Practice Base Case For Merging (19.6.1) - Mergesort - Part A - Data Structures and Algorithms in Python
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Base Case for Merging

Practice - Base Case for Merging

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is merge sort primarily based on?

💡 Hint: Think about breaking problems into smaller pieces.

Question 2 Easy

What do we call the situation when we stop breaking the list down?

💡 Hint: It involves lists of size one or zero.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the worst-case time complexity of merge sort?

O(n)
O(n log n)
O(n^2)

💡 Hint: Recall the efficiency compared to simpler sorting algorithms.

Question 2

True or False: Merge sort is an in-place sorting algorithm.

True
False

💡 Hint: Consider what 'in-place' means regarding storage.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have to sort a list of 10 random numbers using merge sort. Write pseudocode to demonstrate each step including the divide and merge operations.

💡 Hint: Focus on both the divide and merge phases to keep track.

Challenge 2 Hard

Evaluate the efficiency of merge sort compared to quicksort and the implications of their space complexities.

💡 Hint: Consider the scenarios where each algorithm excels or falters.

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