Alphabeta Math
TheoremStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedprecheck passverified 2026-10-22 (gpt-6.1-sol)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Conditional convergence of double-discrepancy composite inversion

Statement

Assume Countable Choice. Let X,Y,Z be real or complex Hilbert spaces over one scalar field. Let compact linear maps F:X→Y and G:Y→Z have a common singular system indexed by a finite or countable set J: (xj)j∈J, (yj)j∈J, and (zj)j∈J are orthonormal bases of R(F∗)‾, R(F)‾=R(G∗)‾, and R(G)‾, and Fxj=λjyj, F∗yj=λjxj, Gyj=μjzj, and G∗zj=μjyj with 0<λj,μj≤1. Put A:=G∘F. Let ν0>2, 1<ν≤ν0−1, ρ>0, x†=∣A∣νw with ∥w∥X≤ρ, and z:=Ax†≠0; set y†:=Fx†. The common system gives the compatible minimum-norm factorization on this exact datum: A†z=F†G†z=x† and G†z=Fx†=y†. For H=F,G, let RηH=fηH(H∗H)H∗ and pηH(t):=1−tfηH(t). Assume finite constants CH,SH,KH>0 satisfy ∥HRηH∥≤CH, ∥I−HRηH∥≤SH, and ∥RηH∥≤KHη−1/2 for every η>0; and, for each 0<s≤ν0, sup⁡j∈JsH,js∣pηH(sH,j2)∣≤DH(s)ηs/2 for finite DH(s), where sF,j=λj and sG,j=μj. Fix τG>SG and τF>SFCG. Let αk,γℓ>0 be decreasing sequences with successive ratios in [θ,1) for one fixed 0<θ<1. For noisy data zδ with ∥zδ−z∥Z≤δ, choose first indices k,ℓ (when they exist) such that ∥GRαkGzδ−zδ∥Z≤τGδ<∥GRαiGzδ−zδ∥Z for every i<k, and ∥ARγℓFRαkGzδ−GRαkGzδ∥Z≤τFδ<∥ARγiFRαkGzδ−GRαkGzδ∥Z for every i<ℓ. For any sequence δ↓0 for which both selected indices exist for all sufficiently small δ, there are constants C1,C2 independent of δ and ρ such that, for all sufficiently small δ, ∥x†−RγℓFRαkGzδ∥X≤C1ρ1/(ν+1)δν/(ν+1)+C2ρ2/(ν+1)δ(ν−1)/(ν+1). In particular, for fixed ρ this two-stage reconstruction converges to x†. The statement is conditional on existence of both stopping indices and makes no claim that (G∘F)†=F†G† for arbitrary factorizations.

Facts & Assumptions

Given: Countable Choice; compact real or complex Hilbert maps F:X→Y and G:Y→Z with the common positive singular system in the Statement; A=G∘F; x†=∣A∣νw, ∥w∥≤ρ, z=Ax†≠0; the compatible minimum-norm factorization; spectral filters with the stated residual, norm, and qualification bounds; the two geometric first-crossing rules; and noisy data ∥zδ−z∥≤δ for a sequence δ↓0.

[A1]

The Axiom of Countable Choice (ACω): Every sequence of nonempty sets admits a choice function selecting one member from each set.

[F1]

Composite double regularization: The forward map is A=G∘F and the reconstruction applies RαG first, then RγF.

[F2]

Compact linear inverse model between Hilbert spaces: A compact Hilbert inverse model has a compact linear forward map and exact data z=Ax† for a truth x†.

[F3]

Power source ball: Xν,ρ(A)={∣A∣νw:∥w∥≤ρ}, where ∣A∣=(A∗A)1/2; source elements lie in (ker⁡A)⊥ and are minimum-norm representatives.

[F4]

Hilbert-space Moore–Penrose inverse: The inverse domain is R(A)⊕R(A)⊥, and on exact range data it returns the unique minimum-norm solution.

