A beginner’s guide to Big O Notation

Big O notation is used to describe the performance or complexity of an algorithm Also to describe the execution time required or the space used such as in memory or on desk by an algorithm.

We have these types of notation of Big O:

1. O(1): it describes the algorithm that will always be executed in the same time or space regard to the size of the input data.

2. O(N): it describes the algorithm whose performance will grow linearly and in direct proportion to the size of the input data.

3. O(N^2): it describes the algorithm whose performance is directly proportioning to the squared size of the input data. it is common with the nested iterations over data.

4. O(2^N): It describes the algorithm whose growth doubles with each addition to the input data.