Algorithmic complexity of naive code for processing all consecutive subsequences of a list: n^2 or n^3?

Question

I\'m studying for a test and found this question:

I can\'t really determine the complexity, I figured it\'s either O(n²) or O(n³) and I\'m lea

Answer 1

You have 3 for statements. For large n, it is quite obvious that is O(n^3). i and j have O(n) each, k is a little shorter, but still O(n).

The algorithm returns the biggest sum of consecutive terms. That's why for the last one it returns 99, even if you have 0 and 1, you also have -3 that will drop your sum to a maximum 97.

PS: Triangle shape means 1 + 2 + ... + n = n(n+1) / 2 = O(n^2)