Data Structures and Algorithms The Complete Masterclass - Big O(1) Complexity

The lecture introduces Big O notation, focusing on constant time complexity (O(1)). It uses a student list example to demonstrate that the number of operations remains constant regardless of input size. The lecture explains that even if multiple operations are performed, the complexity is still considered constant. It emphasizes that Big O notation simplifies to O(1) for constant operations, regardless of the number of operations. The lecture concludes with a preview of calculating complexity step-by-step in the next session.

What does Big O of 1 signify in terms of algorithm performance?

• The algorithm's performance is unpredictable.

• The algorithm's performance remains constant regardless of input size.

• The algorithm's performance improves as input size increases.

• The algorithm's performance decreases as input size increases.

In the example provided, what operation is performed on the student list?

• Calculating the average age

• Printing the first student

• Removing duplicates

• Sorting the list

How does the number of operations change when the input size increases in a constant time complexity?

• It doubles.

• It increases linearly.

• It decreases.

• It remains the same.

What is the Big O notation for an algorithm that performs two operations regardless of input size?

• Big O of n

• Big O of n^2

• Big O of 1

• Big O of 2

Why are constants like Big O of 2 or 3 considered as Big O of 1?

• Because they represent linear time complexity.

• Because they are not significant in Big O notation.

• Because they are considered as constant time complexity.

• Because they represent exponential time complexity.