<div class="csl-bib-body">
<div class="csl-entry">Schidler, A., & Szeider, S. (2023). Computing optimal hypertree decompositions with SAT. <i>Artificial Intelligence</i>, <i>325</i>, Article 104015. https://doi.org/10.1016/j.artint.2023.104015</div>
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dc.identifier.issn
0004-3702
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/190258
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dc.description.abstract
Hypertree width is a prominent hypergraph invariant with many algorithmic applications in constraint satisfaction and databases. We propose two novel characterisations for hypertree width in terms of linear orderings. We utilize these characterisations to obtain SAT, MaxSAT, and SMT encodings for computing the hypertree width exactly. We evaluate the encodings on an extensive set of benchmark instances and compare them to state-of-the-art exact methods for computing optimal hypertree width. Our results show that our approach outperforms these state-of-the-art algorithms.
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dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.description.sponsorship
WWTF Wiener Wissenschafts-, Forschu und Technologiefonds
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dc.language.iso
en
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dc.publisher
Elsevier
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dc.relation.ispartof
Artificial Intelligence
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
Constraint programming
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dc.subject
Hypertree width
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dc.subject
MaxSAT
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dc.subject
Optimization
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dc.subject
SAT
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dc.subject
SAT encoding
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dc.subject
SMT
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dc.title
Computing optimal hypertree decompositions with SAT