Abstract
We introduce sharpSAT, a new #SAT solver that is based on the well known DPLL algorithm and techniques from SAT and #SAT solvers. Most importantly, we introduce an entirely new approach of coding components, which reduces the cache size by at least one order of magnitude, and a new cache management scheme. Furthermore, we apply a well known look ahead based on BCP in a manner that is well suited for #SAT solving. We show that these techniques are highly beneficial, especially on large structured instances, such that our solver performs significantly better than other #SAT solvers.
This is a preview of subscription content, log in via an institution to check access.
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
Bayardo, R.J., Pehoushek, J.D.: Counting models using connected components. In: Proceedings, AAAI 2000: 17th International Conference on Artificial Intelligence, pp. 157–162 (2000)
Birnbaum, E., Lozinskii, E.L.: The good old davis-putnam procedure helps counting models. Journal of Artificial Intelligence Research (1999)
Davis, M., Logeman, G., Loveland, D.: A machine program for theorem-proving. Communications of the ACM (1962)
Goldberg, E., Novikov, Y.: Berkmin: A fast and robust sat-solver. Design, Automation and Test in Europe (DATE 2002), 142–149 (2002)
Li, C.M., Anbulagan: Heuristics based on unit propagation for satisfiability problems. In: IJCAI (1), pp. 366–371 (1997)
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) (June 2001)
Ryan, L.: Efficient algorithms for clause learning sat solvers. Master’s thesis, Simon Fraser University (2004)
Sang, T., Bacchus, F., Beame, P., Kautz, H., Pitassi, T.: Combining component caching and clause learning for effective model counting. In: Seventh International Conference on Theory and Applications of Satisfiability Testing (2004)
Sang, T., Beame, P., Kautz, H.: Heuristics for fast exact model counting. In: Bacchus, F., Walsh, T. (eds.) SAT 2005. LNCS, vol. 3569, Springer, Heidelberg (2005)
Silva, J.P.M., Sakallah, K.A.: Grasp - a new search algorithm for satisfiability. In: Proceedings of IEEE/ACM International Conference on Computer-Aided Design, November 1996, pp. 220–227 (1996)
Zhang, L., Madigan, C.F., Moskewicz, M.W., Malik, S.: Efficient conflict driven learning in a boolean satisfiability solver. In: International Conference on Computer-Aided Design (ICCAD 2001), pp. 279–285 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Thurley, M. (2006). sharpSAT – Counting Models with Advanced Component Caching and Implicit BCP. In: Biere, A., Gomes, C.P. (eds) Theory and Applications of Satisfiability Testing - SAT 2006. SAT 2006. Lecture Notes in Computer Science, vol 4121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11814948_38
Download citation
DOI: https://doi.org/10.1007/11814948_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37206-6
Online ISBN: 978-3-540-37207-3
eBook Packages: Computer ScienceComputer Science (R0)