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DOI: 10.4230/LIPIcs.SAT.2023.18

Learning Shorter Redundant Clauses in SDCL Using MaxSAT

Authors: Albert Oliveras, Chunxiao Li, Darryl Wu, Jonathan Chung, and Vijay Ganesh

Published in: LIPIcs, Volume 271, 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)

Abstract

In this paper we present the design and implementation of a Satisfaction-Driven Clause Learning (SDCL) SAT solver, MapleSDCL, which uses a MaxSAT-based technique that enables it to learn shorter, and hence better, redundant clauses. We also perform a thorough empirical evaluation of our method and show that our SDCL solver solves Mutilated Chess Board (MCB) problems significantly faster than CDCL solvers, without requiring any alteration to the branching heuristic used by the underlying CDCL SAT solver.

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Albert Oliveras, Chunxiao Li, Darryl Wu, Jonathan Chung, and Vijay Ganesh. Learning Shorter Redundant Clauses in SDCL Using MaxSAT. In 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 271, pp. 18:1-18:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{oliveras_et_al:LIPIcs.SAT.2023.18,
  author =	{Oliveras, Albert and Li, Chunxiao and Wu, Darryl and Chung, Jonathan and Ganesh, Vijay},
  title =	{{Learning Shorter Redundant Clauses in SDCL Using MaxSAT}},
  booktitle =	{26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)},
  pages =	{18:1--18:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-286-0},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{271},
  editor =	{Mahajan, Meena and Slivovsky, Friedrich},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2023.18},
  URN =		{urn:nbn:de:0030-drops-184803},
  doi =		{10.4230/LIPIcs.SAT.2023.18},
  annote =	{Keywords: SAT, SDCL, MaxSAT}
}

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DOI: 10.4230/LIPIcs.SAT.2023.27

Limits of CDCL Learning via Merge Resolution

Authors: Marc Vinyals, Chunxiao Li, Noah Fleming, Antonina Kolokolova, and Vijay Ganesh

Published in: LIPIcs, Volume 271, 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)

Abstract

In their seminal work, Atserias et al. and independently Pipatsrisawat and Darwiche in 2009 showed that CDCL solvers can simulate resolution proofs with polynomial overhead. However, previous work does not address the tightness of the simulation, i.e., the question of how large this overhead needs to be. In this paper, we address this question by focusing on an important property of proofs generated by CDCL solvers that employ standard learning schemes, namely that the derivation of a learned clause has at least one inference where a literal appears in both premises (aka, a merge literal). Specifically, we show that proofs of this kind can simulate resolution proofs with at most a linear overhead, but there also exist formulas where such overhead is necessary or, more precisely, that there exist formulas with resolution proofs of linear length that require quadratic CDCL proofs.

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Marc Vinyals, Chunxiao Li, Noah Fleming, Antonina Kolokolova, and Vijay Ganesh. Limits of CDCL Learning via Merge Resolution. In 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 271, pp. 27:1-27:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{vinyals_et_al:LIPIcs.SAT.2023.27,
  author =	{Vinyals, Marc and Li, Chunxiao and Fleming, Noah and Kolokolova, Antonina and Ganesh, Vijay},
  title =	{{Limits of CDCL Learning via Merge Resolution}},
  booktitle =	{26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)},
  pages =	{27:1--27:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-286-0},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{271},
  editor =	{Mahajan, Meena and Slivovsky, Friedrich},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2023.27},
  URN =		{urn:nbn:de:0030-drops-184894},
  doi =		{10.4230/LIPIcs.SAT.2023.27},
  annote =	{Keywords: proof complexity, resolution, merge resolution, CDCL, learning scheme}
}

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Learning Shorter Redundant Clauses in SDCL Using MaxSAT

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Limits of CDCL Learning via Merge Resolution

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