A Quick Guide to Big-O Notation, Memoization, Tabulation, and Sorting Algorithms by Example

Curating Complexity: A Guide to Big-O Notation

Curating Complexity: A Guide to Big-O Notation

Why is looking at runtime not a reliable method of calculating time complexity?
Not all computers are made equal( some may be stronger and therefore boost our runtime speed )
How many background processes ran concurrently with our program that was being tested?
We also need to ask if our code remains performant if we increase the size of the input.
The real question we need to answering is: How does our performance scale?.

Big O Notation is a tool for describing the efficiency of algorithms with respect to the size of the input arguments.
Since we use mathematical functions in Big-O, there are a few big picture ideas that we’ll want to keep in mind:
The function should be defined by the size of the input.
Smaller Big O is better (lower time complexity)
Big O is used to describe the worst case scenario.
Big O is simplified to show only its most dominant mathematical term.