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Exploiting subproblem optimization in SAT-based MaxSAT algorithms

Author

Listed:
  • Carlos Ansótegui

    (Universitat de Lleida)

  • Joel Gabàs

    (Universitat de Lleida)

  • Jordi Levy

    (IIIA-CSIC)

Abstract

The Maximum Satisfiability (MaxSAT) problem is an optimization variant of the Satisfiability (SAT) problem. Several combinatorial optimization problems can be translated into a MaxSAT formula. Among exact MaxSAT algorithms, SAT-based MaxSAT algorithms are the best performing approaches for real-world problems. We have extended the WPM2 algorithm by adding several improvements. In particular, we show that by solving some subproblems of the original MaxSAT instance we can dramatically increase the efficiency of WPM2. This led WPM2 to achieve the best overall results at the international MaxSAT Evaluation 2013 (MSE13) on industrial instances. Then, we present additional techniques and heuristics to further exploit the information retrieved from the resolution of the subproblems. We exhaustively analyze the impact of each improvement what contributes to our understanding of why they work. This architecture allows to convert exact algorithms into efficient incomplete algorithms. The resulting solver had the best results on industrial instances at the incomplete track of the latest international MSE.

Suggested Citation

  • Carlos Ansótegui & Joel Gabàs & Jordi Levy, 2016. "Exploiting subproblem optimization in SAT-based MaxSAT algorithms," Journal of Heuristics, Springer, vol. 22(1), pages 1-53, February.
  • Handle: RePEc:spr:joheur:v:22:y:2016:i:1:d:10.1007_s10732-015-9300-7
    DOI: 10.1007/s10732-015-9300-7
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    References listed on IDEAS

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    1. Brian Borchers & Judith Furman, 1998. "A Two-Phase Exact Algorithm for MAX-SAT and Weighted MAX-SAT Problems," Journal of Combinatorial Optimization, Springer, vol. 2(4), pages 299-306, December.
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    Cited by:

    1. Carlos Ansótegui & Felip Manyà & Jesus Ojeda & Josep M. Salvia & Eduard Torres, 2022. "Incomplete MaxSAT approaches for combinatorial testing," Journal of Heuristics, Springer, vol. 28(4), pages 377-431, August.

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