Practice Realistic Inputs For Algorithms (15.7) - Efficiency - Data Structures and Algorithms in Python
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Realistic Inputs for Algorithms

Practice - Realistic Inputs for Algorithms

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does algorithm efficiency refer to?

💡 Hint: Think about time versus input amount.

Question 2 Easy

Define worst-case behavior in an algorithm.

💡 Hint: Consider the example where you need to check every element.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is big O notation used for?

To measure speed of an algorithm
To provide a precise time value
To describe performance growth with input size

💡 Hint: Consider the notation's function in comparing different algorithms.

Question 2

True or False: All polynomial time algorithms are considered efficient.

True
False

💡 Hint: Reflect on polynomial degrees and practical execution limits.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Analyze the efficiency of different sorting algorithms (like bubble sort vs. quicksort). Provide inputs of varying sizes and classify them using big O notation.

💡 Hint: Compare their executions with growing input sizes.

Challenge 2 Hard

Propose an empirical method to determine the maximum feasible input size for a binary search algorithm. What factors would influence your results?

💡 Hint: Think about conducting tests on sorted arrays of increasing size.

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Reference links

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