SLR 26

Question 1

What is O(1)?

Answer 1

Constant

Always executes the in the same amount of time regardless of the size of the data set

Question 2

What is O(log n)?

Answer 2

Logarithmic

Halves the data set in each pass, opposite to exponential

Question 3

What is O(n)?

Answer 3

Linear

Performance declines as the data set grows, meaning efficiency decreases as the data set increases in size

Question 4

What is O(n²)?

Answer 4

Polynomial

Performance is proportional to the square of the size of the data set. Has significantly reduced efficiency when dealing with large data sets

Question 5

What is O(n^2)?

Answer 5

Quadratic

The algorithm’s execution time grows proportionally to the square of the input size

Question 6

What is O(n!)?

Answer 6

Factorial

The algorithm’s execution time grows proportionally to the factorial of the input size

Question 7

What is O(2n)?

Answer 7

Exponential

Runtime doubles with each increment, opposite to logarithmic