Dirac delta function

Pseudo-function δ such that an integral of δ(x-c)f(x) always takes the value of f(c)

"Delta function" redirects here. For other uses, see Delta function (disambiguation).

Differential equations

Navier–Stokes differential equations used to simulate airflow around an obstruction

Navier–Stokes differential equations used to simulate airflow around an obstruction

Scope

Fields

* Natural sciences
* Engineering

* Astronomy
* Physics
* Chemistry

Biology
* Geology

Applied mathematics

* Continuum mechanics
* Chaos theory
* Dynamical systems

Social sciences

* Economics
* Population dynamics

* * *

List of named differential equations

Classification

Types

* Ordinary
* Partial
* Differential-algebraic
* Integro-differential
* Fractional
* Linear
* Non-linear

By variable type

* Dependent and independent variables

* Autonomous
* Coupled / Decoupled
* Exact
* Homogeneous / Nonhomogeneous

Features

* Order
* Operator

* Notation

Relation to processes

* Difference (discrete analogue)

* Stochastic
* Stochastic partial
* Delay

Solution

Existence and uniqueness

* Picard–Lindelöf theorem
* Peano existence theorem
* Carathéodory's existence theorem
* Cauchy–Kowalevski theorem

General topics

* Initial conditions
* Boundary values
* Dirichlet
* Neumann
* Robin
* Cauchy problem
* Wronskian
* Phase portrait
* Lyapunov / Asymptotic / Exponential stability
* Rate of convergence
* Series / Integral solutions
* Numerical integration
* Dirac delta function

Solution methods

* Inspection
* Method of characteristics

Euler
* Exponential response formula
* Finite difference (Crank–Nicolson)
* Finite element
* Infinite element
* Finite volume
* Galerkin
* Petrov–Galerkin
* Green's function
* Integrating factor
* Integral transforms
* Perturbation theory
* Runge–Kutta

* Separation of variables
* Undetermined coefficients
* Variation of parameters

People

List

* Isaac Newton
* Gottfried Leibniz
* Jacob Bernoulli
* Leonhard Euler
* Józef Maria Hoene-Wroński
* Joseph Fourier
* Augustin-Louis Cauchy
* George Green
* Carl David Tolmé Runge
* Martin Kutta
* Rudolf Lipschitz
* Ernst Lindelöf
* Émile Picard
* Phyllis Nicolson
* John Crank

* v
* t
* e

File:Dirac distribution PDF.svg

Schematic representation of the Dirac delta by a line surmounted by an arrow. The height of the arrow is usually meant to specify the value of any multiplicative constant, which will give the area under the function. The other convention is to write the area next to the arrowhead.

File:Dirac function approximation.gif

The Dirac delta as the limit as Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle a\to 0} (in the sense of distributions) of the sequence of zero-centered normal distributions Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta _{a}(x)={\frac {1}{\left|a\right|{\sqrt {\pi }}}}e^{-(x/a)^{2}}}

In mathematics, the Dirac delta distribution (δ distribution), also known as the unit impulse,[1] is a generalized function or distribution over the real numbers, whose value is zero everywhere except at zero, and whose integral over the entire real line is equal to one.[2][3][4]

The current understanding of the unit impulse is as a linear functional that maps every continuous function (e.g.,  f ( x ) {\displaystyle f(x)} ) to its value at zero of its domain ( f ( 0 ) {\displaystyle f(0)} ),[5][6] or as the weak limit of a sequence of bump functions (e.g., Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (x)=\lim _{b\to 0}{\frac {1}{|b|{\sqrt {\pi }}}}e^{-(x/b)^{2}}} ), which are zero over most of the real line, with a tall spike at the origin. Bump functions are thus sometimes called "approximate" or "nascent" delta distributions.

The delta function was introduced by physicist Paul Dirac as a tool for the normalization of state vectors. It also has uses in probability theory and signal processing. Its validity was disputed until Laurent Schwartz developed the theory of distributions where it is defined as a linear form acting on functions.

The Kronecker delta function, which is usually defined on a discrete domain and takes values 0 and 1, is the discrete analog of the Dirac delta function.

## Contents

* 1 Motivation and overview
* 2 History
* 3 Definitions
* 3.1 As a measure
* 3.2 As a distribution
* 3.3 Generalizations
* 4 Properties
* 4.1 Scaling and symmetry
* 4.2 Algebraic properties
* 4.3 Translation
* 4.4 Composition with a function
* 4.5 Properties in n dimensions
* 5 Fourier transform
* 6 Derivatives of the Dirac delta function
* 6.1 Higher dimensions
* 7 Representations of the delta function
* 7.1 Approximations to the identity
* 7.2 Probabilistic considerations
* 7.3 Semigroups
* 7.4 Oscillatory integrals
* 7.5 Plane wave decomposition
* 7.6 Fourier kernels
* 7.7 Hilbert space theory
* 7.7.1 Spaces of holomorphic functions
* 7.7.2 Resolutions of the identity
* 7.8 Infinitesimal delta functions
* 8 Dirac comb
* 9 Sokhotski–Plemelj theorem
* 10 Relationship to the Kronecker delta
* 11 Applications
* 11.1 Probability theory
* 11.2 Quantum mechanics
* 11.3 Structural mechanics
* 12 See also
* 13 Notes
* 14 References
* 15 External links

## Motivation and overview[edit | edit source]

The graph of the Dirac delta is usually thought of as following the whole x-axis and the positive y-axis.[7]: 174 The Dirac delta is used to model a tall narrow spike function (an impulse), and other similar abstractions such as a point charge, point mass or electron point. For example, to calculate the dynamics of a billiard ball being struck, one can approximate the force of the impact by a Dirac delta. In doing so, one not only simplifies the equations, but one also is able to calculate the motion of the ball by only considering the total impulse of the collision without a detailed model of all of the elastic energy transfer at subatomic levels (for instance).

To be specific, suppose that a billiard ball is at rest. At time Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle t=0} it is struck by another ball, imparting it with a momentum P {\displaystyle P} , in Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle {\text{kg m s}}^{-1}} . The exchange of momentum is not actually instantaneous, being mediated by elastic processes at the molecular and subatomic level, but for practical purposes it is convenient to consider that energy transfer as effectively instantaneous. The force therefore is Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle P\,\delta (t)} . (The units of Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (t)} are Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \mathrm {s} ^{-1}} .)

To model this situation more rigorously, suppose that the force instead is uniformly distributed over a small time interval  Δ t = [ 0 , T ] {\displaystyle \Delta t=[0,T]} . That is,

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle F_{\Delta t}(t)={\begin{cases}P/\Delta t&0<t\leq T,\\\0&{\text{otherwise}}.\end{cases}}}

Then the momentum at any time t is found by integration:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle p(t)=\int _{0}^{t}F_{\Delta t}(\tau )\,\mathrm {d} \tau ={\begin{cases}P&t\geq T\\\P\,t/\Delta t&0\leq t\leq T\\\0&{\text{otherwise.}}\end{cases}}}

Now, the model situation of an instantaneous transfer of momentum requires taking the limit as Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \Delta t\to 0} , giving a result everywhere except at 0:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle p(t)={\begin{cases}P&t>0\\\0&t<0.\end{cases}}}

Here the functions Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle F_{\Delta t}} are thought of as useful approximations to the idea of instantaneous transfer of momentum.

The delta function allows us to construct an idealized limit of these approximations. Unfortunately, the actual limit of the functions (in the sense of pointwise convergence) Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\textstyle \lim _{\Delta t\to 0}F_{\Delta t}} is zero everywhere but a single point, where it is infinite. To make proper sense of the Dirac delta, we should instead insist that the property

∫ − ∞ ∞ F Δ t ( t ) d t = P , {\displaystyle \int _{-\infty }^{\infty }F_{\Delta t}(t)\,\mathrm {d} t=P,}

which holds for all Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \Delta t>0} , should continue to hold in the limit. So, in the equation Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\textstyle F(t)=P\,\delta (t)=\lim _{\Delta t\to 0}F_{\Delta t}(t)} , it is understood that the limit is always taken outside the integral.

In applied mathematics, as we have done here, the delta function is often manipulated as a kind of limit (a weak limit) of a sequence of functions, each member of which has a tall spike at the origin: for example, a sequence of Gaussian distributions centered at the origin with variance tending to zero.

The Dirac delta is not truly a function, at least not a usual one with domain and range in real numbers. For example, the objects f(x) = δ(x) and g(x) = 0 are equal everywhere except at x = 0 yet have integrals that are different. According to Lebesgue integration theory, if f and g are functions such that f = g almost everywhere, then f is integrable if and only if g is integrable and the integrals of f and g are identical. A rigorous approach to regarding the Dirac delta function as a mathematical object in its own right requires measure theory or the theory of distributions.

