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Banach range invariance and EIT rates

1 · Prerequisites

2 · Logical flowchart

3 · Definitions, theorems and proofs

LemmaStatement: Literature-sourcedProof: AI-adaptedverified 2026-10-22 (gpt-6.1-sol)Open item page →

Besov smoothness gives the exact collared Sobolev penalty a cutoff source bound

Statement

Assume Countable Choice. Put Ω=B1(0)⊂R3, fix 0<ρ<1, and let 5/2<s<7/2 and s<t<2s−1. Use the following precise intrinsic Hilbert norm on Hs(Ω). If s=m+θ with integer m≥0 and 0<θ<1, set ∥v∥s,Ω2=∑∣α∣≤m∥Dαv∥L2(Ω)2+∑∣α∣=m∫Ω×Ω∣Dαv(x)−Dαv(y)∣2∣x−y∣3+2θ dx dy. For integer s=3 use just the derivative sum with m=3. Denote the corresponding real inner product by (⋅,⋅)s,Ω. On R3 use the unitary Fourier transform and ∥f∥Hr2=∫⟨ξ⟩2r∣f^(ξ)∣2dξ. Let B2,∞r(R3) have norm sup⁡j≥02jr∥1Ajf^∥2, where A0={∣ξ∣<2} and Aj={2j≤∣ξ∣<2j+1} for j≥1; the restriction space on Ω consists of restrictions of members of this whole-space Besov space.

Let σ†∈Hs(Ω)∩B2,∞t(Ω) be real and equal to 1 almost everywhere on Ω∖Bρ. Then there is C>0, depending on σ†,s,t,ρ, such that for every real σ∈Hs(Ω) also equal to 1 there and every integer J≥0, −(σ†,σ−σ†)s,Ω≤C2J(2s−t−1)∥σ−σ†∥H1(Ω)+C2−J(t−s)∥σ−σ†∥s,Ω. Consequently, suppose on a specified admissible sublevel a forward map G satisfies ∥σ−σ†∥H1(Ω)≤C0ω(∥G(σ)−G(σ†)∥) with ω(0)=0 and 0≤ω≤1. For the exact regularizer R(σ)=12∥σ∥s,Ω2, its exact Bregman distance is DR(σ,σ†)=12∥σ−σ†∥s,Ω2, and −(σ†,σ−σ†)s,Ω≤34DR(σ,σ†)+C1ω(∥G(σ)−G(σ†)∥)2(t−s)/(t−1). In particular, an H1 stability modulus of order [log⁡(1/u)]−(s−3/2) yields a VSC source term of order [log⁡(1/u)]−ν with ν=(t−s)(2s−3)/(t−1). The conditional stability estimate is a hypothesis here, not a conclusion.

Facts & Assumptions

Given: Countable Choice and the intrinsic penalty, exponents, collar and truth in the Statement.

[F1]

Plancherel supplies the unitary L2 transform. Its extension to tempered distributions, agreement with the L2 transform, and derivative rule are supplied separately by Fourier transform is a topological automorphism of tempered distributions, Fourier transform agrees with l one and plancherel transforms, and Fourier differentiation and multiplication identities on tempered distributions. Plancherel theorem

[F2]

Smooth dyadic Fourier blocks characterize the Bessel Hilbert Sobolev norms. Littlewood-Paley characterisation of the Hilbert-Sobolev spaces

[F3]

Young's convolution inequality in the L1∗L2→L2 case bounds convolution by an L1 kernel on L2. Young's convolution inequality under Countable Choice

[F4]

Tonelli's theorem permits interchanging nonnegative measurable integrals on the sigma-finite Lebesgue product used below. Tonelli's theorem for nonnegative measurable functions on a sigma-finite product

[A1]

Countable Choice is used at the Fourier and Hilbert-Sobolev interfaces. The Axiom of Countable Choice (ACω)

Proof

1.1A1F1F3givenalgebra

Use the angular-frequency convention f^(ξ)=(2π)−3/2∫e−ix⋅ξf(x)dx. The 2π-frequency results in [F1] transfer by f^(ξ)=(2π)−3/2(F2πf)(ξ/(2π)), with the same change of variables on distributional tests; hence Dαf^=(iξ)αf^ and the transform remains unitary. The fixed frequency dilation changes only equivalence constants in the dyadic norms of [F2]. We record explicitly the smooth-multiplication bound for the stated Besov convention. Write Qj=1Aj(D). For χ∈Cc∞(R3), QjχQk has Fourier kernel (2π)−3/21Aj(ξ)χ^(ξ−η)1Ak(η). If ∣j−k∣≤2, Young's convolution inequality [F3] bounds its L2 operator norm by a constant times ∥χ^∥1. If ∣j−k∣>2, then ∣ξ−η∣≥c2max⁡(j,k) on these supports. Repeated integration by parts in the Fourier integral of χ gives ∣χ^(ζ)∣≤CN(1+∣ζ∣)−N. Cauchy–Schwarz in the two frequency variables therefore bounds the operator norm by CN2−Nmax⁡(j,k)23(j+k)/2. For any real r, multiply these bounds by 2jr−kr and sum over k. Choosing N>2∣r∣+8 makes both off-diagonal geometric sums uniformly bounded in j, while the near-diagonal sum has at most five terms. Hence ∥χf∥B2,∞r≤Cr,χ∥f∥B2,∞r. These calculations first apply to finite block sums; the same estimates give their convergence in distributions, since a Schwartz test has faster-than-polynomial Fourier decay.

2.1F1F2step 1.1algebra

The sharp annular definition agrees, up to constants, with any usual smooth inhomogeneous dyadic definition at p=2. Indeed a sharp annulus meets at most a fixed number of the smooth supports at neighboring scales. On that annulus, the identity ∑φj=1 and Cauchy–Schwarz give its L2 norm at most the sum of those finitely many smooth-block L2 norms; conversely each smooth symbol has magnitude at most one and support in finitely many sharp annuli. The weights 2jr differ by only a constant at neighboring scales. The low blocks also meet only finitely many supports. This proves both Besov norm comparisons. For Hilbert Sobolev norms, [F1] gives directly ∥f∥Hr2≍∑j22jr∥Qjf∥22, consistently with [F2].

3.1step 1.1step 2.1givenchoosealgebra

Choose χ0∈Cc∞(Ω) equal to one near Bρ‾, and choose η∈Cc∞(R3) equal to one near Ω‾. Let σ~† be one whole-space B2,∞t extension, whose existence is part of the restriction-space hypothesis. The compactly supported distribution f†:=χ0(σ~†−1) is in B2,∞t(R3) by step 1.1 (the constant term multiplied by χ0 is smooth). It equals σ†−1 on Ω and vanishes outside Bρ‾, since the conductivity equals one in the collar. Thus v†=η+f† is a compactly supported B2,∞t extension of σ†. In particular v†∈Hs(R3): the squared block sum is bounded by a constant times ∑j2−2j(t−s)<∞.

3.2F1F4step 2.1givenalgebra

For e=σ−σ†, let e0 be its zero extension. Its support lies in Bρ‾. Zero extension creates no boundary derivatives because e vanishes on an open collar: local distributional tests near ∂Ω see zero, and interior tests give exactly the zero extensions of the weak derivatives. For s=m+θ noninteger, the whole-space top-order difference integral equals the integral over Ω2 plus 2∫Ω∣Dαe(x)∣2∫R3∖Ω∣x−y∣−3−2θdy dx. Where Dαe can be nonzero, the inner integral is bounded by ∫∣z∣≥1−ρ∣z∣−3−2θdz<∞. The whole-space difference norm is therefore bounded by C∥e∥s,Ω, and the integer case is immediate. The translation identity g(⋅+h)^(ξ)=eiξ⋅hg^(ξ) follows by substitution for Schwartz functions and then by L2 density and unitarity for every g∈L2. Thus [F1] and Tonelli [F4] give ∫∫∣g(x+h)−g(x)∣2∣h∣−3−2θdx dh=∫∣g^(ξ)∣2∫∣eiξ⋅h−1∣2∣h∣−3−2θdh dξ. The inner integral equals cθ∣ξ∣2θ by rotation and dilation; 0<cθ<∞ follows by the bounds ∣eiu−1∣≤min⁡{2,∣u∣}. Summing derivative multipliers shows that this whole-space difference norm is equivalent to ∥e0∥Hs. Thus ∥e0∥Hs≤C∥e∥s,Ω, while exactly ∥e0∥H1=∥e∥H1(Ω).

4.1F1step 2.1step 3.1step 3.2algebra

Let Bs(v,w) be the whole-space intrinsic bilinear form associated with the derivative and difference norm in step 3.2. Its Fourier multiplier ps(ξ) equals ∑∣α∣≤mξ2α+cθ∑∣α∣=mξ2α∣ξ∣2θ in the noninteger case, and just the first polynomial in the integer case. Thus ∣ps(ξ)∣≤C⟨ξ⟩2s, and P0:=ps(D)v† belongs to B2,∞t−2s by the annular multiplier bound and step 3.1. Its distributional pairing with e0 is Bs(v†,e0). The pairing is defined by convergent Fourier blocks: high blocks have the product bound C2−j(t−s)∥e0∥Hs, a summable sequence. This also justifies the bilinear identity by truncation and limits, without assuming classical derivatives of order 2s.

5.1F4step 1.1step 3.1step 3.2step 4.1algebra

In the integer case the whole-space and domain pairings are identical, since every derivative of e0 is supported in Bρ‾; take P=P0. For noninteger s=m+θ, their difference is Bs(v†,e0)−(σ†,e)s,Ω=2∑∣α∣=m∫ΩDαe(x)(A(x)Dαv†(x)−bα(x))dx, where A(x)=∫Ωc∣x−y∣−3−2θdy and bα(x)=∫ΩcDαη(y)∣x−y∣−3−2θdy. To obtain the formula, split each whole-space double integral into Ω2, Ω×Ωc and its transpose; Dαe0 is zero outside and v†=η there. On any fixed neighborhood of Bρ‾ compact in Ω, every derivative of A,bα is bounded, by differentiating the separated kernel and using its integrable tail. Choose χ∈Cc∞(Ω) equal to one on that neighborhood. The domain pairing is therefore the pairing of e0 with P=P0−2∑∣α∣=m(−1)mDα(χADαv†−χbα). By step 1.1, its correction terms lie in B2,∞t−2m: differentiation lowers the block regularity by the derivative order, multiplication by the smooth compact coefficient preserves it, and χbα is smooth and compactly supported. Since 2m≤2s, they also lie in B2,∞t−2s. Hence P∈B2,∞t−2s in every case, and ⟨P,e0⟩=(σ†,e)s,Ω. This treats the exact domain penalty pairing rather than replacing its norm by an equivalent one.

6.1F1step 2.1step 3.2step 5.1algebra

Put d=t−s>0 and a=2s−t−1>0. Let P≤J=∑j≤JQjP. By step 5.1 and the Hilbert block norm formula, ∥P≤J∥H−12≤C∑j≤J22j(2s−t−1)≤C22Ja. Similarly ∥P−P≤J∥H−s2≤C∑j>J22j(s−t)≤C2−2Jd. Weighted Fourier Cauchy–Schwarz and step 3.2 now give ∣⟨P,e0⟩∣≤C2Ja∥e∥H1(Ω)+C2−Jd∥e∥s,Ω, the first claimed estimate.

7.1step 6.1givenchoosealgebra∎

Expansion of the quadratic form gives exactly DR(σ,σ†)=∥e∥s,Ω2/2. Set W=ω(∥G(σ)−G(σ†)∥). The conditional stability hypothesis and uv≤3u2/8+2v2/3 applied to the high-frequency term in step 6.1 yield −(σ†,e)s,Ω≤3∥e∥s,Ω2/8+C(2JaW+2−2Jd). If W=0, stability gives e=0. For 0<W≤1, take the integer J≥0 with 2−J(a+2d)≥W>2−(J+1)(a+2d). Then 2−2Jd≤22dW2d/(a+2d) and 2JaW≤W2d/(a+2d). Since a+2d=t−1, this proves the asserted source inequality with coefficient 3/4 on the exact Bregman distance. Substitution of the stated logarithmic stability exponent gives ν=2(s−3/2)(t−s)/(t−1) by multiplying exponents.

LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)Open item page →

Positive-Sobolev CGO remainders from a shifted Fourier lattice

Statement

Assume Countable Choice. Fix 0<τ<1, 0<ρ<1 and M>0. If q∈H1/2+τ(R3;C) has support in Bρ‾ and norm at most M, there are constants T,C>0, depending only on τ,ρ,M, such that for every ζ∈C3 with ζ⋅ζ=0 and ∣ζ∣≥T, there is rζ∈H2(B2) for which (−Δ+q)(eiζ⋅x(1+rζ))=0in B2, ∥rζ∥H1/2(B2)≤C∣ζ∣−1M,∥rζ∥L∞(B2)≤C∣ζ∣−τ/2M,∥rζ∥H2(B2)≤C∣ζ∣M(1+M). Here fractional Sobolev norms on the ball are restriction norms from the whole-space Fourier Sobolev spaces. No boundary condition is imposed. The construction is on a larger cube, so the stated regularity holds on a neighborhood of the closed unit ball.

Facts & Assumptions

Given: The choice assumption, exponents, support, potential and null phase in the Statement.

[F1]

On a finite torus the Fourier characters form a complete orthonormal basis, with Parseval and a surjective coefficient isometry. Multiplication by a fixed unit-modulus exponential gives the shifted basis. The Fourier basis and Parseval's identity on the finite torus

[F2]

Whole-space Fourier transformation is unitary on L2. Plancherel theorem

[A1]

Countable Choice is assumed at [F1]–[F2]. The Axiom of Countable Choice (ACω)

Proof

1.1A1F1givenalgebra

We give the Sobolev product bounds used below. They hold for periodic functions on a fixed three-dimensional cube, and for the product of a periodic function and a function with one fixed half-integer Fourier shift. The constants can be chosen uniformly under rotations of the cube. Split Fourier frequencies into dyadic shells and write fj,gj for the corresponding blocks. A shell contains at most C23j lattice points, so Cauchy–Schwarz gives ∥fj∥∞≤C23j/2∥fj∥2. For r>3/2 and 0≤s≤r, these estimates prove ∥fg∥Hs≤C∥f∥Hr∥g∥Hs. For low f against high g, use ∥f<j−2∥∞≤C∥f∥Hr and square-sum 2js∥gj∥2. For high f against low g, when s<3/2 use ∥g<j−2∥∞≤C2j(3/2−s)∥g∥Hs, and when s>3/2 use the uniform bound by ∥g∥Hs; the critical case has an extra factor (1+j)1/2, absorbed by 2−j(r−3/2). The resulting weighted terms are bounded by 2−j(r−3/2)∥f∥Hr∥g∥Hs or by 2js∥fj∥2∥g∥Hs. For comparable high input shells k that produce an output shell j≤k+C, use ∥Qj(fkgk)∥2≤C23j/2∥fk∥2∥gk∥2, obtained from the L1 bound on the product and the number of output frequencies. Multiplication by 2js and summation over k≥j−C gives a summable geometric kernel, since r+s>0 and r>3/2; at s=r=0 this case is not being used. Cauchy–Schwarz for the two input shell sequences and the geometric kernel completes the Hs bound.

1.2F1givenconstructalgebra

Rotate so that ζ=s(e1+ie2), where s=∣ζ∣/2, and choose a fixed cube Q=(−πL,πL)3 containing B3‾. Put ηk=(k+e2/2)/L and use the shifted orthonormal basis wk=(2πL)−3/2eiηk⋅x. For Aζ=D2+2ζ⋅D, D=−i∇, its symbol is pk=∣ηk∣2+2ζ⋅ηk, with ∣Im⁡pk∣≥s/L. For ∣ηk∣≤4s, this gives ∣pk∣≥s/L; for ∣ηk∣>4s, its real part has magnitude at least ∣ηk∣2/2. Hence for every real μ and 0≤v≤2 the coefficient multiplier Gζ:pk−1 satisfies ∥Gζf∥Hμ+v(Q)≤Cvsv−1∥f∥Hμ(Q) for s≥1. The Sobolev spaces here are defined by the shifted weighted coefficient norms. Parseval proves these bounds and AζGζf=f in distributions inside Q, first on finite sums and then by convergence.

