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#!/usr/bin/env python
# @(#) $Jeannot: test1.py,v 1.11 2005/01/06 21:22:39 js Exp $
# Copywrite 2007 Stuart Mitchell
# Columnwise modelling
# Import PuLP modeler functions
from pulp import *
# A new LP problem
prob = LpProblem("test6", LpMinimize)
# objective
obj = LpConstraintVar("obj")
# constraints
a = LpConstraintVar("Ca", LpConstraintLE, 5)
b = LpConstraintVar("Cb", LpConstraintGE, 10)
c = LpConstraintVar("Cc", LpConstraintEQ, 7)
prob.setObjective(obj)
prob += a
prob += b
prob += c
# Variables
# 0 <= x <= 4
x = LpVariable("x", 0, 4, LpContinuous, obj + a + b)
# -1 <= y <= 1
y = LpVariable("y", -1, 1, LpContinuous, 4*obj + a - c)
# 0 <= z
z = LpVariable("z", 0, None, LpContinuous, 9*obj + b + c)
# Use None for +/- Infinity, i.e. z <= 0 -> LpVariable("z", None, 0)
# Write the problem as an LP file
prob.writeLP("test6.lp")
# Solve the problem using the default solver
prob.solve()
# Use prob.solve(GLPK()) instead to choose GLPK as the solver
# Use GLPK(msg = 0) to suppress GLPK messages
# If GLPK is not in your path and you lack the pulpGLPK module,
# replace GLPK() with GLPK("/path/")
# Where /path/ is the path to glpsol (excluding glpsol itself).
# If you want to use CPLEX, use CPLEX() instead of GLPK().
# If you want to use XPRESS, use XPRESS() instead of GLPK().
# If you want to use COIN, use COIN() instead of GLPK(). In this last case,
# two paths may be provided (one to clp, one to cbc).
# Print the status of the solved LP
print("Status:", LpStatus[prob.status])
# Print the value of the variables at the optimum
for v in prob.variables():
print(v.name, "=", v.varValue)
# Print the value of the objective
print("objective=", value(prob.objective))
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