Self-improving algorithms

Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms

Volumn , Issue , 2006, Pages 261-270

  Ailon, Nir ^a   Chazelle, Bernard ^a   Comandur, Seshadhri ^a   Liu, Ding ^a  

^a Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LOGARITHMIC NUMBER; OPTIMIZED INCARNATION; SELF-IMPROVING ALGORITHMS;

LEARNING ALGORITHMS; LINEAR ALGEBRA; OPTIMIZATION; SORTING; TIME SERIES ANALYSIS;

ALGORITHMS;

EID: 33244468994     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1109557.1109587     Document Type: Conference Paper

References (46)

1. S. Albers and J. Westbrook. Self organizing data structures, Online Algorithms: The state of the art, 1442:13-41, 1998.
2. B. Allen and I. Munro. Self-organizing binary search trees, Journal of the ACM, 25:526-535, 1978.
3. N. Alon, S. Dar, M. Parnas, and D. Ron. Testing of clustering, Proceedings of the 41st Annual IEEE Symposium on Foundations of Computer Science (FOCS), 2000.
4. N. Alon and J. Spencer. The probabilistic method. John Wiley, 2nd edition, 2000.
5. N. Alon and B. Sudakov. On two segmentation problems, Journal of Algorithms, 33:173-184, 1999.
6. S. Ar, B. Chazelle, and A. Tal. Self-customized bsp trees for collision detection, Comput. Geom.: Theory and Applications, 10:23-29, 2000.
7. M. Badoiu and K. L. Clarkson. Smaller core-sets for balls, In Proceedings of the 14th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2003.
8. M. Badoiu, S. Har-Peled, and P. Indyk. Approximate clustering via core-sets, In Proceedings of the 34th Annual ACM Symposium on Theory of Computing (STOC), pages 250-257, 2002.
9. J.L. Bentley and C.C. McGeoch. Amortized analysis of self-organizing sequential search heuristics, C. ACM, 28:404-411, 1985.
10. J.R. Bitner. Heuristics that dynamically organize data structures, SIAM J. Comput., 8:82-110, 1979.
11. A. Blum. On-line algorithms in machine learning, Online Algorithms: The state of the art, 1441, 1998.
12. A. Blum, S. Chawla, and A. Kalai. Static optimality and dynamic search-optimality in lists and trees, In Proc. 13th SODA, pages 1-8, 2002.
13. A. Borodin and R. El-Yaniv. Online computation and competitive analysis, 1998.
14. N. Cesa-Bianchi, Y. Freund, D. Haussler, D.P. Helmbold, R.E. Schapire, and M.K. War. How to use expert advice, Journal of the ACM, 44(3):427-485, 1997.
15. W.F. De la Vega, M. Karpinski, C. Kenyon, and Y. Rabani. Approximation schemes for clustering problems, In Proceedings of the 35th Annual ACM Symposium on Theory of Computing (STOC), pages 50-58, 2003.
16. R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. John Wiley & Sons, 2nd edition, 2000.
17. V. Estivill-Castro and D. Wood. A survey of adaptive sorting algorithms, ACM Computing Surveys, 24:441-476, 1992.
18. A. Flaxman, A. Kalai, and B. McMahan. Online convex optimization in the bandit setting: gradient descent without a gradient , In Proc. 16th SODA, 2005.
19. Fredman, M.L. How good is the information theory bound in sorting?, Theoretical Computer Science 1 (1976), 355-361.
20. Y. Freund and R.E. Schapire. Adaptive game playing using multiplicative weights, Games and Economic Behavior, 29:79-103, 1999.
21. G.H. Gonnet, J.I. Munro, and H. Suwanda. Exegesis of self-organizing linear search, SIAM J. Comput., 10:613-637, 1981.
22. S. Har-Peled. Clustering motion, In Proceedings of the 42nd Annual IEEE Symposium on Foundations of Computer Science (FOGS), pages 84-93, 2001.
23. S. Har-Peled and A. Kushal. Smaller core-sets for k-median and k-means clustering, In Proceedings of the 21st Annual ACM Symposium on Computational Geometry (SoCG), 2005.
24. S. Har-Peled and S. Mazumdar. Core-sets for k-means and k-median clustering and their applications, In Proceedings of the 36th Annual ACM Symposium on Theory of Computing (STOC), pages 291-300, 2004.
25. J.H. Hester and D.S. Hirschberg. Self-organizing linear search, ACM Comp. Surv., 17:295-311, 1985.
26. M. Inaba, N. Katoh, and H. Imai. Applications of weighted voronoi diagrams and randomization to variance-based k-clustering, In Proceedings of the 10th Annual ACM Symposium on Computational Geometry (SoCG), pages 332-339, 1994.
27. A. Kalai and S. Vempala. Efficient algorithms for the online decision problem, In Proc. 16 COLT, 2003.
28. M. Kearns and U. Vazirani. An Introduction to Computational Learning Theory, MIT Press, 1994.
29. M. Kearns, R. Schapire, and L. Sellie. Towards efficient agnostic learning, Machine Learning 17, pages 115-141, 1994.
30. J. Kleinberg, C. Papadimitriou, and R. Prabhakar. Segmentation problems, In Proceedings of the 13th Annual ACM Symposium on Theory of Computing (STOC), pages 473-482, 1998.
31. A. Kumar, Y. Sabharwal, and S. Sen. A simple linear time (1+ε)-approximation algorithm for k-means clustering in any dimension, In Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science (FOGS), pages 454-462, 2004.
32. E. Kushilevitz, R. Ostrovsky, and Y. Rabani. Efficient search for approximate nearest neighbor in high dimensional spaces, SIAM J. Comput. 30 (2000), 457-474.
33. N. Littlestone. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm, Machine Learning, 2:285-318, 1987.
34. N. Littlestone and M.K. Warmuth. The weighted majority algorithm, Information and Computation, 108(2):212-261, 1994.
35. J. McCabe. On serial files with relocatable records, Operations Research, 13:609-618, 1965.
36. B. McMahan and A. Blum. Online geometric optimization in the bandit setting against an adaptive adversary, In Proc. 17th COLT, 2004.
37. K. Mehlhorn. Data structures and algorithms 1: Sorting and searching, EATCS Monographs on Theoretical Computer Science, 1984.
38. N. Mishra, D. Oblinger, and L. Pitt. Sublinear approximate clustering, In Proceedings of the 12th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2001.
39. T. Mitchell. Machine Learning. McGraw Hill, 1997.
40. R. Ostrovsky and Y. Rabani. Polynomial time approximation schemes for geometric k-clustering, J. ACM, 49:139-156, 2002.
41. R. Rivest. On self-organizing sequential search heuristics, C. ACM, 19:63-67, 1976.
42. Albers S. and M. Mitzenmacher. Average case analyses of list update algorithms, Algorithmica, 21:312-329, 1998.
43. D.D. Sleator and R.E. Tarjan. Amortized efficiency of list update and paging rules, C. ACM, 28:202-208, 1985.
44. D.D. Sleator and R.E. Tarjan. Self-adjusting binary search trees, J. ACM, 32:652-686, 1985.
45. F. Takens. Detecting strange attractors in turbulence, Dynamical Systems and Turbulence, 898:366-381, 1981.
46. M. Thorup. Quick k-median, k-center and facility location for sparse graphs, In Proceedings of the 28th International Colloquium on Automata, Languages and Programming (ICALP), volume 2076 of Lecture Notes in Computer Science. Springer, 2001.