MIP emphasis switch
Controls trade-offs between speed, feasibility, optimality, and moving bounds in MIP.
Purpose
MIP emphasis switch
| API | Parameter Name | Name prior to V12.6.0 |
|---|---|---|
| C | CPXPARAM_Emphasis_MIP | CPX_PARAM_MIPEMPHASIS |
| C++ | IloCplex::Param::Emphasis::MIP | MIPEmphasis (int) |
| Java | IloCplex.Param.Emphasis.MIP | MIPEmphasis (int) |
| .NET | Cplex.Param.Emphasis.MIP | MIPEmphasis (int) |
| OPL | mipemphasis | |
| Python | parameters.emphasis.mip | emphasis.mip |
| MATLAB | Cplex.Param.emphasis.mip | emphasis.mip |
| Interactive | emphasis mip | emphasis mip |
| Identifier | 2058 | 2058 |
Description
Controls trade-offs between speed, feasibility, optimality, and moving bounds in MIP.
With the default setting of BALANCED, CPLEX works toward a rapid proof of an optimal solution, but balances that with effort toward finding high quality feasible solutions early in the optimization.
When this parameter is set to FEASIBILITY, CPLEX frequently will generate more feasible solutions as it optimizes the problem, at some sacrifice in the speed to the proof of optimality.
When set to OPTIMALITY, less effort may be applied to finding feasible solutions early.
With the setting BESTBOUND, even greater emphasis is placed on proving optimality through moving the best bound value, so that the detection of feasible solutions along the way becomes almost incidental.
When the parameter is set to HIDDENFEAS, the MIP optimizer works hard to find high quality feasible solutions that are otherwise very difficult to find, so consider this setting when the FEASIBILITY setting has difficulty finding solutions of acceptable quality.
| Value | Symbol | Meaning |
|---|---|---|
| 0 | CPX_MIPEMPHASIS_BALANCED | Balance optimality and feasibility; default |
| 1 | CPX_MIPEMPHASIS_FEASIBILITY | Emphasize feasibility over optimality |
| 2 | CPX_MIPEMPHASIS_OPTIMALITY | Emphasize optimality over feasibility |
| 3 | CPX_MIPEMPHASIS_BESTBOUND | Emphasize moving best bound |
| 4 | CPX_MIPEMPHASIS_HIDDENFEAS | Emphasize finding hidden feasible solutions |