Article

Upper and lower bounds for weak backdoor set detection

Authors:

Neeldhara Misra,

Sebastian Ordyniak,

Venkatesh Raman,

Stefan SzeiderAuthors Info & Claims

SAT'13: Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing

Pages 394 - 402

https://doi.org/10.1007/978-3-642-39071-5_29

Published: 08 July 2013 Publication History

Abstract

We obtain upper and lower bounds for running times of exponential time algorithms for the detection of weak backdoor sets of 3CNF formulas, considering various base classes. These results include (omitting polynomial factors), (i) a 4.54^k algorithm to detect whether there is a weak backdoor set of at most k variables into the class of Horn formulas; (ii) a 2.27^k algorithm to detect whether there is a weak backdoor set of at most k variables into the class of Krom formulas. These bounds improve an earlier known bound of 6^k. We also prove a 2^k lower bound for these problems, subject to the Strong Exponential Time Hypothesis.

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Cited By

Liang JGanesh VCzarnecki KRaman VSchmidt D(2015)SAT-based analysis of large real-world feature models is easyProceedings of the 19th International Conference on Software Product Line10.1145/2791060.2791070(91-100)Online publication date: 20-Jul-2015
https://dl.acm.org/doi/10.1145/2791060.2791070

Upper and lower bounds for weak backdoor set detection
1. Theory of computation

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Published In

cover image Guide Proceedings

SAT'13: Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing

July 2013

436 pages

ISBN:9783642390708

Editors:
Matti Järvisalo
HIIT and Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2 b, Helsinki, Finland
,
Allen Van Gelder
Computer Science Department, University of California at Santa Cruz, 1156 High Street, SOE-3, Santa Cruz, CA, Finland

Sponsors

University of Helsinki
Federation of Finnish Learned Societies
IBMR: IBM Research
SAT Association: SAT Association
AI Journal: AI Journal

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 08 July 2013

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Cited By

Liang JGanesh VCzarnecki KRaman VSchmidt D(2015)SAT-based analysis of large real-world feature models is easyProceedings of the 19th International Conference on Software Product Line10.1145/2791060.2791070(91-100)Online publication date: 20-Jul-2015
https://dl.acm.org/doi/10.1145/2791060.2791070

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