Abstract
In this work, our objective is to study the impact of knowledge sharing on the performance of portfolio-based parallel local search algorithms. Our work is motivated by the demonstrated importance of clause-sharing in the performance of complete parallel SAT solvers. Unlike complete solvers, state-of-the-art local search algorithms for SAT are not able to generate redundant clauses during their execution. In our settings, each member of the portfolio shares its best configuration (i.e., one which minimizes conflicting clauses) in a common structure. At each restart point, instead of classically generating a random configuration to start with, each algorithm aggregates the shared knowledge to carefully craft a new starting point. We present several aggregation strategies and evaluate them on a large set of problems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Hamadi, Y., Jabbour, S., Sais, L.: ManySAT: A Parallel SAT Solver. Journal on Satisfiability, Boolean Modeling and Computation, JSAT 6, 245–262 (2009)
Moskewicz, M.W., Madigan, C.F., Zhao, Y., Zhang, L., Malik, S.: Chaff: Engineering an Efficient SAT Solver. In: Proceedings of the 38th Design Automation Conference (DAC 2001), pp. 530–535 (2001)
Bordeaux, L., Hamadi, Y., Zhang, L.: Propositional Satisfiability and Constraint Programming: A Comparative Survey. ACM Comput. Surv. 38(4) (2006)
Hoos, H.H., Stützle, T.: Local Search Algorithms for SAT: An Empirical Evaluation. J. Autom. Reasoning 24(4), 421–481 (2000)
Selman, B., Levesque, H.J., Mitchell, D.G.: A New Method for Solving Hard Satisfiability Problems. In: AAAI (ed.), pp. 440–446 (1992)
Selman, B., Kautz, H.A., Cohen, B.: Noise Strategies for Improving Local Search. In: AAAI, pp. 337–343 (1994)
McAllester, D.A., Selman, B., Kautz, H.A.: Evidence for Invariants in Local Search. In: AAAI/IAAI, pp. 321–326 (1997)
Li, C.M., Huang, W.Q.: Diversification and Determinism in Local Search for Satisfiability. In: Bacchus, F., Walsh, T. (eds.) SAT 2005. LNCS, vol. 3569, pp. 158–172. Springer, Heidelberg (2005)
Hutter, F., Tompkins, D.A.D., Hoos, H.H.: Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 233–248. Springer, Heidelberg (2002)
Thornton, J., Pham, D.N., Bain, S., Ferreira Jr, V.: Additive versus Multiplicative Clause Weighting for SAT. In: McGuinness, D.L., Ferguson, G. (eds.) AAAI, pp. 191–196. AAAI Press/The MIT Press, San Jose, California, USA (2004)
Hoos, H.H.: An Adaptive Noise Mechanism for WalkSAT. In: AAAI/IAAI, pp. 655–660 (2002)
Li, C.M., Wei, W., Zhang, H.: Combining Adaptive Noise and Look-Ahead in Local Search. In: Marques-Silva, J., Sakallah, K.A. (eds.) SAT 2007. LNCS, vol. 4501, pp. 121–133. Springer, Heidelberg (2007)
Chrabakh, W., Wolski, R.: GridSAT: A System for Solving Satisfiability Problems Using a Computational Grid. Parallel Computing 32(9), 660–687 (2006)
Zhang, W., Huang, Z., Zhang, J.: Parallel Execution of Stochastic Search Procedures on Reduced SAT Instances. In: Ishizuka, M., Sattar, A. (eds.) PRICAI 2002. LNCS (LNAI), vol. 2417, pp. 108–117. Springer, Heidelberg (2002)
Roli, A.: Criticality and Parallelism in Structured SAT Instances. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 714–719. Springer, Heidelberg (2002)
Roli, A., Blesa, M.J., Blum, C.: Random Walk and Parallelism in Local Search. In: Metaheuristic International Conference (MIC 2005), Vienna, Austria (2005)
Pham, D.N., Gretton, C.: gNovelty+ (v.2). In: Solver Description, SAT Competition 2009 (2009)
Kroc, L., Sabharwal, A., Gomes, C.P., Selman, B.: Integrating Systematic and Local Search Paradigms: A New Strategy for MaxSAT. In: Boutilier, C. (ed.) IJCAI, Pasadena, California, pp. 544–551 (July 2009)
Hogg, T., Williams, C.P.: Solving the Really Hard Problems with Cooperative Search. In: AAAI, pp. 231–236 (1993)
Silva, D.L., Burke, E.K.: Asynchronous Cooperative Local Search for the Office-Space-Allocation Problem. INFORMS Journal on Computing 19(4), 575–587 (2007)
Selman, B., Kautz, H.A.: Domain-Independent Extensions to GSAT: Solving Large Structured Satisfiability Problems. In: IJCAI, pp. 290–295 (1993)
Tompkins, D.A.D., Hoos, H.H.: UBCSAT: An Implementation and Experimentation Environment for SLS Algorithms for SAT and MAX-SAT. In: Hoos, H.H., Mitchell, D.G. (eds.) SAT 2004. LNCS, vol. 3542, pp. 306–320. Springer, Heidelberg (2005)
Hutter, F., Hoos, H.H., Leyton-Brown, K.: Tradeoffs in the Empirical Evaluation of Competing Algorithm Designs. Annals of Mathematics and Artificial Intelligence (AMAI), Special Issue on Learning and Intelligent Optimization (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arbelaez, A., Hamadi, Y. (2011). Improving Parallel Local Search for SAT. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-25566-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25565-6
Online ISBN: 978-3-642-25566-3
eBook Packages: Computer ScienceComputer Science (R0)