# Big O Notation

Big O notation describes how well an algorithm scales as the size of the input approaches a large number or infinity. The notation approximates the worst-case (upper-bound) performance of an algorithm.

Notes:
1) `c`

is constant and `n`

is the number of inputs.
2) O is order

**Quadratic time**
every item in the list (aka n for the input size), we have to do n more operations. So n * n == n^2

**Lineaer time**, every item in the list, we have to do n operations.

Constant time means is no matter how big our input is, it always takes the same amount of time to compute things.

Resouces:

MIT’s Lecture Notes on Big O

Justin Abrah’s Big O Notation Explained

Justin Abrah’s How to Calculate Big O