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Weight-Aware Core Extraction in SAT-Based MaxSAT Solving

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Principles and Practice of Constraint Programming (CP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10416))

Abstract

Maximum satisfiability (MaxSAT) is today a competitive approach to tackling NP-hard optimization problems in a variety of AI and industrial domains. A great majority of the modern state-of-the-art MaxSAT solvers are core-guided, relying on a SAT solver to iteratively extract unsatisfiable cores of the soft clauses in the working formula and ruling out the found cores via adding cardinality constraints into the working formula until a solution is found. In this work we propose weight-aware core extraction (WCE) as a refinement to the current common approach of core-guided solvers. WCE integrates knowledge of soft clause weights into the core extraction process, and allows for delaying the addition of cardinality constraints into the working formula. We show that WCE noticeably improves in practice the performance of PMRES, one of the recent core-guided MaxSAT algorithms using soft cardinality constraints, and explain how the approach can be integrated into other core-guided algorithms.

Work supported by Academy of Finland (grants 251170 COIN, 276412, 284591); and DoCS Doctoral School in Computer Science and Research Funds of the University of Helsinki.

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Notes

  1. 1.

    In our implementation used in the experiments of this work, we use the slightly more sophisticated diversity heuristic [5] which attempts to balance the number of new soft clauses introduced and the amount that \(w_{\max }\) is decreased.

  2. 2.

    Unfortunately, we do not have access to the source code of MSCG.

  3. 3.

    Originally in [31] PMRES was formalized as a special case of the so-called MAXRES [19] rule; the specific special case is equivalent to the formalization of PMRES used here.

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Berg, J., Järvisalo, M. (2017). Weight-Aware Core Extraction in SAT-Based MaxSAT Solving. In: Beck, J. (eds) Principles and Practice of Constraint Programming. CP 2017. Lecture Notes in Computer Science(), vol 10416. Springer, Cham. https://doi.org/10.1007/978-3-319-66158-2_42

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