Parameter Tuning of Linear Programming Solvers
Νικόλαος Πλόσκας · 2022
Linear programming solvers include various options that can be used to control algorithmic aspects and considerably impact the solver performance. As it is obvious, manually finding optimal parameters is a very difficult task and sometimes impossible. For this reason, it is necessary to implement smart techniques that will automate this process. Other works have utilized derivative-free optimization solvers to tune solver parameters. In this work, eight open-source derivative-free optimization solvers are utilized for finding (near) optimal tuning parameters of state-of-the-art linear programming solvers. We investigate how sensitive linear programming solvers are to a parameter tuning process. Extensive computational results are presented on tuning four linear programming solvers (CLP, CPLEX, GUROBI, and XPRESS) over a set of 70 benchmark problems. We find better parameters for all linear programming solvers, achieving a reduction in execution time over their default parameters up to 26%. We conclude that several derivative-free optimization solvers outperform others on finding optimal optimal tuning parameters for linear programming solvers.