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Open AccessProceedings ArticleDOI

Efficient conflict driven learning in a boolean satisfiability solver

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TLDR
This paper generalizes various conflict driven learning strategies in terms of different partitioning schemes of the implication graph to re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes.
Abstract
One of the most important features of current state-of-the-art SAT solvers is the use of conflict based backtracking and learning techniques. In this paper, we generalize various conflict driven learning strategies in terms of different partitioning schemes of the implication graph. We re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes. Extensive experiments with real world examples show that the best performing new learning scheme has at least a 2/spl times/ speedup compared with learning schemes employed in state-of-the-art SAT solvers.

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Citations
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Journal ArticleDOI

An exact algorithm with learning for the graph coloring problem

TL;DR: This paper proposes an exact algorithm with learning for GCP which exploits the implicit constraints using propositional logic, and shows that this algorithm outperforms other algorithms on many instances.
Book ChapterDOI

Lazy explanations for constraint propagators

TL;DR: It is shown that it is possible and highly effective to calculate explanations retrospectively when they are needed, and implemented “lazy” explanations in a state of the art learning framework.
Journal ArticleDOI

Satisfiability-based test generation for nonseparable RTL controller-datapath circuits

TL;DR: This paper presents a satisfiability (SAT)-based algorithm for automatically generating test sequences that target gate-level stuck-at faults in a circuit by using its register-transfer level (RTL) description, and shows that this RTL test generator can outperform gate- level sequential automatic test-pattern generation (ATPG), in terms of both fault coverage and test-generation time.
Journal ArticleDOI

SAT Competition 2020

TL;DR: 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 are provided.
Book ChapterDOI

Partial max-SAT solvers with clause learning

TL;DR: The results obtained provide empirical evidence that Partial Max-SAT is a suitable formalism for representing and solving over-constrained problems, and that the learning techniques defined in this paper can give rise to substantial performance improvements.
References
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Book

Computers and Intractability: A Guide to the Theory of NP-Completeness

TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book

Introduction to Algorithms

TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Book

Genetic Algorithms

Journal ArticleDOI

Tabu Search—Part II

TL;DR: The elements of staged search and structured move sets are characterized, which bear on the issue of finiteness, and new dynamic strategies for managing tabu lists are introduced, allowing fuller exploitation of underlying evaluation functions.