Data Structures and Algorithms The Complete Masterclass - Merge Sort Complexity

The video tutorial explains the complexity of merge sort, highlighting its O(N log N) time complexity. It covers the process of logarithmic decomposition and element comparisons at each level of the algorithm. The tutorial also discusses the best, worst, and average cases, emphasizing that the complexity remains consistent regardless of input. The logic and implementation of merge sort are detailed, followed by an analysis of its time and space complexity, which is O(N) for space due to the need for additional storage.

What is the time complexity of merge sort?

• O(N^2)

• O(N log N)

• O(log N)

• O(N)

How many decompositions are there if you have 32 elements?

• 6

• 4

• 3

• 5

In merge sort, what happens at each level of decomposition?

• Elements are multiplied

• Elements are compared

• Elements are ignored

• Elements are divided

Does the complexity of merge sort change with different input cases?

• Yes, it changes with input type

• No, it remains the same

• Yes, it changes with input order

• Yes, it changes with input size

What is the space complexity of merge sort?

• O(N)

• O(N^2)

• O(N log N)

• O(log N)

Why does merge sort require extra space?

• To store the sorted array

• To store the input size

• To store the original array

• To store intermediate arrays

What concept is crucial to understand before implementing merge sort?

• Dynamic programming

• Divide and conquer recursion

• Greedy algorithms

• Backtracking