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Selecting SAT Encodings for Pseudo-Boolean and Linear Integer Constraints

Authors Felix Ulrich-Oltean , Peter Nightingale , James Alfred Walker

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Author Details

Felix Ulrich-Oltean
  • Department of Computer Science, University of York, UK
Peter Nightingale
  • Department of Computer Science, University of York, UK
James Alfred Walker
  • Department of Computer Science, University of York, UK

Funding

  • Ulrich-Oltean, Felix: Supported by grant EP/R513386/1 from the UK Engineering and Physical Sciences Research Council.

Acknowledgements

We are very grateful to Nguyen Dang for helpful conversations about portfolio approaches. The experiments were undertaken on the Viking Cluster, which is a high performance compute facility provided by the University of York. We are grateful for support from the University of York High Performance Computing service, Viking and the Research Computing team.

Cite AsGet BibTex

Felix Ulrich-Oltean, Peter Nightingale, and James Alfred Walker. Selecting SAT Encodings for Pseudo-Boolean and Linear Integer Constraints. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 38:1-38:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.CP.2022.38

Abstract

Many constraint satisfaction and optimisation problems can be solved effectively by encoding them as instances of the Boolean Satisfiability problem (SAT). However, even the simplest types of constraints have many encodings in the literature with widely varying performance, and the problem of selecting suitable encodings for a given problem instance is not trivial. We explore the problem of selecting encodings for pseudo-Boolean and linear constraints using a supervised machine learning approach. We show that it is possible to select encodings effectively using a standard set of features for constraint problems; however we obtain better performance with a new set of features specifically designed for the pseudo-Boolean and linear constraints. In fact, we achieve good results when selecting encodings for unseen problem classes. Our results compare favourably to AutoFolio when using the same feature set. We discuss the relative importance of instance features to the task of selecting the best encodings, and compare several variations of the machine learning method.

Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
Keywords
  • Constraint programming
  • SAT encodings
  • machine learning
  • global constraints
  • pseudo-Boolean constraints
  • linear constraints

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Supplementary Materials

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