Data Structures and Algorithms The Complete Masterclass - Big O(n^2) Complexity

The video tutorial explains quadratic time complexity, often represented as O(N^2), which occurs when using nested loops. It provides an example with a list of seven elements, demonstrating how each element is paired with every other element, resulting in 49 operations. The tutorial also covers calculating big O notation, simplifying it to N^2 by removing constants. Additionally, it discusses complexity when dealing with different input sizes, using N and M to represent the number of inputs for two lists. The video concludes with a brief mention of the next lecture topic.

What is the time complexity of a function with nested loops iterating over a list of n elements?

• O(n)

• O(n log n)

• O(n^2)

• O(log n)

How many operations are performed when a nested loop iterates over a list of 7 elements?

• 49

• 14

• 21

• 35

What is the simplified Big O notation for a function with a complexity of 1 + n^2 + n^2 + 1?

• O(n^2)

• O(n)

• O(1)

• O(n^3)

When dealing with two lists of different sizes, what is the time complexity if one list has n elements and the other has m elements?

• O(m^2)

• O(n^2)

• O(n * m)

• O(n + m)

If a function has a complexity of O(n * m), what do n and m represent?

• The depth of nested loops

• The number of recursive calls

• The number of operations

• The sizes of two different input lists