https://bgoonz-blog.netlify.app/docs/javascript/bigo/

Big O: big picture, broad strokes, not details

For a more complete guide… checkout :

• way we analyze how efficient algorithms are without getting too mired in details
• can model how much time any function will take given n inputs
• interested in order of magnitude of number of the exact figure
• O absorbs all fluff and n = biggest term
• Big O of 3x^2 +x + 1 = O(n^2)

Time Complexity

no loops or exit & return = O(1)

0 nested loops = O(n) 1 nested loops = O(n^2) 2 nested loops = O(n^3) 3 nested loops = O(n^4)

recursive: as you add more terms, increase in time as you add input diminishes recursion: when you define something in terms of itself, a function that calls itself

• used because of ability to maintain state at diffferent levels of recursion