Design and analysis of algorithms Nptel Week 1 Quiz Answers

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Design and analysis of algorithms Week 1 Quiz Answers
Design and analysis of algorithms Week 1 Quiz Answers

Design and analysis of algorithms Week 1 Quiz Answers (Jan-Apr 2026)


Que1. An image processing application begins with two n×n matrices A and B. The first phase of preprocessing the inputs takes O(n²) steps for each of A and B. The second step involves a convolution of A and B to yield a new matrix C in time O(n³). This is followed by an edge detection phase that takes time O(n²) for matrix C. What is the most accurate and concise description of the complexity of the overall algorithm?

a) O(n²)
b) O(n³)
c) O(n² + n³)
d) O(n⁵)

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Que2. We are trying to determine the worst case time complexity of a library function that is provided to us, whose code we cannot read. We test the function by feeding large numbers of random inputs of different sizes. We find that for inputs of size 400 and 4,000, the function always returns well within one second, but for inputs of size 40,000 it sometimes takes a couple of seconds and for inputs of size 400,000 it sometimes takes a few minutes. What is a reasonable conclusion we can draw about the worst case time complexity of the library function? (You can assume that a typical desktop PC performs 10⁹ basic operations per second.)

a) O(n log n)
b) O(n²)
c) O(n³)
d) O(n³ log n)

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Que3. Suppose f(n) is 252n³ + 164n² + 507 and g(n) is n⁴ + 5n + 12. Let h(n) be a third, unknown function. Which of the following is not possible?

a) h(n) is O(f(n)) and h(n) is also O(g(n))
b) h(n) is O(f(n)) but h(n) is not O(g(n))
c) h(n) is O(g(n)) but h(n) is not O(f(n))
d) h(n) is not O(f(n)) and h(n) is also not O(g(n))

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Que4. How many times is the comparison i >= n performed in the following program?

int i = 300, n = 150;
main(){
  while (i >= n){
    i = i - 2;
    n = n + 1; 
  }
}

a) 50
b) 51
c) 52
d) 53

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Que5. If T(n) is O(n²√n), which of the following is false?

a) T(n) is O(n² log n)
b) T(n) is O(n³)
c) T(n) is O(n³ log n)
d) T(n) is O(n⁴)

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Design and analysis of algorithms Week 1 Quiz Answers (July-Dec 2025)


Que.1 What does f(2000, 3) return?

f(m, n) {
  ans = 1;
  count = 0;
  while (ans <= m) {
    count = count + 1;
    ans = ans * n;
  }
  return(count)
}

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Que.2 Suppose someone designs a new airline routing algorithm called MagicPath and claims that its worst-case complexity is O(n² log n). Which of the following statements is inconsistent with this claim?
A) For every n, for every input of size n, MagicPath is able to solve the problem in time proportional to n².
B) For some n, for every input of size n, MagicPath is able to solve the problem in time proportional to n².
C) For every sufficiently large n, there is an input of size n for which MagicPath requires time proportional to n².
D) For every sufficiently large n, there is an input of size n for which MagicPath requires time proportional to n³.

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Que.3 You are executing an algorithm with worst-case time complexity O(n⁴) on a CPU that can perform 10⁸ operations per second. Which of the following is the most accurate guarantee for the time required to solve a worst-case input of size 800?
A) Under 5 minutes
B) Under 5 hours
C) Under 5 days
D) Under 5 weeks

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Que.4 Suppose f(n) is n² log n. Consider the following statements:
(A) f(n) is O(n √n)
(B) f(n) is O(n² √n)
(C) f(n) is O(n³)

Which of the following is true?
A) (A), (B), and (C) are all not true.
B) (B) and (C) are true but (A) is not true.
C) (B) is true but (A) and (C) are not true.
D) (A) and (B) are true but (C) is not true.

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Que.5 In the code fragment below, first and last are integer values and composite(x) is a function that returns true if x is not a prime number and false otherwise:

i = 0; j = 0; k = 0;
for (m = last; m >= first; m = m - 1){
  k = k + m;
  if (composite(m)){
    i = i + m;
  } else {
    j = j - m;
  }
}

if (…) {
  print("True");
} else {
  print("False");
}

Which of the following expressions can we put in place of the missing if condition (…) to ensure that the program prints “True”?
A) k == i + j
B) k == i – j
C) k == j – i
D) None of the other options is universally true. The expression depends on the values of first and last.

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Design and analysis of algorithms Week 1 Quiz Answers (Jan-Apr 2025)

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Que. 1) An algorithm has two phases. The first phase, initialization, takes time O(n3)O(n^3). The second phase, which is the main computation, takes time O(n2)O(n^2). What is the most accurate description of the complexity of the overall algorithm?

a) O(n6)O(n^6)
b) O(n5)O(n^5)
c) O(n3)O(n^3)
d) O(n2)O(n^2)

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These are Design and analysis of algorithms Week 1 Quiz Answers


Que. 2) We are using a computer that performs 10810^8 basic operations per second. We are trying to determine the worst-case time complexity of a library function that is provided to us, whose code we cannot read. We test the function by feeding large numbers of random inputs of different sizes. We find that for inputs of size 50 the function always returns well within one second, for inputs of size 500 it sometimes takes a couple of seconds, and for inputs of size 5,000 it sometimes takes over 15 minutes. What is a reasonable conclusion we can draw about the worst-case time complexity of the library function?

a) O(n4)O(n^4)
b) O(n3)O(n^3)
c) O(n2log⁡n)O(n^2 \log n)
d) O(n2)O(n^2)

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Que. 3) Suppose f(n)=2n2+4n+5f(n) = 2n^2 + 4n + 5 and g(n)=7n3+5n2+12g(n) = 7n^3 + 5n^2 + 12. Let h(n)h(n) be a third, unknown function. Which of the following is not possible?

a) h(n)h(n) is O(f(n))O(f(n)) and h(n)h(n) is also O(g(n))O(g(n))
b) h(n)h(n) is not O(f(n))O(f(n)) and h(n)h(n) is also not O(g(n))O(g(n))
c) h(n)h(n) is O(f(n))O(f(n)) but h(n)h(n) is not O(g(n))O(g(n))
d) h(n)h(n) is O(g(n))O(g(n)) but h(n)h(n) is not O(f(n))O(f(n))

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Que. 4) How many times is the comparison i≤ni \leq n performed in the following program?

int i = 60, n = 300;
main(){
  while (i <= n){
    i = i + 2;
    n = n - 3; 
  }
}

a) 48
b) 49
c) 50
d) 51

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Que. 5) If T(n)T(n) is O(n4/3)O(n^{4/3}), which of the following is false?

a) T(n)T(n) is O(nlog⁡n)O(n \log n)
b) T(n)T(n) is O(n2)O(n^2)
c) T(n)T(n) is O(n2log⁡n)O(n^2 \log n)
d) T(n)T(n) is O(n3)O(n^3)

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