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 where 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 singular, 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, distribution theory reinterprets functions as being equivalent to their dual linear functionals: if every nicely behaved test function integrated with a function gives out the same result, this defines a linear functional equivalent to every conventional function.
However, this kind of definition is much laxer, and admits mathematical objects beyond functions. In particular, these kinds of generalised functions can be used to represent singular measures, such as the delta function, and all of its derivatives. Since the distributional framework is localised, linear, and shiftinvariant, it can represent almost all of the compositions of the basis waveforms as well. It also admits a full Fourier theory, and since the theory is fully localised, there is no need to have functions fall off in time or shift.
In applications to physics and engineering, test functions are usually infinitely differentiable complexvalued (or realvalued) functions with compact support that are defined on some given nonempty open subset (bump functions are examples of test functions). The set of all such test functions forms a vector space that is denoted by or
Most commonly encountered functions, including all continuous maps if using can be canonically reinterpreted as acting via "integration against a test function." Explicitly, this means that "acts on" a test function by "sending" it to the number which is often denoted by This new action of is a scalarvalued map, denoted by whose domain is the space of test functions This functional turns out to have the two defining properties of what is known as a distribution on it is linear and also continuous when is given a certain topology called the canonical LF topology. Distributions like that arise from functions in this way are prototypical examples of distributions, but there are many which cannot be defined by integration against any function. Examples of the latter include the Dirac delta function and distributions defined to act by integration of test functions against certain measures. It is nonetheless still possible to reduce any arbitrary distribution down to a simpler family of related distributions that do arise via such actions of integration.
More generally, a distribution on is by definition a linear functional on that is continuous when is given a topology called the canonical LF topology. This leads to the space of (all) distributions on , 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.
Definitions of the appropriate topologies on spaces of test functions and distributions are given in the article on spaces of test functions and distributions. This article is primarily concerned with the definition of distributions, together with their properties and some important examples.
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 secondorder 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.
The following notation will be used throughout this article:
In this section, some basic notions and definitions needed to define realvalued distributions on U are introduced. Further discussion of the topologies on the spaces of test functions and distributions are given in the article on spaces of test functions and distributions.
For all and any compact subsets K and L of U, we have:
Distributions on U are continuous linear functionals on when this vector space is endowed with a particular topology called the canonical LFtopology. The following proposition states two necessary and sufficient conditions for the continuity of a linear functional on that are often straightforward to verify.
Proposition: A linear functional T on is continuous, and therefore a distribution, if and only if either of the following equivalent conditions are satisfied:
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 nonnegative valued^{[note 2]} seminorms on
Each of the following families of seminorms generates the same locally convex vector topology on :
With this topology, becomes a locally convex (nonnormable) 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 multiindex 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
As before, fix Recall that if is any compact subset of then
If is finite then is a Banach space^{[6]} with a topology that can be defined by the norm
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 and for any compact subset let is the vector subspace of consisting of maps with support contained in Given its trivial extension to V is by definition, the function defined by:
Main article: Spaces of test functions and distributions 
See also: LFspace and Topology of uniform convergence 
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.
The canonical LFtopology is not metrizable and importantly, it is strictly finer than the subspace topology that induces on However, the canonical LFtopology does make into a complete reflexive nuclear^{[8]} Montel^{[9]} bornological barrelled Mackey space; the same is true of its strong dual space (that is, the space of all distributions with its usual topology). The canonical LFtopology can be defined in various ways.
See also: Continuous linear functional 
As discussed earlier, continuous linear functionals on a are known as distributions on Other equivalent definitions are described below.
There 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
Proposition. If is a linear functional on then the following are equivalent:
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 (for example, 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).
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.
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 map is a continuous injection. A distribution is said to be extendible to U if it belongs to the range of the transpose of and it is called extendible if it is extendable to ^{[11]}
For any distribution the restriction is a distribution in defined as the transpose of 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
Theorem^{[12]} — 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^{[12]} — 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^{[12]} — The union of all open subsets of U in which a distribution T vanishes is an open subset of U in which T vanishes.
