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Deep Cooperation of CDCL and Local Search for SAT

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Theory and Applications of Satisfiability Testing – SAT 2021 (SAT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12831))

Abstract

Modern SAT solvers are based on a paradigm named conflict driven clause learning (CDCL), while local search is an important alternative. Although there have been attempts combining these two methods, this work proposes deeper cooperation techniques. First, we relax the CDCL framework by extending promising branches to complete assignments and calling a local search solver to search for a model nearby. More importantly, the local search assignments and the conflict frequency of variables in local search are exploited in the phase selection and branching heuristics of CDCL. We use our techniques to improve three typical CDCL solvers (glucose, MapleLCMDistChronoBT and Kissat). Experiments on benchmarks from the Main tracks of SAT Competitions 2017–2020 and a real world benchmark of spectrum allocation show that the techniques bring significant improvements, particularly on satisfiable instances. For example, the integration of our techniques allow the three CDCL solvers to solve 62, 67 and 10 more instances in the benchmark of SAT Competition 2020. A resulting solver won the Main Track SAT category in SAT Competition 2020 and also performs very well on the spectrum allocation benchmark. As far as we know, this is the first work that meets the standard of the challenge “Demonstrate the successful combination of stochastic search and systematic search techniques, by the creation of a new algorithm that outperforms the best previous examples of both approaches.” [35] on standard application benchmarks.

S. Cai and X. Zhang—The authors are considered to have equal contributions. Cai contributes mostly on the ideas and partly on the implementations and writes the paper, while Zhang contributes mostly on the implementations and partly on the ideas.

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Notes

  1. 1.

    http://sat-race-2019.ciirc.cvut.cz/solvers/glucose-4.2.1.zip.

  2. 2.

    http://sat-race-2019.ciirc.cvut.cz/solvers/MapleLCMDistChronoBT-DL-v2.1.zip.

  3. 3.

    https://github.com/arminbiere/kissat.git.

  4. 4.

    https://www.cs.ubc.ca/labs/beta/www-projects/SATFC/cacm_cnfs.tar.gz.

  5. 5.

    https://github.com/caiswgroup/relaxed-sat.

  6. 6.

    https://www.cs.ubc.ca/labs/beta/www-projects/SATFC/cacm_cnfs.tar.gz.

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Acknowledgement

This work is supported by Beijing Academy of Artificial Intelligence (BAAI), and Youth Innovation Promotion Association, Chinese Academy of Sciences [No. 2017150].

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Authors and Affiliations

  1. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China

    Shaowei Cai & Xindi Zhang

  2. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

    Shaowei Cai & Xindi Zhang

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  1. Shaowei Cai
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  2. Xindi Zhang
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Correspondence to Shaowei Cai .

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Editors and Affiliations

  1. Laboratoire MIS, University of Picardie Jules Verne, Amiens, France

    Chu-Min Li

  2. IIIA-CSIC, Spanish National Research Council (CSIC), Bellaterra, Barcelona, Spain

    Felip Manyà

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Cai, S., Zhang, X. (2021). Deep Cooperation of CDCL and Local Search for SAT. In: Li, CM., Manyà, F. (eds) Theory and Applications of Satisfiability Testing – SAT 2021. SAT 2021. Lecture Notes in Computer Science(), vol 12831. Springer, Cham. https://doi.org/10.1007/978-3-030-80223-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-80223-3_6

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