Binary search has a huge advantage of time complexity over linear search. Linear search has worst-case complexity of O(n) whereas binary search has O(log n).
There are cases where the location of target data may be known in advance. For example, in case of a telephone directory, if we want to search the telephone number of Morphius. Here, linear search and even binary search will seem slow as we can directly jump to memory space where the names start from 'M' are stored.
In binary search, if the desired data is not found then the rest of the list is divided in two parts, lower and higher. The search is carried out in either of them.
Even when the data is sorted, binary search does not take advantage to probe the position of the desired data.
Interpolation search finds a particular item by computing the probe position. Initially, the probe position is the position of the middle most item of the collection.
If a match occurs, then the index of the item is returned. To split the list into two parts, we use the following method:
If the middle item is greater than the item, then the probe position is again calculated in the sub-array to the right of the middle item. Otherwise, the item is searched in the subarray to the left of the middle item. This process continues on the sub-array as well until the size of subarray reduces to zero.
Runtime complexity of interpolation search algorithm is O(log (log n)) as compared to O(log n) of BST in favorable situations.
As it is an improvisation of the existing BST algorithm, we are mentioning the steps to search the 'target' data value index, using position probing: