SATzilla-07: The design and analysis of an algorithm portfolio for SAT

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4741 LNCS, Issue , 2007, Pages 712-727

  Xu, Lin ^a   Hutter, Frank ^a   Hoos, Holger H ^a   Leyton Brown, Kevin ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPETITION; HIERARCHICAL SYSTEMS; MATHEMATICAL MODELS;

CENSORED SAMPLING; DATA SETS; HARDNESS MODELS;

COMPUTATIONAL METHODS;

EID: 38349031300     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74970-7_50     Document Type: Conference Paper

References (29)

1. Bacchus, F., Winter, J.: Effective preprocessing with hyper-resolution and equality reduction. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 341-355. Springer, Heidelberg (2004)
2. Bertsekas, D.P.: Linear Network Optimization, Algorithms and Codes. MIT Press, Cambridge, MA (1991)
3. Carchrae, T., Beck, J.C.: Applying machine learning to low-knowledge control of optimization algorithms. Computational Intelligence 21(4), 372-387 (2005)
4. Dubois, O., Dequen, G.: A backbone-search heuristic for efficient solving of hard 3-SAT formulae. In: IJCAI-01, pp. 248-253 (2001)
5. 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)
6. Eén, N., Sörensson, N.: Minisat v2.0 (beta). Solver description, SAT Race (2006)
7. Gagliolo, M., Schmidhuber, J.: Learning dynamic algorithm portfolios. Annals of Mathematics and Artificial Intelligence 47(3-4), 295-328 (2007)
8. Gebruers, C., Hnich, B., Bridge, D., Freuder, E.: Using CBR to select solution strategies in constraint programming. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 222-236. Springer, Heidelberg (2005)
9. Gomes, C.P., Selman, B.: Problem structure in the presence of perturbations. In: AAAI-97, pp. 221-226 (1997)
10. Gomes, C.P., Selman, B.: Algorithm portfolios. Artificial Intelligence 126(1-2), 43-62 (2001)
11. Heule, M., Maaren, H.V.: march_dl: Adding adaptive heuristics and a new branching strategy. Journal on Satisfiability, Boolean Modeling and Computation 2, 47-59 (2006)
12. Horvitz, E., Ruan, Y., Gomes, C.P., Kautz, H., Selman, B., Chickering, D.M.: A Bayesian approach to tackling hard computational problems. In: Proc. of UAI-01, pp. 235-244 (2001)
13. Hutter, F., Hamadi, Y., Hoos, H.H., Leyton-Brown, K.: Performance prediction and automated tuning of randomized and parametric algorithms. In: Benhamou, F. (ed.) CP 2006. LNCS, vol. 4204, pp. 213-228. Springer, Heidelberg (2006)
14. Krishnapuram, B., Carin, L., Figueiredo, M., Hartemink, A.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 957-968 (2005)
15. Kullmann, O.: Investigating the behaviour of a SAT solver on random formulas (2002), http://cs-svr1.swan.ac.uk/-csoliver/Artikel/0KsolverAnalyse. html
16. Lagoudakis, M.G., Littman, M.L.: Learning to select branching rules in the DPLL procedure for satisfiability. In: LICS/SAT, vol. 9, pp. 344-359 (2001)
17. Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: Boosting as a metaphor for algorithm design. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 899-903. Springer, Heidelberg (2003)
18. Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: A portfolio approach to algorithm selection. In: IJCAI-03, pp. 1542-1543 (2003)
19. Leyton-Brown, K., Nudelman, E., Shoham, Y.: Learning the empirical hardness of optimization problems: The case of combinatorial auctions. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 556-572. Springer, Heidelberg (2002)
20. Lobjois, L., Lemaître, M.: Branch and bound algorithm selection by performance prediction. In: AAAI-98, pp. 353-358 (1998)
21. Schmidhuber, J., Gagliolo, M.: Impact of censored sampling on the performance of restart strategies. In: Benhamou, F. (ed.) CP 2006. LNCS, vol. 4204, pp. 167-181. Springer, Heidelberg (2006)
22. Mahajan, Y.S., Fu, Z., Malik, S.: Zchaff2004: an efficient SAT solver. In: Bacchus, F., Walsh, T. (eds.) SAT 2005. LNCS, vol. 3569, pp. 360-375. Springer, Heidelberg (2005)
23. Nadel, A., Gordon, M., Palti, A., Hanna, Z.: Eureka-2006 SAT solver. Solver description, SAT Race (2006)
24. Nudelman, E., Leyton-Brown, K., Hoos, H.H., Devkar, A., Shoham, Y.: Understanding random SAT: Beyond the clauses-to-variables ratio. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 438-452. Springer, Heidelberg (2004)
25. Pipatsrisawat, K., Darwiche, A.: Rsat 1.03: SAT solver description. Technical Report D-152, Automated Reasoning Group, UCLA (2006)
26. Rice, J.R.: The algorithm selection problem. Advances in Computers 15, 65-118 (1976)
27. Schmee, J., Hahn, G.J.: A simple method for regression analysis with censored data. Technometrics 21(4), 417-432 (1979)
28. Vallstrom, D.: Vallst documentation (2005), http://vallst.satcompetition. org/index.html
29. Xu, L., Hoos, H.H., Leyton-Brown, K.: Hierarchical hardness models for SAT. In: CP-07 (2007)