1999Unpublished venueRequires access

The Difference All-Difference Makes

Kostas Stergiou, Toby Walsh

Open publisher page 44 citations

Abstract

We perform a comprehensive theoretical and experimental analysis of the use of all-different constraints. We prove that generalizedarcconsistencyon such constraints lies between neighborhoodinverseconsistencyand, under a simplerestriction, path inverse consistency on thebinaryrepresentationoftheproblem.By generalizingtheargumentsof Kondrak and van Beek, weprovethatasearchalgorithmthat maintainsgeneralizedarc-consistency on all-different constraintsdominatesasearch algorithmthatmaintainsarc-consistencyonthebinaryrepresentation. Ourexperimentsshowthe practicalvalueofachievingthesehighlevelsof consistency. For example, wecansolvealmost allbenchmarkquasigroupcompletionproblems up to order 25 with just a few branches of search. These results demonstrate the benefits of using non-binary constraints like all-different to identify structure in problems.

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What this paper is about

We perform a comprehensive theoretical and experimental analysis of the use of all-different constraints. We prove that generalizedarcconsistencyon such constraints lies between neighborhoodinverseconsistencyand, under a simplerestriction, path inverse consistency on thebinaryrepresentationoftheproblem.By generalizingtheargumentsof Kondrak and van Beek, weprovethatasearchalgorithmthat maintainsgeneralizedarc-consistency on all-different constraintsdominatesasearch algorithmthatmaintainsarc-consistencyonthebinaryrepresentation. Ourexperimentsshowthe practicalvalueofachievingthesehighlevelsof consistency. For example, wecansolvealmost allbenchmarkquasigroupcompletionproblems up to order 25 with just a few branches of search. These results demonstrate the benefits of using non-binary constraints like all-different to identify structure in problems.

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Available abstract

We perform a comprehensive theoretical and experimental analysis of the use of all-different constraints. We prove that generalizedarcconsistencyon such constraints lies between neighborhoodinverseconsistencyand, under a simplerestriction, path inverse consistency on thebinaryrepresentationoftheproblem.By generalizingtheargumentsof Kondrak and van Beek, weprovethatasearchalgorithmthat maintainsgeneralizedarc-consistency on all-different constraintsdominatesasearch algorithmthatmaintainsarc-consistencyonthebinaryrepresentation. Ourexperimentsshowthe practicalvalueofachievingthesehighlevelsof consistency. For example, wecansolvealmost allbenchmarkquasigroupcompletionproblems up to order 25 with just a few branches of search. These results demonstrate the benefits of using non-binary constraints like all-different to identify structure in problems.

Key concepts: Consistency (knowledge bases), Local consistency, Benchmark (surveying), Representation (politics), Binary number, Inverse, Mathematics, Algorithm

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