Changing parameters
Rather than continuing optimization on a difficult model likeglass4, it is sometimes useful to try different parameter settings. When the lower bound moves slowly, as it does on this model, one potentially useful parameter isMIPFocus, which adjusts the high-level MIP solution strategy. Let us now set this parameter to value 1, which changes the focus of the MIP search to finding good feasible solutions. There are two ways to change the parameter value. You can either use methodm.setParam():
gurobi> m.setParam('MIPFocus', 1) Set parameter MIPFocus to value 1...or you can use them.Paramsclass...gurobi> m.Params.MIPFocus = 1 Set parameter MIPFocus to value 1Once the parameter has been changed, we callm.reset()to reset the optimization on our model and thenm.optimize()to start a new optimization run:
gurobi> m.reset()
Discarded solution information
gurobi> m.optimize()
Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (win64)
Optimize a model with 396 rows, 322 columns and 1815 nonzeros Model fingerprint: 0x541d0ad3 Variable types: 20 continuous, 302 integer (0 binary) Coefficient statistics: Matrix range [1e+00, 8e+06] Objective range [1e+00, 1e+06] Bounds range [1e+00, 8e+02] RHS range [1e+00, 8e+06] Presolve removed 4 rows and 5 columns Presolve time: 0.00s Presolved: 392 rows, 317 columns, 1815 nonzeros Variable types: 19 continuous, 298 integer (298 binary) Found heuristic solution: objective 3.133356e+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 3.1334e+09 8.0000e+08 74.5% - 0s H 0 0 2.400019e+09 8.0000e+08 66.7% - 0s H 0 0 2.220019e+09 8.0000e+08 64.0% - 0s 0 0 8.0000e+08 0 72 2.2200e+09 8.0000e+08 64.0% - 0s H 0 0 2.166685e+09 8.0000e+08 63.1% - 0s 0 0 8.0000e+08 0 72 2.1667e+09 8.0000e+08 63.1% - 0s 0 0 8.0000e+08 0 77 2.1667e+09 8.0000e+08 63.1% - 0s H 0 0 2.133351e+09 8.0000e+08 62.5% - 0s 0 0 8.0000e+08 0 80 2.1334e+09 8.0000e+08 62.5% - 0s 0 0 8.0000e+08 0 80 2.1334e+09 8.0000e+08 62.5% - 0s 0 0 8.0000e+08 0 83 2.1334e+09 8.0000e+08 62.5% - 0s 0 0 8.0000e+08 0 78 2.1334e+09 8.0000e+08 62.5% - 0s 0 0 8.0000e+08 0 83 2.1334e+09 8.0000e+08 62.5% - 0s 0 0 8.0000e+08 0 83 2.1334e+09 8.0000e+08 62.5% - 0s 0 0 8.0000e+08 0 88 2.1334e+09 8.0000e+08 62.5% - 0s 0 0 8.0000e+08 0 66 2.1334e+09 8.0000e+08 62.5% - 0s H 0 0 2.050017e+09 8.0000e+08 61.0% - 0s 0 2 8.0000e+08 0 65 2.0500e+09 8.0000e+08 61.0% - 0s H 1 4 2.050017e+09 8.0000e+08 61.0% 74.0 0s H 6 8 2.000016e+09 8.0000e+08 60.0% 41.8 0s H 130 128 1.700015e+09 8.0000e+08 52.9% 12.7 0s H 199 203 1.644459e+09 8.0000e+08 51.4% 10.8 0s H 213 213 1.644459e+09 8.0000e+08 51.4% 10.8 1s H 244 269 1.633347e+09 8.0001e+08 51.0% 11.0 1s 1428 1027 1.5333e+09 40 44 1.6333e+09 8.0001e+08 51.0% 15.5 5s 3138 1602 1.3750e+09 58 22 1.6333e+09 8.0001e+08 51.0% 20.5 10s * 4233 2185 66 1.600017e+09 8.0001e+08 50.0% 21.5 12s * 4238 2082 67 1.550017e+09 8.0001e+08 48.4% 21.5 12s H 4308 2026 1.500016e+09 8.0001e+08 46.7% 21.6 14s 4457 2226 1.1000e+09 36 65 1.5000e+09 8.0001e+08 46.7% 22.6 15s H 4809 2136 1.450016e+09 8.0001e+08 44.8% 23.4 16s H 4908 2043 1.400013e+09 8.0001e+08 42.9% 23.9 17s H 5098 2027 1.350013e+09 8.0001e+08 40.7% 24.8 18s H 5282 1752 1.200013e+09 8.0001e+08 33.3% 25.7 18s Interrupt request received Cutting planes: Gomory: 37 Cover: 9 Implied bound: 41 MIR: 51 Flow cover: 266 RLT: 107 Relax-and-lift: 99 Explored 5332 nodes (140122 simplex iterations) in 19.00 seconds Thread count was 8 (of 8 available processors) Solution count 10: 1.20001e+09 1.35001e+09 1.40001e+09 ... 1.64446e+09 Solve interrupted Best objective 1.200012600000e+09, best bound 8.000066838804e+08, gap 33.3335%
Results are consistent with our expectations. We find a better solution sooner by shifting the focus towards finding feasible solutions (objective value1.2e9versus1.5e9).
ThesetParam()method is designed to be quite flexible and forgiving. It accepts wildcards as arguments, and it ignores character case. Thus, the following commands are all equivalent:
gurobi> m.setParam('NODELIMIT', 100) gurobi> m.setParam('NodeLimit', 100) gurobi> m.setParam('Node*', 100) gurobi> m.setParam('N???Limit, 100)You can use wildcards to get a list of matching parameters:gurobi> m.setParam('*Cuts', 2) Matching parameters: ['Cuts', 'CliqueCuts', 'CoverCuts', 'FlowCoverCuts', 'FlowPathCuts', 'GUBCoverCuts', 'ImpliedCuts', 'MIPSepCuts', 'MIRCuts', 'ModKCuts', 'NetworkCuts', 'SubMIPCuts', 'ZeroHalfCuts']
Note thatModel.Paramsis a bit less forgiving thansetParam(). In particular, wildcards are not allowed with this approach. You don't have to worry about capitalization of parameter names in either approach, though, som.Params.Heuristicsandm.Params.heuristicsare equivalent.
The full set of available parameters can be browsed using theparamHelp()命令。你可以获得更多的信息pecific parameter (e.g.,MIPGap) by typingparamHelp('MIPGap').
