O notation has a precise definition. A function f : N -> R+ is said to be O(g(x)) (for some g : N -> R) if there exists a constant c so that f(n) <= cg(n) for all sufficiently large n. If f is bounded, then f is O(1).
Yeah, you’re right, I’m not being rigorous here. I’m just co-opting big O notation somewhat inaccurately to express that this isn’t going to get as big as it seems because the number of upvotes isn’t going to increase all that much.
According to that logic, everything is O(1) because at some point you go out of memory or your computer crashes.
“How fast is your sorting algorithm?” – “It can’t sort all the atoms in the universe so O(1).”
That’s not how O notation works. It is about asymptomatics. It is about “What if it doesn’t crash”, “what if infinity”.
So, again, what exactly is your question if your answer is O(1)?
O notation has a precise definition. A function f : N -> R+ is said to be O(g(x)) (for some g : N -> R) if there exists a constant c so that f(n) <= cg(n) for all sufficiently large n. If f is bounded, then f is O(1).
Yeah, you’re right, I’m not being rigorous here. I’m just co-opting big O notation somewhat inaccurately to express that this isn’t going to get as big as it seems because the number of upvotes isn’t going to increase all that much.