There are lots of existing algorithms; some are fast and some are slow. Some use lots of memory. It can be hard to decide which algorithm is the best to solve a particular problem. “Big O” analysis (pronounced “Big Oh”, not “Big Zero”) is one way to compare algorithms.

Big O is a characterization of algorithms according to their worst-case growth rates

We write Big-O notation like this:

O(formula)

Where formula describes how an algorithm’s run time or space requirements grow as the input size grows.

1. O(1) - constant
2. O(n) - linear
3. O(n^2) - squared
4. O(2^n) - exponential
5. O(n!) - factorial

References

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