Documentation

Changing parameters

Rather than continuing optimization on a difficult model like glass4, 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 is MIPFocus, 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 method m.setParam():

gurobi> m.setParam('MIPFocus', 1)
Changed value of parameter MIPFocus to 1
   Prev: 0   Min: 0   Max: 3   Default: 0

...or you can use the m.Params class...

gurobi> m.Params.MIPFocus = 1
Changed value of parameter MIPFocus to 1
   Prev: 0   Min: 0   Max: 3   Default: 0

Once the parameter has been changed, we call m.reset() to reset the optimization on our model and then m.optimize() to start a new optimization run:

gurobi> m.reset()
gurobi> m.optimize()
Optimize a model with 396 rows, 322 columns and 1815 nonzeros
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]
Found heuristic solution: objective 2.40002e+09
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)

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 2.4000e+09 8.0000e+08  66.7%     -    0s
H    0     0                    2.283353e+09 8.0000e+08  65.0%     -    0s
H    0     0                    2.283353e+09 8.0000e+08  65.0%     -    0s
     0     0 8.0000e+08    0   72 2.2834e+09 8.0000e+08  65.0%     -    0s
     0     0 8.0000e+08    0   72 2.2834e+09 8.0000e+08  65.0%     -    0s
     0     0 8.0000e+08    0   72 2.2834e+09 8.0000e+08  65.0%     -    0s
     0     0 8.0000e+08    0   72 2.2834e+09 8.0000e+08  65.0%     -    0s
     0     0 8.0000e+08    0   72 2.2834e+09 8.0000e+08  65.0%     -    0s
     0     0 8.0000e+08    0   88 2.2834e+09 8.0000e+08  65.0%     -    0s
     0     0 8.0000e+08    0   71 2.2834e+09 8.0000e+08  65.0%     -    0s
H    0     0                    2.100018e+09 8.0000e+08  61.9%     -    0s
H    0     2                    2.075016e+09 8.0000e+08  61.4%     -    0s
     0     2 8.0000e+08    0   70 2.0750e+09 8.0000e+08  61.4%     -    0s
H    3     8                    1.950016e+09 8.0000e+08  59.0%  47.3    0s
H  148   134                    1.933349e+09 8.0000e+08  58.6%   4.2    0s
H  285   288                    1.900014e+09 8.0000e+08  57.9%   4.2    0s
H  286   288                    1.825015e+09 8.0000e+08  56.2%   4.2    0s
H  286   286                    1.733347e+09 8.0000e+08  53.8%   4.2    0s
H  300   282                    1.733347e+09 8.0000e+08  53.8%   4.2    1s
H  312   295                    1.700014e+09 8.0000e+08  52.9%   4.4    1s
H  340   315                    1.700014e+09 8.0000e+08  52.9%   4.7    1s
H  356   324                    1.666680e+09 8.0000e+08  52.0%   4.6    1s
H  396   383                    1.666680e+09 8.0000e+08  52.0%   4.6    1s
H  615   524                    1.633347e+09 8.0000e+08  51.0%   4.4    1s
  1936  1388 1.2500e+09   58   42 1.6333e+09 9.0000e+08  44.9%   8.6    5s
H 3651  2060                    1.540012e+09 9.0001e+08  41.6%   9.4    7s
H 4900  2566                    1.480013e+09 9.0001e+08  39.2%  10.1    9s
  4985  2616 1.0295e+09   65   52 1.4800e+09 9.0001e+08  39.2%  10.3   11s

Interrupt request received

Cutting planes:
  Gomory: 51
  Cover: 1
  Implied bound: 69
  MIR: 11
  Flow cover: 221

Explored 5508 nodes (62529 simplex iterations) in 11.39 seconds
Thread count was 8 (of 8 available processors)

Solution count 10: 1.48001e+09 1.54001e+09 1.63335e+09 ... 1.82501e+09

Solve interrupted
Best objective 1.480012800002e+09, best bound 9.000054375256e+08, gap 39.1893%

Results are consistent with our expectations. We find a better solution sooner by shifting the focus towards finding feasible solutions (objective value 1.525e9 versus 1.6e9).

The setParam() 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 that Model.Params is a bit less forgiving than setParam(). In particular, wildcards are not allowed with this approach. You don't have to worry about capitalization of parameter names in either approach, though, so m.Params.Heuristics and m.Params.heuristics are equivalent.

The full set of available parameters can be browsed using the paramHelp() command. You can obtain further information on a specific parameter (e.g., MIPGap) by typing paramHelp('MIPGap').