fixanddive.py
#!/usr/bin/python
# Copyright 2016, Gurobi Optimization, Inc.
# Implement a simple MIP heuristic. Relax the model,
# sort variables based on fractionality, and fix the 25% of
# the fractional variables that are closest to integer variables.
# Repeat until either the relaxation is integer feasible or
# linearly infeasible.
import sys
from gurobipy import *
# Key function used to sort variables based on relaxation fractionality
def sortkey(v1):
sol = v1.x
return abs(sol-int(sol+0.5))
if len(sys.argv) < 2:
print('Usage: fixanddive.py filename')
quit()
# Read model
model = gurobi.read(sys.argv[1])
# Collect integer variables and relax them
intvars = []
for v in model.getVars():
if v.vType != GRB.CONTINUOUS:
intvars += [v]
v.vType = GRB.CONTINUOUS
model.Params.outputFlag = 0
model.optimize()
# Perform multiple iterations. In each iteration, identify the first
# quartile of integer variables that are closest to an integer value in the
# relaxation, fix them to the nearest integer, and repeat.
for iter in range(1000):
# create a list of fractional variables, sorted in order of increasing
# distance from the relaxation solution to the nearest integer value
fractional = []
for v in intvars:
sol = v.x
if abs(sol - int(sol+0.5)) > 1e-5:
fractional += [v]
fractional.sort(key=sortkey)
print('Iteration %d, obj %g, fractional %d' % \
(iter, model.objVal, len(fractional)))
if len(fractional) == 0:
print('Found feasible solution - objective %g' % model.objVal)
break
# Fix the first quartile to the nearest integer value
nfix = max(int(len(fractional)/4), 1)
for i in range(nfix):
v = fractional[i]
fixval = int(v.x+0.5)
v.lb = fixval
v.ub = fixval
print(' Fix %s to %g (rel %g)' % (v.varName, fixval, v.x))
model.optimize()
# Check optimization result
if model.status != GRB.Status.OPTIMAL:
print('Relaxation is infeasible')
break