Can I read Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching on EtoBox?

Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching by Chen, Yanguang; Gao, Wenzhi; Zhang, Wanyu; Ge, Dongdong; Liu, Huikang; Ye, Yinyu is a scholarly article available to read on EtoBox.

What is Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching about?

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.

Author

Chen, Yanguang; Gao, Wenzhi; Zhang, Wanyu; Ge, Dongdong; Liu, Huikang; Ye, Yinyu

Published

2023

Language

EN