OP solved it, closing

@jacosta66, if you're asking about what O(n) means (or the "O(...)" notation in general is), its basically a way to estimate what the upper bound of execution time a certain piece of code would take to complete.

An example of O(n) means that a function will complete in the worst case by parsing a given list a constant number of times. So with a list of 100 elements, we can expect the upper time complexity limit to be a multiple of the time it takes to process all 100 elements.

Conversely, O(n^2) means that for every element in the list, we have to parse the entire rest of the list elements. So for 100 elements, we can parse somewhere around 100^2 or 10000 elements total. That difference in time taken is what makes the 10 million element case impossible to solve within a reasonable timeframe.

While you do bring up a strange logic scenario, there are approaches to this problem that don't rely on length checking or bifurcation. I recommend trying out different approaches!

guys, when you find yourself unable to optimize your greedy algorithm for longer than a week (no jk), as i did, just remember that www is your friend. every-single-one top programmer around this planet has resorted to it at least once in his life. besides, you cannot use something, unless you are aware of its existence. now, reset, your strategy. this exercise regards algorithmic optimization, and imo, is harder than 5 kyu. literally a Big-OH in one's neck. thank you for this. whoever you are.