What are the common Big O notations in computer science?

What are the common Big O notations in computer science?

Common Big O notations include O(1), O(log n), O(n), O(n log n), O(n^2), and O(2^n). Each describes the time complexity of algorithms.

Can you explain what O(1) means?

O(1), or constant time complexity, means that the algorithm's runtime does not change regardless of the input size.

What about O(n log n)? When is it used?

O(n log n) is often seen in efficient sorting algorithms like mergesort and heapsort, where the time grows with the input size.

How does O(n^2) compare to O(n log n)?

O(n^2) is quadratic time complexity, which grows much faster than O(n log n) as the input size increases, making it less efficient for large datasets.

Can you give me an example of an algorithm with O(2^n) complexity?

Sure! The recursive solution for the Fibonacci sequence has O(2^n) complexity, as it makes two calls for each number.