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.^{[12]} 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.^{[12]} If is continuous, then the support of is equal to the closure of the set of points in U at which does not vanish.^{[12]} The support of the distribution associated with the Dirac measure at a point is the set ^{[12]} 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 nonnegative 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 ^{[13]} Furthermore, if is a differential operator in U, then for all distributions T on U and all we have and ^{[13]}
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 ^{[14]} If in addition the order of T is then there exist constants such that:^{[15]}
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
Theorem^{[7]} — Suppose T is a distribution on U with compact support K. There exists a continuous function defined on U and a multiindex p such that
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
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.
Theorem^{[16]} — 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:
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:
Theorem^{[17]} — Let T be a distribution on U. For every multiindex p there exists a continuous function on U such that
Moreover, if T has finite order, then one can choose in such a way that only finitely many of them are nonzero.
Note that the infinite sum above is welldefined as a distribution. The value of T for a given can be computed using the finitely many that intersect the support of
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 6]}^{[citation needed]}^{[clarification needed]}
Main article: Transpose of a linear map 
Operations on distributions and spaces of distributions are often defined by means of the transpose of a linear operator. This is because the transpose allows for a unified presentation of the many definitions in the theory of distributions and also because its properties are well known in functional analysis.^{[18]} For instance, the wellknown Hermitian adjoint of a linear operator between Hilbert spaces is just the operator's transpose (but with the Riesz representation theorem used to identify each Hilbert space with its continuous dual space). In general the transpose of a continuous linear map is the linear map
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:
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 multiindex, 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
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:
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
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 7]} 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, ^{[20]} 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.^{[20]} Its action on an arbitrary distribution is defined via:
If converges to then for every multiindex converges to
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 (that is, ). 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 (that is, ), then
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.^{[21]}
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 ^{[22]}
If converges to and converges to then converges to
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 wellbehaved 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
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^{[citation needed]} 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,^{[23]} Martin Hairer proposed a consistent way of multiplying distributions with certain structure (regularity structures^{[24]}), 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.
Let T be a distribution on Let V be an open set in and If is a submersion, it is possible to define
This is the composition of the distribution with , and is also called the pullback of along , sometimes written
The 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 be a submersion is equivalent to the requirement that the Jacobian derivative of is a surjective linear map for every A necessary (but not sufficient) condition for extending to distributions is that be an open mapping.^{[25]} The Inverse function theorem ensures that a submersion satisfies this condition.
If 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.^{[26]}
In the special case when 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:
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
Importantly, if has compact support then for any the convolution map is continuous when considered as the map or as the map ^{[27]}
Given the translation operator sends to defined by This can be extended by the transpose to distributions in the following way: given a distribution the translation of by is the distribution defined by ^{[28]}^{[29]}
Given define the function by Given a distribution let be the distribution defined by The operator is called the symmetry with respect to the origin.^{[28]}
Convolution with defines a linear map:
Convolution of with a distribution can be defined by taking the transpose of relative to the duality pairing of with the space of distributions.^{[30]} If then by Fubini's theorem
Extending by continuity, the convolution of with a distribution is defined by
An alternative way to define the convolution of a test function and a distribution is to use the translation operator The convolution of the compactly supported function and the distribution 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 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 ^{[28]} If the distribution has compact support as well, then is a compactly supported function, and the Titchmarsh convolution theorem Hörmander (1983, Theorem 4.3.3) implies that:
Let and and assume that at least one of and has compact support. The convolution of and denoted by or by is the smooth function:^{[28]}
If is a distribution then the map is continuous as a map where if in addition has compact support then it is also continuous as the map and continuous as the map ^{[28]}
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 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:
It is also possible to define the convolution of two distributions and on provided one of them has compact support. Informally, in order to define where has compact support, the idea is to extend the definition of the convolution to a linear operation on distributions so that the associativity formula
It is also possible to provide a more explicit characterization of the convolution of distributions.^{[30]} Suppose that and are distributions and that has compact support. Then the linear maps