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Once we've added the model constraints, we call optimize
and
then output the optimal solution:
if m.status == GRB.status.OPTIMAL: for h in commodities: print '\nOptimal flows for', h, ':' for i,j in arcs: if flow[h,i,j].x > 0: print i, '->', j, ':', flow[h,i,j].x
If you run the example (gurobi.bat netflow.py
on Windows,
or gurobi.sh netflow.py
on Linux and Mac), you should
see the following output:
Optimize a model with 16 rows, 12 columns and 36 nonzeros Presolve removed 16 rows and 12 columns Presolve time: 0.00s Presolve: All rows and columns removed Iteration Objective Primal Inf. Dual Inf. Time 0 5.5000000e+03 0.000000e+00 0.000000e+00 0s Solved in 0 iterations and 0.00 seconds Optimal objective 5.500000000e+03 Optimal flows for Pencils : Detroit -> Boston : 50.0 Denver -> New York : 50.0 Denver -> Seattle : 10.0 Optimal flows for Pens : Detroit -> Boston : 30.0 Detroit -> New York : 30.0 Denver -> Boston : 10.0 Denver -> Seattle : 30.0
Next: Building and running the Up: Python Dictionary Example Previous: Flow conservation constraints