DAY 32 - Complexity Analysis & Big O Notations-23rd July 24

What is the primary purpose of complexity analysis in computer science?

• To determine the syntax of an algorithm

• To understand the efficiency of an algorithm

• To debug code

• To design user interfaces

Which notation is used to describe the upper bound of an algorithm's complexity?

• Big Omega notation

• Big Theta notation

• Big O notation

• Little o notation

What does O(1) represent in time complexity?

• Constant time

• Logarithmic time

• Linear time

• Exponential time

Which of the following time complexities is commonly seen in efficient sorting algorithms?

• O(n)

• O(n^2)

• O(log n)

• O(n log n)

What is the time complexity of a binary search algorithm?

• O(n)

• O(log n)

• O(n^2)

• O(1)

In Big O notation, what happens to constants?

• They are multiplied

• They are ignored

• They are added

• They are squared

What is the space complexity of an algorithm that uses a fixed amount of space regardless of input size?

• O(n)

• O(log n)

• O(1)

• O(n^2)

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

• Binary search

• Linear search

• Merge sort

• Recursive Fibonacci

When analyzing algorithms, what should you focus on when there are multiple terms?

• The constant term

• The logarithmic term

• The dominant term

• The smallest term

What is the time complexity of a simple linear search algorithm?

• O(1)

• O(log n)

• O(n)

• O(n^2)