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dc.contributor.authorThornton, Johnen_US
dc.contributor.editorDeepak Kapuren_US
dc.date.accessioned2017-05-03T12:54:30Z
dc.date.available2017-05-03T12:54:30Z
dc.date.issued2006en_US
dc.date.modified2011-05-05T07:56:03Z
dc.identifier.issn01687433en_US
dc.identifier.doi10.1007/s10817-005-9010-1en_AU
dc.identifier.urihttp://hdl.handle.net/10072/13781
dc.description.abstractThis paper investigates the necessary features of an effective clause weighting local search algorithm for propositional satisfiability testing. Using the recent history of clause weighting as evidence, we suggest that the best current algorithms have each discovered the same basic framework, that is, to increase weights on false clauses in local minima and then to periodically normalize these weights using a decay mechanism. Within this framework, we identify two basic classes of algorithm according to whether clause weight updates are performed additively or multiplicatively. Using a state-of-the-art multiplicative algorithm (SAPS) and our own pure additive weighting scheme (PAWS), we constructed an experimental study to isolate the effects of multiplicative in comparison to additive weighting, while controlling other key features of the two approaches, namely, the use of pure versus flat random moves, deterministic versus probabilistic weight smoothing and multiple versus single inclusion of literals in the local search neighbourhood. In addition, we examined the effects of adding a threshold feature to multiplicative weighting that makes it indifferent to similar cost moves. As a result of this investigation, we show that additive weighting can outperform multiplicative weighting on a range of difficult problems, while requiring considerably less effort in terms of parameter tuning. Our examination of the differences between SAPS and PAWS suggests that additive weighting does benefit from the random flat move and deterministic smoothing heuristics, whereas multiplicative weighting would benefit from a deterministic/probabilistic smoothing switch parameter that is set according to the problem instance. We further show that adding a threshold to multiplicative weighting produces a general deterioration in performance, contradicting our earlier conjecture that additive weighting has better performance due to having a larger selection of possible moves. This leads us to explain differences in performance as being mainly caused by the greater emphasis of additive weighting on penalizing clauses with relatively less weight.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent253757 bytes
dc.format.extent96562 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringeren_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom97en_US
dc.relation.ispartofpageto142en_US
dc.relation.ispartofjournalJournal of Automated Reasoningen_US
dc.relation.ispartofvolume35en_US
dc.rights.retentionNen_AU
dc.subject.fieldofresearchcode280213en_US
dc.titleClause Weighting Local Search for SATen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2006 Springer. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.comen_AU
gro.date.issued2006
gro.hasfulltextFull Text


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