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On optimal parallel algorithm for building a data structure for planar point location

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Published by Courant Institute of Mathematical Sciences, New York University in New York .
Written in English


Book details:

Edition Notes

Statementby Richard Cole, Ofer Zajicek.
SeriesUltracomputer note -- 128
ContributionsZajicek, Ofer
The Physical Object
Pagination15 p.
Number of Pages15
ID Numbers
Open LibraryOL17977297M

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Klappentext Excerpt from On Optimal Parallel Algorithm for Building a Data Structure for Planar Point Location Remark: Due to the use of expander graphs, the constants in the running time for the above procedure are large. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at For a given planar subdivision with n vertices a point location data structure supporting O(log n)time queries can be constructed in O(log n) time on an EREW-PRAM with O(n) processors [14]. Thus   () An optimal parallel algorithm for building a data structure for planar point location. Journal of Parallel and Distributed Computing , () Parallel algorithms for contour extraction and coding on an EREW PRAM ://   A planar subdivision is any partition of the plane into (possibly unbounded) polygonal regions. The subdivision search problem is the following: given a subdivision S with n line segments and a query point P, determine which region of S contains P. We present a practical algorithm for subdivision search that achieves the same (optimal) worst case complexity bounds as the significantly more

  A planar subdivision is any partition of the plane into (possibly unbounded) polygonal regions. The subdivision search problem is the following: given a subdivision S with n line segments and a query point P, determine which region of S contains present a practical algorithm for subdivision search that achieves the same (optimal) worst case complexity bounds as the significantly more   () A parallel algorithm for the visibility problem from a point. Journal of Parallel and Distributed Computing , () Optimal parallel merging and sorting algorithms using √N processors without memory ://   For functional persistence, we show a data structure for balanced BST with O(lg n) per op [Okasaki-book ], a data structure for link-cut tree with the same bound [Demaine, Langerman, Price], one for deques with concatenation in O(1) per op [Kaplan,Okasaki, Tarjan - SICOMP ] and update and search in O(lg n) per op [Brodal, Makris   This set of MCQ on tree and graph in data structure includes multiple-choice questions on the introduction of trees, definitions, binary tree, tree traversal parallel arrays. A. 4 Data Structure MCQ Questions Download: Pdf E-Book Of Data Structure MCQ ://

Data sets in large applications are often too massive to fit completely inside the computers internal memory. The resulting input/output communication (or I/O) between fast internal memory and slower external memory (such as disks) can be a major performance ://   We consider planar n-point sets V with L p metric, 1 parallel, β-spectrum labeling. We also show a parallel algorithm, which for a given β ∈ [1, 2] finds the lune-based β-skeleton in O (log 2 ⁡ n) time. The parallel algorithms use O (n) processors in Geometric partitioning made easier, even in parallel. Share on. Author: Michael T. Goodrich. View Profile. Authors Info & Affiliations ; Publication: SCG ' Proceedings of the ninth annual symposium on Computational geometry We present an efficient external-memory dynamic data structure for point location in monotone planar subdivisions. Our data structure uses disk blocks to store a monotone subdivision of size, where is the size of a disk block. It supports queries in I/Os (worst-case) and updates in I/Os (amortized)~jsv/Papers/catalog/