts and Lazy Constraints [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/branch-and-bound/write-branch-and-bound.html b/tutorials/mixed-integer-programming/branch-and-bound/write-branch-and-bound.html new file mode 100644 index 00000000..3a51634d --- /dev/null +++ b/tutorials/mixed-integer-programming/branch-and-bound/write-branch-and-bound.html @@ -0,0 +1,151 @@ + + + + + + + + + Writing an LP-based Branch-and-Bound [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Writing an LP-based Branch-and-Bound [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/dantzig-wolfe/dantzig-wolfe.html b/tutorials/mixed-integer-programming/dantzig-wolfe/dantzig-wolfe.html new file mode 100644 index 00000000..b6cf0282 --- /dev/null +++ b/tutorials/mixed-integer-programming/dantzig-wolfe/dantzig-wolfe.html @@ -0,0 +1,151 @@ + + + + + + + + + Modeling and Solving A Dantzig-Wolfe Reformulation [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Modeling and Solving A Dantzig-Wolfe Reformulation [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/dantzig-wolfe/in-out-stabilization.html b/tutorials/mixed-integer-programming/dantzig-wolfe/in-out-stabilization.html new file mode 100644 index 00000000..03248cca --- /dev/null +++ b/tutorials/mixed-integer-programming/dantzig-wolfe/in-out-stabilization.html @@ -0,0 +1,151 @@ + + + + + + + + + Using In-Out Stabilization [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

Using In-Out Stabilization [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/dantzig-wolfe/index.html b/tutorials/mixed-integer-programming/dantzig-wolfe/index.html new file mode 100644 index 00000000..6b84b7c1 --- /dev/null +++ b/tutorials/mixed-integer-programming/dantzig-wolfe/index.html @@ -0,0 +1,161 @@ + + + + + + + + + Column Generation and Branch-and-Price Algorithm — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/dantzig-wolfe/integer-master.html b/tutorials/mixed-integer-programming/dantzig-wolfe/integer-master.html new file mode 100644 index 00000000..9ca04b28 --- /dev/null +++ b/tutorials/mixed-integer-programming/dantzig-wolfe/integer-master.html @@ -0,0 +1,151 @@ + + + + + + + + + The Integer Master Heuristic [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

The Integer Master Heuristic [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/dantzig-wolfe/old/dantzig_wolfe.html b/tutorials/mixed-integer-programming/dantzig-wolfe/old/dantzig_wolfe.html new file mode 100644 index 00000000..2a9d128e --- /dev/null +++ b/tutorials/mixed-integer-programming/dantzig-wolfe/old/dantzig_wolfe.html @@ -0,0 +1,365 @@ + + + + + + + + + Branch-and-Price 101 — idol + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Branch-and-Price 101

+

In this section, we will show how to use the Branch-and-Price solver to solve the Generalized Assignment Problem (GAP) +using an external solver to solve each sub-problem.

+
+

Hint

+

This tutorial regards the advanced topic of Column Generation and Dantzig-Wolfe decomposition. +Rudimentary notions in the following subjects are recommended:

+ +
+
+

See also

+

A Benchmark on Generalized Assignment Problem is +available.

+
+
+

Mathematical Models

+

In this section, we assume that the reader is familiar with GAP. +Let \(m\) be the number of agents and let \(n\) be the number of tasks to be performed.

+

Let \(c_{ij}\) be the cost for assigning task \(j\) to agent \(i\), \(w_{ij}\) be the resource +consumption of task \(j\) when performed by agent \(i\) and let \(t_i\) be the resource capacity of agent +\(i\).

+
+

Original Formulation

+

The Generalized Assignment Problem (GAP) can be modeled as

+
+\[\begin{split}\min_x \quad & \sum_{i=1}^m\sum_{j=1}^n c_{ij} x_{ij} \\ +\textrm{s.t.} \quad & \sum_{j=1}^n w_{ij} x_{ij} \le t_i & i=1,...,m, \\ +& \sum_{i=1}^m x_{ij} = 1 & j = 1,...,n, \\ +& x_{ij}\in\{0,1\} & i=1,...,m, j=1,...,n.\end{split}\]
+

Here, variable \(x_{ij}\) encodes the assignment decision and equals 1 if and only if task \(j\) is assigned to +agent \(i\).

+
+
+

Dantzig-Wolfe Reformulation

+

Let us enumerate the list of all feasible assignments, i.e., let

+
+\[\{\bar x^e_{ij} \}_{e\in E} = \left\{ x \in \{ 0,1 \}^{mn} : \sum_{j=1}^n w_{ij}x_{ij} \le t_i \quad i=1,...,m \right\},\]
+

in which \(E\) denotes a list for their indices. The Dantzig-Wolfe reformulation of GAP reads

+
+\[\begin{split}\min_{\lambda} \quad & \sum_{e\in E} \lambda_e\left( \sum_{i=1}^m\sum_{j=1}^n c_{ij}\bar x_{ij}^e \right) \\ +\textrm{s.t.} \quad & \sum_{e\in E} \lambda_e \left( \sum_{i=1}^m \bar x_{ij}^e \right) = 1 & j=1,...,n, \\ +& \sum_{e\in E} \lambda_e = 1, \\ +& \lambda_e \in \{ 0, 1 \} & \forall e\in E.\end{split}\]
+

Though this model contains an exponential number of variables (i.e., columns) it can be solved efficiently using +Column Generation and Branch-and-price. In such a case, the pricing problem is a Knapsack Problem.

+
+
+
+

Automatic Reformulation

+

The simplest way to solve a problem using Column Generation and idol is through its automatic reformulation feature. +To use this, one simply needs to give the original space formulation of the problem +and to indicate which constraints should be moved to the pricing problem (here, the knapsack constraints).

+
+

The Original Formulation

+

To read an instance for GAP, we first need to include the header file located in "idol/problems/generalized-assignment-problem/GAP_Instance.h". +This will allow us to use idol’s instance parser for GAP. +Assuming that the instance file is named instance.txt, it can be loaded as follows.

+
const auto instance = Problems::GAP::read_instance("instance.txt");
+
+const unsigned int n_agents = instance.n_agents();
+const unsigned int n_jobs = instance.n_jobs();
+
+
+

We are now ready to model our problem (for more details, refer to this tutorial on modeling)

+
// Create optimization environment
+Env env;
+
+// Create model
+Model model(env);
+
+// Create assignment variables (x_ij binaries)
+auto x = model.add_vars(Dim<2>(n_agents, n_jobs), 0., 1., Binary, "x");
+
+// Create knapsack constraints (i.e., capacity constraints)
+for (unsigned int i = 0 ; i < n_agents ; ++i) {
+    model.add_ctr(idol_Sum(j, Range(n_jobs), instance.resource_consumption(i, j) * x[i][j]) <= instance.capacity(i), "capacity_" + std::to_string(i));
+}
+
+// Create assignment constraints
+for (unsigned int j = 0 ; j < n_jobs ; ++j) {
+    model.add(idol_Sum(i, Range(n_agents), x[i][j]) == 1, "assignment_" + std::to_string(j));
+}
+
+// Set the objective function
+model.set_obj_expr(idol_Sum(i, Range(n_agents), idol_Sum(j, Range(n_jobs), instance.cost(i, j) * x[i][j])));
+
+
+
+
+

Giving Decomposition Instructions

+

We are now at the crucial step of indicating which constraint should be moved to the pricing problem. In idol, this is done by using +annotations. Annotations are additional information associated to an optimization object (e.g., a constraint or a variable). +Note that annotations are global, i.e., they do not relate to a given optimization model.

+

Every annotation is formed with two template arguments: an optimization object type and a value type. Here, we want to add +annotations to constraints, thus, the optimization object type must be Ctr. The value type can typically be any desired +type. Here, however, the Dantzig-Wolfe decomposition needs an annotation corresponding to an unsigned int which corresponds +to the sub-problem index to which the constraint shall be moved to. Thus, we create the annotation as follows.

+
Annotation<Ctr, unsigned int> decomposition(env, "decomposition", MasterId);
+
+
+

Here, we pass three arguments to the constructor of Annotation<Ctr, unsigned int>. First, we pass the optimization +environment which will store the annotation. Then, a name (mandatory) is given to the annotation, here, “decomposition”. +Finally, a default value is given and equals “MasterId”. This will tell idol that constraints which have not been annotated +should remain in the master problem.

+

Now, observe how the annotation is applied to the capacity constraints.

+
for (unsigned int i = 0 ; i < n_agents ; ++i) {
+    Ctr capacity = model.add_ctr(idol_Sum(j, Range(n_jobs), instance.resource_consumption(i, j) * x[i][j]) <= instance.capacity(i), "capacity_" + std::to_string(i));
+    capacity.set(decomposition, i); // <-- Annotating the capacity constraint
+}
+
+
+

Here, the first capacity constraint is moved to the first pricing problem (id: 0), +the second constraint to the second pricing problem (id: 1), and so on.

+

Note that another decomposition would be materialized as follows.

+
for (unsigned int i = 0 ; i < n_agents ; ++i) {
+    Ctr capacity = model.add_ctr(idol_Sum(j, Range(n_jobs), instance.resource_consumption(i, j) * x[i][j]) <= instance.capacity(i), "capacity_" + std::to_string(i));
+    capacity.set(decomposition, 0); // <-- Annotating the capacity constraint
+}
+
+
+

Here, all the knapsack constraints would be moved to the same pricing problem (id: 0).

+
+
+
+

Creating the Branch-and-Price Algorithm

+

Now that the desired decomposition has been specified, we can specify the desired optimizer to solve our model. +Here, we want to solve our problem using a Branch-and-Price algorithm, i.e., a Branch-and-Bound algorithm where each node +in the Branch-and-Bound tree is solved by a Dantzig-Wolfe decomposition.

+

To begin with, we need to give some specification about how each sub-problem will be solved. In other words, we need +to specify the optimizer(s) used for pricing during the column generation process. This is done by first creating a +DantzigWolfe::SubProblem object.

+
const auto sub_problem_specifications = DantzigWolfe::SubProblem()
+                                            .add_optimizer(Gurobi());
+
+
+

Then, we can create our Column Generation algorithm (factory) in the following way.

+
const auto column_generation = DantzigWolfeDecomposition(decomposition)
+                                    .with_master_optimizer(Gurobi::ContinuousRelaxation())
+                                    .with_default_sub_problem_spec(sub_problem_specifications);
+
+
+

Here, we are solving the relaxed master problem using Gurobi. We also define our default sub-problem specifications to +be the one we just defined.

+

Our remaining task is to embed our Column Generation routine inside of a Branch-and-Bound algorithm. +This can be done as follows.

+
const auto branch_and_bound = BranchAndBound()
+                                /* Each node is solved by Column Generation */
+                                .with_node_optimizer(column_generation)
+
+                                /* Variables are selected for branching using the most-infeasible rule */
+                                .with_branching_rule(MostInfeasible())
+
+                                /* Nodes are selected using the best-bound rule */
+                                .with_node_selection_rule(BestBound())
+
+                                /* The algorithm will run with a time limit of 3600 */
+                                .with_time_limit(3600)
+
+);
+
+
+

Then, we can tell idol to use this algorithm for solving our model by using the Model::use method.

+
model.use(branch_and_bound);
+
+
+

Finally, one can simply call the Model::optimize method as follows.

+
model.optimize();
+
+
+

That’s it! The problem is being solved by column generation, and possibly branching on fractional variables.

+
+

Hint

+

Note that it is possible to obtain logs using the with_log_level method on the desired optimizer. +For instance, one may want to have +logs for the branch-and-bound optimizer. Then, one should do as follows.

+
model.use(
+    BranchAndBound()
+
+        /* ... omitting identical details */
+
+        .with_log_level(Info, Blue)
+);
+
+
+
+

The rest remains unchanged and one can use Model::optimize to solve the problem and retrieve the solution +through methods like Model::get_status and Model::get_var_primal.

+
+

Example

+

Here, we can solve our model using a Dantzig-Wolfe decomposition.

+
model.optimize();
+
+std::cout << save_primal(model) << std::endl;
+
+
+

This will produce the following output (e.g.).

