Similar to big O notation, big Omega(Ω) function is used in computer science to describe the performance or complexity of an algorithm.

If a running time is Ω(f(n)), then for large enough n, the running time is at least k⋅f(n) for some constant k. Here’s how to think of a running time that is Ω(f(n)):

We say that the running time is “big-Ω of f(n).” We use big-Ω notation for ** asymptotic lower bounds**, since it bounds the growth of the running time from below for large enough input sizes.

**Difference between Big O and Big Ω**

The difference between Big O notation and Big Ω notation is that Big O is used to describe the worst case running time for an algorithm. But, Big Ω notation, on the other hand, is used to describe the best case running time for a given algorithm.