Simple experimentation with a more difficult model
Let us now consider a more difficult model, glass4.mps.
Again, we read the model and begin the optimization:
gurobi> m = read('c:/gurobi560/win64/examples/data/glass4')
Read MPS format model from file c:/gurobi560/win64/examples/data/glass4.mps
Reading time = 0.00 seconds
glass4: 396 Rows, 322 Columns, 1815 NonZeros
gurobi> m.optimize()
Optimize a model with 396 Rows, 322 Columns and 1815 NonZeros
Presolve removed 4 rows and 5 columns
Presolve time: 0.00s
Presolved: 392 Rows, 317 Columns, 1815 Nonzeros
Found heuristic solution: objective 3.691696e+09
Root relaxation: objective 8.000024e+08, 72 iterations, 0.00 seconds
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
0 0 8.0000e+08 0 72 3.6917e+09 8.0000e+08 78.3% - 0s
0 0 8.0000e+08 0 72 3.6917e+09 8.0000e+08 78.3% - 0s
0 0 8.0000e+08 0 72 3.6917e+09 8.0000e+08 78.3% - 0s
0 0 8.0000e+08 0 72 3.6917e+09 8.0000e+08 78.3% - 0s
0 2 8.0000e+08 0 72 3.6917e+09 8.0000e+08 78.3% - 0s
H 769 732 2.800024e+09 8.0000e+08 71.4% 5.2 0s
H 834 781 2.666693e+09 8.0000e+08 70.0% 5.3 0s
H 1091 984 2.475023e+09 8.0000e+08 67.7% 5.1 0s
H 1092 986 2.400020e+09 8.0000e+08 66.7% 5.1 0s
H 1092 984 2.380021e+09 8.0000e+08 66.4% 5.1 0s
H 1095 988 2.350020e+09 8.0000e+08 66.0% 5.1 0s
* 1845 1543 94 2.316685e+09 8.0000e+08 65.5% 4.9 0s
* 2131 1627 126 2.150018e+09 8.0000e+08 62.8% 4.8 0s
H 2244 1580 2.100019e+09 8.0000e+08 61.9% 4.8 0s
H 2248 1341 1.900018e+09 8.0000e+08 57.9% 5.0 0s
H 3345 1816 1.900018e+09 8.0000e+08 57.9% 4.1 0s
H 3346 1744 1.900017e+09 8.0000e+08 57.9% 4.1 0s
H15979 10383 1.900017e+09 8.0000e+08 57.9% 2.5 1s
H19540 13051 1.900016e+09 8.0000e+08 57.9% 2.4 1s
*21124 13489 101 1.866683e+09 8.0000e+08 57.1% 2.4 1s
*23011 14690 100 1.850015e+09 8.0000e+08 56.8% 2.3 1s
*25630 15679 143 1.800016e+09 8.0000e+08 55.6% 2.3 1s
*28365 15421 113 1.700015e+09 8.0000e+08 52.9% 2.3 1s
H29910 16333 1.700014e+09 8.0000e+08 52.9% 2.3 1s
*30582 16765 124 1.700014e+09 8.0000e+08 52.9% 2.3 1s
*33238 16251 92 1.677794e+09 8.0000e+08 52.3% 2.3 1s
*37319 18258 85 1.633349e+09 8.0000e+08 51.0% 2.2 1s
H40623 19584 1.600015e+09 8.0000e+08 50.0% 2.3 2s
81781 42951 1.1000e+09 49 51 1.6000e+09 8.0001e+08 50.0% 2.2 5s
199990 100088 1.6000e+09 82 28 1.6000e+09 8.0001e+08 50.0% 2.3 10s
*242810 116891 97 1.600015e+09 8.2001e+08 48.8% 2.3 11s
*243703 116786 95 1.600014e+09 8.2001e+08 48.8% 2.3 11s
Interrupt request received
Explored 255558 nodes (588336 simplex iterations) in 12.36 seconds
Thread count was 8 (of 8 available processors)
Solve interrupted
Best objective 1.6000142000e+09, best bound 8.5000490000e+08, gap 46.8752%
It quickly becomes apparent that this model is quite a bit more
difficult than the earlier coins model. The optimal solution
is actually 1,200,000,000, but finding that solution takes a
while. After letting the model run for 10 seconds, we interrupt the
run (by hitting CTRL-C, which produces the Interrupt request
received message) and consider our options. Typing
m.optimize() would resume the run from the point at which it was
interrupted.
