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mip2.py
#!/usr/bin/env python3.7 # Copyright 2023, Gurobi Optimization, LLC # 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 import gurobipy as gp from gurobipy import GRB if len(sys.argv) < 2: print('Usage: mip2.py filename') sys.exit(0) # Read and solve model model = gp.read(sys.argv[1]) if model.IsMIP == 0: print('Model is not a MIP') sys.exit(0) model.optimize() if model.Status == GRB.OPTIMAL: print('Optimal objective: %g' % model.ObjVal) elif model.Status == GRB.INF_OR_UNBD: print('Model is infeasible or unbounded') sys.exit(0) elif model.Status == GRB.INFEASIBLE: print('Model is infeasible') sys.exit(0) elif model.Status == GRB.UNBOUNDED: print('Model is unbounded') sys.exit(0) else: print('Optimization ended with status %d' % model.Status) sys.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 print('Solution %d has objective %g' % (k, model.PoolObjVal)) print('') model.Params.OutputFlag = 1 fixed = model.fixed() fixed.Params.Presolve = 0 fixed.optimize() if fixed.Status != GRB.OPTIMAL: print("Error: fixed model isn't optimal") sys.exit(1) diff = model.ObjVal - fixed.ObjVal if abs(diff) > 1e-6 * (1.0 + abs(model.ObjVal)): print('Error: objective values are different') sys.exit(1) # Print values of nonzero variables for v in fixed.getVars(): if v.X != 0: print('%s %g' % (v.VarName, v.X))