## History[edit | edit source]

Joseph Fourier presented what is now called the Fourier integral theorem in his treatise Théorie analytique de la chaleur in the form:[8]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle f(x)={\frac {1}{2\pi }}\int _{-\infty }^{\infty }\ \ d\alpha \,f(\alpha )\ \int _{-\infty }^{\infty }dp\ \cos(px-p\alpha )\ ,}

which is tantamount to the introduction of the δ-function in the form:[9]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (x-\alpha )={\frac {1}{2\pi }}\int _{-\infty }^{\infty }dp\ \cos(px-p\alpha )\ .}

Later, Augustin Cauchy expressed the theorem using exponentials:[10][11]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle f(x)={\frac {1}{2\pi }}\int _{-\infty }^{\infty }\ e^{ipx}\left(\int _{-\infty }^{\infty }e^{-ip\alpha }f(\alpha )\,d\alpha \right)\,dp.}

Cauchy pointed out that in some circumstances the order of integration is significant in this result (contrast Fubini's theorem).[12][13]

As justified using the theory of distributions, the Cauchy equation can be rearranged to resemble Fourier's original formulation and expose the δ-function as

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle {\begin{aligned}f(x)&={\frac {1}{2\pi }}\int _{-\infty }^{\infty }e^{ipx}\left(\int _{-\infty }^{\infty }e^{-ip\alpha }f(\alpha )\,d\alpha \right)\,dp\\\\[4pt]&={\frac {1}{2\pi }}\int _{-\infty }^{\infty }\left(\int _{-\infty }^{\infty }e^{ipx}e^{-ip\alpha }\,dp\right)f(\alpha )\,d\alpha =\int _{-\infty }^{\infty }\delta (x-\alpha )f(\alpha )\,d\alpha ,\end{aligned}}}

where the δ-function is expressed as

δ ( x − α ) = 1 2 π ∫ − ∞ ∞ e i p ( x − α ) d p . {\displaystyle \delta (x-\alpha )={\frac {1}{2\pi }}\int _{-\infty }^{\infty }e^{ip(x-\alpha )}\,dp\ .}

A rigorous interpretation of the exponential form and the various limitations upon the function f necessary for its application extended over several centuries. The problems with a classical interpretation are explained as follows:[14]

The greatest drawback of the classical Fourier transformation is a rather narrow class of functions (originals) for which it can be effectively computed. Namely, it is necessary that these functions decrease sufficiently rapidly to zero (in the neighborhood of infinity) to ensure the existence of the Fourier integral. For example, the Fourier transform of such simple functions as polynomials does not exist in the classical sense. The extension of the classical Fourier transformation to distributions considerably enlarged the class of functions that could be transformed and this removed many obstacles.

Further developments included generalization of the Fourier integral, "beginning with Plancherel's pathbreaking L2-theory (1910), continuing with Wiener's and Bochner's works (around 1930) and culminating with the amalgamation into L. Schwartz's theory of distributions (1945) ...",[15] and leading to the formal development of the Dirac delta function.

An infinitesimal formula for an infinitely tall, unit impulse delta function (infinitesimal version of Cauchy distribution) explicitly appears in an 1827 text of Augustin Louis Cauchy. [16] Siméon Denis Poisson considered the issue in connection with the study of wave propagation as did Gustav Kirchhoff somewhat later. Kirchhoff and Hermann von Helmholtz also introduced the unit impulse as a limit of Gaussians, which also corresponded to Lord Kelvin's notion of a point heat source. At the end of the 19th century, Oliver Heaviside used formal Fourier series to manipulate the unit impulse.[17] The Dirac delta function as such was introduced by Paul Dirac in his 1927 paper The Physical Interpretation of the Quantum Dynamics[18] and used in his textbook The Principles of Quantum Mechanics.[3] He called it the "delta function" since he used it as a continuous analogue of the discrete Kronecker delta.

## Definitions[edit | edit source]

The Dirac delta can be loosely thought of as a function on the real line which is zero everywhere except at the origin, where it is infinite,

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (x)\simeq {\begin{cases}+\infty ,&x=0\\\0,&x\neq 0\end{cases}}}

and which is also constrained to satisfy the identity

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{-\infty }^{\infty }\delta (x)\,\mathrm {d} x=1.} [19]

This is merely a heuristic characterization. The Dirac delta is not a function in the traditional sense as no function defined on the real numbers has these properties.[20] The Dirac delta function can be rigorously defined either as a distribution or as a measure.

### As a measure[edit | edit source]

One way to rigorously capture the notion of the Dirac delta function is to define a measure, called Dirac measure, which accepts a subset A of the real line R as an argument, and returns δ(A) = 1 if 0 ∈ A, and δ(A) = 0 otherwise.[21] If the delta function is conceptualized as modeling an idealized point mass at 0, then δ(A) represents the mass contained in the set A. One may then define the integral against δ as the integral of a function against this mass distribution. Formally, the Lebesgue integral provides the necessary analytic device. The Lebesgue integral with respect to the measure δ satisfies

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{-\infty }^{\infty }f(x)\,\delta (\mathrm {d} x)=f(0)}

for all continuous compactly supported functions f. The measure δ is not absolutely continuous with respect to the Lebesgue measure—in fact, it is a singular measure. Consequently, the delta measure has no Radon–Nikodym derivative (with respect to Lebesgue measure)—no true function for which the property

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{-\infty }^{\infty }f(x)\,\delta (x)\,\mathrm {d} x=f(0)}

holds.[22] As a result, the latter notation is a convenient abuse of notation, and not a standard (Riemann or Lebesgue) integral.

As a probability measure on R, the delta measure is characterized by its cumulative distribution function, which is the unit step function.[23]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle H(x)={\begin{cases}1&{\text{if }}x\geq 0\\\0&{\text{if }}x<0.\end{cases}}}

This means that H(x) is the integral of the cumulative indicator function 1(−∞, x] with respect to the measure δ; to wit,

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle H(x)=\int _{\mathbf {R} }\mathbf {1} _{(-\infty ,x]}(t)\,\delta (\mathrm {d} t)=\delta (-\infty ,x],}

the latter being the measure of this interval; more formally, δ((−∞, x]). Thus in particular the integration of the delta function against a continuous function can be properly understood as a Riemann–Stieltjes integral:[24]

∫ − ∞ ∞ f ( x ) δ ( d x ) = ∫ − ∞ ∞ f ( x ) d H ( x ) . {\displaystyle \int _{-\infty }^{\infty }f(x)\,\delta (\mathrm {d} x)=\int _{-\infty }^{\infty }f(x)\,\mathrm {d} H(x).}

All higher moments of δ are zero. In particular, characteristic function and moment generating function are both equal to one.

### As a distribution[edit | edit source]

In the theory of distributions, a generalized function is considered not a function in itself but only about how it affects other functions when "integrated" against them.[25] In keeping with this philosophy, to define the delta function properly, it is enough to say what the "integral" of the delta function is against a sufficiently "good" test function φ. Test functions are also known as bump functions. If the delta function is already understood as a measure, then the Lebesgue integral of a test function against that measure supplies the necessary integral.

A typical space of test functions consists of all smooth functions on R with compact support that have as many derivatives as required. As a distribution, the Dirac delta is a linear functional on the space of test functions and is defined by[26]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta [\varphi ]=\varphi (0)}

|
|

(1)

for every test function  φ {\displaystyle \varphi } .

For δ to be properly a distribution, it must be continuous in a suitable topology on the space of test functions. In general, for a linear functional S on the space of test functions to define a distribution, it is necessary and sufficient that, for every positive integer N there is an integer MN and a constant CN such that for every test function φ, one has the inequality[27]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \left|S[\varphi ]\right|\leq C_{N}\sum _{k=0}^{M_{N}}\sup _{x\in [-N,N]}\left|\varphi ^{(k)}(x)\right|}

where sup represents the supremum. With the δ distribution, one has such an inequality (with CN = 1) with MN = 0 for all N. Thus δ is a distribution of order zero. It is, furthermore, a distribution with compact support (the support being {0}).

The delta distribution can also be defined in several equivalent ways. For instance, it is the distributional derivative of the Heaviside step function. This means that for every test function φ, one has

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta [\varphi ]=-\int _{-\infty }^{\infty }\varphi '(x)\,H(x)\,\mathrm {d} x.}

Intuitively, if integration by parts were permitted, then the latter integral should simplify to

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{-\infty }^{\infty }\varphi (x)\,H'(x)\,\mathrm {d} x=\int _{-\infty }^{\infty }\varphi (x)\,\delta (x)\,\mathrm {d} x,}

and indeed, a form of integration by parts is permitted for the Stieltjes integral, and in that case, one does have

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle -\int _{-\infty }^{\infty }\varphi '(x)\,H(x)\,\mathrm {d} x=\int _{-\infty }^{\infty }\varphi (x)\,\mathrm {d} H(x).}

In the context of measure theory, the Dirac measure gives rise to distribution by integration. Conversely, equation (1) defines a Daniell integral on the space of all compactly supported continuous functions φ which, by the Riesz representation theorem, can be represented as the Lebesgue integral of φ concerning some Radon measure.

Generally, when the term "Dirac delta function" is used, it is in the sense of distributions rather than measures, the Dirac measure being among several terms for the corresponding notion in measure theory. Some sources may also use the term Dirac delta distribution.

### Generalizations[edit | edit source]

The delta function can be defined in n-dimensional Euclidean space Rn as the measure such that

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{\mathbf {R} ^{n}}f(\mathbf {x} )\,\delta (\mathrm {d} \mathbf {x} )=f(\mathbf {0} )}

for every compactly supported continuous function f. As a measure, the n-dimensional delta function is the product measure of the 1-dimensional delta functions in each variable separately. Thus, formally, with x = (x1, x2, ..., xn), one has[28]

δ ( x ) = δ ( x 1 ) δ ( x 2 ) ⋯ δ ( x n ) . {\displaystyle \delta (\mathbf {x} )=\delta (x_{1})\,\delta (x_{2})\cdots \delta (x_{n}).}

|
|

(2)

The delta function can also be defined in the sense of distributions exactly as above in the one-dimensional case.[29] However, despite widespread use in engineering contexts, (2) should be manipulated with care, since the product of distributions can only be defined under quite narrow circumstances.[30][31]

The notion of a Dirac measure makes sense on any set.[32] Thus if X is a set, x0 ∈ X is a marked point, and Σ is any sigma algebra of subsets of X, then the measure defined on sets A ∈ Σ by

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta _{x_{0}}(A)={\begin{cases}1&{\text{if }}x_{0}\in A\\\0&{\text{if }}x_{0}\notin A\end{cases}}}

is the delta measure or unit mass concentrated at x0.

