UWrMaxSat: Efficient Solver for MaxSAT and Pseudo-Boolean Problems

Piotrów, Marek

doi:10.1109/ictai50040.2020.00031

Cited by 10 publications

(10 citation statements)

References 19 publications

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“…To compute a minimal-cost hitting set, MaxHS employs integer linear programming (ILP) as well as a branch-and-bound approach. A more recent MaxSAT solver is UWrMaxSat [Piotrow, 2020], which also solves PB problems. UWrMaxSat uses a core-guided approach and translates cardinality constraints into CNF.…”

Section: Mpe-satmentioning

confidence: 99%

“…We also considered the top three solvers in the complete weighted track of the MaxSAT Evaluation 2021: CASHWMaxSAT [Lei et al, 2021], MaxHS [Davies and Bacchus, 2011], and UWrMaxSat [Piotrow, 2020]. But we had to exclude CASHWMaxSAT because it did not report numeric optimal costs; we contacted the authors and are waiting for their response.…”

Section: Solversmentioning

confidence: 99%

“…To find maximizers, we also adapt an iterative technique [Kyrillidis et al, 2022] that was recently proposed for MaxSAT. To demonstrate the advantage of our approach, we generate random XOR-CNF benchmarks on which DPO significantly outperforms MaxHS [Davies and Bacchus, 2011], UWrMaxSat [Piotrow, 2020], and GaussMaxHS [Soos and Meel, 2021], which are state-of-the-art exact solvers for MaxSAT. [Sang et al, 2007] is an exact Boolean MPE solver that is based on DPLL and techniques in WMC [Sang et al, 2004].…”

Section: Introductionmentioning

confidence: 99%

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DPO: Dynamic-Programming Optimization on Hybrid Constraints

Phan¹,

Vardi²

2022

Preprint

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In Bayesian inference, the most probable explanation (MPE) problem requests a variable instantiation with the highest probability given some evidence. Since a Bayesian network can be encoded as a literal-weighted CNF formula ϕ, we study Boolean MPE, a more general problem that requests a model τ of ϕ with the highest weight, where the weight of τ is the product of weights of literals satisfied by τ . It is known that Boolean MPE can be solved via reduction to (weighted partial) MaxSAT. Recent work proposed DPMC, a dynamic-programming model counter that leverages graph-decomposition techniques to construct project-join trees. A project-join tree is an execution plan that specifies how to conjoin clauses and project out variables. We build on DPMC and introduce DPO, a dynamic-programming optimizer that exactly solves Boolean MPE. By using algebraic decision diagrams (ADDs) to represent pseudo-Boolean (PB) functions, DPO is able to handle disjunctive clauses as well as XOR clauses. (Cardinality constraints and PB constraints may also be compactly represented by ADDs, so one can further extend DPO's support for hybrid inputs.) To test the competitiveness of DPO, we generate random XOR-CNF formulas. On these hybrid benchmarks, DPO significantly outperforms MaxHS, UWrMaxSat, and GaussMaxHS, which are state-of-the-art exact solvers for MaxSAT.

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Section: Mpe-satmentioning

confidence: 99%

Section: Solversmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DPO: Dynamic-Programming Optimization on Hybrid Constraints

Phan¹,

Vardi²

2022

Preprint

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“…There have been proposed several algorithms to compute the lean kernel, e.g. [36,43]; we have implemented the algorithm by Marques-Silva et al [43] using a MaxSAT solver UWrMaxSat [54] as a back-end.…”

Section: W 3 -The Union Of Musesmentioning

confidence: 99%

“…To determine the value maxOnes, we define a partial MaxSAT problem using the formula W ∧ ai∈A a i , where W are the hard clauses and ai∈A a i are the soft clauses. To solve the problem, we employ the MaxSAT solver UWrMaxSat [54].…”

Section: W 5 -Maximum Mus Cardinalitymentioning

confidence: 99%

Counting Minimal Unsatisfiable Subsets

Bendík

Meel

2021

Computer Aided Verification

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Given an unsatisfiable Boolean formula F in CNF, an unsatisfiable subset of clauses U of F is called Minimal Unsatisfiable Subset (MUS) if every proper subset of U is satisfiable. Since MUSes serve as explanations for the unsatisfiability of F, MUSes find applications in a wide variety of domains. The availability of efficient SAT solvers has aided the development of scalable techniques for finding and enumerating MUSes in the past two decades. Building on the recent developments in the design of scalable model counting techniques for SAT, Bendík and Meel initiated the study of MUS counting techniques. They succeeded in designing the first approximate MUS counter, $$\mathsf {AMUSIC}$$ AMUSIC , that does not rely on exhaustive MUS enumeration. $$\mathsf {AMUSIC}$$ AMUSIC , however, suffers from two shortcomings: the lack of exact estimates and limited scalability due to its reliance on 3-QBF solvers.In this work, we address the two shortcomings of $$\mathsf {AMUSIC}$$ AMUSIC by designing the first exact MUS counter, $$\mathsf {CountMUST}$$ CountMUST , that does not rely on exhaustive enumeration. $$\mathsf {CountMUST}$$ CountMUST circumvents the need for 3-QBF solvers by reducing the problem of MUS counting to projected model counting. While projected model counting is #NP-hard, the past few years have witnessed the development of scalable projected model counters. An extensive empirical evaluation demonstrates that $$\mathsf {CountMUST}$$ CountMUST successfully returns MUS count for 1500 instances while $$\mathsf {AMUSIC}$$ AMUSIC and enumeration-based techniques could only handle up to 833 instances.

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