+
[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Master> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <ObjVal=110000> <NGen=0> <BestBnd=-inf> <BestObj=110000.00> <RGap=90909090909090928.00 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-50093.00000> <NGen=0> <BestBnd=-inf> <BestObj=110000.00000> <RGap=90909090909090928.00000 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-40096.00000> <NGen=0> <BestBnd=-inf> <BestObj=110000.00000> <RGap=90909090909090928.00000 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-30068.00000> <NGen=0> <BestBnd=-inf> <BestObj=110000.00000> <RGap=90909090909090928.00000 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Master> <Iter=10> <TimT=0.01> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <ObjVal=-242.5> <NGen=1> <BestBnd=-277.86> <BestObj=-242.50> <RGap=14.58 %> <AGap=35.36>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=10> <TimT=0.01> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-7.90269> <NGen=1> <BestBnd=-277.85653> <BestObj=-242.50000> <RGap=14.58001 %> <AGap=35.35653>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=10> <TimT=0.01> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-14.49817> <NGen=1> <BestBnd=-277.85653> <BestObj=-242.50000> <RGap=14.58001 %> <AGap=35.35653>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=10> <TimT=0.01> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-1.36131> <NGen=1> <BestBnd=-277.85653> <BestObj=-242.50000> <RGap=14.58001 %> <AGap=35.35653>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Master> <Iter=16> <TimT=0.02> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <ObjVal=-242.5> <NGen=0> <BestBnd=-242.51> <BestObj=-242.50> <RGap=0.00 %> <AGap=0.01>
+[2023-04-07 13:45:44]   [info]  [branch-and-bound]      <Node=   0 > <TimT=   0.02> <TimI=   0.02> <Levl=  0> <Stat=1> <Reas=0> <ObjVal=  -242.50> <BestBnd=     -inf> <BestObj=     +inf> <RelGap=200.00> <AbsGap= +inf>
+[2023-04-07 13:45:44]   [info]  [branch-and-bound]      <Node=   H-> <TimT=   0.02> <TimI=   0.02> <Levl=  0> <Stat=1> <Reas=1> <ObjVal=  -233.00> <BestBnd=     -inf> <BestObj=  -233.00> <RelGap=42918454935603896320.00> <AbsGap= +inf>
+[2023-04-07 13:45:44]   [info]  [branch-and-bound]      <Node=   0+> <TimT=   0.02> <TimI=   0.02> <Levl=  0> <Stat=1> <Reas=0> <ObjVal=  -242.50> <BestBnd=  -242.50> <BestObj=  -233.00> <RelGap= 4.08> <AbsGap= 9.50>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Master> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <ObjVal=-233> <NGen=0> <BestBnd=-inf> <BestObj=-233.00> <RGap=42918454935603896320.00 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-22579.50000> <NGen=0> <BestBnd=-inf> <BestObj=-233.00000> <RGap=42918454935603896320.00000 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-17547.50000> <NGen=0> <BestBnd=-inf> <BestObj=-233.00000> <RGap=42918454935603896320.00000 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-17552.50000> <NGen=0> <BestBnd=-inf> <BestObj=-233.00000> <RGap=42918454935603896320.00000 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Master> <Iter=10> <TimT=0.01> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <ObjVal=-233> <NGen=2> <BestBnd=-235.81> <BestObj=-233.00> <RGap=1.21 %> <AGap=2.81>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=10> <TimT=0.01> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-0.25760> <NGen=2> <BestBnd=-235.80933> <BestObj=-233.00000> <RGap=1.20572 %> <AGap=2.80933>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=10> <TimT=0.01> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-0.22397> <NGen=2> <BestBnd=-235.80933> <BestObj=-233.00000> <RGap=1.20572 %> <AGap=2.80933>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=10> <TimT=0.01> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-0.36123> <NGen=2> <BestBnd=-235.80933> <BestObj=-233.00000> <RGap=1.20572 %> <AGap=2.80933>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Master> <Iter=13> <TimT=0.01> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <ObjVal=-233> <NGen=2> <BestBnd=-233.02> <BestObj=-233.00> <RGap=0.01 %> <AGap=0.02>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Master> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <ObjVal=4747> <NGen=0> <BestBnd=-inf> <BestObj=4747.00> <RGap=2106593638087168512.00 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-12451.00000> <NGen=0> <BestBnd=-inf> <BestObj=4747.00000> <RGap=2106593638087168512.00000 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=-7492.00000> <NGen=0> <BestBnd=-inf> <BestObj=4747.00000> <RGap=2106593638087168512.00000 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Pricing> <Iter=0> <TimT=0.00> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <Obj=0.00000> <NGen=0> <BestBnd=-inf> <BestObj=4747.00000> <RGap=2106593638087168512.00000 %> <AGap=+inf>
+[2023-04-07 13:45:44]   [info]  [dantzig-wolfe] <Type=Master> <Iter=8> <TimT=0.01> <TimI=0.00> <Stat=Optimal> <Reas=Proved> <ObjVal=-218> <NGen=1> <BestBnd=-224.13> <BestObj=-218.00> <RGap=2.81 %> <AGap=6.13>
+
++-----------------------
+| Status: Optimal
+| Reason: Proved
+| ObjVal: -233.00
+| Values:
+|       x_1_7 = 1.00
+|       x_2_0 = 1.00
+|       x_2_1 = 1.00
+|       x_1_4 = 1.00
+|       x_1_5 = 1.00
+|       x_0_6 = 1.00
+|       x_0_2 = 1.00
+|       x_0_3 = 1.00
++-----------------------
+
+
+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Henri Lefebvre.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/dantzig-wolfe/old/heuristic.html b/tutorials/mixed-integer-programming/dantzig-wolfe/old/heuristic.html new file mode 100644 index 00000000..5bffe6de --- /dev/null +++ b/tutorials/mixed-integer-programming/dantzig-wolfe/old/heuristic.html @@ -0,0 +1,127 @@ + + + + + + + + + Heuristic Pricing (to come) — idol + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Heuristic Pricing (to come)

+

Hey, you’ve implemented your first Branch-and-Price algorithm and are curious about how to use your own heuristic for +pricing during the Column Generation algorithm? Then you are at the right place! If not, make sure to check the +Generalized Assignment Problem tutorial.

+
+

Warning

+

This tutorial is still under construction.

+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Henri Lefebvre.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/dantzig-wolfe/old/stabilization.html b/tutorials/mixed-integer-programming/dantzig-wolfe/old/stabilization.html new file mode 100644 index 00000000..e39bb8f7 --- /dev/null +++ b/tutorials/mixed-integer-programming/dantzig-wolfe/old/stabilization.html @@ -0,0 +1,127 @@ + + + + + + + + + In-Out Stabilization (to come) — idol + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

In-Out Stabilization (to come)

+

Hey, you’ve implemented your first Branch-and-Price algorithm and are curious about how to stabilize your Column +Generation algorithm? Then you are at the right place! If not, make sure to check the +Generalized Assignment Problem tutorial.

+
+

Warning

+

This tutorial is still under construction.

+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Henri Lefebvre.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/dantzig-wolfe/strong-branching.html b/tutorials/mixed-integer-programming/dantzig-wolfe/strong-branching.html new file mode 100644 index 00000000..a2c76a28 --- /dev/null +++ b/tutorials/mixed-integer-programming/dantzig-wolfe/strong-branching.html @@ -0,0 +1,323 @@ + + + + + + + + + Using Strong Branching — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

Using Strong Branching

+ +
+

Basics

+

Strong Branching is a technique that falls into the category of variable selection rules, a crucial aspect of +Branch-and-Bound algorithms.

+

More specifically, the task is to decide which variable to branch on at each node of the Branch-and-Bound tree, i.e, +among a set of branching candidates whose value must be integer, one must +decide which variable with fractional value in the current solution of the relaxation to choose for creating +child nodes.

+

The most common rule is the so-called Most-Infeasible rule, which selects a variable whose fractional +part is closest to 0.5. Unfortunately, this rule performs badly in practice. Most importantly, if solving a node is +computationally hard, it makes sense to spend some time in carefully choosing the variable to branch on. This is typically +the case when nodes are solved using Column Generation.

+
+

Hint

+

Clearly, Strong Branching is not only used in the context of column generation. It is a general technique that can be +used in any context where solving a node is computationally expensive. Thus, this tutorial is not specific to the +context of column generation, though we will use it as an example.

+
+

The idea of Strong Branching is to evaluate the effect of branching on a variable before branching actually happens.

+

To be more clear, let \(C\) be a set of indices for branching candidates at a given node, i.e., for each \(j\in C\), +\(x_j\) is an integer variable in the original problem but with \(x_j^*\notin\mathbb Z\) at the current node. +Strong Branching tries to evaluate the effect of applying branching constraints \(x_j \le \lfloor x_j^* \rfloor\) and +\(x_j \ge \lceil x_j^* \rceil\) to the current node. To do so, it solves, before branching happens, and for each \(j\in C\), +both the left and right child node. Let \(z_i^\le\) and \(z_i^\ge\) denote the (optimal) value of the +left and right nodes if branching is performed on \(x_j\). The “effect of branching on \(x_j\)” is then estimated by +computing a score, noted \(\text{Score}(j)\), based on \(z_j^\le\) and \(z_j^\ge\). Then Strong Branching +selects the variable with an index \(j^*\) such that (for minimization problems)

+
+\[j^* \in \text{argmax}\{ \text{Score}(j) : j\in C \}.\]
+

Empirically, Strong Branching is known to produce substantially smaller Branch-and-Bound trees compared to other +branching rules. Unfortunately, computing \(\text{Score}(j)\) typically requires a lot of time. To avoid this, +several techniques have been designed such as Restricted Strong Branching and Strong Branching with Phases (see below).

+

For more details, please refer to [1].

+
+

Scoring Functions

+

Two common scoring functions \(\text{Score}(j)\) are found in the literature. The linear formula [9]

+
+\[\text{LinearScore}(j) := (1 - \mu) \min(\Delta_j^\le, \Delta_j^\ge) + \mu \max(\Delta_j^\le, \Delta_j^\ge),\]
+

and the product formula

+
+\[\text{ProductScore}(j) := \max(\Delta_j^\le, \varepsilon) \max(\Delta_j^\ge, \varepsilon),\]
+

in which \(\Delta_j^\le := z_j^\le - z^*\) and \(\Delta_j^\ge := z_j^\ge - z^*\) with \(z^*\) denoting the +(optimal) value of the current node. Parameters \(\mu\in[0,1]\) and \(\varepsilon > 0\) are given. +In idol, \(\mu = 1/6\) and \(\varepsilon = 10^{-6}\).

+
+
+

Variants

+

There are several variants of Strong Branching. The most common ones are:

+
    +

  • Full Strong Branching denotes the standard Strong Branching rule which solves all \(2|C|\) nodes at each branching +decision. The drawback of this approach is that it may take a lot of time to solve all these sub-problems before branching +actually happens.

    • +

  • Restricted Strong Branching is an attempt to reduce the computational burden of Full Strong Branching. The idea is to +consider only a maximum of \(K\) branching candidates at each branching decision instead of the whole set \(C\). +Thus, \(C\) is replaced by a smaller set \(R\subseteq C\) such that \(|R| = K\) with \(K\) fixed. +The “restricted branching candidate set” \(R\) is created by taking the \(K\) first variables selected by, yet +another, branching rule, e.g., the most-infeasible rule.

    • +

  • Strong Branching with Look Ahead is similar to Restricted Strong Branching yet differs from it by not specifying a +fixed size for the “restricted branching candidate set” \(R\). Instead, it considers a look ahead parameter, noted +\(L\), and applies the Full Strong Branching rule. However, if the branching candidate does not change after \(L\) +iterations, the algorithm stops and the current branching candidate is returned.

    • +

  • Strong Branching with Phases is a combination of the above three approaches which applies different schemes depending +on the level of the current node in the Branch-and-Bound tree. Additionally, it allows to solve each node only approximately +by, e.g., imposing a maximum number of iterations for the underlying Column Generation algorithm.

    • +
+
+
+
+

Implementation

+

This section explains how to use the Strong Branching rule in idol. +It is based on the Generalized Assignment Problem example from the Column Generation. +More specifically, we will assume that you have a variable +model of type Model which has a decomposable structure specified by the annotation (Annotation<Ctr, unsigned int>) decomposition.

+
+

Full Strong Branching

+

Recall that the Branch-and-Price algorithm is created by the following code.

+
const auto column_generation =
+    DantzigWolfeDecomposition(decomposition)
+        .with_master_optimizer(Gurobi::ContinuousRelaxation())
+        .with_default_sub_problem_spec(
+            DantzigWolfe::SubProblem()
+                .add_optimizer(Gurobi())
+        );
+
+
+

Now, we will show how to use Strong Branching as a branching rule. This is done while creating our Branch-and-Bound algorithm. In +particular, we will use the StrongBranching class to define our branching rule. We can, for instance, simply +declare

+
const auto branching_rule =
+    StrongBranching();
+
+
+

which will create a new Full Strong Branching rule. Just like any other branching rule, it can be used by calling the +BranchAndBound::with_branching_rule method.

