Dictionary
(statistics) an arrangement of values of a variable showing their observed or theoretical frequency of occurrence the spatial or geographic property of being scattered about over a range, area, or volume "worldwide in distribution" "the distribution of nerve fibers" "in complementary distribution" the act of distributing or spreading or apportioning the commercial activity of transporting and selling goods from a producer to a consumer
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Wikipedia
This page deals with generalized functions in mathematical analysis. It is not about probability distributions.''In mathematical analysis, distributions (also known as generalized functions) are objects which generalize function (mathematics)functions and probability distributions. They extend the concept of derivative to all continuous functioncontinuous functions and beyond and are used to formulate generalized solutions of partial differential equations. They are important in physics and engineering where many non-continuous problems naturally lead to differential equations whose solutions are distributions, such as the Dirac delta distribution."Generalized functions" were introduced by SobolevSergei Sobolev in 1935. They were independently discovered in late 1940s by Laurent Schwartz, who developed a comprehensive theory of distributions.Sometimes, people talk of a "probability theoryprobability distribution" when they just mean "probability measure (mathematics)measure", especially if it is obtained by taking the product of the Lebesgue measure by a positive, real-valued measurable function of integral equal to 1.
Basic idea - The basic idea is as follows. If ''f'' : R → R is an integrationintegrable function, and φ : R → R is a smooth (that is, infinitely derivativedifferentiable) function with compact support (that is, it is identically zero except on some bounded set), then ∫''f''φd''x'' is a real number which linear operatorlinearly and Continuous functioncontinuously depends on φ. One can therefore think of the function ''f'' as a continuous linear functional (mathematics)functional on the space which consists of all the "test functions" φ. Similarly, if ''P'' is a probability distribution on the reals and φ is a test function, then ∫φd''P'' is a real number that continuously and linearly depends on φ: probability distributions can thus also be viewed as continuous linear functionals on the space of test functions. This notion of "continuous linear functional on the space of test functions" is therefore used as the definition of a distribution.Such distributions may be multiplied with real numbers and can be added together, so they form a real vector space. In general it is not possible to define a multiplication for distributions, but distributions may be multiplied with infinitely differentiable functions.To define the derivative of a distribution, we first consider the case of a differentiable and integrable function ''f'' : R → R. If is a test function, then we have :using integration by parts (note that φ is zero outside of a bounded set and that therefore no boundary values have to be taken into account). This suggests that if ''S'' is a ''distribution'', we should define its derivative S' as the linear functional which sends the test function φ to -''S''(φ'). It turns out that this is the proper definition; it extends the ordinary definition of derivative, every distribution becomes infinitely differentiable and the usual properties of derivatives hold.The Dirac delta (so-called Dirac delta function) is the distribution which sends the test function φ to φ(0). It is the derivative of the Heaviside step function ''H''(''x'') = 0 if ''x'' < 0 and ''H''(''x'') = 1 if ''x'' ≥ 0. The derivative of the Dirac delta is the distribution which sends the test function φ to -φ'(0). This latter distribution is our first example of a distribution which is neither a function nor a probability distribution.An alternate definition is the limit of a sequence of functions. For instance the delta function is given bywhere !δa Formal definition - In the sequel, real-valued distributions on an open setopen subset ''U'' of R''n'' will be formally defined. (With minor modifications, one can also define complex-valued distributions, and one can replace R''n'' by any smooth manifold.) First, the space D(''U'') of test functions on ''U'' needs to be explained. A function φ : ''U'' → R is said to have ''compact support'' if there exists a compact spacecompact subset ''K'' of ''U'' such that φ(''x'') = 0 for all ''x'' in ''U'' \ ''K''. The elements of D(''U'') are the infinitely often differentiable functions φ : ''U'' → R with compact support. This is a real vector space. We turn it into a topological vector space by requiring that a sequence (or net (mathematics)net) !(φ''k'')? converges to 0 if and only if there exists a compact subset ''K'' of ''U'' such that all !φ''k'' 0 and natural number ''d'' ≥ 0 there exists a natural number ''k''0 such that for all ''k'' ≥ ''k''0 the absolute value of all ''d''-th derivatives of !φ''k''complete topological vector space (in fact, a so-called LF-space).The dual space of the topological vector space D(''U''), consisting of all continuous linear functionals ''S'' : D(''U'') → R, is the space of all distributions on ''U''; it is a vector space and is denoted by D'(''U'').The function ''f'' : ''U'' → R is called ''locally integrable'' if it is Lebesgue integrationLebesgue integrable over every compact subset ''K'' of ''U''. This is a large class of functions which includes all continuous functions. The topology on D(''U'') is defined in such a fashion that any locally integrable function ''f'' yields a continuous linear functional on D(''U'') whose value on the test function φ is given by the Lebesgue integral !∫''U''''k'')? converges towards 0 if and only if !''S''''k''strong (operator) topology. This is the case if and only if !''S''''k''converges uniformly to 0 on all bounded subsets of D(''U''). (A subset of ''E'' of D(''U'') is bounded if there exists a compact subset ''K'' of ''U'' and numbers !''d''''n''''n''R is an infinitely often differentiable function and ''S'' is a distribution on ''U'', we define the product ''S''ψ by (''S''ψ)(φ) = ''S''(ψφ) for all test functions φ. The ordinary product rule of calculus remains valid.
