Dynamic interpolation search in o(log log n) time
A new efficient data structure, based on the augmentation technique used in the interpolation search tree by Mehlhorn and Tsakalidis, is presented. We achieve:
a trade-off between input distribution and search cost for dynamic interpolation search.
θ(log log n) expected time for search and update operations for a larger class of densities than Mehlhorn and Tsakalidis.
o(log log n) expected time for search and update operations for a large class of densities. As an example, we give an unbounded density for which we achieve θ(log* n) expected time. We also show θ(1) expected time for all bounded densities, in particular, the uniform distribution.
improved worst-case cost from θ(log2 n) to θ(log n) for searches and from θ(n) to θ(log n) for updates.
We also include a discussion of terminology: which methods should be termed “interpolation search”?
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- Dynamic interpolation search in o(log log n) time
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- Automata, Languages and Programming
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- 20th International Colloquium, ICALP 93 Lund, Sweden, July 5–9, 1993 Proceedings
- pp 15-27
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- Lecture Notes in Computer Science
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