Big O in plain language flashcards
Understanding Big O notation for algorithms made simple with flashcards.
Quiz(12 spørgsmål)
1. What does O(n^2) signify?
Begreber i dette studiesæt(15)
What is Big O notation?
A mathematical representation that describes the efficiency of an algorithm in terms of time or space.
O(1)
Constant time complexity; execution time remains the same regardless of input size.
O(n)
Linear time complexity; execution time increases linearly with input size.
O(n^2)
Quadratic time complexity; performance is proportional to the square of the input size.
True or false: O(n log n) is better than O(n^2).
True, because O(n log n) grows slower than O(n^2) with increasing n.
Difference between O(n) and O(n^2)
O(n) grows linearly, while O(n^2) grows quadratically, making O(n^2) slower.
O(log n)
Logarithmic time complexity; execution time grows logarithmically with input size.
What does upper bound mean?
The worst-case scenario performance of an algorithm, described using Big O.
Fill the blank: O(2^n) is called _____ time complexity.
Exponential time complexity.
True or false: O(n) and O(2n) are the same.
False, because O(2n) is still linear but usually considered O(n).
Which is NOT a valid Big O notation?
O(n!), as it's factorial time complexity and grows very fast.
What is space complexity?
A measure of the amount of working storage an algorithm needs.
When is Big O used?
To analyze and compare the efficiency of algorithms.
What is O(n^3)?
Cubic time complexity; execution time increases with the cube of input size.
Fill the blank: The best complexity you can achieve is _____
O(1), which is constant time.
Spørgsmål i dette studiesæt(12)
1. What does O(n^2) signify?
2. Which of the following is the fastest growth rate?
3. When discussing Big O, what does 'n' typically represent?
4. Which time complexity is worse for large inputs?
5. What is the average time complexity of quicksort?
6. O(n log n) is commonly associated with which algorithm?
7. What is a key characteristic of O(2^n)?
8. Which complexity indicates a linear relationship?
9. Which algorithm typically runs in O(n^2) time?
10. What does a lower complexity indicate?
11. If an algorithm is O(n^2), how does it perform as n doubles?
12. Which is NOT a common Big O notation?
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