Delta Dirac: Showing it's a Distribution

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    Delta Dirac Function
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Discussion Overview

The discussion centers around the characterization of the Dirac delta function as a distribution, exploring its properties of continuity and linearity. Participants delve into the definitions and mathematical framework surrounding distributions, particularly in the context of functional analysis.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification

Main Points Raised

  • One participant notes that the Dirac delta function is not a classical function and seeks to understand how to demonstrate its properties as a distribution.
  • Another participant asks for clarification on the definition of the Dirac delta function, indicating that definitions may vary among contributors.
  • A participant defines the delta function as a distribution that evaluates a test function at a specific point, suggesting a formal mathematical representation.
  • It is mentioned that the set of test functions forms a vector space, which can be used to demonstrate linearity, while the smoothness of test functions can be leveraged for continuity.
  • One contributor elaborates on the concept of distributions, linking them to functional analysis and the Riesz Representation Theorem, emphasizing the linear and continuous nature of functionals.
  • This participant argues that the Dirac delta function can be viewed as a continuous linear functional, providing a specific formulation for its action on functions.
  • Continuity is discussed in terms of boundedness, with a claim that the Dirac delta function is continuous because it is bounded when acting on continuous functions.

Areas of Agreement / Disagreement

Participants express varying definitions and interpretations of the Dirac delta function and its properties. While some points of agreement exist regarding its characterization as a distribution, multiple competing views and interpretations remain unresolved.

Contextual Notes

The discussion reflects differing assumptions about the nature of distributions and the mathematical framework required to define them. There is an emphasis on the need for generalized functions and the limitations of classical definitions.

sdickey9480
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I realize it's not a function in the classical sense, but how would one show that the delta dirac function is a distribution i.e. how do I show it's continuous and linear given that it's not truly a function?
 
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What is your definition of the Dirac delta function??
 
Given any x_0 ∈ Rn, the delta function is the distribution, δ_{x_0} :D(Rn)→C
given by the evaluation of a test function at x_0: ⟨δ_{x_0} , φ⟩ = φ(x_0)
 
The set of set of test functions is a vector space. The reals are are also a vector space. Use that for linearity.
Test functions are smooth, use that for continuity.
 
Hi sdickey9480!

I think your question really amounts to 'what is a distribution'? As mentioned by the previous posters it has to do with test functions, and more generally with special vector spaces (usually complete ones and those endowed with a norm).

An amazing triumph of functional analysis is representing vectors (in this case non pathological functions) in terms of their actions on other vectors. By action on other vectors, I mean given any vector v in the vector space V, define a mapping \hat{v}:V\rightarrow \mathbb{R}. This mapping is given by the Riesz Representation Theorem, and in our case it means \hat{v}(g):=\int fg \mathrm{d}x

\hat{v} is called linear because \hat{v}(f+g)=\hat{v}(f)+\hat{v}(g).

The second important property we want \hat{v} to have is that of continuity. Another surprising result of functional analysis says that a functional (any linear map from the vector space into the reals, like \hat{v} for example) is continuous if and only if it is bounded in the operator sense. That is, \hat{v} is bounded if and only if sup\{\hat{v}(f): ||f||_\infty = 1 \}< \infty

What I have done is built the necessary machinery to generalize functions. What I have shown is that any vector (or in this case non-pathological function) can be thought of as a continuous linear functional. A distribution is then just one of these continuous linear functionals.

So to answer your question, the dirac delta function \delta is defined as a functional, mapping some space of functions to the real line by \delta (f) = \int f\delta \mathrm{d}x := f(0). It is clear that \delta is linear because integral is linear (actually strictly speaking the integral doesn't make sense, hence the need for generalized functions to begin with. We really define \delta to be linear).

Why is \delta continuous? Because if ||f||_\infty = 1 and f is continuous, then f(x)\leq 1 for any x. Hence \delta is bounded by 1, and therefore continuous.
 

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