+
const auto branch_and_bound =
+    BranchAndBound()
+        .with_branching_rule(branching_rule)
+        .with_node_selection_strategy(BestBound());
+
+
+

Then, we can write a Branch-and-Price algorithm and solve our problem as follows.

+
const auto branch_and_price = branch_and_bound + column_generation;
+
+model.use(branch_and_price);
+
+model.optimize();
+
+
+

Beware that here, we only implemented Full Strong Branching which, as we saw, is not computationally convenient… +Let’s see how to implemented Restricted Strong Branching.

+
+
+

Restricted Strong Branching

+

To implement Restricted Branching, one simply needs to call the StrongBranching::with_max_n_variables method. +This is done as follows.

+
const auto branching_rule =
+        StrongBranching()
+            .with_max_n_variables(50);
+
+
+

Here, we set the maximum number of considered variables equal to \(K = 50\).

+
+
+

Phases

+

In this section, we will discuss how to implement phases with the strong branching rule. This is done by using the +StrongBranching::add_phase method. This method takes three arguments: a phase type, which is used to indicate +how each node should be solved, e.g., with some iteration limit, a maximum number of variables to consider, for restricted +strong branching, and a maximum depth, used to trigger the phase based on the level of the current node in the Branch-and-Bound +tree.

+

Here is an instance of strong branching with phases which, for nodes whose level is below or equal to 3, applies Full +Strong Branching, then switches to Restricted Strong Branching with \(K = 30\) and which solves nodes with an iteration +limit of 20.

+
const auto branching_rule =
+            StrongBranching()
+                .add_phase(StrongBranchingPhases::WithNodeOptimizer(), std::numeric_limits<unsigned int>::max(), 3)
+                .add_phase(StrongBranchingPhases::WithIterationLimit(20), 30, std::numeric_limits<unsigned int>::max());
+
+
+

Observe how we used std::numeric_limits<unsigned int>::max() to remove restrictions on the number of +considered variables and on the maximum depth for the final phase. Note that, by default, if no phase is triggered for a +given depth, e.g., because it was not specified, Full Strong Branching is applied. Here, however, we make sure that the +second phase is always triggered.

+
+
+
+

Changing the Scoring Function

+

The scoring function can be changed by calling the StrongBranching::with_scoring_function method. This method +takes a scoring function as an argument. The scoring function is a sub-class of NodeScoreFunction and can be +Linear or Product.

+

By default, idol uses the product scoring function. To change it to the linear scoring function, one can simply write

+
const auto branching_rule =
+    StrongBranching()
+        .with_scoring_function(NodeScoreFunctions::Linear());
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-cut-and-price.html b/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-cut-and-price.html new file mode 100644 index 00000000..aad35c1a --- /dev/null +++ b/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-cut-and-price.html @@ -0,0 +1,151 @@ + + + + + + + + + Writing A Branch-and-Cut-and-Price Algorithm [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Writing A Branch-and-Cut-and-Price Algorithm [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-price.html b/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-price.html new file mode 100644 index 00000000..19ef6e60 --- /dev/null +++ b/tutorials/mixed-integer-programming/dantzig-wolfe/writing-branch-and-price.html @@ -0,0 +1,151 @@ + + + + + + + + + Writing A Branch-and-Price Algorithm [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Writing A Branch-and-Price Algorithm [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/index.html b/tutorials/mixed-integer-programming/index.html new file mode 100644 index 00000000..2cdcf9e3 --- /dev/null +++ b/tutorials/mixed-integer-programming/index.html @@ -0,0 +1,177 @@ + + + + + + + + + Mixed-Integer Programming — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Mixed-Integer Programming

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/modeling/constraints.html b/tutorials/mixed-integer-programming/modeling/constraints.html new file mode 100644 index 00000000..4712015e --- /dev/null +++ b/tutorials/mixed-integer-programming/modeling/constraints.html @@ -0,0 +1,235 @@ + + + + + + + + + Constraints — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

Constraints

+ +
+

Creating Constraints

+

Similarly to variables, constraints are easily created and added to a given Model.

+

Constraints can be created by calling the constructor of the Ctr class and added to a model by means of +Model::add or by calling the Model::add_ctr method. See for instance.

+
Env env;
+Model model(env);
+
+Var x_0(env, 0., Inf, Continuous, "x_0");
+Var x_1(env, 0., Inf, Continuous, "x_1");
+Ctr constraint(env, x_0 + x_1 >= 1);
+
+model.add(x_0);
+model.add(x_1);
+model.add(constraint);
+
+
+

A more compact version of this code is obtained by making use of the Model::add_vars and Model::add_ctr methods.

+
Env env;
+Model model(env);
+
+const auto x = model.add_vars(Dim<1>(2), 0., Inf, Continuous, "x");
+const auto constraint = model.add_ctr(x[0] + x[1] >= 1);
+
+
+

As you can see, a constraint is created using the pattern {expression} {sign} {expression} where

+
    +

  • {sign} is one of <=, >= and ==;

    • +

  • {expression} is an expression, i.e., an instance of Expr.

    • +
+

Actually, the pattern {expression} {sign} {expression} only creates a “temporary” constraint, i.e., a constraint +which is not associated to any model. This is why we need to add it to a model by calling the Model::add_ctr method. +Temporary constraints are objects of the class TempCtr. An equivalent code would be.

+
Env env;
+Model model(env);
+
+const auto x = model.add_vars(Dim<1>(2), 0., Inf, Continuous, "x");
+const auto temporary_constraint = TempCtr(Row(x[0] + x[1], 1), GreaterThan);
+
+model.add_ctr(temporary_constraint);
+
+
+

Here, we used the class Row to create the row associated to the constraint. The sign of the constraint is +specified by the second argument of the constructor of the TempCtr class, and can take values +LessThan, GreaterThan and EqualTo.

+
+
+

Accessing Constraints

+

Information about a given constraint in a model can be accessed by calling the corresponding methods of the model. +The type of a constraint can be accessed by calling the Model::get_ctr_type method. The row of a constraint can be +accessed by calling the Model::get_ctr_row method.

+

If a given model has been solved and feasibility could be proved (or better, optimality), the dual value of a constraint +can be accessed by calling the Model::get_ctr_dual method. +For infeasible models, a Farkas certificate (dual ray) can be accessed by calling the Model::get_ctr_farkas method.

+

The Model::has method can be used to check if a given constraint is in the model.

+

The current index of a constraint in the model can be accessed by calling the Model::get_ctr_index method. +Beware, however, that the index may change if the model is modified.

+

For more details, see the Model class.

+
+
+

Modifying Constraints

+

The type of a constraint can be modified by calling the Ctr::set_ctr_type method. +The value of the right-hand side of a constraint can be modified by calling the Ctr::set_rhs method.

+

The row of a constraint in the model can be modified by calling the Ctr::set_row method. +For instance.

+
Row row;
+row.set_rhs(2);
+row.linear().set(x, coefficient_for_x_in_constraint);
+row.linear().set(y, coefficient_for_y_in_constraint);
+
+constraint.set_ctr_row(constraint, row);
+
+
+

For more details, see the Model class.

+
+
+

Removing Constraints

+

A constraint can be removed from a model by calling the Model::remove method.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/modeling/environment.html b/tutorials/mixed-integer-programming/modeling/environment.html new file mode 100644 index 00000000..97522959 --- /dev/null +++ b/tutorials/mixed-integer-programming/modeling/environment.html @@ -0,0 +1,155 @@ + + + + + + + + + The Environment — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

The Environment

+

Every optimization object (such as variables and constraints) are managed by an “optimization environment”. +It is the environment that controls the death and life of such objects. It is also through the environment that idol +manages the different versions each optimization object may have during the execution of your program.

+

Typically, only one environment should be used by your code. Yet, it is not forbidden to have several environments at the same time (not advised).

+

Environments are objects of the Env class, and can be created as follows.

+
Env env; // Creates a new optimization environment.
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/modeling/expressions.html b/tutorials/mixed-integer-programming/modeling/expressions.html new file mode 100644 index 00000000..ef38e5e4 --- /dev/null +++ b/tutorials/mixed-integer-programming/modeling/expressions.html @@ -0,0 +1,212 @@ + + + + + + + + + Expressions — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

Expressions

+

An expression generically refers to any mathematical expression involving variables or constraints. In idol, expressions +are represented by the Expr class.

+

An expression can be created by adding, subtracting or multiplying variables with constants or other expressions. For instance, +the following code creates the mathematical expression \(1 + 3 x_0 + x_1 + x_0 + 2 x_0 x_1\).

+
const Expr expr = 1 + 3 * x[0] + x[1] + x[0] + 2 * x[0] * x[1];
+
+std::cout << expr << std::endl; // "1 + 4 * x[0] + 1 * x[1] + 2 * x[0] * x[1]"
+
+
+

Expressions are composed of three parts:

+
    +

  • A constant (or offset) which is an instance of the Constant class;

    • +

  • A linear part which is an instance of the LinExpr class;

    • +

  • A quadratic part which is an instance of the QuadExpr class.

    • +
+

Each of these parts can be accessed using the methods Expr::constant, Expr::linear and Expr::quadratic, respectively. +For instance, consider the following code.

+
for (const auto& [var, coefficient] : expr.linear()) {
+    std::cout << var << " is multiplied by " << coefficient << std::endl;
+}
+
+
+

This code iterates over the linear part of the expression and prints the variables and their coefficients.

+
+

About constants in expressions

+

Without going into too much detail, we should here precise that each constant multiplying a variable in an Expr, +as well as the offset constant, can actually be composite. For instance, this is the case when a variable is multiplied by +a parameter which is considered fixed in the current model but cannot be evaluated at the time the expression is created.

+

Creating such “parameters” can be done by prepending a variable with the ! symbol. This will automatically +create an instance of the Param class. +For instance, the following code creates +a constant which involves the variables \(\xi_0\) and \(\xi_1\), viewed as parameters.

+
Env env;
+const Model model(env);
+const auto x = model.add_vars(Dim<1>(2), 0., Inf, Continuous, "x");
+
+const Model model2(env);
+const auto xi = model2.add_vars(Dim<1>(2), 0., 1., Continuous, "xi");
+
+const Expr expr = (1 + 2 * !xi[0]) * x[0] + 3 * !xi[1] * x[1];
+
+
+

Here, 1 + 2 * !xi_0 is an instance of the Constant object and can be used as follows.

+
Constant constant = 1 + 2 * !xi_0;
+
+std::cout << constant.numerical() << std::endl; // output: 1
+
+for (const auto& [param, coeff] : constant) {
+    std::cout << coeff << " * " << param << std::endl; // output: 2 * !xi_0
+}
+
+
+

Note that a parameter can be turned back into a variable by calling the Param::as<T> template method. +See, for instance.

+
const auto param = !xi[0];
+
+if (param.is<Var>()) {
+    const auto var = param.as<Var>();
+    // do somthing with the variable
+}
+
+
+

Parameters can be variables or constraints.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/modeling/index.html b/tutorials/mixed-integer-programming/modeling/index.html new file mode 100644 index 00000000..c6be5545 --- /dev/null +++ b/tutorials/mixed-integer-programming/modeling/index.html @@ -0,0 +1,164 @@ + + + + + + + + + Modeling — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

Modeling

+

This page introduces the basic concepts of modeling optimization problems in idol. +We will discuss how to create optimization models and add variables, constraints and objective functions to that model. +We will also see how to access the different components of an existing model.

+

All classes which are used for modeling (standard) optimization problems can be accessed by including #include <idol/modeling.h>.

+

To simplify things, we will use the namespace idol in the following examples. This dispenses us from prefixing +every class with idol::. For instance, we will use Model instead of idol::Model.

+

Let’s get started.

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/modeling/models.html b/tutorials/mixed-integer-programming/modeling/models.html new file mode 100644 index 00000000..29d07c92 --- /dev/null +++ b/tutorials/mixed-integer-programming/modeling/models.html @@ -0,0 +1,220 @@ + + + + + + + + + Models — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

Models

+

The Model class is used to represent a (standard) optimization model, i.e., a mathematical program of the form

+
+\[\begin{split}\begin{align} + \min_x \ & c^\top x + x^\top D x + c_0 \\ + \text{s.t.} \ & a_{(i)}^\top x + x^\top Q^i x \le b_i \quad i=1,\dotsc,m \\ + & x_j \in \mathbb Z \quad j=1,\dotsc,p +\end{align}\end{split}\]
+

Here, \(x_j\) are the decision variables of this optimization problem while matrices \(c, D, A\) and \(Q^i\) are given +input parameters for this model. Additionally, variables \(x_j\) having \(j\in\{ 1,\dotsc,p \}\) have to be integer-valued.

