A General Large Neighborhood Search Framework for Solving Integer Linear Programs

• URL: http://arxiv.org/abs/2004.00422v3
• Date: Tue, 22 Dec 2020 22:21:18 GMT
• Title: A General Large Neighborhood Search Framework for Solving Integer Linear Programs
• Authors: Jialin Song, Ravi Lanka, Yisong Yue, Bistra Dilkina
• Abstract summary: We focus on solving integer programs, and ground our approach in the large neighborhood search (SLN) paradigm. We show that our LNS framework can significantly outperform compared to state-of-the-art commercial solvers such as Gurobi.
• Score: 46.62993477453986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies a strategy for data-driven algorithm design for large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways. The goal is to arrive at new approaches that can reliably outperform existing solvers in wall-clock time. We focus on solving integer programs, and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed. The appeal of LNS is that it can easily use any existing solver as a subroutine, and thus can inherit the benefits of carefully engineered heuristic or complete approaches and their software implementations. We show that one can learn a good neighborhood selector using imitation and reinforcement learning techniques. Through an extensive empirical validation in bounded-time optimization, we demonstrate that our LNS framework can significantly outperform compared to state-of-the-art commercial solvers such as Gurobi.

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