Big O notation is used in algorithm analysis to describe the asymptotic complexity — that is, how the execution time or the amount of memory used grows depending on the size of the input data.

For example, if an algorithm has a complexity of O(n), it means that its running time grows linearly with the input size n. If it is O(n²), the time grows proportionally to the square of the input size.

This helps compare the efficiency of algorithms, especially with large volumes of data, ignoring constants and less significant terms.

Eelmine Edasi