Algorithm Quizz 2

What is time complexity?

• The amount of memory used by an algorithm
• The amount of time taken by an algorithm to run
• The input size of an algorithm
• The output size of an algorithm

Which of the following is not an example of asymptotic notation?

• O(n)
• Ω(n)
• Θ(n)
• A(n)

Which of the following statements about space complexity is true?

• Space complexity is the amount of memory used by an algorithm
• Space complexity is the time taken by an algorithm to run
• Space complexity is the input size of an algorithm
• Space complexity is the output size of an algorithm

What is the best case time complexity of a linear search algorithm?

• O(n)
• O(1)
• O(log n)
• O(n log n)

Which of the following is an example of worst case time complexity?

• O(n)
• O(1)
• O(log n)
• O(n log n)

Which of the following is an example of constant time complexity?

• O(1)
• O(n)
• O(log n)
• O(n log n)

Which of the following is not an asymptotic notation used to represent the upper bound of an algorithm's time complexity?

• a) O
• b) Ω
• c) Θ
• d) ω

Which of the following statements is true regarding the big-O notation?

• a) It represents the best-case time complexity of an algorithm.
• b) It represents the average-case time complexity of an algorithm.
• c) It represents the worst-case time complexity of an algorithm.
• d) It represents the exact running time of an algorithm.

Which of the following statements is true regarding the big-Ω notation?

• a) It represents the best-case time complexity of an algorithm.
• b) It represents the average-case time complexity of an algorithm.
• c) It represents the worst-case time complexity of an algorithm.
• d) It represents the lower bound of an algorithm's time complexity.

Which of the following is true regarding the big-Θ notation?

• a) It represents the best-case time complexity of an algorithm.
• b) It represents the average-case time complexity of an algorithm.
• c) It represents the worst-case time complexity of an algorithm.
• d) It represents both the upper and lower bounds of an algorithm's time complexity.