# Interpolation Search vs. Binary Search

Searching is a common task that needs to be performed on different datasets. In today's fast-growing world, we always look to save our time. An efficient searching algorithm helps us perform efficient searches.

Binary search and interpolation search are two popular search algorithms that differ in their approach and efficiency.

In this article, we will deep dive into the key differences between binary search and interpolation search, and will also discuss which one to prefer for specific searches.

## Binary Search

Binary search is a classic algorithm for searching in a sorted array.

By continually dividing the search space in half, it uses a divide-and-conquer technique until the target element is located or the search space becomes zero.

The algorithm can be summarized as follows:

• Begin with the entire sorted array.
• Compute the middle index of the array.
• If the element at the middle index matches the target, the search is successful.
• If the target is smaller than the middle element, repeat the process on the left half of the array.
• If the target is larger than the middle element, repeat the process on the right half of the array.
• Continue this process until the target element is found or the search space becomes empty.

Below is the Python implementation of the Binary Search:

Binary search is highly efficient for large datasets as it eliminates half of the remaining search space at each step.

Time complexity: O(log n), where n is the number of elements in the array.

However, it requires the array to be sorted in ascending or descending order, and any changes to the array may necessitate re-sorting.

## Interpolation Search:

Interpolation search is an improvement over binary search and works efficiently when the elements in the dataset are uniformly distributed.

It uses a formulaic approach to determine the position of the target element within the array.

The interpolation search algorithm can be summarized as follows:

• Begin with the sorted array.
• Estimate the position of the target element using a formula based on the minimum and maximum values in the array.
• If the estimated position matches the target, the search is successful.
• If the target is smaller than the estimated element, repeat the process on the left portion of the array.
• If the target is larger than the estimated element, repeat the process on the right portion of the array.
• Continue this process until the target element is found or the search space becomes empty.

Below is the Python implementation of the Interpolation Search:

Output:

Interpolation search can be more efficient than binary search when the dataset is uniformly distributed because it approximates the position of the target element rather than dividing the search space by half.

However, if the dataset is not uniformly distributed, interpolation search may perform worse than binary search.

The time complexity of interpolation search can vary depending on the dataset and is approximately O(log log n) on average, with a worst-case time complexity of O(n) in scenarios where the dataset is not uniformly distributed.

## Is Interpolation Search Better than Binary Search?

Whether interpolation search is better than binary search depends on the characteristics of the dataset being searched.

Here are two factors to consider when comparing the two algorithms:

Factors Interpolation Search Binary Search
Dataset Distribution Interpolation search performs better when the dataset is uniformly distributed. It estimates the position of the target element based on the values at the ends of the array. On the other hand, binary search always divides the search space in half, regardless of the distribution of values.
Time Complexity Interpolation search has an average time complexity of O(log log n). However, Interpolation search can degrade to a worst-case time complexity of O(n), which is slower than binary search. Binary search has a time complexity of O(log n).

## When to Use Interpolation Search and Binary Search:

The choice between interpolation search and binary search depends on the characteristics of the dataset being searched.

Here are some tips for selecting the appropriate algorithm:

Use binary search when:

• The dataset is large and uniformly distributed.
• The dataset is sorted and static (i.e., it doesn't change frequently).
• Memory usage needs to be minimal, as binary search only requires indexing the array.

Use interpolation search when:

• The dataset is large and uniformly distributed.
• The dataset is sorted and dynamic (i.e., it changes frequently).
• The dataset is sorted in a non-uniform distribution but exhibits some degree of linearity.

## Key Difference Between Interpolation Search and Binary Search:

Factors Interpolation Search Binary Search
Approach Interpolation search estimates the position of the target element based on the dataset's distribution. While binary search follows a divide-and-conquer strategy, dividing the search space in half at each step.
Time Complexity Average Case: O(log logn)
Worst Case: O(n)
Average Case: Θ(logn)
Worst Case: O(logn)
Dataset Distribution Interpolation search requires sorted data as well as uniform distribution. Binary Search requires sorted data.
Static and Dynamic Interpolation search handles dynamic datasets more efficiently. Whereas binary search is better suited for static datasets.

## CONCLUSION:

In conclusion, both binary search and interpolation search are important and valuable search algorithms with distinct advantages.

Binary search is a reliable choice for sorted, static datasets while interpolation search excels when the dataset is sorted, dynamic, or exhibits uniformity.

Understanding the characteristics of the dataset and considering the time complexity trade-offs will help in making an informed decision when choosing between these search algorithms.