+ +
+

Creating a Model

+

A Model is created by calling the constructor of the Model class. Note that it is necessary to pass an environment +to the constructor.

+

The following code creates a new optimization model.

+
Env env;
+Model model(env);
+
+
+

We can now define our decision variables and constraints, and add an objective function to our model. +This is detailed in the Variables, Constraints and Objective sections.

+
+
+

Reading a Model from a File

+

A Model can also be created by reading a model from a file (typically, an MPS or an LP file). +To do this, idol relies on an external solver. The following code reads a model from a file using Gurobi.

+
Env env;
+auto model = Gurobi::read_from_file(env, "/path/to/model.mps");
+// auto model = GLPK::read_from_file(env, "/path/to/model.mps");
+
+
+
+
+

Writing a Model to a File

+

To write an optimization model to a file, the easiest way is again to rely on an external solver. +This is done by attaching a solver to a model, then by calling the Model::write method.

+
Env env;
+Model model = some_function_creating_my_model(env); // this is assumed to create the desired optimization model
+
+model.use(Gurobi()); // We will use Gurobi as the optimizer
+
+model.write("instance.lp"); // Creates a new file "instance.lp" storing the model
+
+
+
+
+

Iterating over the Variables and Constraints

+

The Model class provides methods to iterate over the variables and constraints of the model. +See for instance, the following example.

+
for (const auto& var : model.vars()) {
+    std::cout << "Variable " << var.name() << " belongs to the model." << std::endl;
+}
+
+for (const auto& ctr : model.ctrs()) {
+    std::cout << "Constraint " << ctr.name() << " belongs to the model." << std::endl;
+}
+
+
+

The number of variables and constraints can be obtained by doing the following.

+
std::cout << "Nb. of variables: " << model.vars().size() << std::endl;
+std::cout << "Nb. of constraints: " << model.ctrs().size() << std::endl;
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/modeling/objective-function.html b/tutorials/mixed-integer-programming/modeling/objective-function.html new file mode 100644 index 00000000..5fd701a5 --- /dev/null +++ b/tutorials/mixed-integer-programming/modeling/objective-function.html @@ -0,0 +1,184 @@ + + + + + + + + + Objective Functions — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Objective Functions

+ +
+

Adding an Objective Function

+

The objective function of an optimization model can specified by the Model::set_obj_expr method. The objective function is a linear expression of the decision variables.

+

Here is an example which sets the objective function to \(-x_0 + 2 x_1\);

+
model.set_obj_expr(-x_0 + 2 * x_1);
+
+
+
+
+

Accessing the Objective Function

+

The objective function can be accessed using the Model::get_obj_expr method. +The objective sense can be accessed using the Model::get_obj_sense method.

+

If a given model has been solved, the best objective function value can be accessed using the Model::get_best_obj method. +The best bound can be accessed using the Model::get_best_bound method.

+
+
+

Changing the Optimization Sense

+

To change the optimization sense, use the Model::set_obj_sense method. +The optimization sense can be either Minimize or Maximize. For instance,

+
model.set_obj_sense(Maximize);
+
+
+

Alternatively, one can use the constructor of the Model class to set the optimization sense.

+
Env env;
+Model model(env, Maximize); // Creates a model for maximization
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/modeling/variables.html b/tutorials/mixed-integer-programming/modeling/variables.html new file mode 100644 index 00000000..24257a57 --- /dev/null +++ b/tutorials/mixed-integer-programming/modeling/variables.html @@ -0,0 +1,268 @@ + + + + + + + + + Variables — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

Variables

+

Variables are the decision variables of an optimization problem. They are the objects that we want to find the best +values for, with respect to some objective function given the specified constraints.

+ +
+

Creating Variables

+

There are mainly two ways to create variables in idol. The first one is to use the Var class directly, and the +second one is to use the Model::add_var method.

+

Using the Var class directly creates a variable that is not associated to any model. We will there need to add it +explicitly to a model. This is done by calling the Model::add method. See for instance the following code which +creates a continuous variable \(x\), with bounds \([0,\infty)\), and adds it to the model.

+
Var x(env, 0., Inf, Continuous, "x");
+
+model.add(x); // Variable x is added to the model
+
+
+

Note that a variable may belong to several models at the same time. Each version of the variable in each model is then independent. +For instance, one could do the following.

+
Var x(env, 0., Inf, Continuous, "x");
+
+model.add(x);
+
+Model model2(env);
+model2.add(x);
+model2.set_var_ub(x, 10.);
+
+
+

In this example, the variable \(x\) is added to two different models. The upper bound of \(x\) in the second model +is then set to 10. The upper bound of \(x\) in the first model is not affected by this change.

+

A more straightforward way to create variables is to call the add_var method of the Model class. +For instance, one can do:

+
const auto x = model.add_var(0, Inf, Continuous, "x");
+
+
+

Here, we do not need to repeat the environment since the model’s environment is used by default. Moreover, we do not need to +explicitly add the variable to the model since it is done automatically by the add_var method.

+

Again, a variable may belong to several models at the same time. And we can do as follows.

+
const auto x = model.add_var(0, Inf, Continuous, "x");
+
+Model model2(env);
+model2.add(x);
+model2.set_var_ub(x, 10.);
+
+
+

Note that an even more compact version of the previous code is obtained by using the Model::add method with additional +parameters. For instance, the following code is equivalent to the previous one.

+
const auto x = model.add(0, Inf, Continuous, "x");
+
+Model model2(env);
+model2.add(x, TempVar(0, 10, Continuous));
+
+
+

Here, we used the class TempVar to create a temporary variable. A temporary variable is a variable +which has not been instantiate yet. It is only a “template” of a variable specifying its bounds and type. Here, we therefore +add the variable \(x\) to the second model and directly set its attributes (bounds and type) accordingly.

+
+
+

Creating Several Variables at Once

+

Sometimes, you will find it convenient to create several variables at once. This can be done by calling the Var::make_vector +function, or the Model::add_vars method. These functions require +an extra parameter specifying the dimension of the new variable.

+

For instance, the following code creates variables \(y_{ij}\) with \(i=1,...,K\) and \(j=1,...,T\).

+
const auto y = Var::make_vector(env, Dim<2>(K, T), 0., Inf, Continuous, "y");
+
+model.add_vector<Var, 2>(y);
+
+std::cout << y[0][0] << std::endl; // "y_0_0"
+
+
+

Similarly, one could use the more direct Model::add_vars method.

+
const auto y = model.add_vars(Dim<2>(K, T), 0., Inf, Continuous, "y");
+
+std::cout << y[0][0] << std::endl; // "y_0_0"
+
+
+

Integer and binary variables can be created by setting the type of the variable to Integer and Binary respectively.

+
+
+

Accessing Variables

+

Information about a given variable in a model can be accessed by calling the corresponding methods of the model. For instance, +one can access the lower bound of a variable by calling the Model::get_var_lb method. Similarly, the upper bound of a +variable can be accessed by calling the Model::get_var_ub method. The type of a variable can be accessed by calling the +Model::get_var_type method. The column of a variable in the model can be accessed by calling the Model::get_var_column +method.

+

If a given model has been solved and feasibility could be proved (or better, optimality), the value of a variable +can be accessed by calling the Model::get_var_primal method. +When applicable, its reduced cost can be accessed by calling the Model::get_var_reduced_cost method. +Similarly, for unbounded models, a primal ray can be accessed by calling the Model::get_var_ray method.

+

The Model::has method can be used to check if a given variable is in the model.

+

The current index of a variable in the model can be accessed by calling the Model::get_var_index method. +Beware, however, that the index may change if the model is modified.

+

For more details, see the Model class.

+
+
+

Modifying Variables

+

The bounds of a variable can be modified by calling the Model::set_var_lb and Model::set_var_ub methods. +The type of a variable can be modified by calling the Model::set_var_type method.

+

The column of a variable in the model can be modified by calling the Model::set_var_column method. +For instance.

+
Column column(objective_coefficient_for_x);
+column.linear().set(constraint1, constraint1_coefficient_for_x);
+column.linear().set(constraint2, constraint2_coefficient_for_x);
+
+model.set_var_column(x, column);
+
+
+

For more details, see the Model class.

+
+
+

Removing Variables

+

A variable can be removed from a model by calling the Model::remove method.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/optimizers/access-optimizer.html b/tutorials/mixed-integer-programming/optimizers/access-optimizer.html new file mode 100644 index 00000000..bc442291 --- /dev/null +++ b/tutorials/mixed-integer-programming/optimizers/access-optimizer.html @@ -0,0 +1,171 @@ + + + + + + + + + Getting Access to The Underlying Optimizer — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Getting Access to The Underlying Optimizer

+

In some situations, you may want to access the underlying optimizer. +For instance, this could be the case if you want to get optimizer-specific information which are not exposed by the Model interface.

+

In this case, you can use the Model::optimizer method. Note that this method +will return a reference of type Optimizer&.

+

To access optimizer-specific routines, you will need to cast the reference to the appropriate optimizer type. +This is made easier by the Optimizer::as and Optimizer::is methods, which allow you to cast the optimizer to a specific type or check if it is of a specific type.

+

For instance, the following code shows how to access the underlying Gurobi optimizer of model.

+
Env env;
+Model model(env);
+const auto x = model.add_var(0.0, 1.0, Continuous, Column(-1), "x");
+
+model.use(Gurobi());
+model.optimize();
+
+auto& gurobi_optimizer = model.optimizer().as<Optimizers::Gurobi>(); // Cast the optimizer to "Optimizers::Gurobi"
+
+GRBVar& var = gurobi_optimizer[x]; // Access the optimizer-specific routines
+
+
+
+

Warning

+

A common mistake is to try to cast the optimizer to the wrong type. In particular, to cast optimizer to its +optimizer factory type (e.g., Gurobi instead of Optimizers::Gurobi). +This will result in a runtime error.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/optimizers/create-optimizer.html b/tutorials/mixed-integer-programming/optimizers/create-optimizer.html new file mode 100644 index 00000000..523ab10d --- /dev/null +++ b/tutorials/mixed-integer-programming/optimizers/create-optimizer.html @@ -0,0 +1,150 @@ + + + + + + + + + Creating Your Own Optimizer (Advanced) [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Creating Your Own Optimizer (Advanced) [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/optimizers/create-universal-callback.html b/tutorials/mixed-integer-programming/optimizers/create-universal-callback.html new file mode 100644 index 00000000..0d2df1f7 --- /dev/null +++ b/tutorials/mixed-integer-programming/optimizers/create-universal-callback.html @@ -0,0 +1,329 @@ + + + + + + + + + Creating Your Own Universal Callback — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Creating Your Own Universal Callback

+

In this page, we will show you how to create your own universal callback. +Universal callbacks are callbacks which are not specific to a particular solver. In that sense, they are generic and can be +used with any solver.

+

Note that there are also solver-specific callbacks, which are specific to a particular solver. +If you are looking for this, you can refer to this page.

+

Creating your own callback can useful if you want to create a callback that is not included in the +default set of callbacks implemented in idol. +If you are looking for quick and easy-to-use callbacks for separating user cuts or lazy constraints, +you can refer to this page.

+ +
+

Basics

+

Creating your callback is done by creating a sub-class of the Callback class and overriding the +Callack::operator() method. It is this method that will be called by the solver at each iteration.

+

Note, however, that callbacks cannot be given “as-is” to an optimizer but must be passed through a CallbackFactory. A callback +factory is a class whose role is to create a new callback object whenever it is needed. Every callback factories must be +a child (or little child) of the CallbackFactory class.

+

The following code shows how to create a callback factory and a callback that prints the value of the primal solution at each +iteration.

+
class MyCallback : public CallbackFactory {
+public:
+
+    class Strategy { // Real callback implementation
+    protected:
+        void operator()(CallbackEvent t_event) {
+
+               if (t_event != IncumbentSolution) {
+                    return;
+               }
+
+               std::cout << primal_solution() << std::endl;
+        }
+    }
+
+    Callback* operator()() { // Creates a new callback object
+        return new Strategy();
+    }
+
+    CallbackFactory* clone() const { // Creates a copy of the callback factory
+        return new MyCallback(*this);
+    }
+
+}
+
+
+

As you can see, the callback factory has two important methods: operator() and clone(). The operator() +method is used to create a new callback object, while the clone() method is used to create a copy of the callback factory.

