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Data Structures, Near Neighbor Searches, and Methodology: Fifth and Sixth DIMACS Implementation Challenges
About this Title
Michael H. Goldwasser, Loyola University of Chicago, Chicago, IL, David S. Johnson, AT&T Bell Laboratories, Florham Park, NJ and Catherine C. McGeoch, Amherst College, Amherst, MA, Editors
Publication: DIMACS Series in Discrete Mathematics and Theoretical Computer Science
Publication Year:
2002; Volume 59
ISBNs: 978-0-8218-2892-2 (print); 978-1-4704-4017-6 (online)
DOI: https://doi.org/10.1090/dimacs/059
MathSciNet review: MR1967938
MSC: Primary 68-06; Secondary 68P05, 68P10, 68U05, 68W01
Table of Contents
Front/Back Matter
Chapters
- Partially persistent dynamic sets for history-sensitive heuristics
- A practical perfect hashing algorithm
- Computational evaluation of hot queues
- Nearest neighbor search for data compression
- Experimental evaluation of disk-based data structures for nearest neighbor searching
- Analysis of approximate nearest neighbor searching with clustered point sets
- Approximate nearest neighbor search using the extended general space-filling curves heuristic
- Locally lifting the curse of dimensionality for nearest neighbor search
- The role of experiment in the theory of algorithms
- Towards a discipline of experimental algorithmics
- A theoretician’s guide to the experimental analysis of algorithms
- A bibliography of algorithm experimentation