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TheoremStatement: AI-adaptedProof: AI-generatedPipeline-generatedprecheck passverified 2026-10-22 (gpt-6.1-sol)
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Residual distance and isolated-eigenspace certificate

Statement

Assume the Axiom of Choice and Countable Choice. Let H≠{0} be a complex Hilbert space and let T=T∗ be self-adjoint on H, with spectral projection-valued measure ET. Let u∈D(T) satisfy ∥u∥=1, let λ∈R, and define the residual r=(T−λI)u. For a Borel set Δ⊆R, write PΔ=ET(Δ). If g>0 and ∣x−λ∣≥g for every x∈σ(T)∖Δ, then

dist⁡(λ,σ(T))≤∥r∥,∥(I−PΔ)u∥≤∥r∥g,

where dist⁡(λ,S)=inf⁡x∈S∣x−λ∣. If σ(T)∖Δ=∅, the second conclusion is ∥(I−PΔ)u∥=0.

Facts & Assumptions

Given: Choice and Countable Choice; a nonzero complex Hilbert space H; a self-adjoint operator T with spectral PVM ET; u∈D(T) with ∥u∥=1; a real λ; and a Borel set Δ and gap g as in the Statement.

[A1]

The Axiom of Choice and Countable Choice are the set-theoretic assumptions of the spectral theorem and scalar-measure suppliers (The Axiom of Choice, The Axiom of Countable Choice (ACω)).

[F1]

T=∫x dET(x) with domain D(T)={v:∫x2 dμv(x)<∞}, where ET is a regular PVM (Spectral theorem for unbounded self-adjoint operators (PVM form)).

[F2]

μu(B)=⟨ET(B)u,u⟩ is a positive measure of total mass ∥u∥2=1 (Scalar and complex measures from a pvm).

[F3]

For real Borel functions, the unbounded PVM integral has its squared-integrability domain and norm identity; the Borel calculus identifies σ(T) as the essential range of the identity function, and its proof shows ET(R∖σ(T))=0 (The unbounded PVM integral is densely defined, closed and normal, Unbounded Borel functional calculus: domains, products, spectral mapping).

Proof

1.1A1F1F2F3given

Apply the Borel sum rule (Unbounded Borel functional calculus: domains, products, spectral mapping, Statement, clause 3) to f(x)=x and h(x)=−λ. By [F1], f(T)=T, while h(T)=−λI has domain H; thus u∈D(f(T))∩D(h(T)) and r=(x↦x−λ)(T)u. The norm identity in [F3] therefore gives ∥r∥2=∫R∣x−λ∣2 dμu(x). The measure μu has total mass one by [F2].

2.1F2F3step 1.1algebra

By [F3], μu is supported on σ(T). Set d=dist⁡(λ,σ(T)). For every x∈σ(T), ∣x−λ∣≥d by the definition of infimum, so step 1.1 yields ∥r∥2≥d2μu(σ(T))=d2. Both sides are nonnegative, hence d≤∥r∥.

3.1F2F3step 1.1algebra∎

Since I−PΔ=ET(R∖Δ), the PVM norm identity gives ∥(I−PΔ)u∥2=μu(R∖Δ)=μu(σ(T)∖Δ) by support from [F3]. On this set ∣x−λ∣≥g, so step 1.1 implies ∥r∥2≥g2∥(I−PΔ)u∥2. Taking nonnegative square roots and dividing by g>0 proves the cluster estimate. If σ(T)∖Δ is empty, [F3] directly gives μu(R∖Δ)=0, yielding the stated zero bound.

Remarks

The residual is the continuum quantity (T−λI)u and requires u∈D(T). A raw discrete-matrix residual is not a continuum residual unless its vector and operator action are shown to satisfy these domain hypotheses. The distance estimate locates some spectrum; only the separate gap-and-cluster hypothesis bounds amplitude outside a specified spectral set.

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