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Boosting branch-and-bound MaxSAT solvers with clause learning

Abstract : The Maximum Satisfiability Problem, or MaxSAT, offers a suitable problem solving formalism for combinatorial optimization problems. Nevertheless, MaxSAT solvers implementing the Branch-and-Bound (BnB) scheme have not succeeded in solving challenging real-world optimization problems. It is widely believed that BnB MaxSAT solvers are only superior on random and some specific crafted instances. At the same time, SAT-based MaxSAT solvers perform particularly well on real-world instances. To overcome this shortcoming of BnB MaxSAT solvers, this paper proposes a new BnB MaxSAT solver called MaxCDCL. The main feature of MaxCDCL is the combination of clause learning of soft conflicts and an efficient bounding procedure. Moreover, the paper reports on an experimental investigation showing that MaxCDCL is competitive when compared with the best performing solvers of the 2020 MaxSAT Evaluation. MaxCDCL performs very well on real-world instances, and solves a number of instances that other solvers cannot solve. Furthermore, MaxCDCL, when combined with the best performing MaxSAT solvers, solves the highest number of instances of a collection from all the MaxSAT evaluations held so far.
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https://hal-amu.archives-ouvertes.fr/hal-03594950
Contributor : Djamal Habet Connect in order to contact the contributor
Submitted on : Thursday, March 3, 2022 - 8:58:15 AM
Last modification on : Friday, August 5, 2022 - 11:22:16 AM

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Chu-Min Li, Zhenxing Xu, Jordi Coll, Felip Manyà, Djamal Habet, et al.. Boosting branch-and-bound MaxSAT solvers with clause learning. AI Communications, 2021, pp.1-21. ⟨10.3233/AIC-210178⟩. ⟨hal-03594950⟩

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