SRG: Score-based Relaxation-guided Generation for Mixed Integer Linear Programming

TL;DR

SRG introduces a novel score-based generative framework using relaxation-guided SDEs and Transformers to produce high-quality, diverse solutions for mixed-integer linear programming, enhancing solver performance.

Contribution

The paper presents SRG, a new generative approach leveraging relaxation-guided SDEs and Transformers to improve solution quality and transferability in MILP problems.

Findings

SRG matches or surpasses strong learning-based baselines in solution quality.

SRG improves solver objectives and reduces search time in challenging instances.

SRG demonstrates promising zero-shot transferability to unseen problems.

Abstract

We propose Score-based Relaxation-guided Generation (SRG), a generative framework based on an approximate formulation of relaxation-guided stochastic differential equations (SDEs) for mixed-integer linear programming. SRG employs a Transformer-based score network that incorporates feasibility and optimality signals into score modeling, encouraging the learned generative model to place more probability mass on feasible, high-quality regions of the solution space. At inference time, SRG directly samples diverse candidate solutions from the learned score model without requiring any additional guidance module. These candidates are then used to construct compact trust-region subproblems for standard MILP solvers. Across multiple public benchmarks, SRG matches or improves upon the solution quality of the strongest learning-based baselines, with particularly strong gains in challenging candidate-generation settings. Moreover, SRG shows promising zero-shot transferability to unseen cross-scale and cross-problem instances, improving solver objectives and reducing search time in several cases through higher-quality initial candidates and compact trust-region search.