Article

Exponential recency weighted average branching heuristic for SAT solvers

Authors:

Pascal Poupart,

Krzysztof CzarneckiAuthors Info & Claims

AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence

Pages 3434 - 3440

Published: 12 February 2016 Publication History

Abstract

Modern conflict-driven clause-learning SAT solvers routinely solve large real-world instances with millions of clauses and variables in them. Their success crucially depends on effective branching heuristics. In this paper, we propose a new branching heuristic inspired by the exponential recency weighted average algorithm used to solve the bandit problem. The branching heuristic, we call CHB, learns online which variables to branch on by leveraging the feedback received from conflict analysis. We evaluated CHB on 1200 instances from the SAT Competition 2013 and 2014 instances, and showed that CHB solves significantly more instances than VSIDS, currently the most effective branching heuristic in widespread use. More precisely, we implemented CHB as part of the MiniSat and Glucose solvers, and performed an apple-to-apple comparison with their VSIDS-based variants. CHB-based MiniSat (resp. CHB-based Glucose) solved approximately 16.1% (resp. 5.6%) more instances than their VSIDS-based variants. Additionally, CHB-based solvers are much more efficient at constructing first preimage attacks on step-reduced SHA-1 and MD5 cryptographic hash functions, than their VSIDS-based counterparts. To the best of our knowledge, CHB is the first branching heuristic to solve significantly more instances than VSIDS on a large, diverse benchmark of real-world instances.

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Cited By

Huang GHu JHe YLiu JMa MShen ZWu JXu YZhang HZhong KNing XMa YYang HYu BYang HWang Y(2021)Machine Learning for Electronic Design Automation: A SurveyACM Transactions on Design Automation of Electronic Systems10.1145/345117926:5(1-46)Online publication date: 5-Jun-2021
https://dl.acm.org/doi/10.1145/3451179
Yolcu EPóczos BWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Learning local search heuristics for boolean satisfiabilityProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3455005(7992-8003)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3455005
Habet DTerrioux CHung CPapadopoulos G(2019)Conflict history based search for constraint satisfaction problemProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297389(1117-1122)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297389
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AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence

February 2016

4406 pages

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Association for the Advancement of Artificial Intelligence

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AAAI Press

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Published: 12 February 2016

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Cited By

Huang GHu JHe YLiu JMa MShen ZWu JXu YZhang HZhong KNing XMa YYang HYu BYang HWang Y(2021)Machine Learning for Electronic Design Automation: A SurveyACM Transactions on Design Automation of Electronic Systems10.1145/345117926:5(1-46)Online publication date: 5-Jun-2021
https://dl.acm.org/doi/10.1145/3451179
Yolcu EPóczos BWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Learning local search heuristics for boolean satisfiabilityProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3455005(7992-8003)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3455005
Habet DTerrioux CHung CPapadopoulos G(2019)Conflict history based search for constraint satisfaction problemProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297389(1117-1122)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297389
Liang JGovind HPoupart PCzarnecki KGanesh V(2018)An empirical study of branching heuristics through the lens of global learning rateProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304758(5319-5323)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304652.3304758
Luo MLi CXiao FManyà FLü Z(2017)An effective learnt clause minimization approach for CDCL SAT solversProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3171642.3171743(703-711)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3171642.3171743
Zulkoski EBright CHeinle AKotsireas ICzarnecki KGanesh V(2017)Combining SAT Solvers with Computer Algebra Systems to Verify Combinatorial ConjecturesJournal of Automated Reasoning10.1007/s10817-016-9396-y58:3(313-339)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s10817-016-9396-y

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