+

The nested class Strategy is the actual callback implementation. It is a sub-class of the Callback class and +overrides the operator() method. In this example, the callback prints the value of the primal solution whenever the event +triggering the callback is IncumbentSolution. In other words, this callback will print out all incumbent solutions found by the solver.

+

As for the UserCutCallback and LazyConstraintCallback classes, +our new callback can be added to an optimizer as follows.

+
model.use(
+    Gurobi().add_callback(MyCallback())
+);
+
+model.optimize();
+
+
+
+
+

A Simple Example: Knapsack Cover Cuts

+
+

Hint

+

This section is dedicated to the “advanced topic” of knapsack cover inequalities. +Rudimentary notions on Knapsack problems and +Cover inequalities are recommended.

+
+

In this example, we will show how to create a callback that separates knapsack cover cuts. A knapsack cover cut is a valid inequality +for the knapsack problem. It is defined as follows:

+
+\[\sum_{i \in C} x_i \leq |C| - 1\]
+

where \(C\) defines a cover of the knapsack, i.e., a set of items such that the sum of their weights is greater than the capacity of the knapsack.

+

Given a solution \(\hat x\) to the continuous relaxation of the knapsack problem, we can check whether it violates a cover inequality. +This is done by solving the following separation problem.

+
+\[\begin{split}\begin{align} + \max_{z} \ & (1 - \hat x)^\top z & \ge 1 \\ + \text{s.t.} \ & w^\top z \ge W + 1, \\ + & z\in\{0,1\}^n. +\end{align}\end{split}\]
+

A cover inequality is violated if and only if the optimal objective value of this problem is strictly less than 1. +In such a case, a new cut should be added.

+

We will write a callback that separates knapsack cover cuts.

+

To this end, we first create our knapsack problem model. This is done as follows.

+
Env env;
+Model knapsack(env, Maximize);
+
+const auto x = knapsack.add_vars(Dim<1>(n), 0, 1, Binary, "x");
+
+knapsack.add_ctr(idol_Sum(i, Range(n_items), w[i] * x[i]) <= W);
+knapsack.set_obj_expr(idol_Sum(i, Range(n_items), p[i] * x[i]));
+
+
+

Then, we create our callback factory. It is this factory that will be used to create a new callback object when needed. +Since we need to pass some parameters to the callback, we will use the constructor of the callback factory to pass these parameters. +This is done as follows.

+
class KnapsackCover : public CallbackFactory {
+    const std::vector<Var> m_x;
+    const std::vector<double> m_weights;
+    const std::vector<double> m_profits;
+    const double m_capacity;
+public:
+    KnapsackCover(const std::vector<Var>& t_x,
+                  const std::vector<double>& t_weights,
+                  const std::vector<double>& t_profits,
+                  double t_capacity)
+                    : m_x(t_x), m_weights(t_weights), m_profits(t_profits), m_capacity(t_capacity) {}
+
+    class Strategy;
+
+    Callback* operator()() { // Creates a new callback object
+        return new Strategy(m_x, m_weights, m_profits, m_capacity);
+    }
+
+    CallbackFactory* clone() const { // Creates a copy of the callback factory
+        return new MyCallback(*this);
+    }
+
+}
+
+
+

The real implementation of the callback is done in the nested class Strategy. +This class is a sub-class of the Callback class and is defined as follows.

+
class KnapsackCover::Strategy { // Real callback implementation
+    const std::vector<Var> m_x;
+    const std::vector<double> m_weights;
+    const std::vector<double> m_profits;
+    const double m_capacity;
+protected:
+    Strategy(const std::vector<Var>& t_x,
+             const std::vector<double>& t_weights,
+             const std::vector<double>& t_profits,
+             double t_capacity)
+                : m_x(t_x), m_weights(t_weights), m_profits(t_profits), m_capacity(t_capacity) {}
+
+    void operator()(CallbackEvent t_event) {
+
+           if (t_event != InvalidSolution) {
+                return;
+           }
+
+           auto& env = parent().env();
+           const auto fractional_point = primal_solution();
+
+            Model separation(env, Maximize);
+
+            const auto z = separation.add_vars(Dim<1>(m_x.size()), 0, 1, Binary, "z");
+            separation.add_ctr(idol_Sum(i, Range(m_x.size()), m_weights[i] * z[i]) >= m_capacity + 1);
+            separation.set_obj_expr(idol_Sum(i, Range(m_x.size()), (1 - fractional_point[i]) * z[i]));
+
+            separation.use(Gurobi());
+
+            separation.optimize();
+
+            if (separation.get_best_obj() < 1) {
+                return;
+            }
+
+            const auto cut = idol_Sum(i, Range(m_x.size()), separation.get_var_primal(z[i]) * (1 - x[i])) >= 1;
+
+            add_user_cut(cut);
+
+    }
+}
+
+
+

Finally, we can add our callback to the optimizer as follows.

+
knapsack.use(
+    Gurobi::Continuous().add_callback(KnapsackCover(x, w, p, W))
+);
+
+knapsack.optimize();
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/optimizers/index.html b/tutorials/mixed-integer-programming/optimizers/index.html new file mode 100644 index 00000000..57194054 --- /dev/null +++ b/tutorials/mixed-integer-programming/optimizers/index.html @@ -0,0 +1,237 @@ + + + + + + + + + Optimizers — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Optimizers

+

This page discusses the concept of optimizers and optimizer factories in idol. +We will discuss how to set up an optimizer for solving a model and how to access the solution of the model.

+

In idol, an optimizer is a class that is responsible for solving a given optimization problem. +It is created by an optimizer factory, which is a class that is responsible for assembling and creating optimizers.

+

We start with an example. Consider the following code, which models a simple knapsack problem:

+
Env env;
+Model model(env, Maximize);
+const auto x = model.add_vars(Dim<1>(3), 0, 1, Binary, "x");
+model.add_ctr(x[0] + 2 * x[1] + 3 * x[2] <= 5);
+model.set_obj_expr(x[0] + x[1] + x[2]);
+
+
+

The code creates a model with three binary variables, and adds a constraint and an objective function to it. +Currently, the model does not have an optimizer, so it cannot be solved. +Calling model.optimize() will result in an exception being thrown.

+

To set up an optimizer, we need to call the Model::use method, which takes an optimizer factory as an argument. +For instance, let us use the GLPK optimizer factory, which creates an optimizer using GLPK to solve our model:

+
model.use(GLPK());
+
+
+

Here, we assume that idol was linked with the GLPK library during installation. +Now, calling model.optimize() will cause the optimizer factory to create an optimizer, which will solve the model. +Since we are using the GLPK optimizer factory, an instance of Optimizers::GLPK +will be created and used to solve the model.

+

Note that we can modify the model after setting up the optimizer. The optimizer will be updated accordingly. For instance.

+
model.add_ctr(x[0] + x[1] <= 1); // Add a new constraint
+model.optimize(); // The optimizer will solve the model with the new constraint
+
+
+

Idol provides several optimizer factories, each of which creates an optimizer using a different solvers or implementing different algorithms. +For instance, the Gurobi optimizer factory creates an optimizer using the Gurobi solver, +while the BranchAndBound optimizer factory creates an optimizer implementing a branch-and-bound algorithm.

+

Typically, users only work with optimizer factories, and do not need to create optimizers directly. +Passing arguments to an optimizer is always possible at creation time through the optimizer factory. +For instance, here is a small example of how to pass a time limit to the GLPK optimizer:

+
model.use(GLPK().with_time_limit(10)); // add time limit of 10 seconds
+
+
+

As it can be seen, arguments are given by calling the with_ method of the optimizer factory, followed by the name of the argument.

+

In some cases, it is useful to nest optimizers to, e.g., to use an optimizer to solve a sub-problem of another optimization problem. +The process of nesting optimizers is straightforward and follows the same pattern as setting up an optimizer for a model.

+

For instance, consider the following code, which implements a simple branch-and-bound algorithm to solve our knapsack problem:

+
model.use(
+    /* The overall algorithm is a branch-and-bound */
+    BranchAndBound()
+
+        /* Each node is solved by the continuous relaxation of the problem, with GLPK */
+        .with_node_solver(GLPK::ContinuousRelaxation())
+
+        /* Variables are selected for branching using the most-infeasible rule */
+        .with_branching_rule(MostInfeasible())
+
+        /* Nodes are selected using the best-bound rule */
+        .with_node_selection_rule(BestBound())
+
+        /* Turn on logs */
+        .with_logs(true)
+
+        /* The algorithm will run with a time limit of 3600 */
+        .with_time_limit(3600)
+    )
+);
+
+
+

In this example, we create a branch-and-bound algorithm to solve the model, and we use GLPK to solve the continuous relaxation of the problem at each node. +Also note that we can nest optimizers as many times as we want with no restrictions.

+

To simplify the writing of complex optimizer factories, it may be useful to use temporary variables to store intermediate results. +For instance.

+
const auto continuous_relaxation = GLPK::ContinuousRelaxation().with_logs(true);
+const auto branching_rule = MostInfeasible();
+const auto node_selection_rule = BestBound();
+
+const auto branch_and_bound = BranchAndBound()
+    .with_node_solver(continuous_relaxation)
+    .with_branching_rule(branching_rule)
+    .with_node_selection_rule(node_selection_rule)
+    .with_logs(true)
+    .with_time_limit(3600);
+
+model.use(branch_and_bound);
+
+
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/optimizers/solving.html b/tutorials/mixed-integer-programming/optimizers/solving.html new file mode 100644 index 00000000..164305ce --- /dev/null +++ b/tutorials/mixed-integer-programming/optimizers/solving.html @@ -0,0 +1,270 @@ + + + + + + + + + Solving a Model and Accessing the Solution — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Solving a Model and Accessing the Solution

+ +
+

Solving a Model

+

This page shoes how to solve a model and access the solution. +The reader should be familiar with the concept of optimizer and optimizer factories. If this is not the case, +please refer to the page on Optimizers.

+

Let us consider the following code.

+
using namespace idol;
+
+const unsigned int n_items = 5;
+const double[] profit = { 40., 50., 100., 95., 30., };
+const double[] weight = { 2., 3.14, 1.98, 5., 3., };
+const double capacity = 10.;
+
+Env env;
+Model model(env, Maximize);
+
+const auto x = model.add_vars(Dim<1>(n_items), 0., 1., Binary, "x");
+model.add(idol_Sum(j, Range(n_items), weight[j] * x[j] ) <= capacity);
+model.set_obj_expr(idol_Sum(j, Range(n_items), profit[i] * x[i]);
+
+
+

This code creates a model for the knapsack problem. As described in the page on this page, +we now set up an optimizer and solve the model. We use GLPK.

+
model.use(GLPK());
+
+
+

Solving the model is done by a single class to the optimize method.

+
model.optimize();
+
+
+

Then, idol provides several methods to access the solution.

+

First, get_status and get_reason return the status and the reason of the solution. +The status can be, for instance, Optimal or Infeasible. The reason provides more details about the status. +For instance, if the status is Infeasible, the reason can be that the original problem is infeasible (Proved), +or that the solver reached a time limit before finding a feasible solution (TimeLimit).

+

The best objective value found can be accessed using get_best_obj. This is the best objective value among feasible solutions considered during the execution of the algorithm. +The best objective value bound can be accessed using get_best_bound. For instance, this can be a dual bound. +The methods get_relative_gap and get_absolute_gap return the relative and absolute optimality gaps, respectively. +See this page for more details about gaps and tolerances.

+

The methods get_var_primal and get_var_ray return the primal value and the (primal) ray value of a given variable, respectively. +Note that primal values are only accessible if the model has status Feasible, SubOptimal or Optimal. +Similarly, a primal ray is only accessible if the model has status Unbounded.

+

The methods get_ctr_dual and get_ctr_farkas return the dual value and the Farkas certificate value of a given constraint, respectively. +Note that dual values are only accessible if the model is continuous and has status Optimal or Feasible. +Similarly, a Farkas certificate is only accessible if the model has status Infeasible.

+
+
+

Saving a Solution

+

Sometimes, you will find it useful to save a solution to access it later. idol provides the following functions to do so: +save_primal, save_ray, save_dual and save_farkas. +Each of these functions takes a model as argument and returns an object of the class Solution::Primal or Solution::Primal depending on the function. +The returned object stores the results of corresponding calls to get_var_primal, get_var_ray, get_ctr_dual or get_ctr_farkas methods.

+
+

Example

+

This example shows how to solve a model using HiGHS and retrieves some piece of information about its solution.

