Abstract
Sequential algorithm portfolios for satisfiability testing (SAT), such as SATzilla and 3S, have enjoyed much success in the last decade. By leveraging the differing strengths of individual SAT solvers, portfolios employing older solvers have often fared as well or better than newly designed ones, in several categories of the annual SAT Competitions and Races. We propose two simple yet powerful techniques to further boost the performance of sequential portfolios, namely, a generic way of knowledge sharing suitable for sequential SAT solver schedules which is commonly employed in parallel SAT solvers, and a meta-level guardian classifier for judging whether to switch the main solver suggested by the portfolio with a recourse action solver. With these additions, we show that the performance of the sequential portfolio solver 3S, which dominated other sequential categories but was ranked 10th in the application category of the 2011 SAT Competition, can be boosted significantly, bringing it just one instance short of matching the performance of the winning application track solver, while still outperforming all other solvers submitted to the crafted and random categories.
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Notes
- 1.
For the specific case of population-based algorithm portfolios, Peng et al. [11] have proposed sharing information through migration of individuals across populations.
- 2.
We also tried labels that identify top performer (e.g., not more than \(x\,\%\) slower than the best solver, for various \(x\)), but obtained much worse results. The issue here is that it is more ambitious than necessary to predict which solver is best or close to best. Instead, we need to be able to distinguish solvers that are good enough from those that fail. That is, rather than aiming for speed, we optimize for solver robustness.
- 3.
The source code of 3S can be obtained from http://www.satcompetition.org/
- 4.
- 5.
Note that the numbers do not add up to 300 since, with the classifier, we only consider instances that have not been solved yet by the pre-scheduler and can be solved by at least one of the solvers in our portfolio.
References
Audemard, G., Simon, L.: Predicting learnt clauses quality in modern SAT solvers. In: 21st IJCAI, Pasadena, CA, pp. 399–404, July 2009
Biere, A.: Plingeling: solver description, SAT Race (2010)
Cook, S.A.: The complexity of theorem-proving procedures. In: STOC, pp. 151–158. ACM (1971)
Gomes, C.P., Selman, B.: Algorithm portfolios. AI J. 126(1–2), 43–62 (2001)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Hamadi, Y., Sais, L.: ManySAT: a parallel SAT solver. JSAT 6, 245–262 (2009)
Rice, J.R.: The algorithm selection problem. Adv. Comput. 15, 65–118 (1976)
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)
Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: A portfolio approach to algorithm selection. In: IJCAI, pp. 1542–1543 (2003)
Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Non-model-based algorithm portfolios for SAT. In: Sakallah, K.A., Simon, L. (eds.) SAT 2011. LNCS, vol. 6695, pp. 369–370. Springer, Heidelberg (2011)
Peng, F., Tang, K., Chen, G., Yao, X.: Population-based algorithm portfolios for numerical optimization. IEEE Trans. Evol. Comput. 14(5), 782–800 (2010)
Soos, M.: CryptoMiniSat 2.9.0. http://www.msoos.org/cryptominisat2 (2010)
Sorensson, N., Een, N.: SatELite 1.0. http://minisat.se (2005)
Sorensson, N., Een, N.: MiniSAT 2.2.0. http://minisat.se (2010)
Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla-07: the design and analysis of an algorithm portfolio for SAT. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 712–727. Springer, Heidelberg (2007)
Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla: portfolio-based algorithm selection for SAT. JAIR 32(1), 565–606 (2008)
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Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M. (2013). Boosting Sequential Solver Portfolios: Knowledge Sharing and Accuracy Prediction. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_17
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