Practice Polynomial Time Algorithms (15.8) - Efficiency - Data Structures and Algorithms in Python
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Polynomial Time Algorithms

Practice - Polynomial Time Algorithms

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does 'input size' refer to in algorithm efficiency?

💡 Hint: Think about how data is processed.

Question 2 Easy

What is big O notation?

💡 Hint: It's used to describe algorithm efficiency.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary measure of algorithm efficiency?

Execution Time
Code Length
Input Complexity

💡 Hint: Consider what ultimately matters in performance.

Question 2

In Big O notation, O(n) means the algorithm runs in a time proportional to what?

True
False

💡 Hint: Think about how you would measure the time.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Analyze an O(n log n) algorithm versus an O(n^2) algorithm for a dataset of 10,000 elements. Which would perform better, and why?

💡 Hint: Consider how both complexities increase with larger datasets.

Challenge 2 Hard

Why is it critical to choose algorithms with polynomial complexity in a large-scale application?

💡 Hint: Think about the limitations of operations with increasing inputs.

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Reference links

Supplementary resources to enhance your learning experience.