Practice Big-o Notation (o) (8.2.1.2.3) - Undecidability and Introduction to Complexity Theory
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Big-O Notation (O)

Practice - Big-O Notation (O)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is Big-O notation?

💡 Hint: Think about how it relates to input size and running time.

Question 2 Easy

Give an example of a constant time complexity.

💡 Hint: This time complexity doesn't change with the size of the input.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does Big-O Notation represent?

Efficiency of algorithms
Only worst-case scenarios
Complete algorithm classification

💡 Hint: Think about what aspect of running time it measures.

Question 2

True or False - O(1) is a constant growth rate.

True
False

💡 Hint: Does the running time change based on input size?

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a sorting algorithm that operates within O(n log n) complexity and explain why it is efficient for large datasets.

💡 Hint: What method do these sorting algorithms use?

Challenge 2 Hard

Conceptualize an algorithm to find the maximum element in an unsorted list of n elements, and analyze its time complexity.

💡 Hint: What is the process of finding elements in an unsorted list?

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