# 🍩 Big "O" Notation

Module Summary: Learn how to use Big "O" notation to describe the performance of algorithms.

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Big O notation is a way to describe the performance of algorithms. It considers how an algorithm grows with respect to its input. Algorithms that grow slowly when their inout gets larger are considered good algorithms. Algorithms that grow quickly when their input gets larger are considered bad algorithms. We can group algorithms that grow at similar mathematical rates into groups as seen below.

## Big O Growth Graph View Full Image

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Big 0 Growth Graph by https://www.bigocheatsheet.com

## Big O Table

Here is a table of the above graph (with the addition of n^3).

Big O NotationName
O(1)Constant
O(log n)Logarithmic
O(n)Linear
O(n log n)Linearithmic
O(n^3)Cubic
O(2^n)Exponential
O(n!)Factorial

In the next modules, we will classify each algorithm into its Big O notation. Each algorithm will have a best, average, and worst case Big O.

Last updated on January 29, 2022

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