Practice Big O Notation (15.4) - Efficiency - Data Structures and Algorithms in Python
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Big O Notation

Practice - Big O Notation

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does Big O Notation describe?

💡 Hint: Think about what efficiency means in terms of input size.

Question 2 Easy

Give an example of an algorithm with O(n) time complexity.

💡 Hint: Consider how you would search through a list.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does Big O Notation measure?

Efficiency
Input Size
Memory Usage

💡 Hint: Remember the focus on performance measurement.

Question 2

True or False: Linear search has a time complexity of O(log n).

True
False

💡 Hint: Think about how items in a list are processed.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have two algorithms, A with O(n) and B with O(n^2). If you are required to process an input of size 10,000 for both algorithms, compare the time taken assuming a constant factor of 1.

💡 Hint: Calculate the time taken by each algorithm by substituting n into their respective complexities.

Challenge 2 Hard

Explain why an algorithm with exponential time complexity might be infeasible for a small increase in input size, say from n=20 to n=21.

💡 Hint: Think about how quickly powers of 2 increase.

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Reference links

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