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
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   73 3.6917e+09 8.0000e+08  78.3%     -    0s
H    0     0                    3.075022e+09 8.0000e+08  74.0%     -    0s
H    0     0                    3.020023e+09 8.0000e+08  73.5%     -    0s
     0     0 8.0000e+08    0   76 3.0200e+09 8.0000e+08  73.5%     -    0s
     0     0 8.0000e+08    0   75 3.0200e+09 8.0000e+08  73.5%     -    0s
H    0     0                    2.550024e+09 8.0000e+08  68.6%     -    0s
H    0     2                    2.175020e+09 8.0000e+08  63.2%     -    0s
     0     2 8.0000e+08    0   75 2.1750e+09 8.0000e+08  63.2%     -    0s
H   95    98                    2.150020e+09 8.0000e+08  62.8%   4.6    0s
H   96    98                    2.120018e+09 8.0000e+08  62.3%   4.6    0s
H  101   103                    2.116687e+09 8.0000e+08  62.2%   4.5    0s
H  110   103                    2.100017e+09 8.0000e+08  61.9%   4.3    0s
H  352   325                    2.000018e+09 8.0000e+08  60.0%   4.2    0s
H  406   375                    1.991686e+09 8.0000e+08  59.8%   4.0    0s
H 1074   888                    1.981836e+09 8.0000e+08  59.6%   3.5    0s
H 1078   889                    1.966686e+09 8.0000e+08  59.3%   3.5    0s
H 1107   878                    1.900018e+09 8.0000e+08  57.9%   3.5    0s
H 1696  1125                    1.800017e+09 8.0000e+08  55.6%   3.4    0s
H 1845  1146                    1.800017e+09 8.0000e+08  55.6%   4.2    1s
H 1863  1087                    1.733350e+09 8.0000e+08  53.8%   4.3    1s
H 2353  1273                    1.733350e+09 8.0000e+08  53.8%   4.3    1s
H 2517  1299                    1.700016e+09 8.0000e+08  52.9%   4.3    1s
H 2598  1248                    1.666682e+09 8.0000e+08  52.0%   4.3    1s
H 2733  1252                    1.633349e+09 8.0000e+08  51.0%   4.2    1s
 14259  7927 1.5000e+09   85   28 1.6333e+09 8.0000e+08  51.0%   3.5    5s
 24846 14278 1.1000e+09   49   55 1.6333e+09 8.0001e+08  51.0%   3.5   10s
H25035 13985                    1.600016e+09 8.0001e+08  50.0%   3.5   10s
H25066 14020                    1.600016e+09 8.0001e+08  50.0%   3.5   10s
H25072 13532                    1.583350e+09 8.0001e+08  49.5%   3.5   10s
H26218 14083                    1.575016e+09 8.0001e+08  49.2%   3.5   10s
H26326 14118                    1.566682e+09 8.0001e+08  48.9%   3.5   10s
H26577 13650                    1.525016e+09 8.0001e+08  47.5%   3.5   10s

Interrupt request received

Cutting planes:
  Gomory: 6
  Implied bound: 26
  MIR: 60

Explored 30546 nodes (107810 simplex iterations) in 11.81 seconds
Thread count was 8 (of 8 available processors)

Solve interrupted
Best objective 1.5250155750e+09, best bound 8.0000520000e+08, gap 47.5412%

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').