Practice - Polynomial Time Algorithms
Practice Questions
Test your understanding with targeted questions
What does 'input size' refer to in algorithm efficiency?
💡 Hint: Think about how data is processed.
What is big O notation?
💡 Hint: It's used to describe algorithm efficiency.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary measure of algorithm efficiency?
💡 Hint: Consider what ultimately matters in performance.
In Big O notation, O(n) means the algorithm runs in a time proportional to what?
💡 Hint: Think about how you would measure the time.
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Challenge Problems
Push your limits with advanced challenges
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.
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.