Goal

This project provides several algorithms implemented in python to solve linear programs of the form

$latex:\large $\mathbf{x}^*=argmin_\mathbf{x} \mathbf{c}^t\mathbf{x} ~ s.t.~ A_e\mathbf{x}=\mathbf{b_e},A_i\mathbf{x}\leq\mathbf{ b_i}, \mathbf{l}\leq \mathbf{x}\leq \mathbf{u}$$

where A_e and A_i are sparse matrices

The different algorithms that are implemented here are documented in the pdf:

a dual coordinate ascent method with exact line search
a dual gradient ascent with exact line search
a first order primal-dual algorithm adapted from chambolle pock [2]
three methods based on the Alternating Direction Method of Multipliers [3]

Note These methods are not meant to be efficient methods to solve generic linear programs. They are simple and quite naive methods I implemented while exploring different possibilities to solve very large sparse linear programs that are too big to be solved using the standard simplex method or standard interior point methods.

This project also provides:

a python implementation of Mehrotra's Predictor-Corrector Pimal-Dual Interior Point method.
a python class SparseLP (in SparseLP.py) that makes it easier to build linear programs from python
methods to convert between the different common forms of linear programs (slack form, standard form etc),
methods to import and export the linear program from and to standard file formats (MPS), It is used here to run netlib LP problems. Using mps files, one can upload and solve LP on the neos servers.
a simple constraint propagation method with back-tracking to find feasible integer values solutions (for integer programs)
an interface to the OSQP solver [11]
interfaces to other solvers (SCS, ECOS, CVXOPT) through CVXPY
interfaces to other LP and MILP solvers (CLP, CBC, MIPLC, GLPSOL, QSOPT) using mps text files

Build and test status

Installation

using pip

sudo pip install git+git://github.com/martinResearch/PySparseLP.git

otherwise you can dowload it, decompress it and compile it locally using

python setup.py build_ext --inplace

If you want to be able to run external solvers using mps files in windows then download the following executables and copy them in the solvers\windows subfolder

LP problem modeling

This library provides a python class SparseLP (in SparseLP.py) that aims at making it easier to build linear programs from python. It is easy to derive a specialize class from it and add specialized constraints creations methods (see potts penalization in example 1). SparseLP is written in python and relies on scipy sparse matrices and numpy matrices to represent constraint internally and for its interface. There is no variables class binding to c++ objects. This makes it potentially easier to interface with the python scientific stack.

Debuging

Constructing a LP problem is often error prone. If we can generate a valid solution before constructing the LP we can check that the constraints are not violated while we add them to the LP using the method check_solution. This make it easier to pin down which constraint is causing problem. We could add a debug flag so that this check is automatic done as we add constraints.

Other modeling tools

Other libraries provide linear program modeling tools:

CyLP. It uses operator overloading so that we can use notations that are close to the mathematical notations. Variables are defined as 1D vectors wich is sometimes not convenient.
GLOP. The variables are added as scalars, one at a time, instead of using arrays, which make the creation of large LPs very slow in python.
PuLP. The variables are added as scalars, one at a time, instead of using arrays, which make the creation of large LPs very slow in python.
Pyomo
CVXPY

The approach taken here is lower level than these tools (no variable class and no operator overloading to define the constraints) but provides more control and flexibility for the user to define the constraints and the objective function. It is made easy by using numpy arrays to store variables indices.

Examples

Image segmentation

We consider the image segmentation problem with Potts regularization:

$latex: \large $min_s c^ts + \sum_{(i,j)\in E} |s_i-s_j| ~s.t. ~0 \leq s\leq 1$$

with E the list of indices of pairs of neighbouring pixels and c a cost vector that is obtained from color distribution models of the two regions. This problem can be rewritten as a linear program by adding an auxiliary variable d_ij for each edge with the constraints

$latex: \large $min_s c^ts + \sum_{(i,j)\in E} d_{ij} ~s.t. ~0 \leq s\leq 1, ~d_{ij}\geq s_j-s_j, ~d_{ij}\geq s_i-s_i $$

