MindOpt Adapter for CPLEX Benchmarking Performance Analysis
Mou Sun, Tao Li, Wotao Yin
TL;DR
The paper addresses suboptimal MILP performance of CPLEX under default configurations on benchmark sets. It proposes MindOpt Adapter to automatically tune solver configurations based on problem characteristics. Empirical results show that MindOpt Adapter for CPLEX solves 232 of 240 MIPLIB 2017 problems and demonstrates superior running-time efficiency, outperforming all competing solvers; it also solves all 45 slightly pathological problems and 31 of 32 infeasibility-detection problems, underscoring robustness. The results support the practical impact of automated solver configuration for robust MILP performance and provide reproducible configuration tables for researchers and practitioners.
Abstract
This report provides a comprehensive analysis of the performance of MindOpt Adapter for CPLEX 12.9 in benchmark testing. CPLEX, recognized as a robust Mixed Integer Programming (MIP) solver, has faced some scrutiny regarding its performance on MIPLIB 2017 when configured to default settings. MindOpt Adapter aims to enhance CPLEX's performance by automatically applying improved configurations for solving optimization problems. Our testing demonstrates that MindOpt Adapter for CPLEX yields successfully solved 232 of the 240 problems in the MIPLIB 2017 benchmark set. This performance surpasses all the other solvers in terms of the number of problems solved and the geometric mean of running times. The report provides a comparison of the benchmark results against the outcomes achieved by CPLEX under its default configuration.