In mathematics, big O notation describes the limiting behavior of a function when the argument tends towards a particular value or infinity, usually in terms of simpler functions. It is a member of a larger family of notations that is called Landau notation, Bachmann–Landau notation (after Edmund Landau and Paul Bachmann), or asymptotic notation. In computer science, big O notation is used to classify algorithms by how they respond (e.g., in their processing time or working space requirements) to changes in input size. In analytic number theory, it is used to estimate the "error committed" while replacing the asymptotic size, or asymptotic mean size, of an arithmetical function, by the value, or mean value, it takes at a large finite argument. A famous example is the problem of estimating the remainder term in the prime number theorem.
Big O notation characterizes functions according to their growth rates: different functions with the same growth rate may be represented using the same O notation. The letter O is used because the growth rate of a function is also referred to as order of the function. A description of a function in terms of big O notation usually only provides an upper bound on the growth rate of the function. Associated with big O notation are several related notations, using the symbols o, Ω, ω, and Θ, to describe other kinds of bounds on asymptotic growth rates.
Big O notation is also used in many other fields to provide similar estimates.
Formal definition
Let f(x) and g(x) be two functions defined on some subset of the real numbers. One writes
 $f(x)=O(g(x))\backslash text\{\; as\; \}x\backslash to\backslash infty\backslash ,$
if and only if there is a positive constant M such that for all sufficiently large values of x, f(x) is at most M multiplied by g(x) in absolute value. That is, f(x) = O(g(x)) if and only if there exists a positive real number M and a real number x_{0} such that
 $f(x)\; \backslash le\; \backslash ;\; M\; g(x)\backslash text\{\; for\; all\; \}x>x\_0.$
In many contexts, the assumption that we are interested in the growth rate as the variable x goes to infinity is left unstated, and one writes more simply that f(x) = O(g(x)).
The notation can also be used to describe the behavior of f near some real number a (often, a = 0): we say
 $f(x)=O(g(x))\backslash text\{\; as\; \}x\backslash to\; a\backslash ,$
if and only if there exist positive numbers δ and M such that
 $f(x)\; \backslash le\; \backslash ;\; M\; g(x)\backslash text\{\; for\; \}x\; \; a\; <\; \backslash delta.$
If g(x) is nonzero for values of x sufficiently close to a, both of these definitions can be unified using the limit superior:
 $f(x)=O(g(x))\backslash text\{\; as\; \}x\; \backslash to\; a\backslash ,$
if and only if
 $\backslash limsup\_\{x\backslash to\; a\}\; \backslash left\backslash frac\{f(x)\}\{g(x)\}\backslash right\; <\; \backslash infty.$
Example
In typical usage, the formal definition of O notation is not used directly; rather, the O notation for a function f(x) is derived by the following simplification rules:
 If f(x) is a sum of several terms, the one with the largest growth rate is kept, and all others omitted.
 If f(x) is a product of several factors, any constants (terms in the product that do not depend on x) are omitted.
For example, let $f(x)\; =\; 6x^4\; \; 2x^3\; +5$, and suppose we wish to simplify this function, using O notation, to describe its growth rate as x approaches infinity. This function is the sum of three terms: 6x^{4}, −2x^{3}, and 5. Of these three terms, the one with the highest growth rate is the one with the largest exponent as a function of x, namely 6x^{4}. Now one may apply the second rule: 6x^{4} is a product of 6 and x^{4} in which the first factor does not depend on x. Omitting this factor results in the simplified form x^{4}. Thus, we say that f(x) is a "bigoh" of (x^{4}). Mathematically, we can write f(x) = O(x^{4}).
One may confirm this calculation using the formal definition: let f(x) = 6x^{4} − 2x^{3} + 5 and g(x) = x^{4}. Applying the formal definition from above, the statement that f(x) = O(x^{4}) is equivalent to its expansion,
 $f(x)\; \backslash le\; \backslash ;\; M\; g(x)$
for some suitable choice of x_{0} and M and for all x > x_{0}. To prove this, let x_{0} = 1 and M = 13. Then, for all x > x_{0}:
 $\backslash begin\{align\}6x^4\; \; 2x^3\; +\; 5\; \&\backslash le\; 6x^4\; +\; 2x^3\; +\; 5\backslash \backslash $
&\le 6x^4 + 2x^4 + 5x^4\\
&\le 13x^4\\
&\le 13x^4\end{align}
so
 $6x^4\; \; 2x^3\; +\; 5\; \backslash le\; 13\; \backslash ,x^4\; .$
Usage
Big O notation has two main areas of application. In mathematics, it is commonly used to describe how closely a finite series approximates a given function, especially in the case of a truncated Taylor series or asymptotic expansion. In computer science, it is useful in the analysis of algorithms. In both applications, the function g(x) appearing within the O(...) is typically chosen to be as simple as possible, omitting constant factors and lower order terms.
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) = 4n^{2} − 2n + 2.
As n grows large, the n^{2} term will come to dominate, so that all other terms can be neglected—for instance when n = 500, the term 4n^{2} is 1000 times as large as the 2n term. Ignoring the latter would have negligible effect on the expression's value for most purposes.
