Eigenvalues for a bounded operator

In summary: The equation ##f(1) = \lambda f(1)## implies ##\lambda = 1## only in the case ##f(1) \neq 0##; otherwise, if ##f(1) = 0## there is no restriction on ##\lambda##.
  • #1
Wuberdall
34
0

Homework Statement


Let [itex]C[/itex] be the composition operator on the Hilbert space [itex]L_{2}(\mathbb{R})[/itex] with the usual inner product. Let [itex]f\in L_{2}(\mathbb{R})[/itex], then [itex]C[/itex] is defined by

[itex](Cf)(x) = f(2x-1)[/itex], [itex]\hspace{9pt}x\in\mathbb{R}[/itex]

give a demonstration, which shows that [itex]C[/itex] does not have any eigenvalues.

Homework Equations


[itex]C[/itex] is a unitary operator.

Let [itex]\mathcal{F}[/itex] denote the Fourier Transformation on [itex]L_{2}(\mathbb{R})[/itex], then
[itex](\mathcal{F}C\mathcal{F}^{\ast}f)(p) =
\frac{\exp\big(-i\tfrac{1}{2}p\big)}{\sqrt{2}}\hspace{1pt}f\big(\tfrac{1}{2}p\big)
[/itex]

The Attempt at a Solution


Direct application of the eigenvalue equation of course yields Schröders equation, that is
[itex]f(2x-1) = \lambda f(x)[/itex]

I don't have a slightest idea on how to proceed from here.Any good suggestions are more than welcome!
 
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  • #2
If C were to have any eigenvalues, they would apply to any choice of f in your space.
Consdider a function that is has the property f(0)=0 and f(-1) ##\neq## 0.
Such a function clearly exists in your space.
 
  • #3
RUber said:
If C were to have any eigenvalues, they would apply to any choice of f in your space.
Consdider a function that is has the property f(0)=0 and f(-1) ##\neq## 0.
Such a function clearly exists in your space.
Thanks for your reply.
I still don't see how this implies that [itex]C[/itex] doesn't have any eigenvalues ?

I mean, does i not only show that there doesn't exist an eigenfunction having these properties?
 
  • #4
Is this a valid argument ?
Assume that [itex]f:\mathbb{R}\rightarrow\mathbb{R}[/itex] is non vanishing almost everywhere and [itex]\lambda[/itex] is an eigenvalue of [itex]C[/itex], then
[itex]f(2x-1) = \lambda f(x)[/itex]
[itex]2f'(2x-1) = \lambda f'(x)[/itex]

For x=1 the first of the equations yields : [itex]\hspace{3pt} f(1) = \lambda f(1) \hspace{12pt}\Rightarrow\hspace{12pt} \lambda=1[/itex].
While the second of the equations yields : [itex]\hspace{3pt} 2f'(1) = \lambda f'(1) \hspace{12pt}\Rightarrow\hspace{12pt} \lambda=2[/itex].
This is obvious a contradiction, therefore [itex]\lambda[/itex] can't be a eigenvalue.

Of course [itex] f [/itex] must behave properly around [itex] x=1 [/itex], but this is guaranteed by [itex] f \in L_{2}(\mathbb{R}) [/itex].
 
  • #5
RUber said:
If C were to have any eigenvalues, they would apply to any choice of f in your space.
Consdider a function that is has the property f(0)=0 and f(-1) ##\neq## 0.
Such a function clearly exists in your space.

No, the eigenvalue equation ##f(2x-1) = \lambda f(x) \; \forall x## need apply only to an eigenfunction ##f = f_{\lambda}##.
 
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  • #6
Thanks, Ray. I must have been operating without caffeine when I posted that.

Wuberdall, your contradiction argument looks reasonable to me.
 
  • #7
But ##f\in \mathbb L^2## doesn't give you that ##f## is differentiable.
 
