Dirac distribution: identity

In summary, the author proves that if you use the scaling property and the definition of the distributional derivative, you can find that:\left( \delta'(\lambda x), f \right) = \dfrac{1}{\vert \lambda \vert} \left( \delta', f(x/\lambda) \right) = \dfrac{(-1)}{\lambda \vert \lambda \vert} \left( \delta, f'(x/\lambda) \right) = \dfrac{1}{\lambda \vert \lambda \vert} \left( \delta', f(x/\lambda) \right)
  • #1
parton
83
1
Hello!

I should prove:

[tex] \delta'(\lambda x) = \dfrac{1}{\lambda \vert \lambda \vert} \delta(x), [/tex]
where lambda is just a constant.

If we make use of the scaling property and the definition of the distributional derivative, we find:

[tex] \left( \delta'(\lambda x), f \right) = \dfrac{1}{\vert \lambda \vert} \left( \delta', f(x/\lambda) \right) = \dfrac{(-1)}{\lambda \vert \lambda \vert} \left( \delta, f'(x/\lambda) \right) = \dfrac{1}{\lambda \vert \lambda \vert} \left( \delta', f(x/\lambda) \right) [/tex]
Because this is true for all testfunctions f, we have shown the identity.

Now I think that my last step is wrong.

Because of
[tex] f'(x/\lambda) = \left( \dfrac{\partial f}{\partial x} \right) (x/\lambda) \neq \dfrac{\partial}{\partial x} \left( f(x/\lambda) \right) = \dfrac{1}{\lambda} f'(x/\lambda) [/tex]
there should be an additional factor of lambda if I 'shift' the derivative back to the delta distribution.
You can also see this by (formally) writing the last step:

[tex] \dfrac{(-1)}{\lambda \vert \lambda \vert} \left( \delta, f'(x/\lambda) \right) = \dfrac{(-1)}{\lambda \vert \lambda \vert} \int_{-\infty}^{\infty} \delta(x) f'(x/\lambda) \, \mathrm{d}x = \dfrac{1}{\vert \lambda \vert} \int_{-\infty}^{\infty} \delta'(x) f(x/\lambda) \, \mathrm{d} x = \dfrac{1}{\vert \lambda \vert} \left( \delta', f(x/\lambda) \right), [/tex]
where I used integration by parts.

So something is really wrong here.
Maybe someone could help me.
 
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  • #2
I believe you are right.

You can also look at this physically the dimensions of this distribution has [1/x].


So you cannot have on one side dimensions of [1/(x\lambda)] and on the other side dimensions of [1/x\lambda^2].
 
  • #3
parton said:
Hello!

I should prove:

[tex] \delta'(\lambda x) = \dfrac{1}{\lambda \vert \lambda \vert} \delta(x), [/tex]
where lambda is just a constant.

What does the prime denote differentiation with respect to? Is it ##d\delta(\lambda x)/dx## or ##d \delta(\lambda x)/d(\lambda x)##?

Also, I don't think the identity as you have written it can be correct. Are you trying to prove

$$\delta'(\lambda x) = \dfrac{1}{\lambda \vert \lambda \vert} \delta'(x)?$$

i.e., did you make a typo and mean to write the derivative of the dirac function on the RHS?

You either need both sides to be derivatives of the Dirac function or you need the test function on the LHS and its derivative on the RHS to appear explicitly. Otherwise, for ##\lambda = 1##, your identity reduces to ##\delta'(x) = \delta(x)##, which is not true of course.
 
  • #4
Mute said:
What does the prime denote differentiation with respect to? Is it ##d\delta(\lambda x)/dx## or ##d \delta(\lambda x)/d(\lambda x)##?

I don't know. But, I think if you write
[tex] \delta'(\lambda x)[/tex] you usually mean [tex] \left[ \dfrac{\partial \delta}{\partial x} \right] (\lambda x), [/tex]
so you first build the derivative and then you evaluate it with lambda*x.

I also tried ##\partial \delta(\lambda x)/ \partial x##, but I nevertheless cannot show the identity.

Mute said:
Also, I don't think the identity as you have written it can be correct. Are you trying to prove

$$\delta'(\lambda x) = \dfrac{1}{\lambda \vert \lambda \vert} \delta'(x)?$$

i.e., did you make a typo and mean to write the derivative of the dirac function on the RHS?

Yes, your are right, it is a typo, thanks. It should be
[tex] \delta'(\lambda x) = \dfrac{1}{\lambda \vert \lambda \vert} \delta'(x) [/tex]

But still, I am not able to show the identity. Maybe the identity itself is wrong? What do you think?
 
  • #5
Ok, I should have somehting.

[tex] (\delta'(\lambda x), f) = \dfrac{1}{\vert \lambda \vert} \left( \delta', f(x/\lambda) \right) = \dfrac{(-1)}{\vert \lambda \vert} \left( \delta, \partial [ f(x/\lambda) ]/ \partial x \right) = \dfrac{(-1)}{\lambda \vert \lambda \vert} \left( \delta, f'(x/\lambda) \right) = \dfrac{(-1)}{\lambda \vert \lambda \vert} f'(0) = \dfrac{1}{\lambda \vert \lambda \vert} \left( \delta', f \right) [/tex]

So I used the chain rule in the 3rd step and
in the last one just the definition of the distributional derivative, i.e., ##(\delta', f) = - f'(0)##
 

What is the Dirac distribution?

The Dirac distribution, also known as the Dirac delta function, is a mathematical concept used to describe a point mass or a spike at a specific point in a continuous function. It is a generalized function that is zero everywhere except at a single point where it is infinite.

What is the purpose of the Dirac distribution?

The Dirac distribution is useful in physics and engineering for modeling point sources, such as the mass of an electron or the charge of a point particle. It is also used in mathematical analysis to simplify expressions involving integrals and differential equations.

How is the Dirac distribution defined mathematically?

The Dirac distribution is defined as a limit of a sequence of functions that have increasingly narrow peaks centered at the point of interest. Formally, it can be written as δ(x-c) = 0 for x≠c and ∫f(x)δ(x-c)dx = f(c) for all continuous functions f.

What are some properties of the Dirac distribution?

The Dirac distribution has several important properties, including symmetry (δ(x)=δ(-x)), scaling (δ(ax)=|a|⁻¹δ(x)), and sifting (∫f(x)δ(x-c)dx = f(c)). It is also a linear operator, meaning that it follows the rules of linearity (αδ(x)+βδ(x)= (α+β)δ(x)).

How is the Dirac distribution used in practical applications?

The Dirac distribution has a wide range of applications, including signal processing, quantum mechanics, and control theory. It is used to model impulse responses in systems, calculate Fourier transforms, and solve differential equations. It is also used in probability and statistics to describe point events in a continuous distribution.

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