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Recent questions tagged time-complexity
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Recent questions tagged time-complexity
0
votes
1
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1
UGCNET-Oct2020-II: 25
If algorithm $A$ and another algorithm $B$ take $\log_2 (n)$ and $\sqrt{n}$ microseconds, respectively, to solve a problem, then the largest size $n$ of a problem these algorithms can solve, respectively, in one second are ______ and ______. $2^{10^n}$ and $10^6$ $2^{10^n}$ and $10^{12}$ $2^{10^n}$ and $6.10^6$ $2^{10^n}$ and $6.10^{12}$
If algorithm $A$ and another algorithm $B$ take $\log_2 (n)$ and $\sqrt{n}$ microseconds, respectively, to solve a problem, then the largest size $n$ of a problem these algorithms can solve, respectively, in one second are ______ and ______. $2^{10^n}$ and $10^6$ $2^{10^n}$ and $10^{12}$ $2^{10^n}$ and $6.10^6$ $2^{10^n}$ and $6.10^{12}$
asked
Nov 20, 2020
in
Algorithms
jothee
165
views
ugcnet-oct2020-ii
algorithms
time-complexity
0
votes
0
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2
UGCNET-Oct2020-II: 52
The running time of an algorithm is $O(g(n))$ if and only if its worst-case running time is $O(g(n))$ and its best-case running time is $\Omega(g(n)) \cdot (O= \textit{ big }O)$ its worst-case running time is $\Omega (g(n))$ and its best-case running ... $(a)$ only $(b)$ only $(c)$ only $(d)$ only
The running time of an algorithm is $O(g(n))$ if and only if its worst-case running time is $O(g(n))$ and its best-case running time is $\Omega(g(n)) \cdot (O= \textit{ big }O)$ its worst-case running time is $\Omega (g(n))$ ... , $(o = \textit{ small } o)$ Choose the correct answer from the options given below: $(a)$ only $(b)$ only $(c)$ only $(d)$ only
asked
Nov 20, 2020
in
Algorithms
jothee
77
views
ugcnet-oct2020-ii
algorithms
time-complexity
0
votes
0
answers
3
NTA NET NOV2020( Big O )
asked
Nov 17, 2020
in
CBSE/UGC NET
Sanjay Sharma
33
views
time-complexity
1
vote
1
answer
4
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, 2020
in
Algorithms
Lakshman Patel RJIT
195
views
nielit2016mar-scientistc
algorithms
time-complexity
0
votes
1
answer
5
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, 2020
in
DS
Lakshman Patel RJIT
627
views
nielit2016mar-scientistc
data-structures
binary-search
time-complexity
heap
0
votes
1
answer
6
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, 2020
in
Algorithms
Lakshman Patel RJIT
325
views
nielit2017oct-assistanta-it
algorithms
time-complexity
0
votes
1
answer
7
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, 2020
in
Algorithms
Lakshman Patel RJIT
241
views
nielit2017oct-assistanta-it
algorithms
recurrence-relations
time-complexity
master-theorem
0
votes
3
answers
8
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, 2020
in
Algorithms
Lakshman Patel RJIT
250
views
nielit2017oct-assistanta-cs
algorithms
recurrence-relations
time-complexity
master-theorem
0
votes
2
answers
9
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, 2020
in
Algorithms
Lakshman Patel RJIT
239
views
nielit2017oct-assistanta-cs
algorithms
time-complexity
0
votes
3
answers
10
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, 2020
in
Algorithms
Lakshman Patel RJIT
231
views
nielit2017oct-assistanta-cs
algorithms
time-complexity
3
votes
12
answers
11
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, 2020
in
Algorithms
Lakshman Patel RJIT
1k
views
nielit2016mar-scientistb
algorithms
sorting
time-complexity
0
votes
3
answers
12
NIELIT 2016 MAR Scientist B - Section C: 22
Time complexity of an algorithm $T(n)$, where $n$ is the input size is given by $\begin{array}{ll}T(n) & =T(n-1)+\frac{1}{n}, \text{ if }n>1\\ & =1, \text{ otherwise} \end{array}$ 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 $\begin{array}{ll}T(n) & =T(n-1)+\frac{1}{n}, \text{ if }n>1\\ & =1, \text{ otherwise} \end{array}$ The order of this algorithm is $\log n$ $n$ $n^2$ $n^n$
asked
Mar 31, 2020
in
Algorithms
Lakshman Patel RJIT
456
views
nielit2016mar-scientistb
algorithms
recurrence-relations
time-complexity
3
votes
2
answers
13
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, 2020
in
Algorithms
Lakshman Patel RJIT
392
views
nielit2016dec-scientistb-it
algorithms
spanning-tree
time-complexity
0
votes
3
answers
14
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, 2020
in
Algorithms
Lakshman Patel RJIT
1.8k
views
nielit2016dec-scientistb-cs
algorithms
time-complexity
0
votes
2
answers
15
NIELIT 2016 DEC Scientist B (CS) - Section B: 27
What is the solution to the recurrence $T(n)=T \bigg (\dfrac{n}{2} \bigg )+n$? $O(\log n)$ $O(n)$ $O(n\log n)$ None of these
What is the solution to the recurrence $T(n)=T \bigg (\dfrac{n}{2} \bigg )+n$? $O(\log n)$ $O(n)$ $O(n\log n)$ None of these
asked
Mar 31, 2020
in
Algorithms
Lakshman Patel RJIT
321
views
nielit2016dec-scientistb-cs
algorithms
recurrence-relations
time-complexity
0
votes
2
answers
16
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, 2020
in
Algorithms
Lakshman Patel RJIT
311
views
nielit2016dec-scientistb-cs
algorithms
time-complexity
0
votes
2
answers
17
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, 2020
in
Algorithms
Lakshman Patel RJIT
440
views
nielit2017july-scientistb-it
algorithms
time-complexity
asymptotic-notations
1
vote
2
answers
18
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, 2020
in
Algorithms
Lakshman Patel RJIT
272
views
nielit2017july-scientistb-cs
algorithms
time-complexity
5
votes
4
answers
19
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, 2020
in
Algorithms
Lakshman Patel RJIT
609
views
tifr2020
algorithms
asymptotic-notations
time-complexity
5
votes
2
answers
20
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, 2020
in
Algorithms
Satbir
1.3k
views
isro-2020
algorithms
time-complexity
normal
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