Another common generalization of the delta function is to a differentiable manifold where most of its properties as a distribution can also be exploited because of the differentiable structure. The delta function on a manifold M centered at the point x0 ∈ M is defined as the following distribution:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta _{x_{0}}[\varphi ]=\varphi (x_{0})}

|
|

(3)

for all compactly supported smooth real-valued functions φ on M.[33] A common special case of this construction is a case in which M is an open set in the Euclidean space Rn.

On a locally compact Hausdorff space X, the Dirac delta measure concentrated at a point x is the Radon measure associated with the Daniell integral (3) on compactly supported continuous functions φ.[34] At this level of generality, calculus as such is no longer possible, however a variety of techniques from abstract analysis are available. For instance, the mapping Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle x_{0}\mapsto \delta _{x_{0}}} is a continuous embedding of X into the space of finite Radon measures on X, equipped with its vague topology. Moreover, the convex hull of the image of X under this embedding is dense in the space of probability measures on X.[35]

## Properties[edit | edit source]

### Scaling and symmetry[edit | edit source]

The delta function satisfies the following scaling property for a non-zero scalar α:[36]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{-\infty }^{\infty }\delta (\alpha x)\,\mathrm {d} x=\int _{-\infty }^{\infty }\delta (u)\,{\frac {\mathrm {d} u}{|\alpha |}}={\frac {1}{|\alpha |}}}

and so

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (\alpha x)={\frac {\delta (x)}{|\alpha |}}.}

|
|

(4)

Scaling property proof:Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle {\begin{aligned}\delta (\alpha x)&=\lim _{b\to 0}{\frac {1}{|b|{\sqrt {\pi }}}}e^{-(\alpha x/b)^{2}}\qquad {\text{since }}b{\text{ is a dummy variable, we set }}b=\alpha c\\\&=\lim _{c\to 0}{\frac {1}{|\alpha c|{\sqrt {\pi }}}}e^{-(\alpha x/(\alpha c))^{2}}\\\&=\lim _{c\to 0}{\frac {1}{|\alpha |}}{\frac {1}{|c|{\sqrt {\pi }}}}e^{-(x/c)^{2}}={\frac {1}{|\alpha |}}\delta (x)\end{aligned}}}

In this proof, the delta function representation as the limit of the sequence of zero-centered normal distributions δ ( x ) = lim b -> 0 1 | b | π e − ( x / b ) 2 {\displaystyle \delta (x)=\lim _{b\to 0}{\frac {1}{|b|{\sqrt {\pi }}}}e^{-(x/b)^{2}}} is used. This proof can be made by using other delta function representations as the limits of sequences of functions, as long as these are even functions.

In particular, the delta function is an even distribution (symmetry), in the sense that

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (-x)=\delta (x)}

which is homogeneous of degree −1.

### Algebraic properties[edit | edit source]

The distributional product of δ with x is equal to zero:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle x\,\delta (x)=0.}

Conversely, if xf(x) = xg(x), where f and g are distributions, then

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle f(x)=g(x)+c\delta (x)}

for some constant c.[37]

### Translation[edit | edit source]

The integral of the time-delayed Dirac delta is[38]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{-\infty }^{\infty }f(t)\,\delta (t-T)\,\mathrm {d} t=f(T).}

This is sometimes referred to as the shifting property[39] or the sampling property.[40] The delta function is said to "shift out" the value at t = T.[41]

It follows that the effect of convolving a function f(t) with the time-delayed Dirac delta Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta _{T}(t)=\delta (t-T)} is to time-delay f(t) by the same amount:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle {\begin{aligned}(f*\delta _{T})(t)\ &{\stackrel {\mathrm {def} }{=}}\ \int _{-\infty }^{\infty }f(\tau )\,\delta (t-T-\tau )\,\mathrm {d} \tau \\\&=\int _{-\infty }^{\infty }f(\tau )\,\delta (\tau -(t-T))\,\mathrm {d} \tau \qquad {\text{since}}~\delta (-x)=\delta (x)~~{\text{by (4)}}\\\&=f(t-T).\end{aligned}}}

This holds under the precise condition that f be a tempered distribution (see the discussion of the Fourier transform below). As a special case, for instance, we have the identity (understood in the distribution sense)

∫ − ∞ ∞ δ ( ξ − x ) δ ( x − η ) d x = δ ( η − ξ ) . {\displaystyle \int _{-\infty }^{\infty }\delta (\xi -x)\delta (x-\eta )\,\mathrm {d} x=\delta (\eta -\xi ).}

### Composition with a function[edit | edit source]

More generally, the delta distribution may be composed with a smooth function g(x) in such a way that the familiar change of variables formula holds, that

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{\mathbb {R} }\delta {\bigl (}g(x){\bigr )}f{\bigl (}g(x){\bigr )}\left|g'(x)\right|\mathrm {d} x=\int _{g(\mathbb {R} )}\delta (u)\,f(u)\,\mathrm {d} u}

provided that g is a continuously differentiable function with g′ nowhere zero.[42] That is, there is a unique way to assign meaning to the distribution Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta \circ g} so that this identity holds for all compactly supported test functions f. Therefore, the domain must be broken up to exclude the g′ = 0 point. This distribution satisfies δ(g(x)) = 0 if g is nowhere zero, and otherwise if g has a real root at x0, then

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (g(x))={\frac {\delta (x-x_{0})}{|g'(x_{0})|}}.}

It is natural therefore to define the composition δ(g(x)) for continuously differentiable functions g by

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (g(x))=\sum _{i}{\frac {\delta (x-x_{i})}{|g'(x_{i})|}}}

where the sum extends over all roots (i.e., all the different ones) of g(x), which are assumed to be simple. Thus, for example

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta \left(x^{2}-\alpha ^{2}\right)={\frac {1}{2|\alpha |}}{\Big [}\delta \left(x+\alpha \right)+\delta \left(x-\alpha \right){\Big ]}.}

In the integral form, the generalized scaling property may be written as

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{-\infty }^{\infty }f(x)\,\delta (g(x))\,\mathrm {d} x=\sum _{i}{\frac {f(x_{i})}{|g'(x_{i})|}}.}

### Properties in n dimensions[edit | edit source]

The delta distribution in an n-dimensional space satisfies the following scaling property instead,

δ ( α x ) = | α | − n δ ( x ) , {\displaystyle \delta (\alpha \mathbf {x} )=|\alpha |^{-n}\delta (\mathbf {x} )~,}

so that δ is a homogeneous distribution of degree −n.

Under any reflection or rotation ρ, the delta function is invariant,

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (\rho \mathbf {x} )=\delta (\mathbf {x} )~.}

As in the one-variable case, it is possible to define the composition of δ with a bi-Lipschitz function[43] g: Rn → Rn uniquely so that the identity

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{\mathbb {R} ^{n}}\delta (g(\mathbf {x} ))\,f(g(\mathbf {x} ))\left|\det g'(\mathbf {x} )\right|\mathrm {d} \mathbf {x} =\int _{g(\mathbb {R} ^{n})}\delta (\mathbf {u} )f(\mathbf {u} )\,\mathrm {d} \mathbf {u} }

for all compactly supported functions f.

Using the coarea formula from geometric measure theory, one can also define the composition of the delta function with a submersion from one Euclidean space to another one of different dimension; the result is a type of current. In the special case of a continuously differentiable function g : Rn → R such that the gradient of g is nowhere zero, the following identity holds[44]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{\mathbb {R} ^{n}}f(\mathbf {x} )\,\delta (g(\mathbf {x} ))\,\mathrm {d} \mathbf {x} =\int _{g^{-1}(0)}{\frac {f(\mathbf {x} )}{|\mathbf {\nabla } g|}}\,\mathrm {d} \sigma (\mathbf {x} )}

where the integral on the right is over g−1(0), the (n − 1)-dimensional surface defined by g(x) = 0 with respect to the Minkowski content measure. This is known as a simple layer integral.

More generally, if S is a smooth hypersurface of Rn, then we can associate to S the distribution that integrates any compactly supported smooth function g over S:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta _{S}[g]=\int _{S}g(\mathbf {s} )\,\mathrm {d} \sigma (\mathbf {s} )}

where σ is the hypersurface measure associated to S. This generalization is associated with the potential theory of simple layer potentials on S. If D is a domain in Rn with smooth boundary S, then δS is equal to the normal derivative of the indicator function of D in the distribution sense,

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle -\int _{\mathbb {R} ^{n}}g(\mathbf {x} )\,{\frac {\partial 1_{D}(\mathbf {x} )}{\partial n}}\,\mathrm {d} \mathbf {x} =\int _{S}\,g(\mathbf {s} )\,\mathrm {d} \sigma (\mathbf {s} ),}

where n is the outward normal.[45][46] For a proof, see e.g. the article on the surface delta function.

