Match the sorting algorithms with their corresponding computational complexity. bubble sort O(n^2) selection sort O(n^2) insertion sort O(n^2) merge sort O(n^2)

One disadvantage of using a For-loop in sorting algorithms is that the body of theFor-loop is repeatedly executed even if it is no longer necessary. One example isBubble Sort:Input: Array A[1 . . . n]Output: Array A sortedfor i ←1 to (n −1) dofor j ←1 to (n −i) doif A[j] > A[j + 1] thenA[j] ↔A[j + 1]return AIn the array [2, 1, 3, 7, 9] we know that when 2 and 1 are interchanged, early on in theexecution of the algorithm, the array is now sorted, and all subsequent iterations ofthe outer loop are unnecessary.  *Write a version of Bubble Sort that avoids unnecessary comparisons*

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:

Merge sort is an efficient sorting algorithm with a time complexity of O(n log n). This means that as the number of elements (chocolates or students) increases significantly, the efficiency of merge sort remains relatively stable compared to other sorting algorithms. Merge sort achieves this efficiency by recursively dividing the input array into smaller sub-arrays, sorting them individually, and then merging them back together. The efficiency of merge sort is primarily determined by its time complexity, which is , where n is the number of elements in the array. This time complexity indicates that the time taken by merge sort grows logarithmically with the size of the input array. Therefore, even as the number of chocolates or students increases significantly, merge sort maintains its relatively efficient performance. Regarding the distribution of a given set of x to y using iterative and recursive functions, the complexity analysis depends on the specific implementation of each…

There exist sorting algorithms which can sort any list with N elements in O(N log N) time. True False

The order of inserting an element into a sorted list of size N implemented using array is 0(1) O(logN) O(N) O(N logN)

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Write an array that when sorting using Selection Sort, requires O(n^2) time complexity and only O(n) using Insertion Sort. Provide explanations for how you got your answer.

When it comes to sorting, simple algorithms have a tendency to run inO(N^2) time. Why is this and how do more advanced algorithms break this lower boundary?

Question in image. Please explain the algorithm with the answer Thank you.