Practice Prof. Madhavan Mukund (7.3) - Design and Analysis of Algorithms

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11 questions

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6 sample questions

Question 1.
What does Big O notation represent?
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Think about what happens as input size grows.
Question 2.
Give an example of a function that is O(n).
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Consider linear functions.
3Question 3.
What does Big O notation describe?
- Lower bound of time complexity
- Upper bound of time complexity
- Exact running time
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Think about the worst-case situation.
4Question 4.
True or False: Omega notation represents an upper bound.
- True
- False
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What does Omega represent?
Question 5.
Given the function t(n) = n² log(n), determine its Big O notation.
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Consider dominant growth terms in polynomial expressions.
Question 6.
Prove that sorting algorithms based on comparison will always have a lower bound of Ω(n log n).
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Think about the number of comparisons taken in worst-case scenarios.

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