Practice Asymptotic Complexity (1.5.1) - Design and Analysis of Algorithms

Practice: Asymptotic Complexity

Lesson 1.5.1Design and Analysis of Algorithms

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9 questions

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Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is Big O notation?
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Think of it as a way to categorize how algorithms behave as input sizes increase.
Question 2.
Explain the significance of asymptotic complexity.
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Focus on the importance of comparing different algorithms.
3Question 3.
What does Big O notation represent?
- Upper limit of algorithm performance
- Lower limit of algorithm performance
- Exact performance
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Look at how we express the efficiency of algorithms.
4Question 4.
Is Linear Search O(n) better, worse, or equivalent to Binary Search O(log n) for large datasets?
- True
- False
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Consider their respective time complexities.
Question 5.
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.
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What conditions can help demonstrate the scaling effects?
Question 6.
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.
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How do both algorithms perform as input sizes increase?

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