distribution
Motivation
The main motivation behind distribution theory is to
extend the common linear operators on functions,
such as the derivative, convolution, and the Fourier transform
,
so that they also apply to the singular, non-smooth, or non-integrable
functions that regularly appear in both theoretical and applied
analysis
.
Distribution theory also seeks to define suitable structures
on the spaces of functions involved
to ensure the convergence of suitable approximating functions,
and the continuity of certain operators
.
For example, the limit of derivatives should be equal
to the derivative of the limit, with some definition of the limiting
operation
.
When this program is carried out,
inevitably we find that we have to enlarge the space of objects that we
would consider as “functions”. For example, the derivative of a step
function is the Dirac delta function with a spike at the discontinuous
step;
the Fourier transform of a constant function is also a Dirac delta
function, with the spike representing infinite
spectral magnitude
at one single frequency. (These facts, of course, had long been
used in engineering mathematics.)
Remark:
Dirac’s notion of delta distributions was introduced to facilitate computations in Quantum Mechanics,
however without having at the beginning a proper mathematical definition. In part as
a (negative) reaction to such a state of affairs, von Neumann produced a mathematically
well-defined foundation of Quantum Mechanics (http://planetmath.org/QuantumGroupsAndVonNeumannAlgebras) based on actions of
self-adjoint operators on Hilbert spaces which is still currently in use, with several significant
additions such as Frechét nuclear spaces and quantum groups
.
There are several theories of such ‘generalized functions’. In this entry, we describe Schwartz’ theory of distributions, which is probably the most widely used.
Essentially, a distribution on ℝ is a linear mapping that takes a
smooth function (with compact support) on ℝ into a real number.
For example, the delta distribution is the map,
f↦f(0) |
while any smooth function g on ℝ induces a distribution
f↦∫ℝfg. |
Distributions are also well behaved under coordinate changes, and
can be defined onto a manifold. Differential forms with
distribution valued coefficients are called currents.
However, it is not possible to define a product of two
distributions generalizing the product of usual functions.
Formal definition
A note on notation. In distribution theory, the
topological vector space of smooth functions with compact support on
an open set U⊆ℝn
is traditionally denoted by 𝒟(U). Let us also denote by
𝒟K(U) the subset of 𝒟(U) of functions with support
in a
compact set K⊂U.
Definition 1 (Distribution).
A distribution is a linear continuous functional on D(U), i.e., a linear continuous mapping D(U)→C. The set of all distributions on U is denoted by D′(U).
Suppose T is a linear functional on 𝒟(U).
Then T is continuous
if and only if T is continuous
in the origin (see this page (http://planetmath.org/ContinuousLinearMapping)).
This condition can be rewritten in various ways, and
the below theorem gives two convenient conditions that can be used to prove
that a linear mapping is a distribution.
Theorem 1.
Let U be an open set in Rn,
and let T be a linear functional on D(U). Then the
following are equivalent:
-
1.
T is a distribution.
-
2.
If K is a compact set in U, and {ui}∞i=1 be a sequence in 𝒟K(U), such that for any multi-index α, we have
Dαui→0 in the supremum norm
as i→∞, then T(ui)→0 in ℂ.
-
3.
For any compact set K in U, there are constants C>0 and k∈{1,2,…} such that for all u∈𝒟K(U), we have
|T(u)| ≤ C∑|α|≤k||Dαu||∞, (1) where α is a multi-index, and ||⋅||∞ is the supremum norm.
Proof The equivalence of (2) and (3) can be found on this page (http://planetmath.org/EquivalenceOfConditions2And3), and the equivalence of (1) and (3) is shown in [1].
Distributions of order k
If T is a distribution on an open set U,
and the same k can be used for any K
in the above inequality, then T is a
distribution of order k.
The set of all such distributions is denoted by D′k(U).
Both usual functions and the delta distribution are of order 0. One can also show that by differentiating a distribution its order increases by at most one. Thus, in some sense, the order is a measure of how ”smooth” a distribution is.
Topology for 𝒟′(U)
The standard topology for 𝒟′(U) is the weak∗ topology.
In this topology, a sequence {Ti}∞i=1 of distributions
(in 𝒟′(U)) converges
to a distribution T∈𝒟′(U) if and only if
Ti(u)→T(u)(in ℂ) as i→∞ |
for every u∈𝒟(U).
Notes
A common notation for the action of a distribution T onto a test function u∈𝒟(U) (i.e., T(u) with above notation) is ⟨T,u⟩. The motivation for this comes from this example (http://planetmath.org/EveryLocallyIntegrableFunctionIsADistribution).
References
-
1
W. Rudin, Functional Analysis
, McGraw-Hill Book Company, 1973.
- 2 L. Hörmander, The Analysis of Linear Partial Differential Operators I, (Distribution theory and Fourier Analysis), 2nd ed, Springer-Verlag, 1990.
Title | distribution |
Canonical name | Distribution |
Date of creation | 2013-03-22 13:44:08 |
Last modified on | 2013-03-22 13:44:08 |
Owner | matte (1858) |
Last modified by | matte (1858) |
Numerical id | 23 |
Author | matte (1858) |
Entry type | Definition |
Classification | msc 46-00 |
Classification | msc 46F05 |
Synonym | ‘generalized function’ |
Related topic | ExampleOfDiracSequence |
Related topic | DiracDeltaFunction |
Related topic | DiscreteTimeFourierTransformInRelationWithItsContinousTimeFourierTransfrom |
Related topic | QuantumGroups |
Related topic | FourierStieltjesAlgebraOfAGroupoid |
Related topic | QuantumOperatorAlgebrasInQuantumFieldTheories |
Related topic | QFTOrQuantumFieldTheories |
Related topic | QuantumGroup |
Defines | distribution of finite order |