A Python interface to COIN-OR’s Linear and mixed-integer program solvers (CLP, CBC, and CGL)

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

CyLP depends on Numpy (www.numpy.org) and Scipy (www.scipy.org). Please note that Numpy does need to be installed prior to installing CyLP, even though it is listed as a dependency in the setup.py file.

You will also need to install binaries for Cbc. The version should be 2.10 (recommended) or earlier (current master branch of Cbc will not work with this version of CyLP). You can install Cbc by either by installing with a package manager, by downloading pre-built binaries, or by building yourself from source using coinbrew.

To install Cbc in Linux, the easiest way is to use a package manager. Install coinor-libcbc-dev on Ubuntu/Debian or coin-or-Cbc-devel on Fedora. Cbc is also available on Linux through conda with

$ conda create -n cbc coin-or-cbc -c conda-forge
On OS X, it is easiest to install Cbc with homebrew:

$ brew tap coin-or-tools/coinor

$ brew install coin-or-tools/coinor/cbc pkg-config

Cbc is also available on OS X through conda with

$ conda create -n cbc coin-or-cbc -c conda-forge
On Windows, a binary wheel is available and it is not necessary to install Cbc.

You should no longer need to build Cbc from source on any platform unless for some reason, none of the above recipes applies to you. If you do need to build from source, please go to the Cbc project page and follow the instructions there. After building and installing, make sure to either set the COIN_INSTALL_DIR variable to point to the installation or set PKG_CONFIG_PATH to point to the directory where the .pc files are installed. You may also need to set either LD_LIBRARY_PATH (Linux) or DYLD_LIBRARY_PATH (OS X).

Installation

If you are building against the coin-or-cbc package installed by conda, be sure to conda install pkg-config, which is needed to find he Cbc libraries installed by conda. Once Numpy and Cbc are installed, simply do:

$ pip install cylp

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.

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:

Online : Please visit http://coin-or.github.io/CyLP/
Offline : To install CyLP’s documentation in your repository, you need Sphinx (http://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

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

PyArt, The Python ARM Radar Toolkit, used by Atmospheric Radiation Measurement (U.S. Department of energy). https://github.com/ARM-DOE/pyart
Meteorological Institute University of Bonn.
Sherbrooke university hospital (Centre hospitalier universitaire de Sherbrooke): CyLP is used for nurse scheduling.
Maisonneuve-Rosemont hospital (L’hôpital HMR): CyLP is used for physician scheduling with preferences.
Lehigh University: CyLP is used to teach mixed-integer cuts.
IBM T. J. Watson research center
Saarland University, Germany