# TIL about little-o notation

I've just read this Wolfram MathWorld article, apparently, big O notation is actually one of two "Landau symbols". A Landau symbol is a function that takes a function as a parameter, so \(O(n)\) is equivalent to \(O(\phi)\) where \(\phi(n) = n\). The formal definition is that given some function \(f(x)\) (such as the runtime of an algorithm) and some other function \(\phi(x)\) (such as the asymptotic complexity class of an algorithm),

for all values of \(x\) where \(A\) is some constant. In other words, \(f\) will never grow faster than \(\phi\).

There's also little-o notation, which implies that \(f/\phi\) approaches 0. In other words, \(\phi\) grows much faster than \(f\). For example, we could say that bubble sort runs in \(o(n^3)\) time because it runs in much fewer than \(n^3\) steps.

Actually, the mathematical definition of big-O notation would seem to imply that \(f = o(\phi) \implies f = O(\phi)\), since \(f\) will never grow faster than \(\phi\). If that's right, then we could for example call bubble-sort an \(O(n^3)\) algorithm because it will never take longer than \(n^3\) time. Of course, it also won't take longer than \(n^2\) time, which is more impressive, but the first statement is still technically true.

I can't be bothered to find an example right now, but some computer science problems ask for "the most restrictive time-complexity in big-O notation" rather than "the time complexity in big-O notation", I guess that's why.

By the way, you probably shouldn't trust these blogs as an authoritative source on anything, I'm just some kid from Texas who likes computer science and math.