Run-time of a summation function and its complexity

Question

I am trying to analyze the running time of the following function:

def algo(array: List[int]):
    x = 1
    y = 0
    sigma = 0
    for ix in range(1, len(array)):  #len(array) always >= 1
        summation = 0 
        for jx in range(ix, 0, -1):
            summation = summation + array[jx]
            if summation > sigma:
                x = jx
                y = ix 
                sigma = summation
    return (x,y)

I have identified a basic unit of the algorithm to count as the number of iterations/total loops, $L$, it runs through. So for instance, if the length of array, $n$, is $1$, then $L = 1$. If, $n = 5$, then $L = 15$. The pattern follows that of the triangular numbers sequence. If you plot a set of points $(n, L)$ you will see that it kind of looks exponential. Is my line of thinking so far okay?

Answer 1

The body of the inner for loop is executed in constant time per iteration and it does not affect the number of iterations of the outer loops.

Let $n$ be the length of array. The outer for loop iterates $n$ times. During the $i$-th iteration of the outer loop, the inner for loop iterates $i$ times.

The overall time complexity is therefore: $$ \Theta(1) \cdot \sum_{i=1}^{n} i = \Theta(1) \cdot \Theta(n^2) = \Theta(n^2). $$