Applying to Algorithms

Also, in using these functions to describe algorithms, there are a few things to note.

• If we say the algorithm's running time is O(n²), it means its worst-case running time is O(n²), but its best case might be better.
• If we say the algorithm's running time is $\Omega(n)$, it means its best-case running time is $\Omega(n)$, but its worst-case might be worse.
• Therefore, an algorithm with running time $\Theta(n\log n)$ takes $\Theta(n\log n)$ on all inputs of size n.