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2014 | 24 | 4 | 901-916
Tytuł artykułu

Accelerating backtrack search with a best-first-search strategy

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Backtrack-style exhaustive search algorithms for NP-hard problems tend to have large variance in their runtime. This is because "fortunate" branching decisions can lead to finding a solution quickly, whereas “unfortunate” decisions in another run can lead the algorithm to a region of the search space with no solutions. In the literature, frequent restarting has been suggested as a means to overcome this problem. In this paper, we propose a more sophisticated approach: a best-firstsearch heuristic to quickly move between parts of the search space, always concentrating on the most promising region. We describe how this idea can be efficiently incorporated into a backtrack search algorithm, without sacrificing optimality. Moreover, we demonstrate empirically that, for hard solvable problem instances, the new approach provides significantly higher speed-up than frequent restarting.
Rocznik
Tom
24
Numer
4
Strony
901-916
Opis fizyczny
Daty
wydano
2014
otrzymano
2014-02-02
poprawiono
2014-05-07
Twórcy
  • Department of Computer Science and Information Theory, Budapest University of Technology and Economics, Magyar tudósok körútja 2., 1117 Budapest, Hungary
autor
  • Department of Computer Science and Information Theory, Budapest University of Technology and Economics, Magyar tudósok körútja 2., 1117 Budapest, Hungary
Bibliografia
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  • Dechter, R. (2003). Constraint Processing, Morgan Kaufmann, San Francisco, CA.
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  • Gomes, C.P., Selman, B., Crato, N. and Kautz, H. (2000). Heavy-tailed phenomena in satisfiability and constraint satisfaction problems, Journal of Automated Reasoning 24(1-2): 67-100.
  • Gomes, C., Selman, B. and Kautz, H. (1998). Boosting combinatorial search through randomization, Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), Madison, WI, USA, pp. 431-437.
  • Haim, S. and Heule, M. (2010). Towards ultra rapid restarts, Technical report, UNSW/TU Delft, Sydney/Delft.
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  • Hutter, F., Hamadi, Y., Hoos, H. and Leyton-Brown, K. (2006). Performance prediction and automated tuning of randomized and parametric algorithms, in F. Benhamou (Ed.), Principles and Practice of Constraint Programming-CP 2006, Springer, Berlin/Heidelberg, pp. 213-228.
  • Jia, H. and Moore, C. (2004). How much backtracking does it take to color random graphs? Rigorous results on heavy tails, Principles and Practice of Constraint Programming (CP 2004), Toronto, Canada, pp. 742-746.
  • Kautz, H., Horvitz, E., Ruan, Y., Gomes, C. and Selman, B. (2002). Dynamic restart policies, 18th National Conference on Artificial Intelligence, Edmonton, Canada, pp. 674-681.
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  • Mann, Z. and Szajkó, A. (2012). Complexity of different ILP models of the frequency assignment problem, in Z. Mann (Ed.), Linear Programming-New Frontiers in Theory and Applications, Nova Science Publishers, New York, NY, pp. 305-326.
  • Mann, Z. and Szajkó, A. (2010a). Determining the expected runtime of exact graph coloring, Proceedings of the 13th International Multiconference on Information Society-IS 2010, Ljubljana, Slovenia, Vol. A, pp. 389-393.
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Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv24i4p901bwm
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