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• W4281718597 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." @default.
• W4281718597 created "2022-06-13" @default.
• W4281718597 creator A5003919374 @default.
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• W4281718597 date "2022-06-03" @default.
• W4281718597 modified "2023-10-14" @default.
• W4281718597 title "Fast and parallel decomposition of constraint satisfaction problems" @default.
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• W4281718597 doi "https://doi.org/10.1007/s10601-022-09332-1" @default.
• W4281718597 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35965950" @default.
• W4281718597 hasPublicationYear "2022" @default.
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