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On Modern Clause-Learning Satisfiability Solvers

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Abstract

In this paper, we present a perspective on modern clause-learning SAT solvers that highlights the roles of, and the interactions between, decision making and clause learning in these solvers. We discuss two limitations of these solvers from this perspective and discuss techniques for dealing with them. We show empirically that the proposed techniques significantly improve state-of-the-art solvers.

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Authors and Affiliations

  1. Computer Science Department, University of California, Los Angeles, 405 Hilgard Ave, Los Angeles, CA, 90024, USA

    Knot Pipatsrisawat & Adnan Darwiche

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  1. Knot Pipatsrisawat
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  2. Adnan Darwiche
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Correspondence to Knot Pipatsrisawat.

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Pipatsrisawat, K., Darwiche, A. On Modern Clause-Learning Satisfiability Solvers. J Autom Reasoning 44, 277–301 (2010). https://doi.org/10.1007/s10817-009-9156-3

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