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Random Walk with Continuously Smoothed Variable Weights

  • Steven Prestwich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Random Walk Local Search Variable Weight Local Search Algorithm Tabu Tenure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Steven Prestwich
    • 1
  1. 1.Cork Constraint Computation Centre, Department of Computer ScienceUniversity College CorkIreland

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