# Distribution (mathematics)

**Distributions**, also known as **Schwartz distributions** or **generalized functions**, are objects that generalize the classical notion of functions in mathematical analysis. Distributions make it possible to differentiate functions whose derivatives do not exist in the classical sense. In particular, any locally integrable function has a distributional derivative. Distributions are widely used in the theory of partial differential equations, where it may be easier to establish the existence of distributional solutions than classical solutions, or appropriate classical solutions may not exist. Distributions are also important in physics and engineering where many problems naturally lead to differential equations whose solutions or initial conditions are distributions, such as the Dirac delta function.

A function is normally thought of as *acting* on the *points* in its domain by "sending" a point x in its domain to the point Instead of acting on points, distribution theory reinterprets functions such as as acting on *test functions* in a certain way. ** Test functions** are usually infinitely differentiable complex-valued (or sometimes real-valued) functions with compact support (bump functions are examples of test functions). Many "standard functions" (meaning for example a function that is typically encountered in a Calculus course), say for instance a continuous map can be canonically reinterpreted as acting on test functions (instead of their usual interpretation as acting on points of their domain) via the action known as "integration against a test function"; explicitly, this means that "acts on" a test function g by "sending" g to the number This new action of is thus a complex (or real)-valued map, denoted by whose domain is the space of test functions; this map turns out to have two additional properties[note 1] that make it into what is known as a

*distribution on*Distributions that arise from "standard functions" in this way are the prototypical examples of a distributions. But there are many distributions that do not arise in this way and these distributions are known as "generalized functions." Examples include the Dirac delta function or some distributions that arise via the action of "integration of test functions against measures." However, by using various methods it is nevertheless still possible to reduce any arbitrary distribution down to a simpler

*family*of related distributions that do arise via such actions of integration.

In applications to physics and engineering, the space of test functions usually consists of smooth functions with compact support that are defined on some given non-empty open subset This space of test functions is denoted by or and a * distribution on U* is by definition a linear functional on that is continuous when is given a topology called the

**. This leads to**

*canonical LF topology**the*space of (all) distributions on U, usually denoted by (note the prime), which by definition is the space of all distributions on (that is, it is the continuous dual space of ); it is these distributions that are the main focus of this article.

The formal definitions of the spaces of test functions and distributions, their topologies, and their properties is highly technical and so a full presentation of this is given in the article on spaces of test functions and distributions.
Whereas that article is primarily concerned with *spaces* of test functions and distributions, this article is primarily concerned with *individual* test functions and distributions.

## History

The practical use of distributions can be traced back to the use of Green functions in the 1830s to solve ordinary differential equations, but was not formalized until much later. According to Kolmogorov & Fomin (1957), generalized functions originated in the work of Sergei Sobolev (1936) on second-order hyperbolic partial differential equations, and the ideas were developed in somewhat extended form by Laurent Schwartz in the late 1940s. According to his autobiography, Schwartz introduced the term "distribution" by analogy with a distribution of electrical charge, possibly including not only point charges but also dipoles and so on. Gårding (1997) comments that although the ideas in the transformative book by Schwartz (1951) were not entirely new, it was Schwartz's broad attack and conviction that distributions would be useful almost everywhere in analysis that made the difference.

## Notation

The following notation will be used throughout this article:

- is a fixed positive integer and is a fixed non-empty open subset of Euclidean space
- denotes the natural numbers.
- will denote a non-negative integer or
- If is a function then will denote its domain and the
of denoted by is defined to be the closure of the set in*support* - For two functions , the following notation defines a canonical pairing:
- A
is an element in (given that is fixed, if the size of multi-indices is omitted then the size should be assumed to be ). The*multi-index*of sizeof a multi-index is defined as and denoted by Multi-indices are particularly useful when dealing with functions of several variables, in particular we introduce the following notations for a given multi-index :*length*We also introduce a partial order of all multi-indices by if and only if for all When we define their multi-index binomial coefficient as:

## Definitions of test functions and distributions

In this section, some basic notions and defintions needed to define real-valued distributions on U are introduced. The formal definitions of the spaces of test functions and distributions, their topologies, and their properties is highly technical and so a full presentation of this is given in the article on spaces of test functions and distributions. Content related to the formal definition of these spaces that is also used in this article is described in this subsection.

