Practice Week - 01 (7.5) - Design and Analysis of Algorithms - Design & Analysis of Algorithms

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8 questions

Flashcard drill
2 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
2 questions · about 1 min · pass mark 70%. Hints available.
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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 increases.
Question 2.
Is O(n) better than O(n^2)? Why?
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Comparing growth rates can help clarify this.
3Question 3.
Which of the following notations describes an upper bound for an algorithm's performance?
- Big O
- Omega
- Theta
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Remember the purpose of Big O notation.
4Question 4.
True or False: Omega notation provides the maximum time complexity possible for an algorithm.
- True
- False
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Think about what Omega notation signifies.
Question 5.
Demonstrate that any function f(n) = an^k + bn^(k-1) + ... + c is O(n^k) for any constants a, b, ..., c.
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Examine what happens as n becomes very large.
Question 6.
Consider t(n) = 3n^3 + n; does it fulfill both Big O and Omega requirements for n^3?
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Estimate t(n) for large n and compare to n^3.

4 more questions in this section. Enroll free to practise them all.