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Abstract: OF THE THESIS
This dissertation develops and studies fast algorithms for solving closest point problems.
Algorithms for such problems have applications in many areas including statistical classification,
crystallography, data compression, and finite element analysis. In addition to a comprehensive
empirical study of known sequential methods, I introduce new parallel algorithms for these problems
that are both efficient and practical. I present a simple and flexible programmingmodel for... (Update)
Context of citations to this paper: More
...algorithms based upon divide andconquer and sweepline techniques, which will be discussed here only briefly. Refer to Su and Drysdale [68, 67] for an overview of these and other two dimensional Delaunay triangulation algorithms. Incremental insertion algorithms operate by...
...experiments. One drawback is that many implemented parallel algorithms are tuned to work most efficiently for the uniform distributions [37, 35] but fail to be efficient for nonuniform distributions. Here we define new point distributions for which we can find efficient...
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Design and Implementation of a Practical Parallel.. - Blelloch, Hardwick, .. (1999)
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A Comparison of Sequential Delaunay Triangulation Algorithms - Su, Drysdale (1996)
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0.8: Voronoi Diagrams and Delaunay Triangulations - Fortune (1992)
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0.6: Dynamic Randomized Simulation of Hierarchical PRAMs on Meshes - Heywood, Leopold (1995)
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6: Computational Geometry: An Introduction (context) - Preparata, Shamos - 1985
6: A data-parallel algorithm for three-dimensional Delaunay triangulation and its i.. (context) - Teng, Sullivan et al. - 1993
6: A comparison of sequential delaunay triangulation algorithms
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BibTeX entry: (Update)
Peter Su. Efficient parallel algorithms for closest point problems. PhD thesis, Dartmouth College, 1994. PCS-TR94-238. http://citeseer.ist.psu.edu/su94efficient.html More
@techreport{ susuefficient,
author = "Peter Su.",
title = "Efficient Parallel Algorithms for Closest Point Problems",
publisher = "Department of Computer Science, Duke University",
url = "citeseer.ist.psu.edu/su94efficient.html" }
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