**Notation**: Suppose

- Let denote the vector space of all k-times continuously differentiable real-valued functions on U.
- For any compact subset let and both denote the vector space of all those functions such that
- Note that depends on both K and U but we will only indicate K, where in particular, if then the domain of is U rather than K. We will use the notation only when the notation risks being ambiguous.
- Clearly, every contains the constant 0 map, even if

- Let denote the set of all such that for some compact subset K of U.
- Equivalently, is the set of all such that has compact support.
- is equal to the union of all as ranges over all compact subsets of
- If is a real-valued function on U, then is an element of if and only if is a bump function. Every real-valued test function on is always also a complex-valued test function on

For all and any compact subsets K and L of U, we have:

**Definition**: Elements of are called

**on U and is called the**

*test functions***on U. We will use both and to denote this space.**

*space of test function*Distributions on U are defined to be the continuous linear functionals on when this vector space is endowed with a particular topology called the ** canonical LF-topology**.
This topology is unfortunately not easy to define but it is nevertheless still possible to characterize distributions in a way so that no mention of the canonical LF-topology is made.

**Proposition**: If T is a linear functional on then the T is a distribution if and only if the following equivalent conditions are satisfied:

- For every compact subset there exist constants and such that for all [1]
- For every compact subset there exist constants and such that for all with support contained in [2]
- For any compact subset and any sequence in if converges uniformly to zero on for all multi-indices , then

The above characterizations can be used to determine whether or not a linear functional is a distribution, but more advanced uses of distributions and test functions (such as applications to differential equations) is limited if no topologies are placed on and

### Topology on *C*^{k}(*U*)

*C*

^{k}(

*U*)

We now introduce the seminorms that will define the topology on Different authors sometimes use different families of seminorms so we list the most common families below. However, the resulting topology is the same no matter which family is used.

Each of the functions above are non-negative -valued[note 3] seminorms on

Each of the following families of seminorms generates the same locally convex vector topology on :

*all*of the seminorms above (that is, by the union of the four families of seminorms).

With this topology, becomes a locally convex (*non*-normable) Fréchet space and all of the seminorms defined above are continuous on this space. *All* of the seminorms defined above are continuous functions on
Under this topology, a net in converges to if and only if for every multi-index with and every compact the net of partial derivatives converges uniformly to on [3] For any any (von Neumann) bounded subset of is a relatively compact subset of [4] In particular, a subset of is bounded if and only if it is bounded in for all [4] The space is a Montel space if and only if [5]

A subset of is open in this topology if and only if there exists such that is open when is endowed with the subspace topology induced on it by

#### Topology on *C*^{k}(*K*)

*C*

^{k}(

*K*)

As before, fix Recall that if is any compact subset of then

**Assumption**: For any compact subset we will henceforth assume that is endowed with the subspace topology it inherits from the Fréchet space

If is finite then is a Banach space[6] with a topology that can be defined by the norm

And when then is even a Hilbert space.[6]

#### Trivial extensions and independence of *C*^{k}(*K*)'s topology from *U*

*C*

^{k}(

*K*)'s topology from

*U*

The definition of depends on U so we will let denote the topological space which by definition is a topological subspace of Suppose is an open subset of containing Given its * trivial extension to V* is by definition, the function defined by:

so that Let denote the map that sends a function in to its trivial extension on V. This map is a linear injection and for every compact subset we have where is the vector subspace of consisting of maps with support contained in (since is also a compact subset of ). It follows that If I is restricted to then the following induced linear map is a homeomorphism (and thus a TVS-isomorphism):

and thus the next map (which like the previous map is defined by ) is a topological embedding:

Using we identify with its image in Because through this identification, can also be considered as a subset of Thus the topology on is independent of the open subset U of that contains K.[7] This justifies the practice of written instead of

### Canonical LF topology

Recall that denote all those functions in that have compact support in where note that is the union of all as ranges over all compact subsets of Moreover, for every is a dense subset of The special case when gives us the space of test functions.