2.1F1step 1.1algebra

A second bound is ∥fg∥H−1/2≤Cε∥f∥H1/2+ε∥g∥H1/2 for every 0<ε<1. Here the low-high and high-low estimates from step 1.1, with output weight 2−j/2, have the additional decay 2−εj. For comparable high shells, the output estimate becomes 2j∑k≥j−C∥fk∥2∥gk∥2=2−εj∑k≥j−C2−(k−j)(1+ε)FkGk, with Fk=2k(1/2+ε)∥fk∥2 and Gk=2k/2∥gk∥2. The geometric kernel has finite sum, and Cauchy–Schwarz bounds the product sequence in ℓ1 by ∥F∥2∥G∥2. This proves the claimed bound. All product calculations first hold for finite Fourier sums; the bounds give convergence in the target Sobolev space and identify the product as the distributional limit. Whenever one factor belongs to Hr, r>3/2, its Fourier coefficients are absolutely summable by weighted Cauchy–Schwarz, so it is a bounded continuous multiplier and this distributional product agrees with the ordinary product.

2.2F1F2step 1.2algebra

The periodic and shifted Sobolev norms of the compactly supported potential are bounded by C∥q∥H1/2+τ(R3)≤CM. For completeness, choose a fixed smooth cutoff equal to one on B1 and supported in B2. Since it multiplies q without changing it, each Fourier coefficient of q is the convolution of its whole-space Fourier transform with a translate of the rapidly decreasing transform of this cutoff, sampled at an integer or shifted lattice point. Weighted Cauchy–Schwarz, followed by summation over the lattice, bounds the coefficient norm by the whole-space norm: the sums sup⁡ξ∑k(1+∣ηk−ξ∣)−N and the corresponding integrals are finite for N>3, and polynomial Sobolev weights are absorbed by increasing N. The reverse local bound needed later follows in the same way by multiplying the Fourier series by a smooth cutoff supported inside Q and equal to one on B2, then estimating the resulting convolution sum in whole-space weighted L2. The cutoffs can be chosen radial before rotation, so all constants are independent of the phase direction.

3.1step 1.1step 2.1step 1.2step 2.2choosealgebra

Put r0=3/2+τ/2 and h0=r0−1=1/2+τ/2. Split the periodic potential into q=q♯+q♭ by retaining frequencies of size at most 2J in q♯. Then ∥q♭∥Hh0≤C2−Jτ/2M, whereas q♯ has bounded Sobolev norms of every order with bounds depending only on J,M. Choose J, depending only on τ,M, so that Gζq♭ as a multiplication-composition operator has norm at most 1/4 both on shifted Hr0 and on shifted H1/2. On Hr0 this follows from the product bound in step 1.1, with multiplier r∈Hr0 and q♭∈Hh0, and the v=1 bound of step 1.2. On H1/2 it follows from step 2.1 with ε=τ/2 and the same v=1 bound. Next choose s large enough, depending only on J,M,τ, that Gζq♯ has norm at most 1/4 on both spaces, using the smooth multiplier bound from step 1.1 and the v=0 bound C/s from step 1.2. Thus I+Gζq has inverse given by a convergent geometric series, of norm at most two, on both spaces.

4.1step 1.2step 2.2step 3.1constructalgebra

Define rζ=−(I+Gζq)−1Gζq in shifted Hr0. The forcing q in the last position is the actual compactly supported function on Q, represented in the shifted basis; the multiplication operators use the periodic representation of the same function. These agree pointwise inside Q. Step 1.2 with μ=1/2+τ and v=1−τ/2 gives ∥rζ∥Hr0(Q)≤Cs−τ/2M. Since r0>3/2, weighted Cauchy–Schwarz for its coefficients gives ∥rζ∥∞≤C∥rζ∥Hr0(Q). This solution is also the unique shifted H1/2 solution, by the second invertibility statement in step 3.1; using step 1.2 with μ=1/2,v=0 gives ∥rζ∥H1/2(Q)≤Cs−1M. Finally, its equation is rζ=−Gζ(q+qrζ); since q∈L2 and rζ∈L∞, the v=2 bound from L2 gives ∥rζ∥H2(Q)≤CsM(1+M), enlarging the threshold to keep s≥1.

5.1step 1.2step 2.2step 4.1algebra∎

Applying Aζ to the equation in step 4.1 gives Aζrζ+q(1+rζ)=0 in the interior of Q. Direct conjugation and ζ⋅ζ=0 show this is exactly (−Δ+q)(eiζ⋅x(1+rζ))=0. The local comparison in step 2.2 transfers the fractional bounds to restriction norms on B2, and restriction gives the integer H2 bound there. Pulling back the initial rotation preserves the whole-space potential norm, the support ball, and the Euclidean PDE. Since s=∣ζ∣/2, step 4.1 yields all displayed estimates. The construction was on a fixed larger cube, so it imposes no boundary condition on the unit ball and supplies regularity on its full neighborhood.

LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)Open item page →

Sharp weighted absorption of modulated CGO remainders

Statement

Assume Countable Choice. Write ⟨ξ⟩=(1+∣ξ∣2)1/2 and use unitary Fourier transformation on R3. There is an absolute constant C such that for every b≥1 and Q∈H−1/2(R3), ∫∣ξ∣≤b⟨ξ⟩−1∥e−iξ⋅xQ∥H−1/22 dξ≤Cb2∥Q∥H−1/22. Consequently, if t>0, L≥0 and aξ∈H1/2(R3) is any family with ∥aξ∥H1/2≤L/t for ∣ξ∣≤b, its modulated bilinear distributional remainders R(ξ)=⟨e−iξ⋅xQ,aξ⟩ satisfy (∫∣ξ∣≤b⟨ξ⟩−1∣R(ξ)∣2 dξ)1/2≤CLbt∥Q∥H−1/2, whenever the scalar family R is measurable. No continuity or derivative bound in ξ is required.

Facts & Assumptions

Given: Countable Choice and the whole-space Sobolev norms, radius, potential difference, remainder bounds in the Statement.

[A1]

Countable Choice is assumed for the Fourier isometry in [F1]. The Axiom of Countable Choice (ACω)

[F1]

Under [A1], the unitary Fourier transform is an isometry on L2 and defines the Fourier Sobolev norms by ∥f∥Hu2=∫⟨η⟩2u∣f^(η)∣2 dη. Plancherel theorem

[F2]

Tonelli's theorem permits interchanging the nonnegative measurable Fourier integrals on the sigma-finite Lebesgue product used below. Tonelli's theorem for nonnegative measurable functions on a sigma-finite product

[F3]

The positive-Sobolev CGO construction supplies remainders with H1/2 norm O(t−1) and uniformly bounded L∞ norm for the phases used in the application. Positive-Sobolev CGO remainders from a shifted Fourier lattice

[F4]

A Sobolev class supported in a compact subset of an open set extends by zero with its weak derivatives unchanged. Compactly supported Sobolev functions extend by zero in every integer order

Proof

1.1givenalgebra

Define Jb(η)=∫∣ξ∣≤b⟨ξ⟩−1⟨η−ξ⟩−1 dξ. For ∣η∣≥2b, ∣η−ξ∣≥∣η∣/2 throughout the integration ball, so ⟨η−ξ⟩−1≤2⟨η⟩−1. Polar integration gives ∫∣ξ∣≤b⟨ξ⟩−1 dξ≤Cb2 for b≥1. Hence Jb(η)≤Cb2⟨η⟩−1 in this region.

2.1step 1.1algebra

If ∣η∣<2b, Cauchy–Schwarz gives Jb(η)≤(∫∣ξ∣≤b⟨ξ⟩−2 dξ)1/2(∫∣ξ∣≤b⟨η−ξ⟩−2 dξ)1/2. The first integral is at most Cb by polar integration. In the second, the substitution z=η−ξ places its domain inside B3b, so it is also at most Cb. Thus Jb(η)≤Cb. Since ⟨η⟩≤1+4b2≤5b, this is at most C′b2⟨η⟩−1. Together with step 1.1 this proves Jb(η)≤Cb2⟨η⟩−1 for every η.

3.1A1F1F2step 2.1algebra

Fourier translation gives ∥e−iξ⋅xQ∥H−1/22=∫⟨η−ξ⟩−1∣Q^(η)∣2 dη. This identity holds for Sobolev distributions as well as smooth functions, because the Fourier transform is represented by a locally square integrable function with the indicated weight, and translation preserves weighted square integrability for every fixed ξ. Tonelli [F2] and step 2.1 now give ∫∣ξ∣≤b⟨ξ⟩−1∥e−iξ⋅xQ∥H−1/22 dξ=∫∣Q^(η)∣2Jb(η) dη≤Cb2∥Q∥H−1/22.

4.1F1step 3.1givenalgebra∎

For the bilinear duality pairing, weighted Fourier Cauchy–Schwarz gives ∣⟨e−iξ⋅xQ,aξ⟩∣≤∥e−iξ⋅xQ∥H−1/2∥aξ∥H1/2. The absence of complex conjugation changes the second Fourier factor to its reflection and does not change its Sobolev norm. Squaring, integrating with weight ⟨ξ⟩−1, using the bound L/t, and applying step 3.1 prove the remainder estimate.

Application to the conductivity remainder

For w∈L2(R3), set [w]1/22:=∬∣w(x)−w(y)∣2∣x−y∣−4 dx dy. Translation, Plancherel and Tonelli [F1]–[F2] give

[w]1/22=∫∣w^(ξ)∣2(∫∣eiξ⋅h−1∣2∣h∣4 dh)dξ=c3∫∣ξ∣ ∣w^(ξ)∣2dξ,

where rotation and dilation give the inner integral, and 0<c3<∞ follows from ∣eiu−1∣≤min⁡{2,∣u∣}. The identity holds with values in [0,∞], since Plancherel applies to each L2 translate difference and Tonelli to its nonnegative squared integrand. Thus w∈H1/2 exactly when ∥w∥22+[w]1/22<∞, and this quantity is equivalent to ∥w∥H1/22. Cutoff remainders are whole-space H2 functions by [F4], so this identity applies directly to them. The identity f(x)g(x)−f(y)g(y)=f(x)(g(x)−g(y))+g(y)(f(x)−f(y)) therefore yields

∥fg∥H1/2≤C(∥f∥∞∥g∥H1/2+∥g∥∞∥f∥H1/2)

for bounded cutoff remainders. For the exact CGO identity, take aξ=χ(r1+r2+r1r2), with a fixed smooth cutoff equal to one on the support of Q. For phases with ∣ζj∣≍t≥T, [F3] gives ∥χrj∥H1/2≤C/t and uniformly bounded L∞ norms. Insert another cutoff equal to one on the support of χ to apply the product estimate to χr1r2. Consequently ∥aξ∥H1/2≤C/t, uniformly in ξ. One may combine this bound with the separate exact boundary identity, high frequency cutoff and Liouville recovery suppliers. No measurable phase selection is necessary for that application: bound the remainder pointwise by (C/t)∥e−iξ⋅xQ∥H−1/2, which is measurable by step 3.1, before integrating the resulting scalar Fourier inequality.

Source-reading note

The source text at printed pages 172–179, including the full proofs of Theorems 6.8–6.11, and the norm convention (5.8b) and Theorem 5.8 were read. The source norm is an unmodulated localized Sobolev norm. The printed passage on page 176 bounds an absolute volume integral by negative-Sobolev duality and omits the actual Fourier modulation. The proof above uses neither that passage nor its pointwise conclusion: it keeps the modulation, moves it to Q, and uses the integrated kernel estimate in steps 1.1–3.1. No independent proof review or native workflow acceptance is asserted.

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Boundary trace quotient and chart norm equivalence

Statement

Assume the Axiom of Choice and Countable Choice. Let n≥2 and let Ω⊂Rn be a nonempty bounded connected open set whose boundary is covered by finitely many C1 graph charts. Give H1(Ω;K)=W1,2(Ω;K) its Sobolev norm, let H01(Ω;K) be the H1-closure of Cc∞(Ω;K), and let W1/2,2(∂Ω;K) have the finite-chart Slobodeckij norm. Define D(Ω):=H1(Ω;K)/H01(Ω;K) with its quotient norm. The trace induces a bounded linear bijection T‾:D(Ω)→W1/2,2(∂Ω;K) with bounded inverse, so these two norms are equivalent under the induced identification. The induced map after quotienting by constants is also a bounded linear bijection D(Ω)/K1→W1/2,2(∂Ω;K)/K, with equivalent quotient norms. A continuous linear functional on W1/2,2(∂Ω;K) descends to the constant quotient exactly when it annihilates constants, and its descended functional has the quotient dual norm.

Facts & Assumptions

Given: The Axiom of Choice and Countable Choice; the bounded C1 domain; the trace operator T; and the quotient spaces in the statement.

[F1]

The trace is bounded from H1(Ω) into W1/2,2(∂Ω) and is onto. (The sharp trace theorem: boundedness and range in the fractional space)

[F2]

The kernel of the trace is exactly H01(Ω), the closure of compactly supported smooth functions. (The kernel of the trace is the closure of the test functions, Zero-boundary Sobolev space as a norm closure)

[F3]

There is a bounded right inverse R:W1/2,2(∂Ω)→H1(Ω) with TRf=f. (A bounded right inverse of the trace, supported in a prescribed collar)

[F4]

The trace-page boundary norm is a finite-chart Slobodeckij norm on almost-everywhere boundary classes. (The fractional Sobolev space on a compact C1 boundary)

[A1]

The Axiom of Choice and Countable Choice are assumed at the cited trace and boundary-space interfaces. (The Axiom of Choice, The Axiom of Countable Choice (ACω))

Proof

Given: The data in the statement.

1.1F1F2F4givenalgebra

If u−v∈H01(Ω), then [F2] gives Tu=Tv, so T‾[u]:=Tu is well-defined on D(Ω). By [F1], ∥T‾[u]∥W1/2,2(∂Ω)≤C∥u∥H1(Ω) for every representative u; taking the infimum over its coset gives ∥T‾[u]∥≤C∥[u]∥D. If T‾[u]=0, [F2] gives u∈H01, hence [u]=0; thus the induced map is injective.

1.2A1F1F3givenconstruct

By [F1], every f∈W1/2,2(∂Ω) is Tu for some u∈H1(Ω), so T‾ is onto. More quantitatively, choose the lift Rf from [F3]. Then T‾[Rf]=f and ∥[Rf]∥D≤∥Rf∥H1≤C∥f∥W1/2,2, proving that the inverse is bounded.

2.1step 1.1step 1.2givenalgebra

The trace of a constant function is that same constant, so T‾ maps the closed constant subspace K1⊂D(Ω) onto the constant subspace of W1/2,2(∂Ω). The bounded isomorphism and its bounded inverse from steps 1.1–1.2 therefore induce a bounded isomorphism on the two quotient spaces, with equivalent quotient norms.

3.1givenalgebra∎

If ℓ is a continuous linear functional on W1/2,2(∂Ω) that annihilates constants, then ℓ˙([f]):=ℓ(f) is independent of the representative because ℓ(f+c)=ℓ(f) for every constant c. By the quotient-norm definition, ∣ℓ˙([f])∣≤∥ℓ∥inf⁡c∥f+c∥W1/2,2, so ℓ˙ is continuous with quotient dual norm at most ∥ℓ∥. Conversely, any functional that factors as ℓ=φ∘q vanishes on constants because q(c)=0; and ∣φ(qf)∣≤∥φ∥∥qf∥≤∥φ∥∥f∥ because the quotient map q has norm at most one. Hence ∥ℓ∥≤∥φ∥, while the first inequality gives ∥φ∥≤∥ℓ∥ for the induced functional. This proves the equivalence and equality of dual norms.

