Which of the following Big O notations is appropriate for the complexity of a sort algorithm?(1 point)

Question

Responses

O(n)
upper O left parenthesis n right parenthesis

O(1)
upper O left parenthesis 1 right parenthesis

O(n2)
upper O left parenthesis n squared right parenthesis

O(logn)

Answer 1

The appropriate Big O notation for the complexity of a sorting algorithm typically depends on the specific algorithm being considered. Common sorting algorithms have the following complexities:

1. O(n) - This is typically not applicable for comparison-based sorting algorithms, but can be relevant for non-comparison sorts like Counting Sort or Radix Sort under certain conditions.
2. O(1) - This denotes constant time complexity and is not applicable for sorting algorithms as they generally require at least linear time to process the input.
3. O(n²) - This is an appropriate notation for simple sorting algorithms like Bubble Sort, Insertion Sort, and Selection Sort.
4. O(log n) - This complexity can be associated with specific operations, like binary search, but is not suitable for sorting algorithms.

Overall, the most common Big O notation associated with general sorting algorithms, particularly for basic algorithms, is O(n²). However, more efficient sorting algorithms, like Merge Sort and Quick Sort, have average time complexities of O(n log n).

Among the provided options, O(n²) is the most appropriate for the complexity of certain sorting algorithms.