This problem can be more efficiently solved using graph-cuts than with a generic linear program solver but it is still interesting to compare performance of the different generic LP solvers on this problem.

from pysparselp.example_pott_segmentation import run
run()

Here are the resulting segmentations obtained with the various LP solvers, with the same random data term with the optimizations limited to 15 seconds for each solver. convergence curves

Note that instead of using a simple Potts model we could try to solve the LP from [5]

Sparse inverse convariance matrix

The Sparse Inverse Covariance Estimation problem aims to find a sparse matrix B that approximate the inverse of Covariance matrix A.

$latex:\large $B^*=argmin_B |B|1~ s.t.~ |A B-I_d|\infty\leq \lambda$$

Let denote f the fonction that take a matrix as an input an yield the vector of coefficient of the matrix in row-major order. Let b=f(B) we have f(AB)=Mb with M=kron(A, I_d) The problem rewrites

$latex: \large $ min_{b,c} \sum_i c_i s.t. -b\leq c,b\leq c,-\lambda\leq M b-f(I_d)\leq \lambda$$

We take inspiration from this scikit-learn example here to generate samples of a gaussian with a sparse inverse covariance (precision) matrix. From the sample we compute the empirical covariance A and the we estimate a sparse inverse covariance (precision) matrix B from that empirical covariance using the LP formulation above.

from pysparselp.example_sparse_inv_covariance import run
run()

L1 regularised multi-class SVM

Given n examples of vector-class pairs (x_i,y_i), with x_i a vector of size m and y_i an integer representing the class, we aim at estimating a matrix W of size k by m that allows to discriminate the right class, with k the number of classes. We assume that the last component of x_i is a one in order to represent the offset constants in W. we denote W_k the k^th line of the matrix W

$latex:\large $W^*=argmin_W min_{\epsilon}|W|1+\sum{i=1}^n \epsilon_i\ s.t.~ W_{y_i}x_i-W_kx_i>1-\epsilon_i \forall{(i,k)|k\neq y_i}$$

By adding auxiliary variables in a matrix S of the same size as the matrix W we can rewrite the absolute value as follow: $latex:\large $|W|1=min_S \sum{ij}S_{ij} \ s.t.~ W_{ij}<S_{ij}, -W_{ij}<S_{ij} \forall(ij)$$

We obtain the LP formulation:

$latex:\large $W^*=argmin_{W} min_{\epsilon,S} \sum_{ij}S_{ij} +\sum_{i=1}^n \epsilon_i\s.t.~W_{y_i}x_i-W_kx_i>1-\epsilon_i \forall{(i,k)|k\neq y_i},W_{ij}<S_{ij}, -W_{ij}<S_{ij} \forall(ij)$$

The example can be executed using the following line in python

from pysparselp.example_sparse_l1_svm import run
run()

The support vectors are represented by black circles.

Bipartite matching

Bipartite matching can be reformulated as an integer linear program:

$latex: $$ max \sum_{ij\in {1,\dots,n}^2} M_{ij} C_{i,j} ~ s.t~ M_{ij}\in{0,1}, \sum_j M_{ij}\leq 1 \sum_i M_{ij}\leq 1 $$$

We relax it into an continuous variables LP.

from pysparselp.example_bipartite_matching import run
run()

K-medians

Given n points we want to cluster them into k set by minimizing

$latex: $min_ {C \subset {1,\dots,n}} \sum_i min_{j\in C}d_{ij}~ s.t~ card(C)\leq k$$ with d_ij the distance between point i and point j This can be reformulated as an integer program:

$latex: $$ min \sum_{ij\in {1,\dots,n}^2} L_{ij} d_{ij} ~ s.t~ L_{ij}\in{0,1}, \sum_j L_{ij}=1 \forall i, L_{ij}<u_i \forall (i,j),\sum_i u_i\leq k $$$

We relax it into a continuous variables LP using $latex: $$ L_{ij}\in[0,1]$$$

from pysparselp.example_kmedians import run
run()

