Problem
Research questions and friction points this paper is trying to address.
Accelerates MIP solving with data-driven branching
Uses probabilistic multi-variable cardinality branching
Provides simple, provable ML-based MIP optimization
Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching
🤖 AI Summary
To address the low online solving efficiency of Mixed-Integer Programming (MIP), this paper proposes PreMIO: a framework that pretrains lightweight machine learning models on offline data to devise the first data-driven, multi-variable branching strategy with both theoretical provability and interpretability. Leveraging concentration inequalities, the strategy guides hyperplane-based cuts to dynamically partition the feasible region—bridging the long-standing gap between theoretical guarantees and engineering practicality in ML-augmented MIP. PreMIO seamlessly integrates with mainstream MIP solvers without modifying their core algorithms. Evaluated on standard operations research benchmarks (e.g., MIPLIB) and real-world industrial instances, PreMIO reduces average solving time by 32%–57% and significantly decreases the number of explored nodes, demonstrating strong generalization and deployment feasibility.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Pre-trained framework accelerates MIP solving
Data-driven multi-variable cardinality branching
Simple, flexible, provable, explainable method
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