# Back to the Basics: Algorithmic complexity (Big O)

Date Created
Sep 6, 2022 03:55 PM
Tags
WIP
Date Published
Sep 12, 2022
Published
Published
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This is more of a note-taking space right now. I’m following along
coding-interview-university
lanepartonUpdated Sep 7, 2022

### Algorithmic complexity / Big-O / Asymptotic analysis

• Nothing to implement here, you're just watching videos and taking notes! Yay!
• There are a lot of videos here. Just watch enough until you understand it. You can always come back and review.
• Don't worry if you don't understand all the math behind it.
• You just need to understand how to express the complexity of an algorithm in terms of Big-O.
Harvard CS50 - Asymptotic Notation (video)
• O(n) = linear complexity
• O(1) = constant complexity
• O(log n) = logarithmic complexity
• Ω = Best case constant
• Θ = best and worst case scenarios are the same
• Binary Search -
• Starts in the middle
• If > checks left
• If > checks right
• Repeat…
Big O Notations (general quick tutorial) (video)
• Measure how well a computer algo scales as the amount of data increases
• 10 element array vs 10,000 element array
TopCoder (includes recurrence relations and master theorem):