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feasopt.py
#!/usr/bin/env python3.7 # Copyright 2021, Gurobi Optimization, LLC # 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 import gurobipy as gp if len(sys.argv) < 2: print('Usage: feasopt.py filename') sys.exit(0) feasmodel = gp.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=gp.Column([-1], [c])) if sense != '<': feasmodel.addVar(obj=1.0, name="ArtP_" + c.constrName, column=gp.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()