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Recent questions tagged time-complexity

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Recent questions tagged time-complexity

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1 answer
1
NIELIT 2016 MAR Scientist C - Section C: 51
The most efficient algorithm for finding the number of connected components in a $n$ undirected graph on $n$ vertices and $m$ edges has time complexity $\Theta (n)$ $\Theta (m)$ $\Theta (m+n)$ $\Theta (mn)$
The most efficient algorithm for finding the number of connected components in a $n$ undirected graph on $n$ vertices and $m$ edges has time complexity $\Theta (n)$ $\Theta (m)$ $\Theta (m+n)$ $\Theta (mn)$
asked Apr 2 in Algorithms Lakshman Patel RJIT 68 views
0 votes
1 answer
2
NIELIT 2016 MAR Scientist C - Section C: 52
Consider the process of inserting an element into a $Max\ Heap$, where the $Max\ Heap$ is represented by an $array$. Suppose we perform a binary search on the path from the new leaf to the root to find the position for the newly inserted element, the number of $comparisons$ ... $\Theta(n\log _{2} \log_2 n)$ $\Theta (n)$ $\Theta(n\log _{2}n)$
Consider the process of inserting an element into a $Max\ Heap$, where the $Max\ Heap$ is represented by an $array$. Suppose we perform a binary search on the path from the new leaf to the root to find the position for the newly inserted element, the number of $comparisons$ performed is $\Theta(\log _{2}n)$ $\Theta(n\log _{2} \log_2 n)$ $\Theta (n)$ $\Theta(n\log _{2}n)$
asked Apr 2 in DS Lakshman Patel RJIT 448 views
0 votes
1 answer
3
NIELIT 2017 OCT Scientific Assistant A (IT) - Section C: 2
An algorithm is made up pf two modules $M1$ and $M2.$ If order of $M1$ is $f(n)$ and $M2$ is $g(n)$ then the order of algorithm is $max(f(n),g(n))$ $min(f(n),g(n))$ $f(n) + g(n)$ $f(n) \times g(n)$
An algorithm is made up pf two modules $M1$ and $M2.$ If order of $M1$ is $f(n)$ and $M2$ is $g(n)$ then the order of algorithm is $max(f(n),g(n))$ $min(f(n),g(n))$ $f(n) + g(n)$ $f(n) \times g(n)$
asked Apr 1 in Algorithms Lakshman Patel RJIT 252 views
0 votes
1 answer
4
NIELIT 2017 OCT Scientific Assistant A (IT) - Section B: 14
The running time of an algorithm $T(n),$ where $’n’$ is the input size , is given by $T(n) = 8T(n/2) + qn,$ if $n>1$ $= p,$ if $n = 1$ Where $p,q$ are constants. The order of this algorithm is $n^{2}$ $n^{n}$ $n^{3}$ $n$
The running time of an algorithm $T(n),$ where $’n’$ is the input size , is given by $T(n) = 8T(n/2) + qn,$ if $n>1$ $= p,$ if $n = 1$ Where $p,q$ are constants. The order of this algorithm is $n^{2}$ $n^{n}$ $n^{3}$ $n$
asked Apr 1 in Algorithms Lakshman Patel RJIT 159 views
0 votes
3 answers
5
NIELIT 2017 OCT Scientific Assistant A (CS) - Section C: 4
The running time of an algorithm $T(n),$ where $’n’$ is the input size , is given by $T(n) = 8T(n/2) + qn,$ if $n>1$ $ = p,$ if $n = 1$ Where $p,q$ are constants. The order of this algorithm is $n^{2}$ $n^{n}$ $n^{3}$ $n$
The running time of an algorithm $T(n),$ where $’n’$ is the input size , is given by $T(n) = 8T(n/2) + qn,$ if $n>1$ $ = p,$ if $n = 1$ Where $p,q$ are constants. The order of this algorithm is $n^{2}$ $n^{n}$ $n^{3}$ $n$
asked Apr 1 in Algorithms Lakshman Patel RJIT 158 views
0 votes
2 answers
6
NIELIT 2017 OCT Scientific Assistant A (CS) - Section B: 8
Consider the following C code segment: int Ls Prime(n) { int i,n; for(i=2;i<=sqrt(n);i++) if(n%i ==0) { printf( NOT Prime.\n ); return 0; } return 1; } Let $T(n)$ denote the number of times the for loop is executed by the program on input $n.$ ... $T(n) = \Omega (1)$ $T(n) = O(n)$ and $T(n) = \Omega (\sqrt{n})$ None of these
Consider the following C code segment: int Ls Prime(n) { int i,n; for(i=2;i<=sqrt(n);i++) if(n%i ==0) { printf( NOT Prime.\n ); return 0; } return 1; } Let $T(n)$ denote the number of times the for loop is executed by the program on input $n.$ ... $T(n) = O(\sqrt{n})$ and $T(n) = \Omega (1)$ $T(n) = O(n)$ and $T(n) = \Omega (\sqrt{n})$ None of these
asked Apr 1 in Algorithms Lakshman Patel RJIT 146 views
0 votes
3 answers
7
NIELIT 2017 OCT Scientific Assistant A (CS) - Section B: 30
An algorithm is made up of two modules $M1$ and $M2.$ If order of $M1$ is $f(n)$ and $M2$ is $g(n)$ then he order of algorithm is $max(f(n),g(n))$ $min(f(n),g(n))$ $f(n) + g(n)$ $f(n) \times g(n)$
An algorithm is made up of two modules $M1$ and $M2.$ If order of $M1$ is $f(n)$ and $M2$ is $g(n)$ then he order of algorithm is $max(f(n),g(n))$ $min(f(n),g(n))$ $f(n) + g(n)$ $f(n) \times g(n)$
asked Apr 1 in Algorithms Lakshman Patel RJIT 120 views
3 votes
12 answers
8
NIELIT 2016 MAR Scientist B - Section C: 12
Which of the following sorting algorithms does not have a worst case running time of $O(n​^2​)$? Insertion sort. Merge sort. Quick sort. Bubble sort.