## Fourier transform[edit | edit source]

The delta function is a tempered distribution, and therefore it has a well-defined Fourier transform. Formally, one finds[47]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle {\widehat {\delta }}(\xi )=\int _{-\infty }^{\infty }e^{-2\pi ix\xi }\,\delta (\mathrm {d} x)=1.}

Properly speaking, the Fourier transform of a distribution is defined by imposing self-adjointness of the Fourier transform under the duality pairing  ⟨ ⋅ , ⋅ ⟩ {\displaystyle \langle \cdot ,\cdot \rangle } of tempered distributions with Schwartz functions. Thus Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle {\widehat {\delta }}} is defined as the unique tempered distribution satisfying

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \langle {\widehat {\delta }},\varphi \rangle =\langle \delta ,{\widehat {\varphi }}\rangle }

for all Schwartz functions  φ {\displaystyle \varphi } . And indeed it follows from this that Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle {\widehat {\delta }}=1.}

As a result of this identity, the convolution of the delta function with any other tempered distribution S is simply S:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle S*\delta = S.}

That is to say that δ is an identity element for the convolution on tempered distributions, and in fact, the space of compactly supported distributions under convolution is an associative algebra with identity the delta function. This property is fundamental in signal processing, as convolution with a tempered distribution is a linear time-invariant system, and applying the linear time-invariant system measures its impulse response. The impulse response can be computed to any desired degree of accuracy by choosing a suitable approximation for δ, and once it is known, it characterizes the system completely. See LTI system theory § Impulse response and convolution.

The inverse Fourier transform of the tempered distribution f(ξ) = 1 is the delta function. Formally, this is expressed

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{-\infty }^{\infty }1\cdot e^{2\pi ix\xi }\,\mathrm {d} \xi =\delta (x)}

and more rigorously, it follows since

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \langle 1, \widehat{f}\rangle = f(0) = \langle\delta,f\rangle}

for all Schwartz functions f.

In these terms, the delta function provides a suggestive statement of the orthogonality property of the Fourier kernel on R. Formally, one has

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{-\infty }^{\infty }e^{i2\pi \xi _{1}t}\left[e^{i2\pi \xi _{2}t}\right]^{*}\,\mathrm {d} t=\int _{-\infty }^{\infty }e^{-i2\pi (\xi _{2}-\xi _{1})t}\,\mathrm {d} t=\delta (\xi _{2}-\xi _{1}).}

This is, of course, shorthand for the assertion that the Fourier transform of the tempered distribution

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f(t) = e^{i2\pi\xi_1 t}}

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \widehat{f}(\xi_2) = \delta(\xi_1-\xi_2)}

which again follows by imposing self-adjointness of the Fourier transform.

By analytic continuation of the Fourier transform, the Laplace transform of the delta function is found to be[48]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \int_{0}^{\infty}\delta(t-a)\,e^{-st} \, \mathrm dt=e^{-sa}.}

## Derivatives of the Dirac delta function[edit | edit source]

The derivative of the Dirac delta distribution, denoted Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta^\prime} and also called the Dirac delta prime or Dirac delta derivative as described in Laplacian of the indicator, is defined on compactly supported smooth test functions Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \varphi} by[49]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta'[\varphi] = -\delta[\varphi']=-\varphi'(0).}

The first equality here is a kind of integration by parts, for if Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta} were a true function then

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \int_{-\infty}^\infty \delta'(x)\varphi(x)\,dx = -\int_{-\infty}^\infty \delta(x) \varphi'(x)\,dx.}

The Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle k} -th derivative of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta} is defined similarly as the distribution given on test functions by

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta^{(k)}[\varphi] = (-1)^k \varphi^{(k)}(0).}

In particular, Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta} is an infinitely differentiable distribution.

The first derivative of the delta function is the distributional limit of the difference quotients:[50]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta'(x) = \lim_{h\to 0} \frac{\delta(x+h)-\delta(x)}{h}.}

More properly, one has

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta' = \lim_{h\to 0} \frac{1}{h}(\tau_h\delta - \delta)}

where Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \tau_h} is the translation operator, defined on functions by Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \tau_h \varphi(x) = \varphi(x + h)} , and on a distribution Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle S} by

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (\tau_h S)[\varphi] = S[\tau_{-h}\varphi].}

In the theory of electromagnetism, the first derivative of the delta function represents a point magnetic dipole situated at the origin. Accordingly, it is referred to as a dipole or the doublet function.[51]

The derivative of the delta function satisfies a number of basic properties, including:[52]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \begin{align} & \delta'(-x) = -\delta'(x) \\\ & x\delta'(x) = -\delta(x) \end{align} }

which can be shown by applying a test function and integrating by parts.

The latter of these properties can also be demonstrated by applying distributional derivative definition, Liebnitz's theorem and linearity of inner product:[53]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \begin{align} \langle x\delta', \varphi \rangle \, &= \, \langle \delta', x\varphi \rangle \, = \, -\langle\delta,(x\varphi)'\rangle \, = \, - \langle \delta, x'\varphi + x\varphi'\rangle \, = \, - \langle \delta, x'\varphi\rangle - \langle\delta, x\varphi'\rangle \, = \, - x'(0)\varphi(0) - x(0)\varphi'(0) \\\ &= \, -x'(0) \langle \delta , \varphi \rangle - x(0) \langle \delta, \varphi' \rangle \, = \, -x'(0) \langle \delta,\varphi\rangle + x(0) \langle \delta', \varphi \rangle \, = \, \langle x(0)\delta' \- x'(0)\delta, \varphi \rangle \\\ \Longrightarrow x(t)\delta'(t) &= x(0)\delta'(t) - x'(0)\delta(t) = -x'(0)\delta(t) = -\delta(t) \end{align} }

Furthermore, the convolution of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta'} with a compactly-supported, smooth function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f} is

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta'*f = \delta*f' = f',}

which follows from the properties of the distributional derivative of a convolution.

### Higher dimensions[edit | edit source]

More generally, on an open set Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U} in the Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle n} -dimensional Euclidean space Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathbb{R}^n} , the Dirac delta distribution centered at a point Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle a \in U} is defined by[54]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta_a[\varphi]=\varphi(a)}

for all Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \varphi \in C_c^\infty(U)} , the space of all smooth functions with compact support on Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U} . If Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \alpha = (\alpha_1, \ldots, \alpha_n)} is any multi-index with Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle |\alpha|=\alpha_1+\cdots+\alpha_n} and Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \partial^\alpha} denotes the associated mixed partial derivative operator, then the Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \alpha} -th derivative Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \partial^\alpha \delta_a} of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta_a} is given by[54]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \left\langle \partial^\alpha \delta_{a}, \, \varphi \right\rangle = (-1)^{| \alpha |} \left\langle \delta_{a}, \partial^{\alpha} \varphi \right\rangle = (-1)^{| \alpha |} \partial^\alpha \varphi (x) \Big|_{x = a} \quad \text{ for all } \varphi \in C_c^\infty(U).}

That is, the Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \alpha} -th derivative of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta_a} is the distribution whose value on any test function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \varphi} is the Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \alpha} -th derivative of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \varphi} at Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle a} (with the appropriate positive or negative sign).

The first partial derivatives of the delta function are thought of as double layers along the coordinate planes. More generally, the normal derivative of a simple layer supported on a surface is a double layer supported on that surface and represents a laminar magnetic monopole. Higher derivatives of the delta function are known in physics as multipoles.

Higher derivatives enter into mathematics naturally as the building blocks for the complete structure of distributions with point support. If Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle S} is any distribution on Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U} supported on the set Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \\{ a \\}} consisting of a single point, then there is an integer Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle m} and coefficients Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle c_\alpha} such that[54][55]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle S = \sum_{|\alpha|\le m} c_\alpha \partial^\alpha\delta_a.}

## Representations of the delta function[edit | edit source]

The delta function can be viewed as the limit of a sequence of functions

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta (x) = \lim_{\varepsilon\to 0^+} \eta_\varepsilon(x), }

where ηε(x) is sometimes called a nascent delta function. This limit is meant in a weak sense: either that

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \lim_{\varepsilon\to 0^+} \int_{-\infty}^\infty \eta_\varepsilon(x)f(x) \, dx = f(0) }

|
|

(5)

for all continuous functions f having compact support, or that this limit holds for all smooth functions f with compact support. The difference between these two slightly different modes of weak convergence is often subtle: the former is convergence in the vague topology of measures, and the latter is convergence in the sense of distributions.

### Approximations to the identity[edit | edit source]

Typically a nascent delta function ηε can be constructed in the following manner. Let η be an absolutely integrable function on R of total integral 1, and define

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta_\varepsilon(x) = \varepsilon^{-1} \eta \left (\frac{x}{\varepsilon} \right). }

In n dimensions, one uses instead the scaling

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta_\varepsilon(x) = \varepsilon^{-n} \eta \left (\frac{x}{\varepsilon} \right). }

Then a simple change of variables shows that ηε also has integral 1. One may show that (5) holds for all continuous compactly supported functions f,[56] and so ηε converges weakly to δ in the sense of measures.

The ηε constructed in this way are known as an approximation to the identity.[57] This terminology is because the space L1(R) of absolutely integrable functions is closed under the operation of convolution of functions: f ∗ g ∈ L1(R) whenever f and g are in L1(R). However, there is no identity in L1(R) for the convolution product: no element h such that f ∗ h = f for all f. Nevertheless, the sequence ηε does approximate such an identity in the sense that

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f*\eta_\varepsilon \to f \quad \text{as }\varepsilon\to 0.}

This limit holds in the sense of mean convergence (convergence in L1). Further conditions on the ηε, for instance that it be a mollifier associated to a compactly supported function,[58] are needed to ensure pointwise convergence almost everywhere.

If the initial η = η1 is itself smooth and compactly supported then the sequence is called a mollifier. The standard mollifier is obtained by choosing η to be a suitably normalized bump function, for instance

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta(x) = \begin{cases} e^{-\frac{1}{1-|x|^2}}& \text{if } |x| < 1\\\ 0 & \text{if } |x|\geq 1. \end{cases}}

In some situations such as numerical analysis, a piecewise linear approximation to the identity is desirable. This can be obtained by taking η1 to be a hat function. With this choice of η1, one has

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta_\varepsilon(x) = \varepsilon^{-1}\max \left (1-\left|\frac{x}{\varepsilon}\right|,0 \right) }

which are all continuous and compactly supported, although not smooth and so not a mollifier.

