Big O asks what dominates after the small stuff has stopped mattering.
Big O notation describes how the time or memory required by an algorithm grows as the size of its input increases. Expressions such as O(1), O(log n), O(n), and O(n2) classify algorithms by their dominant growth rate rather than exact execution time. This makes it possible to compare algorithms independently of hardware, programming language, or small implementation details. Big O usually gives an upper-bound description of asymptotic behavior and becomes most useful for large inputs. It helps identify which algorithms will remain practical as problems scale. It is used in algorithm design, performance analysis, and complexity theory.
