Big-O notation describes how an algorithm’s work grows as its input grows.
- O(1): the work stays roughly constant.
- O(log n): the problem shrinks dramatically each step.
- O(n): each item is examined once.
- O(n log n): common territory for efficient comparison sorting.
- O(n²): many pairs are compared.
The goal is not to memorize symbols in isolation. Ask what operation repeats, how often it repeats, and which term dominates for large inputs.
See the full complexity table in the free quick-reference PDF.