#!/usr/bin/python
# Copyright 2017, Gurobi Optimization, Inc.
# This example reads a MIP model from a file, adds artificial
# variables to each constraint, and then minimizes the sum of the
# artificial variables. A solution with objective zero corresponds
# to a feasible solution to the input model.
#
# We can also use FeasRelax feature to do it. In this example, we
# use minrelax=1, i.e. optimizing the returned model finds a solution
# that minimizes the original objective, but only from among those
# solutions that minimize the sum of the artificial variables.
import sys
from gurobipy import *
if len(sys.argv) < 2:
print('Usage: feasopt.py filename')
quit()
feasmodel = gurobi.read(sys.argv[1])
#create a copy to use FeasRelax feature later
feasmodel1 = feasmodel.copy()
# clear objective
feasmodel.setObjective(0.0)
# add slack variables
for c in feasmodel.getConstrs():
sense = c.sense
if sense != '>':
feasmodel.addVar(obj=1.0, name="ArtN_" + c.constrName,
column=Column([-1], [c]))
if sense != '<':
feasmodel.addVar(obj=1.0, name="ArtP_" + c.constrName,
column=Column([1], [c]))
# optimize modified model
feasmodel.optimize()
feasmodel.write('feasopt.lp')
# use FeasRelax feature
feasmodel1.feasRelaxS(0, True, False, True);
feasmodel1.write("feasopt1.lp");
feasmodel1.optimize();