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Centered Neumann voltage sequences on the three-dimensional ball

Statement

Assume the Axiom of Choice, Countable Choice and Dependent Choice. Let Ω=B1(0)⊂R3, fix 0<ρ<1 and 0<m<1<M, and let E be the closed real subspace of L∞(Ω) consisting of functions vanishing almost everywhere outside Bρ(0). Set A={a∈E:ess inf⁡(1+a)>m, ess sup⁡(1+a)<M} and σ=1+a. Choose an orthonormal spherical-harmonic basis Yℓk of mean-zero L2(S2), with ℓ≥1 and 1≤k≤2ℓ+1. For each basis current there is a unique weak solution uσ,ℓk∈H1(Ω) with boundary mean zero such that ∫Ωσ∇uσ,ℓk⋅∇v‾=∫S2YℓkTr⁡v‾(v∈H1(Ω)). The centered sequence G(a):=(Tr⁡(uσ,ℓk−u1,ℓk))ℓ,k belongs to Y=ℓ2(L2(S2)). The map G:A→Y is Fréchet differentiable, and both G and G′ are Lipschitz on A, with constants depending only on m,M,ρ and the ball. Its derivative in direction h∈E is the trace sequence of the unique boundary-mean-zero solutions ∫Ωσ∇vh,ℓk⋅∇w‾=−∫Ωh∇uσ,ℓk⋅∇w‾. The uncentered sequence (Tr⁡u1,ℓk)ℓ,k does not belong to Y. Thus absolute voltage data in this convention lie in an affine translate of Y, whereas data differences belong to Y. All complex spaces here may be regarded as real Banach spaces when taking derivatives with respect to real conductivities.

Facts & Assumptions

Given: The choices, fixed collar radius, uniformly elliptic conductivities and mean-zero current basis in the Statement.

[F1]

The degree-ℓ harmonic space has dimension 2ℓ+1, and these spaces form a complete orthogonal expansion on S2. Spherical harmonics form a complete basis with the addition formula

[F2]

The ball is an extension domain, satisfies the mean-zero Poincaré inequality, and has a bounded trace H1(Ω)→W1/2,2(S2)⊂L2(S2). Bounded C^k domains admit integer-order Sobolev extension, Poincare-Wirtinger on bounded connected extension domains by Rellich compactness, Boundary trace quotient and chart norm equivalence

[F3]

Bounded coercive forms have unique solutions for bounded conjugate-linear functionals. The Lax--Milgram theorem

[F4]

The divergence theorem on a bounded C1 domain gives Green's identity for smooth functions; the complex form follows by real and imaginary parts. Divergence on a bounded C1 Euclidean domain

[F5]

Restrictions of ambient smooth functions are dense in H1(B1), so identities proved for smooth tests pass to all H1 tests. Ambient smooth restrictions are dense on bounded C^k domains

[A1]

Choice assumptions are used at [F1]–[F5]; the Axiom of Choice selects the countable family of finite-dimensional bases. The Axiom of Choice, The Axiom of Countable Choice (ACω), The axiom of dependent choice: a relation in which every element is related to something admits an N-indexed chain

Proof

1.1A1F2F3givenalgebra

Let V={w∈H1(Ω):∫S2Tr⁡w=0}. This is closed by [F2]. For w∈V, write wˉ for its volume mean. Poincaré gives ∥w−wˉ∥H1≤C∥∇w∥2, and the trace bound and boundary-mean condition give ∣wˉ∣ ∣S2∣≤∣S2∣1/2∥Tr⁡(w−wˉ)∥2≤C∥∇w∥2. Thus ∥w∥H1≤C∥∇w∥2 on V. The form Aσ(w,v)=∫σ∇w⋅∇v‾ is bounded and coercive there, uniformly for m<σ<M. By [F3], any functional v↦−∫H⋅∇v‾ with H∈L2(Ω)3 has a unique solution in V, of H1 norm at most C∥H∥2.

2.1A1F1F2F4F5step 1.1givenalgebra

The background solution is u1,ℓk(r,ω)=rℓYℓk(ω)/ℓ. It is a harmonic polynomial with normal derivative Yℓk and boundary mean zero. For smooth test functions, the divergence theorem [F4] gives its weak equation. The density [F5] and continuity of the trace pass the identity to every H1 test. The same integration by parts on Bρ gives ∥∇u1,ℓk∥L2(Bρ)2=ρ2ℓ+1/ℓ. In particular Aρ:=∑ℓ≥1(2ℓ+1)ρ2ℓ+1/ℓ<∞, since (2ℓ+1)/ℓ≤3.

3.1F2step 1.1step 2.1algebra

Apply step 1.1 with H=a∇u1,ℓk, supported in Bρ, to obtain wa,ℓk∈V satisfying Aσ(wa,ℓk,v)=−∫a∇u1,ℓk⋅∇v‾. The sum u1,ℓk+wa,ℓk solves the required Neumann equation, first for v∈V and then for all v∈H1 by subtracting its boundary mean. The current annihilates constants. Uniqueness follows by testing the difference with itself and using step 1.1. Summing the energy estimates gives ∑∥wa,ℓk∥H12≤C∥a∥∞2Aρ. The trace bound proves G(a)∈Y. Moreover ∑∥∇uσ,ℓk∥L2(Bρ)2≤2Aρ+2∑∥wa,ℓk∥H12≤C, uniformly over A, since ∥a∥∞≤max⁡{1−m,M−1}.

4.1F2step 1.1step 3.1algebra

For a,b∈A, subtract the weak equations to obtain A1+a(u1+a,ℓk−u1+b,ℓk,v)=−∫(a−b)∇u1+b,ℓk⋅∇v‾. By step 1.1 and the summed interior bound in step 3.1, the sequence of differences has ℓ2(H1) norm at most C∥a−b∥∞. For h∈E, the same estimate constructs the asserted derivative sequence va,h with norm at most C∥h∥∞. Subtracting its equation from the difference equation for a+h,a shows that the remainder has forcing −h∇(u1+a+h−u1+a) and hence ℓ2(H1) norm at most C∥h∥∞2. Taking traces proves Fréchet differentiability. Finally, subtract the derivative equations at a,b: A1+a(va,h−vb,h,w)=−∫h∇(u1+a−u1+b)⋅∇w‾−∫(a−b)∇vb,h⋅∇w‾. The established estimates bound its sequence norm by C∥a−b∥∞∥h∥∞, proving the operator-norm Lipschitz derivative bound and the asserted Lipschitz bound for G.

5.1F1step 2.1step 3.1algebra∎

By step 2.1, Tr⁡u1,ℓk=Yℓk/ℓ. Its squared sequence norm is ∑ℓ≥1(2ℓ+1)/ℓ2, which diverges since each summand is at least 2/ℓ. Thus the absolute background voltage sequence is not in Y, while every centered difference is in Y by step 3.1. This establishes the affine-data distinction without changing the set of measured currents.

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Logarithmic source indices and their admissible parameter scales

Statement

Fix C,ν>0 and 0<t0<e−(ν+1). Define ψ(0)=0, ψ(t)=C[log⁡(1/t)]−ν for 0<t≤t0, and ψ(t)=ψ(t0) for t>t0. Then ψ is continuous, nondecreasing and concave, and H(q):=sup⁡t≥0(ψ(t)−qt)∼C(log⁡q)−ν(q→∞). For each fixed c>0, one consequently has H(cq)/H(q)→1. For p>0, fixed D,a,C∗>0 and nonnegative errors satisfying Sδ+aαeδ≤Dδp+αψ(C∗Sδ), the balanced choice α≍δp[log⁡(1/δ)]ν gives eδ=O([log⁡(1/δ)]−ν),Sδ=O(δp). For a pure-power choice α≍δk with 0<k<p, the same error rate follows, with residual bound Sδ=O(δk[log⁡(1/δ)]−ν). No assertion for k=p is made. If eδ=12∥uδ−u†∥2, the corresponding norm rate has exponent ν/2.

Facts & Assumptions

Given: The constants, index, scalar inequality and parameter choices in the Statement.

[F1]

A conjugate bound for residual and Bregman errors: the scalar inequality implies eδ≤a−1(Dδp/α+H(1/(C∗α))) and Sδ≤2(Dδp+αH(1/(2C∗α))).

Proof

1.1givenalgebra

On (0,t0) write L=log⁡(1/t). Direct differentiation gives ψ′(t)=Cνt−1L−ν−1>0 and ψ′′(t)=Cνt−2L−ν−2(ν+1−L)<0. At t0 the derivative drops from a positive value to zero; above it the function is constant. Thus the derivative is nonincreasing on each smooth piece and has a downward jump, giving concavity on (0,∞); taking limits at zero extends the chord inequalities there. Continuity and monotonicity are immediate from the formula. In particular H is finite because 0≤ψ≤ψ(t0), and H≥0 by testing t=0.

2.1step 1.1givenalgebra

Put Q=log⁡q and take q sufficiently large. For 0<t≤q−1Qν+1, one has log⁡(1/t)≥Q−(ν+1)log⁡Q, so ψ(t)−qt≤C[Q−(ν+1)log⁡Q]−ν. For t>q−1Qν+1, one has qt>Qν+1>ψ(t0), so the expression is negative. This bounds H(q) above by CQ−ν(1+o(1)). Testing t=q−1Q−ν−1 gives ψ(t)−qt=C[Q+(ν+1)log⁡Q]−ν−Q−ν−1=CQ−ν(1+o(1)), proving the matching lower bound. Replacing q by cq changes Q by the fixed constant log⁡c, hence H(cq)/H(q)→1.

3.1F1step 2.1givenalgebra

For the balanced scale, log⁡(1/α)∼plog⁡(1/δ), so step 2.1 gives both conjugate values in [F1] of order [log⁡(1/δ)]−ν. Moreover δp/α has this same order, and αH(1/(2C∗α))=O(δp). Substitution in [F1] proves the balanced error and residual bounds.

4.1F1step 2.1givenalgebra∎

If α≍δk with 0<k<p, step 2.1 again gives the two conjugate values of order [log⁡(1/δ)]−ν. Since δp−k[log⁡(1/δ)]ν→0 (put x=log⁡(1/δ) and compare the exponential e(p−k)x with its power-series term of integer degree greater than ν), δp/α is bounded by the desired logarithmic order. Also δp=O(δk[log⁡(1/δ)]−ν). Apply [F1] for the two asserted bounds. Finally, eδ=12∥uδ−u†∥2 gives the norm conclusion by taking square roots.

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Collared Sobolev conductivities have regular Liouville potentials and H1 recovery

Statement

Assume Countable Choice. Fix 5/2<s<7/2, 0<ρ<1, 0<m<1<M and A>0. Suppose σj=1+aj on R3, where aj is real, supported in Bρ‾, ∥aj∥Hs(R3)≤A, and m≤σj≤M, for j=1,2. Then gj=σj and log⁡gj belong to C1, with gj−1,log⁡gj∈Hs(R3) supported in Bρ‾ and uniformly bounded in Hs and C1. The real Liouville potentials qj=gj−1Δgj belong to Hs−2(R3), have support in Bρ‾, and have norms bounded in terms of s,ρ,m,M,A. Moreover, ∥σ1−σ2∥H1(R3)≤C∥q1−q2∥H−1(R3), with the same dependence. In particular the right side may be replaced by C∥q1−q2∥H−1/2 when that stronger norm is used. The Fourier and intrinsic norms for collared variations are those of the collared Sobolev source-functional lemma.

Facts & Assumptions

Given: The choice assumption and collared positive conductivities, Sobolev exponent, and uniform norm bounds in the Statement.

[F1]

Whole-space Fourier transformation is unitary on L2. Plancherel theorem Its distributional extension, agreement with Plancherel and derivative rule are given by Fourier transform is a topological automorphism of tempered distributions, Fourier transform agrees with l one and plancherel transforms, and Fourier differentiation and multiplication identities on tempered distributions. We use their angular-frequency form: f^(ξ)=(2π)−3/2(F2πf)(ξ/(2π)), so Dαf^=(iξ)αf^. Weighted Sobolev pairings then follow by Fourier Cauchy–Schwarz.

[F2]

The intrinsic and Fourier norm conventions are specified in Besov smoothness gives the exact collared Sobolev penalty a cutoff source bound. The fractional norm identity needed here is proved in step 2.1 below, rather than imported as a conclusion of that lemma.

[F3]

A Sobolev function supported in a compact subset of an open set extends by zero with all weak derivatives through the stated integer order. Compactly supported Sobolev functions extend by zero in every integer order

[F4]

A nonnegative unit-mass smooth bump generates a compactly supported L1 approximate identity. The mollifier family generated by a unit-mass smooth bump, An L1 approximate identity on Rn

[F5]

Mollification commutes with weak derivatives on the whole space, and approximate identities converge in finite L2. Interior mollification commutes with weak derivatives, Every L1 approximate identity converges to the identity in Lp for 1≤p<∞

[F6]

Tonelli's theorem interchanges the nonnegative measurable fractional-difference integrals below. Tonelli's theorem for nonnegative measurable functions on a sigma-finite product

[A1]

Countable Choice is used at the Fourier interfaces. The Axiom of Countable Choice (ACω)

Proof

1.1A1F1givenalgebra

Weighted Fourier Cauchy–Schwarz gives ∥a∥∞+∥∇a∥∞≤Cs∥a∥Hs because ∫(1+∣ξ∣2)−s(1+∣ξ∣)2dξ<∞ for s>5/2. The Fourier integrals for a and its first derivatives converge absolutely and are continuous by dominated convergence, so these bounds hold for a C1 representative. We shall also use ∥fg∥Hb≤C∥f∥Hr∥g∥Hb when r>3/2 and 0≤b≤r. Here is a direct justification: split Fourier space into dyadic annuli, use ∥fj∥∞≤C23j/2∥fj∥2 for low-high products, and ∥Qj(fkgk)∥2≤C23j/2∥fk∥2∥gk∥2 when comparable high shells k produce a shell j≤k+C. The low-high sum is bounded by ∥f∥Hr∥g∥Hb since r>3/2. For high-low products, sum the low g blocks with bound C2j(3/2−b)∥g∥Hb if b<3/2, a square-root logarithmic factor if b=3/2, or C∥g∥Hb if b>3/2; the excess r−3/2>0 makes the resulting geometric sum converge. For high-high products, weighted summation over k≥j−C has geometric kernel 2−(k−j)(r+b) and excess factor 2j(3/2−r); Cauchy–Schwarz for the input shell sequences completes the bound. The estimates are first proved for Fourier truncations and then passed to their norm limits.

2.1F1F2F6step 1.1algebra

For f∈L2 and 0<θ<1, Plancherel and [F6] give ∫R3∫R3∣f(x+h)−f(x)∣2∣h∣−3−2θdx dh=∫R3∣f^(ξ)∣2∫R3∣eiξ⋅h−1∣2∣h∣−3−2θdh dξ. The translation formula follows by substitution on Schwartz functions and then L2 density. Rotation and dilation make the inner integral cθ∣ξ∣2θ, where 0<cθ<∞ because ∣eiu−1∣≤min⁡{2,∣u∣}. Adding ∥f∥22 proves equivalence with the Fourier Hθ norm. Applying this identity to the weak derivatives of order k and using [F1] proves the analogous whole-space intrinsic/Fourier comparison at order k+θ; at integer orders use the derivative rule alone. For 0<θ<1, multiplication by a bounded C1 function h is bounded on Hθ. Indeed ∣h(x)f(x)−h(y)f(y)∣2≤2∥h∥∞2∣f(x)−f(y)∣2+2∣f(y)∣2∣h(x)−h(y)∣2. In the last term use ∣h(x)−h(y)∣≤min⁡{2∥h∥∞,∥∇h∥∞∣x−y∣}; integrating the kernel ∣x−y∣−3−2θ near and far from the diagonal gives a finite constant times ∥f∥22. Apply the identity just proved to get the Fourier Sobolev bound. For θ=0 this is just the L2 multiplier bound.