Which of the following sorting algorithms does not have a worst case running time of $O(n​^2​)$? Insertion sort. Merge sort. Quick sort. Bubble sort.
asked Mar 31 in Algorithms Lakshman Patel RJIT 627 views
0 votes
3 answers
9
NIELIT 2016 MAR Scientist B - Section C: 22
Time complexity of an algorithm $T(n)$, where $n$ is the input size is given by $\begin{array}{ll}T(n) & =T(n-1)+1/n, \text{ if }n>1\\ & =1, \text{ otherwise} \end{array}$ The order of this algorithm is $\log n$ $n$ $n^2$ $n^n$
Time complexity of an algorithm $T(n)$, where $n$ is the input size is given by $\begin{array}{ll}T(n) & =T(n-1)+1/n, \text{ if }n>1\\ & =1, \text{ otherwise} \end{array}$ The order of this algorithm is $\log n$ $n$ $n^2$ $n^n$
asked Mar 31 in Algorithms Lakshman Patel RJIT 301 views
1 vote
2 answers
10
NIELIT 2016 DEC Scientist B (IT) - Section B: 27
Complexity of Kruskal's algorithm for finding minimum spanning tree of an undirected graph containing $n$ vertices and $m$ edges if the edges are sorted is: $O(mn)$ $O(m)$ $O(m+n)$ $O(n)$
Complexity of Kruskal's algorithm for finding minimum spanning tree of an undirected graph containing $n$ vertices and $m$ edges if the edges are sorted is: $O(mn)$ $O(m)$ $O(m+n)$ $O(n)$
asked Mar 31 in Algorithms Lakshman Patel RJIT 217 views
0 votes
3 answers
11
NIELIT 2016 DEC Scientist B (CS) - Section B: 16
Two main measures for the efficiency of an algorithm are: Processor and Memory Complexity and Capacity Time and Space Data and Space
Two main measures for the efficiency of an algorithm are: Processor and Memory Complexity and Capacity Time and Space Data and Space
asked Mar 31 in Algorithms Lakshman Patel RJIT 368 views
0 votes
2 answers
13
NIELIT 2016 DEC Scientist B (CS) - Section B: 52
The concept of order Big O is important because It can be used to decide the best algorithm that solves a given problem It is the lower bound of the growth rate of algorithm It determines the maximum size of a problem that can be solved in a given amount of time Both (A) and (B)
The concept of order Big O is important because It can be used to decide the best algorithm that solves a given problem It is the lower bound of the growth rate of algorithm It determines the maximum size of a problem that can be solved in a given amount of time Both (A) and (B)
asked Mar 31 in Algorithms Lakshman Patel RJIT 166 views
0 votes
2 answers
14
NIELIT 2017 July Scientist B (IT) - Section B: 59
What is the running time of the following function (specified as a function of the input value)? void Function(int n){ int i=1; int s=1; while(s<=n){ i++; s=s+i; } } $O(n)$ $O(n^2)$ $O(1)$ $O(\sqrt n)$
What is the running time of the following function (specified as a function of the input value)? void Function(int n){ int i=1; int s=1; while(s<=n){ i++; s=s+i; } } $O(n)$ $O(n^2)$ $O(1)$ $O(\sqrt n)$
asked Mar 30 in Algorithms Lakshman Patel RJIT 309 views
0 votes
2 answers
15
NIELIT 2017 July Scientist B (CS) - Section B: 9