### Probabilistic considerations[edit | edit source]

In the context of probability theory, it is natural to impose the additional condition that the initial η1 in an approximation to the identity should be positive, as such a function then represents a probability distribution. Convolution with a probability distribution is sometimes favorable because it does not result in overshoot or undershoot, as the output is a convex combination of the input values, and thus falls between the maximum and minimum of the input function. Taking η1 to be any probability distribution at all, and letting ηε(x) = η1(x/ε)/ε as above will give rise to an approximation to the identity. In general this converges more rapidly to a delta function if, in addition, η has mean 0 and has small higher moments. For instance, if η1 is the uniform distribution on [−1/2, 1/2], also known as the rectangular function, then:[59]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta_\varepsilon(x) = \frac{1}{\varepsilon}\operatorname{rect}\left(\frac{x}{\varepsilon}\right)= \begin{cases} \frac{1}{\varepsilon},&-\frac{\varepsilon}{2}<x<\frac{\varepsilon}{2}, \\\ 0, &\text{otherwise}. \end{cases}}

Another example is with the Wigner semicircle distribution

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta_\varepsilon(x)= \begin{cases} \frac{2}{\pi \varepsilon^2}\sqrt{\varepsilon^2 - x^2}, & -\varepsilon < x < \varepsilon, \\\ 0, & \text{otherwise}. \end{cases}}

This is continuous and compactly supported, but not a mollifier because it is not smooth.

### Semigroups[edit | edit source]

Nascent delta functions often arise as convolution semigroups.[60] This amounts to the further constraint that the convolution of ηε with ηδ must satisfy

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta_\varepsilon * \eta_\delta = \eta_{\varepsilon+\delta}}

for all ε, δ > 0. Convolution semigroups in L1 that form a nascent delta function are always an approximation to the identity in the above sense, however the semigroup condition is quite a strong restriction.

In practice, semigroups approximating the delta function arise as fundamental solutions or Green's functions to physically motivated elliptic or parabolic partial differential equations. In the context of applied mathematics, semigroups arise as the output of a linear time-invariant system. Abstractly, if A is a linear operator acting on functions of x, then a convolution semigroup arises by solving the initial value problem

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \begin{cases} \dfrac{\partial}{\partial t}\eta(t,x) = A\eta(t,x), \quad t>0 \\\\[5pt] \displaystyle\lim_{t\to 0^+} \eta(t,x) = \delta(x) \end{cases}}

in which the limit is as usual understood in the weak sense. Setting ηε(x) = η(ε, x) gives the associated nascent delta function.

Some examples of physically important convolution semigroups arising from such a fundamental solution include the following.

The heat kernel

The heat kernel, defined by

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta_\varepsilon(x) = \frac{1}{\sqrt{2\pi\varepsilon}} \mathrm{e}^{-\frac{x^2}{2\varepsilon}}}

represents the temperature in an infinite wire at time t > 0, if a unit of heat energy is stored at the origin of the wire at time t = 0. This semigroup evolves according to the one-dimensional heat equation:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \frac{\partial u}{\partial t} = \frac{1}{2}\frac{\partial^2 u}{\partial x^2}.}

In probability theory, ηε(x) is a normal distribution of variance ε and mean 0. It represents the probability density at time t = ε of the position of a particle starting at the origin following a standard Brownian motion. In this context, the semigroup condition is then an expression of the Markov property of Brownian motion.

In higher-dimensional Euclidean space Rn, the heat kernel is

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta_\varepsilon = \frac{1}{(2\pi\varepsilon)^{n/2}}\mathrm{e}^{-\frac{x\cdot x}{2\varepsilon}},}

and has the same physical interpretation, mutatis mutandis. It also represents a nascent delta function in the sense that ηε → δ in the distribution sense as ε → 0.

The Poisson kernel

The Poisson kernel

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta_\varepsilon(x) = \frac{1}{\pi}\mathrm{Im}\left\\{\frac{1}{x-\mathrm{i}\varepsilon}\right\\}=\frac{1}{\pi} \frac{\varepsilon}{\varepsilon^2 + x^2}=\frac{1}{2\pi}\int_{-\infty}^{\infty}\mathrm{e}^{\mathrm{i} \xi x-|\varepsilon \xi|}\,d\xi}

is the fundamental solution of the Laplace equation in the upper half-plane.[61] It represents the electrostatic potential in a semi-infinite plate whose potential along the edge is held at fixed at the delta function. The Poisson kernel is also closely related to the Cauchy distribution and Epanechnikov and Gaussian kernel functions.[62] This semigroup evolves according to the equation

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \frac{\partial u}{\partial t} = -\left (-\frac{\partial^2}{\partial x^2} \right)^{\frac{1}{2}}u(t,x)}

where the operator is rigorously defined as the Fourier multiplier

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathcal{F}\left[\left(-\frac{\partial^2}{\partial x^2} \right)^{\frac{1}{2}}f\right](\xi) = |2\pi\xi|\mathcal{F}f(\xi).}

### Oscillatory integrals[edit | edit source]

In areas of physics such as wave propagation and wave mechanics, the equations involved are hyperbolic and so may have more singular solutions. As a result, the nascent delta functions that arise as fundamental solutions of the associated Cauchy problems are generally oscillatory integrals. An example, which comes from a solution of the Euler–Tricomi equation of transonic gas dynamics,[63] is the rescaled Airy function

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \varepsilon^{-1/3}\operatorname{Ai}\left (x\varepsilon^{-1/3} \right). }

Although using the Fourier transform, it is easy to see that this generates a semigroup in some sense—it is not absolutely integrable and so cannot define a semigroup in the above strong sense. Many nascent delta functions constructed as oscillatory integrals only converge in the sense of distributions (an example is the Dirichlet kernel below), rather than in the sense of measures.

Another example is the Cauchy problem for the wave equation in R1+1:[64]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \begin{align} c^{-2}\frac{\partial^2u}{\partial t^2} - \Delta u &= 0\\\ u=0,\quad \frac{\partial u}{\partial t} = \delta &\qquad \text{for }t=0. \end{align} }

The solution u represents the displacement from equilibrium of an infinite elastic string, with an initial disturbance at the origin.

Other approximations to the identity of this kind include the sinc function (used widely in electronics and telecommunications)

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta_\varepsilon(x)=\frac{1}{\pi x}\sin\left(\frac{x}{\varepsilon}\right)=\frac{1}{2\pi}\int_{-\frac{1}{\varepsilon}}^{\frac{1}{\varepsilon}} \cos(kx)\,dk }

and the Bessel function

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \eta_\varepsilon(x) = \frac{1}{\varepsilon}J_{\frac{1}{\varepsilon}} \left(\frac{x+1}{\varepsilon}\right). }

### Plane wave decomposition[edit | edit source]

One approach to the study of a linear partial differential equation

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle L[u]=f,}

where L is a differential operator on Rn, is to seek first a fundamental solution, which is a solution of the equation

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle L[u]=\delta.}

When L is particularly simple, this problem can often be resolved using the Fourier transform directly (as in the case of the Poisson kernel and heat kernel already mentioned). For more complicated operators, it is sometimes easier first to consider an equation of the form

L [ u ] = h {\displaystyle L[u]=h}

where h is a plane wave function, meaning that it has the form

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle h=h(x\cdot \xi )}

for some vector ξ. Such an equation can be resolved (if the coefficients of L are analytic functions) by the Cauchy–Kovalevskaya theorem or (if the coefficients of L are constant) by quadrature. So, if the delta function can be decomposed into plane waves, then one can in principle solve linear partial differential equations.

Such a decomposition of the delta function into plane waves was part of a general technique first introduced essentially by Johann Radon, and then developed in this form by Fritz John (1955).[65] Choose k so that n \+ k is an even integer, and for a real number s, put

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle g(s)=\operatorname {Re} \left[{\frac {-s^{k}\log(-is)}{k!(2\pi i)^{n}}}\right]={\begin{cases}{\frac {|s|^{k}}{4k!(2\pi i)^{n-1}}}&n{\text{ odd}}\\\\[5pt]-{\frac {|s|^{k}\log |s|}{k!(2\pi i)^{n}}}&n{\text{ even.}}\end{cases}}}

Then δ is obtained by applying a power of the Laplacian to the integral with respect to the unit sphere measure dω of g(x · ξ) for ξ in the unit sphere Sn−1:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (x)=\Delta _{x}^{(n+k)/2}\int _{S^{n-1}}g(x\cdot \xi )\,d\omega _{\xi }.}

The Laplacian here is interpreted as a weak derivative, so that this equation is taken to mean that, for any test function φ,

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \varphi (x)=\int _{\mathbf {R} ^{n}}\varphi (y)\,dy\,\Delta _{x}^{\frac {n+k}{2}}\int _{S^{n-1}}g((x-y)\cdot \xi )\,d\omega _{\xi }.}

The result follows from the formula for the Newtonian potential (the fundamental solution of Poisson's equation). This is essentially a form of the inversion formula for the Radon transform because it recovers the value of φ(x) from its integrals over hyperplanes. For instance, if n is odd and k = 1, then the integral on the right hand side is

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle {\begin{aligned}&c_{n}\Delta _{x}^{\frac {n+1}{2}}\iint _{S^{n-1}}\varphi (y)|(y-x)\cdot \xi |\,d\omega _{\xi }\,dy\\\\[5pt]={}&c_{n}\Delta _{x}^{(n+1)/2}\int _{S^{n-1}}\,d\omega _{\xi }\int _{-\infty }^{\infty }|p|R\varphi (\xi ,p+x\cdot \xi )\,dp\end{aligned}}}

where Rφ(ξ, p) is the Radon transform of φ:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle R\varphi (\xi ,p)=\int _{x\cdot \xi =p}f(x)\,d^{n-1}x.}

An alternative equivalent expression of the plane wave decomposition, from Gelfand & Shilov (1966–1968, I, §3.10), is

δ ( x ) = ( n − 1 ) ! ( 2 π i ) n ∫ S n − 1 ( x ⋅ ξ ) − n d ω ξ {\displaystyle \delta (x)={\frac {(n-1)!}{(2\pi i)^{n}}}\int _{S^{n-1}}(x\cdot \xi )^{-n}\,d\omega _{\xi }}

for n even, and

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (x)={\frac {1}{2(2\pi i)^{n-1}}}\int _{S^{n-1}}\delta ^{(n-1)}(x\cdot \xi )\,d\omega _{\xi }}

for n odd.

