An adaptive prefix-assignment technique for symmetry reduction

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Abstract

This paper presents a technique for symmetry reduction that adaptively assigns a prefix of variables in a system of constraints so that the generated prefix-assignments are pairwise nonisomorphic under the action of the symmetry group of the system. The technique is based on McKay’s canonical extension framework [J. Algorithms 26 (1998), no. 2, 306–324]. Among key features of the technique are (i) adaptability—the prefix sequence can be user-prescribed and truncated for compatibility with the group of symmetries; (ii) parallelisability—prefix-assignments can be processed in parallel independently of each other; (iii) versatility—the method is applicable whenever the group of symmetries can be concisely represented as the automorphism group of a vertex-colored graph; and (iv) implementability—the method can be implemented relying on a canonical labeling map for vertex-colored graphs as the only nontrivial subroutine. To demonstrate the tentative practical applicability of our technique we have prepared a preliminary implementation and report on a limited set of experiments that demonstrate ability to reduce symmetry on hard instances.

Details

Original languageEnglish
Title of host publicationTheory and Applications of Satisfiability Testing – SAT 2017 - 20th International Conference, Proceedings
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Theory and Applications of Satisfiability Testing - Melbourne, Australia
Duration: 28 Aug 20171 Sep 2017
Conference number: 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume10491 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

ConferenceInternational Conference on Theory and Applications of Satisfiability Testing
Abbreviated titleSAT
CountryAustralia
CityMelbourne
Period28/08/201701/09/2017

ID: 15307353