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Have you ever encountered a question, maybe on leetcode, or some other source, that required you to either get the minimum or maximum subarray size whose sum is equivalent to a target? If so, you might find this article useful.

In this piece, I’ll explain the sliding window technique and give an example of it implementation in a coding interview question from leetcode. In addition, I’ll explain it’s time complexity.

What is the sliding window technique?
This technique is an algorithm used to find the smallest or the largest subarray with a given property. It has a time complexity of O(n), to be specific, O(2n).

How does it really work?
It involves using a window to look at a bunch of elements in an array and performing various computations on each element in that window.

Two pointers are used, one points to the first element, and the other to the last element in the subarray. Changing the position of these pointers is equivalent to moving the window.

For more information, visit:- https://codalien.com/blog/sliding-window-technique/