Mastering Algorithms with C

O-Notation

O -notation is the most common notation used to express an algorithm’s performance in a formal manner. Formally, O -notation expresses the upper bound of a function within a constant factor. Specifically, if g (n) is an upper bound of f (n), then for some constant c it is possible to find a value of n, call it n ₀, for which any value of n ≥ n ₀ will result in f (n) ≤ cg (n).

Normally we express an algorithm’s performance as a function of the size of the data it processes. That is, for some data of size n, we describe its performance with some function f (n). However, while in many cases we can determine f exactly, usually it is not necessary to be this precise. Primarily we are interested only in the growth rate of f, which describes how quickly the algorithm’s performance will degrade as the size of the data it processes becomes arbitrarily large. An algorithm’s growth rate, or order of growth, is significant because ultimately it describes how efficient the algorithm is for arbitrary inputs. O -notation reflects an algorithm’s order of growth.

Simple Rules for O-Notation

When we look at some function f (n) in terms of its growth rate, a few things become apparent. First, we can ignore constant terms because as the value of n becomes larger and larger, eventually constant terms will become insignificant. For example, if T (n) = n + 50 describes the running time of an algorithm, and n, the size of the data it processes, is only 1024, the constant term in this expression ...