[F5]

Singular value decomposition for compact operators: A compact Hilbert operator has a positive singular system with orthonormal right and left singular families and the corresponding operator expansion.

[F6]

The Hilbert-space adjoint of a bounded operator: The adjoint is characterized by ⟨Tx,y⟩=⟨x,T∗y⟩.

[F7]

Compact spectral filters, source conditions, and qualification: For a filter family, the residual multiplier is 1−tf(t) and qualification bounds positive spectral powers times the absolute residual.

[F8]

Spectral interpolation for power source balls: If e=∣H∣νv, then ∥e∥≤∥He∥ν/(ν+1)∥v∥1/(ν+1).

Proof

1.1givenA1F1F2F3F4F5F6algebraconstruct

Because F and G are compact, hence bounded, A=G∘F is compact: the image under F of any bounded sequence is bounded, and compactness of G gives a subsequence whose images under G converge. Put aj=λjμj and wj=⟨w,xj⟩. The adjoint identity [F6] and the singular-vector equations show N(F)={xj:j∈J}⊥ and N(G)={yj:j∈J}⊥: each kernel vector is orthogonal to the indicated family, and conversely a vector orthogonal to the family is orthogonal to the full range of the adjoint, hence is killed by the operator. Since G is injective on R(F)‾=R(G∗)‾ (its coefficients there are the positive μj), ker⁡A=ker⁡F; hence (xj) is a basis of N(A)⊥. By [F1], Axj=ajzj. The adjoint identity [F6] gives A∗=F∗G∗, so [F5] yields A∗zj=ajxj, A∗Axj=aj2xj, and ∣A∣xj=ajxj. The vectors zj span R(A)‾, since the range of A is generated by Axj=ajzj and aj>0. Compact spectral calculus and [F3] therefore give x†=∑j∈Jajνwjxj and z=Ax†=∑j∈Jajν+1wjzj. The source element is perpendicular to ker⁡A, so [F4] gives A†z=x†. Thus y†=Fx† lies in N(G)⊥ and x† lies in N(F)⊥; since Gy†=z and Fx†=y†, the minimum-norm property [F4] gives G†z=y† and F†y†=x†. Finally, v:=∑jλjν+1wjyj satisfies y†=∣G∣νv and ∥v∥2=∑jλj2ν+2∣wj∣2≤∥w∥2≤ρ2.

2.1givenstep 1.1F3F7F8algebra

Let eG:=RαGz−y†. On each yj, IY−RαGG has multiplier pαG(μj2), whose absolute value is at most SG because IZ−GRαG has that same multiplier on zj. Thus eG=−∣G∣νpαG(G∗G)v=∣G∣νv′ with ∥v′∥≤SG∥v∥≤SGρ. Also GeG=GRαGz−z. The difference between the exact and noisy first residuals is (GRαG−IZ)(z−zδ), of norm at most SGδ; the stopping inequality therefore gives ∥GeG∥≤(τG+SG)δ. Applying [F8] to eG=∣G∣νv′ proves ∥RαGz−y†∥≤((τG+SG)δ)ν/(ν+1)(SGρ)1/(ν+1).

2.2givenstep 1.1F1F3F7F8algebra

Put eA:=x†−RγFRαGz. On the common singular system, RγFRαGA has multiplier (1−pγF(λj2))(1−pαG(μj2)). The bounds on FRγF and GRαG bound the two factors by CF,CG; on N(A) the product with A is zero. Therefore eA=∣A∣νu with u=(I−RγFRαGA)w and ∥u∥≤(1+CFCG)ρ. The same coefficient formula, now on Z, gives ∥ARγFRαG∥≤CFCG. The exact composite residual decomposes as (z−zδ)+(zδ−GRαGzδ)+(GRαGzδ−ARγFRαGzδ)+ARγFRαG(zδ−z). These four terms have norms at most δ,τGδ,τFδ,CFCGδ, respectively, by the noise bound, the two stopping rules, and the composite operator bound. Since AeA=z−ARγFRαGz, it follows that ∥AeA∥≤(1+τG+τF+CFCG)δ. Applying [F8] to eA=∣A∣νu proves ∥eA∥≤C1ρ1/(ν+1)δν/(ν+1).

