Kazuo Iwama (auth.), Tetsuo Asano (eds.)'s Algorithms and Computation: 17th International Symposium, PDF

By Kazuo Iwama (auth.), Tetsuo Asano (eds.)

ISBN-10: 3540496947

ISBN-13: 9783540496946

ISBN-10: 3540496963

ISBN-13: 9783540496960

This e-book constitutes the refereed court cases of the seventeenth overseas Symposium on Algorithms and Computation, ISAAC 2006, held in Kolkata, India in December 2006.

The seventy three revised complete papers offered have been rigorously reviewed and chosen from 255 submissions. The papers are prepared in topical sections on algorithms and information constructions, on-line algorithms, approximation set of rules, graphs, computational geometry, computational complexity, community, optimization and biology, combinatorial optimization and quantum computing, in addition to dispensed computing and cryptography.

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Selection and sorting with limited storage. Theoretical Computer Science, 12:315–323, 1980. 8. J. I. Munro and V. Raman. Selection from read-only memory and sorting with minimum data movement. Theoretical Computer Science, 165(2):311–323, 1996. 9. M. Paterson. Progress in selection. In SWAT ’96: Proceedings of the 5th Scandinavian Workshop on Algorithm Theory, pages 368–379, 1996. 10. J. S. Vitter. Random sampling with a reservoir. ACM Trans. Math. , 11(1):37–57, 1985. Optimal Algorithms for Tower of Hanoi Problems with Relaxed Placement Rules Yefim Dinitz and Shay Solomon Dept.

Guha, A. McGregor, and S. Venkatasubramanian. Streaming and sublinear approximation of entropy and information distances. In SODA, 2006. 5. R. Jain and I. Chlamtac. The p2 algorithm for dynamic calculation of quantiles and histograms without storing observations. Commun. ACM, 28(10):1076–1085, 1985. 6. G. S. Manku, S. Rajagopalan, and B. G. Lindsay. Approximate medians and other quantiles in one pass and with limited memory. , 27(2):426–435, 1998. 7. J. I. Munro and M. Paterson. Selection and sorting with limited storage.

G. [1]). So, a quite natural approach to solve the independent set problem would be to branch on vertices of high degree and if a subproblem with all vertices of small degrees is obtained, then use dynamic programming. Unfortunately, such a simple approach still provides poor running time mainly because the best known upper bounds on treewidth of graphs with small maximum degree are too large to be useful. In this paper we show two di«erent approaches based on combinations of branching and treewidth techniques.

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Algorithms and Computation: 17th International Symposium, ISAAC 2006, Kolkata, India, December 18-20, 2006. Proceedings by Kazuo Iwama (auth.), Tetsuo Asano (eds.)


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