mip2.py
#!/usr/bin/python
# Copyright 2016, Gurobi Optimization, Inc.
# This example reads a MIP model from a file, solves it and prints
# the objective values from all feasible solutions generated while
# solving the MIP. Then it creates the associated fixed model and
# solves that model.
import sys
from gurobipy import *
if len(sys.argv) < 2:
print('Usage: mip2.py filename')
quit()
# Read and solve model
model = read(sys.argv[1])
if model.isMIP == 0:
print('Model is not a MIP')
exit(0)
model.optimize()
if model.status == GRB.Status.OPTIMAL:
print('Optimal objective: %g' % model.objVal)
elif model.status == GRB.Status.INF_OR_UNBD:
print('Model is infeasible or unbounded')
exit(0)
elif model.status == GRB.Status.INFEASIBLE:
print('Model is infeasible')
exit(0)
elif model.status == GRB.Status.UNBOUNDED:
print('Model is unbounded')
exit(0)
else:
print('Optimization ended with status %d' % model.status)
exit(0)
# Iterate over the solutions and compute the objectives
model.Params.outputFlag = 0
print('')
for k in range(model.solCount):
model.Params.solutionNumber = k
objn = 0
for v in model.getVars():
objn += v.obj * v.xn
print('Solution %d has objective %g' % (k, objn))
print('')
model.Params.outputFlag = 1
fixed = model.fixed()
fixed.Params.presolve = 0
fixed.optimize()
if fixed.status != GRB.Status.OPTIMAL:
print("Error: fixed model isn't optimal")
exit(1)
diff = model.objVal - fixed.objVal
if abs(diff) > 1e-6 * (1.0 + abs(model.objVal)):
print('Error: objective values are different')
exit(1)
# Print values of nonzero variables
for v in fixed.getVars():
if v.x != 0:
print('%s %g' % (v.varName, v.x))
