Practice Department Of Computer Science And Engineering (7.4)

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9 questions

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6 sample questions

Question 1.
What is big O notation?
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Think about how algorithms are compared in terms of efficiency.
Question 2.
What does it mean if a function is O(n^2)?
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Consider how the input size influences the number of operations.
3Question 3.
What does big O notation represent?
- An upper bound
- A lower bound
- A constant
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Think about how we measure algorithm efficiency.
4Question 4.
If a function is Ω(g(n)), what does that mean?
- True
- False
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Consider the implications of lower bounds.
Question 5.
Consider the recursive function t(n) = 2t(n/2) + n, apply the Master theorem to classify its complexity.
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Identify constants a and b within the given relation.
Question 6.
Establish whether the function f(n) = n^2 + n + 1 is Θ(n²).
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Use the definitions of big O and ω to prove both conditions.

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