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Python optimization library for mathematical programming.

Project description

pytimize

Python optimization library for mathematical programming.

PyPI Apache Build

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.formulations.linear import variables, minimize
>>> a, b, c, d, e = variables(5)
>>> p = minimize(4*c - 11*d - e + 17).subject_to(
        a + 2*c + 7*d <= 2 + e,
        b - 4*c - 5*d >= 1 - 3*e
    ).where(
        a >= 0,
        b >= 0,
        c >= 0,
        d <= 0,
        e <= 0
    )
>>> 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.dual()
Max [2. 1.]x
Subject To:

[ 1.   0.]        [  0.]
[ 0.   1.]        [  0.]
[ 2.  -4.]x       [  4.]
[ 7.  -5.]        [-11.]
[-1.   3.]        [ -1.]
x  0
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
>>> p.optimal_value()
17.0

You can also formulate the exact same program by specifying the objective function and constraints in matrix form:

>>> 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])
>>> 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

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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