3.1F1F2step 1.1step 2.1algebra

If F is any smooth scalar function on a neighborhood of [m,M], then F(1+a)−F(1) belongs to Hs with a uniform bound over this class. To verify the fractional highest derivative directly, write s=k+θ with k=2 or 3 and 0≤θ<1. For k=2, the chain rule gives terms F′(σ)D2a and F′′(σ)Da Da. The first term is in Hθ by step 2.1, since F′(σ) is bounded C1 uniformly; for the second, Da∈Hs−1 with s−1>3/2, so the algebra bound in step 1.1 and then step 2.1 apply. For k=3, the terms are F′(σ)D3a, F′′(σ)D2a Da, and F′′′(σ)(Da)3. Here Da∈Hs−1 is a multiplier on Hs−2 by step 1.1, so D2a Da∈Hs−2⊂Hθ; the remaining products follow from the same algebra and step 2.1. Lower derivatives have the corresponding easier estimates; the function itself has an L2 bound by the mean value theorem and vanishes wherever a=0. These calculations may first be made for smooth approximations: convolve a with a nonnegative mollifier, which preserves m≤1+a≤M and uniform Hs and C1 bounds, and then use convergence in lower Sobolev orders and weak compactness in the Hilbert Hs space to identify the asserted weak derivatives and bound. A bounded sequence in this separable Fourier Hilbert space has a weakly convergent subsequence by diagonal convergence of its countable Fourier coordinates and the norm bound; hence only Countable Choice is used.

4.1step 1.1step 2.1step 3.1algebra

Apply step 3.1 to F(t)=t, 12log⁡t, and t−1/2 to obtain the stated regularity and support of gj−1, log⁡gj, and gj−1−1. To bound qj=gj−1Δgj in Hs−2, for s<3 use step 2.1 with θ=s−2∈(1/2,1) and the uniform C1 norm of gj−1; for s≥3 use the multiplier bound in step 1.1 with r=s and b=s−2. Split gj−1=1+(gj−1−1) for that whole-space bound. The support statement follows from gj=1 outside the collar ball and from the local definition of weak derivatives.

5.1A1F1F3F4F5step 1.1step 4.1algebra

Put w=log⁡g1−log⁡g2, H=g1g2 and Q=q1−q2. Since H∇w=g2∇g1−g1∇g2, distributional differentiation gives div⁡(H∇w)=g2Δg1−g1Δg2=HQ. The equality follows first for smooth positive approximations and then in distributions using step 4.1 and the Sobolev products from steps 1.1–2.1; all terms also have their ordinary weak-product interpretation because gj are C1 and Δgj∈L2. The functions w and its gradient vanish outside Bρ‾. By [F3], extend w by zero to R3. Choose a nonnegative unit-mass smooth mollifier from [F4]. For small ε, its convolution with the zero extension is supported in B2; by [F5], the mollifications converge to w in H1. Thus the distributional identity may be tested against w: Q∈H−1 and multiplication by the bounded C1 function H is bounded on H1. It gives ∫H∣∇w∣2=−⟨Q,Hw⟩.

6.1F1step 1.1step 5.1algebra∎

A compactly supported H1 function in Bρ⊂(−2,2)3 obeys ∥w∥2≤4∥∂1w∥2: for a smooth approximation supported in the same larger cube, write w(x1,x′)=∫−2x1∂1w(t,x′)dt, apply Cauchy–Schwarz, and integrate in x1,x′; passing to H1 limits gives the inequality. Since H≥m, step 5.1 and the Fourier dual bound yield m∥∇w∥22≤∥Q∥H−1∥Hw∥H1≤C∥Q∥H−1∥w∥H1. Poincaré in the first sentence then implies ∥w∥H1≤C∥Q∥H−1, dividing by its norm if nonzero. Finally σ1−σ2=σ2(e2w−1); the uniform bounds for w, σ2 and ∇σ2, the mean value theorem, and the ordinary weak product rule give ∥σ1−σ2∥H1≤C∥w∥H1. Also ∥Q∥H−1≤∥Q∥H−1/2 by the Fourier weights. This proves all recovery claims.

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A logarithmic endpoint rate with explicit effective noise and tolerance control

Statement

Fix p,ν,a,C∗,C>0 and let ψ be a continuous nondecreasing concave source index with ψ(0)=0. Suppose its conjugate H(q)=sup⁡t≥0(ψ(t)−qt) satisfies H(q)≤C(log⁡q)−ν for all sufficiently large q. Let δ↓0 be a nominal parameter, and put Lδ=log⁡(1/δ). Suppose nonnegative errors satisfy Sδ+aαδeδ≤dδ+αδψ(C∗Sδ),αδ≍δp,dδ=O(δpLδ−ν). Then eδ=O(Lδ−ν),Sδ=O(δpLδ−ν). For the joint objective estimate with p≥1 and actual data noise εδ=∥yδ−y∥, the hypothesis on dδ is supplied by 2εδp+ηδ=O(δpLδ−ν). This condition explicitly strengthens the usual nominal bound εδ≤δ, ηδ=O(δp) at the endpoint. If eδ=12∥uδ−u†∥2, the norm rate is O(Lδ−ν/2).

The universal assertion that α≍δ2 yields a vanishing norm error for every deterministic noise family bounded by δ is false, even for an exact quadratic Tikhonov minimizer and a truth satisfying every nonnegative variational source inequality: on real ℓ2, take Kej=j−1ej, x†=0, yδj=δjej, δj=1/j, αj=δj2, and objective ∥Kx−yδj∥2+αj2∥x∥2. Its unique minimizer is 2ej/3. This counterexample refutes the universal abstract endpoint assertion; it makes no assertion that these data arise from an EIT conductivity map.

Facts & Assumptions

Given: The scalar comparison, conjugate bound, nominal endpoint parameter, and effective noise hypothesis in the Statement.

[F1]

A conjugate bound for residual and Bregman errors gives eδ≤a−1(dδ/αδ+H(1/(C∗αδ))) and Sδ≤2(dδ+αδH(1/(2C∗αδ))).

Proof

1.1F1givenalgebra

Put dδ+:=max⁡{dδ,0}. The scalar inequality remains true with dδ+ on its right, and dδ+=O(δpLδ−ν). Since αδ≍δp, one has log⁡(1/(cαδ))∼pLδ for either fixed c=C∗ or c=2C∗. Thus both conjugate terms in [F1] are O(Lδ−ν), while dδ+/αδ=O(Lδ−ν). Substitution into [F1] proves both error estimates. If the joint approximate-minimum comparison is made with actual noise εδ and p≥1, its comparison term is dδ=2εδp+ηδ; this follows by replacing the noise upper bound by its actual value in the triangle-power comparison. The explicitly stated effective noise hypothesis therefore suffices. The quadratic norm rate follows by taking square roots.

2.1step 1.1givenalgebra∎

For the asserted counterexample, complete squares coordinatewise in ∥Kx−yδj∥2+(αj/2)∥x∥2. Every coordinate i≠j has unique minimizer zero. The jth coordinate has unique minimizer j−1δjj−2+αj/2=2/3. All quadratic coefficients are positive and the penalty is strictly positive definite, so this point is the unique global minimizer on ℓ2. Its norm error is 2/3 for every j. At truth zero the regularizer subgradient is zero, so the variational source left side is zero and any nonnegative source index satisfies the inequality. Also ∥yδj−Kx†∥=δj, so the ordinary deterministic noise bound holds exactly. This proves the false universal endpoint assertion fails and explains the need for an explicit additional noise condition in step 1.1.

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Centered NtD data control the exact CGO identity with its modulation factor

Statement

Assume the Axiom of Choice, Countable Choice and Dependent Choice. Let Ω=B1⊂R3, and let σj=1+aj be positive real conductivities with aj supported in Bρ‾, ∥aj∥Hs≤A, 5/2<s<7/2, 0<ρ<1, and m≤σj≤M, for fixed positive bounds. Put gj=σj, qj=gj−1Δgj, Q=q1−q2, and N=∥Q∥H−1/2(R3). Let Nj be the boundary-mean-zero Neumann-to-Dirichlet map for σj on L02(S2), and define the actual data difference by the centered voltage sequences δ=(∑ℓ≥1∑k=12ℓ+1∥(N1−N2)Yℓk∥L2(S2)2)1/2. This quantity is finite. If Uj=eiζj⋅x(1+rj) are the solutions from the positive-Sobolev CGO lemma, then ∫ΩQU1U2 dx=∫S2f1(N2−N1)f2 dS,fj=∂νUj∣S2, and the fluxes fj have mean zero. The boundary pairing is bilinear, without complex conjugation. In particular, there are C,T>0 depending only on the fixed class such that for t≥T and ∣ξ∣≤t one can choose null phases with ζ1+ζ2=−ξ, ∣ζj∣=2t, and obtain the rigorous estimate ∣Q^(ξ)∣≤CδeCt+C⟨ξ⟩1/2t−1N. The factor ⟨ξ⟩1/2 is part of this conclusion; this lemma does not assert the sharper stability estimate that would follow from dropping it.

Facts & Assumptions

Given: The choice assumptions, conductivity class, potentials, data norm and CGO solutions in the Statement.

[F1]

The centered voltage sequences are square summable, and the boundary-mean-zero variational Neumann solutions obey uniform H1 energy estimates. Centered Neumann voltage sequences on the three-dimensional ball

[F2]

The potentials belong uniformly to Hs−2 and have compact support in Bρ‾; the conductivities and their square roots and inverses have uniformly bounded C1 norms. Collared Sobolev conductivities have regular Liouville potentials and H1 recovery

[F3]

The CGO remainders have H1/2 norm O(t−1), uniformly bounded L∞ norm, and H2 norm O(t) on B2. Positive-Sobolev CGO remainders from a shifted Fourier lattice

[F4]

The boundary trace is bounded from H1(B1) into L2(S2). Boundary trace quotient and chart norm equivalence

[F5]

The divergence theorem on a bounded C1 domain gives Green's identity for smooth functions; the complex bilinear form follows by real and imaginary parts. Divergence on a bounded C1 Euclidean domain

[F6]

Ambient smooth restrictions are dense in H2(B1), so Green identities for smooth approximants pass to the H2 CGO solutions using the trace bound [F4]. Ambient smooth restrictions are dense on bounded C^k domains

[F7]

Tonelli's theorem permits interchanging the nonnegative Fourier integrals in the fractional difference identity below. Tonelli's theorem for nonnegative measurable functions on a sigma-finite product

[F8]

Compactly supported Sobolev classes extend by zero with their weak derivatives unchanged. Compactly supported Sobolev functions extend by zero in every integer order

Proof

1.1A1F1F4givenalgebra

The energy argument in [F1] applies to every mean-zero f∈L2(S2): the functional v↦∫fTr⁡v‾ is bounded on the boundary-mean-zero H1 subspace by [F4], and its uniformly coercive conductivity form has a unique solution with norm at most C∥f∥2. This defines a bounded map Nj:L02→L02. Its centered sequence columns are those in [F1], so their difference has finite squared sum. For a finite current expansion f=∑cℓkYℓk, Cauchy–Schwarz gives ∥(N1−N2)f∥2≤δ(∑∣cℓk∣2)1/2. Density and boundedness extend this to every f∈L02. Finally the bilinear symmetry ∫fNjh=∫hNjf follows by testing the two weak equations against one another, using real coefficients and complex bilinear extension.

2.1A1F1F2F3F4F5F6step 1.1algebra

Put uj=Uj/gj. The weak Liouville identity gives div⁡(σj∇uj)=0: expand σj∇(Uj/gj)=gj∇Uj−Uj∇gj, whose divergence is gjΔUj−UjΔgj=0. Products are legitimate by [F2]–[F3]; one may also verify the equality first on smooth approximations and pass in distributions. Since gj=1 on a full collar of S2, the boundary normal flux of uj is exactly fj=∂νUj. It belongs to L2 because Uj∈H2(B2) and [F4] applies to each first derivative. The divergence theorem [F5] gives the Green identity for smooth approximants; density [F6] and trace continuity [F4] pass it to the H2 CGO solutions on B1. On the collar Uj is harmonic and the coefficient is identically one. Taking the test function one shows ∫fj=0. Thus Tr⁡uj=Njfj+cj for a constant cj.

3.1A1F2F3F4F5F6step 1.1step 2.1algebra

Subtract the two bilinear Schrödinger Green identities [F5]–[F6], testing the j=1 equation against U2 and the j=2 equation against U1. Their gradient terms cancel, giving ∫QU1U2=∫S2(f1Tr⁡U2−f2Tr⁡U1). Since Uj=uj on the boundary, step 2.1 identifies these traces with Njfj+cj. The constant terms disappear because the fluxes have mean zero; the symmetry from step 1.1 gives exactly the displayed centered NtD identity. All volume terms are integrable since Q∈L2 and Uj∈H2(B2)⊂L∞(B1); boundedness follows, for example, by weighted Fourier Cauchy–Schwarz after multiplication by an interior cutoff.

4.1F3F4step 1.1step 3.1algebra

If ∣ζj∣=2t, [F3] and differentiation of the exponential give ∥Uj∥H2(B1)≤CeCtt3 for t≥T. The trace bound on first derivatives then gives ∥fj∥2≤CeCt, increasing C to absorb the polynomial. The operator-norm bound in step 1.1 and Cauchy–Schwarz in the bilinear boundary pairing yield ∣∫QU1U2∣≤CδeCt.

5.1A1F2F3F6F7F8step 4.1choosealgebra

For smooth compactly supported w put [w]1/22:=∬∣w(x)−w(y)∣2∣x−y∣−4 dx dy. Translation, Plancherel and Tonelli [F7] give [w]1/22=∫∣w^(ξ)∣2(∫∣eiξ⋅h−1∣2∣h∣4 dh)dξ=c3∫∣ξ∣∣w^(ξ)∣2dξ, where rotation and dilation give the inner integral, and 0<c3<∞ follows from ∣eiu−1∣≤min⁡{2,∣u∣}. Thus ∥w∥H1/22≍∥w∥22+[w]1/22. Restrict each H2(B2) remainder to B1 and use [F6] to approximate it there by ambient smooth functions; multiplication by a cutoff supported in B1 and [F8] extend the products to H2(R3), so the norm comparison extends to these cutoff remainders. For bounded compactly supported factors the identity f(x)g(x)−f(y)g(y)=f(x)(g(x)−g(y))+g(y)(f(x)−f(y)) then yields ∥fg∥H1/2≤C(∥f∥∞∥g∥H1/2+∥g∥∞∥f∥H1/2). For ξ≠0, choose real orthonormal d1,d2 perpendicular to ξ and set ζ1=−ξ/2+t2−∣ξ∣2/4 d2+itd1, ζ2=−ξ/2−t2−∣ξ∣2/4 d2−itd1; for ξ=0 choose any orthonormal pair. Direct expansion gives ζj⋅ζj=0, ζ1+ζ2=−ξ and ∣ζj∣=2t. Hence U1U2=e−iξ⋅x(1+r1+r2+r1r2). Choose cutoffs χ,η∈Cc∞(B1) with χ=1 on Bρ and η=1 on supp⁡χ. Applying the product estimate to χrj and ηrj, and [F3], gives ∥χ(r1+r2+r1r2)∥H1/2(R3)≤C/t. Fourier translation and ⟨η+ξ⟩≤2⟨η⟩⟨ξ⟩ then give ∥e−iξ⋅xχ(r1+r2+r1r2)∥H1/2≤C⟨ξ⟩1/2/t.

6.1F2step 4.1step 5.1algebra∎

Extract the Fourier coefficient of Q from the exact product identity in step 5.1. Its remainder is precisely the signed pairing of Q with e−iξ⋅xχ(r1+r2+r1r2), not with the unmodulated remainder. Weighted Fourier Cauchy–Schwarz bounds its absolute value by CN⟨ξ⟩1/2/t. Combine this with step 4.1 and absorb the fixed unitary Fourier factor (2π)−3/2 into C. This proves the stated estimate with its modulation factor retained.

