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

## Project description

A comprehensive wiki can be found at http://pulp-or.googlecode.com/

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’

You can get the value of the variables using value(). ex: >>> value(x) 2.0

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 epression to be used as a constraint or variable