Big O notation

It was first introduced by German number theorist Paul Bachmann in his 1892 book Analytische Zahlentheorie. The notation was popularized in the work of another German number theorist Edmund Landau, hence it is sometimes called a Landau symbol. The big-O, standing for "order of", was originally a capital omicron; today the capital letter O is used, but never the digit
zero.

Omega and theta notation are also used for approximating formulae (see sum), analyzing algorithms (see heapsort), and for defining terms in complexity theory (see polynomial time).

Uses

There are two formally close, but noticeably different usages of this notation: infinite asymptotics and infinitesimal asymptotics. This distinction is only in application and not in principle, however—the formal definition for the "big O" is the same for both cases, only with different limits for the function argument.

Infinite asymptotics

Big O notation is useful when analyzing algorithms
for efficiency. For example, the time (or the number of steps) it takes to
complete a problem of size n might be found to be
T(n) = 4n² - 2n + 2.

As n grows large, the n² term will come to
dominate, so that all other terms can be neglected. Further, the
coefficients will depend on the precise details of the implementation and
the hardware it runs on, so they should also be neglected. Big O
notation captures what remains: we write

Infinitesimal asymptotics

Big O can also be used to describe the error term in an approximation
to a mathematical function. For example,
:
expresses the fact that the error is smaller in absolute value
than some constant times x³ if x is close enough to 0.

Formal definition

Suppose f(x) and g(x) are two functions defined on
some subset of the real numbers. We say
:f(x) is O(g(x)) as x ∞
if and only if
:there exist numbers x₀ and M such that |f(x)| ≤ M |g(x)| for x > x₀.

The notation can also be used to describe the behavior of f near
some real number a: we say
:f(x) is O(g(x)) as x a
if and only if
:there exists numbers δ>0 and M such that |f(x)| ≤ M |g(x)| for |x - a| < δ.

If g(x) is non-zero for values of x sufficiently close to a, both of these definitions can be unified using the limit superior:
:f(x) is O(g(x)) as x a
if and only if
:

In mathematics, both asymptotic behaviors near ∞ and near a are considered.
In computational complexity theory, only asymptotics near ∞ are used; furthermore,
only positive functions are considered, so the absolute value bars may
be left out.

Example

Take the polynomials:

:

Proof:
:         where x > 1
:     because x³ < x⁴, and so on.
:
:

Matters of notation

The statement "f(x) is
O(g(x))" as defined above is often written as f(x) = O(g(x)). This is a slight abuse
of notation: we are not really asserting the equality of two functions.
Here is a bad example:

Furthermore, an "equation" of the form
:f(x) = h(x) + O(g(x))
is to be understood as "the difference of f(x) and h(x) is O(g(x))".

Common orders of functions

Here is a list of classes of functions that are commonly encountered when analyzing algorithms. All of these are as n increases to infinity. The slower-growing functions are listed first. c is an arbitrary constant.

Properties

If a function f(n) can be written as a finite sum of other
functions, then the fastest growing one determines the order of
f(n). For example
:.

In particular, if a function may be bounded by a polynomial in n, then as n tends to infinity, one may disregard lower-order terms of the polynomial.

O(n^c) and O(cⁿ) are
very different. The latter grows much, much faster, no matter how big
the constant c is (so long as it is greater than one). A function that grows faster than any power of
n is called superpolynomial. One that grows slower than an
exponential function of the form cⁿ is called
subexponential. An algorithm can require time that is both
superpolynomial and subexponential; examples of this include the
fastest algorithms known for integer factorization.

O(log n) is exactly the same as O(log(n^c)).
The logarithms differ only by a constant factor, (since
log(n^c)=c log n) and thus the big O
notation ignores that. Similarly, logs with different constant bases
are equivalent.

Related asymptotic notations: O, o, Ω, ω, Θ, Õ

Big O is the most commonly used asymptotic notation for comparing
functions, although it is often actually an informal substitute for Θ (see below). Here, we define some related notations in terms of "big O":

(A mnemonic for these Greek letters is that "omicron" can be read "o-micron", i.e., "o-small", whereas "omega" can be read "o-mega" or "o-big".)

The relation f(n) = o(g(n)) is read as "f(n) is little-oh of g(n)". Intuitively, it means that g(n) grows much faster than f(n). Formally, it states that the limit of f(n)/g(n) is zero.

Aside from big-O, the notations Θ and Ω are the two most often used in computer science;
the lower-case o is common in mathematics but rarer in computer science. The lower-case ω is rarely used.

In casual use, O is commonly used where Θ is meant, i.e., when a tight estimate is implied.
For example, one might say "heapsort is O(n log n) in the
average case" when the intended meaning was "heapsort is
Θ(n log n) in the average case". Both statements are true,
but the latter is a stronger claim.

Another notation sometimes used in computer science is Õ (read
Soft-O). f(n) = Õ(g(n)) is shorthand
for f(n) = O(g(n) log^kg(n)) for some
k. Essentially, it is Big-O, ignoring logarithmic factors.
This notation is often used to describe a class of "nitpicking" estimates (since log^kn is always o(n) for any constant k).

Big O and little o

The following formal properties can be useful:

• O(O(f)) = O(f)
• o(o(f)) = o(f)
• O(o(f)) = o(O(f)) = o(f)
• O(f) + O(f) = O(f)
• o(f) + o(f) = o(f)
• O(f) + o(f) = O(f)
• O(fg) = O(f)O(g)
• o(fg) = O(f)o(g) = O(g)o(f)
  

  Multiple variables

External links

Cprogramming.com: Algorithm Efficiency An article on the Big O in C