Sorting

Question 1
The worst case running times of Insertion sort, Merge sort and Quick sort, respectively, are:
A
Θ(nlogn),Θ(nlogn),and Θ(n2)
B
Θ(n2 ),Θ(n2 ),and Θ(nlogn)
C
Θ(n2),Θ(nlogn),and Θ(nlogn)
D
Θ(n2),Θ(nlogn),and Θ(n2)
       Algorithms       Sorting       2016 set-01
Question 1 Explanation: 
Question 2
   
A
I and II only
B
I and III only
C
II and IV only
D
I and IV only
       Algorithms       Sorting       GATE 2016 set-2
Question 2 Explanation: 
If input sequence is already sorted then the time complexity of Quick sort will take O(n2) and Bubble sort will take O(n) and Merge sort will takes O(nlogn) and insertion sort will takes O(n).
→ The recurrence relation for Quicksort, if elements are already sorted, T(n) = T(n-1)+O(n) with the help of substitution method it will take O(n2).
→ The recurrence relation for Merge sort, is elements are already sorted,T(n) = 2T(n/2) + O(n) with the help of substitution method it will take O(nlogn). We can also use master's theorem [a=2,b=2,k=1,p=0 ] for above recurrence.
Question 3
You have an array of n elements. Suppose you implement quicksort by always choosing the central element of the array as the pivot. Then the tightest upper bound for the worst case performance is
A
O(n2)
B
O(n log n)
C
Θ(n log⁡n)
D
O(n3)
       Algorithms       Sorting       Gate 2014 Set -03
Question 3 Explanation: 
The Worst case time complexity of quick sort is O (n2). This will happen when the elements of the input array are already in order (ascending or descending), irrespective of position of pivot element in array.
Question 4
Which one of the following is the tightest upper bound that represents the number of swaps required to sort n numbers using selection sort?
A
O(log n)
B
O(n)
C
O(n log n)
D
O(n2)
       Algorithms       Sorting       Gate 2013
Question 4 Explanation: 
Best, Average and worst case will take maximum O(n) swaps.
Selection sort time complexity O(n2) in terms of number of comparisons. Each of these scans requires one swap for n-1 elements (the final element is already in place).
Question 5
Which one of the following is the tightest upper bound that represents the time complexity of inserting an object into a binary search tree of n nodes?
A
O(1)
B
O(log n)
C
O(n)
D
O(n log n)
       Algorithms       Sorting       Gate 2013
Question 5 Explanation: 
For skewed binary search tree on n nodes, the tightest upper bound to insert a node is O(n).
Question 6
What is the time complexity of Bellman-Ford single-source shortest path algorithm on a complete graph of n vertices?
A
Θ(n2)
B
Θ(n2 log n)
C
Θ(n3)
D
Θ(n3 log n)
       Algorithms       Sorting       Gate 2013
Question 6 Explanation: 
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is capable of handling graphs in which some of the edge weights are negative numbers.
Bellman–Ford runs in O(|V| * |E|)} time, where |V| and |E| are the number of vertices and edges respectively.
Note: For complete graph: |E|=n(n-1)/2=(n3)
Question 7
The number of elements that can be sorted in Θ(log n) time using heap sort is
A
Θ(1)
B
Θ(√log⁡n)
C
Θ (log⁡n/log⁡log⁡n)
D
Θ(log n)
       Algorithms       Sorting       Gate 2013
Question 7 Explanation: 
Using heap sort to n elements it will take O(n log n) time. Assume there are log n/ log log n elements in the heap.
So, Θ((logn/ log log n)log(logn/log log n))
= Θ(logn/log logn (log logn - log log logn))
= Θ((log n/log logn) × log log n)
= Θ (log n)
Hence, option (C) is correct answer.
Question 8
What is the number of swaps required to sort n elements using selection sort, in the worst case?
A
θ(n)
B
θ(n log n)
C
θ(n2)
D
θ(n2 logn)
       Algorithms        Sorting       2009
Question 8 Explanation: 
Selection sort – There is no Worst case input for selection sort. Since it searches for the index of kth minimum element in kth iteration and then in one swap, it places that element into its correct position. For n-1 iterations of selection sort, it can have O(n) swaps. Selection Sort does a significant number of comparisons before moving each element directly to its final intended position. At most the algorithm requires N swaps. once we swap an element into place, you never go back again.So it is great for writes O(n) but not so great (at all) for reads — O(n2). It isn’t well-suited to generalized sorting, but might work well in specialized situations like EEPROM (where writes are inordinately expensive).
Question 9
In quick sort, for sorting n elements, the (n/4)th smallest element is selected as pivot using an O(n) time algorithm. What is the worst case time complexity of the quick sort? 
A
θ(n)
B
θ(n log n)
C
θ(n2)
D
θ(n2 log n )
       Algorithms        Sorting       2009
Question 9 Explanation: 

