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A parametric propagator for pairs of Sum constraints with a discrete convexity property

Authors :
Nicolas Beldiceanu
Justin Pearson
Pierre Flener
Jean-Noël Monette
Department of Information Technology (DIT-UPPSALA)
Uppsala University
Laboratoire d'Informatique de Nantes Atlantique (LINA)
Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)
Theory, Algorithms and Systems for Constraints (TASC)
Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Département informatique - EMN
Mines Nantes (Mines Nantes)-Inria Rennes – Bretagne Atlantique
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Source :
Artificial Intelligence, Artificial Intelligence, Elsevier, 2016, 241, pp.170-190. ⟨10.1016/j.artint.2016.08.006⟩, Artificial Intelligence, 2016, 241, pp.170-190. ⟨10.1016/j.artint.2016.08.006⟩
Publication Year :
2016
Publisher :
Elsevier BV, 2016.

Abstract

International audience; We introduce a propagator for pairs of Sum constraints, where the expressions in the sums respect a form of convexity. This propagator is parametric and can be instantiated for various concrete pairs, including Deviation, Spread, and the conjunction of Linear ≤ and Among. We show that despite its generality , our propagator is competitive in theory and practice with state-of-the-art propagators.

Details

ISSN :
00043702
Volume :
241
Database :
OpenAIRE
Journal :
Artificial Intelligence
Accession number :
edsair.doi.dedup.....7cd8461a2e59bc472ed4984c0034783d
Full Text :
https://doi.org/10.1016/j.artint.2016.08.006