Time Complexity: O(N^3), where N is the size of the array. This is because we have three nested loops: one for the starting index, one for the ending index, and one for calculating the sum of the subarray.

Space Complexity: O(1), as we are using a constant amount of space for variables, regardless of the input size.

Better Approach

Algorithm

Iterate through the array with variable i, which represents the starting index of each subarray. The possible values for i range from 0 to n-1, where n is the size of the array.
Inside the first loop, iterate again with variable j to signify the ending index of the subarray and the current element of the subarray. For each i, j can range from i to n-1.
For each subarray defined by i and j, add the current element at arr[j] to the sum of the previous subarray.
Keep track of the maximum sum encountered during the iteration using a variable, say maxSum, and update it whenever a greater sum is found.
Once all iterations are complete, return maxSum as the maximum sum of all subarrays.

Code