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Extended clause learning
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DOI：10.1016/j.artint.2010.07.008.png)
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Abstract
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The past decade has seen clause learning as the most successful algorithm for SAT instances arising from real-world applications. This practical success is accompanied by theoretical results showing clause learning as equivalent in power to resolution. There exist, however, problems that are intractable for resolution, for which clause-learning solvers are hence doomed. In this paper, we present extended clause learning, a practical SAT algorithm that surpasses resolution in power. Indeed, we prove that it is equivalent in power to extended resolution, a proof system strictly more powerful than resolution. Empirical results based Oil an initial implementation suggest that the additional theoretical power can indeed translate into substantial practical gains. (c) 2010 Elsevier B.V. All rights reserved.
Keywords:
SAT
Clause learning
Resolution
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