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arXiv · 2305.12352

Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching

Yanguang Chen·Wenzhi Gao·Wanyu Zhang·Dongdong Ge·Huikang Liu·Yinyu Ye
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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.

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Yanguang Chen, Wenzhi Gao, Wanyu Zhang, Dongdong Ge, Huikang Liu, Yinyu Ye. 2023-05-21. Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching. https://arxiv.org/abs/2305.12352

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