[Help-glpk] PuLP 1.1: A Python Linear Programming modeler

[Jean-Sebastien Roy]

I would like to announce the release of PuLP v 1.1.

PuLP is an LP modeler written in python. PuLP can generate MPS or LP
files and call GLPK[1], COIN CLP/SBB[2], CPLEX[3] and XPRESS[4] to solve 
linear problems.

PuLP provides a nice syntax for the creation of linear problems, and a
simple way to call the solvers to perform the optimization. See the 
example below.

This version adds C modules to use the GLPK, COIN and CPLEX solvers 
without using intermediate MPS or LP files. It is faster and more reliable.

While distribution of C sources is not a problem under Unix where a 
compiler is almost always available, it is a problem under Windows, so I 
would like to provide compiled modules for this platform. The problem 
is, I do not have access to a computer running Windows (much less Visual 
C++). Would someone be interested in compiling and testing these modules 
under Windows ?

You can get it at:
http://www.jeannot.org/~js/code/pulp-1.1.tgz

The latest version is always available at:
http://www.jeannot.org/~js/code/index.en.html

Multiple examples are provided.

Thanks,

Jean-Sebastien

References:
[1] http://www.gnu.org/software/glpk/glpk.html
[2] http://www.coin-or.org/
[3] http://www.cplex.com/
[4] http://www.dashoptimization.com/

Example script:

from pulp import *

prob = LpProblem("test1", LpMinimize)

# Variables
x = LpVariable("x", 0, 4)
y = LpVariable("y", -1, 1)
z = LpVariable("z", 0)

# Objective
prob += x + 4*y + 9*z

# Constraints
prob += x+y <= 5
prob += x+z >= 10
prob += -y+z == 7

GLPK().solve(prob)

# Solution
for v in prob.variables():
   print v.name, "=", v.varValue

print "objective=", value(prob.objective)