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Geometric parameters satisfy the logarithmic frozen stopping and growth conditions

Statement

Let ν,c,α∗,τ>0, 0<θ<1 and 0<q<1. Suppose H is the conjugate of a truncated logarithmic source index with H(u)∼C(log⁡u)−ν as u→∞. Starting sufficiently far down the geometric sequence, relabel it as αn=α∗θn+n0, n≥1, and put hn=H(c/αn) and ρn=αnhn. Then one can choose n0 and CH>1, fixed independently of noise, such that hn≤CHhn+1,qCH<1,ρnρn+1≤M:=θ−1CH(n≥1). The positive sequence ρn decreases strictly to zero. For any positive target w↓0, let N(w) be the last index with ρN(w)≥τw. For sufficiently small w this index exists and is at least two, and τw≤ρN(w)<Mτw,hN(w)≍[log⁡(1/w)]−ν,αN(w)≍w[log⁡(1/w)]ν. In particular w=δ2 gives hN≍[log⁡(1/δ)]−ν and αN≍δ2[log⁡(1/δ)]ν. The nominal endpoint target w=δ2[log⁡(1/δ)]−ν gives the same hN and αN≍δ2. In that endpoint case, an actual noise bound εδ2=O(w) and tolerances ηn≤Cηρn give exactly the noise/tolerance scale required for applying a frozen recursion at this stopping index.

Facts & Assumptions

Given: The source index, fixed geometric sequence, contraction number and target in the Statement.

[F1]

The truncated logarithmic conjugate is positive and finite at every positive argument, is nonincreasing, and has the stated asymptotic. Logarithmic source indices and their admissible parameter scales

Proof

1.1F1givenchoosealgebra

Positivity follows directly by testing the supremum at a sufficiently small s>0: the logarithmic index has ψ(s)/s→∞, so ψ(s)−us>0 for any fixed u>0. Finiteness and monotonicity follow from boundedness of ψ and from its defining supremum. By [F1], hn∼C[(n+n0)∣log⁡θ∣]−ν, so hn/hn+1→1. Fix any CH∈(1,1/q) and choose n0 large enough that all ratios in the relabeled tail are at most CH. Since αn decreases strictly and hn is positive and nonincreasing, ρn decreases strictly; its asymptotic is a positive constant times θn+n0(n+n0)−ν, hence it tends to zero and its consecutive ratio is at most M.

2.1step 1.1givenalgebra

For sufficiently small w, ρ2≥τw, and strict decrease to zero gives a unique last index N=N(w)≥2. It satisfies ρN≥τw>ρN+1, so ρN≤MρN+1<Mτw. In particular N→∞ as w→0. Taking logarithms in the geometric asymptotic gives (N+n0)∣log⁡θ∣+νlog⁡(N+n0)=log⁡(1/w)+O(1). Since log⁡N/N→0, it follows that N∼log⁡(1/w)/∣log⁡θ∣. Substituting in hN yields hN≍[log⁡(1/w)]−ν, and αN=ρN/hN gives the asserted parameter scale.

3.1step 2.1givenalgebra∎

For w=δ2, log⁡(1/w)=2log⁡(1/δ), giving the first specialization. For w=δ2[log⁡(1/δ)]−ν, log⁡(1/w)=2log⁡(1/δ)+νlog⁡log⁡(1/δ)∼2log⁡(1/δ), and the same formulas give hN≍[log⁡(1/δ)]−ν and αN≍δ2. If εδ2≤Cw, use the positive noise upper bound Cw, increasing C if necessary. The stopping bracket then has fixed constants τ/C and M, while every earlier ρn≥ρN≥τw and the stated tolerance bound is already ηn≤Cηαnhn. Thus all parameter/noise/tolerance conditions hold at that upper-bound scale.

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Differential range invariance for Banach-space maps

Definition

Let X, Y, and Z be real Banach spaces, let U⊆X be nonempty, let x0∈U, and let F:U→Y be a map. A differential range-invariance factorization at x0 on U consists of a bounded linear map K:Z→Y and a map r:U→Z such that

F(x)−F(x0)=Kr(x)(x∈U).

Thus every data increment lies in the range of the fixed operator K. The factorization alone does not assert that K is injective, that r is unique, or that a tangential-cone inequality holds. If U is open and F and r are Fréchet differentiable, differentiating this identity gives F′(x)=Kr′(x) at every x∈U; no converse is asserted without further hypotheses.

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H1 logarithmic stability from centered three-dimensional NtD data

Statement

Assume the Axiom of Choice, Countable Choice and Dependent Choice. Fix 5/2<s<7/2, 0<ρ<1, 0<m<1<M and A>0. Let σj=1+aj be real conductivities on R3, with aj supported in Bρ‾, ∥aj∥Hs≤A, and m≤σj≤M, for j=1,2. Let Nj be their boundary-mean-zero NtD maps on S2=∂B1, and let δ=(∑ℓ≥1∑k=12ℓ+1∥(N1−N2)Yℓk∥L2(S2)2)1/2. Then there is C>0 depending only on s,ρ,m,M,A such that ∥σ1−σ2∥H1(R3)≤C[log⁡(3+δ−1)]−(s−3/2)(δ>0). If δ=0, the conductivities are equal. The zero-noise modulus is therefore defined to be zero. Restricting the left side to B1 gives the same H1 norm because the difference is supported in Bρ‾.

Facts & Assumptions

Given: The choice assumptions and fixed collared Sobolev conductivity class in the Statement.

[F1]

With qj=(Δσj)/σj and Q=q1−q2, one has ∥Q∥Hs−2≤C, and ∥σ1−σ2∥H1≤C∥Q∥H−1. Collared Sobolev conductivities have regular Liouville potentials and H1 recovery

[F2]

The exact centered NtD/CGO identity has boundary term at most CδeCt. For each ∣ξ∣≤t, its remaining Fourier term is the bilinear pairing of e−iξ⋅xQ with a cutoff remainder of H1/2 norm at most C/t, uniformly in phase direction. Centered NtD data control the exact CGO identity with its modulation factor

[F3]

For every b≥1, ∫∣ξ∣≤b⟨ξ⟩−1∥e−iξ⋅xQ∥H−1/22dξ≤Cb2∥Q∥H−1/22. Sharp weighted absorption of modulated CGO remainders

Proof

1.1A1F1givenalgebra

Put N=∥Q∥H−1/2 and κ=s−3/2>0. For b≥1, the high-frequency contribution satisfies ∫∣ξ∣>b⟨ξ⟩−1∣Q^(ξ)∣2dξ≤Cb−2κ∥Q∥Hs−22≤Cb−2κ. Indeed the ratio of the two Fourier weights is ⟨ξ⟩−1−2(s−2)=⟨ξ⟩−2κ.

2.1F2F3step 1.1algebra

For t≥max⁡{T,b}, [F2] gives the pointwise inequality ∣Q^(ξ)∣≤CδeCt+(C/t)∥e−iξ⋅xQ∥H−1/2 for ∣ξ∣≤b. The right side is measurable by the nonnegative Fourier integral in [F3]; thus this inequality can be integrated without selecting a measurable family of CGO phases. Square it using (u+v)2≤2u2+2v2, multiply by ⟨ξ⟩−1 and integrate. Since ∫∣ξ∣≤b⟨ξ⟩−1dξ≤Cb2, [F3] yields a low-frequency bound Cb2δ2eCt+Cb2t−2N2, after increasing the exponential constant. Combining with step 1.1 gives N2≤Cb−2κ+Cb2δ2eCt+Cb2t−2N2. This uses the integrated modulated estimate, not an unmodulated pointwise Sobolev pairing.

3.1step 2.1choosealgebra

Choose a fixed K≥max⁡{T,1} large enough that C/K2≤1/2 for the last constant in step 2.1, and set t=Kb. Absorb that term into the left side and take square roots. Increasing fixed constants and using b≤eb for b≥1 gives N≤Cb−κ+CδeDb for some D>0 depending only on the fixed class. If δ=0, let b→∞ to obtain N=0. If 0<δ<e−2D, take b=log⁡(1/δ)/(2D)≥1. Then δeDb=δ, which is bounded by a constant times [log⁡(1/δ)]−κ because e−x/2xκ is bounded for x≥1. Hence N≤C[log⁡(1/δ)]−κ for all sufficiently small positive δ.

4.1F1step 3.1algebra∎

By [F1] and the Fourier weight inequality, ∥σ1−σ2∥H1≤C∥Q∥H−1≤CN. This proves the claimed small-noise rate and, if δ=0, equality of the conductivities. For larger positive δ, the uniform bound ∥a1−a2∥H1≤2A and the positive lower bound for [log⁡(3+δ−1)]−κ on that region prove the same displayed estimate after increasing C. On a sufficiently small noise interval, log⁡(3+δ−1) is comparable to log⁡(1/δ), so the two small-noise formulations agree. All constants came from the fixed class.

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Joint and frozen-Newton objectives for range-invariant relaxations

Definition

Let X,Y,Z be real Banach spaces, let X0 be a closed linear subspace of X, and let D⊆X contain X0 and a nonempty set U. Fix x0∈X0∩U, a map F:D→Y, a bounded linear map K:Z→Y, and a map r:D→Z such that F(x)−F(x0)=Kr(x) for every x∈D. Assume that r∣U is a bijection from U onto a set U~⊆Z, and that (I−P)r−1(r^)∈X0∩D for every r^∈U~ admitted below. Here I is the identity on X and P:X→X is a bounded linear projection with P2=P, P∣X0=0, and (I−P)(X)⊆X0. Let Rˇ:X0→[0,+∞] be a proper convex regularizer, let RP:P(X)→[0,+∞] be a nonnegative penalty, and let Q:Z×Z→[0,+∞] satisfy Q(a,b)=Q(b,a) and Q(a,b)=0 exactly when a=b. For r^∈U~, set

R(r^):=Rˇ((I−P)r−1(r^))+RP(Pr−1(r^)).

For p∈[1,∞), data yδ∈Y, and parameters α,β>0, the joint relaxed objective is

Jα,βδ(r^,x):=∥Kr^+F(x0)−yδ∥Yp+αR(r^)+βQ(r(x),r^)p+∥Px∥Xp,(r^,x)∈U~×U.

The joint source-condition objective takes RP=0. The term βQ(r(x),r^)p ties the relaxed range variable to the physical variable, while ∥Px∥Xp penalizes the component outside X0.

If r is Gâteaux differentiable at a current iterate xn∈U, the frozen-Newton objective with parameters αn,βn>0 is

Jnδ(r^,x):=∥Kr^+F(x0)−yδ∥Yp+αnR(r^)+βnQ(r(xn)+r′(xn)(x−xn),r^)p+∥Px∥Xp.

Here r′(xn) is the bounded linear Gâteaux derivative used to form the affine range proxy; no minimizer existence or uniqueness is part of either definition.

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The joint relaxed objective controls Bregman and range errors

Statement

Let 1≤p<∞. Let X,Y,Z be real Banach spaces, X0⊆X a closed linear subspace, D⊆X a domain containing X0 and U, and x0∈U∩X0. Let F:D→Y, K∈L(Z,Y), and r:D→Z satisfy F(x)−F(x0)=Kr(x) on D, with r∣U:U→U~ bijective and r Lipschitz with constant Lr on D. Let P:X→X be a bounded linear projection such that P∣X0=0, (I−P)(X)⊆X0, and P(x0)=P(x†)=0 for an exact solution x†∈U∩X0; put y:=F(x†). Assume the projected points (I−P)r−1(r^) belong to D∩X0 for all admitted r^∈U~.

Let Rˇ:X0→[0,+∞] be proper and convex, choose ξ†∈∂Rˇ(x†)⊆X0∗, and suppose that for some R0≥Rˇ(x†), b∈[0,1), and continuous nondecreasing concave ψ:[0,∞)→[0,∞) with ψ(0)=0, the variational source inequality

−⟨ξ†,xˇ−x†⟩≤bDRˇξ†(xˇ,x†)+ψ(∥F(xˇ)−F(x†)∥Yp)

holds whenever xˇ∈D∩X0 and Rˇ(xˇ)≤R0, where DRˇξ†(xˇ,x†):=Rˇ(xˇ)−Rˇ(x†)−⟨ξ†,xˇ−x†⟩. Set RP=0 in the joint objective. Let Q:U~×U~→[0,∞] satisfy Q(a,b)=Q(b,a) and Q(a,b)=0 exactly when a=b, and assume there is a constant CQ≥0 such that

∥P(r−1(a))−P(r−1(b))∥X≤CQQ(a,b)(a,b∈U~).

For δ>0, let yδ∈Y satisfy ∥yδ−y∥Y≤δ. Let α,β>0, Cη≥0, and let (r^δ,xδ)∈U~×U be an ηδ-minimizer of the joint objective, meaning

Jα,βδ(r^δ,xδ)≤Jα,βδ(r^,x)+ηδ((r^,x)∈U~×U),0≤ηδ≤Cηδp.

Assume xˇδ:=(I−P)r−1(r^δ) lies in the stated VSC sublevel. Define

sδ:=21−p∥K(r^δ−r(x†))∥Yp+βQ(r(xδ),r^δ)p+∥Pxδ∥Xp,eδ:=DRˇξ†(xˇδ,x†),

and

C∗:=22p−2max⁡{1,∥K∥pLrp,∥K∥pLrpCQpβ},dδ:=(2+Cη)δp.

Then

sδ+(1−b)αeδ≤dδ+αψ(C∗sδ).

Facts & Assumptions

Given: The Banach spaces, range factorization, projection, regularizer, VSC, compatibility inequality, exact datum, noise bound, and approximate minimizer in the Statement.

[A1]

The candidate projected point xˇδ belongs to D∩X0 and satisfies Rˇ(xˇδ)≤R0, so the VSC applies to it.

[A2]

The noisy datum and approximate-minimizer tolerance satisfy the bounds in the Statement.

[A3]

The projection-coupling bound holds for all range variables in the Statement.

[F1]

Differential range invariance for Banach-space maps: F(x)−F(x0)=Kr(x) for every x in the range-factorization domain.

[F2]

Joint and frozen-Newton objectives for range-invariant relaxations: with RP=0, the joint objective is ∥Kr^+F(x0)−yδ∥Yp+αRˇ((I−P)r−1(r^))+βQ(r(x),r^)p+∥Px∥Xp.

[F3]

Banach-space convex subgradients and dual pairings: ξ†∈∂Rˇ(x†) means Rˇ(z)≥Rˇ(x†)+⟨ξ†,z−x†⟩ for every z∈X0.

[F4]

Bregman distances for convex regularizers: DRˇξ†(z,x†)=Rˇ(z)−Rˇ(x†)−⟨ξ†,z−x†⟩.

[F5]

Variational source conditions on a regularizer sublevel: on the named regularizer sublevel, −⟨ξ†,z−x†⟩≤bDRˇξ†(z,x†)+ψ(∥F(z)−F(x†)∥Yp).

Proof

1.1A2F1F2givenalgebra

Compare the approximate-minimum inequality with (r^,x)=(r(x†),x†). By [F1], Kr(x†)+F(x0)=F(x†)=y, while Q(r(x†),r(x†))=0 and Px†=0. Since r−1(r(x†))=x† and P(x†)=0, the comparison regularizer is Rˇ(x†), so its objective is at most δp+αRˇ(x†). Writing the estimated data residual as K(r^δ−r(x†))+(y−yδ), the triangle inequality gives ∥K(r^δ−r(x†))∥≤∥Kr^δ+F(x0)−yδ∥+δ. For p=1 the power inequality is equality; for p>1, f(t)=tp has f′′(t)=p(p−1)tp−2≥0 for t>0 and is continuous at zero, so it is convex on [0,∞); midpoint convexity gives (a+b)p≤2p−1(ap+bp) for a,b≥0. Rearranging yields 21−p∥K(r^δ−r(x†))∥p≤∥Kr^δ+F(x0)−yδ∥p+δp. Combining this with the comparison and ηδ≤Cηδp gives sδ+α(Rˇ(xˇδ)−Rˇ(x†))≤dδ.