+
model.use(HiGHS());
+
+model.optimize();
+
+const auto status = model.get_status();
+
+if (status == Optimal) {
+
+    std::cout << "Optimal solution found!" << std::endl;
+
+    const auto primal_values = save_primal(model);
+
+    std::cout << primal_values << std::endl;
+
+} else {
+
+    std::cout << "An optimal solution could not be found." << std::endl;
+
+    std::cout << "HiGHS returned status " << status << std::endl;
+
+    std::cout << "The reason for this status is " << model.get_reason() << std::endl;
+
+    if (status == Feasible) {
+
+        std::cout << "The optimality gap is " << model.get_relative_gap() * 100 << " %" << std::endl;
+
+    } else if (status == Unbounded) {
+
+        std::cout << "An unbounded ray is" << std::endl;
+
+        const auto primal_ray = save_ray(model);
+
+        std::cout << primal_ray << std::endl;
+
+    } else if (status == Infeasible) {
+
+        std::cout << "A Farkas certificate is" << std::endl;
+
+        const auto farkas = save_farkas(model);
+
+        std::cout << farkas << std::endl;
+
+    }
+
+}
+
+
+
+
+
+

Saving a Projected Solution

+

In a more advanced solution scheme, you may deal with extended formulations of an original model, and may want to save +the projected solution on the original problem space. +In such a case, you can use the functions save_* with an additional argument to specify the original model. +For instance.

+
const auto primal_values = save_primal(original_model, higher_dimensional_model);
+
+
+

This code will return an object of the class Solution::Primal storing the results of corresponding calls to get_var_primal methods on the higher dimensional model +for the original model variables.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/mixed-integer-programming/optimizers/universal-callbacks.html b/tutorials/mixed-integer-programming/optimizers/universal-callbacks.html new file mode 100644 index 00000000..aa216620 --- /dev/null +++ b/tutorials/mixed-integer-programming/optimizers/universal-callbacks.html @@ -0,0 +1,349 @@ + + + + + + + + + Adding User Cuts and Lazy Constraints — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Adding User Cuts and Lazy Constraints

+ +
+

Introduction

+

When solving a mixed-integer program (MIP), the underlying optimizers typically make use of a branch-and-cut algorithm. +This algorithm iteratively solves a(n LP) relaxation of the problem, and then adds cuts to the problem to strengthen the relaxation. +Typically, these cuts are generated by the solver itself. However, the user can also add his or her own cuts to the problem. +This is useful when the user has domain-specific knowledge that can be used to strengthen the relaxation.

+

Classically, there are two types of cuts that can be added to a MIP: user cuts and lazy constraints.

+
    +

  • User cuts are cuts which are not necessary to define the feasible region of the problem, but which can be used to strengthen the relaxation. As a matter of fact, User cuts never cut off feasible solutions to the original problem.

    • +

  • Lazy constraints are constraints which are necessary to define the feasible region of the problem but are either unlikely to be violated or impractical to generate in advance. Lazy constraints are only added to the problem if they are violated by a current solution.

    • +
+

In both cases, user cuts and lazy constraints are added to the problem within a callback function, given to the optimizer. +Then, the optimizer will call this callback function at each node of the branch-and-cut tree, allowing to look for violated cuts and constraints that can be added to the problem.

+

Though it is possible for the user to create his or her own callback functions, +the idol library provides a simple way to add user cuts and lazy constraints to a MIP, using the UserCutCallback and LazyCutCallback classes. +In case you are interested in creating your own callback functions, you can refer to this page.

+

Now, both classes are derived from the CallbackFactory class, and are used to create callback objects that can be passed to the optimizer. +They both have a similar interface: First, the user defines a model for the separation problem (i.e., the problem that will be solved to generate the cuts or constraints), +and the corresponding cut to be added to the original problem. +Then, the user creates a callback factory object, and passes the model and the cut to the factory. +The user also specifies which optimizer should be used to solve the separation problem. +Finally, the user passes the callback factory to the optimizer, which will manage the execution of the callback.

+

The main difference between the two classes is that the UserCutCallback class is called +whenever an invalid solution is found, e.g., a point which violates integer requirements, while the LazyCutCallback class is called +whenever a new valid solution is found to check that it satisfies all the lazy constraints.

+

In the next section, we will show how to implement

+
    +

  • a simple separation procedure for knapsack cover inequalities using the UserCutCallback, and

    • +

  • a straightforward Bender’s decomposition algorithm using the LazyCutCallback class.

    • +
+
+
+

An Example of User Cuts: Knapsack Cover Inequalities

+
+

Hint

+

This section is dedicated to the “advanced topic” of knapsack cover inequalities. +Rudimentary notions on Knapsack problems and +Cover inequalities are recommended.

+
+

Consider the following knapsack problem.

+
+\[\begin{split}\begin{align} + \max_{x} \ & p^\top x \\ + \text{s.t.} \ & w^\top x \le W, \\ + & x\in\{0,1\}^n. +\end{align}\end{split}\]
+

Here, \(x\) is a binary vector, \(p\) is a vector of profits, \(w\) is a vector of weights, and \(W\) is the capacity of the knapsack.

+

It is well-known that the knapsack problem can be strengthened by adding cover inequalities. A cover inequality is a constraint of the form

+
+\[\sum_{i\in C} x_i \le |C| - 1,\]
+

where \(C\) defines a cover of the knapsack, i.e., a set of items such that the sum of their weights is greater than the capacity of the knapsack.

+

Given a solution \(\hat x\) to the continuous relaxation of the knapsack problem, we can check whether it violates a cover inequality. +This is done by solving the following separation problem.

+
+\[\begin{split}\begin{align} + \max_{z} \ & (1 - \hat x)^\top z & \ge 1 \\ + \text{s.t.} \ & w^\top z \ge W + 1, \\ + & z\in\{0,1\}^n. +\end{align}\end{split}\]
+

A cover inequality is violated if and only if the optimal objective value of this problem is strictly less than 1. +In such a case, a new cut should be added.

+

As anticipated, we need to define three different things:

+
    +

  • the original problem, i.e., the problem to be solved by the branch-and-cut algorithm;

    • +

  • the feasible region of the separation problem, i.e., the set of all cover inequalities;

    • +

  • the shape of the cuts to be added.

    • +
+

Defining the original problem is straightforward and can be done as follows.

+
Env env;
+Model knapsack(env, Maximize);
+
+const auto x = knapsack.add_vars(Dim<1>(n), 0, 1, Binary, "x");
+
+knapsack.add_ctr(idol_Sum(i, Range(n_items), w[i] * x[i]) <= W);
+knapsack.set_obj_expr(idol_Sum(i, Range(n_items), p[i] * x[i]));
+
+
+

Similarly, the feasible region of the separation problem can be defined as follows.

+
Model cover(env);
+
+const auto z = cover.add_vars(Dim<1>(n), 0, 1, Binary, "z");
+
+cover.add_ctr(idol_Sum(i, Range(n_items), w[i] * z[i]) >= W + 1);
+
+
+

Finally, we need to define the cuts to be added to the original problem for a given cover inequality \(C\). +Cuts are always expressed as if they were part of the original problem. What we mean by this is that, here, \(x\) +should be seen as a variable while \(z\) should be seen as a constant.

+

We therefore have, for a given \(z\),

+
const auto cover_cut = idol_Sum(i, Range(n_items), !z[i] * x[i]) <= idol_Sum(i, Range(n_items), 1 - !z[i]);
+
+
+

See how the z variables are “turned into” constants by prepending them with an “!” symbol.

+

We are now ready to create the callback factory and pass it to the optimizer. This is done as follows.

+
knapsack.use(
+    Gurobi::Continuous()
+        .add_callback(
+            UserCutCallback(cover, cover_cut)
+                .with_separation_optimizer(Gurobi())
+        )
+);
+
+knapsack.optimize();
+
+
+

Here, we solve the continuous relaxation of the knapsack problem using the Gurobi optimizer, and add the cover inequalities using the UserCutCallback.

+
+
+

An Example of Lazy Cut Constraints: Benders Optimality Cuts

+
+

Hint

+

This section is dedicated to the “advanced topic” of Benders Decomposition. +Rudimentary notions on Linear Programming duality and +Benders Decomposition the following subjects are recommended.

+
+

We will base our example on the following model taken from Blanco, V., (2016), Benders Decomposition, MINLP School: Theory +and Applications.

+
+\[\begin{split}\begin{align} + \min_{x,y} \ & 2 x_0 + 3x_1 + 2y \\ + \text{s.t.} \ & x_0 + 2x_1 + y \ge 3, \\ + & 2x_0 - x_1 + 3y \ge 4, \\ + & x\ge 0, y\in \mathbb N. +\end{align}\end{split}\]
+

The Benders reformulation of this problem, by considering \(y\) as the complicating variable, leads to

+
+\[\begin{split}\begin{align} + \min_{y,z} \ & 2y + z \\ + \text{s.t.} \ & z \ge \lambda_1 ( 3 - y ) + \lambda_2(4 - 3y) \qquad \forall \lambda \in \Lambda, \\ + & z \ge 0, y\in\mathbb N, +\end{align}\end{split}\]
+

with \(\Lambda\) defined as the set of all dual feasible points, i.e., those \(\lambda\in\mathbb R^2_+\) such that

+
+\[\begin{split}\begin{align} + & \lambda_0 + 2 \lambda_1 \le 2, \\ + & 2\lambda_0 - \lambda_1 \le 3. +\end{align}\end{split}\]
+

Now, we will show how to implement the Benders decomposition algorithm using the LazyCutCallback class. +This will be done so that constraints \(z \ge \hat \lambda_1 ( 3 - y ) + \hat \lambda_2(4 - 3y)\) are added to the master problem +whenever a violated Benders cut is found.

+

As anticipated, we need to define three different things:

+
    +

  • the master problem, i.e., the problem to be solved at each node of the branch-and-cut tree;

    • +

  • the dual space \(\Lambda\), i.e., the feasible region of the separation problem;

    • +

  • the shape of the cuts to be added.

    • +
+

Defining the master problem is straightforward and can be done as follows.

+
Env env;
+
+Model master(env);
+
+const auto y = master.add_var(0, Inf, Integer, "y");
+const auto z = master.add_var(0, Inf, Continuous, "z");
+
+master.set_obj_expr(2 * y + z);
+
+
+

Similarly, the dual space \(\Lambda\) can be defined as follows.

+
Model dual_space(env);
+
+const auto lambda = dual_space.add_vars(Dim<1>(2), 0, Inf, Continuous, "lambda");
+
+dual_space.add_ctr(lambda[0] + 2 + lambda[1] <= 2);
+dual_space.add_ctr(2 * lambda[0] - lambda[1] <= 3);
+
+
+

Finally, we need to define the cuts to be added to the master problem for a given dual variable \(\lambda\). +Cuts are always expressed as if they were part of the master problem. What we mean by this is that, here, \(y\) +should be seen as a variable while \(\lambda\) should be seen as a constant.

+

We therefore have, for a given \(\lambda\),

+
const auto benders_cut = z >= !lambda[0] * (3 - y) + !lambda[1] * (4 - 3 * y);
+
+
+

See how the lambda variables are “turned into” constants by prepending them with an “!” symbol.

+

We are now ready to create the callback factory and pass it to the optimizer. This is done as follows.

+
master.use(
+    Gurobi()
+        .add_callback(
+            LazyCutCallback(dual_space, benders_cut)
+                .with_separation_optimizer(Gurobi())
+        )
+        .with_lazy_cut(true)
+);
+
+master.optimize();
+
+
+

That’s it! The optimizer will now call the callback function at each node of the branch-and-cut tree, and add the cuts to the master problem whenever a violated constraint is found.

+
+

Hint

+

Here, we added a call to Gurobi::with_lazy_cut. This is because the Gurobi optimizer does not support lazy cuts by default +and one needs to explicitly enable them.

+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/robust-optimization/column-and-constraint-generation/index.html b/tutorials/robust-optimization/column-and-constraint-generation/index.html new file mode 100644 index 00000000..2130fd59 --- /dev/null +++ b/tutorials/robust-optimization/column-and-constraint-generation/index.html @@ -0,0 +1,151 @@ + + + + + + + + + Column-and-Constraint-Generation Algorithm — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/robust-optimization/column-and-constraint-generation/introduction.html b/tutorials/robust-optimization/column-and-constraint-generation/introduction.html new file mode 100644 index 00000000..58306846 --- /dev/null +++ b/tutorials/robust-optimization/column-and-constraint-generation/introduction.html @@ -0,0 +1,267 @@ + + + + + + + + + Introduction — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ +
+

Introduction

+

The Column-and-Constraint Generation (CCG) algorithm is a classical method for solving two-stage robust optimization problems. +It was originally introduced by [14].

