Pre-trained Mixed Integer Optimization through Multi-variable Cardinality Branching

Yanguang Chen, Wenzhi Gao, Dongdong Ge, Yinyu Ye

Abstract: In this paper, we propose a Pre-trained Mixed Integer Optimization framework (PreMIO) that accelerates online mixed integer program (MIP) solving with offline datasets and machine learning models. Our method is based on a data-driven multi-variable cardinality branching procedure that splits the MIP feasible region using hyperplanes chosen by the concentration inequalities. Unlike most previous ML+MIP approaches that either require complicated implementation or suffer from a lack of theoretical justification, our method is simple, flexible, provable, and explainable. Numerical experiments on both classical OR benchmark datasets and real-life instances validate the efficiency of our proposed method.

External IDs:dblp:journals/corr/abs-2305-12352