1. An O function has an order. In this context, the order quantifies how rapidly ____________________ and the shape of that increase.

2. _______means that the algorithm performance is constant and does not depend on n

3. ________ is when the algorithm is factorial n and is extremely sensitive to the input size and it rapidly becomes impractical.

4. ____ is when the algorithm performance changes linearly with n. The FOR loop example is of order O(n).

5. A binary search algorithm to find an item in an ordered array is of this order. This is not so good as linear

6. One of these parts __________________ This is the dominant effect of the algorithm. When converted to Big O notation, this is the only part that matters, and the rest will be discarded

7. In general, you want your program to have as little complexity as possible, so it executes quickly

8. You lost your wedding ring on the beach, and have no memory of when or where it was lost. The big-O performance of your ring finding algorithm will be O(L)

9. Determine the running time in Big O notation of the second algorithm (Algorithm 2)

10. Determine the running time in Big O notation of the first algorithm (Algorithm 1)

11. O(1) is a linear-time algorithm runs in time proportional to the input

12. The following code is an example of:

13. This is the holy grail of scalability. With an O(n) algorithm, you can increase your inputs forever and never bog down.

14. For the following example, in Big O terms it would be: O(N)

15. For a problem of size N: a linear-time algorithm is "order N": O(N)