Big O (often pronounced order) is an abstract function that describes how fast a function grows as the size of the problem becomes large. The order given by Big O is a least upper bound on the rate of growth.
We say that a function T(n) has order O(f(n)) if there exist
positive constants c and n0 such that:
T(n) ≤ c * f(n) when n ≥ n0.
For example, the function T(n) = 2 * n2 + 5 * n + 100 has order O(n2) because 2 * n2 + 5 * n + 100 ≤ 3 * n2 for n ≥ 13.
We don't care about performance for small values of n, since small problems are usually easy to solve. In fact, we can make that a rule:
If the input is small, any algorithm is okay.
In such cases, the simplest (easiest to code) algorithm is best.
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