*and it may also be denoted by Unless indicated otherwise, it is endowed with a topological called*

**space of test functions**on**.**

*the canonical LF topology*The canonical LF-topology is *not* metrizable and importantly, it is * strictly finer* than the subspace topology that induces on However, the canonical LF-topology does make into a complete reflexive nuclear[8] Montel[9] bornological barrelled Mackey space; the same is true of its strong dual space (i.e. the space of all distributions with its usual topology). The canonical LF-topology can be defined in various ways.

### Distributions

As discussed earlier, continuous linear functionals on a are known as distributions on Other equivalent definitions are described below.

*is a continuous linear functional on Said differently, a distribution on is an element of the continuous dual space of when is endowed with its canonical LF topology.*

**distribution**onThere is a canonical duality pairing between a distribution on and a test function which is denoted using angle brackets by

One interprets this notation as the distribution acting on the test function to give a scalar, or symmetrically as the test function acting on the distribution

#### Topology on the space of distributions and its relation to the weak-* topology

The set of all distributions on is the continuous dual space of which when endowed with the strong dual topology is denoted by Importantly, unless indicated otherwise, the topology on is the strong dual topology; if the topology is instead the weak-* topology then this will be clearly indicated. Neither topology is metrizable although unlike the weak-* topology, the strong dual topology makes into a complete nuclear space, to name just a few of its desirable properties.

Neither nor its strong dual is a sequential space and so neither of their topologies can be fully described by sequences (in other words, defining only what sequences converge in these spaces is *not* enough to fully/correctly define their topologies).
However, a *sequence* in converges in the strong dual topology if and only if it converges in the weak-* topology (this leads many authors to use pointwise convergence to actually *define* the convergence of a sequence of distributions; this is fine for sequences but this is *not* guaranteed to extend to the convergence of nets of distributions because a net may converge pointwise but fail to converge in the strong dual topology).
More information about the topology that is endowed with can be found in the article on spaces of test functions and distributions and in the articles on polar topologies and dual systems.

A *linear* map from into another locally convex topological vector space (such as any normed space) is continuous if and only if it is sequentially continuous at the origin. However, this is no longer guaranteed if the map is not linear or for maps valued in more general topological spaces (e.g. that are not also locally convex topological vector spaces). The same is true of maps from (more generally, this is true of maps from any locally convex bornological space).

#### Characterizations of distributions

**Proposition.** If is a linear functional on then the following are equivalent:

- T is a distribution;
: T is continuous;**Definition**- T is continuous at the origin;
- T is uniformly continuous;
- T is a bounded operator;
- T is sequentially continuous;
- explicitly, for every sequence in that converges in to some [note 4]

- T is sequentially continuous at the origin; in other words, T maps null sequences[note 5] to null sequences;
- explicitly, for every sequence in that converges in to the origin (such a sequence is called a
*null sequence*), - a
*null sequence*is by definition a sequence that converges to the origin;

- explicitly, for every sequence in that converges in to the origin (such a sequence is called a
- T maps null sequences to bounded subsets;
- explicitly, for every sequence in that converges in to the origin, the sequence is bounded;

- T maps Mackey convergent null sequences to bounded subsets;
- explicitly, for every Mackey convergent null sequence in the sequence is bounded;
- a sequence is said to be
*Mackey convergent to 0*if there exists a divergent sequence of positive real number such that the sequence is bounded; every sequence that is Mackey convergent to 0 necessarily converges to the origin (in the usual sense);

- The kernel of T is a closed subspace of
- The graph of T is closed;
- There exists a continuous seminorm g on such that
- There exists a constant a collection of continuous seminorms, that defines the canonical LF topology of and a finite subset such that [note 6]
- For every compact subset there exist constants and such that for all [1]
- For every compact subset there exist constants and such that for all with support contained in [2]
- For any compact subset and any sequence in if converges uniformly to zero for all multi-indices p, then

## Localization of distributions

There is no way to define the value of a distribution in at a particular point of U. However, as is the case with functions, distributions on U restrict to give distributions on open subsets of U. Furthermore, distributions are *locally determined* in the sense that a distribution on all of U can be assembled from a distribution on an open cover of U satisfying some compatibility conditions on the overlaps. Such a structure is known as a sheaf.

