Snappy: A Simple Algorithm Portfolio

  • Horst Samulowitz
  • Chandra Reddy
  • Ashish Sabharwal
  • Meinolf Sellmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7962)


Algorithm portfolios try to combine the strength of individual algorithms to tackle a problem instance at hand with the most suitable technique. In the context of SAT the effectiveness of such approaches is often demonstrated at the SAT Competitions. In this paper we show that a competitive algorithm portfolio can be designed in an extremely simple fashion. In fact, the algorithm portfolio we present does not require any offline learning nor knowledge of any complex Machine Learning tools. We hope that the utter simplicity of our approach combined with its effectiveness will make algorithm portfolios accessible by a broader range of researchers including SAT and CSP solver developers.


Test Instance Collaborative Filter Aggregation Scheme Base Solver Portfolio Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amadini, R., Gabbrielli, M., Mauro, J.: An empirical evaluation of portfolios approaches for solving csps. In: Gomes, C., Sellmann, M. (eds.) CPAIOR 2013. LNCS, vol. 7874, pp. 316–324. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Audemard, G., Simon, L.: Predicting learnt clauses quality in modern sat solver. In: IJCAI 2009 (July 2009)Google Scholar
  3. 3.
    Biere, A.: Lingeling and friends at the sat competition 2011. Technical report, Johannes Kepler University, Altenbergerstr. 69, 4040 Linz, Austria (2011)Google Scholar
  4. 4.
    Cnrs, L.: Choco: an open source java constraint programming library. In: White Paper 14th International Conference on Principles and Practice of Constraint Programming CPAI 2008 Competition, pp. 7–14 (2008),
  5. 5.
    Gecode Team. Gecode: Generic constraint development environment (2006),
  6. 6.
    Gomes, C., Selman, B.: Algorithm portfolios. Artificial Intelligence Journal 126(1-2), 43–62 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Huda, M.S., Alam, K.M.R., Mutsuddi, K., Rahman, M.K.S., Rahman, C.M.: A dynamic k-nearest neighbor algorithm for pattern analysis problem. In: 3rd International Conference on Electrical & Computer Engineering (2004)Google Scholar
  8. 8.
    Rice, J.R.: The algorithm selection problem. Advances in Computers 15, 65–118 (1976)CrossRefGoogle Scholar
  9. 9.
    Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm selection and scheduling. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 454–469. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: A portfolio approach to algorithm selection. In: Proc. of the 15th Int. Joint Conference on Artificial Intelligence (IJCAI), pp. 1542–1543 (2003)Google Scholar
  11. 11.
    O’Mahony, E., Hebrard, E., Holland, A., Nugent, C., O’Sullivan, B.: Using case-based reasoning in an algorithm portfolio for constraint solving. In: Irish Conference on Artificial Intelligence and Cognitive Science (2008)Google Scholar
  12. 12.
    Soos, M.: CryptoMiniSat 3.1 (2013),
  13. 13.
    Sorensson, N., Een, N.: MiniSAT 2.2.0 (2010),
  14. 14.
    Stern, D., Samulowitz, H., Herbrich, R., Graepel, T., Pulina, L., Tacchella, A.: Collaborative expert portfolio management. In: AAAI (2010)Google Scholar
  15. 15.
    Streeter, M., Smith, S.: Using decision procedures efficiently for optimization. In: ICAPS, pp. 312–319 (2007)Google Scholar
  16. 16.
    Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: Satzilla: Portfolio-based algorithm selection for sat. JAIR 32(1), 565–606 (2008)zbMATHGoogle Scholar
  17. 17.
    Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: Evaluating component solver contributions to portfolio-based algorithm selectors. In: Cimatti, A., Sebastiani, R. (eds.) SAT 2012. LNCS, vol. 7317, pp. 228–241. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Xu, L., Hutter, F., Shen, J., Hoos, H., Leyton-Brown, K.: Satzilla2012: Improved algorithm selection based on cost-sensitive classification models. solver description. In: SAT Challenge 2012 (2012b)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Horst Samulowitz
    • 1
  • Chandra Reddy
    • 1
  • Ashish Sabharwal
    • 1
  • Meinolf Sellmann
    • 1
  1. 1.IBM Watson Research CenterYorktown HeightsUSA

Personalised recommendations