  • #8
LCKurtz makes a good point. The goal should be to contradict your assumptions.
As it stands, 1 is the only potential eigenvalue, as long as f(x) is defined at x=1.
So if f(x) is defined at ##x = 1+ \delta##, ##f(1+ \delta) = f(1+ 2\delta)=f(1+2^k \delta) ## for any delta and integer k .
You may be able to show that this sort of function is either the zero function (trivial) or not in ##L^2##.
 
  • #9
RUber said:
Thanks, Ray. I must have been operating without caffeine when I posted that.

Wuberdall, your contradiction argument looks reasonable to me.

More generally: if ##f \in C^{\infty}## near ##x = 1## then we have ##2^n f^{(n)}(2x-1) = \lambda f^{(n)}(x), n = 0,1,2,\ldots##, where ##f^{(n)}## denotes the ##n##th derivative. Thus ##2^n f^{(n)}(1) = \lambda f^{(n)}(1), n = 0,1,2, \ldots##.
(1) If ##\lambda = 0## that would imply that ##f^{(n) = 0, n = 0,1,2, \ldots##, meaning that ##f## would be non-analytic (but still ##C^{\infty}##. That means that ##f## would not equal its own Taylor series. There are functions like that, and our ##f## would have to be one of them.
(2) If ##\lambda \neq 0## then either (i) ##f(0) = 0##; or (ii) ##\lambda = 1##. In case (i) we have ##2 f'(1) = \lambda f'(1), 4 f''(1) = \lambda f''(1), \ldots##, and this allows ##\lambda = 2, f^{(n)}(1) = 0## for ##n \geq 2## or ##\lambda = 4, f^{(n)}(1)=0## for ##n = 1,3,4,\ldots#, etc. Again, ##f## would not be analytic
RUber said:
LCKurtz makes a good point. The goal should be to contradict your assumptions.
As it stands, 1 is the only potential eigenvalue, as long as f(x) is defined at x=1.
So if f(x) is defined at ##x = 1+ \delta##, ##f(1+ \delta) = f(1+ 2\delta)=f(1+2^k \delta) ## for any delta and integer k .
You may be able to show that this sort of function is either the zero function (trivial) or not in ##L^2##.

The equation ##f(1) = \lambda f(1)## implies ##\lambda = 1## only in the case ##f(1) \neq 0##; otherwise, if ##f(1) = 0## there is no restriction on ##\lambda##.
 

Related to Eigenvalues for a bounded operator

1. What are eigenvalues for a bounded operator?

Eigenvalues for a bounded operator are the special values that satisfy the equation Av = λv, where A is the bounded operator, v is a non-zero vector, and λ is a scalar. They represent the scaling factor by which the vector v is multiplied when it is transformed by the operator A.

2. How do eigenvalues affect the behavior of a bounded operator?

Eigenvalues play a crucial role in understanding the behavior of a bounded operator. The number and type of eigenvalues determine important properties such as invertibility, diagonalizability, and stability of the operator. They also provide insights into the geometric transformations performed by the operator on the vector space.

3. Can a bounded operator have complex eigenvalues?

Yes, a bounded operator can have complex eigenvalues. In fact, every square complex matrix has at least one complex eigenvalue. This is because the eigenvalues are solutions to the characteristic polynomial of the matrix, which can have complex roots. Complex eigenvalues can also occur in real matrices, but they always occur in complex-conjugate pairs.

4. How can eigenvalues be calculated for a bounded operator?

There are various methods for calculating eigenvalues for a bounded operator. The most common method is to solve the characteristic equation, det(A-λI) = 0, where A is the operator and I is the identity matrix. This will give the eigenvalues as the roots of the polynomial. Other methods include using the power method or the QR algorithm.

5. What is the significance of the multiplicity of an eigenvalue?

The multiplicity of an eigenvalue is the number of times it appears as a root of the characteristic polynomial. It is important because it determines the geometric and algebraic properties of the operator. For example, if an eigenvalue has multiplicity 1, it is a simple eigenvalue and the corresponding eigenspace has dimension 1. But if the multiplicity is greater than 1, the eigenspace can have a higher dimension and the operator may not be diagonalizable.

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