Compact support and convolution - We say that a distribution ''S'' has ''compact support'' if there is a compact subset ''K'' of ''U'' such that for every test function φ whose support is completely outside of ''K'', we have ''S''(φ) = 0. Alternatively, one may define distributions with compact support as continuous linear functionals on the space !C∞∞''k''''k''R''n'' and one of them has compact support, then one can define a new distribution, the ''convolution'' ''S''*''T'' of ''S'' and ''T'', as follows: if φ is a test function in D(R''n'') and ''x'', ''y'' elements of R''n'', write !φ''x''''x''< ;/sub>)? and (''S''*''T'')(φ) = ''S''(ψ).This generalizes the classical notion of convolution of functions and is compatible with differentiation in the following sense::d/d''x'' (''S'' * ''T'') = (d/d''x'' ''S'') * ''T'' = ''S'' * (d/d''x'' ''T'').
Tempered distributions and Fourier transform - By using a larger space of test functions, one can define the ''tempered distributions'', a subspace of D'(R''n''). These distributions are useful if one studies the Fourier transform in generality: all tempered distributions have a Fourier transform, but not all distributions have one.The space of test functions employed here, the so-called Schwartz-space, is the space of all infinitely differentiable rapidly decreasing functions, where φ : R''n'' → R is called ''rapidly decreasing'' if any derivative of φ, multiplied with any power of ''x'', converges towards 0 for ''x'' → ∞. These functions form a complete topological vector space with a suitably defined family of seminorms. More precisely, let:for α, β multi-indices of size ''n''. Then φ is rapidly-decreasing if all the values:The family of seminorms ''p''α, β defines a locally convex topology on the Schwartz-space. It is metrizable and complete.The derivative of a tempered distribution is again a tempered distribution.Tempered distributions generalize the bounded (or slow-growing) locally integrable functions; all distributions with compact support and all square-integrable functions can be viewed as tempered distributions. To study the Fourier transform, it is best to consider ''complex''-valued test functions and complex-linear distributions. The ordinary continuous Fourier transform ''F'' yields then an automorphism of Schwartz-space, and we can define the Fourier transform of the tempered distribution ''S'' by (''FS'')(φ) = ''S''(''F''φ) for every test function φ. ''FS'' is thus again a tempered distribution. The Fourier transform is a continuous, linear, bijective operator from the space of tempered distributions to itself. This operation is compatible with differentiation in the sense that F'' (d/d''x'' ''S'') = ''ix'' ''FS'' and also with convolution: if ''S'' is a tempered distribution and ψ is a ''slowly increasing'' infinitely differentiable function on R''n'' (meaning that all derivatives of ψ grow at most as fast as polynomials), then ''S''ψ is again a tempered distribution and F''(''S''ψ) = ''FS'' * ''F''ψ.
Using holomorphic functions as test functions - The success of the theory led to investigation of the idea of hyperfunction, in which spaces of holomorphic functions are used as test functions. A refined theory has been developed, in particular by Mikio Sato, using sheaf theory and several complex variables. This extends the range of symbolic methods that can be made into rigorous mathematics, for example Path integral formulation Feynman integrals.
Problem of multiplication - The main problem of the theory of distributions (and hyperfunctions) is that it is a purely linear theory, in the sense that the product of two distributions cannot consistently be defined (in general), as has been proved by Laurent Schwartz in the 1950's.Thus, nonlinear problems cannot be posed and thus not solved in distribution theory.In the context of quantum field theory, the non-respect of this fact is one of the sources of the "divergencies". Although in the context of the latter theory, Epstein and Glaser developed a mathematically rigorous (but extremely technical) theory, this does not solve the problem. Many other interesting theories are non linear, like for example Navier-Stokes equations of fluid dynamics.In view of this, several theories of algebra (ring theory)algebras of generalized functions have been developed, among which Colombeau algebraColombeau's (simplified) algebra is maybe the most popular in use today.
See also - generalized functionColombeau algebra
References - M. J. Lighthill (1958). ''Introduction to Fourier Analysis and Generalized Functions''. Cambridge University Press. ISBN 0-521-09128-4 (defines distributions as limits of sequences of functions under integrals) L. Schwartz (1954), ''Sur l'impossibilité de la multiplications des distributions'', C.R.Acad.Sci. Paris 239, pp 847-848.Category:Functional analysisde:Distribution (Mathematik)fr:Distribution (analyse !mathématique)nl:Distributieja :超関数
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