Practice Randomized Quicksort (22.1.5) - Quicksort analysis - Data Structures and Algorithms in Python
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Randomized Quicksort

Practice - Randomized Quicksort

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the time complexity of Quicksort in the average case?

💡 Hint: Think about how good pivots lead to efficient sorting.

Question 2 Easy

Define a pivot in the context of Quicksort.

💡 Hint: It's crucial for partitioning in sorting.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the average-case complexity of Quicksort?

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

💡 Hint: Think about how randomness impacts efficiency.

Question 2

True or False: Quicksort can be made stable easily.

True
False

💡 Hint: Consider the definition of stability in sorting.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Using a dataset of numbers, simulate the execution of Quicksort with both randomized and fixed pivot strategies. Analyze the time taken for each approach.

💡 Hint: Use a variety of datasets, including sorted and randomly ordered lists.

Challenge 2 Hard

Research and explain how a stable Quicksort can be implemented, discussing trade-offs in performance.

💡 Hint: Look into partitioning algorithms that maintain the sequence of equal elements.

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Reference links

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