Practice Lecture - 07 (7.5.2) - Design and Analysis of Algorithms - Design & Analysis of Algorithms

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6 sample questions

Question 1.
What does Big O notation represent?
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Think about maximum performance.
Question 2.
If t(n) = 50n + 10, is this O(n)?
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Consider the dominating term.
3Question 3.
What does Big O notation signify?
- Lower bound
- Upper bound
- Exact value
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Think of its role in algorithm efficiency.
4Question 4.
True or False: n³ is O(n²).
- True
- False
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Consider the growth rates as n gets very big.
Question 5.
Consider the function t(n) = 4n² + 3n + 5. Establish both upper and lower bounds using O and Ω notation.
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Focus on dominating terms and established inequalities.
Question 6.
Prove or disprove that for any algorithm with time complexity f(n) = 100n log n, there's no algorithm that could run faster than it on the same input.
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Contextualize sorting problems where growth behavior cannot be improved below the derived bounds.

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