+

In idol, the implementation of CCG alows to solve a class of problems which is slightly more general than classical +two-stage robust optimization problems. This class of problems is defined in the following section.

+ +
+

Problem Definition and Notation

+

The CCG algorithm can solve optimization problems of the form

+
+(1)\[\begin{split}\begin{align} + \min_x \quad & f(x) \\ + \text{s.t.} \quad & x\in X, \\ + & \forall \xi\in\Xi, \ \exists y\in Y(x,\xi), \ G(x,y,\xi) \le 0. +\end{align}\end{split}\]
+

in which \(f:\mathbb R^{n_x}\rightarrow\mathbb R\) is a given function, +\(X\subseteq\mathbb R^{n_x}\) is a given set, called the first-stage feasible set, +\(\Xi\subseteq\mathbb R^{n_\xi}\) is a given set, called the uncertainty set, typically compact, +\(Y(x,\xi) \subseteq \mathbb R^{n_y}\) is a set, defined for all \(x\in X\) and \(\xi\in\Xi\), called the second-stage feasible set, and +\(G:\mathbb R^{n_x+n_y+n_\xi}\rightarrow\mathbb R^\ell\) is a given vector of functions, defining the so-called coupling constraints between the uncertainty, the first- and the second-stage decisions.

+
+

Regarding Coupling Constraints

+

It is clear that coupling constraints are redundant since they can be incorporated into the definition of the second-stage feasible set. +However, we will see that explicitly defining coupling constraints may lead to different implementations of the CCG algorithm. +We will dive into this aspect after introducing the concept of separators.

+
+
+

Hint

+

Consider the Min-Max-Min problem

+
+\[\min_{x\in X} \ \max_{\xi\in\Xi} \ \min_{y\in Y(x,\xi)} \ \psi(x,y,\xi).\]
+

Clearly, this is a special case of (1) since it can be written as

+
+\[\begin{split}\begin{align} + \min_{x_0,x} \quad & x_0 \\ + \text{s.t.} \quad & (x_0,x) \in\mathbb R\times X, \\ + & \forall \xi\in\Xi, \ \exists y\in Y(x,\xi), \ \psi(x,y,\xi) \le x_0. +\end{align}\end{split}\]
+

Here, there is only one coupling constraint, which is \(\psi(x,y,\xi) \le x_0\).

+
+
+
+

The Algorithm

+
+

Basic Idea

+

The idea of the CCG algorithm is to consider a finite subset of points in \(\Xi\), say \(\{ \xi^1, \dotsc, \xi^k \}\), +and to solve the following problem instead of (1):

+
+(2)\[\begin{split}\begin{align} + \min_{x_0,x,y^1,\dotsc,y^K} \quad & f(x) \\ + \text{s.t.} \quad & x\in X, \\ + & G(x,y^t,\xi^t) \le 0 \qquad k=1,\dotsc,K, \\ + & y^t\in Y(x,\xi^t) \qquad k=1,\dotsc,K. +\end{align}\end{split}\]
+

Here, a new variable \(y^t\) has been introduced for each \(t=1,...,k\), enforcing that, indeed, +there exists \(y^t\in Y(x,\xi^t)\) such that \(G(x,y^t,\xi^t) \le 0\).

+

Note that Problem (2) is a relaxation of Problem (1) since +any feasible point of (1) is also feasible for (2) (for some \(y^1,\dotsc,y^K\)).

+

Now, given a solution \(\hat x\in X\) to the relaxed problem (2), +one needs to check whether \(\hat x\) is feasible for Problem (1). +Thus, one seeks a scenario \(\xi^*\in\Xi\) such that, either \(Y(\hat x, \xi^*)\) is empty, or \(G(\hat x,y,\xi^*) > 0\) for all \(y\in Y(\hat x, \xi^*)\). +If no such scenario exists, then \(\hat x\) is feasible for (1). Otherwise, the new scenario \(\xi^*\) is added to the set of considered scenarios and the process is repeated.

+

Identifying a missing scenario is called separation, and can be done by solving the following problem:

+
+(3)\[ \max_{\xi\in \Xi} \ \max_{\ell=1,...,L} \left\{ \ \min_{ y\in Y(\hat x,\xi) } \ G_\ell(\hat x,y,\xi) \right\}.\]
+

If the optimal value of the separation problem is non-positive, then \(\hat x\) is feasible for (1). +Otherwise, the solution to the separation problem gives a new scenario \(\xi^*\) to be added to the set of scenarios.

+

Note that we use the convention \(\max \emptyset = -\infty\) and \(\min \emptyset = +\infty\).

+
+
+

Separators

+

Clearly, the separation problem (3) can be solved in many different ways. In idol, +it is therefore possible to give a user-defined functor, called a separator, which solves the separation problem. +Note that the most common ways to solve the separation problem are already implemented in idol. +Yet, if you wish to implement your own separator, you should refer to this tutorial.

+

Shortly put, the separator solves problems of the form

+
+(4)\[ \max_{\xi\in \Xi} \ \min_{ y\in Y(\hat x,\xi) } \ G_\ell(\hat x,y,\xi),\]
+

for a given \(G_\ell\) (\(\ell\in\{1,...,L\}\)).

+

Note that it is ensured that the separator always solves a problem which is feasible. +Indeed, in case Problem (3) +is not known to satisfy the complete recourse assumption (i.e., it is not known whether \(\forall x\in X, \forall\xi\in\Xi, \exists y\in Y(x,\xi)\) holds), +the CCG algorithm will first solve a feasibility version of the separation problem to check whether +\(\hat x\) is such that for all \(\xi\in\Xi\) there exists \(y\in Y(\hat x,\xi)\). +Fortunately, it is also possible to specify that the complete recourse assumption holds, in which case the feasibility version of the separation problem is not solved.

+

Let \(\xi^{\ell}\) denote the solution to the separation problem (4) for a given \(\ell\in\{1,...,L\}\). +Then, a scenario \(\xi^{\ell^*}\) is added to Problem (2) if and only if

+
+\[\ell^* \in \underset{\ell=1,...,L}{\text{argmax}} \ \min_{ y\in Y(\hat x,\xi^\ell) } \ G_\ell(\hat x,y,\xi^\ell) > \varepsilon_\text{feas}.\]
+

See the dedicated page for more details.

+
+
+
+

On the Impact of Coupling Constraints

+

We now discuss the impact of the definition of the coupling constraints \(G\) on the implementation of the CCG algorithm. +Clearly, one obtains an equivalent problem to (1) by defining the second-stage feasible set as

+
+\[\tilde Y(x,\xi) = \{ y\in Y(x,\xi) \ | \ G(x,y,\xi) \le 0 \},\]
+

and by considering the problem

+
+\[\begin{split}\begin{align} + \min_x \quad & f(x) \\ + \text{s.t.} \quad & x\in X, \\ + & \forall \xi\in\Xi, \ \exists y\in \tilde Y(\hat x,\xi). +\end{align}\end{split}\]
+

In this case, the separation problem becomes

+
+\[\max_{\xi\in\Xi} \ \min_{ y\in \tilde Y(\hat x,\xi) } \ 0,\]
+

which is a feasibility problem.

+

Though the two approaches are equivalent, in the sense that they will both lead to a solution to (1), +they may lead to different computational performances. An interested reader may refer to, e.g., [2] for more details.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/robust-optimization/column-and-constraint-generation/trust-region-stabilization.html b/tutorials/robust-optimization/column-and-constraint-generation/trust-region-stabilization.html new file mode 100644 index 00000000..3b266860 --- /dev/null +++ b/tutorials/robust-optimization/column-and-constraint-generation/trust-region-stabilization.html @@ -0,0 +1,251 @@ + + + + + + + + + Using Trust Region Stabilization — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Using Trust Region Stabilization

+

This tutorial describes how to use the trust region stabilization in the CCG algorithm. +This stabilization technique is inspired by the work of [3] on Benders decomposition.

+
+

Assumption

+

This feature is only applicable if the first-stage decisions are binary, i.e., \(X \subseteq \{0,1\}^{n_x}\).

+
+ +
+

Introduction

+

In this section, we discuss stabilization of the CCG algorithm if the first-stage decisions are binary, i.e., \(X \subseteq \{0,1\}^{n_x}\).

+

Let \(\bar x\) be a given stability center, i.e., a point that is thought to be a “good” solution to the problem. +The following constraint is called a local branching constraint:

+
+\[\Delta(x,\bar x) := \sum_{i:\bar x_i = 1} (1 - x_i) + \sum_{i:\bar x_i = 0} x_i \le \rho,\]
+

with \(\rho\) a given radius. Additionally, we call reversed local branching constraint the following constraint:

+
+\[\Delta(x,\bar x) \ge \rho + 1.\]
+

At each iteration, the stabilized CCG algorithm solves the following problem instead of (2):

+
+\[\begin{split}\begin{align} + \min_{x_0,x} \quad & f(x) \\ + \text{s.t.} \quad & x\in X, \\ + & G(x,y^t,\xi^t) \le 0 \quad t=1,\dotsc,k, \\ + & y^t\in Y(x,\xi^t) \quad t=1,\dotsc,k, \\ + & \Delta(x,\bar x) \le \rho, \\ + & \Delta(x,\bar x') \ge \rho' + 1 \quad \forall (\bar x', \rho') \in R, +\end{align}\end{split}\]
+

for some set \(R\subseteq X\times\mathbb N\) of reversed local branching constraints.

+

The complete stabilized CCG algorithm is described with the following pseudocode.

+
+    \begin{algorithm}
+    \caption{Stabilized CCG Algorithm}
+    \begin{algorithmic}
+    \REQUIRE An initial radius \( \rho\in\{ 1,\dotsc, n_x \} \) and an initial point \( \bar x\in X \).
+    \STATE Initialize \( k \gets 0 \), \( UB \gets +\infty \), \( LB \gets -\infty \), \( R \gets \emptyset \)
+    \STATE Solve the restricted master problem (RMP)
+    \IF{the RMP is infeasible}
+        \IF{ \( \rho \ge n_x \) }
+            \STATE STOP, \( UB \) is the optimal value.
+        \ENDIF
+        \STATE Add a reversed local branching constraint, \( R \gets R \cup (\bar x, \rho) \)
+        \STATE Increase \( \rho \)
+    \ELSE
+        \STATE Let \( x^k \) be the solution of the RMP and \( v^k \) be its value
+        \STATE Solve the separation problem, let \( \xi^k \) be the solution and \( s^k \) be its value
+        \IF{ \( s^k \le \varepsilon_\text{feas} \) }
+            \STATE \( UB \gets \min\{ UB, v^k \} \)
+            \STATE Solve the RMP without the stabilization constraints, let $\underline v^k$ be its value, set \( LB \gets \underline v^k \)
+            \IF{ \( UB - LB \le \varepsilon \) }
+                \STATE STOP, \( UB \) is the optimal value.
+            \ENDIF
+            \STATE Add a reversed local branching constraint, \( R \gets R \cup (x^k, \rho) \)
+            \STATE Update the stability center \( \bar x \gets x^k \)
+            \STATE Optionally, reset \( \rho \gets 1 \)
+        \ENDIF
+        \STATE \( k \gets k + 1 \)
+        \STATE Go back to step 2
+    \ENDIF
+    \end{algorithmic}
+    \end{algorithm}
+
+

Note that if \(\rho \ge n_x\), the stabilized CCG is exactly the CCG algorithm.

+
+
+

Implementation in idol

+

Activating the trust region stabilization in idol is done through the method with_stabilization of the idol::Robust::ColumnAndConstraintGeneration class. +An object of the class Robust::CCGStabilizers::TrustRegion must be passed as an argument to the method.

+

The following code shows how to use the trust region stabilization in idol.

+
model.use(
+        Robust::ColumnAndConstraintGeneration(stages, uncertainty_set)
+                .with_master_optimizer(Gurobi())
+                .with_separator(Robust::CCGSeparators::Bilevel())
+                .with_stabilization(Robust::CCGStabilizers::TrustRegion())
+                .with_logs(true)
+);
+
+model.optimize();
+
+
+

Note that the radius \(\rho\) is set to

+
+\[\rho \gets \lceil \mu_i n_x \rceil\]
+

where \(\mu_i\) is a parameter controlling the size of the trust region and \(n_x\) is the number of (binary) first-stage variables. By default, \(\mu_i\) takes value in +:\(\lbrace .01, .02, .5 \rbrace\). +You can set the values of \(\mu_i\) by calling the method with_trust_factors of the class Robust::CCGStabilizers::TrustRegion. +For instance, the following code sets the trust factors to \(\lbrace .02, .5 \rbrace\).

