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Learning to Schedule Heuristics in Branch and Bound

  • 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.
Author:Antonia ChmielaORCiD, Elias B. Khalil, Ambros GleixnerORCiD, Andrea Lodi, Sebastian Pokutta
Document Type:In Proceedings
Parent Title (English):Thirty-fifth Conference on Neural Information Processing Systems, NeurIPS 2021
Date of first Publication:2021/12/07
ArXiv Id:http://arxiv.org/abs/2103.10294
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
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