Analyzing multiple conflicts in SAT: an experimental evaluation

Unit propagation and conflict analysis are two essential ingredients of CDCL SAT Solving. The order in which unit propagation is computed does not matter when no conflict is found, because it is well known that there exists a unique unit-propagation fixpoint. However, when a conflict is found, current CDCL implementations stop and analyze that concrete conflict, even though other conflicts may exist in the unit-propagation closure. In this experimental evaluation, we report on our experience in modifying this concrete aspect in the CaDiCaL SAT Solver and try to answer the question of whether we can improve the performance of SAT Solvers by the analysis of multiple conflicts.All authors are supported by grant PID2021-122830OB-C43, funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF: A way of making Europe”Peer ReviewedPostprint (published version

SAT Competition 2020

The SAT Competitions constitute a well-established series of yearly open international algorithm implementation competitions, focusing on the Boolean satisfiability (or propositional satisfiability, SAT) problem. In this article, we provide a detailed account on the 2020 instantiation of the SAT Competition, including the new competition tracks and benchmark selection procedures, overview of solving strategies implemented in top-performing solvers, and a detailed analysis of the empirical data obtained from running the competition. (C) 2021 The Authors. Published by Elsevier B.V.Peer reviewe

SAT Competition 2020

The SAT Competitions constitute a well-established series of yearly open international algorithm implementation competitions, focusing on the Boolean satisfiability (or propositional satisfiability, SAT) problem. In this article, we provide a detailed account on the 2020 instantiation of the SAT Competition, including the new competition tracks and benchmark selection procedures, overview of solving strategies implemented in top-performing solvers, and a detailed analysis of the empirical data obtained from running the competition

Incremental Maximum Satisfiability

Peer reviewe

Learning Shorter Redundant Clauses in SDCL Using MaxSAT

In this paper we present the design and implementation of a Satisfaction-Driven Clause Learning (SDCL) SAT solver, MapleSDCL, which uses a MaxSAT-based technique that enables it to learn shorter, and hence better, redundant clauses. We also perform a thorough empirical evaluation of our method and show that our SDCL solver solves Mutilated Chess Board (MCB) problems significantly faster than CDCL solvers, without requiring any alteration to the branching heuristic used by the underlying CDCL SAT solver

Recognition and Exploitation of Gate Structure in SAT Solving

In der theoretischen Informatik ist das SAT-Problem der archetypische Vertreter der Klasse der NP-vollständigen Probleme, weshalb effizientes SAT-Solving im Allgemeinen als unmöglich angesehen wird. Dennoch erzielt man in der Praxis oft erstaunliche Resultate, wo einige Anwendungen Probleme mit Millionen von Variablen erzeugen, die von neueren SAT-Solvern in angemessener Zeit gelöst werden können. Der Erfolg von SAT-Solving in der Praxis ist auf aktuelle Implementierungen des Conflict Driven Clause-Learning (CDCL) Algorithmus zurückzuführen, dessen Leistungsfähigkeit weitgehend von den verwendeten Heuristiken abhängt, welche implizit die Struktur der in der industriellen Praxis erzeugten Instanzen ausnutzen. In dieser Arbeit stellen wir einen neuen generischen Algorithmus zur effizienten Erkennung der Gate-Struktur in CNF-Encodings von SAT Instanzen vor, und außerdem drei Ansätze, in denen wir diese Struktur explizit ausnutzen. Unsere Beiträge umfassen auch die Implementierung dieser Ansätze in unserem SAT-Solver Candy und die Entwicklung eines Werkzeugs für die verteilte Verwaltung von Benchmark-Instanzen und deren Attribute, der Global Benchmark Database (GBD)

Proceedings of SAT Competition 2018 : Solver and Benchmark Descriptions

Non peer reviewe