How to Handle the Distribution 1/(x-i0)^2?

In summary, the conversation discusses the use of the identity \frac{1}{x-i0}=PP\frac{1}{x}+i\pi\delta\left(x\right) to evaluate the Fourier-transform of the temperate distribution f(x)=\frac{1}{\left(x-i0\right)^2}, which has one double pole. The need for using distribution theory is debated, with the suggestion to use the Cauchy Integral Theorem instead. The idea is to gain familiarity with distribution theory, and the conversation ends with the solution being \frac{1}{\left( x - i \epsilon \right)^2} = - \frac{d}{dx} \left[ \frac{1
  • #1
ziojoe
3
0
I have a little problem with the following exercise:
"Consider the temperate distribution

[tex] f\left(x\right)=\frac{1}{\left(x-i0\right)^2} [/tex]

Write f(x) like function of elementary temperate distributions and calculate its Fourier-transform."
I am almost sure I have to use the identity

[tex] \frac{1}{x-i0}=PP\frac{1}{x}+i\pi\delta\left(x\right) [/tex]

But the square makes appear terms like [tex] \delta^2\left(x\right) [/tex], that is not a distribution.

Any idea?
 
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  • #2
What is "the temperate distribution", and what is an "elementary temperate distribution"?

What is "i0"? E.g. is 0 simply an arbitrarily small positive number?

The original equation defines a function with one double pole (assuming my example interpretation of 0 above). I don't see any need to use distribution theory. You can evaluate the Fourier transform using the Cauchy Integral Theorem (assuming my example interpretation of 0 above).
 
  • #3
turin said:
What is "the temperate distribution", and what is an "elementary temperate distribution"?

I think this means "tempered distribution" and "regular distribution that is also a tempered distribution."
turin said:
What is "i0"? E.g. is 0 simply an arbitrarily small positive number?

I think so. This is usually denoted [itex]x- i \epsilon[/itex].
turin said:
The original equation defines a function with one double pole (assuming my example interpretation of 0 above). I don't see any need to use distribution theory. You can evaluate the Fourier transform using the Cauchy Integral Theorem (assuming my example interpretation of 0 above).

But I think the idea behind the question is to gain familiarity with distribution theory.
ziojoe said:
Any idea?

[tex]\frac{1}{\left( x - i \epsilon \right)^2} = - \frac{d}{dx} \left[ \frac{1}{ x - i \epsilon} \right][/tex]
 
  • #4
Thanks, that was exactly the answer I got myself after a while. Thanks again.
 

FAQ: How to Handle the Distribution 1/(x-i0)^2?

What is distribution theory?

Distribution theory is a branch of mathematics that deals with the analysis and properties of distributions, which are generalized functions used to represent objects that cannot be defined by traditional functions. It is often used in fields such as physics, engineering, and economics to model phenomena that are not captured by standard functions.

What are some common problems in distribution theory?

Some common problems in distribution theory include the existence and uniqueness of solutions, convergence and stability of distributions, and the calculation of moments and derivatives. These problems often arise when dealing with non-smooth or discontinuous functions, which cannot be handled by traditional calculus methods.

How is distribution theory used in real-world applications?

Distribution theory has many applications in real-world problems, particularly in fields such as signal processing, image analysis, and probability theory. It is used to model and analyze data that exhibit non-smooth or discontinuous behavior, such as noise in signals or random fluctuations in financial markets.

What are some key concepts in distribution theory?

Some key concepts in distribution theory include the Dirac delta function, which represents a point mass at a specific location, and the Heaviside step function, which is used to model sudden changes in a system. Other important concepts include the Fourier transform, convolution, and the notion of weak convergence of distributions.

What are some challenges in studying distribution theory?

One of the main challenges in studying distribution theory is understanding the abstract nature of distributions and how they differ from traditional functions. Another challenge is dealing with the non-intuitive properties of distributions, such as their non-uniqueness and non-differentiability. Additionally, the use of distribution theory often requires a strong background in advanced mathematics, making it a difficult subject for many students to grasp.

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