Abstract
This paper introduces a new SLS-solver for the satisfiability problem. It is based on the solver gNovelty+. In contrast to gNovelty+, when our solver reaches a local minimum, it computes a probability distribution on the variables from an unsatisfied clause. It then flips a variable picked according to this distribution. Compared with other state-of-the-art SLS-solvers this distribution needs neither noise nor a random walk to escape efficiently from cycles. We compared this algorithm which we called Sparrow to the winners of the SAT 2009 competition on a broad range of 3-SAT instances. Our results show that Sparrow is significantly outperforming all of its competitors on the random 3-SAT problem.
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References
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Balint, A., Fröhlich, A. (2010). Improving Stochastic Local Search for SAT with a New Probability Distribution. In: Strichman, O., Szeider, S. (eds) Theory and Applications of Satisfiability Testing – SAT 2010. SAT 2010. Lecture Notes in Computer Science, vol 6175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14186-7_3
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DOI: https://doi.org/10.1007/978-3-642-14186-7_3
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