cylp 0.92.2

CyLP

CyLP is a Python interface to COIN-OR’s Linear and mixed-integer program solvers (CLP, CBC, and CGL). CyLP’s unique feature is that you can use it to alter the solution process of the solvers from within Python. For example, you may define cut generators, branch-and-bound strategies, and primal/dual Simplex pivot rules completely in Python.

You may read your LP from an mps file or use the CyLP’s easy modeling facility. Please find examples in the documentation.

Docker

If you’re comfortable with Docker, you can get started right away with the container available on Dockerhub that comes with CyLP pre-installed.

https://hub.docker.com/repository/docker/coinor/cylp

Otherwise, read on.

Prerequisites and installation

$ python -m pip install cylp

$ python -m pip install cylp

$ conda create -n cylp python=3.9
$ conda activate cylp

$ conda create -n cylp coin-or-cbc cython numpy pkg-config scipy -c conda-forge
$ conda activate cylp

$ pip install --no-build-isolation cylp

$ pip install --no-build-isolation .

$ python -m pip install cylp

Testing your installation

Optional step:

If you want to run the doctests (i.e. make doctest in the doc directory) you should also define:

$ export CYLP_SOURCE_DIR=/Path/to/cylp

Now you can use CyLP in your python code. For example:

>>> from cylp.cy import CyClpSimplex
>>> s = CyClpSimplex()
>>> s.readMps('../input/netlib/adlittle.mps')
0
>>> s.initialSolve()
'optimal'
>>> round(s.objectiveValue, 3)
225494.963

Or simply go to CyLP and run:

$ python -m unittest discover

to run all CyLP unit tests (this is currently broken).

Modeling Example

Here is an example of how to model with CyLP’s modeling facility:

import numpy as np
from cylp.cy import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPArray

s = CyClpSimplex()

# Add variables
x = s.addVariable('x', 3)
y = s.addVariable('y', 2)

# Create coefficients and bounds
A = np.matrix([[1., 2., 0],[1., 0, 1.]])
B = np.matrix([[1., 0, 0], [0, 0, 1.]])
D = np.matrix([[1., 2.],[0, 1]])
a = CyLPArray([5, 2.5])
b = CyLPArray([4.2, 3])
x_u= CyLPArray([2., 3.5])

# Add constraints
s += A * x <= a
s += 2 <= B * x + D * y <= b
s += y >= 0
s += 1.1 <= x[1:3] <= x_u

# Set the objective function
c = CyLPArray([1., -2., 3.])
s.objective = c * x + 2 * y.sum()

# Solve using primal Simplex
s.primal()
print(s.primalVariableSolution['x'])

This is the expected output:

Clp0006I 0  Obj 1.1 Primal inf 2.8999998 (2) Dual inf 5.01e+10 (5) w.o. free dual inf (4)
Clp0006I 5  Obj 1.3
Clp0000I Optimal - objective value 1.3
[ 0.2  2.   1.1]

Documentation

You may access CyLP’s documentation:

1. Online : Please visit http://coin-or.github.io/CyLP/

2. Offline : To install CyLP’s documentation in your repository, you need Sphinx (https://www.sphinx-doc.org/). You can generate the documentation by going to cylp/doc and run make html or make latex and access the documentation under cylp/doc/build. You can also run make doctest to perform all the doctest.

Who uses CyLP

The following software packages make use of CyLP:

1. CVXPY, a Python-embedded modeling language for convex optimization problems, uses CyLP for interfacing to CBC, which is one of the supported mixed-integer solvers.

CyLP has been used in a wide range of practical and research fields. Some of the users include:

1. PyArt, The Python ARM Radar Toolkit, used by Atmospheric Radiation Measurement (U.S. Department of energy).

2. Meteorological Institute University of Bonn.

3. Sherbrooke university hospital (Centre hospitalier universitaire de Sherbrooke): CyLP is used for nurse scheduling.

4. Maisonneuve-Rosemont hospital (L’hôpital HMR): CyLP is used for physician scheduling with preferences.

5. Lehigh University: CyLP is used to teach mixed-integer cuts.

6. IBM T. J. Watson research center

7. Saarland University, Germany