n→n/(4/3)→n/(4/3)2→n/(4/3)3-----n/(4/3)k=1
n/(4/3)k=1
⇒n=(4/3)k⇒k=log4/3n [k=no. of levels]
In each level workdone = Cn
So, total workdone = Cn⋅log4/3n=(nlog4/3n)
Question 10
Consider the Quicksort algorithm. Suppose there is a procedure for finding a pivot element which splits the list into two sub-lists each of which contains at least one-fifth of the elements. Let T(n) be the number of comparisons required to sort n elements. Then
A
T(n) ≤ 2T(n/5) + n
B
T(n) ≤ T(n/5) + T(4n/5) + n
C
T(n) ≤ 2T(4n/5) + n
D
T(n) ≤ 2T(n/2) + n
       Algorithms       Sorting       Gate-2008
Question 10 Explanation: 
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.
Question 11
If we use Radix Sort to sort n integers in the range [nk/2, nk], for some k>0 which is independent of n, the time taken would be?
A
Θ(n)
B
Θ(kn)
C
Θ(nlogn)
D
Θ(n2)
       Algorithms        Sorting       Gate 2008-IT
Question 11 Explanation: 
Question 12
Which one of the following in place sorting algorithms needs the minimum number of swaps?
A
Quick sort
B
Insertion sort
C
Selection sort
D
Heap sort
       Algorithms        Sorting       Gate-2006
Question 12 Explanation: 
Selection sort requires minimum number of swaps i.e O(n). The algorithm finds the minimum value, swaps it with the value in the first position, and repeats these steps for the remainder of the list. It does no more than n swaps, and thus is useful where swapping is very expensive.
Question 13
 
A
only I and II
B
only I and IV
C
only II and III
D
only III and IV
       Programming       Sorting       Gate 2005-IT
Question 13 Explanation: 
a[i] ≥ b[i]
Since both 'a' and 'b' are sorted in the beginning, there are 'i' elements than or equal to a[i] and similarly 'i' elements smaller than or equal to b[i]. So, a[i] ≥ b[i] means there are 2i elements smaller than or equal to a[i] and hence in the merged array, a[i] will come after these 2i elements. So, c[2i] ≤ a[i].
Similarly, a[i] ≥ b[i] says for b that, there are not more than 2i elements smaller than b[i] in the sorted array. So, b[i] ≤ c[2i].
So, option (C) is correct.
Question 14
Let A be a sequence of 8 distinct integers sorted in ascending order. How many distinct pairs of sequences, B and C are there such that (i) each is sorted in ascending order, (ii) B has 5 and C has 3 elements, and (iii) the result of merging B and C gives A?  
A
2
B
30
C
56
D
256
       Algorithms        Sorting       Gate-2003
Question 14 Explanation: 
A can have sequence of 8 distinct integers which are sorted in ascending order.
→ If we are pick 3 elements from 8 sequence integers then remaining 5 elements are already in ascending order. After merging these elements then it gives A.
→ No. of possibilities of choosing 8 elements from total of 8 = 8C3
= 8!/3!5!
= 8 * 7
= 56
Question 15

A
B
C
D
       Algorithms        Sorting       Gate-2003
Question 15 Explanation: 
Probability of inverse (ai, aj(i Probability of expected no. of inversions = (1/2) × (n(n-1)/2) = n(n-1)/4
Question 16
 
A
Θ(n2)
B
Θ(n log n)
C
Θ(n1.5)
D
Θ(n)
       Algorithms        Sorting       Gate-2003
Question 16 Explanation: 
Here the inputs are to be restricted to 1...n with atmost 'n' inversions. Then the worst case time complexity of inversion sort reduces to Θ(n).
Question 17

Let s be a sorted array of n integers. Let t(n) denote the time taken for the most efficient algorithm to determined if there are two elements with sum less than 1000 in s. Which of the following statements is true?

