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[Help-glpk] Re: PuLP 1.1: A Python Linear Programming modeler
From: |
Peter Notebaert |
Subject: |
[Help-glpk] Re: PuLP 1.1: A Python Linear Programming modeler |
Date: |
Sat, 7 May 2005 01:25:54 +0200 |
You can download a free version of the microsoft compiler at the Microsoft
site at following link:
http://www.microsoft.com/downloads/details.aspx?FamilyId=272BE09D-40BB-49FD-9CB0-4BFA122FA91B&displaylang=en
Peter
"Jean-Sebastien Roy" <address@hidden> wrote in message
news:address@hidden
>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)