Algorithm selection and scheduling

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

Volumn 6876 LNCS, Issue , 2011, Pages 454-469

  Kadioglu, Serdar ^a   Malitsky, Yuri ^a   Sabharwal, Ashish ^b   Samulowitz, Horst ^b   Sellmann, Meinolf ^b  

^a Brown Univerity   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM SELECTION; COLUMN GENERATION; COMPUTATIONAL COSTS; DYNAMIC SCHEDULING; HIGH-QUALITY SOLUTIONS; OPTIMIZATION PROBLEMS; PERFORMANCE OF ALGORITHM; TIME SLOTS; ALGORITHM PORTFOLIOS;

COMPUTER PROGRAMMING; CONSTRAINT THEORY; LINEAR PROGRAMMING; SCHEDULING;

SCHEDULING ALGORITHMS;

EID: 80052997439     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23786-7_35     Document Type: Conference Paper

References (41)

1. Audemard, G., Simon, L.: GLUCOSE: a solver that predicts learnt clauses quality. SAT Competition, 7-8 (2009)
2. Balint, A., Henn, M., Gableske, O.: hybridGM. Solver description. SAT Competition (2009)
3. Biere, A.: Picosat version 846. Solver description. SAT Competition (2007)
4. Biere, A.: Pre,icoSATSC 2009. SAT Competition, 41-43 (2009)
5. Biere, A.: Lingeling. SAT Race (2010)
6. Bregman, D.R.: The SAT Solver MXC, Version 0.99. SAT Competition, 37-38 (2009)
7. Dantzig, G.B., Wolfe, P.: The decomposition algorithm for linear programs. Econometrica 29(4), 767-778 (1961)
8. Dequen, G., Dubois, O.: kcnfs. Solver description. SAT Competition (2007)
9. Gebser, M., Kaufmann, B., Schaub, T.: Solution Enumeration for Projected Boolean Search Problems. In: van Hoeve,W.-J., Hooker, J.N. (eds.) CPAIOR 2009. LNCS, vol. 5547, pp. 71-86. Springer, Heidelberg (2009)
10. Gilmore, P.C., Gomory, R.E.: A linear programming approach to the cutting stock problem. Operations Research 9, 849-859 (1961)
11. Gomes, C.P., Selman, B.: Algorithm Portfolios. Artificial Intelligence 126(1-2), 43-62 (2001)
12. Hamadi, Y., Jabbour, S., Sais, L.: LySAT: solver description. SAT Competition, 23-24 (2009)
13. Hamerly, G., Elkan, C.: Learning the K in K-Means. NIPS (2003)
14. Heule, M., van Marren, H.: march hi: solver description. SAT Competition, 27-28 (2009)
15. Heule, M., van Marren, H.: march nn, http://www.st.ewi.tudelft.nl/sat/ download.php
16. Heule, M., Zwieten, J., Dufour, M., Maaren, H.: March eq: implementing additional reasoning into an efficient lookahead SAT solver. In: Hoos, H.H., Mitchell, D.G. (eds.) SAT 2004. LNCS, vol. 3542, pp. 345-359. Springer, Heidelberg (2005)
17. Huberman, B., Lukose, R., Hogg, T.: An Economics Approach to Hard Computational Problems. Science 265, 51-54 (2003)
18. Hutter, F., Tompkins, D.A.D., Hoos, H.H.: Scaling and probabilistic smoothing: Efficient dynamic local search for SAT. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 233-248. Springer, Heidelberg (2002)
19. KhudaBukhsh, A.R., Xu, L., Hoos, H.H., Leyton-Brown, K.: SATenstein: Automatically Building Local Search SAT Solvers From Components. In: IJCAI (2009)
20. Lagoudakis, M.G., Littman, M.L.: Learning to select branching rules in the DPLL procedure for satisfiability. In: SAT (2001)
21. Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: A Portfolio Approach to Algorithm Selection. In: IJCAI, pp. 1542-1543 (2003)
22. Li, C.M., Wei, W.: Combining Adaptive Noise and Promising Decreasing Variables in Local Search for SAT. Solver description. SAT Competition (2009)
23. Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Non-Model-Based Algorithm Portfolios for SAT. SAT (to be published, 2011)
24. Nikolic, M., Maric, F., Janici, P.: Instance Based Selection of Policies for SAT Solvers. In: Kullmann, O. (ed.) SAT 2009. LNCS, vol. 5584, pp. 326-340. Springer, Heidelberg (2009)
25. 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)
26. Pham, D.N., Gretton, C.: gnovelty+. Solver description. SAT Competition (2007)
27. Pham, D.N., Gretton, C.: gnovelty+ (v.2). Solver description. SAT Competition (2009)
28. Rice, J.R.: The algorithm selection problem. Advances in Computers, 65-118 (1976)
29. SAT Competition, http://www.satcomptition.org
30. Silverthorn, B., Miikkulainen, R.: Latent Class Models for Algorithm Portfolio Methods. In: AAAI (2010)
31. Smith-Miles, K.A.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41(1), 6:1-6:25 (2009)
32. Soos, M.: CryptoMiniSat 2.5.0. Solver description. SAT Race (2010)
33. Sorensson, N., Een, N.: MiniSAT 2.2.0 (2010), http://minisat.se
34. Streeter, M., Golovin, D., Smith, S.F.: Combining Multiple Heuristics Online. In: AAAI, pp. 1197-1203 (2007)
35. Wei, W., Li, C.M.: Switching Between Two Adaptive Noise Mechanisms in Local Search for SAT. Solver description. SAT Competition (2009)
36. Wei, W., Li, C.M., Zhang, H.: Combining adaptive noise and promising decreasing variables in local search for SAT. Solver description. SAT Competition (2007)
37. Wei, W., Li, C.M., Zhang, H.: Deterministic and random selection of variables in local search for SAT. Solver description. SAT Competition (2007)
38. Xu, L., Hoos, H.H., Leyton-Brown, K.: Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection. In: AAAI (2010)
39. Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla2009: an Automatic Algorithm Portfolio for SAT. Solver description. SAT Competition (2009)
40. Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla: Portfolio-based Algorithm Selection for SAT. JAIR 32(1), 565-606 (2008)
41. 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)