QMaxSATpb: A Certified MaxSAT Solver

Vandesande, Dieter; Wulf, Wolf De; Bogaerts, Bart

doi:10.1007/978-3-031-15707-3_33

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…While developing proof logging methods that can support the full range of modern MaxSAT solving techniques remains a formidable challenge, we want to point out that significant progress has been made of late. The proof system introduced by Gocht and Nordström (2021) has been used to certify correctness of the translations of pseudo-Boolean constraints into CNF for a range of encodings , and quite similar ideas have been employed to design proof logging for an objective-improving solver for unweighted MaxSAT (Vandesande, De Wulf, & Bogaerts, 2022). Very recently, this has been extended also to state-of-the-art core-guided MaxSAT solvers (Berg, Bogaerts, Nordström, Oertel, & Vandesande, 2023).…”

Section: Discussionmentioning

confidence: 99%

Certified Dominance and Symmetry Breaking for Combinatorial Optimisation

Bogaerts

Gocht²,

McCreesh³

et al. 2023

jair

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Symmetry and dominance breaking can be crucial for solving hard combinatorial search and optimisation problems, but the correctness of these techniques sometimes relies on subtle arguments. For this reason, it is desirable to produce efficient, machine-verifiable certificates that solutions have been computed correctly. Building on the cutting planes proof system, we develop a certification method for optimisation problems in which symmetry and dominance breaking is easily expressible. Our experimental evaluation demonstrates that we can efficiently verify fully general symmetry breaking in Boolean satisfiability (SAT) solving, thus providing, for the first time, a unified method to certify a range of advanced SAT techniques that also includes cardinality and parity (XOR) reasoning. In addition, we apply our method to maximum clique solving and constraint programming as a proof of concept that the approach applies to a wider range of combinatorial problems.

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Section: Discussionmentioning

confidence: 99%

Certified Dominance and Symmetry Breaking for Combinatorial Optimisation

Bogaerts

Gocht²,

McCreesh³

et al. 2023

jair

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“…VeriPB has been shown to be capable of efficient justification of important constraint programming techniques [EGMN20,GMN22], and can also provide proof logging for a wide range of graph problem solvers [GMN20, GMM + 20]. Furthermore, the papers [GMNO22,VWB22] have used VeriPB to develop proof logging methods that seem to have the potential to support a range of SAT-based optimization approaches using maximum satisfiability (MaxSAT) solvers. The pseudo-Boolean rules for reasoning with 0-1 linear constraints provide a simple yet very expressive formalism, and it does not seem out of the question to hope that they could be extended to deal with proof logging for mixed integer programming (MIP).…”

Section: Discussionmentioning

confidence: 99%

“…Since the conference version of this paper appeared, our pseudo-Boolean proof logging method has been extended further to deal with fully general symmetry breaking in SAT solving [BGMN22], and also to support pseudo-Boolean solving using SAT solvers [GMNO22]. Furthermore, there have been promising preliminary results on providing proof logging for MaxSAT solvers [VWB22] and constraint programming solvers [GMN22].…”

Section: Subsequent Developmentsmentioning

confidence: 99%

Certifying Parity Reasoning Efficiently Using Pseudo-Boolean Proofs

Gocht¹,

Nordström

2021

AAAI

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The dramatic improvements in combinatorial optimization algorithms over the last decades have had a major impact in artificial intelligence, operations research, and beyond, but the output of current state-of-the-art solvers is often hard to verify and is sometimes wrong. For Boolean satisfiability (SAT) solvers proof logging has been introduced as a way to certify correctness, but the methods used seem hard to generalize to stronger paradigms. What is more, even for enhanced SAT techniques such as parity (XOR) reasoning, cardinality detection, and symmetry handling, it has remained beyond reach to design practically efficient proofs in the standard DRAT format. In this work, we show how to instead use pseudo-Boolean inequalities with extension variables to concisely justify XOR reasoning. Our experimental evaluation of a SAT solver integration shows a dramatic decrease in proof logging and verification time compared to existing DRAT methods. Since our method is a strict generalization of DRAT, and readily lends itself to expressing also 0-1 programming and even constraint programming problems, we hope this work points the way towards a unified approach for efficient machine-verifiable proofs for a rich class of combinatorial optimization paradigms.

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“…The most commonly used proof formalisms are based on reverse asymmetric tautology (Järvisalo, Heule, and Biere 2012), such as DRAT (Heule, Hunter, and Wetzler 2013a,b;Wetzler, Heule, and Hunter 2014) or LRAT (Cruz-Filipe et al 2017). More recent development in the area uses the general purpose proof system VeriPB (e. g. Elffers et al 2020;Bogaerts et al 2022) for turning MaxSAT solvers into certifying algorithms ( Vandesande, Wulf, and Bogaerts 2022).…”

Section: Introductionmentioning

confidence: 99%

Optimality Certificates for Classical Planning

Mugdan,

Christen,

Eriksson

2023

ICAPS

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Algorithms are usually shown to be correct on paper, but bugs in their implementations can still lead to incorrect results. In the case of classical planning, it is fortunately straightforward to check whether a computed plan is correct. For optimal planning however, plans are additionally required to have minimal cost, which is significantly more difficult to verify. While some domain-specific approaches exists, we lack a general tool to verify optimality for arbitrary problems. We bridge this gap and introduce two approaches based on the principle of certifying algorithms, which provide a computer-verifiable certificate of correctness alongside their answer. We show that both approaches are sound and complete, analyze whether they can be generated and verified efficiently, and show how to apply them to concrete planning algorithms. The experimental evaluation shows that verifying optimality comes with a cost but is still practically feasible. Furthermore it confirms that the tested planner configurations provide optimal plans on the given instances, as all certificates were verified successfully.

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QMaxSATpb: A Certified MaxSAT Solver

Abstract: with details of the nature of the infringement. We will investigate the claim and if justified, we will take the appropriate steps.

Cited by 8 publications

References 35 publications

Certified Dominance and Symmetry Breaking for Combinatorial Optimisation

Certified Dominance and Symmetry Breaking for Combinatorial Optimisation

Certifying Parity Reasoning Efficiently Using Pseudo-Boolean Proofs

Optimality Certificates for Classical Planning

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