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Refining Restarts Strategies for SAT and UNSAT

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Principles and Practice of Constraint Programming (CP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 7514))

Abstract

So-called Modern SAT solvers are built upon a few – but essential – ingredients: branching, learning, restarting and clause database cleaning. Most of them have been greatly improved since their first introduction, more than ten years ago. In many cases, the initial reasons that lead to their introduction do not explain anymore their current usage (for instance: very rapid restarts, aggressive clause database cleaning). Modern SAT solvers themselves share fewer and fewer properties with their ancestor, the classical backtrack search DPLL procedure.

In this paper, we explore restart strategies in the light of a new vision of SAT solvers. Following the successful results of Glucose, we consider CDCL solvers as resolution-based producers of clauses. We show that this vision is particularly salient for targeting UNSAT formulae. In a second part, we show how detecting sudden increases in the number of variable assignments can help the solver to target SAT instances too. By varying our restart strategy, we show an important improvement over Glucose 2.0, the winner of the 2011 SAT Competition, category Application SAT+UNSAT formulae. Finally we would like to point out that this new version of Glucose was the winner of the SAT Challenge 2012.

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Audemard, G., Simon, L. (2012). Refining Restarts Strategies for SAT and UNSAT. In: Milano, M. (eds) Principles and Practice of Constraint Programming. CP 2012. Lecture Notes in Computer Science, vol 7514. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33558-7_11

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  • DOI: https://doi.org/10.1007/978-3-642-33558-7_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33557-0

  • Online ISBN: 978-3-642-33558-7

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