2.1A1F3F4F5step 1.1algebra

By [F3], ξ† is a subgradient at x†, and [F4] gives Rˇ(xˇδ)−Rˇ(x†)=eδ+⟨ξ†,xˇδ−x†⟩. The VSC [F5] therefore implies Rˇ(xˇδ)−Rˇ(x†)≥(1−b)eδ−ψ(∥F(xˇδ)−F(x†)∥p). Substituting this lower bound in step 1.1 yields sδ+(1−b)αeδ≤dδ+αψ(∥F(xˇδ)−F(x†)∥p).

2.2A3F1step 1.1algebra

Put uδ=r−1(r^δ), so xˇδ=(I−P)uδ. Since r−1(r(xδ))=xδ, assumption [A3] and the triangle inequality give ∥Puδ∥≤∥Pxδ∥+CQQ(r(xδ),r^δ). Lipschitz continuity of r gives ∥r(xˇδ)−r^δ∥=∥r((I−P)uδ)−r(uδ)∥≤Lr∥Puδ∥. Applying [F1] at xˇδ and x†, then applying (a+b)p≤2p−1(ap+bp) twice, yields ∥F(xˇδ)−F(x†)∥p≤2p−1∥K(r^δ−r(x†))∥p+22p−2∥K∥pLrp(∥Pxδ∥p+CQpQ(r(xδ),r^δ)p)≤C∗sδ. Monotonicity of ψ gives ψ(∥F(xˇδ)−F(x†)∥p)≤ψ(C∗sδ).

3.1step 2.1step 2.2algebra∎

Substitute the last bound from step 2.2 into the inequality from step 2.1. This gives sδ+(1−b)αeδ≤dδ+αψ(C∗sδ), as claimed.

LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)Open item page →

A frozen Newton step yields a controlled error inequality

Statement

Let 1≤p<∞. Let X,Y,Z be real Banach spaces, let X0 be a closed linear subspace of X, and let D,U⊆X. Let x0,x†∈D∩U∩X0, let F:D→Y, K∈L(Z,Y), and r:D→Z satisfy F(x)−F(x0)=Kr(x) for all x∈D. Assume r∣U:U→U~ is a bijection, r is Lipschitz on D with constant Lr, and r has bounded linear Gâteaux derivatives on U. Let P:X→X be a bounded linear projection with P2=P, P∣X0=0, and (I−P)(X)⊆X0. Assume P(x†)=0 and (I−P)r−1(r^)∈D∩X0 for each admitted r^∈U~.

Let Rˇ:X0→[0,+∞] be proper and convex, choose ξ†∈∂Rˇ(x†), and write

DRˇξ†(z,x†):=Rˇ(z)−Rˇ(x†)−⟨ξ†,z−x†⟩X0∗,X0.

Suppose that, on the stated sublevel Rˇ(z)≤R0, the variational source inequality

−⟨ξ†,z−x†⟩X0∗,X0≤bDRˇξ†(z,x†)+ψ ⁣(∥F(z)−F(x†)∥Yp)

holds for some R0≥Rˇ(x†) and 0≤b<1. Assume that ψ:[0,∞)→[0,∞) is continuous, nondecreasing and concave, with ψ(0)=0, and that for a constant Cψ>0,

ψ(a+b)≤Cψ(ψ(a)+ψ(b))(a,b≥0).

Let H(q):=sup⁡s≥0(ψ(s)−qs), and assume the values of H used below are finite. Let γ>Cψ, CP≥2p−1∥K∥pLrp, and C~Q>0. Let RP:P(X)→[0,+∞] satisfy RP(0)=0 and

RP(z)≥γψ(CP∥z∥Xp)(z∈P(X)).

For each admitted r^∈U~, put u(r^):=r−1(r^) and xˇ(r^):=(I−P)u(r^). Assume the compatibility bound

∥r^−r(x†)∥Zp≤C~Q(DRˇξ†(xˇ(r^),x†)+ψ(CP∥Pu(r^)∥Xp)).

Choose cβα>0 with cβα≤min⁡{1−b,γ−Cψ}/(4C~Q), and set

m:=min⁡{1−b,γ−Cψ},a:=21−p,λ:=21−p,t:=22−2p−2p−1ctcrp,v:=(1+2p−1)ctcrp,C‾:=Cψ22p−2.

Assume 0<ctcr<1 and t>0, and for all x1,x2∈U assume

∥r(x1)−r(x2)−r′(x2)(x1−x2)∥Z≤ctcr∥r(x1)−r(x2)∥Z.

Assume the candidate projected point satisfies Rˇ(xˇ(r^n+1))≤R0.

Let Q(a,b):=∥a−b∥Z. Fix δ>0, yδ∈Y with ∥yδ−F(x†)∥Y≤δ, and αn>0, with βn:=cβααn. Suppose (r^n+1,xn+1)∈U~×U satisfies, for some ηn≥0,

Jnδ(r^n+1,xn+1)≤Jnδ(r^,x)+ηn((r^,x)∈U~×U),

where Jnδ is the frozen objective in the Joint and frozen-Newton objectives definition, with regularizer R(r^)=Rˇ(xˇ(r^))+RP(Pu(r^)) and distance Q.

Define

En+1:=cβαCψ(λ∥r^n+1−r(x†)∥Zp+t∥r(xn+1)−r(x†)∥Zp),

Sn+1:=1Cψ(21−p∥K(r^n+1−r(x†))∥Yp+∥Pxn+1∥Xp),

and

dn:=cβαCψαnv∥r(xn)−r(x†)∥Zp+2δp+ηnCψ.

Then

Sn+1+αnEn+1≤dn+αnψ(C‾Sn+1).

Consequently,

En+1≤dnαn+H ⁣(1C‾αn),Sn+1≤2(dn+αnH ⁣(12C‾αn)).

Facts & Assumptions

Given: All spaces, maps, penalties, parameters, source inequalities, compatibility inequalities, and the approximate frozen step satisfy the hypotheses in the Statement.

[A1]

The projected point xˇ(r^n+1) belongs to the regularizer sublevel where the VSC holds.

[F1]

Differential range invariance for Banach-space maps: F(x)−F(x0)=Kr(x) on the specified domain, and r∣U is invertible onto U~.

[F2]

Joint and frozen-Newton objectives for range-invariant relaxations: the frozen objective has data term ∥Kr^+F(x0)−yδ∥p, regularizer αnR(r^), coupling βn∥ℓn(x)−r^∥p, and penalty ∥Px∥p, where ℓn(x)=r(xn)+r′(xn)(x−xn).

[F3]

Banach-space convex subgradients and dual pairings: ξ†∈∂Rˇ(x†) means Rˇ(z)≥Rˇ(x†)+⟨ξ†,z−x†⟩ for every z∈X0.

[F4]

Bregman distances for convex regularizers: DRˇξ†(z,x†)=Rˇ(z)−Rˇ(x†)−⟨ξ†,z−x†⟩.

[F5]

Variational source conditions on a regularizer sublevel: −⟨ξ†,z−x†⟩≤bDRˇξ†(z,x†)+ψ(∥F(z)−F(x†)∥p) on the stated sublevel.

[F6]

A conjugate bound for residual and Bregman errors: if r+αe≤d+αψ(Cr), then e≤d/α+H(1/(Cα)) and r≤2(d+αH(1/(2Cα))).

Proof

1.1A1F1F2givenalgebra

Write r†:=r(x†), u:=r−1(r^n+1), xˇ:=(I−P)u, and ℓn(x):=r(xn)+r′(xn)(x−xn). Compare the approximate-minimum inequality with (r^,x)=(r†,x†). The competitor has data residual at most δp, penalty RP(0)=0, and Px†=0. If g:=Kr^n+1+F(x0)−yδ, then K(r^n+1−r†)=g+(yδ−y); convexity of s↦sp gives ∥K(r^n+1−r†)∥p≤2p−1(∥g∥p+δp). Thus the comparison gives 21−p∥K(r^n+1−r†)∥p+αn(R(r^n+1)−Rˇ(x†))+βn∥ℓn(xn+1)−r^n+1∥p+∥Pxn+1∥p≤2δp+ηn+βn∥ℓn(x†)−r†∥p. The remainder hypothesis at (x†,xn) and ctcr<1 imply ∥ℓn(x†)−r†∥p≤ctcrp∥r(xn)−r†∥p.

1.2givenalgebra

Let A:=∥r(xn+1)−r^n+1∥, B:=∥ℓn(xn+1)−r^n+1∥, and Dn:=∥r(xn+1)−ℓn(xn+1)∥. The triangle inequality gives A≤B+Dn, hence Bp≥21−pAp−Dnp. Applying the same power inequality to ∥r(xn+1)−r†∥≤A+∥r^n+1−r†∥ gives Ap≥21−p∥r(xn+1)−r†∥p−∥r^n+1−r†∥p. The Taylor hypothesis and another triangle-power estimate give Dnp≤2p−1ctcrp(∥r(xn+1)−r†∥p+∥r(xn)−r†∥p). Therefore ∥ℓn(xn+1)−r^n+1∥p≥t∥r(xn+1)−r†∥p−λ∥r^n+1−r†∥p−2p−1ctcrp∥r(xn)−r†∥p, with t=22−2p−2p−1ctcrp and λ=21−p.

1.3A1F1F4F5givenalgebra

Set z:=Pu. Since ∥r(xˇ)−r(u)∥≤Lr∥z∥, the factorization and power inequality give ∥F(xˇ)−F(x†)∥p≤2p−1∥K(r^n+1−r†)∥p+2p−1∥K∥pLrp∥z∥p. By the VSC [F5], the Bregman identity [F4], the penalty lower bound, and ψ(a+b)≤Cψ(ψ(a)+ψ(b)), we obtain R(r^n+1)−Rˇ(x†)≥m(DRˇξ†(xˇ,x†)+ψ(CP∥z∥p))−Cψψ(2p−1∥K(r^n+1−r†)∥p). Indeed, the VSC yields coefficient 1−b on the Bregman term, and the penalty leaves coefficient γ−Cψ on the penalty-index term; their minimum is m.

2.1step 1.1step 1.2step 1.3A1F1F3F4F5givenalgebra

Substituting steps 1.1–1.3 into the objective comparison and applying the compatibility inequality gives CψSn+1+αnmT+βnt∥r(xn+1)−r†∥p≤βnv∥r(xn)−r†∥p+βnλ∥r^n+1−r†∥p+αnCψψ(2p−1∥K(r^n+1−r†)∥p)+2δp+ηn, where T:=DRˇξ†(xˇ,x†)+ψ(CP∥Pu∥p). By [F3], DRˇξ†(xˇ,x†)≥0, so T≥0. Since ∥r^n+1−r†∥p≤C~QT and βnλC~Q≤αnm/4, the left regularizer term absorbs the displayed compatibility term and a second copy of it, leaving αnmT/2; in particular it bounds another βnλ∥r^n+1−r†∥p. The resulting inequality is CψSn+1+βnλ∥r^n+1−r†∥p+βnt∥r(xn+1)−r†∥p≤βnv∥r(xn)−r†∥p+αnCψψ(2p−1∥K(r^n+1−r†)∥p)+2δp+ηn.

3.1step 2.1F6givenalgebra∎

Divide step 2.1 by Cψ and use βn=cβααn. Since CψSn+1≥21−p∥K(r^n+1−r†)∥p, we have 2p−1∥K(r^n+1−r†)∥p≤Cψ22p−2Sn+1=C‾Sn+1. Monotonicity of ψ gives Sn+1+αnEn+1≤dn+αnψ(C‾Sn+1). Applying [F6] with c1=c2=1, C3=C‾, r=Sn+1, and e=En+1 proves both conjugate bounds.

LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)Open item page →

The full collared EIT class satisfies the centered range and source interfaces

Statement

Assume the Axiom of Choice, Countable Choice and Dependent Choice. Let Ω=B1⊂R3, 0<ρ<1, 0<m<1<M, and 5/2<s1<7/2. Use the exact intrinsic Hs1(Ω) Hilbert norm from the collared source-functional lemma, and put V={a∈Hs1(Ω;R):a=0 a.e. on Ω∖Bρ},C={σ∈1+V:m≤σ≤M}. Let σ0,σ†∈C, and assume σ†∈B2,∞s2(Ω) for s1<s2<2s1−1. Set a†=σ†−σ0, and choose R>∥a†∥V. Write Y=ℓ2(L2(S2;C)) for the centered mean-zero Neumann voltage sequence space, regarded as a real Hilbert space, and give X=Z=V×Y the sum norm. Let X0=V×{0}, D=X, x0=(0,0) and P(a,z)=(0,z).

There is a globally Lipschitz, continuously Fréchet differentiable map Γ:V→Y with globally Lipschitz derivative, agreeing for σ0+a∈C with the centered physical observation ((Nσ0+a−N1)Yℓk)ℓ,k. Define T(a)=Γ(a)−Γ(0)−Γ′(0)a,r(a,z)=(a,z+T(a)),K(a,b)=Γ′(0)a+b,F(a,z)=Γ(a)+z. Then F(x)−F(x0)=Kr(x) on D, r and its inverse r−1(a,b)=(a,b−T(a)) are globally Lipschitz, and these facts hold on the admitted set U={(a,z):σ0+a∈C, ∥a∥V<R, ∥z∥Y<R}. The projected points (I−P)r−1(r^) belong to U∩X0 for r^∈r(U).

On the ambient linear space V define the proper convex regularizer Rˇ(a)=12∥σ0+a∥s1,Ω2+IC(σ0+a), where the indicator is zero on C and +∞ elsewhere. On every admitted physical coefficient this is exactly the original quadratic Sobolev penalty. The functional ξ†(v)=(σ†,v)s1,Ω is a subgradient at a†, and its Bregman distance on C−σ0 is exactly 12∥a−a†∥V2. On every fixed sublevel containing the truth, the VSC holds with b=3/4 and a continuous nondecreasing concave truncated logarithmic index ψ(u)=C[log⁡(1/u)]−ν(0<u≤u0),ψ(0)=0,ν=(s2−s1)(2s1−3)s2−1, continued constantly above a sufficiently small u0>0. Its subadditivity constant may be taken as Cψ=1.

Put LT=Lip⁡(T) and let L=Lip⁡(Γ′). For Q(r^,r^′)=∥r^−r^′∥Z, the projection-coupling constant may be taken as 1+LT, and ∥r(x1)−r(x2)−r′(x2)(x1−x2)∥Z≤LR∥r(x1)−r(x2)∥Z(x1,x2∈U). For the frozen method choose CP≥2∥K∥2Lip⁡(r)2, γ>1 and RP(z)=γψ(CP∥z∥Y2). Then the required compatibility bound holds with C~Q=max⁡{4(1+LT)2,2R2ψ(CPR2)}. In particular, the structural hypotheses of the joint and frozen one-step estimates hold with p=2, the displayed exact regularizer, RP=0 for the joint method, and the displayed RP for the frozen method. For frozen rates additionally require LR<ctcr<1/20, choose cβα≤min⁡{1/4,γ−1}/(4C~Q), and use the remaining parameter, tolerance and stopping conditions of the frozen rate theorem. This small-neighborhood hypothesis is local around the chosen initial conductivity; it restricts neither the stated Besov truth class nor its logarithmic exponent.

Facts & Assumptions

Given: The choice assumptions and full conductivity, Sobolev, Besov, collar and local-admission data in the Statement.

