Finding Orthonormal Basis of Hilbert Space wrt Lattice of Subspaces

In summary, the problem is to find a normalized vector v in W⊥ such that for every U in L, v is an eigenvector of PU.
  • #1
adriank
534
1
I have a Hilbert space H; given a closed subspace U of H let PU denote the orthogonal projection onto U. I also have a lattice L of closed subspaces of H, such that for all U and U' in L, PU and PU' commute. The problem is to find an orthonormal basis B of H, such that for every element b of B and every element U of L, b is an eigenvector of PU (equivalently, b is in U or U).

The obvious thing to do is to apply Zorn's lemma to obtain a maximal orthonormal subset B of H satisfying the above condition, and this part works. For some reason or other, though, I'm having trouble showing that span B = H. If not, then letting W = span B, I need to find a normalized vector v in W such that for every U in L, v is an eigenvector of PU; then B ∪ {v} contradicts the maximality of B. (The following may or may not be helpful: It suffices to consider the case where U contains W.)

The idea I have right now is this: Suppose I could find a one-dimensional subspace V of W such that PV commutes with PU for all U in L, and let v be a normalized vector in V. Then for every U in L, PVPU(v) = PUPV(v) = PU(v), so PU(v) is in V. Since V is 1-dimensional, v is an eigenvector of PU(v), as desired.

The problem is that I have no idea how to choose V. I feel like this should be really easy, but for some reason I'm not seeing it.
 
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  • #2


It is impossible, here is a counterexample:

Let your Hilbert space be [tex]L^2([0,1])[/tex], and let you lattice of subspaces is parametrized by [tex]a\in[0,1][/tex], namely

[tex] U_a = \{ f\in L^2([0,1]) : f(x) =0\ \forall x>a\}[/tex]

It is easy to see that the projections [tex]P_{U_a}[/tex] do not have common eigenvectors (because [tex]\displaystyle\cap_{a\in (0,1)}U_a =\varnothing[/tex])
 
  • #3


Interesting, so it's impossible even if H is separable. Thanks.

I was more generally considering normal operators, rather than projections (although the case of normal operators can be reduced to the above problem). Wikipedia says it works if the operators are compact, or if one of them is compact and injective. I'll have to check if this is the case for the application I had in mind.
 

FAQ: Finding Orthonormal Basis of Hilbert Space wrt Lattice of Subspaces

What is an orthonormal basis in a Hilbert space?

An orthonormal basis in a Hilbert space is a set of vectors that are mutually perpendicular (orthogonal) and have a unit length (normalized). These vectors form a basis for the Hilbert space, meaning that any vector in the space can be expressed as a linear combination of these basis vectors.

What is a lattice of subspaces?

A lattice of subspaces is a collection of subspaces that are partially ordered by inclusion. This means that each subspace is a subset of another subspace, and there is a unique "greatest common divisor" of any two subspaces. In other words, the lattice structure allows for the identification of subspaces that are common to multiple subspaces.

Why is it important to find an orthonormal basis of a Hilbert space with respect to a lattice of subspaces?

Finding an orthonormal basis with respect to a lattice of subspaces allows for a more efficient representation of vectors in the Hilbert space. This is because the basis vectors have a special relationship with the subspaces in the lattice, making it easier to identify and manipulate vectors within the space. It also allows for a more elegant and concise description of the space, which can be useful in various applications.

How is an orthonormal basis of a Hilbert space found with respect to a lattice of subspaces?

The process of finding an orthonormal basis of a Hilbert space with respect to a lattice of subspaces involves identifying a subset of the lattice that forms an orthonormal basis for the space. This can be done using various techniques, such as Gram-Schmidt orthogonalization or using the singular value decomposition (SVD) of a matrix representation of the space. The resulting basis will have the desired properties of being orthogonal and normalized.

What are the applications of finding an orthonormal basis of a Hilbert space with respect to a lattice of subspaces?

The applications of finding an orthonormal basis with respect to a lattice of subspaces are numerous. In mathematics, this technique is used in functional analysis, signal processing, and quantum mechanics. It also has practical applications in areas such as data compression, image processing, and machine learning. By finding a more efficient representation of vectors in a Hilbert space, complex computations and analyses can be simplified and streamlined.

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