# Run-time of a summation function and its complexity

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?

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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: $$\Theta(1) \cdot \sum_{i=1}^{n} i = \Theta(1) \cdot \Theta(n^2) = \Theta(n^2).$$