Practice Asymptotic Complexity - 1.5.1 | 1. Welcome to the NPTEL MOOC on Design and Analysis of Algorithms | Design & Analysis of Algorithms - Vol 1
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1.5.1 - Asymptotic Complexity

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is Big O notation?

💡 Hint: Think of it as a way to categorize how algorithms behave as input sizes increase.

Question 2

Easy

Explain the significance of asymptotic complexity.

💡 Hint: Focus on the importance of comparing different algorithms.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does Big O notation represent?

  • Upper limit of algorithm performance
  • Lower limit of algorithm performance
  • Exact performance

💡 Hint: Look at how we express the efficiency of algorithms.

Question 2

Is Linear Search O(n) better, worse, or equivalent to Binary Search O(log n) for large datasets?

  • True
  • False

💡 Hint: Consider their respective time complexities.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given an algorithm with a complexity of O(n^3) and another of O(n), design a test to showcase the effects of input size on performance, providing graphs of their running times.

💡 Hint: What conditions can help demonstrate the scaling effects?

Question 2

You are tasked with processing sales data for an eCommerce site. Choose between a quicksort algorithm O(n log n) and a bubble sort O(n^2). Discuss implications of each choice based on expected data size and performance.

💡 Hint: How do both algorithms perform as input sizes increase?

Challenge and get performance evaluation