 
A
t(n) is O(1)
B
n ≤ t(n) ≤ n log2 n
C
D
       Algorithms        Sorting       Gate-2000
Question 17 Explanation: 
Since the array is sorted. Now just pick the first two minimum elements and check if their sum is less than 1000 or not. If it is less than 1000 then we found it and if not then it is not possible to get the two elements whose sum is less than 1000. Hence, it takes constant time. So, correct option is (A).
Question 18
A sorting technique is called stable if
A
it takes O (nlog n) time
B
it maintains the relative order of occurrence of non-distinct elements
C
it uses divide and conquer paradigm
D
it takes O(n) space
       Algorithms        Sorting       Gate-1999
Question 18 Explanation: 
Sorting techniques are said to be stable if it maintains the relative order of occurence of non-distinct element.
Question 19
     
A
8, 9, 15, 20, 47, 4, 12, 17, 30, 40
B
8, 15, 20, 47, 4, 9, 30, 40, 12, 17
C
15, 20, 47, 4, 8, 9, 12, 30, 40, 17
D
4, 8, 9, 15, 20, 47, 12, 17, 30, 40
       Algorithms        Sorting       Gate-1999
Question 19 Explanation: 
Question 20
Merge sort uses
A
Divide and conquer strategy
B
Backtracking approach
C
Heuristic search
D
Heuristic search
       Algorithms       Sorting       Gate-1995
Question 20 Explanation: 
Merge sort uses the divide and conquer strategy.
Question 21
For merging two sorted lists of sizes m and n into a sorted list of size m+n, we required comparisons of
A
O(m)
B
O(n)
C
O(m+n)
D
O(logm+logn)
       Algorithms       Sorting       Gate-1995
Question 21 Explanation: 
In best case, no. of comparisons is Min(m,n).
In worst case, no. of comparisons is m+n-1.
Then we require O(m+n) comparisons to merging two sorted lists.
Question 22
Following algorithm(s) can be used to sort n integers in the range [1...n3] in O(n) time  
A
Heapsort
B
Quicksort
C
Mergesort
D
Radixsort
       Algorithms       Sorting       Gate-1992
Question 22 Explanation: 
As no comparison based sort can ever do any better than nlogn. So option (A), (B), (C) are eliminated. O(nlogn) is lower bound for comparison based sorting.
As Radix sort is not comparison based sort (it is counting sort), so can be done in linear time.
Question 23
The minimum number of comparisons required to sort 5 elements is _____  
A
7
       Algorithms       Sorting       Gate-1991
Question 23 Explanation: 
Minimum no. of comparisons
= ⌈log(n!)⌉
= ⌈log(5!)⌉
= ⌈log(120)⌉
= 7
Question 24

Randomized quicksort is an extension of quicksort where the pivot is chosen randomly. What is the worst case complexity of sorting n numbers using randomized quicksort?

A
O(n)
B
O(n log n)
C
O(n2)
D
O(n!)
       Algorithms        Sorting       Gate-2001
Question 24 Explanation: 
In worst case Randomized quicksort execution time complexity is same as quicksort.
Question 25
Quicksort is ________ efficient than heapsort in the worst case.
A
LESS.
       Algorithms        Sorting       Gate-1988
Question 25 Explanation: 
As worst case time for quicksort is O(n2) and worst case for heap sort is O(n logn).
Question 26
Let P be a quicksort program to sort numbers in ascending order. Let t1 and t2 be the time taken by the program for the inputs [1 2 3 4] and [5 4 3 2 1] respectively. Which of the following holds?
A
t1 = t2
B
t1 > t2
C
t1 < t2
D
t1 = t2 + 5 log 5
       Algorithms        Sorting       GATE-1987
Question 26 Explanation: 
Since both are in sorted order and no. of elements in second list is greater.
There are 26 questions to complete.