By Joel Spencer (auth.), Tandy Warnow, Binhai Zhu (eds.)
This e-book constitutes the refereed lawsuits of the ninth Annual overseas Computing and Combinatorics convention, COCOON 2003, held in vast Sky, MT, united states in July 2003.
The fifty two revised complete papers provided including three invited contributions have been conscientiously reviewed and chosen from 114 submissions. The papers are prepared in topical sections on computational geometry, computational biology, computability and complexity thought, graph conception and graph algorithms, automata and Petri internet concept, disbursed computing, Web-based computing, scheduling, graph drawing, and fixed-parameter complexity theory.
Read Online or Download Computing and Combinatorics: 9th Annual International Conference, COCOON 2003 Big Sky, MT, USA, July 25–28, 2003 Proceedings PDF
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Additional info for Computing and Combinatorics: 9th Annual International Conference, COCOON 2003 Big Sky, MT, USA, July 25–28, 2003 Proceedings
2. The relation between the execution times of our exact centroid AHC algorithm and the dimensions, for data sets of sizes 10K, 20K, and 30K. Fig. 3. The relation between the execution times of our approximate single-link AHC algorithm and the data sizes, for 2-D data sets. 5M, in ﬁxed dimensions d ≥ 2. Figure 1 shows the execution times of our exact centroid AHC algorithm as functions of n, in dimensions 2, 3, and 4. In Figure 1, the three curves for d = 2, 3, and 4 all indicate that the execution times of our exact centroid AHC algorithm increase almost linearly with respect to the increase of the data sizes.
Bathe. The method of ﬁnite spheres. Computational Mechanics, 25:329–345, 2000. 5. L. Guibas, F. Xie, and L. Zhang. Kinetic data structures for eﬃcient simulation. In Proc. IEEE Intern. Conf. on Robotics and Automation, 3:2903–2910, 2001. 6. L. J. Guibas. Kinetic data structures — a state of the art report. In P. K. Agarwal, L. E. Kavraki, and M. Mason, editors, Proc. Workshop Algorithmic Found. , pp. 191–209. A. K. Peters, Wellesley, MA, 1998. 7. L. J. Guibas, A. Nguyen, D. Russel, and L. Zhang.
Chen and B. Xu Fig. 1. The relation between the execution times of our exact centroid AHC algorithm and the data sizes, for data sets in 2-D, 3-D, and 4-D. develop eﬃcient software for our exact and approximate AHC algorithms, and make it available to real AHC applications. , O(n log n + (sd log s)n) for a constant s > 2) appear to be quite high for various practical settings in which d and s must be rather “big”. Hence, our third goal is to compare the experimental results with these theoretical time bounds to determine the practical eﬃciency of our AHC algorithms.