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Random Walk with Continuously Smoothed Variable Weights

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Book cover Theory and Applications of Satisfiability Testing (SAT 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3569))

Abstract

Many current local search algorithms for SAT fall into one of two classes. Random walk algorithms such as Walksat/SKC, Novelty+ and HWSAT are very successful but can be trapped for long periods in deep local minima. Clause weighting algorithms such as DLM, GLS, ESG and SAPS are good at escaping local minima but require expensive smoothing phases in which all weights are updated. We show that Walksat performance can be greatly enhanced by weighting variables instead of clauses, giving the best known results on some benchmarks. The new algorithm uses an efficient weight smoothing technique with no smoothing phase.

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Author information

Authors and Affiliations

  1. Cork Constraint Computation Centre, Department of Computer Science, University College Cork, Ireland

    Steven Prestwich

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  1. Steven Prestwich
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Toronto, Canada

    Fahiem Bacchus

  2. Dept. of Computer Science, Trinity College, Dublin 2

    Toby Walsh

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© 2005 Springer-Verlag Berlin Heidelberg

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Prestwich, S. (2005). Random Walk with Continuously Smoothed Variable Weights. In: Bacchus, F., Walsh, T. (eds) Theory and Applications of Satisfiability Testing. SAT 2005. Lecture Notes in Computer Science, vol 3569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499107_15

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  • DOI: https://doi.org/10.1007/11499107_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26276-3

  • Online ISBN: 978-3-540-31679-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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