Learning to Schedule Heuristics in Branch-and-Bound

• URL: http://arxiv.org/abs/2103.10294v1
• Date: Thu, 18 Mar 2021 14:49:52 GMT
• Title: Learning to Schedule Heuristics in Branch-and-Bound
• Authors: Antonia Chmiela, Elias B. Khalil, Ambros Gleixner, Andrea Lodi, Sebastian Pokutta
• Abstract summary: Real-world applications typically require finding good solutions early in the search to enable fast decision-making. We propose the first data-driven framework for schedulings in an exact MIP solver. Compared to the default settings of a state-of-the-art academic MIP solver, we are able to reduce the average primal integral by up to 49% on a class of challenging instances.
• Score: 25.79025327341732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Primal heuristics play a crucial role in exact solvers for Mixed Integer Programming (MIP). While solvers are guaranteed to find optimal solutions given sufficient time, real-world applications typically require finding good solutions early on in the search to enable fast decision-making. While much of MIP research focuses on designing effective heuristics, the question of how to manage multiple MIP heuristics in a solver has not received equal attention. Generally, solvers follow hard-coded rules derived from empirical testing on broad sets of instances. Since the performance of heuristics is instance-dependent, using these general rules for a particular problem might not yield the best performance. In this work, we propose the first data-driven framework for scheduling heuristics in an exact MIP solver. By learning from data describing the performance of primal heuristics, we obtain a problem-specific schedule of heuristics that collectively find many solutions at minimal cost. We provide a formal description of the problem and propose an efficient algorithm for computing such a schedule. Compared to the default settings of a state-of-the-art academic MIP solver, we are able to reduce the average primal integral by up to 49% on a class of challenging instances.

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