BigOnotation

Big-O notation, often written as O(...), is a mathematical concept used in computer science to describe the upper bound on the growth rate of an algorithm's running time or space usage as the input size n grows. It characterizes asymptotic behavior, focusing on how the resource consumption scales rather than exact values for finite inputs. A function T(n) is O(g(n)) if there exist constants c > 0 and n0 ≥ 1 such that T(n) ≤ c · g(n) for all n ≥ n0. Here, T(n) might represent time or memory, and g(n) is a simple growth function such as 1, log n, n, n log n, n^2, etc.

Common examples include O(1) for constant time, O(log n) for logarithmic time, O(n) for linear time, O(n log n) for linearithmic time, and O(n^2) for quadratic time. These forms describe how running time grows in the worst case as input size increases. Big-O is an upper bound; it does not imply that all inputs take that long, only that the running time will not grow faster than c·g(n) beyond some n0.

Other related notations provide tighter or complementary information. Theta notation, Θ(g(n)), denotes a tight bound where T(n) grows at the rate of g(n) up to constant factors. Omega notation, Ω(g(n)), gives a lower bound. Together they can describe the precise asymptotic behavior of T(n).

Big-O is a tool for comparing algorithms and understanding scalability. It abstracts away constant factors and lower-order terms, but it has limitations: it may not reflect performance for small inputs or practical hardware effects.