Theoretical average case runtime complexity of `numpy.argmax()`

Question

I was looking at the code of numpy.argmax function. I am confused which data structure numpy maintains for the argmax function.

https://numpy.org/doc/stable/reference/generated/numpy.argmax.html

Eventually, I want to know what is the theoretical average case running time complexity of numpy argmax function for primitive data types. Is it O(logN) or O(N) in the average case?

This may be a relevant question as well: Faster alternatives to numpy.argmax/argmin which is slow

Thanks in advance.

Answer 1

Here is a performance analysis using benchit:

def m(x):
  return np.argmax(x)

in_ = [np.random.rand(n) for n in [10,100,1000,10000]]

As you can see it is O(N) as it should be. You iterate over array once to find the maximum.