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SAT
2005
Springer

Random Walk with Continuously Smoothed Variable Weights

13 years 9 months ago
Random Walk with Continuously Smoothed Variable Weights
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.
Steven David Prestwich
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where SAT
Authors Steven David Prestwich
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