The provided algorithm implements the QuickSort sorting algorithm with a recursive partitioning approach. In terms of efficiency, QuickSort has an average case time complexity of O(n log n), but its worst-case performance is O(n^2), which occurs on already sorted or reverse-sorted data. Correctness is maintained as the partition function correctly sorts elements around a pivot, ensuring that all elements less than the pivot come before it, and elements greater come after, which is fundamental to QuickSort's operation. However, the implementation itself could be clearer: the variable names (like i, pi, j) are not descriptive, and missing comments or structure could make understanding the process difficult for someone unfamiliar with the algorithm. Overall, while functional, the implementation could benefit from improved clarity through better naming conventions and documentation.
Evaluate the following algorithms in terms of its efficiency, correctness, and clarity. Explain your answer in 3-5 sentences.
def partition(arr, low, high): i = (low - 1) pivot = arr[high] for j in range(low, high): if arr[j] <= pivot: i = i + 1 arr[i], arr[j] = arr[j], arr[i] arr[i + 1], arr[high] = arr[high], arr[i + 1] return (i + 1) def quickSort(arr, low, high): if len(arr) == 1: return arr if low < high: pi = partition(arr, low, high) quickSort(arr, low, pi - 1) quickSort(arr, pi + 1, high)
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