Practice Measuring As A Function Of Input Size (n) (8.2.1.2.2) - Undecidability and Introduction to Complexity Theory
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Measuring as a Function of Input Size (n)

Practice - Measuring as a Function of Input Size (n)

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does time complexity measure?

💡 Hint: Think about the resources needed as you increase the input.

Question 2 Easy

What does O(1) represent in time complexity?

💡 Hint: Consider operations that take the same amount of time, regardless of input.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does time complexity measure?

💡 Hint: Remember about the growth of resource requirements.

Question 2

Which of the following is an example of O(log n) complexity?

Linear Search
Bubble Sort
Binary Search

💡 Hint: Think about how the search space reduces.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design an algorithm for searching an element in a sorted array and analyze its time complexity. Explain how it compares to searching in an unsorted array.

💡 Hint: Think about how the order of elements affects the search strategy.

Challenge 2 Hard

Create a sorting algorithm. Demonstrate its efficiencies and inefficiencies using time complexity analysis.

💡 Hint: Consider how dividing and conquering helps in sorting.

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Reference links

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