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

Answer 1

The complexity of sorting algorithms varies depending on the specific algorithm used. Here are some common sorting algorithms and their typical time complexities:

• Bubble Sort: O(n²)
• Selection Sort: O(n²)
• Insertion Sort: O(n²) (O(n) in the best case)
• Merge Sort: O(n log n)
• Quick Sort: O(n log n) on average (O(n²) in the worst case)
• Heap Sort: O(n log n)
• Radix Sort: O(nk), where k is the number of digits in the numbers being sorted (not a comparison sort)

Given this information, the O(log n) notation is not appropriate for the time complexity of comparison-based sorting algorithms, as they generally have a minimum average time complexity of O(n log n).

For a sorting algorithm, O(n log n) would be a more appropriate designation for average time complexity, particularly for efficient algorithms like Merge Sort or Quick Sort.

So, among the options generally expected for sorting algorithm complexities, O(log n) is not suitable.

Answer 2

What the answer

Answer 3

The answer is that O(log n) is not appropriate for the complexity of a sorting algorithm.

The typical time complexities for sorting algorithms are usually O(n²) for simpler algorithms like Bubble Sort and Selection Sort, and O(n log n) for more efficient algorithms like Merge Sort and Quick Sort. Thus, the correct complexity for sorting algorithms is generally O(n log n), not O(log n).