### Extensions and restrictions to an open subset

Let U and V be open subsets of with . Let be the operator which *extends by zero* a given smooth function compactly supported in V to a smooth function compactly supported in the larger set U. The transpose of is called the restriction mapping and is denoted by

The map is a continuous injection where if then it is *not* a topological embedding and its range is *not* dense in which implies that this map's transpose is neither injective nor surjective.[10] A distribution is said to be ** extendible to U** if it belongs to the range of the transpose of and it is called

**if it is extendable to [10]**

*extendible*For any distribution the restriction is a distribution in defined by:

Unless the restriction to V is neither injective nor surjective. Lack of surjectivity follows since distributions can blow up towards the boundary of V. For instance, if and then the distribution

is in but admits no extension to

### Gluing and distributions that vanish in a set

**Theorem[11]** — Let be a collection of open subsets of For each let and suppose that for all the restriction of to is equal to the restriction of to (note that both restrictions are elements of ). Then there exists a unique such that for all the restriction of T to is equal to

Let V be an open subset of U. is said to ** vanish in V** if for all such that we have T vanishes in V if and only if the restriction of T to V is equal to 0, or equivalently, if and only if T lies in the kernel of the restriction map

**Corollary[11]** — Let be a collection of open subsets of and let if and only if for each the restriction of T to is equal to 0.

**Corollary[11]** — The union of all open subsets of U in which a distribution T vanishes is an open subset of U in which T vanishes.

### Support of a distribution

This last corollary implies that for every distribution T on U, there exists a unique largest subset V of U such that T vanishes in V (and does not vanish in any open subset of U that is not contained in V); the complement in U of this unique largest open subset is called *the support of T*.[11] Thus

If is a locally integrable function on U and if is its associated distribution, then the support of is the smallest closed subset of U in the complement of which is almost everywhere equal to 0.[11] If is continuous, then the support of is equal to the closure of the set of points in U at which does not vanish.[11] The support of the distribution associated with the Dirac measure at a point is the set [11] If the support of a test function does not intersect the support of a distribution T then A distribution T is 0 if and only if its support is empty. If is identically 1 on some open set containing the support of a distribution T then If the support of a distribution T is compact then it has finite order and furthermore, there is a constant and a non-negative integer such that:[7]

If T has compact support then it has a unique extension to a continuous linear functional on ; this functional can be defined by where is any function that is identically 1 on an open set containing the support of T.[7]

If and then and Thus, distributions with support in a given subset form a vector subspace of [12] Furthermore, if is a differential operator in U, then for all distributions T on U and all we have and [12]

#### Support in a point set and Dirac measures

For any let denote the distribution induced by the Dirac measure at For any and distribution the support of T is contained in if and only if T is a finite linear combination of derivatives of the Dirac measure at [13] If in addition the order of T is then there exist constants such that:[14]

Said differently, if T has support at a single point then T is in fact a finite linear combination of distributional derivatives of the function at P. That is, there exists an integer m and complex constants such that

where is the translation operator.

#### Distribution with compact support

**Theorem[7]** — Suppose T is a distribution on U with compact support K. There exists a continuous function defined on U and a multi-index *p* such that

where the derivatives are understood in the sense of distributions. That is, for all test functions on U,

#### Distributions of finite order with support in an open subset

**Theorem[7]** — Suppose T is a distribution on U with compact support K and let V be an open subset of U containing K. Since every distribution with compact support has finite order, take N to be the order of T and define There exists a family of continuous functions defined on U with support in V such that

where the derivatives are understood in the sense of distributions. That is, for all test functions on U,

### Global structure of distributions

The formal definition of distributions exhibits them as a subspace of a very large space, namely the topological dual of (or the Schwartz space for tempered distributions). It is not immediately clear from the definition how exotic a distribution might be. To answer this question, it is instructive to see distributions built up from a smaller space, namely the space of continuous functions. Roughly, any distribution is locally a (multiple) derivative of a continuous function. A precise version of this result, given below, holds for distributions of compact support, tempered distributions, and general distributions. Generally speaking, no proper subset of the space of distributions contains all continuous functions and is closed under differentiation. This says that distributions are not particularly exotic objects; they are only as complicated as necessary.

#### Distributions as sheafs

**Theorem[15]** — Let T be a distribution on U.
There exists a sequence in such that each T_{i} has compact support and every compact subset intersects the support of only finitely many and the sequence of partial sums defined by converges in to T; in other words we have:

Recall that a sequence converges in (with its strong dual topology) if and only if it converges pointwise.

#### Decomposition of distributions as sums of derivatives of continuous functions

By combining the above results, one may express any distribution on U as the sum of a series of distributions with compact support, where each of these distributions can in turn be written as a finite sum of distributional derivatives of continuous functions on U. In other words for arbitrary we can write:

where are finite sets of multi-indices and the functions are continuous.

**Theorem[16]** — Let T be a distribution on U. For every multi-index p there exists a continuous function on U such that

- any compact subset K of U intersects the support of only finitely many and

Moreover, if T has finite order, then one can choose in such a way that only finitely many of them are non-zero.

Note that the infinite sum above is well-defined as a distribution. The value of T for a given can be computed using the finitely many that intersect the support of

## Operations on distributions

Many operations which are defined on smooth functions with compact support can also be defined for distributions. In general, if is a linear map which is continuous with respect to the weak topology, then it is possible to extend A to a map by passing to the limit.[note 7]

### Preliminaries: Transpose of a linear operator

Operations on distributions and spaces of distributions are often defined by means of the transpose of a linear operator because it provides a unified approach that the many definitions in the theory of distributions and because of its many well-known topological properties.[17] In general the transpose of a continuous linear map is the linear map defined by or equivalently, it is the unique map satisfying for all and all Since A is continuous, the transpose is also continuous when both duals are endowed with their respective strong dual topologies; it is also continuous when both duals are endowed with their respective weak* topologies (see the articles polar topology and dual system for more details).

In the context of distributions, the characterization of the transpose can be refined slightly. Let be a continuous linear map. Then by definition, the transpose of A is the unique linear operator that satisfies:

However, since the image of is dense in it is sufficient that the above equality hold for all distributions of the form where Explicitly, this means that the above condition holds if and only if the condition below holds:

#### Differentiation of distributions

Let be the partial derivative operator In order to extend we compute its transpose:

Therefore Therefore the partial derivative of with respect to the coordinate is defined by the formula

With this definition, every distribution is infinitely differentiable, and the derivative in the direction is a linear operator on

More generally, if is an arbitrary multi-index, then the partial derivative of the distribution is defined by

Differentiation of distributions is a continuous operator on this is an important and desirable property that is not shared by most other notions of differentiation.

If T is a distribution in then

where is the derivative of T and is translation by x; thus the derivative of T may be viewed as a limit of quotients.[18]

#### Differential operators acting on smooth functions

A linear differential operator in U with smooth coefficients acts on the space of smooth functions on Given such an operator we would like to define a continuous linear map, that extends the action of on to distributions on In other words, we would like to define such that the following diagram commutes:

where the vertical maps are given by assigning its canonical distribution which is defined by:

With this notation the diagram commuting is equivalent to:

In order to find the transpose of the continuous induced map defined by is considered in the lemma below.
This leads to the following definition of the differential operator on called *the formal transpose of * which will be denoted by to avoid confusion with the transpose map, that is defined by

**Lemma** — Let be a linear differential operator with smooth coefficients in Then for all we have

which is equivalent to:

Proof |
---|

As discussed above, for any the transpose may be calculated by: For the last line we used integration by parts combined with the fact that and therefore all the functions have compact support.[note 8] Continuing the calculation above, for all |

The Lemma combined with the fact that the formal transpose of the formal transpose is the original differential operator, that is, [19] enables us to arrive at the correct definition: the formal transpose induces the (continuous) canonical linear operator defined by We claim that the transpose of this map, can be taken as To see this, for every compute its action on a distribution of the form with :

We call the continuous linear operator the ** differential operator on distributions extending P**.[19] Its action on an arbitrary distribution is defined via:

If converges to then for every multi-index converges to

#### Multiplication of distributions by smooth functions

A differential operator of order 0 is just multiplication by a smooth function. And conversely, if is a smooth function then is a differential operator of order 0, whose formal transpose is itself (i.e., ). The induced differential operator maps a distribution T to a distribution denoted by We have thus defined the multiplication of a distribution by a smooth function.

We now give an alternative presentation of multiplication by a smooth function. If is a smooth function and T is a distribution on U, then the product is defined by

This definition coincides with the transpose definition since if is the operator of multiplication by the function m (i.e., ), then

so that

Under multiplication by smooth functions, is a module over the ring With this definition of multiplication by a smooth function, the ordinary product rule of calculus remains valid. However, a number of unusual identities also arise. For example, if is the Dirac delta distribution on then and if is the derivative of the delta distribution, then

The bilinear multiplication map given by is *not* continuous; it is however, hypocontinuous.[20]

**Example.** For any distribution T, the product of T with the function that is identically 1 on U is equal to T.

**Example.** Suppose is a sequence of test functions on U that converges to the constant function For any distribution T on U, the sequence converges to [21]

If converges to and converges to then converges to

##### Problem of multiplying distributions

It is easy to define the product of a distribution with a smooth function, or more generally the product of two distributions whose singular supports are disjoint. With more effort it is possible to define a well-behaved product of several distributions provided their wave front sets at each point are compatible. A limitation of the theory of distributions (and hyperfunctions) is that there is no associative product of two distributions extending the product of a distribution by a smooth function, as has been proved by Laurent Schwartz in the 1950s. For example, if is the distribution obtained by the Cauchy principal value

If is the Dirac delta distribution then

but

so the product of a distribution by a smooth function (which is always well defined) cannot be extended to an associative product on the space of distributions.

Thus, nonlinear problems cannot be posed in general and thus not solved within distribution theory alone. In the context of quantum field theory, however, solutions can be found. In more than two spacetime dimensions the problem is related to the regularization of divergences. Here Henri Epstein and Vladimir Glaser developed the mathematically rigorous (but extremely technical) *causal perturbation theory*. This does not solve the problem in other situations. Many other interesting theories are non linear, like for example the Navier–Stokes equations of fluid dynamics.

Several not entirely satisfactory theories of algebras of generalized functions have been developed, among which Colombeau's (simplified) algebra is maybe the most popular in use today.

Inspired by Lyons' rough path theory,[22] Martin Hairer proposed a consistent way of multiplying distributions with certain structure (regularity structures[23]), available in many examples from stochastic analysis, notably stochastic partial differential equations. See also Gubinelli–Imkeller–Perkowski (2015) for a related development based on Bony's paraproduct from Fourier analysis.

### Composition with a smooth function

Let T be a distribution on Let V be an open set in and If F is a submersion, it is possible to define

This is *the composition of the distribution T with F*, and is also called

*the*, sometimes written

**pullback**of T along FThe pullback is often denoted although this notation should not be confused with the use of '*' to denote the adjoint of a linear mapping.

The condition that F be a submersion is equivalent to the requirement that the Jacobian derivative of F is a surjective linear map for every A necessary (but not sufficient) condition for extending to distributions is that F be an open mapping.[24] The Inverse function theorem ensures that a submersion satisfies this condition.

If F is a submersion, then is defined on distributions by finding the transpose map. Uniqueness of this extension is guaranteed since is a continuous linear operator on Existence, however, requires using the change of variables formula, the inverse function theorem (locally) and a partition of unity argument.[25]

In the special case when F is a diffeomorphism from an open subset V of onto an open subset U of change of variables under the integral gives

In this particular case, then, is defined by the transpose formula:

### Convolution

Under some circumstances, it is possible to define the convolution of a function with a distribution, or even the convolution of two distributions.
Recall that if and are functions on then we denote by *the convolution of and *, defined at to be the integral

provided that the integral exists. If are such that then for any functions and we have and [26] If and g are continuous functions on at least one of which has compact support, then and if then the value of on A do *not* depend on the values of outside of the Minkowski sum [26]

Importantly, if has compact support then for any the convolution map is continuous when considered as the map or as the map [26]

#### Translation and symmetry

Given the translation operator sends to defined by This can be extended by the transpose to distributions in the following way: given a distribution T, *the translation of by * is the distribution defined by [27][28]

