The appropriate Big O notation for the complexity of a sorting algorithm typically depends on the specific algorithm being used. However, commonly analyzed sorting algorithms have the following complexities:
- O(n log n): This is the best average and worst-case complexity for efficient comparison-based sorting algorithms like mergesort and heapsort.
- O(n^2): This is the complexity for simpler sorting algorithms like bubble sort, insertion sort, and selection sort in their average and worst cases.
- O(n): This can be applicable for non-comparison-based sorting algorithms like counting sort or radix sort under specific conditions.
Given your options, the most commonly recognized complexities for sorting algorithms would be:
- O(n^2) (for simpler algorithms in worst case).
So, if you must choose between the provided options that correlate with sorting algorithms, O(n^2) is an appropriate selection for some sorting algorithms.