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LemmaStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedprecheck pendingjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)
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Regularized LM decreases the local solution error

Statement

Assume Countable Choice. Let X,Y be real Hilbert spaces, let T:X→Y be bounded linear with Hilbert adjoint T∗, and let e∈X and r∈Y∖{0}. Suppose 0<σ<1, γ>1, α>0, and h is the unique minimizer of h⟼12∥Th−r∥Y2+α2∥h∥X2. Assume its residual forcing and the range-approximation condition are ∥Th−r∥Y=σ∥r∥Y, ∥Te+r∥Y≤σγ∥r∥Y. Then ∥e∥X2−∥e+h∥X2≥∥h∥X2+2(γ−1)σ2γα∥r∥Y2. If also ∥T∥≤M for some M>0, then α≤σM21−σ.

Facts & Assumptions

Given: Countable Choice; real Hilbert spaces X,Y; bounded linear T:X→Y with adjoint T∗; e∈X; r≠0; 0<σ<1, γ>1, α>0; a unique minimizer h with ∥Th−r∥=σ∥r∥; and ∥Te+r∥≤(σ/γ)∥r∥.

[A1]

Countable Choice is the assumed principle used by the adjoint and shifted-normal-operator interfaces (The Axiom of Countable Choice (ACω)).

[F1]

In the substitution T=F′(x) and r=yδ−F(x), the regularized LM algorithm takes its exact increment to be a global minimizer of this same quadratic (Regularized nonlinear Levenberg–Marquardt with discrepancy stopping).

[F2]

The residual after the selected increment has norm σ∥r∥, and the range-approximation residual satisfies ∥Te+r∥≤(σ/γ)∥r∥ (The regularized LM step and residual-forcing parameter exist, Product condition gives the LM range-approximation bound).

[F3]

The adjoint identity gives ⟨T∗q,e⟩X=⟨q,Te⟩Y (The Hilbert-space adjoint of a bounded operator).

[F4]

For a real scalar a, a≤∣a∣; Cauchy–Schwarz gives ∣⟨q,w⟩Y∣≤∥q∥Y∥w∥Y (Basic properties of the absolute value, Cauchy–Schwarz: ∣⟨x,y⟩∣≤∥x∥ ∥y∥, with equality exactly for dependent pairs).

[F5]

The adjoint satisfies ∥T∗∥=∥T∥ (Hilbert-adjoint identities).

[F7]

The operator norm of a bounded map is the supremum of ∥Sz∥ over the unit ball (The operator norm as the least bound and as the unit-sphere or unit-ball supremum).

[F8]

The real Hilbert norm satisfies the triangle inequality (The induced length is a norm).

[F6]

In a real Hilbert space, ∥e+h∥2=∥e∥2+2⟨e,h⟩+∥h∥2 (Real and complex inner-product spaces and their induced length).

Proof

1.1F3F6givenconstructalgebra

For any z∈X and t∈R, expansion using [F6] gives Φα(h+tz)−Φα(h)=t(⟨Th−r,Tz⟩Y+α⟨h,z⟩X)+t22(∥Tz∥Y2+α∥z∥X2). Since h is a global minimizer, this is nonnegative for every t. Its linear coefficient must therefore vanish: if it were nonzero, a sufficiently small t of the opposite sign would make the displayed difference negative. By [F3], ⟨T∗(Th−r)+αh,z⟩X=0 for every z; taking z=T∗(Th−r)+αh and using positive definiteness gives T∗(Th−r)+αh=0. Set q:=r−Th, so αh=T∗q. Therefore [F3] gives α⟨h,e⟩X=⟨q,Te⟩Y, and taking the inner product of αh=T∗q with h gives α∥h∥X2=⟨q,Th⟩Y=⟨q,r⟩Y−∥q∥Y2.

2.1F3F6step 1.1algebra

Put w:=Te+r. By [F6] and step 1.1, ∥e+h∥X2−∥e∥X2=2α⟨q,w⟩Y−∥h∥X2−2α∥q∥Y2. Indeed, Te=w−r; substitute this into 2⟨e,h⟩+∥h∥2 and use ⟨q,r⟩=α∥h∥2+∥q∥2.

3.1F2F4step 2.1algebra

By [F2], ∥q∥Y=σ∥r∥Y and ∥w∥Y≤(σ/γ)∥r∥Y. Thus [F4] gives 2⟨q,w⟩Y≤2∣⟨q,w⟩Y∣≤2∥q∥Y∥w∥Y≤2σ2∥r∥Y2/γ. Substituting this and ∥q∥Y2=σ2∥r∥Y2 into step 2.1 yields ∥e∥X2−∥e+h∥X2≥∥h∥X2+2(γ−1)σ2γα∥r∥Y2, which is the first claim.

4.1A1F2F5F7F8givenstep 1.1algebra∎

Applying T to αh=T∗q and using q=r−Th gives (TT∗+αIY)q=αr. By [F8], α∥r∥Y=∥(TT∗+αIY)q∥Y≤∥TT∗q∥Y+α∥q∥Y. The unit-ball definition [F7] gives ∥T∗q∥X≤∥T∗∥∥q∥Y and ∥T(T∗q)∥Y≤∥T∥∥T∗q∥X: if an input vector is nonzero, divide it by its norm to place it in the unit ball; if it is zero, the bound is immediate. Then [F5] gives ∥TT∗q∥Y≤∥T∥∥T∗∥∥q∥Y=∥T∥2∥q∥Y. Thus, using ∥T∥≤M and the forcing equality, α∥r∥Y≤(M2+α)σ∥r∥Y. Since r≠0, divide by ∥r∥Y and rearrange using 1−σ>0 to obtain α≤σM2/(1−σ).

Remarks

Apply the lemma in the iteration with T=F′(x), e=x−x†, and r=yδ−F(x). By [F1], the algorithm's exact increment is the required minimizer. Then Te+r is the negative of the linearized residual at x†, so their norms agree. The proof uses Countable Choice through the Hilbert-adjoint and normal-operator interfaces; it does not use full AC, DC, or HB. The first estimate is conditional on the exact residual-forcing step and the stated range-approximation inequality. It is not a claim about arbitrary inexact solutions of the normal equation.

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