<P> 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 . </P> <P> 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 . </P> <P> 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 . </P> <P> When sort keys for a dataset are uniformly distributed numbers, linear interpolation is straightforward to implement and will find an index very near the sought value . </P>

Runtime complexity of interpolation search algorithm is equal to