Problem Statement - Maximum Sum of K Elements

### Problem Statement

You are given an array A containing N elements and an integer K. You have to find the subarray with the maximum sum among all the K-sized sub-arrays and output this maximum sum.

This is a perfect example of a question that can be efficiently solved using the sliding window approach.

Normally, we would calculate the sum of all K-sized sub-arrays which would have the time complexity of O(N).

But, using the Sliding Window approach we can assume the K-sized subarray to be a window. Now we just need to calculate the sum of the first window as the sum of the next window can be derived from the previous sum by subtracting the first element and adding the next element.

### Approach

The code idea involves using a **sliding window** to efficiently calculate the maximum sum of **K-sized** subarrays. After reading N and K, we initialize the sum with the first K elements and set it as the maximum sum. As we slide the window by updating the sum (subtracting the leftmost element and adding the next element), we continually check and update the maximum sum. This approach ensures we only traverse the array once, making it optimal for this problem.

### Time Complexity

The time complexity of the solution is O(N).

### Space Complexity

The space complexity of the solution is O(1).