Further, the coefficients become irrelevant if we compare to any other order of expression, such as an expression containing a term n^{3} or n^{4}. Even if T(n) = 1,000,000n^{2}, if U(n) = n^{3}, the latter will always exceed the former once n grows larger than 1,000,000 (T(1,000,000) = 1,000,000^{3}= U(1,000,000)). Additionally, the number of steps depends on the details of the machine model on which the algorithm runs, but different types of machines typically vary by only a constant factor in the number of steps needed to execute an algorithm.
So the big O notation captures what remains: we write either
 $\backslash \; T(n)=\; O(n^2)\; \backslash ,$
or
 $T(n)\backslash in\; O(n^2)\; \backslash ,$
and say that the algorithm has order of n^{2} time complexity.
Note that "=" is not meant to express "is equal to" in its normal mathematical sense, but rather a more colloquial "is", so the second expression is technically accurate (see the "Equals sign" discussion below) while the first is a common abuse of notation.^{[1]}
Infinitesimal asymptotics
Big O can also be used to describe the error term in an approximation to a mathematical function. The most significant terms are written explicitly, and then the leastsignificant terms are summarized in a single big O term. For example,
 $e^x=1+x+\backslash frac\{x^2\}\{2\}+O(x^3)\; \backslash ,,\; \backslash qquad\backslash text\{as\; \}\; x\backslash to\; 0\; \backslash ,,$
expresses the fact that the error, the difference $\backslash \; e^x\; \; (1\; +\; x\; +\; x^2/2)$, is smaller in absolute value than some constant times $x^3$ when $x$ is close enough to 0.
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
 $f(n)\; =\; 9\; \backslash log\; n\; +\; 5\; (\backslash log\; n)^3\; +\; 3n^2\; +\; 2n^3\; =\; O(n^3)\; \backslash ,,\; \backslash qquad\backslash text\{as\; \}\; n\backslash to\backslash infty\; \backslash ,\backslash !.$
In particular, if a function may be bounded by a polynomial in n, then as n tends to infinity, one may disregard lowerorder terms of the polynomial.
O(n^{c}) and O(c^{n}) are very different. If c is greater than one, then the latter grows much faster. A function that grows faster than n^{c} for any c is called superpolynomial. One that grows more slowly than any exponential function of the form $c^n$ is called subexponential. An algorithm can require time that is both superpolynomial and subexponential; examples of this include the fastest known algorithms for integer factorization.
O(log n) is exactly the same as O(log(n^{c})). The logarithms differ only by a constant factor (since
$\backslash log(n^c)=c\; \backslash log\; n$) and thus the big O notation ignores that. Similarly, logs with different constant bases are equivalent. Exponentials with different bases, on the other hand, are not of the same order. For example, $2^n$ and $3^n$ are not of the same order.
Changing units may or may not affect the order of the resulting algorithm. Changing units is equivalent to multiplying the appropriate variable by a constant wherever it appears. For example, if an algorithm runs in the order of n^{2}, replacing n by cn means the algorithm runs in the order of $c^2n^2$, and the big O notation ignores the constant $c^2$. This can be written as $c^2n^2\; \backslash in\; O(n^2)$. If, however, an algorithm runs in the order of $2^n$, replacing n with cn gives $2^\{cn\}\; =\; (2^c)^n$. This is not equivalent to $2^n$ in general.
Changing of variable may affect the order of the resulting algorithm. For example, if an algorithm's running time is O(n) when measured in terms of the number n of digits of an input number x, then its running time is O(log x) when measured as a function of the input number x itself, because n = Θ(log x).
Product
 $f\_1\; \backslash in\; O(g\_1)\; \backslash text\{\; and\; \}\; f\_2\backslash in\; O(g\_2)\backslash ,\; \backslash Rightarrow\; f\_1\; f\_2\backslash in\; O(g\_1\; g\_2)\backslash ,$
 $f\backslash cdot\; O(g)\; \backslash subset\; O(f\; g)$
Sum
 $f\_1\; \backslash in\; O(g\_1)\; \backslash text\{\; and\; \}$
f_2\in O(g_2)\, \Rightarrow f_1 + f_2\in O(g_1 + g_2)\,
 This implies $f\_1\; \backslash in\; O(g)\; \backslash text\{\; and\; \}\; f\_2\; \backslash in\; O(g)\; \backslash Rightarrow\; f\_1+f\_2\; \backslash in\; O(g)$, which means that $O(g)$ is a convex cone.
 If f and g are positive functions, $f\; +\; O(g)\; \backslash in\; O(f\; +\; g)$
Multiplication by a constant
 Let k be a constant. Then:
 $\backslash \; O(k\; g)\; =\; O(g)$ if k is nonzero.
 $f\backslash in\; O(g)\; \backslash Rightarrow\; kf\backslash in\; O(g).$
Multiple variables
Big O (and little o, and Ω...) can also be used with multiple variables.
To define Big O formally for multiple variables, suppose $f(\backslash vec\{x\})$ and $g(\backslash vec\{x\})$ are two functions defined on some subset of $\backslash mathbb\{R\}^n$. We say
 $f(\backslash vec\{x\})\backslash text\{\; is\; \}O(g(\backslash vec\{x\}))\backslash text\{\; as\; \}\backslash vec\{x\}\backslash to\backslash infty$
if and only if
 $\backslash exists\; C\backslash ,\backslash exists\; M>0\backslash text\{\; such\; that\; \}\; f(\backslash vec\{x\})\; \backslash le\; C\; g(\backslash vec\{x\})\backslash text\{\; for\; all\; \}\backslash vec\{x\}\; \backslash text\{\; with\; \}\; x\_i>M\; \backslash text\{\; for\; all\; \}i.$
For example, the statement
 $f(n,m)\; =\; n^2\; +\; m^3\; +\; O(n+m)\; \backslash text\{\; as\; \}\; n,m\backslash to\backslash infty\backslash ,$
asserts that there exist constants C and M such that
 $\backslash forall\; n,\; m>M\backslash colon\; g(n,m)\; \backslash le\; C(n+m),$
where g(n,m) is defined by
