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Core-Boosted Linear Search for Incomplete MaxSAT

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Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11494))

Abstract

Maximum Satisfiability (MaxSAT), the optimisation extension of the well-known Boolean Satisfiability (SAT) problem, is a competitive approach for solving NP-hard problems encountered in various artificial intelligence and industrial domains. Due to its computational complexity, there is an inherent tradeoff between scalability and guarantee on solution quality in MaxSAT solving. Limitations on available computational resources in many practical applications motivate the development of complete any-time MaxSAT solvers, i.e. algorithms that compute optimal solutions while providing intermediate results. In this work, we propose core-boosted linear search, a generic search-strategy that combines two central approaches in modern MaxSAT solving, namely linear and core-guided algorithms. Our experimental evaluation on a prototype combining reimplementations of two state-of-the-art MaxSAT solvers, PMRES as the core-guided approach and LinSBPS as the linear algorithm, demonstrates that our core-boosted linear algorithm often outperforms its individual components and shows competitive and, on many domains, superior results when compared to other state-of-the-art solvers for incomplete MaxSAT solving.

The first author is financially supported by the University of Helsinki Doctoral Program in Computer Science and the Academy of Finland (grant 312662). We thank the University of Melbourne and the Melbourne School of Engineering Visiting Fellows scheme for supporting the visit of Jeremias Berg.

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Notes

  1. 1.

    A consequence of the metric we use is that the scores of the other solvers we report are lower than in the evaluation. Their relative ranking is however the same.

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Author information

Authors and Affiliations

  1. HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland

    Jeremias Berg

  2. University of Melbourne, Melbourne, Australia

    Emir Demirović

  3. Monash University, Melbourne, Australia

    Peter J. Stuckey

  4. Data61, CSIRO, Canberra, Australia

    Peter J. Stuckey

Authors
  1. Jeremias Berg
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  2. Emir Demirović
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  3. Peter J. Stuckey
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Correspondence to Jeremias Berg .

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Editors and Affiliations

  1. Ecole Polytechnique de Montréal, Montréal, QC, Canada

    Louis-Martin Rousseau

  2. University of Western Macedonia, Kozani, Greece

    Kostas Stergiou

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Berg, J., Demirović, E., Stuckey, P.J. (2019). Core-Boosted Linear Search for Incomplete MaxSAT. In: Rousseau, LM., Stergiou, K. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2019. Lecture Notes in Computer Science(), vol 11494. Springer, Cham. https://doi.org/10.1007/978-3-030-19212-9_3

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