Time Complexity

Describe how the runtime of an algorithm changes as a Big O function of the size of its input. For example, the Big O time complexity of Bubble Sort is . Furthermore, there is Big Omega, which indicates the best case scenario specifically, whereas Big O usually implies worst case time complexity. In the case of bubble sort it is , due to the trivial case of already sorted data. In other words, Big O is the asymptotic upper bound and the big Omega is the asymptotic lower bound of the algorithm.

See also

Auxiliary Space Complexity