Update Documentation URLs

doc/OnlineDocs/explanation/analysis/doe/doe.rst

@@ -26,7 +26,7 @@ Pyomo.DoE provides the exploratory analysis and MBDoE capabilities to the Pyomo
 the allowable design spaces for design variables, and the assumed observation error model.
 During exploratory analysis, Pyomo.DoE checks if the model parameters can be inferred from the postulated measurements or preliminary data.
 MBDoE then recommends optimized experimental conditions for collecting more data.
-Parameter estimation packages such as `Parmest <https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html>`_ can perform parameter estimation using the available data to infer values for parameters,
+Parameter estimation packages such as `Parmest <https://pyomo.readthedocs.io/en/stable/explanation/analysis/parmest/index.html>`_ can perform parameter estimation using the available data to infer values for parameters,
 and facilitate an uncertainty analysis to approximate the parameter covariance matrix.
 If the parameter uncertainties are sufficiently small, the workflow terminates and returns the final model with quantified parametric uncertainty.
 If not, MBDoE recommends optimized experimental conditions to generate new data.

doc/OnlineDocs/explanation/solvers/mindtpy.rst

@@ -26,7 +26,7 @@ at Purdue University and Carnegie Mellon University.
 .. _Duran & Grossmann, 1986: https://dx.doi.org/10.1007/BF02592064
 .. _Westerlund & Petterson, 1995: http://dx.doi.org/10.1016/0098-1354(95)87027-X
 .. _Kesavan & Allgor, 2004: https://link.springer.com/article/10.1007/s10107-004-0503-1
-.. _MC++: https://pyomo.readthedocs.io/en/stable/contributed_packages/mcpp.html
+.. _MC++: https://pyomo.readthedocs.io/en/stable/explanation/solvers/mcpp.html
 .. _Bernal & Peng, 2021: http://www.optimization-online.org/DB_HTML/2021/06/8452.html
 .. _Kronqvist & Bernal, 2018: https://link.springer.com/article/10.1007%2Fs10107-018-1356-3
 .. _Bonami & Cornuéjols, 2009: https://link.springer.com/article/10.1007/s10107-008-0212-2
@@ -132,7 +132,7 @@ The LP/NLP based branch-and-bound algorithm in MindtPy is implemented based on t
 
    In Pyomo, `persistent solvers`_ are necessary to set or register callback functions. The single tree implementation currently only works with CPLEX and GUROBI, more exactly ``cplex_persistent`` and ``gurobi_persistent``. To use the `LazyConstraintCallback`_ function of CPLEX from Pyomo, the `CPLEX Python API`_ is required. This means both IBM ILOG CPLEX Optimization Studio and the CPLEX-Python modules should be installed on your computer. To use the `cbLazy`_ function of GUROBI from pyomo, `gurobipy`_ is required.
 
-.. _`persistent solvers`: https://pyomo.readthedocs.io/en/stable/advanced_topics/persistent_solvers.html?highlight=persistent
+.. _`persistent solvers`: https://pyomo.readthedocs.io/en/stable/explanation/solvers/persistent.html
 .. _CPLEX Python API: https://www.ibm.com/docs/en/icos/20.1.0?topic=cplex-setting-up-python-api
 .. _gurobipy: https://www.gurobi.com/documentation/9.1/quickstart_mac/cs_grbpy_the_gurobi_python.html
 .. _LazyConstraintCallback: https://www.ibm.com/docs/en/icos/20.1.0?topic=classes-cplexcallbackslazyconstraintcallback

pyomo/contrib/incidence_analysis/README.md

@@ -7,7 +7,7 @@ These tools can be used to detect whether and (approximately) why the Jacobian
 of equality constraints is structurally or numerically singular, which
 commonly happens as the result of a modeling error.
 See the
-[documentation](https://pyomo.readthedocs.io/en/stable/contributed_packages/incidence/index.html)
+[documentation](https://pyomo.readthedocs.io/en/stable/explanation/analysis/incidence/index.html)
 for more information and examples.
 
 ## Dependencies

pyomo/contrib/mindtpy/algorithm_base_class.py

@@ -185,7 +185,7 @@ def _log_solver_intro_message(self):
             '               Mixed-Integer Nonlinear Decomposition Toolbox in Pyomo (MindtPy)                \n'
             '-----------------------------------------------------------------------------------------------\n'
             'For more information, please visit \n'
-            'https://pyomo.readthedocs.io/en/stable/contributed_packages/mindtpy.html'
+            'https://pyomo.readthedocs.io/en/stable/explanation/solvers/mindtpy.html'
         )
         self.config.logger.info(
             'If you use this software, please cite the following:\n'

pyomo/contrib/mindtpy/global_outer_approximation.py

@@ -56,7 +56,7 @@ def check_config(self):
             if config.mip_solver not in {'cplex_persistent', 'gurobi_persistent'}:
                 raise ValueError(
                     "Only cplex_persistent and gurobi_persistent are supported for LP/NLP based Branch and Bound method."
-                    "Please refer to https://pyomo.readthedocs.io/en/stable/contributed_packages/mindtpy.html#lp-nlp-based-branch-and-bound."
+                    "Please refer to https://pyomo.readthedocs.io/en/stable/explanation/solvers/mindtpy.html#lp-nlp-based-branch-and-bound."
                 )
             if config.threads > 1:
                 config.threads = 1

pyomo/contrib/mindtpy/outer_approximation.py

@@ -63,7 +63,7 @@ def check_config(self):
             if config.mip_solver not in {'cplex_persistent', 'gurobi_persistent'}:
                 raise ValueError(
                     "Only cplex_persistent and gurobi_persistent are supported for LP/NLP based Branch and Bound method."
-                    "Please refer to https://pyomo.readthedocs.io/en/stable/contributed_packages/mindtpy.html#lp-nlp-based-branch-and-bound."
+                    "Please refer to https://pyomo.readthedocs.io/en/stable/explanation/solvers/mindtpy.html#lp-nlp-based-branch-and-bound."
                 )
             if config.threads > 1:
                 config.threads = 1

pyomo/contrib/mpc/README.md

@@ -2,7 +2,7 @@
 
 Pyomo MPC is an extension for developing model predictive control simulations
 using Pyomo models. Please see the
-[documentation](https://pyomo.readthedocs.io/en/stable/contributed_packages/mpc/index.html)
+[documentation](https://pyomo.readthedocs.io/en/stable/explanation/analysis/mpc/index.html)
 for more detailed information.
 
 Pyomo MPC helps with, among other things, the following use cases: