Improving implementation of SLS solvers for SAT and new heuristics for k-SAT with long clauses

Adrian Balint, Armin Biere, Andreas Fröhlich, Uwe Schöning

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

Stochastic Local Search (SLS) solvers are considered one of the best solving technique for randomly generated problems and more recently also have shown great promise for several types of hard combinatorial problems. Within this work, we provide a thorough analysis of different implementation variants of SLS solvers on random and on hard combinatorial problems. By analyzing existing SLS implementations, we are able to discover new improvements inspired by CDCL solvers, which can speed up the search of all types of SLS solvers. Further, our analysis reveals that the multilevel break values of variables can be easily computed and used within the decision heuristic. By augmenting the probSAT solver with the new heuristic, we are able to reach new stateof- the-art performance on several types of SAT problems, especially on those with long clauses. We further provide a detailed analysis of the clause selection policy used in focused search SLS solvers.
Original languageEnglish
Title of host publicationProc. 17th Intl. Conf. on Theory and Applications of Satisfiability Testing (SAT'14)
PublisherSpringer
Pages302-316
Number of pages15
Volume8561
DOIs
Publication statusPublished - 2014

Publication series

NameLecture Notes in Computer Science (LNCS)

Fields of science

  • 102 Computer Sciences
  • 603109 Logic
  • 202006 Computer hardware

JKU Focus areas

  • Computation in Informatics and Mathematics

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