Practice - Quantifying Computational Resources: Time and Space Complexity
Practice Questions
Test your understanding with targeted questions
Define time complexity.
💡 Hint: Consider what aspect of an algorithm is being measured over varying input sizes.
What does O(1) mean?
💡 Hint: Think of an operation that does not depend on how many elements it processes.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary purpose of Big-O notation?
💡 Hint: Think about how it relates to performance over varying input sizes.
True or False: An algorithm with O(2^n) time complexity is generally efficient.
💡 Hint: How do you feel about the feasibility of large input sizes?
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Challenge Problems
Push your limits with advanced challenges
Consider an algorithm that consists of nested loops where the outer loop runs n times and the inner loop runs n times as well. Calculate the time complexity, and explain the reasoning behind it.
💡 Hint: Think about how many times does each operation in the inner loop get executed based on the outer loop.
You have two algorithms: one runs in O(n log n) time and the other in O(n^2) time. Explain in what scenario one would be preferable over the other when handling large datasets.
💡 Hint: Consider the scalability of each algorithm as data grows.
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Reference links
Supplementary resources to enhance your learning experience.