RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs

Tae-Hoon Lee, Min-Soo Kim

Keywords: Combinatorial Optimization, Integer Linear Programming, Graph Neural Networks, Reinforcement Learning, Primal Heuristics

TL;DR: We propose a novel reinforcement learning-based start primal heuristic (RL-SPH) that can independently generate high-quality feasible solutions for ILP with a theoretical guarantee.

Abstract: Primal heuristics play a crucial role in quickly finding feasible solutions for NP-hard integer linear programming (ILP). Although $\textit{end-to-end learning}$-based primal heuristics (E2EPH) have recently been proposed, they are typically unable to independently generate feasible solutions. To address this challenge, we propose RL-SPH, a novel reinforcement learning-based start primal heuristic capable of independently generating feasible solutions, even for ILP involving non-binary integers. Empirically, RL-SPH rapidly obtains high-quality feasible solutions with a 100% feasibility rate, achieving on average a 44× lower primal gap and a 2.3× lower primal integral compared to existing start primal heuristics.

Primary Area: other topics in machine learning (i.e., none of the above)

Submission Number: 24665