PuLP

From NEOS

PuLP is an LP modeler written in python. PuLP can generate MPS or LP files and call GLPK, COIN CLP/CBC, CPLEX, Gurobi and XPRESS to solve linear problems.

A comprehensive wiki can be found at here (in progress) or the more complete version at http://130.216.209.237/engsci392/pulp/OptimisationWithPuLP

A newsgroup pulp-or-discuss@googlegroups.com is operational for any questions

http://groups.google.co.nz/group/pulp-or-discuss

Use LpVariable() to create new variables. To create a variable 0 <= x <= 3

>>> x = LpVariable("x", 0, 3)

To create a variable 0 <= y <= 1

>>> y = LpVariable("y", 0, 1)

Use LpProblem() to create new problems. Create "myProblem"

>>> prob = LpProblem("myProblem", LpMinimize)

Combine variables to create expressions and constraints and add them to the problem.

>>> prob += x + y <= 2

If you add an expression (not a constraint), it will become the objective.

>>> prob += -4*x + y

Choose a solver and solve the problem. ex:

>>> status = prob.solve(GLPK(msg = 0))

Display the status of the solution

>>> LpStatus[status]
'Optimal'

Exported Classes:

   LpProblem -- Container class for a Linear programming problem
   LpVariable -- Variables that are added to constraints in the LP
   LpConstraint -- A constraint of the general form 
     {{{a1x1+a2x2 ...anxn (<=, =, >=) b}}} 
   LpConstraintVar -- Used to construct a column of the model in column-wise 
     modelling

Exported Functions:

   value() -- Finds the value of a variable or expression
   lpSum() -- given a list of the form [a1*x1, a2x2, ..., anxn] will construct 
     a linear expression to be used as a constraint or variable
   lpDot() --given two lists of the form [a1, a2, ..., an] and 
     [ x1, x2, ..., xn]will construct a linear expression to be used 
     as a constraint or variable