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Completeness and linear domain of the diagonal sequence model

Statement

For the finite or explicitly enumerated countable index set I of Diagonal self-adjoint operator model, the inner-product space ℓ2(I) is complete and therefore a complex Hilbert space, with inner product linear in the first variable. For any real family (Ei), the displayed domain D(HE) is a linear subspace containing every finite-support vector, is dense in ℓ2(I), and x↦(Eixi)i is a linear map from that domain into ℓ2(I). These assertions also hold for the empty finite index set.

Facts & Assumptions

Given: The coefficient space and real energies in the diagonal definition.

[F1]

Square-summable families on an arbitrary index set and the space ℓ2(I): finite-subset suprema define the square sum and its norm, and the coordinate pairing defines an inner-product space.

[F3]

Hilbert space: a complete inner-product space is a Hilbert space.

[F4]

Diagonal self-adjoint operator model: D(HE) consists of coefficient families whose energy-weighted square sum is finite.

Proof

Given: A Cauchy sequence (x(n)) in the stated coefficient norm.

1.1F1F2algebra

The sequence is norm bounded, say ∥x(n)∥≤M. For each coordinate, ∣xi(n)−xi(m)∣≤∥x(n)−x(m)∥, so [F2] gives a unique limit xi. For every finite F⊂I, passing to the limit in its finite sum gives ∑i∈F∣xi∣2≤M2. Taking the finite-subset supremum proves x∈ℓ2(I). No selection of nonunique coordinate limits is involved.

1.2F1F4algebra

If x,y∈D(HE) and a,b∈C, then for every finite F, ∑i∈F∣Ei(axi+byi)∣2≤2∣a∣2∑i∈F∣Eixi∣2+2∣b∣2∑i∈F∣Eiyi∣2. The right side is bounded independently of F, so ax+by∈D(HE); the zero vector is included. The coordinate action is linear and has square-summable output by the domain definition. Every finite-support vector belongs to this domain because each Ei is finite.

2.1F1F2F3step 1.1algebra

Given ϵ>0, choose N with ∥x(n)−x(m)∥≤ϵ for all n,m≥N. For fixed n≥N and finite F, let m→∞ to obtain ∑i∈F∣xi(n)−xi∣2≤ϵ2. Taking the supremum over F gives ∥x(n)−x∥≤ϵ. Thus the space is complete and is Hilbert by [F3].

3.1F1F4step 1.2algebra∎

For any x∈ℓ2(I) and ϵ>0, the finite-subset supremum supplies a finite F whose square sum differs from the total by less than ϵ2. For every finite G⊂I∖F, its square sum is at most that difference, by applying the total bound to F∪G. Hence the norm of the omitted tail is at most ϵ. Finite-coordinate truncations therefore approximate x and lie in D(HE) by step 1.2, proving density. If I is empty all spaces and maps are zero, and the same statements hold directly.

Depends on

Dependency tree · 2 levels

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