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Python optimization library for mathematical programming.

Project description

pytimize

Python optimization library for mathematical programming.

Introduction

Pytimize is a python library for

  • Formulating and solving complex linear, integer, and nonlinear programs.
  • Performing combinatorial optimization with directed/undirected graphs and flows.
  • Visualizing polyhedrons and displaying computation process.

Install using pip install pytimize!

Documentation

Coming soon!

Example

The following shows a code snippet for constructing a linear program and solving it with two phase simplex. For more detailed examples, please see pytimize/examples.

>>> from pytimize.programs import LinearProgram
>>> import numpy as np
>>> A = np.array([
      [1, 0, 2, 7, -1], 
      [0, 1, -4, -5, 3]
    ])
>>> b = np.array([2, 1])
>>> c = np.array([0, 0, 4, -11, -1])
>>> z = 17
>>> p = LinearProgram(A, b, c, z, "min", ["<=", ">="], negative_variables=[4, 5])
>>> print(p)
Min [0. 0. 4. -11. -1.]x + 17
Subject To:

[1.  0.   2.   7.  -1.]        [2.]
[0.  1.  -4.  -5.   3.]x       [1.]
x, x  0
x, x, x  0

>>> p.to_sef(in_place=True)
Max [0. 0. -4. -11. -1. 0. 0.]x + 17
Subject To:

[1.  0.   2.  -7.   1.  1.   0.]     =   [2.]
[0.  1.  -4.   5.  -3.  0.  -1.]x    =   [1.]
x  0

>>> solution, optimal_basis, certificate = p.two_phase_simplex()
>>> solution, optimal_basis, certificate
(array([2., 1., 0., 0., 0., 0., 0.]), [1, 2], array([0., 0.]))
>>> p.verify_optimality(certificate)
True

Contributing

Pytimize is a work in progress project. Contributions are welcome on a pull request basis.

Credits

Pytimize is created and maintained by Terry Zheng, Jonathan Wang, and Colin He. Logo is designed by Kayla Estacio.

Project details


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