“Insertion sort” is the sorting algorithm in which the first two elements of the array are taken and place the third element relative to first two elements. Likewise, this process is continued for subsequent elements until the entire array is sorted. Hence, the correct answer is option “ C ”.

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Quick Sort is another sorting algorithm that follows a divide-and-conquer approach. The algorithm can be summarized in 3 steps: A pivot element is chosen, usually the first element. All elements smaller than the pivot are placed to the left of the pivot. This creates 2 partitions, elements greater than the pivot and elements less than the pivot. The 2 partitions are sorted using Quick Sort. Sample code in python3: def quick_sort(arr): def quick_sort_r(arr, start, end): if end - start < 2: # single element base case return # choose a pivot pivot = start # you may choose other elements store = pivot+1 # index to store less than elements # for all elements after the pivot for i in range(pivot+1, end): if arr[i] < arr[pivot]: # if element is less than pivot arr[i], arr[store] = arr[store], arr[i] # swap store += 1 # increment store index # swap pivot with last element in less than…

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Chapter 16, Problem 10MC

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“Insertion sort” is the sorting algorithm in which the first two elements of the array are taken and place the third element relative to first two elements. Likewise, this process is continued for subsequent elements until the entire array is sorted.

Hence, the correct answer is option “C”.

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Quick Sort is another sorting algorithm that follows a divide-and-conquer approach. The algorithm can be summarized in 3 steps: A pivot element is chosen, usually the first element. All elements smaller than the pivot are placed to the left of the pivot. This creates 2 partitions, elements greater than the pivot and elements less than the pivot. The 2 partitions are sorted using Quick Sort. Sample code in python3: def quick_sort(arr): def quick_sort_r(arr, start, end): if end - start < 2: # single element base case return # choose a pivot pivot = start # you may choose other elements store = pivot+1 # index to store less than elements # for all elements after the pivot for i in range(pivot+1, end): if arr[i] < arr[pivot]: # if element is less than pivot arr[i], arr[store] = arr[store], arr[i] # swap store += 1 # increment store index # swap pivot with last element in less than…

Phase 1: Execution time for Sorting Write a program that obtains the execution time of selection sort, bubble sort, merge sort, quick sort, heap sort, and radix sort for input size 50,000, 100,000, 150,000, 200,000, 250,000, and 300,000. Create a method or class for each sorting algorithm and call them from the main(). Your program should create data randomly and print a table like this: Array size 100,000 200,000 300,000 Insertion Sort Bubble Sort Merge Sort Quick Sort Heap Sort Radix Sort (HINT: You can use the following code template to obtain the execution time.) long startTime = System.nanoTime(); perform the task; long endTime = System.nanoTime(); long executionTime = endTime − startTime;

Write the state of the elements of each of the following arrays after each pass of the outermost loop of the selection sort algorithm has occurred (after each element is selected and moved into place).int[] numbers1 = {63, 9, 45, 72, 27, 18, 54, 36};int[] numbers2 = {37, 29, 19, 48, 23, 55, 74, 12};