Fast and Interpretable Mixed-Integer Linear Program Solving by Learning Model Reduction

TL;DR

This paper introduces a preference-based learning approach for model reduction in MILP problems, significantly speeding up solutions while maintaining high accuracy, by learning a simplified, interpretable model instead of direct solutions.

Contribution

It proposes a novel preference-based model reduction learning method with an attention mechanism and pruning strategy, improving scalability and performance over existing methods.

Findings

Achieves nearly 20% better solution accuracy than state-of-the-art reduction methods.

Attains two to four orders of magnitude speedup compared to Gurobi.

Demonstrates effectiveness on real-world MILP problems.

Abstract

By exploiting the correlation between the structure and the solution of Mixed-Integer Linear Programming (MILP), Machine Learning (ML) has become a promising method for solving large-scale MILP problems. Existing ML-based MILP solvers mainly focus on end-to-end solution learning, which suffers from the scalability issue due to the high dimensionality of the solution space. Instead of directly learning the optimal solution, this paper aims to learn a reduced and equivalent model of the original MILP as an intermediate step. The reduced model often corresponds to interpretable operations and is much simpler, enabling us to solve large-scale MILP problems much faster than existing commercial solvers. However, current approaches rely only on the optimal reduced model, overlooking the significant preference information of all reduced models. To address this issue, this paper proposes a preference-based model reduction learning method, which considers the relative performance (i.e., objective cost and constraint feasibility) of all reduced models on each MILP instance as preferences. We also introduce an attention mechanism to capture and represent preference information, which helps improve the performance of model reduction learning tasks. Moreover, we propose a SetCover based pruning method to control the number of reduced models (i.e., labels), thereby simplifying the learning process. Evaluation on real-world MILP problems shows that 1) compared to the state-of-the-art model reduction ML methods, our method obtains nearly 20% improvement on solution accuracy, and 2) compared to the commercial solver Gurobi, two to four orders of magnitude speedups are achieved.