LP Solvers for Python

Wrapper around Linear Programming (LP) solvers in Python, with a unified interface.

Installation

From conda-forge

conda install -c conda-forge lpsolvers

From PyPI

To install the library and all available LP solvers at the same time:

pip install lpsolvers[open_source_solvers]

To install the library only, assuming LP solvers are installed separately: pip install lpsolvers.

Usage

The function solve_lp is called with the solver keyword argument to select the backend solver. The linear program it solves is, in standard form:

$$ \begin{split} \begin{array}{ll} \mbox{minimize} & c^T x \\ \mbox{subject to} & G x \leq h \\ & A x = b \end{array} \end{split} $$

Vector inequalities are taken coordinate by coordinate.

Example

To solve a linear program, build the matrices that define it and call the solve_lp function:

from numpy import array
from lpsolvers import solve_lp

c = array([1., 2., 3.])
G = array([[1., 2., -1.], [2., 0., 1.], [1., 2., 1.], [-1., -1., -1.]])
h = array([4., 1., 3., 2.])

x = solve_lp(c, G, h, solver="cvxopt")  # select solver here
print(f"LP solution: {x=}")

This example outputs the solution [2.2, -0.8, -3.4].

Solvers

The list of supported solvers currently includes:

• cdd
• CVXOPT
• CVXPY (interface)
• PDLP
• ProxQP