+
model.use(
+        Robust::ColumnAndConstraintGeneration(stages, uncertainty_set)
+                .with_master_optimizer(Gurobi())
+                .with_separator(Robust::CCGSeparators::Bilevel())
+                .with_stabilization(
+                    Robust::CCGStabilizers::TrustRegion()
+                        .with_trust_factors({.02, .5})
+                )
+                .with_logs(true)
+);
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/robust-optimization/column-and-constraint-generation/write-ccg.html b/tutorials/robust-optimization/column-and-constraint-generation/write-ccg.html new file mode 100644 index 00000000..6e5a722e --- /dev/null +++ b/tutorials/robust-optimization/column-and-constraint-generation/write-ccg.html @@ -0,0 +1,171 @@ + + + + + + + + + Writing a Column-and-Constraint-Generation Algorithm — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Writing a Column-and-Constraint-Generation Algorithm

+

In this tutorial, we will see how to write a column-and-constraint-generation to solve a two-stage robust problem.

+

To this end, we will assume that you have your two-stage robust problem modeled already in idol. In particular, +we consider that you have

+
    +

  1. (idol::Model) model which is the deterministic model of the problem in which the uncertain data are seen as parameters.

    2. +

  2. (idol::Model) uncertainty_set which is the uncertainty set of the robust problem.

    3. +

  3. (idol::Robust::StageDescription) stages which stores the assignments of variables and constraints to each stage.

    4. +
+

If you do not know what these are, please refer to the tutorial on how to model a two-stage robust problem.

+

Then, you can solve this two-stage robust problem using a column-and-constraint-generation algorithm as follows:

+
 model.use(
+        Robust::ColumnAndConstraintGeneration(stages, uncertainty_set)
+                .with_master_optimizer(Gurobi())
+                .with_separator(Robust::CCGSeparators::Bilevel())
+                .with_logs(true)
+);
+
+model.optimize();
+
+std::cout << save_primal(model) << std::endl;
+
+
+

Notice that the optimizer is attached to the deterministic model and that both the uncertainty set and the stages are passed as arguments. +An optimizer to solve the master problem is necessary and will be called when needed. Here, we use the Gurobi optimizer.

+

Most importantly, it is also necessary to specify a separator. The separator is a sub-routine of the CCG algorithm +which solves the separation problem, i.e., it finds the worst-case scenario for a given solution to the master problem. +In this example, we use the Bilevel separator which calls the MibS bilevel optimization solver at each iteration.

+

Finally, the method optimize is called to solve the problem and the solution is printed.

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/robust-optimization/column-and-constraint-generation/writing-separator.html b/tutorials/robust-optimization/column-and-constraint-generation/writing-separator.html new file mode 100644 index 00000000..9651b157 --- /dev/null +++ b/tutorials/robust-optimization/column-and-constraint-generation/writing-separator.html @@ -0,0 +1,147 @@ + + + + + + + + + Writing Your Own Separator (Advanced) [TODO] — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Writing Your Own Separator (Advanced) [TODO]

+
+

Warning

+

This is a work in progress. Thank you for your understanding.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/robust-optimization/index.html b/tutorials/robust-optimization/index.html new file mode 100644 index 00000000..e68889a6 --- /dev/null +++ b/tutorials/robust-optimization/index.html @@ -0,0 +1,165 @@ + + + + + + + + + Robust Optimization — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/robust-optimization/modeling/index.html b/tutorials/robust-optimization/modeling/index.html new file mode 100644 index 00000000..40379546 --- /dev/null +++ b/tutorials/robust-optimization/modeling/index.html @@ -0,0 +1,145 @@ + + + + + + + + + Modeling — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Modeling

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/robust-optimization/modeling/two-stage-robust.html b/tutorials/robust-optimization/modeling/two-stage-robust.html new file mode 100644 index 00000000..a21de9ce --- /dev/null +++ b/tutorials/robust-optimization/modeling/two-stage-robust.html @@ -0,0 +1,323 @@ + + + + + + + + + Modeling a Two-Stage Robust Problem — idol + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Modeling a Two-Stage Robust Problem

+

In this tutorial, we will see how to model a two-stage robust problem in idol.

+

To follow this tutorial, you should be familiar with two-stage robust optimization and modeling optimization problems in idol. +If this is not the case, we recommend you to read the tutorial on MIP modeling.

+ +
+

A Two-Stage Robust Facility Location Problem

+

We consider a two-stage robust facility location problem (FLP) where demands are uncertain.

+

Given a set of potential facility locations \(V_1\) and a set of customers \(V_2\), the goal is to select a subset of facility locations +to activate in order to serve all customers’ demand, while minimizing the total cost. +This version introduces uncertainty in the customers’ demands.

+

Note that there is also an example for the deterministic version of the FLP using Column Generation.

+

Each facility \(i\in V_1\) has an opening cost \(f_i\) and a maximum capacity \(q_i\). +Each customer \(j\in V_2\) has a demand \(d_j\). +The unitary cost for serving customer \(j\in V_2\) from facility \(i\in V_1\) is \(t_{ij}\). +The uncertainty in customer demands is controlled by a parameter \(\Gamma\).

+

In this robust variant, we consider that the demands are uncertain and can be expressed as \(d_j(\xi) = d_j(1 + p\xi_j)\) +with \(p\) being the maximum increase in demand and \(\xi\) being an unknown vector taken in the uncertainty set

+
+\[\Xi := \left\{ \xi\in[ 0, 1 ]^{|V_2|} : \sum_{j\in V_2} \xi_j \le \Gamma \right\}.\]
+

We model the two-stage robust FLP as

+
+\[\min_{x\in \{0,1\}^{|V_1|}} \ \left\{ \sum_{i\in V_1} f_i x_i + \max_{\xi\in \Xi} \ \min_{y\in Y(x,\xi)} \ \sum_{i\in V_1} \sum_{j\in V_2} t_{ij} y_{ij} \right\}\]
+

where \(Y(x,\xi)\) is the set of feasible solutions for the second stage problem, given the first stage solution \(x\) and the realization \(\xi\) of the uncertain demand vector. +It is defined as the set of vectors \(y\in \mathbb{R}^{|V_1|\times|V_2|}\) that satisfy the following constraints

+
+\[\begin{split}\begin{align*} + & \sum_{i\in V_1} y_{ij} = d_j(\xi) && j\in V_2, \\ + & \sum_{j\in V_2} y_{ij} \le q_i x_i && i\in V_1, \\ + & y_{ij} \ge 0 && i\in V_1, j\in V_2. +\end{align*}\end{split}\]
+

In what follows, we will assume that we have a variable instance of type idol::Problems::FLP::Instance +that contains the data of the problem. Most typically, you will want to read the instance from a file. This is done as follows.

+
// Read instance
+const auto instance = Problems::FLP::read_instance_1991_Cornuejols_eal("robust_ccg.data.txt");
+
+
+

Additionally, we define an optimization environment env and some parameters.

+
Env env;
+
+const double Gamma = 3;
+const double percentage_increase = .2;
+
+
+
+
+

Modeling Steps

+

To model a two-stage robust problem, one needs to perform the following steps:

+
    +

  1. Define an uncertainty set \(\Xi\).

    2. +

  2. Define the deterministic model in which \(\xi\) is a parameter.

    3. +

  3. Assign a stage to each variable and constraint.

    4. +
+

Let’s see how these steps are done in idol.

+
+

Defining the Uncertainty Set

+

Modeling the uncertainty set \(\Xi\) is done in the same way as modeling any classical optimization problem. +For instance, one can do as follows.

+
const unsigned int n_customers = instance.n_customers();
+
+Model uncertainty_set(env);
+
+auto xi = uncertainty_set.add_vars(Dim<1>(n_customers), 0., 1, Binary, "xi");
+uncertainty_set.add_ctr(idol_Sum(j, Range(n_customers), xi[j]) <= Gamma);
+
+
+

Note that defining an objective function is not necessary since the uncertainty set is not optimized over. +If an objective function is defined, it will be ignored by the optimizer.

+
+
+

Defining the Deterministic Model

+

The deterministic model underlying the two-stage robust FLP is the same as the classical FLP, except that the demand is seen as a parameter.

+

Recall that a variable, e.g., xi[0], can be turned into a parameter by prepending it with !.

+

Hence, +we can define the deterministic model as follows.

+
const unsigned int n_facilities = instance.n_facilities();
+
+Model model(env);
+
+const auto x = model.add_vars(Dim<1>(n_facilities), 0., 1., Binary, "x");
+const auto y = model.add_vars(Dim<2>(n_facilities, n_customers), 0., Inf, Continuous, "y");
+
+// Capacity constraints
+for (unsigned int i = 0 ; i < n_facilities ; ++i) {
+    model.add_ctr(idol_Sum(j, Range(n_customers), y[i][j]) <= instance.capacity(i) * x[i]);
+}
+
+// Demand satisfaction constraints
+for (unsigned int j = 0 ; j < n_customers ; ++j) {
+    // IMPORTANT: here we use the parameter "!xi[j]" instead of the variable "xi[j]"
+    model.add_ctr(idol_Sum(i, Range(n_facilities), y[i][j]) == instance.demand(j) * (1 + percentage_increase * !xi[j]));
+}
+
+// Objective function
+model.seobj_expr(idol_Sum(i, Range(n_facilities),
+                            instance.fixed_cost(i) * x[i]
+                            + idol_Sum(j, Range(n_customers),
+                                       instance.per_unit_transportation_cost(i, j) * y[i][j]
+                            )
+                   )
+);
+
+
+
+
+

Assigning Stages

+

The last step is to assign a stage to each variable and constraint. Here, variables \(x\) are first-stage variables +and variables \(y\) are second-stage variables, i.e., they depend on the realization of the uncertain demand. +Similarly, all constraints are second-stage constraints since they are part of the second-stage feasible region.

+

Assigning stages is done by creating a new object of type idol::Robust::StageDescription. +Under the hood, this object does nothing more but defining new annotations for variables and constraints storing +the assigned stage of each variable and constraint. It is created as follows.

+
Robust::StageDescription stages(env);
+
+
+

By default, all variables and constraints are assigned to the first stage. +To assign a variable or constraint to the second stage, one can use the method set_stage of the object stages. +For instance, one can do as follows.

+
for (const auto& var : model.vars()) {
+    if (var.name().front() != 'x') {
+        stages.set_stage(var, 2);
+    }
+}
+
+
+

Similarly, since all constraints are second-stage constraints, one can do as follows.

+
for (const auto& ctr : model.ctrs()) {
+    stages.set_stage(ctr, 2);
+}
+
+
+
+

About stage annotations

+

Note that it is also possible to define your own annotations to assign variables and constraints to stages. +This is a rather advanced feature and it is your responsability to ensure that the annotations are consistent with the model.

+

The annotations are based on the following conventions: all first-stage variables and constraints have the annotation evaluating to MasterId. +All second-stage variables and constraints have the annotation evaluating to 0.

+

For instance, the following code is equivalent to the previous one.

+
Annotation<Var, unsigned int> stage_vars(model, "stage_vars", MasterId); // By default, all variables are first-stage variables
+Annotation<Ctr, unsigned int> stage_ctrs(model, "stage_ctrs", MasterId); // By default, all constraints are first-stage constraints
+
+for (const auto& var : model.vars()) {
+    if (var.name().front() != 'x') {
+        var.set(stage_vars, 0); // Assign variable to the second stage
+    }
+}
+
+for (const auto& ctr : model.ctrs()) {
+    ctr.set(stage_ctrs, 0); // Assign constraint to the second stage
+}
+
+idol::Robust::StageDescription stages(stage_vars, stage_ctrs);
+
+
+

By doing so, a call to stages.stage(var) will return “1” for all first-stage variables and “2” for all second-stage variables. +The underlying annotation can be obtained using

+
Annotation<Var, unsigned int> stage_vars = stages.stage_vars()
+
+
+

Finally, also note the method stages.stage_index(var) that will return “0” for all first-stage variables and “1” for all second-stage variables.

+
+

That’s it! We have now modeled a two-stage robust FLP in idol. Note that you will now need +to attach an optimizer to the model to solve it. +To this end, be sure to check the tutorials on optimizers for two-stage robust problems, e.g., the column-and-constraint generation tutorial.

+
+
+
+

Complete Example

+

A complete example is given here

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file