Open-WBO: A Modular MaxSAT Solver,

  • Ruben Martins
  • Vasco Manquinho
  • Inês Lynce
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8561)


This paper presents open-wbo, a new MaxSAT solver. open-wbo has two main features. First, it is an open-source solver that can be easily modified and extended. Most MaxSAT solvers are not available in open-source, making it hard to extend and improve current MaxSAT algorithms. Second, open-wbo may use any MiniSAT-like solver as the underlying SAT solver. As many other MaxSAT solvers, open-wbo relies on successive calls to a SAT solver. Even though new techniques are proposed for SAT solvers every year, for many MaxSAT solvers it is hard to change the underlying SAT solver. With open-wbo, advances in SAT technology will result in a free improvement in the performance of the solver. In addition, the paper uses open-wbo to evaluate the impact of using different SAT solvers in the performance of MaxSAT algorithms.


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  1. 1.
    Achá, R.A., Nieuwenhuis, R.: Curriculum-based course timetabling with SAT and MaxSAT. Annals of Operations Research, 1–21 (2012)Google Scholar
  2. 2.
    Ansótegui, C., Bonet, M.L., Gabàs, J., Levy, J.: Improving SAT-Based Weighted MaxSAT Solvers. In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 86–101. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Ansótegui, C., Bonet, M.L., Levy, J.: Solving (Weighted) Partial MaxSAT through Satisfiability Testing. In: Kullmann [18], pp. 427–440Google Scholar
  4. 4.
    Ansótegui, C., Manyà, F.: Mapping Problems with Finite-Domain Variables into Problems with Boolean Variables. In: Hoos, H.H., Mitchell, D.G. (eds.) SAT 2004. LNCS, vol. 3542, pp. 1–15. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Asín, R., Nieuwenhuis, R., Oliveras, A., Rodríguez-Carbonell, E.: Practical algorithms for unsatisfiability proof and core generation in SAT solvers. AI Communications 23(2-3), 145–157 (2010)zbMATHMathSciNetGoogle Scholar
  6. 6.
    Asín, R., Nieuwenhuis, R., Oliveras, A., Rodríguez-Carbonell, E.: Cardinality Networks: a theoretical and empirical study. Constraints 16(2), 195–221 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Audemard, G., Lagniez, J.M., Simon, L.: Improving Glucose for Incremental SAT Solving with Assumptions: Application to MUS Extraction. In: Järvisalo, M., Van Gelder, A. (eds.) SAT 2013. LNCS, vol. 7962, pp. 309–317. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Audemard, G., Simon, L.: Predicting Learnt Clauses Quality in Modern SAT Solvers. In: Boutilier, C. (ed.) International Joint Conference on Artificial Intelligence, pp. 399–404 (2009)Google Scholar
  9. 9.
    Audemard, G., Simon, L.: Glucose 2.3 in the SAT 2013 Competition. In: Proceedings of SAT Competition 2013: Solver and Benchmark Descriptions [10], pp. 42–43Google Scholar
  10. 10.
    Balint, A., Belov, A., Heule, M., Järvisalo, M.: Proceedings of SAT Competition 2013: Solver and Benchmark Descriptions. Tech. rep., Department of Computer Science Series of Publications B, vol. B-2013-1, University of Helsinki, Helsinki (2013)Google Scholar
  11. 11.
    Chen, J.: Solvers with a Bit-Encoding Phase Selection Policy and a Decision-Depth-Sensitive Restart Policy. In: Proceedings of SAT Competition 2013: Solver and Benchmark Descriptions [10], pp. 44–45Google Scholar
  12. 12.
    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
  13. 13.
    Fu, Z., Malik, S.: On Solving the Partial MAX-SAT Problem. In: Biere, A., Gomes, C.P. (eds.) SAT 2006. LNCS, vol. 4121, pp. 252–265. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Gent, I.P., Nightingale, P.: A new encoding of All Different into SAT. In: International Workshop on Modelling and Reformulating Constraint Satisfaction Problems (2004)Google Scholar
  15. 15.
