Big O(n^2) Complexity

The video tutorial explains quadratic time complexity, denoted as O(N^2), which often occurs with nested loops. It uses a list example to demonstrate how operations increase quadratically with the number of elements. The tutorial also covers the notation for quadratic functions and discusses complexity when dealing with multiple input lists. The video concludes by summarizing the key points and hinting at the next topic.

What is the typical cause of quadratic time complexity in algorithms?

• Using a hash table

• Using nested loops

• Using a single loop

• Using recursion

In the given example, how many operations are performed when there are seven elements in the list?

• 14 operations

• 21 operations

• 49 operations

• 35 operations

What is the simplified notation for the function's complexity when constants are removed?

• N^3

• 2N^2

• N^2

• N

When dealing with two different lists, what is the resulting time complexity?

• M^2

• N^2

• N + M

• N * M

If one list has 7 elements and another has 5, how many operations will be performed?

• 35 operations

• 25 operations

• 49 operations

• 12 operations