Merge pull request #101 from mmdmthr/89-big-o-notation-cheatseet

89 add article big o notation cheatseet

content/blog/2024/07/big-o-notation-cheatseet.md

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+---
+title: 'Big O Notation Cheatseet'
+date: 2024-07-13
+category: 'notes'
+---
+
+|Notation   |Name   |Description   |Example (Time complexity)   |
+|---|---|---|---|
+|  O(1) | Constant complexity   | The time and space consumed is consistent regardless of input size   | Retrieving a value from a hash table given a specific key   |
+| O(log n)   | Logarithmic complexity   | The time and space consumed grows logarithmically with the input size   | Binary search on a sorted array   |
+|O(n)   | Linear complexity   | The time and space consumed grows in direct proportion to the input size   | Searching for a single element in an array (looping over an array)   |
+| O(n log n)   | Linearithmic complexity  | The time and space consumed grows proportionally to n log n (where n is the size of the input)   | Efficient sorting algorithms like quicksort  |
+| O(n^2)  | Quadratic complexity  | The time and space consumed grows with the square of the input size   | simple sorting algorithms like bubble sort. looping over an array, and comparing the current element with all other elements in the array   |
+| O(n^3)  | Cubic complexity  | The time and space consumed grows with the cube of the input size  | Triple nested loops   |
+| O(2^n)  | Exponential complexity   | The time and space consumed doubles with eacch increment to the input size   | Recursive calculation if Fibonacci numbers   |
+| O(n!)   | Factorial complexity   | The time and space consumed grows factorially to the size of the input   | Solving the traveling salesman problem with brute force   |