This code implements the QuickSort algorithm.
Efficiency: The efficiency of this algorithm is generally good. QuickSort has an average time complexity of O(n log n), which makes it efficient for large datasets. However, the worst-case time complexity is O(n^2), which can occur if the pivot chosen is always the smallest or largest element.
Correctness: The algorithm appears to be correct in terms of sorting the array. It uses the partition function to divide the array into two subarrays, based on a selected pivot element, and recursively applies QuickSort to the subarrays. This process ultimately results in a sorted array.
Clarity: The code is relatively clear and easy to understand. It follows a standard implementation of QuickSort and uses meaningful variable names. However, the use of recursive function calls and array indexing might require some further understanding of the algorithm to fully comprehend. Additional comments or explanations could improve clarity.
Evaluate the following algorithms in terms of its efficiency, correctness, and clarity. Explain your answer in 3-5 sentences.%0D%0A%0D%0Adef partition(arr, low, high):%0D%0A i = (low - 1)%0D%0A pivot = arr[high]%0D%0A for j in range(low, high):%0D%0A if arr[j] <= pivot:%0D%0A i = i + 1%0D%0A arr[i], arr[j] = arr[j], arr[i]%0D%0A arr[i + 1], arr[high] = arr[high], arr[i + 1]%0D%0A return (i + 1)%0D%0A%0D%0Adef quickSort(arr, low, high):%0D%0A if len(arr) == 1:%0D%0A return arr%0D%0A if low < high:%0D%0A pi = partition(arr, low, high)%0D%0A quickSort(arr, low, pi - 1)%0D%0A quickSort(arr, pi + 1, high)%0D%0A(4 points)
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1 answer