Interpolation search for non-independent data

Authors:
Erik D. Demaine;Thouis Jones;Mihai Pătraşcu
Affiliations:
MIT Computer Science and Artificial Intelligence Laboratory;MIT Computer Science and Artificial Intelligence Laboratory;MIT Computer Science and Artificial Intelligence Laboratory
Venue:
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Year:
2004

Citing 3
Cited 4

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Dynamic interpolation search revisited

ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part I

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Abstract

We define a deterministic metric of "well-behaved data" that enables searching along the lines of interpolation search. Specifically, define Δ to be the ratio of distances between the farthest and nearest pair of adjacent elements. We develop a data structure that stores a dynamic set of n integers subject to insertions, deletions, and predecessor/successor queries in O(lg Δ) time per operation. This result generalizes interpolation search and interpolation search trees smoothly to nonrandom (in particular, non-independent) input data. In this sense, we capture the amount of "pseudorandomness" required for effective interpolation search.