Learning Interpretable Heuristics for WalkSAT
Learning Interpretable Heuristics for WalkSAT
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Local search algorithms are well-known methods for solving large, hard instances of the satisfiability problem (SAT). The performance of these algorithms crucially depends on heuristics for setting noise parameters and scoring variables. The optimal setting for these heuristics varies for different instance distributions. In this paper, we present...
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Learning Interpretable Heuristics for WalkSAT
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TN_cdi_proquest_journals_2835673532
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2835673532
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2331-8422