### Fourier kernels[edit | edit source]

See also: Convergence of Fourier series

In the study of Fourier series, a major question consists of determining whether and in what sense the Fourier series associated with a periodic function converges to the function. The nth partial sum of the Fourier series of a function f of period 2π is defined by convolution (on the interval [−π,π]) with the Dirichlet kernel:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle D_{N}(x)=\sum _{n=-N}^{N}e^{inx}={\frac {\sin \left(\left(N+{\frac {1}{2}}\right)x\right)}{\sin(x/2)}}.}

Thus,

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle s_{N}(f)(x)=D_{N}*f(x)=\sum _{n=-N}^{N}a_{n}e^{inx}}

where

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle a_{n}={\frac {1}{2\pi }}\int _{-\pi }^{\pi }f(y)e^{-iny}\,dy.}

A fundamental result of elementary Fourier series states that the Dirichlet kernel tends to the a multiple of the delta function as N → ∞. This is interpreted in the distribution sense, that

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle s_{N}(f)(0)=\int _{\mathbf {R} }D_{N}(x)f(x)\,dx\to 2\pi f(0)}

for every compactly supported smooth function f. Thus, formally one has

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta (x)={\frac {1}{2\pi }}\sum _{n=-\infty }^{\infty }e^{inx}}

on the interval [−π,π].

Despite this, the result does not hold for all compactly supported continuous functions: that is DN does not converge weakly in the sense of measures. The lack of convergence of the Fourier series has led to the introduction of a variety of summability methods to produce convergence. The method of Cesàro summation leads to the Fejér kernel[66]

F N ( x ) = 1 N ∑ n = 0 N − 1 D n ( x ) = 1 N ( sin ⁡ N x 2 sin ⁡ x 2 ) 2 . {\displaystyle F_{N}(x)={\frac {1}{N}}\sum _{n=0}^{N-1}D_{n}(x)={\frac {1}{N}}\left({\frac {\sin {\frac {Nx}{2}}}{\sin {\frac {x}{2}}}}\right)^{2}.}

The Fejér kernels tend to the delta function in a stronger sense that[67]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \int _{\mathbf {R} }F_{N}(x)f(x)\,dx\to 2\pi f(0)}

for every compactly supported continuous function f. The implication is that the Fourier series of any continuous function is Cesàro summable to the value of the function at every point.

### Hilbert space theory[edit | edit source]

The Dirac delta distribution is a densely defined unbounded linear functional on the Hilbert space L2 of square-integrable functions. Indeed, smooth compactly supported functions are dense in L2, and the action of the delta distribution on such functions is well-defined. In many applications, it is possible to identify subspaces of L2 and to give a stronger topology on which the delta function defines a bounded linear functional.

Sobolev spaces

The Sobolev embedding theorem for Sobolev spaces on the real line R implies that any square-integrable function f such that

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \|f\|_{H^{1}}^{2}=\int _{-\infty }^{\infty }|{\widehat {f}}(\xi )|^{2}(1+|\xi |^{2})\,d\xi <\infty }

is automatically continuous, and satisfies in particular

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta [f]=|f(0)|<C\|f\|_{H^{1}}.}

Thus δ is a bounded linear functional on the Sobolev space H1. Equivalently δ is an element of the continuous dual space H−1 of H1. More generally, in n dimensions, one has δ ∈ H−s(Rn) provided s > n / 2.

#### Spaces of holomorphic functions[edit | edit source]

In complex analysis, the delta function enters via Cauchy's integral formula, which asserts that if D is a domain in the complex plane with smooth boundary, then

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle f(z)={\frac {1}{2\pi i}}\oint _{\partial D}{\frac {f(\zeta )\,d\zeta }{\zeta -z}},\quad z\in D}

for all holomorphic functions f in D that are continuous on the closure of D. As a result, the delta function δz is represented in this class of holomorphic functions by the Cauchy integral:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \delta _{z}[f]=f(z)={\frac {1}{2\pi i}}\oint _{\partial D}{\frac {f(\zeta )\,d\zeta }{\zeta -z}}.}

Moreover, let H2(∂D) be the Hardy space consisting of the closure in L2(∂D) of all holomorphic functions in D continuous up to the boundary of D. Then functions in H2(∂D) uniquely extend to holomorphic functions in D, and the Cauchy integral formula continues to hold. In particular for z ∈ D, the delta function δz is a continuous linear functional on H2(∂D). This is a special case of the situation in several complex variables in which, for smooth domains D, the Szegő kernel plays the role of the Cauchy integral.[68]

#### Resolutions of the identity[edit | edit source]

Given a complete orthonormal basis set of functions {φn} in a separable Hilbert space, for example, the normalized eigenvectors of a compact self-adjoint operator, any vector f can be expressed as

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle f=\sum _{n=1}^{\infty }\alpha _{n}\varphi _{n}.}

The coefficients {αn} are found as

α n = ⟨ φ n , f ⟩ , {\displaystyle \alpha _{n}=\langle \varphi _{n},f\rangle ,}

which may be represented by the notation:

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle \alpha _{n}=\varphi _{n}^{\dagger }f,}

a form of the bra–ket notation of Dirac.[69] Adopting this notation, the expansion of f takes the dyadic form:[70]

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle f=\sum _{n=1}^{\infty }\varphi _{n}\left(\varphi _{n}^{\dagger }f\right).}

Letting I denote the identity operator on the Hilbert space, the expression

Failed to parse (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\displaystyle I=\sum _{n=1}^{\infty }\varphi _{n}\varphi _{n}^{\dagger },}

is called a resolution of the identity. When the Hilbert space is the space L2(D) of square-integrable functions on a domain D, the quantity:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \varphi_n \varphi_n^\dagger, }

is an integral operator, and the expression for f can be rewritten

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f(x) = \sum_{n=1}^\infty \int_D\, \left( \varphi_n (x) \varphi_n^*(\xi)\right) f(\xi) \, d \xi.}

The right-hand side converges to f in the L2 sense. It need not hold in a pointwise sense, even when f is a continuous function. Nevertheless, it is common to abuse notation and write

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f(x) = \int \, \delta(x-\xi) f (\xi)\, d\xi, }

resulting in the representation of the delta function:[71]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta(x-\xi) = \sum_{n=1}^\infty \varphi_n (x) \varphi_n^*(\xi). }

With a suitable rigged Hilbert space (Φ, L2(D), Φ*) where Φ ⊂ L2(D) contains all compactly supported smooth functions, this summation may converge in Φ*, depending on the properties of the basis φn. In most cases of practical interest, the orthonormal basis comes from an integral or differential operator, in which case the series converges in the distribution sense.[72]

### Infinitesimal delta functions[edit | edit source]

Cauchy used an infinitesimal α to write down a unit impulse, infinitely tall and narrow Dirac-type delta function δα satisfying Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\textstyle \int F(x)\delta_\alpha(x) \,dx = F(0)} in a number of articles in 1827.[73] Cauchy defined an infinitesimal in Cours d'Analyse (1827) in terms of a sequence tending to zero. Namely, such a null sequence becomes an infinitesimal in Cauchy's and Lazare Carnot's terminology.

Non-standard analysis allows one to rigorously treat infinitesimals. The article by Yamashita (2007) contains a bibliography on modern Dirac delta functions in the context of an infinitesimal-enriched continuum provided by the hyperreals. Here the Dirac delta can be given by an actual function, having the property that for every real function F one has Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\textstyle \int F(x)\delta_\alpha(x) \, dx = F(0)} as anticipated by Fourier and Cauchy.

## Dirac comb[edit | edit source]

Main article: Dirac comb

File:Dirac comb.svg

A Dirac comb is an infinite series of Dirac delta functions spaced at intervals of T

A so-called uniform "pulse train" of Dirac delta measures, which is known as a Dirac comb, or as the Sha distribution, creates a sampling function, often used in digital signal processing (DSP) and discrete time signal analysis. The Dirac comb is given as the infinite sum, whose limit is understood in the distribution sense,

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \operatorname{III}(x) = \sum_{n=-\infty}^\infty \delta(x-n),}

which is a sequence of point masses at each of the integers.

Up to an overall normalizing constant, the Dirac comb is equal to its own Fourier transform. This is significant because if Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f} is any Schwartz function, then the periodization of Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f} is given by the convolution

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (f * \operatorname{III})(x) = \sum_{n=-\infty}^\infty f(x-n).}

In particular,

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (f*\operatorname{III})^\wedge = \widehat{f}\widehat{\operatorname{III}} = \widehat{f}\operatorname{III}}

is precisely the Poisson summation formula.[74][75] More generally, this formula remains to be true if Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f} is a tempered distribution of rapid descent or, equivalently, if Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \widehat{f}} is a slowly growing, ordinary function within the space of tempered distributions.

