# What Is Interpolation Search in Data Structure?

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Scott Campbell

Interpolation search is an efficient searching algorithm used to find a specific element in a sorted array. It works by estimating the position of the desired element based on the values of the first and last elements in the array. This algorithm is particularly useful when dealing with large data sets where there is a uniform distribution of values.

## How Does Interpolation Search Work?

The interpolation search algorithm involves the following steps:

1. Step 1: The first step is to determine the range of indices where the desired element may be located. To do this, we use linear interpolation between the first and last elements of the array.
2. Let’s say we are searching for a key value ‘x’ in an array with indices ranging from ‘low’ to ‘high’. The formula for interpolation can be expressed as:

``position = low + ((x - arr[low]) * (high - low)) / (arr[high] - arr[low])``

The ‘position’ variable represents an estimation of where the desired element might be located.

3. Step 2: Once we have an estimate of the position, we compare the value at that position with our Target key value (‘x’).
• If they match, we have found our desired element and return its index.
• If the estimated value is greater than ‘x’, we update our range to search in a smaller range to the left of ‘position’.
• If the estimated value is less than ‘x’, we update our range to search in a smaller range to the right of ‘position’.
4. Step 3: Repeat step 1 and step 2 until we find the desired element or exhaust our search range.

The interpolation search algorithm has several advantages:

• Efficiency: Interpolation search performs better than binary search for uniformly distributed data. It takes advantage of the estimated position to narrow down the search range.
• Time Complexity: In the average case, interpolation search has a time complexity of O(log log n), where ‘n’ represents the number of elements in the array.

However, in the worst case scenario where the data is not uniformly distributed, it can have a time complexity of O(n).

• Data Distribution: Interpolation search works best when there is a uniform distribution of values in the array. If there are clusters of elements with similar values, it may not perform as well as other searching algorithms.

## Conclusion

In summary, interpolation search is an efficient searching algorithm used to find elements in sorted arrays. It uses linear interpolation to estimate the position of the desired element and narrows down the search range accordingly. This algorithm performs well when dealing with large data sets with uniform data distribution.

By understanding how interpolation search works and considering its advantages and limitations, you can make informed decisions about when to use this algorithm in your projects.