Space Complexity – Part 1

The video tutorial explains space complexity, focusing on how additional memory is required to run a program. It provides examples of constant space complexity (O(1)) and linear space complexity (O(N)), illustrating how space requirements change with input size. The tutorial concludes with a summary of these concepts, emphasizing the importance of understanding space complexity in programming.

What does space complexity measure in a program?

• The number of lines in a program

• The extra memory required to run a program

• The time taken to execute a program

• The number of functions in a program

In the first example, why is the space complexity considered O(1)?

• Because only one extra space is needed regardless of input size

• Because the program uses multiple variables

• Because the space required increases with input size

• Because the program does not use any extra space

What is the space complexity when the space requirement increases linearly with the number of inputs?

• O(N)

• O(N^2)

• O(log N)

• O(1)

In the second example, how does the space complexity change with the number of inputs?

• It increases linearly

• It increases exponentially

• It remains constant

• It decreases

Why is the space complexity O(N) in the second example?

• Because the program uses a fixed amount of space

• Because the space required is independent of input size

• Because the space required increases with each input

• Because the program does not store any results