Example: quality control through the solution pool gap parameter

Illustrates quality control through the solution pool gap parameter.

In many cases, solutions are interesting only if their objective value is close to the optimal objective value of the model. In that context, you can control the quality of solutions generated and saved in the solution pool with the absolute gap for solution pool parameter (SolnPoolAGap, CPX_PARAM_SOLNPOOLAGAP) and the relative gap for solution pool parameter (SolnPoolGap, CPX_PARAM_SOLNPOOLGAP).

To demonstrate this idea, consider again the example cited in Example: simple facility location problem. In order to obtain solutions that are less than 10% worse than the optimal objective value, specify the solution pool relative gap parameter in the Interactive Optimizer like this:

read location.lp
set mip pool relgap 0.1
populate

Then display the objective value of each solution in the Interactive Optimizer with this command:

display solution list *

Afterwards, you see that all solutions in the pool are of a cost less than or equal to 548; that is, within 10% of the optimal objective value of 499.