Projections in L^2 spaces

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This is in fact a theorem: Suppose ##A## is a bounded operator on a Hilbert space ##H##, and ##\lambda## is an eigenvalue of ##A## with multiplicity 1. Then the eigenspace ##E_\lambda## of ##\lambda## admits an orthogonal projection.Note that this theorem is not true anymore if the operator is not bounded. In fact, there are self-adjoint operators on infinite-dimensional Hilbert spaces which have no eigenvectors at all.
  • #1
AxiomOfChoice
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Suppose you have a self-adjoint operator [itex]A[/itex] on [itex]L^2(\mathbb R^n)[/itex] that has exactly one discrete, non-degenerate eigenvalue [itex]a[/itex] with associated normalized eigenfunction [itex]f_a[/itex]. What does the projection onto the associated eigenspace look like? My guess would be:

[tex]
P(f) = (f,f_a)f_a = f_a(x) \int_{\mathbb R^n} f(x) \cdot \overline{f_a(x)}\ dx.
[/tex]

This certainly satisfies [itex]P^2(f) = P(f)[/itex] and [itex]P(f_a) = f_a[/itex]...
 
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  • #2
AxiomOfChoice said:
Suppose you have a self-adjoint operator [itex]A[/itex] on [itex]L^2(\mathbb R^n)[/itex] that has exactly one discrete, non-degenerate eigenvalue [itex]a[/itex] with associated normalized eigenfunction [itex]f_a[/itex]. What does the projection onto the associated eigenspace look like? My guess would be:

[tex]
P(f) = (f,f_a)f_a = f_a(x) \int_{\mathbb R^n} f(x) \cdot \overline{f_a(x)}\ dx.
[/tex]

This certainly satisfies [itex]P^2(f) = P(f)[/itex] and [itex]P(f_a) = f_a[/itex]...

You still need to check that ##P## is self-adjoint. But this is true.

So indeed, the projection operator has the form you indicate.

In physics texts, they denote this operator often by ##\left|f_a\right\rangle \left\langle f_a\right|##. In math texts, it is denoted by ##f_a\otimes f_a##.
 
  • #3
micromass said:
You still need to check that ##P## is self-adjoint.

I'd only need to do this if I were interested in showing [itex]P[/itex] was an orthogonal projection, right? Is that somehow crucial here? If so, I guess I don't see why.
 
  • #4
AxiomOfChoice said:
I'd only need to do this if I were interested in showing [itex]P[/itex] was an orthogonal projection, right? Is that somehow crucial here? If so, I guess I don't see why.

Two remarks on this:

1) If you are working in a Hilbert space and if you say the word projection, then people will automatically assume orthogonal projections. The word "projection operator" in Hilbert space implies for the most people that it is orthogonal. So although you are right, you should specify when you are working with other kind of projections.

2) You said in your post

What does the projection onto ...

The word "the" indicates that the projection is unique. But if you allow non-orthogonal projections, then this is not the case. There are many projections that do the job.

You are correct though, and I'm just nitpicking. But I feel that this was important enough to mention anway.
 
  • #5
micromass said:
The word "the" indicates that the projection is unique. But if you allow non-orthogonal projections, then this is not the case. There are many projections that do the job.
This is interesting. My intuition tells me that whenever you're considering any kind of eigenvalue problem for a (bounded, unbounded, self-adjoint, non-self-adjoint, etc.) linear operator in a Hilbert space, then for any eigenvalue there is an associated eigenspace and (at least) one projection onto that space. But is it *always* true that the projection has to be an orthogonal projection?

I guess this could be a very deep question with a textbook-chapter-length answer, or not very deep at all...
 
  • #6
Yes, on any closed subspace of the Hilbert space, there is an orthogonal projection.

So any eigenspace of a bounded operator admits an orthogonal projection.
 

1. What is a projection in L^2 spaces?

A projection in L^2 spaces is a mathematical operation that maps a vector onto a subspace of that vector space. In simpler terms, it is a way of finding the closest vector in a subspace to a given vector in L^2 space.

2. How is a projection calculated in L^2 spaces?

The projection of a vector onto a subspace in L^2 space can be calculated using the inner product between the vector and the basis vectors of the subspace. This inner product is then divided by the norm of the basis vectors squared, and multiplied by the basis vectors themselves.

3. What is the significance of projections in L^2 spaces?

Projections in L^2 spaces have several important uses in mathematics, physics, and engineering. They can be used to solve optimization problems, find the best fit for data, and represent physical quantities in a simplified manner. They also have applications in signal processing, image compression, and pattern recognition.

4. Are projections unique in L^2 spaces?

No, in general, projections in L^2 spaces are not unique. This is because there are often multiple subspaces that a vector can be projected onto, and thus, multiple projections are possible. However, there are certain cases where a projection is unique, such as when the subspace is orthogonal to the vector being projected.

5. Can projections be extended to other vector spaces?

Yes, projections can be extended to other vector spaces, such as L^p spaces, where p is any real number. However, the method of calculating projections may differ in these spaces due to the different properties of the vector space. Additionally, projections can also be extended to infinite-dimensional spaces, such as Hilbert spaces.

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