Random Walk with Continuously Smoothed Variable Weights

机译：具有连续平滑的可变权重的随机游走

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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.

机译：当前许多针对SAT的本地搜索算法都属于两类之一。诸如Walksat / SKC，Novety +和HWSAT之类的随机游走算法非常成功，但是可以长时间陷入深层的局部最小值中。诸如DLM，GLS，ESG和SAPS之类的子句加权算法擅长逃避局部最小值，但需要昂贵的平滑阶段，在此阶段中，所有权重都会更新。我们表明，通过对变量（而不是从句）进行加权，可以大大提高Walksat的性能，从而在某些基准测试中给出最著名的结果。新算法使用了没有平滑阶段的有效权重平滑技术。

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《International Conference on Theory and Applications of Satisfiability Testing(SAT 2005); 20050619-23; St Andrews(GB)》|2005年|P.203-215|共13页
会议地点 St Andrews(GB)
作者
Steven Prestwich;
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作者单位

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

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原文格式 PDF
正文语种 eng
中图分类计算数学;理论、方法;
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Random Walk with Continuously Smoothed Variable Weights

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