Basis pursuit denoising

Basis pursuit is the mathematical optimization problem of the form:

$latex:\large $x^*=argmin_x |x|_1~ s.t.~ Ax=y$$

where x is a N × 1 solution vector (signal), y is a M × 1 vector of observations (measurements), A is a M × N transform matrix (usually measurement matrix) and M < N. Basis pursuit denoising (BPDN) refers to a mathematical optimization problem of the form:

$latex:\large $x^*=argmin_x \lambda|x|_1~ s.t.~ \frac{1}{2}|Ax-y|^2_2$$

where λ is a parameter that controls the trade-off between sparsity and reconstruction fidelity. This this can be reformulated as a quadratic programming problem. Using a absolute difference loss insead of a squared loss i.e

$latex:\large $x^*=argmin_x \lambda|x|_1~ s.t.~ \frac{1}{2}|Ax-y|_1$$

We can reformulate the problem as a linear program:

$latex: \large $ min_{b,c} \lambda\sum_i c_i + \sum_i d_i s.t. -c\leq x\leq c, ~-d\leq Ax-y\leq d$$

with c and b slack variable vectors respectively of size N and M

Netlib LP problems

We have an interface to easily test the various solvers on netlib problems from netlib. The uncompressed files are downloaded from here. In order to monitor convergence rates, the exact solutions of these problems are found here

from pysparselp.test_netlib import test_netlib
test_netlib('SC50A')

Note: since august 2017, numpy files containing the netlib examples are provided with scipy here

Random problems

Random sparse LP problem can be generate using code in randomLP.py. The approach used to generate random problem is very simple and could be improved in order to generate harder sparse LPs. We could implement the approach used in section 6.2.1 in https://arxiv.org/pdf/1404.6770v3.pdf to generate random problems with the matlab code available here

To Do

translate from Matlab ot python the ADMM methods from https://github.com/nmchaves/admm-for-lp
test mtlab implementatoin of chambolle pock methods with linesearch applied to lp from here and translate to python
add automatic constraint checking if we provide a feasible solution from the beginning. It will help debugging constraints.
convert to python the matlab implementation of the LP solver based on improved version of champolle-pock called Adaptive Primal-Dual Hybrid Gradient Methods available here
create a cython binding for LPsparse [1] using scipy.sparse matrices for the interface and adding the possibility to compute the convergence curve by providing the problem known solution to the solver or by adding the possibility to define a callback to a python function.
implement method [4]
implement method in [5]
add interface to [8] once the code is online.
try to get more meaningful convergence curves for scipy.linprog, or maybe those are the expected curves ?
we provide an implementation of Mehrotra's Predictor-Corrector Pimal-Dual Interior Point method translated to python from Yiming yan's matlab code. We could add other interior point methods by translating into python the code
- https://github.com/YimingYAN/pathfollow (matlab)
- https://github.com/YimingYAN/pipm-lp (matlab)
- http://www.cs.ubc.ca/~pcarbo/convexprog.html
- https://github.com/YimingYAN/cppipm (c++)
- https://github.com/pkhuong/cholesky-is-magic (lisp) described here https://www.pvk.ca/Blog/2013/12/19/so-you-want-to-write-an-lp-solver/
implement some presolve methods to avoid singular matrices in the interior point methods (for example http://www.davi.ws/doc/gondzio94presolve.pdf). For example detect constraints on singletons, duplicated rows etc.

