Merge Sort Concepts and Techniques

The video tutorial introduces merge sort, a recursive algorithm that uses the divide and conquer approach. It explains how to break down an array into smaller parts, sort individual items, and merge them back together. The tutorial provides pseudocode for better understanding and discusses the time complexity of merge sort, which is O(n log n). The video concludes with a request for feedback and suggestions for future topics.

What is the primary strategy used in Merge Sort?

• Backtracking

• Greedy Algorithm

• Divide and Conquer

• Dynamic Programming

How is Merge Sort typically implemented?

• Recursively

• Using a queue

• Using a stack

• Iteratively

What is the first step in the Merge Sort process?

• Merging the array

• Sorting the array

• Splitting the array

• Comparing elements

Why might the steps in the human-friendly order differ from the code implementation?

• Due to sorting algorithms

• Due to iteration

• Due to recursion

• Due to memory constraints

What do we do after breaking down the array into individual items?

• Merge the items

• Insert the items

• Sort the items

• Compare the items

What is the purpose of temporary arrays in Merge Sort?

• To store unsorted items

• To store recursion depth

• To store sorted items

• To store indices

What is the worst-case time complexity of Merge Sort?

• O(n^2)

• O(n)

• O(log n)

• O(n log n)

What does the 'log n' in the time complexity of Merge Sort represent?

• The height of the binary tree

• The number of comparisons

• The number of merges

• The number of elements