Interpolation Search - Performance

Performance

Using big-O notation, the performance of the interpolation algorithm on a data set of size N is O(N); however under the assumption of a uniform distribution of the data on the linear scale used for interpolation, the performance can be shown to be O(log log N). However, Dynamic Interpolation Search is possible in o(log log n) time using a novel data structure.

Practical performance of interpolation search depends on whether the reduced number of probes is outweighed by the more complicated calculations needed for each probe. It can be useful for locating a record in a large sorted file on disk, where each probe involves a disk seek and is much slower than the interpolation arithmetic.

Index structures like B-trees also reduce the number of disk accesses, and are more often used to index on-disk data in part because they can index many types of data and can be updated online. Still, interpolation search may be useful when one is forced to search certain sorted but unindexed on-disk datasets.