Question 1
Which of the following best describes how Big O notation characterizes the growth rate of an algorithm?
Question 2
When analyzing an algorithm, what is the primary reason to consider both time and space complexity?
Question 3
An algorithm has a time complexity of $O(n \log n)$. Which of the following operations is most likely to contribute to this complexity?
Question 4
What is the main distinction between worst-case and average-case analysis of an algorithm?
Question 5
Consider an algorithm that searches for an element in a sorted array using binary search. What is its time complexity?
