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.
Installation¶
- 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:
- Online : Please visit http://mpy.github.io/CyLPdoc/
- 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.