 $f(n,m)\; =\; n^2\; +\; m^3\; +\; g(n,m).\backslash ,$
Note that this definition allows all of the coordinates of $\backslash vec\{x\}$ to increase to infinity. In particular, the statement
 $f(n,m)\; =\; O(n^m)\; \backslash text\{\; as\; \}\; n,m\backslash to\backslash infty\backslash ,$
(i.e., $\backslash exists\; C\backslash ,\backslash exists\; M\backslash ,\backslash forall\; n\backslash ,\backslash forall\; m\backslash dots$) is quite different from
 $\backslash forall\; m\backslash colon\; f(n,m)\; =\; O(n^m)\; \backslash text\{\; as\; \}\; n\backslash to\backslash infty$
(i.e., $\backslash forall\; m\backslash ,\backslash exists\; C\backslash ,\backslash exists\; M\backslash ,\backslash forall\; n\backslash dots$).
It is worth noting that Rodney R. Howell in his paper "On Asymptotic Notation with Multiple Variables" claims that it is impossible to define Big O notation in multiple variables in a way that implies the properties commonly used in algorithms analysis.
Matters of notation
Equals sign
The statement "f(x) is O(g(x))" as defined above is usually written as f(x) = O(g(x)). Some consider this to be an abuse of notation, since the use of the equals sign could be misleading as it suggests a symmetry that this statement does not have. As de Bruijn says, O(x) = O(x^{2}) is true but O(x^{2}) = O(x) is not.^{[2]} Knuth describes such statements as "oneway equalities", since if the sides could be reversed, "we could deduce ridiculous things like n = n^{2} from the identities n = O(n^{2}) and n^{2} = O(n^{2})."^{[3]}
For these reasons, it would be more precise to use set notation and write f(x) ∈ O(g(x)), thinking of O(g(x)) as the class of all functions h(x) such that h(x) ≤ Cg(x) for some constant C.^{[3]} However, the use of the equals sign is customary. Knuth pointed out that "mathematicians customarily use the = sign as they use the word 'is' in English: Aristotle is a man, but a man isn't necessarily Aristotle."^{[4]}
Other arithmetic operators
Big O notation can also be used in conjunction with other arithmetic operators in more complicated equations. For example, h(x) + O(f(x)) denotes the collection of functions having the growth of h(x) plus a part whose growth is limited to that of f(x). Thus,
 $g(x)\; =\; h(x)\; +\; O(f(x))\backslash ,$
expresses the same as
 $g(x)\; \; h(x)\; \backslash in\; O(f(x))\backslash ,.$
Example
Suppose an algorithm is being developed to operate on a set of n elements. Its developers are interested in finding a function T(n) that will express how long the algorithm will take to run (in some arbitrary measurement of time) in terms of the number of elements in the input set. The algorithm works by first calling a subroutine to sort the elements in the set and then perform its own operations. The sort has a known time complexity of O(n^{2}), and after the subroutine runs the algorithm must take an additional $55n^3+2n+10$ time before it terminates. Thus the overall time complexity of the algorithm can be expressed as
 $T(n)=O(n^2)+55n^3+2n+10.\backslash $
This can perhaps be most easily read by replacing O(n^{2}) with "some function that grows asymptotically no faster than n^{2} ". Again, this usage disregards some of the formal meaning of the "=" and "+" symbols, but it does allow one to use the big O notation as a kind of convenient placeholder.
Declaration of variables
Another feature of the notation, although less exceptional, is that function arguments may need to be inferred from the context when several variables are involved. The following two righthand side big O notations have dramatically different meanings:
 $f(m)\; =\; O(m^n)\backslash ,,$
 $g(n)\backslash ,\backslash ,\; =\; O(m^n)\backslash ,.$
The first case states that f(m) exhibits polynomial growth, while the second, assuming m > 1, states that g(n) exhibits exponential growth.
To avoid confusion, some authors use the notation
 $g(x)\; \backslash in\; O(f(x))\backslash ,.$
rather than the less explicit
 $g\; \backslash in\; O(f)\backslash ,,$
Multiple usages
In more complicated usage, O(...) can appear in different places in an equation, even several times on each side. For example, the following are true for $n\backslash to\backslash infty$
 $(n+1)^2\; =\; n^2\; +\; O(n)\backslash $
 $(n+O(n^\{1/2\}))(n\; +\; O(\backslash log\; n))^2\; =\; n^3\; +\; O(n^\{5/2\})\backslash $
 $n^\{O(1)\}\; =\; O(e^n).\backslash $
The meaning of such statements is as follows: for any functions which satisfy each O(...) on the left side, there are some functions satisfying each O(...) on the right side, such that substituting all these functions into the equation makes the two sides equal. For example, the third equation above means: "For any function $f(n)=O(1)\backslash ,$, there is some function $g(n)=O(e^n)\backslash ,$ such that $n^\{f(n)\}=g(n)$." In terms of the "set notation" above, the meaning is that the class of functions represented by the left side is a subset of the class of functions represented by the right side. In this use the "=" is a formal symbol that unlike the usual use of "=" is not a symmetric relation. Thus for example $n^\{O(1)\}\; =\; O(e^n)\backslash ,$ does not imply the false statement $O(e^n)\; =\; n^\{O(1)\}\backslash ,$.
Orders of common functions
Here is a list of classes of functions that are commonly encountered when analyzing the running time of an algorithm. In each case, c is a constant and n increases without bound. The slowergrowing functions are generally listed first.
Notation 
Name 
Example

