A Linear Programming modeler
Project description
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.
Multiple examples are provided.
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)
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.
Multiple examples are provided.
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)