Given define the function by Given a distribution T, let be the distribution defined by The operator is called ** the symmetry with respect to the origin**.[27]

#### Convolution of a test function with a distribution

Convolution with defines a linear map:

which is continuous with respect to the canonical LF space topology on

Convolution of with a distribution can be defined by taking the transpose of relative to the duality pairing of with the space of distributions.[29] If then by Fubini's theorem

Extending by continuity, the convolution of with a distribution T is defined by

An alternative way to define the convolution of a test function and a distribution T is to use the translation operator The convolution of the compactly supported function and the distribution T is then the function defined for each by

It can be shown that the convolution of a smooth, compactly supported function and a distribution is a smooth function. If the distribution T has compact support then if is a polynomial (resp. an exponential function, an analytic function, the restriction of an entire analytic function on to the restriction of an entire function of exponential type in to ) then the same is true of [27] If the distribution T has compact support as well, then is a compactly supported function, and the Titchmarsh convolution theorem Hörmander (1983, Theorem 4.3.3) implies that

where denotes the convex hull and supp denotes the support.

#### Convolution of a smooth function with a distribution

Let and and assume that at least one of and T has compact support. The ** convolution** of and T, denoted by or by is the smooth function:[27]

satisfying for all :

If T is a distribution then the map is continuous as a map where if in addition T has compact support then it is also continuous as the map and continuous as the map [27]

If is a continuous linear map such that for all and all then there exists a distribution such that for all [7]

**Example.**[7] Let *H* be the Heaviside function on . For any

Let be the Dirac measure at 0 and its derivative as a distribution. Then and Importantly, the associative law fails to hold:

#### Convolution of distributions

It is also possible to define the convolution of two distributions S and T on provided one of them has compact support. Informally, in order to define where T has compact support, the idea is to extend the definition of the convolution to a linear operation on distributions so that the associativity formula

continues to hold for all test functions [30]

It is also possible to provide a more explicit characterization of the convolution of distributions.[29] Suppose that S and T are distributions and that S has compact support. Then the linear maps

are continuous. The transposes of these maps:

are consequently continuous and it can also be shown that[27]

This common value is called *the convolution of S and T*and it is a distribution that is denoted by or It satisfies [27] If S and T are two distributions, at least one of which has compact support, then for any [27] If T is a distribution in and if is a Dirac measure then ;[27] thus is the identity element of the convolution operation. Moreover, if is a function then where now the associativity of convolution implies that for all functions and

Suppose that it is T that has compact support. For consider the function

It can be readily shown that this defines a smooth function of which moreover has compact support. The convolution of S and T is defined by

This generalizes the classical notion of convolution of functions and is compatible with differentiation in the following sense: for every multi-index

The convolution of a finite number of distributions, all of which (except possibly one) have compact support, is associative.[27]

This definition of convolution remains valid under less restrictive assumptions about S and T.[31]

The convolution of distributions with compact support induces a continuous bilinear map defined by where denotes the space of distributions with compact support.[20] However, the convolution map as a function is *not* continuous[20] although it is separately continuous.[32] The convolution maps and given by both *fail* to be continuous.[20] Each of these non-continuous maps is, however, separately continuous and hypocontinuous.[20]

#### Convolution versus multiplication

In general, regularity is required for multiplication products and locality is required for convolution products. It is expressed in the following extension of the Convolution Theorem which guarantees the existence of both convolution and multiplication products. Let be a rapidly decreasing tempered distribution or, equivalently, be an ordinary (slowly growing, smooth) function within the space of tempered distributions and let be the normalized (unitary, ordinary frequency) Fourier transform[33] then, according to Schwartz (1951),

hold within the space of tempered distributions.[34][35][36] In particular, these equations become the Poisson Summation Formula if is the Dirac Comb.[37] The space of all rapidly decreasing tempered distributions is also called the space of *convolution operators* and the space of all ordinary functions within the space of tempered distributions is also called the space of *multiplication operators* More generally, and [38][39] A particular case is the Paley-Wiener-Schwartz Theorem which states that and This is because and In other words, compactly supported tempered distributions belong to the space of *convolution operators* and
Paley-Wiener functions better known as bandlimited functions, belong to the space of *multiplication operators* [40]

