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Lecture 17 - Distribution Space

Motivation

Consider

uttc2uxx=0

The solution is given by u(x,t)=f(x+ct)+g(xct) by method of characteristics with uC2(R) but by looking at the form of the solution u we see that from the solution there is no apparent solution why such regularity is needed. (A contrast to the solutions of the Burger's equation which forms singularities).

Example

Consider the Heaviside function H:RR given by:

H(x)={1x>10x0

Classical derivative has a problem at x=0. A more general derivative which we shall call δ function. However notice that:

ϵϵδ(x)dx=H(ϵ)H(ϵ)=1ϵ>0

This cannot hold for any 'ordinary' function. Moreover, Dirac wants to also know the derivatives of this δ.
δ will be defined as a distribution or so called "generalized function".

Definition

A distribution is a function T that takes any test function, ϕ:RR and maps it to RT(x)ϕ(x)dx.

Space of Test Functions

Definition

For any real/complex valued function f defined on ΩRn, the support of f is

supp f={x:f(x)0}
Definition

If Ω is any open set on Rn, the space of test function on Ω is:

Cc(Ω)=C0(Ω)={ϕC(R):supp ϕ is compact in Ω}=D(Ω)
Definition

If ϕnD(Ω) we say ϕn0 in D(Ω) if:

  1. There exists a compact set KΩ such that supp ϕnK n.
  2. limnmaxxΩ|Dαϕn(x)|=0 for any multi index α

Notation: For α1,...,αnN we call α=(α1,...,αn) a multi index and

Dα=α1x1α1α2x2α2...αnxnαn

Remark

||Dα(ϕn)Da(ϕ)||0

Space of Distribution

Definition

A linear mapping T:D(Ω)C is called a distribution if T(ϕn)T(ϕ) whenever ϕnϕ.
The set of all distributions on D(Ω) is denoted by D(Ω).

Definition

We say that two distributions T1 and T2 are equal if:

T1(ϕ)=T2(ϕ)ϕD(Ω)