Many state-of-the-art SAT solvers use the VSIDS heuristic to make branching decisions based on the activity of variables or literals. In combination with rapid restarts and phase saving this yields a powerful decision heuristic in practice. However, there are approaches that motivate more in-depth reasoning to guide the search of the SAT solver. But more reasoning often requires more information and comes along with more complex data structures. This may sometimes even cause strong concepts to be inapplicable in practice.

In this paper we present a suitable data structure for the DMRP approach to overcome the problem above. Moreover, we show how DMRP can be combined with CDCL solving to be competitive to state-of-the-art solvers and to even improve on some families of industrial instances.


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  1. 1.
    Bacchus, F.: Exploring the computational tradeoff of more reasoning and less searching. In: SAT 2002, pp. 7–16 (2002)Google Scholar
  2. 2.
    Biere, A.: Adaptive restart strategies for conflict driven SAT solvers. In: Kleine Büning, H., Zhao, X. (eds.) SAT 2008. LNCS, vol. 4996, pp. 28–33. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Biere, A.: Picosat essentials. JSAT 4, 75–97 (2008)zbMATHGoogle Scholar
  4. 4.
    Davis, M., Logemann, G., Loveland, D.: A machine program for theorem-proving. ACM Commun. 5(7), 394–397 (1962)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Davis, M., Putnam, H.: A computing procedure for quantification theory. J. ACM 7(3), 201–215 (1960)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Eén, N., Sörensson, N.: An extensible SAT-solver. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 502–518. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Fukunaga, A.S.: Efficient Implementations of SAT Local Search. In: SAT (2004)Google Scholar
  8. 8.
    Goldberg, E.: Determinization of resolution by an algorithm operating on complete assignments. In: Biere, A., Gomes, C.P. (eds.) SAT 2006. LNCS, vol. 4121, pp. 90–95. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Goldberg, E.: A decision-making procedure for resolution-based SAT-solvers. In: Kleine Büning, H., Zhao, X. (eds.) SAT 2008. LNCS, vol. 4996, pp. 119–132. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Ivancic, F., Yang, Z., Ganai, M., Gupta, A., Ashar, P.: Efficient SAT-based bounded model checking for software verification. Theoretical Computer Science 404(3) (2008)Google Scholar
  11. 11.
    Kautz, H.A., Selman, B.: Planning as satisfiability. In: Proceedings of the Tenth European Conference on Artificial Intelligence ECAI 1992, pp. 359–363 (1992)Google Scholar
  12. 12.
    Kottler, S.: Solver descriptions for the SAT competition (2009),
  13. 13.
    Küchlin, W., Sinz, C.: Proving consistency assertions for automotive product data management. J. Automated Reasoning 24(1-2), 145–163 (2000)CrossRefzbMATHGoogle Scholar
  14. 14.
    Luby, M., Sinclair, A., Zuckerman, D.: Optimal speedup of las vegas algorithms. In: ISTCS, pp. 128–133 (1993)Google Scholar
  15. 15.
    Lynce, I., Marques-Silva, J.: SAT in bioinformatics: Making the case with haplotype inference. In: Biere, A., Gomes, C.P. (eds.) SAT 2006. LNCS, vol. 4121, pp. 136–141. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Marques-Silva, J.P.: Practical Applications of Boolean Satisfiability. In: Workshop on Discrete Event Systems, WODES 2008 (2008)Google Scholar
  17. 17.
    Marques-Silva, J.P., Sakallah, K.A.: Grasp: A search algorithm for propositional satisfiability. IEEE Trans. Comput. 48(5), 506–521 (1999)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Moskewicz, M.W., Madigan, C.F., Zhao, Y., Zhang, L., Malik, S.: Chaff: engineering an efficient SAT solver. In: DAC (2001)Google Scholar
  19. 19.
    Pipatsrisawat, K., Darwiche, A.: A lightweight component caching scheme for satisfiability solvers. In: Marques-Silva, J., Sakallah, K.A. (eds.) SAT 2007. LNCS, vol. 4501, pp. 294–299. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Selman, B., Levesque, H., Mitchell, D.: A new method for solving hard satisfiability problems. In: Tenth National Conference on Artificial Intelligence (1992)Google Scholar
  21. 21.
    Velev, M.N.: Using rewriting rules and positive equality to formally verify wide-issue out-of-order microprocessors with a reorder buffer. In: DATE 2002 (2002)Google Scholar
  22. 22.
    Zheng, L., Stuckey, P.J.: Improving SAT using 2SAT. In: Proceedings of the 25th Australasian Computer Science Conference, pp. 331–340. E (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Stephan Kottler
    • 1
  1. 1.Wilhelm–Schickard–InstituteUniversity of TübingenGermany

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