Abstract
Traditionally, the propositional satisfiability problem (SAT) was attacked with systematic search algorithms, but more recently, local search methods were shown to be very effective for solving large and hard SAT instances. Generally, it is not well understood which type of algorithm performs best on a specific type of SAT instances. Here, we present results of a comprehensive empirical study, comparing the performance of some of the best performing stochastic local search and systematic search algorithms for SAT on a wide range of problem instances. Our experimental results suggest that, considering the specific strengths and weaknesses of both approaches, hybrid algorithms or portfolio combinations might be most effective for solving SAT problems in practice.
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References
R. J. Bayardo Jr. and R. C. Schrag. Using CSP Look-back Techniques to Solve Real World SAT Instances. In Proceedings of AAAI’97, pages 203–208, 1997.
C. P. Gomes, B. Selman, and H. Kautz. Boosting Combinatorial Search Trough Randomization. In Proceedings of AAAI’98, pages 431–437. MIT press, 1998.
C. P. Gomes and B. Selman. Algorithm Portfolio Design: Theory vs. Practice. In UAI’97, Morgan Kaufmann, 1997.
I. P. Gent, H. H. Hoos, P. Prosser, and T. Walsh. Morphing: Combining Structure and Randomness. To appear in Proceedings of AAAI-99, 1999.
H. H. Hoos. Stochastic Local Search — Methods, Models, Applications. PhD thesis, Department of Computer Science, Darmstadt University of Technology, 1998. Available at http://www.cs.ubc.ca/spider/hoos/publ-ai.html
H. H. Hoos and T. Stützle. Local Search Algorithms for SAT: An Empirical Evaluation. Technical Report TR-99-06. Department of Computer Science, University of BC, Canada, 1999. Available at http://www.cs.ubc.ca/spider/hoos/publ-ai.html
H. H. Hoos and T. Stützle. Local Search Algorithms for SAT: An Empirical Evaluation. Submitted to Journal of Automated Reasoning, 1999.
H. Kautz and B. Selman. Pushing the Envelope: Planning, Propositional Logic, and Stochastic Search. In Proceedings of AAAI’96, pages 1194–1201. MIT Press, 1996.
C. M. Li and Anbulagan Look-Ahead Versus Lock-Back for Satisfiability Problems. In Proceedings of CP’97, pages 341–355. Springer Verlag, 1997.
D. McAllester, B. Selman, and H. Kautz. Evidence for Invariants in Local Search. In Proceedings of AAAI’97, pages 321–326, 1997.
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© 1999 Springer-Verlag Berlin Heidelberg
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Hoos, H.H., Stützle, T. (1999). Systematic vs. Local Search for SAT. In: Burgard, W., Cremers, A.B., Cristaller, T. (eds) KI-99: Advances in Artificial Intelligence. KI 1999. Lecture Notes in Computer Science(), vol 1701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48238-5_25
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DOI: https://doi.org/10.1007/3-540-48238-5_25
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