For an array of sorted items, which algorithm (quick sort, merge sort, selection sort, insertion sort, or bubble sort) would perform the fastest?
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For an array of sorted items, which
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- using selection sort algorithm, the intermediate sorting results of sorting the array (1,3,24,19,5,2} in Descending order are: starting: {12,4,25,20,6,3,100} Result of the first iteration: Result of the second iteration: Result of the third iteration: Result of the fourth iteration: { Result of the fifth iteration:How to implement an Insertion Sort Algorithm?Quick sort is the quickest and utilizes the least auxiliary storage space of all sorting algorithms.
- using Insertion sort algorithm, the intermediate sorting results of sorting the array (1,3,24,19,5,2} in ascending order are: starting: {11 || 3,24,19,5,2,99} Result of the first iteration: { Result of the second iteration: Result of the third iteration: Result of the fourth iteration: { Result of the fifth iteration: {Write a modified version of the selection sort algorithm that selects the largest element each time and moves it to the end of the array, rather than selecting the smallest element and moving it to the beginning. Will this algorithm be faster than the standard selection sort? What will its complexity class (big-Oh) be?Big-O Solving (PYTHON) Question: Does the choice of the pivot affect the running time of quick sort? Why or why not? It would help if you could provide examples or illustrations. Given ONLY: 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]:…
- A sort algorithm that finds the smallest element of the array and interchanges it with the element in the first position of the array. Then it finds the second smallest element from the remaining elements in the array and places it in the second position of the array and so onFor selection sort, how many comparisons would be needed to sort an array containing 100 elements if the original array elements were already sorted? What if the original array elements were sorted in reverse order? What if the original array elements were all identical?If an array is already sorted, which of the following algorithms will exhibit the best performance and why? Be precise in your answer. Selection Sort Insertion Sort • Merge Sort • Quick Sort
- A merge sort is used to sort an array of 1000 test scores in descending order. Which of the following statements is true?a) The sort is fastest if the original test scores are sorted from smallest to largestb) The sort is fastest if the original test scores are in completely sorted order.c) The sort is fastest if the original test scores are sorted from largest to smallest.d) The sort is the same, no matter what the order of the original elements.Write the algorithm which sorts the array by using the selection sort algorithm. Then find the complexity of the algorithm as Big O notationCreate a sort algorithm that counts the variety of key values before sorting the array using key-indexed counting using a symbol table. (This technique should not be used if there are many different key values.)