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A Python interface for CLP, CBC, and CGL

Project description


CyLP now works in Python 3. To get the most recent release, do:

pip install cylp

Please note that numpy does need to be installed prior to installing CyLP, even though it is listed as a dependency in the file. On Windows, installing will download a binary wheel that includes Cbc (no additional steps required). On Linux and OSX, you will get a source distribution, which requires that you first install Cbc. Once Cbc is installed, add the <prefix>/lib/pkgfig directory to your PKG_CONFIG_PATH environment variable. Alternatively, you can build from source first and set the environment variable COIN_INSTALL_DIR to point to the installation directory (if you use coinbrew, this will be the dist/ directory).

What is 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.


CyLP interfaces a limited number of functionalities of COIN-OR’s solvers. If there is any particular class or method in CLP, CBC, and CGL that you would like to use in Python please don’t hesitate to let us know; we will try to make the connections. Moreover, in the case that you find a bug or a mistake, we would appreciate it if you notify us. Contact us at mehdi [dot] towhidi [at] gerad [dot] ca.

Who uses CyLP

CyLP is being 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


The easiest way to install CyLP is by using the binaries. If that’s not possible you may always compile it from source.


CyLP needs Numpy ( and Scipy ( If you wish to install CyLP from source, you will also need to compile Cbc. Details of this process is given below.

Binary Installation

If you have setuptools installed you may run:

$ pip install cylp

If a binary is available for your architecture it will be installed. Otherwise you will see an error telling you to specify where to find a Cbc installation. That’s because easy_install is trying to compile the source. In this case you’ll have to compile Cbc and set and environment variable to point to it before calling easy_install again. The details are given in the Installing from source section.

Installing from source


Install CBC either from source ( or by any of the following methods.

  1. Install the cbc package in Linux (coinor-cbc on Debian or coin-or-Cbc on Fedora).

  2. Install with homebrew on OSX:

    $ brew tap coin-or-tools/coinor

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

  3. Download binaries from Bintray ( for Windows.


In case of installing from Bintray on Windows or from source on any platform, either create an environment variable called COIN_INSTALL_DIR pointing to your installation of COIN, e.g.,

$ export COIN_INSTALL_DIR=/Users/mehdi/Cbc-2.10.3

or add the location of the lib/pkgconfig/ directory (where you should find the cbc.pc file) to your PKG_CONFIG_PATH.


Install CyLP. Go to CyLP’s root directory and run:

$ pip install .

In linux you might also need to add COIN’s lib directory to LD_LIBRARY_PATH as follows:

$ export LD_LIBRARY_PATH=/path/to/Cbc-2.8.5/lib:$LD_LIBRARY_PATH"
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 import CyClpSimplex
>>> s = CyClpSimplex()
>>> s.readMps('../input/netlib/adlittle.mps')
>>> s.initialSolve()
>>> round(s.objectiveValue, 3)

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 import CyClpSimplex
from 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
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]


You may access CyLP’s documentation:

  1. Online : Please visit
  2. Offline : To install CyLP’s documentation in your repository, you need Sphinx ( 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.

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