The worst case running times of Insertion sort, Merge sort and Quick sort, respectively, are $\Theta(n \log n),\Theta(n \log n) \text{ and } \Theta(n^2)$ $\Theta(n^2),\Theta(n^2)\text{ and } \Theta(n \log n)$ $\Theta(n^2), \Theta(n \log n)\text{ and } \Theta(n \log n)$ $\Theta(n^2),\Theta(n\log n) \text{ and } \Theta(n^2)$
The worst case running times of Insertion sort, Merge sort and Quick sort, respectively, are $\Theta(n \log n),\Theta(n \log n) \text{ and } \Theta(n^2)$ $\Theta(n^2),\Theta(n^2)\text{ and } \Theta(n \log n)$ $\Theta(n^2), \Theta(n \log n)\text{ and } \Theta(n \log n)$ $\Theta(n^2),\Theta(n\log n) \text{ and } \Theta(n^2)$
asked Mar 30 in Algorithms Lakshman Patel RJIT 158 views
4 votes
4 answers
16
TIFR2020-B-10
Among the following asymptotic expressions, which of these functions grows the slowest (as a function of $n$) asymptotically? $2^{\log n}$ $n^{10}$ $(\sqrt{\log n})^{\log ^{2} n}$ $(\log n)^{\sqrt{\log n}}$ $2^{2^{\sqrt{\log\log n}}}$
Among the following asymptotic expressions, which of these functions grows the slowest (as a function of $n$) asymptotically? $2^{\log n}$ $n^{10}$ $(\sqrt{\log n})^{\log ^{2} n}$ $(\log n)^{\sqrt{\log n}}$ $2^{2^{\sqrt{\log\log n}}}$
asked Feb 11 in Algorithms Lakshman Patel RJIT 526 views
5 votes
2 answers
17
ISRO2020-36
What is the complexity of the following code? sum=0; for(i=1;i<=n;i*=2) for(j=1;j<=n;j++) sum++; Which of the following is not a valid string? $O(n^2)$ $O(n\log\ n)$ $O(n)$ $O(n\log\ n\log\ n)$
What is the complexity of the following code? sum=0; for(i=1;i<=n;i*=2) for(j=1;j<=n;j++) sum++; Which of the following is not a valid string? $O(n^2)$ $O(n\log\ n)$ $O(n)$ $O(n\log\ n\log\ n)$
asked Jan 13 in Algorithms Satbir 992 views
1 vote
1 answer
18
IIIT BLR TEST 1 : ALGORITHMS 3
Given an array of ( both positive and negative ) integers, $a_0,a_1,….a_{n-1}$ and $l, 1<l<n$. Design a linear time algorithm to compute the maximum product subarray, whose length is atmost $l$.
Given an array of ( both positive and negative ) integers, $a_0,a_1,….a_{n-1}$ and $l, 1<l<n$. Design a linear time algorithm to compute the maximum product subarray, whose length is atmost $l$.
asked Aug 27, 2019 in Algorithms Shaik Masthan 485 views
0 votes
1 answer
19
IIIT BLR TEST 1 : ALGORITHMS 1
​​​​​​Solve the following recursions ( in terms of Θ ). T(0) = T(1) = Θ(1) in all of the following. $T(n) = n + \frac{1}{n}\sum_{i=0}^{i=n-1}T(i)$ $T(n) = n + \frac{2}{n}\sum_{i=0}^{i=n-1}T(i)$ $T(n) = n + \frac{4}{n}\sum_{i=0}^{i=n/2}T(i)$ $T(n) = n + \frac{40}{n}\sum_{i=0}^{i=n/5}T(i)$
​​​​​​Solve the following recursions ( in terms of Θ ). T(0) = T(1) = Θ(1) in all of the following. $T(n) = n + \frac{1}{n}\sum_{i=0}^{i=n-1}T(i)$ $T(n) = n + \frac{2}{n}\sum_{i=0}^{i=n-1}T(i)$ $T(n) = n + \frac{4}{n}\sum_{i=0}^{i=n/2}T(i)$ $T(n) = n + \frac{40}{n}\sum_{i=0}^{i=n/5}T(i)$
asked Aug 27, 2019 in Algorithms Shaik Masthan 331 views
0 votes
1 answer
20
Cormen Edition 3 Exercise 7.4 Question 4 (Page No. 184)
Show that RANDOMIZED-QUICKSORT’s expected running time is $\Omega(n\ lg\ n)$.
Show that RANDOMIZED-QUICKSORT’s expected running time is $\Omega(n\ lg\ n)$.
asked Jun 28, 2019 in Algorithms akash.dinkar12 160 views
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