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

https://replit.com/@bgoonz/Medium-article-comp-complex?lite=true

• 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
• 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.