Abstract
In this paper, we study the approach of dynamic local search for the SAT problem. We focus on the recent and promising Exponentiated Sub-Gradient (ESG) algorithm, and examine the factors determining the time complexity of its search steps. Based on the insights gained from our analysis, we developed Scaling and Probabilistic Smoothing (SAPS), an efficient SAT algorithm that is conceptually closely related to ESG. We also introduce a reactive version of SAPS (RSAPS) that adaptively tunes one of the algorithm’s important parameters. We show that for a broad range of standard benchmark problems for SAT, SAPS and RSAPS achieve significantly better performance than both ESG and the state-of-the-art WalkSAT variant, Novelty+.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
J. Frank. Learning Short-term Clause Weights for GSAT. In Proc. IJCAI-97, pp. 384–389, Morgan Kaufmann Publishers, 1997.
H. H. Hoos. Stochastic Local Search — Methods, Models, Applications, PhD thesis, Darmstadt University of Technology, 1998.
H. H. Hoos. On the Run-time Behaviour of Stochastic Local Search Algorithms for SAT. In Proc. AAAI-99, pp. 661–666. AAAI Press, 1999.
H.H. Hoos. An Adaptive Noise Mechanism for WalkSAT. To appear in Proc.AAAI-02, AAAI Press, 2002.
H. H. Hoos and T. Stützle. Local Search Algorithms for SAT: An Empirical Evaluation. In J. of Automated Reasoning, Vol. 24, No. 4, pp. 421–481, 2000.
H. H. Hoos and T. Stützle. SATLIB: An Online Resource for Research on SAT. In I.P. Gent, H. Maaren, T. Walsh (ed.), SAT 2000, pp. 283–292, IOS Press, 2000.
H. Kautz and B. Selman. Pushing the Envelope: Planning, Propositional Logic, and Stochastic Search. In Proc. AAAI-96, pp. 1194–1201. AAAI Press, 1996.
D.A. McAllester and B. Selman and H.A. Kautz. Evidence for Invariants in Local Search. In Proc. AAAI-97, pp. 321–326, AAAI Press, 1997.
P. Morris. The breakout method for escaping from local minima. In Proc. AAAI-93, pp. 40–45. AAAI Press, 1993.
D. Schuurmans, and F. Southey. Local search characteristics of incomplete SAT procedures. In Proc. AAAI-2000, pp. 297–302, AAAI Press, 2000.
D. Schuurmans, F. Southey, and R. C. Holte. The exponentiated subgradient algorithm for heuristic boolean programming. In Proc. IJCAI-01, pp. 334–341, Morgan Kaufmann Publishers, 2001.
B. Selman and H. A. Kautz. Domain-Independent Extensions to GSAT: Solving Large Structured Satisfiability Problems. In Proc. IJCAI-93, pp. 290–295, Morgan Kaufmann Publishers, 1993.
B. Selman and H. A. Kautz and B. Cohen. Noise Strategies for Improving Local Search. In Proc. AAAI-94, pp. 337–343, AAAI Press, 1994.
B. Selman, H. Levesque and D. Mitchell. A New Method for Solving Hard Satisfiability Problems. In Proc. AAAI-92, pp. 440–446, AAAI Press, 1992.
Z. Wu and B.W. Wah. An Efficient Global-Search Strategy in Discrete Lagrangian Methods for Solving Hard Satisfiability Problems. In Proc. AAAI-00, pp. 310–315, AAAI Press, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hutter, F., Tompkins, D.A.D., Hoos, H.H. (2002). Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT. In: Van Hentenryck, P. (eds) Principles and Practice of Constraint Programming - CP 2002. CP 2002. Lecture Notes in Computer Science, vol 2470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46135-3_16
Download citation
DOI: https://doi.org/10.1007/3-540-46135-3_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44120-5
Online ISBN: 978-3-540-46135-7
eBook Packages: Springer Book Archive