research-article

Fast and parallel decomposition of constraint satisfaction problems

Authors:

Reinhard PichlerAuthors Info & Claims

Constraints, Volume 27, Issue 3

Pages 284 - 326

https://doi.org/10.1007/s10601-022-09332-1

Published: 01 July 2022 Publication History

Abstract

Constraint Satisfaction Problems (CSP) are notoriously hard. Consequently, powerful decomposition methods have been developed to overcome this complexity. However, this poses the challenge of actually computing such a decomposition for a given CSP instance, and previous algorithms have shown their limitations in doing so. In this paper, we present a number of key algorithmic improvements and parallelisation techniques to compute so-called Generalized Hypertree Decompositions (GHDs) faster. We thus advance the ability to compute optimal (i.e., minimal-width) GHDs for a significantly wider range of CSP instances on modern machines. This lays the foundation for more systems and applications in evaluating CSPs and related problems (such as Conjunctive Query answering) based on their structural properties.

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Cited By

Gottlob GLanzinger MOkulmus CPichler R(2023)Fast Parallel Hypertree Decompositions in Logarithmic Recursion DepthACM Transactions on Database Systems10.1145/363875849:1(1-43)Online publication date: 30-Dec-2023
https://dl.acm.org/doi/10.1145/3638758
Schidler ASzeider S(2023)Computing optimal hypertree decompositions with SATArtificial Intelligence10.1016/j.artint.2023.104015325:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.artint.2023.104015

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Published In

cover image Constraints

Constraints Volume 27, Issue 3

Jul 2022

219 pages

ISSN:1383-7133

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© The Author(s) 2022. corrected publication 2022.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2022

Accepted: 23 April 2022

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Cited By

Gottlob GLanzinger MOkulmus CPichler R(2023)Fast Parallel Hypertree Decompositions in Logarithmic Recursion DepthACM Transactions on Database Systems10.1145/363875849:1(1-43)Online publication date: 30-Dec-2023
https://dl.acm.org/doi/10.1145/3638758
Schidler ASzeider S(2023)Computing optimal hypertree decompositions with SATArtificial Intelligence10.1016/j.artint.2023.104015325:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.artint.2023.104015

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