Important Notice

To comply with PEP8 we decided to rename the package name from CyLP to cylp, which was long overdue. It affects the package name ONLY and a simple replace can make your program work with the new settings. Thank you for your understanding.

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.

Note

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). https://github.com/ARM-DOE/pyart
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

Installation

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

Requirements

CyLP needs Numpy (www.numpy.org) and Scipy (www.scipy.org). 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:

$ easy_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

STEP 1:

Install CBC (http://www.coin-or.org/download/source/Cbc/). CyLP can be compiled against Cbc version 2.8.5. Please go to the installation directory and run:

$ ./configure
$ make
$ make install

STEP 2:

Create an environment variable called COIN_INSTALL_DIR pointing to your installation of Coin. For example:

$ export COIN_INSTALL_DIR=/Users/mehdi/Cbc-2.8.5

You may also add this line to your ~/.bash_rc or ~/.profile to make it persistent.

STEP 3:

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

$ python setup.py install

STEP 4 (LINUX):

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 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']

Documentation

You may access CyLP's documentation:

1. Online : Please visit http://mpy.github.io/CyLPdoc/
2. Offline : To install CyLP's documentation in your repository, you need Sphinx (http://sphinx.pocoo.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.