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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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Proceedings Article

A restriction of extended resolution for clause learning sat solvers

TL;DR: A clause learning SAT solver that uses extended resolution based on a restriction of the application of the extension rule, which outperforms existing solvers on application instances from recent SAT competitions as well as on instances that are provably hard for resolution, such as XOR-SAT instances.
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The First Evaluation of Pseudo-Boolean Solvers (PB’05)

TL;DR: The organization and the results of the first evaluation of pseudo-Boolean solvers was organized as a subtrack of the SAT 2005 competition and the goal is to take a snapshot of the current state of the art in the eld of pseudo -Boolean constraints.
Book ChapterDOI

Specification and Model Checking of Temporal Properties in Time Petri Nets and Timed Automata

TL;DR: The paper surveys some of the most recent approaches to verification of properties, expressible in some timed and untimed temporal logics, for real-time systems represented by time Petri nets (TPN’s), and focuses on bounded model checking for TCTL and for reachability properties.
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Adaptive Application of SAT Solving Techniques

TL;DR: This work proposes the notion of Adaptive Solving, and suggests several possible metrics, and shows that applying different heuristics on different parts of the search space can improve run times even beyond what can be achieved by the best heuristic on its own.
Proceedings ArticleDOI

SAT-decoding in evolutionary algorithms for discrete constrained optimization problems

TL;DR: This paper proposes a novel methodology to obtain feasible solutions from constrained discrete problems in population- based optimization heuristics and shows in detail how this methodology is implemented in Multi-objective Evolutionary Algorithms.
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.