Submodular Max-SAT

Yossi Azar; Iftah Gamzu; Ran Roth

doi:10.1007/978-3-642-23719-5_28

Submodular Max-SAT

Yossi Azar^*, Iftah Gamzu, Ran Roth

^*Corresponding author for this work

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

We introduce the submodular Max-SAT problem. This problem is a natural generalization of the classical Max-SAT problem in which the additive objective function is replaced by a submodular one. We develop a randomized linear-time 2/3-approximation algorithm for the problem. Our algorithm is applicable even for the online variant of the problem. We also establish hardness results for both the online and offline settings. Notably, for the online setting, the hardness result proves that our algorithm is best possible, while for the offline setting, the hardness result establishes a computational separation between the classical Max-SAT and the submodular Max-SAT.

Original language	English
Title of host publication	Algorithms, ESA 2011 - 19th Annual European Symposium, Proceedings
Pages	323-334
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-23719-5_28
State	Published - 2011
Event	19th Annual European Symposium on Algorithms, ESA 2011 - Saarbrucken, Germany Duration: 5 Sep 2011 → 9 Sep 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6942 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th Annual European Symposium on Algorithms, ESA 2011
Country/Territory	Germany
City	Saarbrucken
Period	5/09/11 → 9/09/11

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10.1007/978-3-642-23719-5_28

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title = "Submodular Max-SAT",

abstract = "We introduce the submodular Max-SAT problem. This problem is a natural generalization of the classical Max-SAT problem in which the additive objective function is replaced by a submodular one. We develop a randomized linear-time 2/3-approximation algorithm for the problem. Our algorithm is applicable even for the online variant of the problem. We also establish hardness results for both the online and offline settings. Notably, for the online setting, the hardness result proves that our algorithm is best possible, while for the offline setting, the hardness result establishes a computational separation between the classical Max-SAT and the submodular Max-SAT.",

author = "Yossi Azar and Iftah Gamzu and Ran Roth",

year = "2011",

doi = "10.1007/978-3-642-23719-5_28",

language = "אנגלית",

isbn = "9783642237188",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "19th Annual European Symposium on Algorithms, ESA 2011 ; Conference date: 05-09-2011 Through 09-09-2011",

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Azar, Y, Gamzu, I & Roth, R 2011, Submodular Max-SAT. in Algorithms, ESA 2011 - 19th Annual European Symposium, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6942 LNCS, pp. 323-334, 19th Annual European Symposium on Algorithms, ESA 2011, Saarbrucken, Germany, 5/09/11. https://doi.org/10.1007/978-3-642-23719-5_28

Submodular Max-SAT. / Azar, Yossi; Gamzu, Iftah; Roth, Ran.
Algorithms, ESA 2011 - 19th Annual European Symposium, Proceedings. 2011. p. 323-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6942 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We introduce the submodular Max-SAT problem. This problem is a natural generalization of the classical Max-SAT problem in which the additive objective function is replaced by a submodular one. We develop a randomized linear-time 2/3-approximation algorithm for the problem. Our algorithm is applicable even for the online variant of the problem. We also establish hardness results for both the online and offline settings. Notably, for the online setting, the hardness result proves that our algorithm is best possible, while for the offline setting, the hardness result establishes a computational separation between the classical Max-SAT and the submodular Max-SAT.

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Submodular Max-SAT

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