Practice Introduction To Big O (7.6.1) - Design and Analysis of Algorithms

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6 sample questions

Question 1.
What does Big O notation measure?
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Think about how we assess algorithms regarding efficiency.
Question 2.
Define time complexity.
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Consider the relationship between input size and time taken.
3Question 3.
What does Big O notation represent?
- Lower bound of an algorithm
- Upper bound of an algorithm
- Exact time complexity
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Think about what Big O tells us about algorithm performance.
4Question 4.
True or False: Big O can be used to express the exact running time of an algorithm.
- True
- False
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Does Big O focus on exact values or growth trends?
Question 5.
Prove that for the function f(n) = 3n² + 2n + 1, f(n) is O(n²).
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Determine appropriate constants that satisfy the inequality for large n.
Question 6.
Discuss the implications of using Big O notation for algorithm analysis in terms of practicality versus theoretical limits.
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Consider why focusing on growth may be more useful than specific timing in development.

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