[F1]

The centered physical map on uniformly elliptic collared L∞ coefficients is globally Lipschitz on each fixed ellipticity class, is continuously Fréchet differentiable there, and has a uniform Lipschitz derivative. Centered Neumann voltage sequences on the three-dimensional ball

[F2]

Uniform collared Hs1 classes satisfy H1 stability with modulus O([log⁡(1/δ)]−(s1−3/2)) in their centered sequence distance. H1 logarithmic stability from centered three-dimensional NtD data

[F3]

For the exact intrinsic quadratic penalty, the H1 modulus and B2,∞s2 truth imply a VSC with coefficient 3/4 and logarithmic exponent ν in the Statement. The proof treats the exact domain inner product. Besov smoothness gives the exact collared Sobolev penalty a cutoff source bound

[F4]

Truncated logarithmic indices are concave when the truncation point is small enough, and their conjugates satisfy H(q)∼C(log⁡q)−ν. Logarithmic source indices and their admissible parameter scales

Proof

Domains for the generic estimates. The canonical maps iV:V→X0, iV(a)=(a,0), and iY:Y→P(X), iY(z)=(0,z), are linear isometries onto X0=V×{0} and P(X)={0}×Y. In the generic one-step estimates, transport the regularizer and subgradient by Rˇ0(iV(a)):=Rˇ(a),ξ0†(iV(v)):=ξ†(v), and the frozen penalty by RP,0(iY(z)):=γψ(CP∥z∥Y2). The map iV preserves the dual pairing, so the subgradient inequality verified in step 3.2 transports to ξ0†∈∂X0Rˇ0(iV(a†)). It also preserves the Bregman distance: DRˇ0ξ0†(iV(a),iV(a†))=DRˇξ†(a,a†). The penalty is a functional on P(X) under iY and has the required norm there. These are the domains used by the generic joint and frozen estimates.

1.1A1F3givenalgebra

The space V is closed in the intrinsic Sobolev Hilbert space, since convergence there implies L2 convergence and preserves vanishing on the collar. For its elements, zero extension is bounded into Hs1(R3) by [F3]. Fourier Cauchy–Schwarz gives ∥a∥∞≤C∥a∥V because s1>3/2. Thus V embeds continuously into the bounded collared coefficient space. The set C−σ0 is convex and closed in V: convexity preserves the pointwise bounds, and uniform convergence from this embedding preserves them under a norm limit.

2.1F1step 1.1choosealgebra

Choose a smooth scalar function ϑ with 0≤ϑ≤1, equal to one on [m,M] and zero outside (m/2,2M), and set g(t)=1+∫1tϑ(u)du. Then g(t)=t on [m,M], g(1)=1, and m/2≤g(t)≤2M globally. All its first and second derivatives are bounded. Put b(a):=g(σ0+a)−1. Since σ0+a=1 outside Bρ and g(1)=1, b(a)∈E for every a∈V. Moreover, a↦b(a) is globally Lipschitz from V into E and continuously Fréchet differentiable, with derivative b′(a)h=g′(σ0+a)h and globally Lipschitz derivative; its Taylor remainder is bounded by 12∥g′′∥∞∥h∥∞2. Use the centered map [F1] with fixed strict ellipticity bounds m∗:=m/4 and M∗:=4M, and denote it by Gm∗,M∗. Since m∗<m/2≤1+b(a)≤2M<M∗, every b(a) is admissible for that map. Define explicitly Γ(a):=Gm∗,M∗(b(a))=Gm∗,M∗(g(σ0+a)−1)(a∈V). The chain rule gives Γ′(a)h=Gm∗,M∗′(b(a))[g′(σ0+a)h]. The bounded and Lipschitz derivatives of b and the Lipschitz derivative of the centered supplier give all the asserted global bounds for Γ and Γ′. For σ0+a∈C, g(σ0+a)=σ0+a, so this formula agrees with the centered physical observation.

3.1step 2.1givenalgebra

The identities for K,r,F and the inverse follow by direct substitution. The map T is globally Lipschitz with LT≤Lip⁡(Γ)+∥Γ′(0)∥, so both r and r−1 have Lipschitz constant at most 1+LT in the sum norm. In particular ∥Pr−1(a,b)−Pr−1(a′,b′)∥≤(1+LT)∥(a,b)−(a′,b′)∥. The restriction of a bijection to U is a bijection onto r(U); its projected inverse point has the same coefficient and zero artificial voltage, hence remains in U∩X0. The extension to all of D=X supplies the global factorization domain required by the original generic hypotheses without changing the physical forward map on any admitted point.

3.2F2F3F4step 1.1step 2.1algebra

The indicator of the convex set in step 1.1 is convex, and adding the quadratic functional makes Rˇ proper and nonnegative. For any admitted a, expansion of its square gives Rˇ(a)−Rˇ(a†)−ξ†(a−a†)=12∥a−a†∥V2; outside the set the subgradient inequality is automatic because the left value is infinite. This proves the asserted subgradient and exact Bregman formula. On a fixed sublevel Rˇ(a)≤R0, the coefficients have bounded intrinsic Hs1 norm; subtracting the constant background and zero-extending gives a uniform whole-space bound by [F3]. Thus [F2] applies uniformly on that sublevel, including the truth. Applying [F3] yields the VSC source term C[log⁡(1/∥Γ(a)−Γ(a†)∥)]−ν for small data differences. Replacing the norm argument by its square changes only a fixed constant in the logarithm. Choose u0 small enough and then enlarge C to dominate the source inequality also at larger data differences; on the fixed sublevel the absolute linear pairing is uniformly bounded. The truncated logarithmic index in [F4] is then a valid concave VSC index on the entire sublevel.

4.1step 2.1step 3.1step 3.2algebra

Concavity and ψ(0)=0 imply subadditivity: for positive a+b, ψ(a)≥aψ(a+b)/(a+b) and ψ(b)≥bψ(a+b)/(a+b); summing gives ψ(a+b)≤ψ(a)+ψ(b). Hence Cψ=1. The derivative remainder of r has only the second component Γ(a1)−Γ(a2)−Γ′(a2)(a1−a2). Integrating the Lipschitz derivative along the segment gives norm at most L∥a1−a2∥V2/2. Since ∥ai∥<R and the first component of r(x1)−r(x2) is a1−a2, this is at most LR∥r(x1)−r(x2)∥Z. No differentiability of the boundary of C is needed, since the map was differentiated on the full ambient space.

5.1step 3.1step 3.2step 4.1givenalgebra

If r^=r(a,z)∈r(U), then ∥r^−r(a†,0)∥Z≤(1+LT)∥a−a†∥V+∥z∥Y. Squaring gives a bound by 4(1+LT)2DRˇξ†(a,a†)+2∥z∥Y2. Because ∥z∥<R, concavity gives ψ(CP∥z∥2)≥(∥z∥2/R2)ψ(CPR2). The displayed C~Q therefore proves the full frozen compatibility inequality for every admitted range variable. Its denominator is positive since the logarithmic index is positive away from zero. The stated RP has the required lower bound with equality and vanishes at zero.

6.1step 3.1step 3.2step 4.1step 5.1givenalgebra∎

All projected coefficients in U have ∥σ0+a∥s1,Ω≤∥σ0∥s1,Ω+R, so one fixed R0 covers their regularizer values and the truth. The preceding steps verify every structural range, domain, Lipschitz, projection, subgradient, VSC, compatibility and penalty hypothesis. With p=2 and Cψ=1, the frozen constants are t=1/4−2ctcr2, v=3ctcr2, λ=1/2 and C‾=4. The bound ctcr<1/20 makes t>0 and q=v/t<1. The displayed choice of cβα is exactly the required linked-parameter bound because 1−b=1/4 and γ−Cψ=γ−1. Thus the original generic estimates apply on the admitted set with their remaining stated algorithmic hypotheses. The ambient extension and indicator do not change any admitted objective value.

TheoremStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)Open item page →

Joint regularization rates under differential range invariance

Statement

Assume the hypotheses of the joint range-invariance VSC estimate lemma, with the same notation, with fixed β>0, and with RP=0 in the joint objective. Define

H(q):=sup⁡s≥0(ψ(s)−qs)(q>0),ρδ:=H ⁣(1C∗α(δ)).

Assume H(1/(C∗α(δ))) and H(1/(2C∗α(δ))) are finite, α(δ)→0, ρδ→0, ηδ≤Cηδp, and there are constants c−,c+,CH>0 independent of δ such that

c−δp≤α(δ)ρδ≤c+δp,H ⁣(12C∗α(δ))≤CHρδ.

Then the approximate joint reconstructions satisfy, as δ↓0,

DRˇξ†(xˇδ,x†)=O(ρδ),Sδ=O(δp),

where Sδ is the scalar residual, coupling, and projection error defined in the joint range-invariance VSC estimate lemma. In particular,

∥K(r^δ−r(x†))∥Y=O(δ),∥Pxδ∥X=O(δ),Q(r(xδ),r^δ)=O(δ),

and the data residual ∥Kr^δ+F(x0)−yδ∥Y is O(δ).

For the following three-dimensional EIT specialization, assume the Axiom of Choice, Countable Choice and Dependent Choice. Let Ω=B1⊂R3, let the known background be 1 outside Bρ⋐B1, and impose fixed bounds 0<m<1<M and m≤σ≤M. Let s1∈(5/2,7/2), use the intrinsic quadratic penalty 12∥σ∥Hs1(Ω)2 on this admissible class, and let σ†∈B2,∞s2(Ω) with s2∈(s1,2s1−1). The Sobolev norm is the exact intrinsic Hilbert norm in the EIT compatibility lemma. Use centered mean-zero Neumann voltage data (Nσ−N1)Yℓk in Y=ℓ2(L2(S2)), its observation-augmentation range coordinates, and admitted approximate joint minimizers in the set U specified by that lemma. The artificial voltage is z∈Y and the projection is P(σ−σ0,z)=(0,z). Put

ν=(s2−s1)(2s1−3)s2−1,Lδ=log⁡(1/δ).

For fixed β>0, noise bounded by δ, and ηδ=O(δ2), the balanced choice α≍δ2Lδν gives

∥σδ−σ†∥Hs1(Ω)=O(Lδ−ν/2),∥zδ∥Y=O(δ),Sδ=O(δ2).

Every pure power α≍δk with 0<k<2 gives the same conductivity norm rate and Sδ=O(δkLδ−ν), hence ∥zδ∥Y=O(δk/2Lδ−ν/2). At the nominal endpoint α≍δ2, the same conductivity norm rate holds under the explicit actual-noise and tolerance condition

2∥yδ−y∥Y2+ηδ=O(δ2Lδ−ν);

then Sδ=O(δ2Lδ−ν) and ∥zδ∥Y=O(δLδ−ν/2). In each case the coupling distance and relaxed data residual have order O(Sδ+∥yδ−y∥Y). In particular the sum of the conductivity norm error and the artificial-voltage norm is O(Lδ−ν/2) in all three stated regimes.

Facts & Assumptions

Given: For the generic assertion, the joint estimate lemma and parameter-index bounds in the first part of the Statement apply. For each EIT regime, use that regime's own stated parameter and noise conditions in the same joint comparison.

[A1]

For the generic assertion, the parameter coupling satisfies c−δp≤αρδ≤c+δp.

[A2]

For the generic assertion, the conjugate values at 1/(C∗α) and 1/(2C∗α) are finite, and H(1/(2C∗α))≤CHρδ.

[F1]

The joint relaxed objective controls Bregman and range errors: Sδ+(1−b)αeδ≤(2+Cη)δp+αψ(C∗Sδ).

[F2]

A conjugate bound for residual and Bregman errors: if c1r+c2αe≤d+αψ(C3r), then e≤c2−1(d/α+H(c1/(C3α))) and r≤2c1−1(d+αH(c1/(2C3α))).

[F3]

Differential range invariance for Banach-space maps: F(x)−F(x0)=Kr(x), so Kr(x†)+F(x0)=F(x†).

[A3]

The EIT clauses use the Axiom of Choice, Countable Choice and Dependent Choice only at their local elliptic, Fourier, trace and Hilbert suppliers; the generic implication above retains its original assumptions.

[F4]

The full collared EIT class satisfies the centered range and source interfaces verifies the centered EIT structural hypotheses with p=2, exact quadratic Bregman distance, b=3/4, and logarithmic source exponent ν displayed in the Statement.

[F5]

Logarithmic source indices and their admissible parameter scales gives H(q)∼C(log⁡q)−ν and the balanced and strict-power scalar comparison rates.

[F6]

A logarithmic endpoint rate with explicit effective noise and tolerance control gives the endpoint scalar rates with the explicitly strengthened actual noise and tolerance term.

Proof

1.1A1F1F2

Apply [F2] to [F1] with c1=1, c2=1−b, C3=C∗, r=Sδ, e=eδ, and d=(2+Cη)δp. The first bound gives eδ≤11−b((2+Cη)δp/α+ρδ). By [A1], δp/α≤ρδ/c−, hence eδ=O(ρδ).

1.2A1A2F1F2

The second bound from [F2] gives Sδ≤2((2+Cη)δp+αH(1/(2C∗α))). By [A1]–[A2], αH(1/(2C∗α))≤CHc+δp, so Sδ=O(δp).

1.3A3F4givenalgebra

For the EIT clauses use [F4]. Every admitted projected coefficient is physical, the ambient indicator leaves the quadratic objective unchanged, and its Bregman error is eδ=12∥σ^δ−σ†∥Hs12, where σ^δ is the first range coordinate translated by σ0. The first-coordinate identity of the range map gives ∥σδ−σ^δ∥Hs1≤Q(r(xδ),r^δ). Thus a Bregman rate and the coupling rate control the actual physical conductivity, without confusing the relaxed coefficient with the admitted output coefficient.

2.1givenF3step 1.2

Each nonnegative summand defining Sδ is bounded by Sδ. Since β is fixed and positive, the definitions of Sδ and p≥1 imply the three displayed O(δ) bounds. Moreover, Kr^δ+F(x0)−yδ=K(r^δ−r(x†))+(F(x†)−yδ) by [F3]; the triangle inequality and the noise bound then give the O(δ) data-residual estimate.

2.2F1F4F5step 1.3algebra

For α≍δk with 0<k<2, apply the strict-power scalar conclusion of [F5] directly to the joint comparison [F1], with d=(2+Cη)δ2. This yields eδ=O(Lδ−ν) and Sδ=O(δkLδ−ν). The fixed positive coupling coefficient and nonnegative summands of Sδ give the claimed artificial-voltage and coupling bounds. Step 1.3 then gives the same physical conductivity norm rate because δk/2Lδ−ν/2=O(Lδ−ν/2).

3.1F4F5step 1.1step 1.2step 2.1step 1.3algebra

By [F5], for α≍δ2Lδν the values H(1/(C∗α)) and H(1/(2C∗α)) are both of order Lδ−ν, their ratio is bounded, and αH(1/(C∗α))≍δ2. Hence the generic theorem assumptions apply and steps 1.1–2.1 give eδ=O(Lδ−ν), Sδ=O(δ2), and the stated residual, coupling and projection bounds. Taking square roots in step 1.3 and using the coupling bound proves the physical conductivity norm rate.

4.1F1F3F4F6step 2.1step 1.3algebra∎

At the endpoint use the actual noise εδ=∥yδ−y∥ in the approximate-minimum comparison, so its scalar right-side constant is d=2εδ2+ηδ. The explicit endpoint assumption and [F6] give eδ=O(Lδ−ν) and Sδ=O(δ2Lδ−ν). Nonnegativity of the projection and coupling terms and step 1.3 give all displayed norm bounds. In each regime the relaxed data residual is at most ∥K(r^δ−r(x†))∥+εδ=O(Sδ+εδ), exactly as in step 2.1. This completes every stated EIT regime with its explicit noise assumptions.