$O(1)\backslash ,$ 
constant 
Determining if a number is even or odd; using a constantsize lookup table

$O(\backslash log\; \backslash log\; n)\backslash ,$ 
double logarithmic 
Finding an item using interpolation search in a sorted array of uniformly distributed values.

$O(\backslash log\; n)\backslash ,$ 
logarithmic 
Finding an item in a sorted array with a binary search or a balanced search tree as well as all operations in a Binomial heap.

$O(n^c),\backslash ;01\backslash ,\; math>$ 
fractional power 
Searching in a kdtree

$O(n)\backslash ,$ 
linear 
Finding an item in an unsorted list or a malformed tree (worst case) or in an unsorted array; Adding two nbit integers by ripple carry.

$O(n\backslash log^*\; n)\backslash ,$ 
n logstar n 
Performing triangulation of a simple polygon using Seidel's algorithm. (Note $\backslash log^*(n)\; =$ \begin{cases}
0, & \text{if }n \leq 1 \\
1 + \log^*(\log n), & \text{if }n>1
\end{cases}

$O(n\backslash log\; n)=O(\backslash log\; n!)\backslash ,$ 
linearithmic, loglinear, or quasilinear 
Performing a Fast Fourier transform; heapsort, quicksort (best and average case), or merge sort

$O(n^2)\backslash ,$ 
quadratic 
Multiplying two ndigit numbers by a simple algorithm; bubble sort (worst case or naive implementation), Shell sort, quicksort (worst case), selection sort or insertion sort

$O(n^c),\backslash ;c>1$ 
polynomial or algebraic 
Treeadjoining grammar parsing; maximum matching for bipartite graphs

$L\_n[\backslash alpha,c],\backslash ;0\; <\; \backslash alpha\; <\; 1=\backslash ,$ $e^\{(c+o(1))\; (\backslash ln\; n)^\backslash alpha\; (\backslash ln\; \backslash ln\; n)^\{1\backslash alpha\}\}$ 
Lnotation or subexponential 
Factoring a number using the quadratic sieve or number field sieve

$O(c^n),\backslash ;c>1$ 
exponential 
Finding the (exact) solution to the travelling salesman problem using dynamic programming; determining if two logical statements are equivalent using bruteforce search

$O(n!)\backslash ,$ 
factorial 
Solving the traveling salesman problem via bruteforce search; generating all unrestricted permutations of a poset; finding the determinant with expansion by minors.

The statement $f(n)=O(n!)\backslash ,$ is sometimes weakened to $f(n)=O\backslash left(n^n\backslash right)$ to derive simpler formulas for asymptotic complexity.
For any $k>0$ and $c>0$, $O(n^c(\backslash log\; n)^k)$ is a subset of $O(n^\{c+\backslash varepsilon\; \})$ for any $\backslash varepsilon\; >0$, so may be considered as a polynomial with some bigger order.
Related asymptotic notations
Big O is the most commonly used asymptotic notation for comparing functions, although in many cases Big O may be replaced with Big Theta Θ for asymptotically tighter bounds. Here, we define some related notations in terms of Big O, progressing up to the family of Bachmann–Landau notations to which Big O notation belongs.
Littleo notation
The relation $f(x)\; \backslash in\; o(g(x))$ is read as "$f(x)$ is littleo of $g(x)$". Intuitively, it means that $g(x)$ grows much faster than $f(x)$, or similarly, the growth of $f(x)$ is nothing compared to that of $g(x)$. It assumes that f and g are both functions of one variable. Formally, f(n) = o(g(n)) as n → ∞ means that for every positive constant $\backslash epsilon$ there exists a constant N such that
 $f(n)\backslash leq\backslash epsilong(n)\backslash qquad\backslash text\{for\; all\; \}n\backslash geq\; N~.$^{[3]}
Note the difference between the earlier formal definition for the bigO notation, and the present definition of littleo: while the former has to be true for at least one constant M the latter must hold for every positive constant $\backslash epsilon$, however small.^{[1]} In this way littleo notation makes a stronger statement than the corresponding bigO notation: every function that is littleo of g is also bigO of g, but not every function that is bigO g is also littleo of g (for instance g itself is not, unless it is identically zero near ∞).
If g(x) is nonzero, or at least becomes nonzero beyond a certain point, the relation f(x) = o(g(x)) is equivalent to
 $\backslash lim\_\{x\; \backslash to\; \backslash infty\}\backslash frac\{f(x)\}\{g(x)\}=0.$
For example,
 $2x\; \backslash in\; o(x^2)\; \backslash ,\backslash !$
 $2x^2\; \backslash not\; \backslash in\; o(x^2)$
 $1/x\; \backslash in\; o(1)$
Littleo notation is common in mathematics but rarer in computer science. In computer science the variable (and function value) is most often a natural number. In mathematics, the variable and function values are often real numbers. The following properties can be useful:
 $o(f)\; +\; o(f)\; \backslash subseteq\; o(f)$
 $o(f)o(g)\; \backslash subseteq\; o(fg)$
 $o(o(f))\; \backslash subseteq\; o(f)$
 $o(f)\; \backslash subset\; O(f)$ (and thus the above properties apply with most combinations of o and O).
As with big O notation, the statement "$f(x)$ is $o(g(x))$" is usually written as $f(x)\; =\; o(g(x))$, which is a slight abuse of notation.
Big Omega notation
There are two very widespread and incompatible definitions of the statement
 $f(x)=\backslash Omega(g(x))\backslash \; (x\backslash rightarrow\; a),$
where $a$ is some real number, $\backslash infty$ or $\backslash infty$, where $f$ and $g$ are real functions defined in a neighbourhood of $a$, and where $g$ is positive in this neighbourhood.
The first one (chronologically) is used in analytic number theory, and the other one in computational complexity theory. When the two subjects meet, this situation is bound to generate confusion.
The Hardy–Littlewood definition
In 1914 G.H. Hardy and J.E. Littlewood introduced the new symbol $\backslash Omega$,^{[5]} which is defined as follows:
 $f(x)=\backslash Omega(g(x))\backslash \; (x\backslash rightarrow\backslash infty)\backslash ;\backslash Leftrightarrow\backslash ;\backslash limsup\_\{x\; \backslash to\; \backslash infty\}\; \backslash left\backslash frac\{f(x)\}\{g(x)\}\backslash right>\; 0$.
Thus $f(x)=\backslash Omega(g(x))$ is the negation of $f(x)=o(g(x))$.
In 1918 the same authors introduced the two new symbols $\backslash Omega\_R$ and $\backslash Omega\_L$,^{[6]} thus defined:
 $f(x)=\backslash Omega\_R(g(x))\backslash \; (x\backslash rightarrow\backslash infty)\backslash ;\backslash Leftrightarrow\backslash ;\backslash limsup\_\{x\; \backslash to\; \backslash infty\}\; \backslash frac\{f(x)\}\{g(x)\}>\; 0$;
 $f(x)=\backslash Omega\_L(g(x))\backslash \; (x\backslash rightarrow\backslash infty)\backslash ;\backslash Leftrightarrow\backslash ;\backslash liminf\_\{x\; \backslash to\; \backslash infty\}\; \backslash frac\{f(x)\}\{g(x)\}<\; 0$.
Hence $f(x)=\backslash Omega\_R(g(x))$ is the negation of $f(x)(g(x))\; math>,\; and$ f(x)=\backslash Omega\_L(g(x))$the\; negation\; of$ f(x)o(g(x))$.$
Contrary to a later assertion of D.E. Knuth,^{[7]} Edmund Landau did use these three symbols, with the same meanings, in 1924.^{[8]}
These HardyLittlewood symbols are prototypes, which after Landau were never used again exactly thus.
 $\backslash Omega\_R$ became $\backslash Omega\_+$, and $\backslash Omega\_L$ became $\backslash Omega\_$.
These three symbols $\backslash Omega,\; \backslash Omega\_+,\; \backslash Omega\_$, as well as $f(x)=\backslash Omega\_\backslash pm(g(x))$ (meaning that $f(x)=\backslash Omega\_+(g(x))$ and $f(x)=\backslash Omega\_(g(x))$ are both satisfied), are now currently used in analytic number theory.
Simple examples
We have
 $\backslash sin\; x=\backslash Omega(1)\backslash \; (x\backslash rightarrow\backslash infty)$,
and more precisely
 $\backslash sin\; x=\backslash Omega\_\backslash pm(1)\backslash \; (x\backslash rightarrow\backslash infty)$.
We have
 $\backslash sin\; x+1=\backslash Omega(1)\backslash \; (x\backslash rightarrow\backslash infty)$,
and more precisely
 $\backslash sin\; x+1=\backslash Omega\_+(1)\backslash \; (x\backslash rightarrow\backslash infty)$;
however
 $\backslash sin\; x+1\backslash not=\backslash Omega\_(1)\backslash \; (x\backslash rightarrow\backslash infty)$.
The Knuth definition
In 1976 D.E. Knuth published a paper^{[7]} to justify his use of the $\backslash Omega$symbol to describe a stronger property. Knuth wrote: "For all the applications I have seen so far in computer science, a stronger requirement […] is much more appropriate". He defined
 $f(x)=\backslash Omega(g(x))\backslash Leftrightarrow\; g(x)=O(f(x))$
with the comment: "Although I have changed Hardy and Littlewood's definition of $\backslash Omega$, I feel justified in doing so because their definition is by no mean in wide use, and because there are other ways to say what they want to say in the comparatively rare cases when their definition applies". However, the Hardy–Littlewood definition had been well used for at least 25 years.^{[9]}
Family of Bachmann–Landau notations
Notation

Name

Intuition

Informal definition: for sufficiently large $n$...

Formal Definition

$f(n)\; \backslash in\; O(g(n))$

Big Omicron; Big O; Big Oh

$f$ is bounded above by $g$ (up to constant factor) asymptotically

f(n) \leq g(n)\cdot k for some positive k

f(n) \leq g(n)\cdot k or $\backslash exists\; k>0\; \backslash ;\; \backslash exists\; n\_0\; \backslash ;\; \backslash forall\; n>n\_0\; \backslash ;\; f(n)\; \backslash leq\; g(n)\backslash cdot\; k$

$f(n)\; \backslash in\; \backslash Omega(g(n))$

Big Omega

Two definitions :
Number theory:
$f$ is not dominated by $g$ asymptotically
Complexity theory:
$f$ is bounded below by $g$ asymptotically

Number theory:
$f(n)\; \backslash geq\; g(n)\backslash cdot\; k$ for infinitely many values of n and for some positive k
Complexity theory:
$f(n)\; \backslash geq\; g(n)\backslash cdot\; k$ for some positive k

Number theory:
$\backslash exists\; k>0\; \backslash ;\; \backslash forall\; n\_0\; \backslash ;\; \backslash exists\; n>n\_0\; \backslash ;\; g(n)\backslash cdot\; k\; \backslash leq\; f(n)$
Complexity theory:
$\backslash exists\; k>0\; \backslash ;\; \backslash exists\; n\_0\; \backslash ;\; \backslash forall\; n>n\_0\; \backslash ;\; g(n)\backslash cdot\; k\; \backslash leq\; f(n)$

$f(n)\; \backslash in\; \backslash Theta(g(n))$

Big Theta

$f$ is bounded both above and below by $g$ asymptotically

$g(n)\backslash cdot\; k\_1\; \backslash leq\; f(n)\; \backslash leq\; g(n)\backslash cdot\; k\_2$ for some positive k_{1}, k_{2}

$\backslash exists\; k\_1>0\; \backslash ;\; \backslash exists\; k\_2>0\; \backslash ;\; \backslash exists\; n\_0\; \backslash ;\; \backslash forall\; n>n\_0$
$g(n)\; \backslash cdot\; k\_1\; \backslash leq\; f(n)\; \backslash leq\; g(n)\; \backslash cdot\; k\_2$

$f(n)\; \backslash in\; o(g(n))$

Small Omicron; Small O; Small Oh

$f$ is dominated by $g$ asymptotically

f(n) \le k\cdotg(n), for every fixed positive number $k$

f(n) \le k\cdot g(n)

$f(n)\; \backslash in\; \backslash omega(g(n))$

Small Omega

$f$ dominates $g$ asymptotically

f(n) \ge k\cdotg(n), for every fixed positive number $k$

f(n) \ge k\cdot g(n)

$f(n)\backslash sim\; g(n)\backslash !$

On the order of

$f$ is equal to $g$ asymptotically

$f(n)/g(n)\; \backslash to\; 1$

{f(n) \over g(n)}1\right<\varepsilon

When Paul Gustav Heinrich Bachmann and Edmund Landau designed the notation that they utilized when authoring their respective seminal references on asymptotic growthrates from 1894^{[10]}
to 1909,^{[11]} Bachmann–Landau notation was designed around several mnemonics, as shown in the bullets below. To conceptually access these mnemonics, "omicron" can be read "omicron" and "omega" can be read "omega". Also, the lowercase versus capitalization of the Greek letters in Bachmann–Landau notation is mnemonic.
 The omicron mnemonic: The omicron reading of $f(n)\; \backslash in\; O(g(n))$ and of $f(n)\; \backslash in\; o(g(n))$ can be thought of as "Osmaller than" and "osmaller than", respectively. This micro/smaller mnemonic refers to: for sufficiently large input parameter(s), $f$ grows at a rate that may henceforth be less than $cg$ regarding $g\; \backslash in\; O(f)$ or $g\; \backslash in\; o(f)$.
 The omega mnemonic: The omega reading of $f(n)\; \backslash in\; \backslash Omega(g(n))$ and of $f(n)\; \backslash in\; \backslash omega(g(n))$ can be thought of as "Olarger than". This mega/larger mnemonic refers to: for sufficiently large input parameter(s), $f$ grows at a rate that may henceforth be greater than $cg$ regarding $g\; \backslash in\; \backslash Omega(f)$ or $g\; \backslash in\; \backslash omega(f)$.
 The uppercase mnemonic: This mnemonic reminds us when to use the uppercase Greek letters in $f(n)\; \backslash in\; O(g(n))$ and $f(n)\; \backslash in\; \backslash Omega(g(n))$: for sufficiently large input parameter(s), $f$ grows at a rate that may henceforth be equal to $cg$ regarding $g\; \backslash in\; O(f)$.
 The lowercase mnemonic: This mnemonic reminds us when to use the lowercase Greek letters in $f(n)\; \backslash in\; o(g(n))$ and $f(n)\; \backslash in\; \backslash omega(g(n))$: for sufficiently large input parameter(s), $f$ grows at a rate that is henceforth inequal to $cg$ regarding $g\; \backslash in\; O(f)$.
Aside from the Big O notation, the Big Theta Θ and Big Omega Ω notations are the two most often used in computer science; the small omega ω notation is occasionally used in computer science.
Aside from the Big O notation, the small o, Big Omega Ω and $\backslash sim$ notations are the three most often used in number theory; the small omega ω notation is never used in number theory.
Use in computer science
Informally, especially in computer science, the Big O notation often is permitted to be somewhat abused to describe an asymptotic tight bound where using Big Theta Θ notation might be more factually appropriate in a given context. For example, when considering a function $T(n)\; =\; 73n^3\; +\; 22n^2\; +\; 58$, all of the following are generally acceptable, but tightnesses of bound (i.e., numbers 2 and 3 below) are usually strongly preferred over laxness of bound (i.e., number 1 below).
 T(n) = O(n^{100}), which is identical to T(n) ∈ O(n^{100})
 T(n) = O(n^{3}), which is identical to T(n) ∈ O(n^{3})
 T(n) = Θ(n^{3}), which is identical to T(n) ∈ Θ(n^{3}).
The equivalent English statements are respectively:
 T(n) grows asymptotically no faster than n^{100}
 T(n) grows asymptotically no faster than n^{3}
 T(n) grows asymptotically as fast as n^{3}.
So while all three statements are true, progressively more information is contained in each. In some fields, however, the Big O notation (number 2 in the lists above) would be used more commonly than the Big Theta notation (bullets number 3 in the lists above) because functions that grow more slowly are more desirable. For example, if $T(n)$ represents the running time of a newly developed algorithm for input size $n$, the inventors and users of the algorithm might be more inclined to put an upper asymptotic bound on how long it will take to run without making an explicit statement about the lower asymptotic bound.
Extensions to the Bachmann–Landau notations
Another notation sometimes used in computer science is Õ (read softO): f(n) = Õ(g(n)) is shorthand
for f(n) = O(g(n) log^{k} g(n)) for some k. Essentially, it is Big O notation, ignoring logarithmic factors because the growthrate effects of some other superlogarithmic function indicate a growthrate explosion for largesized input parameters that is more important to predicting bad runtime performance than the finerpoint effects contributed by the logarithmicgrowth factor(s). This notation is often used to obviate the "nitpicking" within growthrates that are stated as too tightly bounded for the matters at hand (since log^{k} n is always o(n^{ε}) for any constant k and any ε > 0).
The L notation, defined as
 $L\_n[\backslash alpha,c]=O\backslash left(e^\{(c+o(1))(\backslash ln\; n)^\backslash alpha(\backslash ln\backslash ln\; n)^\{1\backslash alpha\}\}\backslash right),$
is convenient for functions that are between polynomial and exponential.
Generalizations and related usages
The generalization to functions taking values in any normed vector space is straightforward (replacing absolute values by norms), where f and g need not take their values in the same space. A generalization to functions g taking values in any topological group is also possible.
The "limiting process" x→x_{o} can also be generalized by introducing an arbitrary filter base, i.e. to directed nets f and g.
The o notation can be used to define derivatives and differentiability in quite general spaces, and also (asymptotical) equivalence of functions,
 $f\backslash sim\; g\; \backslash iff\; (fg)\; \backslash in\; o(g)$
which is an equivalence relation and a more restrictive notion than the relationship "f is Θ(g)" from above. (It reduces to $\backslash lim\; f/g\; =\; 1$ if f and g are positive real valued functions.) For example, 2x is Θ(x), but 2x − x is not o(x).
History (Bachmann–Landau, Hardy, and Vinogradov notations)
The symbol O was first introduced by number theorist Paul Bachmann in 1894, in the second volume of his book Analytische Zahlentheorie ("analytic number theory"), the first volume of which (not yet containing big O notation) was published in 1892.^{[12]} The number theorist Edmund Landau adopted it, and was thus inspired to introduce in 1909 the notation o;^{[13]} hence both are now called Landau symbols. These notations were used in applied mathematics during the 1950s for asymptotic analysis ^{[14]} The big O was popularized in computer science by Donald Knuth, who reintroduced the related Omega and Theta notations.^{[7]} Knuth also noted that the Omega notation had been introduced by Hardy and Littlewood^{[5]} under a different meaning "≠o" (i.e. "is not an o of"), and proposed the above definition. Hardy and Littlewood's original definition (which was also used in one paper by Landau^{[8]}) is still used in number theory (where Knuth's definition is never used). In fact, Landau introduced in 1924, in the paper just mentioned, the symbols $\backslash Omega\_R$ ("rechts") and $\backslash Omega\_L$ ("links"), precursors for the modern symbols $\backslash Omega\_+$ ("is not smaller than a small o of") and $\backslash Omega\_$ ("is not larger than a o of"). Thus the Omega symbols (with their original meanings) are sometimes also referred to as "Landau symbols". Also, Landau never used the Big Theta and small omega symbols.
Hardy's symbols were (in terms of the modern O notation)
 $f\; \backslash preceq\; g\backslash iff\; f\; \backslash in\; O(g)$ and $f\backslash prec\; g\backslash iff\; f\backslash in\; o(g);$
(Hardy however never defined or used the notation $\backslash prec\backslash !\backslash !\backslash prec$, nor $\backslash ll$, as it has been sometimes reported).
It should also be noted that Hardy introduces the symbols $\backslash preceq$ and $\backslash prec$ (as well as some other symbols) in his 1910 tract "Orders of Infinity", and makes use of it only in three papers (1910–1913). In the remaining papers (nearly 400!) and books he constantly uses the Landau symbols O and o.
Hardy's notation is not used anymore. On the other hand, in the 1930s,^{[15]} the Russian number theorist Ivan Matveyevich Vinogradov introduced his notation
$\backslash ll$, which has been increasingly used in number theory instead of the $O$ notation. We have
 $f\backslash ll\; g\; \backslash iff\; f\; \backslash in\; O(g),$
and frequently both notations are used in the same paper.
The bigO, standing for "order of", was originally a capital omicron; today the identicallooking Latin capital letter O is used, but never the digit zero.
See also
References and Notes
Further reading
 Paul Bachmann. Die Analytische Zahlentheorie. Zahlentheorie. pt. 2 Leipzig: B. G. Teubner, 1894.
 Edmund Landau. Handbuch der Lehre von der Verteilung der Primzahlen. 2 vols. Leipzig: B. G. Teubner, 1909.
 G. H. Hardy. Orders of Infinity: The 'Infinitärcalcül' of Paul du BoisReymond, 1910.
 Donald Knuth. The Art of Computer Programming, Volume 1: Fundamental Algorithms, Third Edition. Addison–Wesley, 1997. ISBN 0201896834. Section 1.2.11: Asymptotic Representations, pp. 107–123.
 Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, Second Edition. MIT Press and McGraw–Hill, 2001. ISBN 0262032937. Section 3.1: Asymptotic notation, pp. 41–50.
 Pages 226–228 of section 7.1: Measuring complexity.
 Jeremy Avigad, Kevin Donnelly. Formalizing O notation in Isabelle/HOL
 Paul E. Black, "bigO notation", in Dictionary of Algorithms and Data Structures [online], Paul E. Black, ed., U.S. National Institute of Standards and Technology. 11 March 2005. Retrieved December 16, 2006.
 Paul E. Black, "littleo notation", in Dictionary of Algorithms and Data Structures [online], Paul E. Black, ed., U.S. National Institute of Standards and Technology. 17 December 2004. Retrieved December 16, 2006.
 Paul E. Black, "Ω", in Dictionary of Algorithms and Data Structures [online], Paul E. Black, ed., U.S. National Institute of Standards and Technology. 17 December 2004. Retrieved December 16, 2006.
 Paul E. Black, "ω", in Dictionary of Algorithms and Data Structures [online], Paul E. Black, ed., U.S. National Institute of Standards and Technology. 29 November 2004. Retrieved December 16, 2006.
 Paul E. Black, "Θ", in Dictionary of Algorithms and Data Structures [online], Paul E. Black, ed., U.S. National Institute of Standards and Technology. 17 December 2004. Retrieved December 16, 2006.
External links
 Introduction to Asymptotic Notations
 Landau Symbols
 ONotation Visualizer: Interactive Graphs of Common ONotations
 BigO Notation – What is it good for
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