## Sokhotski–Plemelj theorem[edit | edit source]

The Sokhotski–Plemelj theorem, important in quantum mechanics, relates the delta function to the distribution p.v. 1/x, the Cauchy principal value of the function 1/x, defined by

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \left\langle\operatorname{p.v.}\frac{1}{x}, \varphi\right\rangle = \lim_{\varepsilon\to 0^+}\int_{|x|>\varepsilon} \frac{\varphi(x)}{x}\,dx.}

Sokhotsky's formula states that[76]

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \lim_{\varepsilon\to 0^+} \frac{1}{x\pm i\varepsilon} = \operatorname{p.v.}\frac{1}{x} \mp i\pi\delta(x),}

Here the limit is understood in the distribution sense, that for all compactly supported smooth functions f,

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \lim_{\varepsilon\to 0^+} \int_{-\infty}^\infty\frac{f(x)}{x\pm i\varepsilon}\,dx = \mp i\pi f(0) + \lim_{\varepsilon\to 0^+} \int_{|x|>\varepsilon}\frac{f(x)}{x}\,dx.}

## Relationship to the Kronecker delta[edit | edit source]

The Kronecker delta δij is the quantity defined by

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta_{ij} = \begin{cases} 1 & i=j\\\ 0 &i\not=j \end{cases} }

for all integers i, j. This function then satisfies the following analog of the sifting property: if Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (a_i)_{i \in \mathbf{Z}}} is any doubly infinite sequence, then

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \sum_{i=-\infty}^\infty a_i \delta_{ik}=a_k.}

Similarly, for any real or complex valued continuous function f on R, the Dirac delta satisfies the sifting property

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \int_{-\infty}^\infty f(x)\delta(x-x_0)\,dx=f(x_0).}

This exhibits the Kronecker delta function as a discrete analog of the Dirac delta function.[77]

## Applications[edit | edit source]

### Probability theory[edit | edit source]

In probability theory and statistics, the Dirac delta function is often used to represent a discrete distribution, or a partially discrete, partially continuous distribution, using a probability density function (which is normally used to represent absolutely continuous distributions). For example, the probability density function f(x) of a discrete distribution consisting of points x = {x1, ..., xn}, with corresponding probabilities p1, ..., pn, can be written as

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f(x) = \sum_{i=1}^n p_i \delta(x-x_i).}

As another example, consider a distribution in which 6/10 of the time returns a standard normal distribution, and 4/10 of the time returns exactly the value 3.5 (i.e. a partly continuous, partly discrete mixture distribution). The density function of this distribution can be written as

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f(x) = 0.6 \, \frac {1}{\sqrt{2\pi}} e^{-\frac{x^2}{2}} + 0.4 \, \delta(x-3.5).}

The delta function is also used to represent the resulting probability density function of a random variable that is transformed by continuously differentiable function. If Y = g(X) is a continuous differentiable function, then the density of Y can be written as

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f_Y(y) = \int_{-\infty}^{+\infty} f_X(x) \delta(y-g(x)) d x. }

The delta function is also used in a completely different way to represent the local time of a diffusion process (like Brownian motion). The local time of a stochastic process B(t) is given by

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \ell(x,t) = \int_0^t \delta(x-B(s))\,ds}

and represents the amount of time that the process spends at the point x in the range of the process. More precisely, in one dimension this integral can be written

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \ell(x,t) = \lim_{\varepsilon\to 0^+}\frac{1}{2\varepsilon}\int_0^t \mathbf{1}_{[x-\varepsilon,x+\varepsilon]}(B(s))\,ds}

where 1[x−ε, x+ε] is the indicator function of the interval [x−ε, x+ε].

### Quantum mechanics[edit | edit source]

The delta function is expedient in quantum mechanics. The wave function of a particle gives the probability amplitude of finding a particle within a given region of space. Wave functions are assumed to be elements of the Hilbert space L2 of square-integrable functions, and the total probability of finding a particle within a given interval is the integral of the magnitude of the wave function squared over the interval. A set {Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle |\varphi_n\rangle} } of wave functions is orthonormal if they are normalized by

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \langle\varphi_n \mid \varphi_m\rangle = \delta_{nm}}

where Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta} is the Kronecker delta. A set of orthonormal wave functions is complete in the space of square-integrable functions if any wave function Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle |\psi\rangle } can be expressed as a linear combination of the {Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle |\varphi_n\rangle} } with complex coefficients:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \psi = \sum c_n \varphi_n, }

with Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle c_n = \langle \varphi_n | \psi \rangle } . Complete orthonormal systems of wave functions appear naturally as the eigenfunctions of the Hamiltonian (of a bound system) in quantum mechanics that measures the energy levels, which are called the eigenvalues. The set of eigenvalues, in this case, is known as the spectrum of the Hamiltonian. In bra–ket notation, as above, this equality implies the resolution of the identity:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle I = \sum |\varphi_n\rangle\langle\varphi_n|.}

Here the eigenvalues are assumed to be discrete, but the set of eigenvalues of an observable may be continuous rather than discrete. An example is the position observable, Qψ(x) = xψ(x). The spectrum of the position (in one dimension) is the entire real line and is called a continuous spectrum. However, unlike the Hamiltonian, the position operator lacks proper eigenfunctions. The conventional way to overcome this shortcoming is to widen the class of available functions by allowing distributions as well: that is, to replace the Hilbert space of quantum mechanics with an appropriate rigged Hilbert space.[78] In this context, the position operator has a complete set of eigen-distributions, labeled by the points y of the real line, given by

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \varphi_y(x) = \delta(x-y).}

The eigenfunctions of position are denoted by Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \varphi_y = |y\rangle} in Dirac notation, and are known as position eigenstates.

Similar considerations apply to the eigenstates of the momentum operator, or indeed any other self-adjoint unbounded operator P on the Hilbert space, provided the spectrum of P is continuous and there are no degenerate eigenvalues. In that case, there is a set Ω of real numbers (the spectrum), and a collection φy of distributions indexed by the elements of Ω, such that

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P\varphi_y = y\varphi_y.}

That is, φy are the eigenvectors of P. If the eigenvectors are normalized so that

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \langle \varphi_y,\varphi_{y'}\rangle = \delta(y-y')}

in the distribution sense, then for any test function ψ,

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \psi(x) = \int_\Omega c(y) \varphi_y(x) \, dy}

where

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle c(y) = \langle \psi, \varphi_y \rangle.}

That is, as in the discrete case, there is a resolution of the identity

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle I = \int_\Omega |\varphi_y\rangle\, \langle\varphi_y|\,dy}

where the operator-valued integral is again understood in the weak sense. If the spectrum of P has both continuous and discrete parts, then the resolution of the identity involves a summation over the discrete spectrum and an integral over the continuous spectrum.

The delta function also has many more specialized applications in quantum mechanics, such as the delta potential models for a single and double potential well.

### Structural mechanics[edit | edit source]

The delta function can be used in structural mechanics to describe transient loads or point loads acting on structures. The governing equation of a simple mass–spring system excited by a sudden force impulse I at time t = 0 can be written

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle m \frac{\mathrm{d}^2 \xi}{\mathrm{d} t^2} + k \xi = I \delta(t),}

where m is the mass, ξ the deflection and k the spring constant.

As another example, the equation governing the static deflection of a slender beam is, according to Euler–Bernoulli theory,

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle EI \frac{\mathrm{d}^4 w}{\mathrm{d} x^4} = q(x),}

where EI is the bending stiffness of the beam, w the deflection, x the spatial coordinate and q(x) the load distribution. If a beam is loaded by a point force F at x = x0, the load distribution is written

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle q(x) = F \delta(x-x_0).}

As the integration of the delta function results in the Heaviside step function, it follows that the static deflection of a slender beam subject to multiple point loads is described by a set of piecewise polynomials.

Also, a point moment acting on a beam can be described by delta functions. Consider two opposing point forces F at a distance d apart. They then produce a moment M = Fd acting on the beam. Now, let the distance d approach the limit zero, while M is kept constant. The load distribution, assuming a clockwise moment acting at x = 0, is written

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \begin{align} q(x) &= \lim_{d \to 0} \Big( F \delta(x) - F \delta(x-d) \Big) \\\\[4pt] &= \lim_{d \to 0} \left( \frac{M}{d} \delta(x) - \frac{M}{d} \delta(x-d) \right) \\\\[4pt] &= M \lim_{d \to 0} \frac{\delta(x) - \delta(x - d)}{d}\\\\[4pt] &= M \delta'(x). \end{align}}

Point moments can thus be represented by the derivative of the delta function. Integration of the beam equation again results in piecewise polynomial deflection.

