# Demystifying Interpolation Search

Demystifying Interpolation Search: Upgrade Your Searching Game! Prior to this article, I have written about Binary Search. Check it out if you haven’t seen it.

Prior to this article, I have written about Binary Search. Check it out if you haven’t seen it. In this article, we will be discussing Interpolation Search, which is an improvement of Binary Search when the values in the sorted array are uniformly distributed.

You might recall that Binary Search is already running in O(log n) time complexity. Can there be any other methods that perform even better? Ummm… partly yes. We will dive into it shortly.

Interpolation Search is another efficient searching technique. This method can outrun binary search in cases where the elements of the array are uniformly distributed. Interpolation Search estimates the position of the key value in the array. This can be achieved by taking into account the smallest and the largest element in the array and the length of the array.

It is based on the thinking that if the key value is larger (closer to the largest element), it’s position is likely located to the end of the array. The same thing goes for a key with a smaller value.

## How Does it Work?

There are the 6 main steps in doing Interpolation Search, which are:

1. Estimates the middle position for the array and compares this element to the desired key.
2. If the key matches the middle element, return the middle location.
3. If the key is smaller than the middle element, then the key can only lie in the left subarray. Focus on the left subarray

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