2.3givenstep 1.1F3F7algebra

Replace DH(ν+1) by max⁡{DH(ν+1),0}+1 for H=F,G; the qualification bounds remain valid and these constants are positive. Define qγ(h):=ARγFRαGh−GRαGh. Its common-system multiplier on zj is −pγF(λj2)(1−pαG(μj2)). Since ajν+1=λjν+1μjν+1, qualification at s=ν+1, μj≤1, and ∣1−pαG(μj2)∣≤CG give ∥qγ(z)∥≤DF(ν+1)CGρ γ(ν+1)/2. On arbitrary noise coordinates, ∣pγF(λj2)∣≤SF and ∣1−pαG(μj2)∣≤CG, so ∥qγ(zδ−z)∥≤SFCGδ. Let ℓ0 denote the first index of (γℓ). If ℓ>ℓ0, the preceding noisy discrepancy is greater than τFδ; subtracting the noise bound gives ∥qγℓ−1(z)∥>(τF−SFCG)δ. The exact-data bound just proved then yields γℓ−1>[(τF−SFCG)/(DF(ν+1)CG)]2/(ν+1)(δ/ρ)2/(ν+1), and γℓ≥θγℓ−1 gives the same lower bound with factor θ. Hence ∥RγℓF∥≤KF/γℓ≤C′′ρ1/(ν+1)δ−1/(ν+1). If ℓ=ℓ0, then γℓ=γℓ0 is fixed. The common system and λj,μj≤1 give ∥A∥≤1, so 0<δ<∥z∥=∥∣A∣ν+1w∥≤ρ for all sufficiently small δ. Thus (ρ/δ)1/(ν+1)≥1, and ∥Rγℓ0F∥≤KF/γℓ0≤C′′(ρ/δ)1/(ν+1) after enlarging C′′.

3.1givenstep 2.1F7algebra

Let k0 denote the first index of (αk). If k>k0, the preceding noisy residual is greater than τGδ. Its difference from the exact-data residual is (GRαk−1G−IZ)(z−zδ), of norm at most SGδ, so ∥GRαk−1Gz−z∥>(τG−SG)δ. Expanding z=G∣G∣νv in the common basis and applying the qualification bound at s=ν+1 gives ∥GRαk−1Gz−z∥≤DG(ν+1)ρ αk−1(ν+1)/2. Thus αk−1>[(τG−SG)/DG(ν+1)]2/(ν+1)(δ/ρ)2/(ν+1), and αk≥θαk−1 gives the same lower bound for αk with the extra factor θ. Consequently ∥RαkG(zδ−z)∥≤KGδ/αk≤Cρ1/(ν+1)δν/(ν+1), where C is independent of ρ,δ. If k=k0, then αk=αk0 is fixed. Since ρ>0, we have δ≤ρ for all sufficiently small δ, and hence ∥Rαk0G(zδ−z)∥≤KGδ/αk0≤KGαk0−1/2ρ1/(ν+1)δν/(ν+1). Combining this noise bound with step 2.1 and the triangle inequality gives ∥RαkGzδ−y†∥≤C′ρ1/(ν+1)δν/(ν+1).

4.1givenstep 2.1step 2.2step 2.3step 3.1F1algebra∎

The exact/noisy decomposition is x†−RγℓFRαkGzδ=eA+RγℓFRαkG(z−zδ). By linearity, ∥RαkG(z−zδ)∥≤∥RαkGz−y†∥+∥RαkGzδ−y†∥, and these terms are bounded by steps 2.1 and 3.1. Combining those estimates with the exact composite bias from step 2.2 and the outer norm bound from step 2.3 gives the two terms in the Statement, with constants independent of δ and ρ. Since ν>1, both powers of δ are positive, so the error tends to zero for fixed ρ.

Depends on

Used by

Dependency tree · two levels

59 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources