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An operator is a mapping from one vector space or module to another. Operators are of critical importance to both linear algebra and functional analysis, and they find application in many other fields of pure and applied mathematics. For example, in classical mechanics, the derivative is used ubiquitously, and in quantum mechanics, observables are represented by hermitian operators. Important properties that various operators may exhibit include linearity, continuity, and boundedness.

Definitions

Let U, V be two vector spaces. Any mapping from U to V is called an operator. Let V be a vector space over the field K. We can define the structure of a vector space on the set of all operators from U to V (A and B are operators):

$(A+B)\mathbf{x}:=A\mathbf{x}+B\mathbf{x},$

$(\alpha A)\mathbf{x}:=\alpha A\mathbf{x}$

for all A, B: U → V, for all x in U and for all α in K.

Additionally, operators from any vector space to itself form a unital associative algebra:

$(AB)\mathbf{x}:=A(B\mathbf{x})$

with the identity mapping (usually denoted E, I or id) being the unit.

Bounded operators and operator norm

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Let U and V be two vector spaces over the same ordered field (for example, $\mathbf{R}$), and they are equipped with norms. Then a linear operator from U to V is called bounded if there exists C > 0 such that

$\left|\right|A\mathbf{x}|{|}_V\le C|\left|\mathbf{x}\right|{|}_U$

for all x in U.

Bounded operators form a vector space. On this vector space we can introduce a norm that is compatible with the norms of U and V:

$\left|\right|A\left|\right|=\inf \{C:|\left|A\mathbf{x}\right|{|}_V\le C\left|\right|\mathbf{x}|{|}_U\}$.

In case of operators from U to itself it can be shown that

$\left|\right|AB\left|\right|\le \left|\right|A\left|\right|\cdot \left|\right|B\left|\right|$.

Any unital normed algebra with this property is called a Banach algebra. It is possible to generalize spectral theory to such algebras. C*-algebras, which are Banach algebras with some additional structure, play an important role in quantum mechanics.

Special cases

Functionals

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Linear operators

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The most common kind of operator encountered are linear operators. Let U and V be vector spaces over a field K. Operator A: U → V is called linear if

$A(\alpha \mathbf{x}+\beta \mathbf{y})=\alpha A\mathbf{x}+\beta A\mathbf{y}$

for all x, y in U and for all α, β in K.

The importance of linear operators is partially because they are morphisms between vector spaces.

In finite-dimensional case linear operators can be represented by matrices in the following way. Let $K$ be a field, and $U$ and $V$ be finite-dimensional vector spaces over $K$. Let us select a basis ${\mathbf{u}}_1,\dots ,{\mathbf{u}}_n$ in $U$ and ${\mathbf{v}}_1,\dots ,{\mathbf{v}}_m$ in $V$. Then let $\mathbf{x}=x^i{\mathbf{u}}_i$ be an arbitrary vector in $U$ (assuming Einstein convention), and $A:U\to V$ be a linear operator. Then

$A\mathbf{x}=x^{i}A{\mathbf{u}}_i=x^i(A{\mathbf{u}}_i{)}^j{\mathbf{v}}_j$.

Then $a_i^j:=(A{\mathbf{u}}_i{)}^j\in K$ is the matrix of the operator $A$ in fixed bases. $a_i^j$ does not depend on the choice of $x$, and $A\mathbf{x}=\mathbf{y}$ iff $a_i^j{x}^i=y^j$. Thus in fixed bases n-by-m matrices are in bijective correspondence to linear operators from $U$ to $V$.

The important concepts directly related to operators between finite-dimensional vector spaces are the ones of rank, determinant, inverse operator, and eigenspace.

Linear operators also play a great role in the infinite-dimensional case. The concepts of rank and determinant cannot be extended to infinite-dimensional matrices. This is why very different techniques are employed when studying linear operators (and operators in general) in the infinite-dimensional case. The study of linear operators in the infinite-dimensional case is known as functional analysis (so called because various classes of functions form interesting examples of infinite-dimensional vector spaces).

The space of sequences of real numbers, or more generally sequences of vectors in any vector space, themselves form an infinite-dimensional vector space. The most important cases are sequences of real or complex numbers, and these spaces, together with linear subspaces, are known as sequence spaces. Operators on these spaces are known as sequence transformations.

Bounded linear operators over Banach space form a Banach algebra in respect to the standard operator norm. The theory of Banach algebras develops a very general concept of spectra that elegantly generalizes the theory of eigenspaces.

Examples

Geometry

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In geometry, additional structures on vector spaces are sometimes studied. Operators that map such vector spaces to themselves bijectively are very useful in these studies, they naturally form groups by composition.

For example, bijective operators preserving the structure of a vector space are precisely the invertible linear operators. They form the general linear group under composition. They do not form a vector space under the addition of operators, e.g. both id and -id are invertible (bijective), but their sum, 0, is not.

Operators preserving the Euclidean metric on such a space form the isometry group, and those that fix the origin form a subgroup known as the orthogonal group. Operators in the orthogonal group that also preserve the orientation of vector tuples form the special orthogonal group, or the group of rotations.

Probability theory

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Operators are also involved in probability theory, such as expectation, variance, covariance, factorials, etc.

Calculus

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Fourier series and Fourier transform

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The Fourier transform is useful in applied mathematics, particularly physics and signal processing. It is another integral operator; it is useful mainly because it converts a function on one (temporal) domain to a function on another (frequency) domain, in a way effectively invertible. Nothing significant is lost, because there is an inverse transform operator. In the simple case of periodic functions, this result is based on the theorem that any continuous periodic function can be represented as the sum of a series of sine waves and cosine waves:

$f(t)=\frac{a_0}{2}+\sum _{n=1}^{\mathrm{\infty }}{a}_n\cos (\omega nt)+b_n\sin (\omega nt)$

Coefficients (a₀, a₁, b₁, a₂, b₂, ...) are in fact an element of an infinite-dimensional vector space ℓ², and thus Fourier series is a linear operator.

When dealing with general function R → C, the transform takes on an integral form:

$f(t)=\frac{1}{\sqrt{2\pi }}\underset{-\mathrm{\infty }}{\overset{+\mathrm{\infty }}{\int }}g(\omega )e^{i\omega t}d\omega .$

Laplace transform

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Given f = f(s), it is defined by:

$F(s)=(\mathcal{ℒ}f)(s)=\underset{0}{\overset{\mathrm{\infty }}{\int }}{e}^{-st}f(t)dt.$

Fundamental operators on scalar and vector fields

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Three operators are key to vector calculus:

• Grad (gradient), (with operator symbol $\mathrm{\nabla }$) assigns a vector at every point in a scalar field that points in the direction of greatest rate of change of that field and whose norm measures the absolute value of that greatest rate of change.
• Div (divergence), (with operator symbol $\mathrm{\nabla }\cdot$) is a vector operator that measures a vector field's divergence from or convergence towards a given point.
• Curl, (with operator symbol $\mathrm{\nabla }\times$) is a vector operator that measures a vector field's curling (winding around, rotating around) trend about a given point.

As an extension of vector calculus operators to physics, engineering and tensor spaces, Grad, Div and Curl operators also are often associatied with Tensor calculus as well as vector calculus.

See also

• Operation
• Function
• List of mathematical operators
• Vector space
• Dual space
• Operator algebra
• Banach algebra
• List of operators
• Operator (physics)
• Operator (programming)

References

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