Alternatives

Linear Program solvers with a python interface

Scipy's linprog. Only the simplex is implemented in october 2016 (Note: an interior point method has been added in august 2017). Note that it is possible to call this solver from within our code using method='ScipyLinProg' when callign the solve method. The simplex method is implemented in python with many loops and is very slow for problems that involve more than a hundred variables. The interior point method has not been tested here.
OSQP. Operator Splitting Quadratic programming [11]. It support support linear programming (with all zeros hessian matrix). OSQP can be executde on GPU with cuosqp and can be use a a subroutine to solve mixted integer quadratic progams using as done in miosqp python interface
GPU implementation of OSQP (can be 2 order of magnitude faster)here
Python bindings for GLPK here . It might not be adapted to very large sparse problems as it uses simplex or interior point methods. The installation is a bit tedious. The licence is GPL which makes it unsuited for use in commercial products.
GLOP, Google's linear programming system has a python interface pywraplp.
CyLP . Python interface to Coin-Or solvers CLP, CBC, and CGL. We can use the first two solvers using mps files using my code. Installing CyLP involves quite a few steps. CyLP also provide LP modeling tools.
CVXOPT. It provides a linear program cone program solvers and also provides interfaces to GLPK,Mosek,DSPD.
CVXPY. Python-embedded modeling language for convex optimization problems. It provide interface to cvxopt solvers and to SCS
SCS, github Solves convex cone programs via operator splitting. Can solve in particular linear programs.

Linear Program solvers without python interface

LIPSOL. Matlab code. Seems to be adequate for sparse problems. Part of the code in Fortran. license GPL
LPsolve license LGPL. Python wrapper here. I cannot find in the windows installer the command line executable mentioned in the documentation that could be executed with mps files.
Joptimize implemented in Java. Appache licence
PCx PCx is an interior-point predictor-corrector linear programming package. Code available here https://github.com/lpoo/PCx. Free but to public domain. Binaries provided for Linux only.
DSDP solve semi-definite programs, which are more general than linear programs. It uses the sparsity of the problem and might still be competitive to solve sparse linear programs. Can be called from python through cvxoptms.

References

[1] Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon , NIPS 2015. code

[2] Diagonal preconditioning for first order primal-dual algorithms in convex optimization T. Pock and A.Chambolle ICCV 2011

[3] Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers Stephen Boyd Foundations and Trends in Machine Learning 2010

[4] Augmented Lagrangian method for large-scale linear programming problems Yu G Evtushenko, A I Golikov, and N Mollaverdy. Optimization Method and Software 2005.

[5] Alternating Direction Method of Multipliers for Linear Programming. He Bingsheng and Yuan Xiaoming. 2015. Paper here

[6] Local Linear Convergence of the Alternating Direction Method of Multipliers on Quadratic or Linear Programs. Daniel Boley. SIAM Journal on Optimization. 2013

[7] Multiblock ADMM Methods for Linear Programming. Nico Chaves, Junjie (Jason) Zhu. 2016. report and matlab code here

[8] A New Alternating Direction Method for Linear Programming. Sinong Wang, Ness Shroff. NIPS 2017 paper here

[9] Equivalence of Linear Programming and Basis Pursuit. paper here

[11] OSQP: An Operator Splitting Solver for Quadratic Programs. B.Stellato, G. Banjac, P. Goulart, A. Bemporad and S. Boyd. ArXiv e-prints 2017

Name		Name	Last commit message	Last commit date
Latest commit History 161 Commits
.github		.github
.vscode		.vscode
images		images
latex		latex
pysparselp		pysparselp
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
activate_venv.bat		activate_venv.bat
compile_locally.bat		compile_locally.bat
create_latex_images.py		create_latex_images.py
create_venv.bat		create_venv.bat
readme.md		readme.md
requirements.txt		requirements.txt
setup.cfg		setup.cfg
setup.py		setup.py

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Goal

Build and test status

Installation

LP problem modeling

Debuging

Other modeling tools

Examples

Image segmentation

Sparse inverse convariance matrix

L1 regularised multi-class SVM

Bipartite matching

K-medians

Basis pursuit denoising

Netlib LP problems

Random problems

To Do

Alternatives

Linear Program solvers with a python interface

Linear Program solvers without python interface

References

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Build and test status

Installation

LP problem modeling

Debuging

Other modeling tools

Examples

Image segmentation

Sparse inverse convariance matrix

L1 regularised multi-class SVM

Bipartite matching

K-medians

Basis pursuit denoising

Netlib LP problems

Random problems

To Do

Alternatives

Linear Program solvers with a python interface

Linear Program solvers without python interface

References

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