On Modern Clause-Learning Satisfiability Solvers

Authors:
Knot Pipatsrisawat;Adnan Darwiche
Affiliations:
Computer Science Department, University of California, Los Angeles, Los Angeles, USA 90024;Computer Science Department, University of California, Los Angeles, Los Angeles, USA 90024
Venue:
Journal of Automated Reasoning
Year:
2010

Citing 23
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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.