Big O(n!) Complexity

The video tutorial discusses the rare and impractical complexity of O(N!) or factorial time complexity. It explains that this complexity involves adding a nested loop for every element in an array, making it highly inefficient and almost impossible to encounter in real-world coding scenarios. The tutorial emphasizes the importance of being aware of this complexity, despite its impracticality, and concludes with a reminder of its significance in understanding scalability issues.

What is the likelihood of encountering O(N!) complexity in practical coding scenarios?

• Rare

• Moderately common

• Very common

• Impossible

What does O(N!) complexity imply in terms of loops?

• A constant number of loops

• No loops involved

• A nested loop for each element

• A single loop for each element

How does the time complexity change as the number of items increases in O(N!)?

• Time complexity increases exponentially

• Time complexity remains constant

• Time complexity decreases

• Time complexity increases linearly

Why is it important to be aware of O(N!) complexity?

• It represents the worst-case scenario in complexity

• It is the best-case scenario in complexity

• It is a common complexity in everyday coding

• It is frequently used in coding interviews

What is the general advice given regarding O(N!) complexity?

• To avoid learning about it

• To use it for scalability

• To implement it in all algorithms

• To be aware of its existence