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Constraint-Based Techniques in Stochastic Local Search MaxSAT Solving

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Principles and Practice of Constraint Programming (CP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11802))

Abstract

The recent improvements in solving Maximum Satisfiability (MaxSAT) problems has allowed the usage of MaxSAT in several application domains. However, it has been observed that finding an optimal solution in a reasonable amount of time remains a challenge. Moreover, in many applications it is enough to provide a good approximation of the optimum. Recently, new local search algorithms have been shown to be successful in approximating the optimum in MaxSAT problems. Nevertheless, these local search algorithms fail in finding feasible solutions to highly constrained instances. In this paper, we propose two constraint-based techniques for improving local search MaxSAT solvers. Firstly, an unsatisfiability-based algorithm is used to guide the local search solver into the feasible region of the search space. Secondly, given a partial assignment, we perform Minimal Correction Subsets (MCS) enumeration in order to improve upon the best solution found by the local search solver. Experimental results using a large set of instances from the MaxSAT evaluation 2018 show the effectiveness of our approach.

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Notes

  1. 1.

    In the case of partial MaxSAT instances, a cardinality constraint is used.

  2. 2.

    We refer to the literature for further details [10, 33].

  3. 3.

    The source code of SATLike is publicly available at the 2018 MaxSAT evaluation https://maxsat-evaluations.github.io/2018/descriptions.html.

  4. 4.

    Instances available at https://maxsat-evaluations.github.io/2018/.

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Acknowledgments

This work was supported by national funds through FCT with references UID/CEC/50021/2019, PTDC/CCI-COM/31198/2017 and DSAIPA/AI/0044/2018.

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Authors and Affiliations

  1. INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Andreia P. Guerreiro, Miguel Terra-Neves, Inês Lynce & Vasco Manquinho

  2. OutSystems, Lisbon, Portugal

    Miguel Terra-Neves

  3. CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    José Rui Figueira

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  1. Andreia P. Guerreiro
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  2. Miguel Terra-Neves
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  3. Inês Lynce
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  5. Vasco Manquinho
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Correspondence to Vasco Manquinho .

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  1. INRA, Castanet Tolosan, France

    Thomas Schiex

  2. INRA, Castanet Tolosan, France

    Simon de Givry

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Guerreiro, A.P., Terra-Neves, M., Lynce, I., Figueira, J.R., Manquinho, V. (2019). Constraint-Based Techniques in Stochastic Local Search MaxSAT Solving. In: Schiex, T., de Givry, S. (eds) Principles and Practice of Constraint Programming. CP 2019. Lecture Notes in Computer Science(), vol 11802. Springer, Cham. https://doi.org/10.1007/978-3-030-30048-7_14

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