Test: Sorting- 2 - Computer Science Engineering (CSE) MCQ

Time complexity of Heap Sort is for m input elements. For m = , the value of will be  which is

See Radix Sort for explanation.

Answer(B) The recursion expression becomes: T(n) = T(n/4) + T(3n/4) + cn After solving the above recursion, we get heta(nLogn).

For the case where n/5 elements are in one subset, T(n/5) comparisons are needed for the first subset with n/5 elements, T(4n/5) is for the rest 4n/5 elements, and n is for finding the pivot. If there are more than n/5 elements in one set then other set will have less than 4n/5 elements and time complexity will be less than T(n/5) + T(4n/5) + n because recursion tree will be more balanced.

When first element or last element is chosen as pivot, Quick Sort's worst case occurs for the sorted arrays. In every step of quick sort, numbers are divided as per the following recurrence. T(n) = T(n-1) + O(n)

The central element may always be an extreme element, therefore time complexity in worst case becomes O(n²)

Insertion sort runs in Θ(n + f(n)) time, where f(n) denotes the number of inversion initially present in the array being sorted.

Randomized quicksort has expected time complexity as O(nLogn), but worst case time complexity remains same. In worst case the randomized function can pick the index of corner element every time.

The 4th optimization is generally not used, it reduces the worst case time complexity to O(nLogn), but the hidden constants are very high.

In worst case, the chosen pivot is always placed at a corner position and recursive call is made for following. a) for subarray on left of pivot which is of size n-1 in worst case. b) for subarray on right of pivot which is of size 0 in worst case.

Time complexity of merge sort is Θ(nLogn)

c*64Log64 is 30
c*64*6 is 30
c is 5/64

For time 6 minutes
5/64*nLogn = 6*60
nLogn = 72*64 = 512 * 9
n = 512.

• Insertion Sort takes Θ(n²) in worst case as we need to run two loops. The outer loop is needed to one by one pick an element to be inserted at right position. Inner loop is used for two things, to find position of the element to be inserted and moving all sorted greater elements one position ahead. Therefore the worst case recursive formula is T(n) = T(n-1) + Θ(n).
• Merge Sort takes Θ(n Log n) time in all cases. We always divide array in two halves, sort the two halves and merge them. The recursive formula is T(n) = 2T(n/2) + Θ(n).
• QuickSort takes Θ(n²) in worst case. In QuickSort, we take an element as pivot and partition the array around it. In worst case, the picked element is always a corner element and recursive formula becomes T(n) = T(n-1) + Θ(n). An example scenario when worst case happens is, arrays is sorted and our code always picks a corner element as pivot.

I. Given an array in ascending order, Recurrence relation for total number of comparisons for quicksort will be T(n) = T(n-1)+O(n) //partition algo will take O(n) comparisons in any case. = O(n^2)
II. Bubble Sort runs in Θ(n^2) time If an array is in ascending order, we could make a small modification in Bubble Sort Inner for loop which is responsible for bubbling the kth largest element to the end in kth iteration. Whenever there is no swap after the completion of inner for loop of bubble sort in any iteration, we can declare that array is sorted in case of Bubble Sort taking O(n) time in Best Case.
III. Merge Sort runs in Θ(n) time Merge Sort relies on Divide and Conquer paradigm to sort an array and there is no such worst or best case input for merge sort. For any sequence, Time complexity will be given by following recurrence relation, T(n) = 2T(n/2) + Θ(n) // In-Place Merge algorithm will take Θ(n) due to copying an entire array. = Θ(nlogn)
IV. Insertion sort runs in Θ(n) time Whenever a new element which will be greater than all the elements of the intermediate sorted sub-array ( because given array is sorted) is added, there won't be any swap but a single comparison. In n-1 passes we will be having 0 swaps and n-1 comparisons. Total time complexity = O(n) // N-1 Comparisons

/// For an array already sorted in ascending order, Quicksort has a complexity Θ(n²) [Worst Case] Bubblesort has a complexity Θ(n) [Best Case] Mergesort has a complexity Θ(n log n) [Any Case] Insertsort has a complexity Θ(n) [Best Case]

Radix sort time complexity = O(wn)
for n keys of word size= w 
=>w = log(n^k) 
O(wn)=O(klogn.n) 
=> kO(nlogn)

In the insertion sort , just imagine that the first element is already sorted and all the right side Elements are unsorted, we need to insert all elements one by one from left to right in the sorted Array. Sorted : 5
unsorted : 4 3 2 1 Insert all elements less than 5 on the left (Considering 5 as the key ) Now key value is 4 and array will look like this Sorted : 45 unsorted : 3 2 1 Similarly for all the cases the key will always be the newly inserted value and all the values will be compared to that key and inserted in to proper position

In best case,

Quick sort: O (nlogn)
Merge sort: O (nlogn)
Insertion sort: O (n)
Selection sort: O (n^2)

Time taken by algorithm is good for small number of elements, but increases quadratically for large number of elements.

Sort array={1,3,4,5,9} and total cost=50 Rupees

the cost associated with each sort is 25 rupees, what is the total cost of the insertion sort when element 1 reaches the first position of the array

• Two comparisons are all that are needed when element 1 reaches the top of the array, therefore

=25 * 2

=50 rupees.

the array A[]= {5,4,9,1,3} apply the insertion sort to sort the array .:

• The straightforward sorting algorithm known as insertion sort functions similarly to how you would arrange playing cards in your hands. In a sense, the array is divided into sorted and unsorted parts. Values are chosen and assigned to the appropriate positions in the sorted part of the data from the unsorted part.

Insertion Sort:

Step1: {5,4,9,1,3}

Step2:{1,3,4,5,9}

Hence, Sort array={1,3,4,5,9} and total cost=50 Rupees