    Hölldobler, S., Manthey, N., Steinke, P.: A Compact Encoding of Pseudo-Boolean Constraints into SAT. In: Glimm, B., Krüger, A. (eds.) KI 2012. LNCS, vol. 7526, pp. 107–118. Springer, Heidelberg (2012)Google Scholar
  16. 16.
    Janota, M., Lynce, I., Manquinho, V., Marques-Silva, J.: PackUp: Tools for Package Upgradability Solving. Journal on Satisfiability, Boolean Modeling and Computation 8(1/2), 89–94 (2012)MathSciNetGoogle Scholar
  17. 17.
    Koshimura, M., Zhang, T., Fujita, H., Hasegawa, R.: QMaxSAT: A Partial Max-SAT Solver. Journal on Satisfiability, Boolean Modeling and Computation 8, 95–100 (2012)MathSciNetGoogle Scholar
  18. 18.
    Kullmann, O. (ed.): SAT 2009. LNCS, vol. 5584. Springer, Heidelberg (2009)zbMATHGoogle Scholar
  19. 19.
    Le Berre, D., Parrain, A.: The Sat4j library, release 2.2. Journal on Satisfiability, Boolean Modeling and Computation 7(2-3), 59–66 (2010)Google Scholar
  20. 20.
    Le Berre, D., Rapicault, P.: Dependency Management for the Eclipse Ecosystem: Eclipse P2, Metadata and Resolution. In: International Workshop on Open Component Ecosystems, pp. 21–30. ACM (2009)Google Scholar
  21. 21.
    Li, C.M., Manyà, F.: MaxSAT, Hard and Soft Constraints. In: Handbook of Satisfiability, pp. 613–631. IOS Press (2009)Google Scholar
  22. 22.
    Luby, M., Sinclair, A., Zuckerman, D.: Optimal Speedup of Las Vegas Algorithms. Information Processing Letters 47(4), 173–180 (1993)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Manquinho, V., Marques-Silva, J., Planes, J.: Algorithms for Weighted Boolean Optimization. In: Kullmann [18], pp. 495–508Google Scholar
  24. 24.
    Marques-Silva, J., Argelich, J., Graça, A., Lynce, I.: Boolean lexicographic optimization: algorithms & applications. Annals of Mathematics and Artificial Intelligence 62(3-4), 317–343 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  25. 25.
    Martins, R., Manquinho, V.M., Lynce, I.: On Partitioning for Maximum Satisfiability. In: Raedt, L.D., Bessière, C., Dubois, D., Doherty, P., Frasconi, P., Heintz, F., Lucas, P.J.F. (eds.) European Conference on Artificial Intelligence. Frontiers in Artificial Intelligence and Applications, vol. 242, pp. 913–914. IOS Press (2012)Google Scholar
  26. 26.
    Morgado, A., Heras, F., Liffiton, M., Planes, J., Marques-Silva, J.: Iterative and core-guided MaxSAT solving: A survey and assessment. Constraints 18(4), 478–534 (2013)CrossRefMathSciNetGoogle Scholar
  27. 27.
    Nabeshima, H., Iwanuma, K., Inoue, K.: GLUEMINISAT2.2.7. In: Proceedings of SAT Competition 2013: Solver and Benchmark Descriptions [10], pp. 46–47Google Scholar
  28. 28.
    Ogawa, T., Liu, Y., Hasegawa, R., Koshimura, M., Fujita, H.: Modulo Based CNF Encoding of Cardinality Constraints and Its Application to MaxSAT Solvers. In: International Conference on Tools with Artificial Intelligence, pp. 9–17. IEEE (2013)Google Scholar
  29. 29.
    Oh, C.: gluH: Modified Version of glucose 2.1. In: Proceedings of SAT Competition 2013: Solver and Benchmark Descriptions [10], p. 48Google Scholar
  30. 30.
    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
  31. 31.
    Wieringa, S.: GlucoRed. In: Proceedings of SAT Competition 2013: Solver and Benchmark Descriptions [10], pp. 40–41Google Scholar
  32. 32.
    Yasumoto, T., Okugawa, T.: SINNminisat. In: Proceedings of SAT Competition 2013 : Solver and Benchmark Descriptions [10], p. 85Google Scholar
  33. 33.
    Yasumoto, T., Okugawa, T.: ZENN. In: Proceedings of SAT Competition 2013: Solver and Benchmark Descriptions [10], p. 95Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ruben Martins
    • 1
  • Vasco Manquinho
    • 2
  • Inês Lynce
    • 2
  1. 1.Department of Computer ScienceUniversity of OxfordUnited Kingdom
  2. 2.INESC-ID / Instituto Superior TécnicoUniversidade de LisboaPortugal

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