For example, let be the Dirac comb and be the Dirac delta then is the function that is constantly one and both equations yield the Dirac comb identity. Another example is to let be the Dirac comb and be the rectangular function then is the sinc function and both equations yield the Classical Sampling Theorem for suitable functions. More generally, if is the Dirac comb and is a smooth window function (Schwartz function), e.g. the Gaussian, then is another smooth window function (Schwartz function). They are known as mollifiers, especially in partial differential equations theory, or as regularizers in physics because they allow turning generalized functions into regular functions.

### Tensor product of distributions

Let and be open sets. Assume all vector spaces to be over the field where or For define for every and every the following functions:

Given and define the following functions:

where and These definitions associate every and with the (respective) continuous linear map:

Moreover if either (resp. ) has compact support then it also induces a continuous linear map of (resp. ).[41]

**Fubini's theorem for distributions[41]** — Let and If then

*The tensor product of and * denoted by or is the distribution in defined by:[41]

## Spaces of distributions

For all and all every one of the following canonical injections is continuous and has an image (also called the range) that is a dense subset of its codomain:

where the topologies on () are defined as direct limits of the spaces in a manner analogous to how the topologies on were defined (so in particular, they are not the usual norm topologies). The range of each of the maps above (and of any composition of the maps above) is dense in its codomain.[42]

Suppose that is one of the spaces (for ) or (for ) or (for ). Because the canonical injection is a continuous injection whose image is dense in the codomain, this map's transpose is a continuous injection. This injective transpose map thus allows the continuous dual space of to be identified with a certain vector subspace of the space of all distributions (specifically, it is identified with the image of this transpose map). This transpose map is continuous but it is *not* necessarily a topological embedding.
A linear subspace of carrying a locally convex topology that is finer than the subspace topology induced by is called ** a space of distributions**.[43]
Almost all of the spaces of distributions mentioned in this article arise in this way (e.g. tempered distribution, restrictions, distributions of order some integer, distributions induced by a positive Radon measure, distributions induced by an -function, etc.) and any representation theorem about the continuous dual space of may, through the transpose be transferred directly to elements of the space

### Radon measures

The inclusion map is a continuous injection whose image is dense in its codomain, so the transpose is also a continuous injection.

Note that the continuous dual space can be identified as the space of Radon measures, where there is a one-to-one correspondence between the continuous linear functionals and integral with respect to a Radon measure; that is,

- if then there exists a Radon measure on U such that for all and
- if is a Radon measure on U then the linear functional on defined by sending to is continuous.

Through the injection every Radon measure becomes a distribution on U. If is a locally integrable function on U then the distribution is a Radon measure; so Radon measures form a large and important space of distributions.

The following is the theorem of the structure of distributions of Radon measures, which shows that every Radon measure can be written as a sum of derivatives of locally functions in U:

** Theorem.[15]** — Suppose is a Radon measure, is a neighborhood of the support of T, and There exists is a family of locally functions in U such that

and for very

#### Positive Radon measures

A linear function on a space of functions is called ** positive** if whenever a function that belongs to the domain of is non-negative (that is, is real-valued and ) then One may show that every positive linear functional on is necessarily continuous (i.e. necessarily a Radon measure).[44] Lebesgue measure is an example of a positive Radon measure.

#### Locally integrable functions as distributions

One particularly important class of Radon measures are those that are induced locally integrable functions. The function is called ** locally integrable** if it is Lebesgue integrable over every compact subset K of U.[note 9] This is a large class of functions which includes all continuous functions and all Lp space functions. The topology on is defined in such a fashion that any locally integrable function yields a continuous linear functional on – that is, an element of – denoted here by whose value on the test function is given by the Lebesgue integral:

Conventionally, one abuses notation by identifying with provided no confusion can arise, and thus the pairing between and is often written

If and are two locally integrable functions, then the associated distributions and are equal to the same element of if and only if and are equal almost everywhere (see, for instance, Hörmander (1983, Theorem 1.2.5)). In a similar manner, every Radon measure on defines an element of whose value on the test function is