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Perturbing Branching Heuristics in Constraint Solving

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Principles and Practice of Constraint Programming (CP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12333))

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Abstract

Variable ordering heuristics are one of the key settings for an efficient constraint solver. During the last two decades, a considerable effort has been spent for designing dynamic heuristics that iteratively change the order of variables as search progresses. At the same time, restart and randomization methods have been devised for alleviating heavy-tailed phenomena that typically arise in backtrack search. Despite restart methods are now well-understood, choosing how and when to randomize a given heuristic remains an open issue in the design of modern solvers. In this paper, we present several conceptually simple perturbation strategies for incorporating random choices in constraint solving with restarts. The amount of perturbation is controlled and learned in a bandit-driven framework under various stationary and non-stationary exploration policies, during successive restarts. Our experimental evaluation shows significant performance improvements for the perturbed heuristics compared to their classic counterpart, establishing the need for incorporating perturbation in modern constraint solvers.

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Notes

  1. 1.

    See http://www.cril.univ-artois.fr/XCSP17.

  2. 2.

    See http://www.cril.fr/~lecoutre/#/softwares.

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Acknowledgments

The authors would like to thank Frederic Koriche for his valuable advices on the Machine Learning aspects of the paper as well as the anonymous reviewers for their constructive remarks. This work has been partially supported by the project Emergence 2020 BAUTOM of INS2I and the project CPER Data from the region “Hauts-de-France”.

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Authors and Affiliations

  1. CRIL, University of Artois & CNRS, Lens, France

    Anastasia Paparrizou & Hugues Wattez

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  1. Anastasia Paparrizou
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  2. Hugues Wattez
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Correspondence to Anastasia Paparrizou .

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  1. Insight Centre for Data Analytics, School for Computer Science and Information Technology, University College Cork, Cork, Ireland

    Helmut Simonis

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Paparrizou, A., Wattez, H. (2020). Perturbing Branching Heuristics in Constraint Solving. In: Simonis, H. (eds) Principles and Practice of Constraint Programming. CP 2020. Lecture Notes in Computer Science(), vol 12333. Springer, Cham. https://doi.org/10.1007/978-3-030-58475-7_29

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  • DOI: https://doi.org/10.1007/978-3-030-58475-7_29

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