## Big O Notation Explained Algorithm efficiency is measured using Big O notation, which describes how execution time or space requirements grow as the input size ($N$) increases. ### Common Complexities - **O(1) - Constant Time**: Running time is independent of input size (e.g. array index lookup). - **O(log N) - Logarithmic Time**: Time increases logarithmically (e.g. binary search). - **O(N) - Linear Time**: Time grows proportionally to input size (e.g. simple list traversal). - **O(N log N) - Linearithmic Time**: Standard sorting algorithm complexity (e.g. merge sort, quicksort). - **O(N^2) - Quadratic Time**: Slow, nested iterations (e.g. bubble sort).