Eigenvectors of Fourier transform operator #F:L^2\to L^2#

In summary, Kolmogorov and Fomin discuss finding an orthogonal basis of eigenvectors for the Fourier transform operator, which can be represented by an infinite diagonal matrix. They use Schwartz functions of the form w(x)e^(-x^2/2) to satisfy a specific equation, and find that the polynomials of these functions satisfy the equation for specific values of mu. They then prove that these functions are eigenvectors of the Fourier transform with eigenvalues of +/- sqrt(2pi) and +/- i sqrt(2pi), and explain the relationship between the eigenvalues and the fourth roots of 4pi^2. They also mention that the Fourier transform has specific properties for functions in S_infinity.
  • #1
DavideGenoa
155
5
Hi, friends! In order to find an orthogonal basis of eigenvectors of the Fourier transform operator ##F : L_2(\mathbb{R})\to L_2(\mathbb{R}),f\mapsto\lim_{N\to\infty}\int_{[-N,N]}f(x)e^{-i\lambda x}d\mu_x## for Euclidean separable space ##L_2(\mathbb{R})##, so that ##F## would be represented by an infinite diagonal matrix, A.N. Kolmogorov and S.V. Fomin, in their Элементы теории функций и функционального анализа (Elements of the theory of functions and functional analysis), pp. 442-445 here, use functions that are, up to a constant factor, the Hermite functions, which I know to constitute an orthogonal basis of ##L_2(\mathbb{R})##.

While looking for such an orthogonal basis of eigenvectors, Kolmogorov and Fomin search for Schwartz functions, belonging to ##S_\infty\subset L_2(\mathbb{R})##, which is dense everywhere, in the form ##w(x)e^{-x^2/2}## where ##w## is a polynomial, satisfying the equation
##\frac{d^2f}{dx^2}-x^2f=\mu g\quad\quad\text{equation }(3)##
where ##\mu## is a constant, which may well be not the same for all solutions. Such an equation change into ##\frac{d^2g}{d\lambda^2}-\lambda^2g=\mu f## when acted upon by operator ##F##, where I have written ##g:= F(f)##. It is shown that the polynomials ##w## of such (Hermite up to a constant factor) functions satisfy equation (3) for ##\mu=-(2n+1)## when ##\deg w=n## (let us call such a polynomial ##P_n##) and they have non-null coefficients ##a_k## of the variable ##x^k## only for the ##k##'s of the same oddity of ##n##. It had also previously proved (p. 401) that ##P_n## is, up to a constant, ##(-1)^ne^{x^2}\frac{d^n e^{-x^2}}{dx^n}## (I am writing this in the case it can be used to prove that ##P_n(x)e^{-x^2/2}## defines an eigenvector of ##F##).

Kolmogorov-Fomin's says that the fact that the ##P_ne^{-x^2/2}## are eigenvectors of ##F## and their eigenvalues are ##\pm\sqrt{2\pi}##, ##\pm i\sqrt{2\pi}## derives from the following fact:

1. Equation (3) is invariant with respect to transformation ##F##.
2. Equation (3) has got, up to a constant factor, one solution of the form ##P_ne^{-x^2/2}##.
3. The Fourier transform maps ##x^ne^{-x^2/2}## to ##i^n\sqrt{2\pi}\frac{d^n}{dx^n}e^{-x^2/2}## (as [I knew][3], by using the fact that ##F[e^{-x^2/2}](\lambda)=\sqrt{2\pi}e^{-\lambda^2/2}##).

The proof of this derivation contained in the book only says that ##F^4[P_n e^{-x^2/2}]=4\pi^2 P_n e^{-x^2/2}##, which I know to be true for all #f\in L_2(\mathbb{R})#, but I could not see how this is related to the fact that the eigenvalues are the fourth roots of ##4\pi^2##. I have found some resources talking about the topic on line, but nothing using Kolmogorov-Fomin's argument, which I would like to understand... Does anybody understand and can explain it? I heartily thank you!

P.S.: If it were useful to understand what Kolmogorov-Fomin's says, I know that ##\forall f\in S_\infty##, ##k\in\mathbb{N}## ##F[f^{(k)}](\lambda)=(i\lambda)^kF[f](\lambda)## and ##\frac{d^k}{d\lambda^k}F[f](\lambda)=(-i)^kF[x^k f](\lambda)##.
 
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  • #2
Davide, just a suggestion: make the question more concise and then maybe do a second post with additional content, or wait until someone asks a followup. This may improve your chances of getting an answer.
 

Related to Eigenvectors of Fourier transform operator #F:L^2\to L^2#

1. What is the Fourier transform operator #F:L^2\to L^2#?

The Fourier transform operator is a mathematical operation that maps a function in the space of square-integrable functions, denoted as L^2, to another function in the same space. It is commonly used in signal processing, image processing, and other areas of mathematics and engineering.

2. What are eigenvectors of the Fourier transform operator?

Eigenvectors of the Fourier transform operator are functions that, when transformed by the operator, result in a scalar multiple of the original function. These eigenvectors are important in understanding the behavior of the operator and its applications.

3. How are eigenvectors of the Fourier transform operator calculated?

The process of calculating eigenvectors of the Fourier transform operator involves solving a system of equations known as the eigenvalue problem. The resulting solutions are the eigenvectors of the operator.

4. What are the properties of eigenvectors of the Fourier transform operator?

There are several important properties of eigenvectors of the Fourier transform operator, including orthogonality and completeness. These properties allow for the representation of any function as a linear combination of the eigenvectors.

5. What are the applications of eigenvectors of the Fourier transform operator?

Eigenvectors of the Fourier transform operator have many applications in signal and image processing, including image compression, filtering, and analysis. They are also used in solving differential equations and other mathematical problems.

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