## See also[edit | edit source]

* Atom (measure theory)
* Laplacian of the indicator

## Notes[edit | edit source]

1. ^ atis 2013, unit impulse.
2. ^ Arfken & Weber 2000, p. 84.
3. ^ a b Dirac 1930, §22 The δ function.
4. ^ Gelfand & Shilov 1966–1968, Volume I, §1.1.
5. ^ Gelfand & Shilov 1966–1968, Volume I, §1.3.
6. ^ Schwartz 1950, p. 3.
7. ^ Zhao, Ji-Cheng (2011-05-05). Methods for Phase Diagram Determination. Elsevier. ISBN 978-0-08-054996-5.
8. ^ Fourier, JB (1822). The Analytical Theory of Heat (English translation by Alexander Freeman, 1878 ed.). The University Press. p. [1]., cf. https://books.google.com/books?id=-N8EAAAAYAAJ&pg=PA449 and pp. 546–551. The original French text may be found here.
9. ^ Komatsu, Hikosaburo (2002). "Fourier's hyperfunctions and Heaviside's pseudodifferential operators". In Takahiro Kawai; Keiko Fujita (eds.). Microlocal Analysis and Complex Fourier Analysis. World Scientific. p. [2]. ISBN 978-981-238-161-3.
10. ^ Myint-U., Tyn; Debnath, Lokenath (2007). Linear Partial Differential Equations for Scientists And Engineers (4th ed.). Springer. p. [3]. ISBN 978-0-8176-4393-5.
11. ^ Debnath, Lokenath; Bhatta, Dambaru (2007). Integral Transforms And Their Applications (2nd ed.). CRC Press. p. [4]. ISBN 978-1-58488-575-7.
12. ^ Grattan-Guinness, Ivor (2009). Convolutions in French Mathematics, 1800–1840: From the Calculus and Mechanics to Mathematical Analysis and Mathematical Physics, Volume 2. Birkhäuser. p. 653. ISBN 978-3-7643-2238-0.
13. ^ See, for example, Cauchy, Augustin-Louis (1789-1857) Auteur du texte (1882–1974). "Des intégrales doubles qui se présentent sous une forme indéterminèe". Oeuvres complètes d'Augustin Cauchy. Série 1, tome 1 / publiées sous la direction scientifique de l'Académie des sciences et sous les auspices de M. le ministre de l'Instruction publique...
14. ^ Mitrović, Dragiša; Žubrinić, Darko (1998). Fundamentals of Applied Functional Analysis: Distributions, Sobolev Spaces. CRC Press. p. 62. ISBN 978-0-582-24694-2.
15. ^ Kracht, Manfred; Kreyszig, Erwin (1989). "On singular integral operators and generalizations". In Themistocles M. Rassias (ed.). Topics in Mathematical Analysis: A Volume Dedicated to the Memory of A.L. Cauchy. World Scientific. p. https://books.google.com/books?id=xIsPrSiDlZIC&pg=PA553 553]. ISBN 978-9971-5-0666-7.
16. ^ Laugwitz 1989, p. 230.
17. ^ A more complete historical account can be found in van der Pol & Bremmer 1987, §V.4.
18. ^ "The physical interpretation of the quantum dynamics". Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character. 113 (765): 621–641. January 1927. doi:10.1098/rspa.1927.0012. ISSN 0950-1207.
19. ^ Gelfand & Shilov 1966–1968, Volume I, §1.1, p. 1.
20. ^ Dirac 1930, p. 63.
21. ^ Rudin 1966, §1.20
22. ^ Hewitt & Stromberg 1963, §19.61.
23. ^ Driggers 2003, p. 2321 See also Bracewell 1986, Chapter 5 for a different interpretation. Other conventions for the assigning the value of the Heaviside function at zero exist, and some of these are not consistent with what follows.
24. ^ Hewitt & Stromberg 1963, §9.19.
25. ^ Hazewinkel 2011, p. 41.
26. ^ Strichartz 1994, §2.2.
27. ^ Hörmander 1983, Theorem 2.1.5.
28. ^ Bracewell 1986, Chapter 5.
29. ^ Hörmander 1983, §3.1.
30. ^ Strichartz 1994, §2.3.
31. ^ Hörmander 1983, §8.2.
32. ^ Rudin 1966, §1.20.
33. ^ Dieudonné 1972, §17.3.3.
34. ^ Krantz, Steven G.; Parks, Harold R. (2008-12-15). Geometric Integration Theory. Springer Science & Business Media. ISBN 978-0-8176-4679-0.
35. ^ Federer 1969, §2.5.19.
36. ^ Strichartz 1994, Problem 2.6.2.
37. ^ Vladimirov 1971, Chapter 2, Example 3(d).
38. ^ Rottwitt, Karsten; Tidemand-Lichtenberg, Peter (2014-12-11). Nonlinear Optics: Principles and Applications. CRC Press. p. [5] 276\. ISBN 978-1-4665-6583-8.
39. ^ Weisstein, Eric W. "Sifting Property". MathWorld.
40. ^ Karris, Steven T. (2003). Signals and Systems with MATLAB Applications. Orchard Publications. p. 15. ISBN 978-0-9709511-6-8.
41. ^ Roden, Martin S. (2014-05-17). Introduction to Communication Theory. Elsevier. p. [6]. ISBN 978-1-4831-4556-3.
42. ^ Gelfand & Shilov 1966–1968, Vol. 1, §II.2.5.
43. ^ Further refinement is possible, namely to submersions, although these require a more involved change of variables formula.
44. ^ Hörmander 1983, §6.1.
45. ^ Lange 2012, pp.29–30.
46. ^ Gelfand & Shilov 1966–1968, p. 212.
47. ^ In some conventions for the Fourier transform.
48. ^ Bracewell 1986.
49. ^ Gelfand & Shilov 1966–1968, p. 26.
50. ^ Gelfand & Shilov 1966–1968, §2.1.
51. ^ Weisstein, Eric W. "Doublet Function". MathWorld.
52. ^ Bracewell 2000, p. 86.
53. ^ "Gugo82's comment on the distributional derivative of Dirac's delta". matematicamente.it. 12 September 2010.
54. ^ a b c Hörmander 1983, p. 56.
55. ^ Rudin 1991, Theorem 6.25.
56. ^ Stein & Weiss 1971, Theorem 1.18.
57. ^ Rudin 1991, §II.6.31.
58. ^ More generally, one only needs η = η1 to have an integrable radially symmetric decreasing rearrangement.
59. ^ Saichev & Woyczyński 1997, §1.1 The "delta function" as viewed by a physicist and an engineer, p. 3.
60. ^ Milovanović, Gradimir V.; Rassias, Michael Th (2014-07-08). Analytic Number Theory, Approximation Theory, and Special Functions: In Honor of Hari M. Srivastava. Springer. p. 748. ISBN 978-1-4939-0258-3.
61. ^ Stein & Weiss 1971, §I.1.
62. ^ Mader, Heidy M. (2006). Statistics in Volcanology. Geological Society of London. p. 81. ISBN 978-1-86239-208-3.
63. ^ Vallée & Soares 2004, §7.2.
64. ^ Hörmander 1983, §7.8.
65. ^ Courant & Hilbert 1962, §14.
66. ^ Lang 1997, p. 312.
67. ^ In the terminology of Lang (1997), the Fejér kernel is a Dirac sequence, whereas the Dirichlet kernel is not.
68. ^ Hazewinkel 1995, p. 357. sfn error: no target: CITEREFHazewinkel1995 (help)
69. ^ The development of this section in bra–ket notation is found in (Levin 2002, Coordinate-space wave functions and completeness, pp.=109ff)
70. ^ Davis & Thomson 2000, Perfect operators, p.344.
71. ^ Davis & Thomson 2000, Equation 8.9.11, p. 344.
72. ^ de la Madrid, Bohm & Gadella 2002.
73. ^ Laugwitz 1989.
74. ^ Córdoba 1988.
75. ^ Hörmander 1983, §7.2.
76. ^ Vladimirov 1971, §5.7.
77. ^ Hartmann 1997, pp. 154–155.
78. ^ Isham 1995, §6.2.

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## External links[edit | edit source]

* Media related to Dirac delta function at Wikimedia Commons
* "Delta-function", Encyclopedia of Mathematics, EMS Press, 2001 [1994]
* KhanAcademy.org video lesson
* The Dirac Delta function, a tutorial on the Dirac delta function.
* Video Lectures – Lecture 23, a lecture by Arthur Mattuck.
* The Dirac delta measure is a hyperfunction
* We show the existence of a unique solution and analyze a finite element approximation when the source term is a Dirac delta measure
* Non-Lebesgue measures on R. Lebesgue-Stieltjes measure, Dirac delta measure. Archived 2008-03-07 at the Wayback Machine

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Attributes of variables|

* Dependent and independent variables
* Homogeneous
* Nonhomogeneous
* Coupled
* Decoupled
* Order
* Degree
* Autonomous
* Exact differential equation
* On jet bundles

Relation to processes|

* Difference (discrete analogue)
* Stochastic
* Stochastic partial
* Delay

Solutions|

| Existence/uniqueness|

* Picard–Lindelöf theorem
* Peano existence theorem
* Carathéodory's existence theorem
* Cauchy–Kowalevski theorem

|

Solution topics|

* Wronskian
* Phase portrait
* Phase space
* Lyapunov stability
* Asymptotic stability
* Exponential stability
* Rate of convergence
* Series solutions
* Integral solutions
* Numerical integration
* Dirac delta function

Solution methods|

* Inspection
* Substitution
* Separation of variables
* Method of undetermined coefficients
* Variation of parameters
* Integrating factor
* Integral transforms
* Euler method
* Finite difference method
* Crank–Nicolson method
* Runge–Kutta methods
* Finite element method
* Finite volume method
* Galerkin method
* Perturbation theory

Applications|

* List of named differential equations

Mathematicians|

* Isaac Newton
* Gottfried Wilhelm Leibniz
* Leonhard Euler
* Jacob Bernoulli
* Émile Picard
* Józef Maria Hoene-Wroński
* Ernst Lindelöf
* Rudolf Lipschitz
* Joseph-Louis Lagrange
* Augustin-Louis Cauchy
* John Crank
* Phyllis Nicolson
* Carl David Tolmé Runge
* Martin Kutta
* Sofya Kovalevskaya

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