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Frozen Newton rates under differential range invariance

Statement

Assume the structural hypotheses of the frozen Newton one-step error lemma hold with fixed constants and notation. Let the initial pair be (r^0,x0)∈U~×U, where x0 is the basepoint from the range-invariance factorization. At step n≥1, freeze the objective at the previous iterate xn−1, use parameters αn and βn=cβααn, and let (r^n,xn) satisfy the resulting approximate-minimum inequality. Let En and Sn be the error and residual quantities from the one-step lemma for this step, with its input iterate relabeled xn−1 and output iterate relabeled xn. Define E0:=cβαCψ(λ∥r^0−r(x†)∥Zp+t∥r(x0)−r(x†)∥Zp). The parameter sequence (αn)n≥1 and the associated hn,ρn are fixed independently of δ. Write

H(q):=sup⁡s≥0(ψ(s)−qs),hn:=H ⁣(12C‾αn),ρn:=αnhn,q:=vt,

where t,v,C‾ are as in that lemma. Let αn>0 decrease strictly to zero, suppose hn is finite and positive, ρn decreases strictly to zero, and assume for some CH≥1 that

hn≤CHhn+1(n≥1),qCH<1.

Assume each step through N satisfies the one-step lemma, has tolerance ηn≤Cηρn, and the initial error E0 is finite. Assume also that for every sufficiently small δ>0 there is an index N=N(δ)≥2 such that

ρN≥τδp,ρN≤Mτδp,ρn≥τδp(1≤n≤N),

for constants τ,M>0 independent of δ. This bounded crossing condition is satisfied, for example, if N is the last index with ρN≥τδp and ρn/ρn+1≤M for all n.

Then the stopping iterate (r^N,xN) satisfies

EN=O(hN),SN=O(δp),

and therefore

∥r^N−r(x†)∥Zp+∥r(xN)−r(x†)∥Zp=O(hN),

∥K(r^N−r(x†))∥Y=O(δ),∥PxN∥X=O(δ),∥Kr^N+F(x0)−yδ∥Y=O(δ).

If Φ(a):=aH(1/(2C‾a)) has a strictly increasing inverse ϕ on the relevant range, then

hN≤H ⁣(12C‾ ϕ(Mτδp)),

which gives the corresponding source-index rate for the range-variable and nonlinear-range errors.

For the following three-dimensional EIT specialization, assume the Axiom of Choice, Countable Choice and Dependent Choice. Let Ω=B1⊂R3, let the known conductivity be 1 outside Bρ⋐B1, impose fixed positive upper and lower conductivity bounds, use the exact intrinsic quadratic Hs1(Ω) penalty with s1∈(5/2,7/2), and assume σ†∈B2,∞s2(Ω) for s2∈(s1,2s1−1). Use the centered mean-zero Neumann voltage map in Y=ℓ2(L2(S2)) and the explicit observation augmentation, admitted neighborhood U, penalty RP, and linked-parameter constants in the EIT compatibility lemma. Choose the initial conductivity sufficiently close to the truth that its radius condition LR<ctcr<1/20 holds, and choose a fixed initial range variable in r(U), independently of noise. Put

ν=(s2−s1)(2s1−3)s2−1,Lδ=log⁡(1/δ).

Take a fixed geometric parameter sequence, starting sufficiently far down its tail that the conjugate growth condition above holds, set βn=cβααn, and use admitted approximate steps with ηn≤Cηρn. For a positive target wδ, stop at the last index N with ρN≥τwδ. If the actual squared data noise is O(wδ), then

∥σN−σ†∥Hs1(Ω)=O(Lδ−ν/2),∥zN∥Y=O(wδ),∥Kr^N+F(x0)−yδ∥Y=O(wδ)

in each of the following regimes: wδ=δ2 for the balanced choice αN≍δ2Lδν and ordinary noise bounded by δ; wδ=δkLδ−ν for every 0<k<2, giving αN≍δk under the same ordinary noise bound; and wδ=δ2Lδ−ν for the nominal endpoint αN≍δ2, under the explicit stronger actual-noise condition ∥yδ−y∥Y2=O(δ2Lδ−ν). All tolerances remain bounded by Cηρn through the stopping index. Thus the total conductivity and artificial-voltage norm error has logarithmic exponent ν/2 in every stated regime.

Statement references

Facts & Assumptions

Given: For the generic assertion, every n≤N satisfies the stated one-step and stopping hypotheses. For the EIT regimes, use their own stated actual-noise upper bound in those same hypotheses, with fixed structural constants and a fixed parameter sequence.

[A1]

ρn=αnhn≥τδp for 1≤n≤N and ρN≤Mτδp.

[A2]

The tolerances satisfy ηn≤Cηαnhn.

[A3]

hn≤CHhn+1 and qCH<1.

[A4]

E0<∞ is independent of δ.

[F1]

A frozen Newton step yields a controlled error inequality: En≤dn/αn+H(1/(C‾αn)) and Sn≤2(dn+αnhn), where dn=(cβα/Cψ)αnv∥r(xn−1)−r(x†)∥p+(2δp+ηn)/Cψ.

[A5]

The EIT clauses use the Axiom of Choice, Countable Choice and Dependent Choice at their actual elliptic, trace, Fourier and Hilbert suppliers only.

[F2]

The full collared EIT class satisfies the centered range and source interfaces verifies the full EIT frozen structural hypotheses with p=2, Cψ=1, C‾=4, the exact intrinsic Sobolev quadratic penalty, q<1 and source exponent ν.

[F3]

Logarithmic source indices and their admissible parameter scales gives the logarithmic conjugate asymptotic.

[F4]

Geometric parameters satisfy the logarithmic frozen stopping and growth conditions verifies the fixed geometric schedule, growth, bounded crossing and parameter-index asymptotics.

Proof

1.1A1A2F1givenalgebra

Since Sn≥0 and H is nonincreasing in its positive argument, [F1] and the definitions of hn and C‾ give En≤dn/αn+hn. The first term in dn/αn that contains the previous nonlinear-range error is at most qEn−1, because En−1 contains (cβα/Cψ)t∥r(xn−1)−r(x†)∥p and q=v/t. Also, δp/αn≤hn/τ follows from [A1], and ηn/αn≤Cηhn follows from [A2]. Therefore En≤qEn−1+C^hn, where C^=2/(τCψ)+Cη/Cψ+1.

1.2A5F1F2F4givenalgebra

Use [F2] for the EIT clauses. Its exact convex regularizer agrees with the physical quadratic penalty on all admitted coefficients, and its local neighborhood verifies every Taylor, compatibility and projection requirement of [F1]. Since q<1, [F4] supplies a fixed tail of any geometric sequence and a constant CH>1 with qCH<1. The initial pair is fixed and lies in the bounded admitted set, so E0 is finite and independent of noise.

2.1A3A4step 1.1algebra

Iterating the recurrence from step 1.1 gives EN≤qNE0+C^∑j=1NqN−jhj. For 1≤j≤N, repeated use of [A3] yields hj≤CHN−jhN, so the sum is at most hN∑k=0N−1(qCH)k≤hN/(1−qCH). Also h1≤CHN−1hN, so qNE0≤(E0/h1)CH−1(qCH)NhN=O(hN), with constant independent of δ. The same estimate holds with N replaced by any k≤N, so Ek=O(hk) throughout the stopped segment. Thus EN=O(hN).

3.1A1A2A3F1step 2.1algebra

The definition of dN, the inequality (cβα/Cψ)v∥r(xN−1)−r(x†)∥p≤qEN−1, and step 2.1 with k=N−1 show that dN=O(αNhN−1+δp+ηN). By [A3], hN−1≤CHhN; by [A1]–[A2], αNhN=ρN≤Mτδp and ηN≤CηρN=O(δp). Therefore dN=O(δp), and the residual estimate in [F1] gives SN=O(δp).

4.1F1givenstep 2.1step 3.1

Since EN is a positive constant multiple of λ∥r^N−r(x†)∥p+t∥r(xN)−r(x†)∥p, its bound controls both displayed range errors. The definition in [F1] gives CψSN=21−p∥K(r^N−r(x†))∥Yp+∥PxN∥Xp. Each nonnegative summand is at most CψSN, and Cψ is fixed, giving the two O(δ) estimates. Finally, Kr^N+F(x0)−yδ=K(r^N−r(x†))+(F(x†)−yδ), so the data residual is O(δ). If Φ is strictly increasing, ρN=Φ(αN)≤Mτδp gives αN≤ϕ(Mτδp); monotonicity of H then gives the stated index bound for hN.

4.2F2F3F4step 1.1step 2.1step 3.1step 1.2algebra

In each displayed regime, wδ→0 and log⁡(1/wδ)≍Lδ. By [F3]–[F4], the selected last-crossing index satisfies ρN≍wδ, hN≍Lδ−ν, and αN≍wδLδν. This gives exactly the three displayed parameter scales. For 0<k<2, the ordinary squared noise δ2 is O(wδ) because δ2−kLδν→0. The same bound is immediate in the balanced regime and is the explicit additional assumption at the endpoint. Hence a fixed positive multiple of wδ is a valid noise upper bound; with this bound, [F4] supplies the original stopping bracket, and the tolerance condition is already the required one. Applying steps 1.1–3.1 at that upper-bound scale gives EN=O(Lδ−ν) and SN=O(wδ).

5.1F1F2step 4.1step 4.2algebra∎

The first component of r(σN−σ0,zN)−r(σ†−σ0,0) is σN−σ†. Its norm is therefore bounded by the controlled range norm in EN, so p=2 gives the stated conductivity error after taking square roots. The projection norm is exactly ∥zN∥Y, and the residual argument in step 4.1 uses the actual noise upper bound O(wδ), giving both remaining estimates. Since every positive power of δ times a fixed power of its logarithm tends to zero, wδ=O(Lδ−ν/2) in all three regimes, proving the total norm-rate assertion.

RemarkRemark: Literature-sourcedProof: AI-adaptedverified 2026-10-22 (gpt-6.1-sol)Open item page →

Source corrections for three-dimensional EIT rates

Remark

Kaltenbacher's 2024 paper treats the three-dimensional unit ball with background conductivity σbg≡1, quadratic penalty Rˇ(σ)=12∥σ∥Hs1(Ω)2, s1∈(5/2,7/2), and truth σ†∈B2,∞s2(Ω) for s2∈(s1,2s1−1). Its Theorem 10 reports a logarithmic VSC exponent

νpaper:=(s2−s1)(2s1−3)s1−1,

with b=3/4, and Corollary 11 reports a conductivity-norm exponent νpaper/p=νpaper/2 for the joint, split, and frozen-Newton methods. The source fixes p=2 and separately uses α≍δp~ with 0<p~≤2; p~ is not the denominator in its displayed norm exponent. These printed formulas are source assertions; the locally proved EIT conclusions use the corrected VSC exponent and parameter qualifications below.

The cited Weidling Theorem 6.15 at p=2 gives denominator s2−1 in the corresponding logarithmic exponent. In addition, the quadratic Bregman distance is one half the squared Hs1 norm error, so a Bregman rate with logarithmic exponent ν yields a norm exponent ν/2; this quadratic norm conversion agrees with the divisor p=2 in Corollary 11, while the VSC exponent and parameter qualifications still require the separate comparisons below. The sufficient parameter coupling in the abstract rate theorem requires αH(c/α)≍δ2 for a fixed c>0. For a logarithmic index with H(q)≍(log⁡q)−ν, no pure-power choice α≍δp~ with 0<p~≤2 satisfies this two-sided coupling. Kaltenbacher's Remark 1 separately asserts that pure powers still give the same logarithmic rate; that separate assertion needs an independent derivation and is not ruled out solely by failure of the sufficient coupling. The local joint and frozen EIT theorems assume the Axiom of Choice, Countable Choice and Dependent Choice, use the corrected exponent ν=(s2−s1)(2s1−3)/(s2−1), retain the entire stated Sobolev/Besov truth range, and prove the conductivity norm exponent ν/2. They give balanced parameters, every strict pure power 0<p~<2, and the nominal endpoint p~=2 with an explicit stronger actual-noise condition. The universal abstract endpoint assertion under arbitrary deterministic noise bounded only by δ is false, as shown by the exact quadratic counterexample in the endpoint lemma; that example does not claim to be an EIT-specific counterexample.

The local application uses mean-zero Neumann currents and boundary-mean-zero Neumann states. Its data space is the centered sequence (Nσ−N1)Yℓk in ℓ2(L2(S2)), or equivalently the corresponding affine translate for absolute voltages. This centering is necessary: at the background conductivity 1, the uncentered voltage columns have norms 1/ℓ with multiplicity 2ℓ+1, so their squared sum diverges. The centered Neumann-map differentiability, H1 logarithmic stability, exact intrinsic Sobolev/Besov source inequality, augmented factorization and inverse, frozen Taylor estimate, and regularizer/penalty compatibility now have local proofs. The stability proof retains the Fourier modulation in the CGO remainder and integrates its negative-Sobolev norm before absorption; it does not use the invalid unmodulated pointwise inference in the printed Weidling proof.

Remark references

Facts & Assumptions

Given: The source assertions, comparison formulas, and quadratic regularizer are those stated in the Remark.

[F1]

[Kaltenbacher, Theorem 10]: the paper gives exponent νpaper=(s2−s1)(2s1−3)/(s1−1) and cites Weidling Theorem 6.15 with p=2.

[F2]

Weidling, Theorem 6.15: its proof sets s1=3/p+1+τ, s2=s, and μ=2(s2−s1)/(s2−1).

[F3]

Bregman distances for convex regularizers: DRξ†(x,x†)=R(x)−R(x†)−⟨ξ†,x−x†⟩.

[F4]

Joint regularization rates under differential range invariance: the generic joint rate theorem assumes c−δp≤αρδ≤c+δp and H(1/(2C∗α))≤CHρδ, where ρδ=H(1/(C∗α)).

[F5]

Kaltenbacher, Theorem 10 and Corollary 11 (printed p. 24): the stated VSC uses Rˇ(σ)=12∥σ∥Hs12. Corollary 11 fixes p=2 and reports the norm exponent νpaper/p=νpaper/2, separately from the parameter choice α≍δp~, 0<p~≤2.

[F6]

Kaltenbacher, equation (1.32b): the logarithmic index is ψ(t)=C(−log⁡(min⁡{t,t0}))−νpaper for constants C,t0>0.

[A1]

The local EIT applications retain the Axiom of Choice, Countable Choice and Dependent Choice at their actual suppliers; the source algebra in steps 1.1–1.3 needs none of these additional assumptions.

[F7]

The full collared EIT class satisfies the centered range and source interfaces supplies the full EIT source/range/penalty interface with the corrected exponent ν and exact quadratic Bregman distance. Its local stability and function-space source suppliers are fully proved.

[F8]

A logarithmic endpoint rate with explicit effective noise and tolerance control proves the explicit effective-noise endpoint and refutes the universal abstract deterministic-noise endpoint by an exact ℓ2 minimizer.

[F9]

Centered Neumann voltage sequences on the three-dimensional ball proves the centered sequence map and computes the divergent uncentered background norm.

Verification

1.1F1F2algebra

Substituting p=2 into [F2] gives s1=5/2+τ and μ=2(s2−s1)/(s2−1). The logarithmic VSC exponent contributed by the source Sobolev gap is therefore (s2−s1)(2s1−3)/(s2−1). This differs from [F1]'s denominator s1−1 because the allowed range has s2>s1; the cited theorem does not directly yield the exponent printed in Kaltenbacher's Theorem 10.

1.2F3F5algebra

For R(σ)=12∥σ∥Hs12, the Hilbert-space subgradient is ξ†=σ† under the Hs1 inner product. Expanding the square gives DRξ†(σ,σ†)=12∥σ−σ†∥Hs12. Therefore DRξ†=O((log⁡(1/δ))−ν) implies only ∥σ−σ†∥Hs1=O((log⁡(1/δ))−ν/2) by taking square roots; it does not imply exponent ν.

1.3F4F5F6algebra

By [F6], ψ(t)≍(log⁡(1/t))−νpaper for sufficiently small positive t, and ψ is bounded because of the truncation at t0. For q large, if 0<t<q−1/2 then ψ(t)≤C2νpaper(log⁡q)−νpaper; if t≥q−1/2 then ψ(t)−qt≤ψ(t)−q<0. Thus H(q)≤C′(log⁡q)−νpaper. Conversely, choosing tq=q−1(log⁡q)−νpaper−1 gives log⁡(1/tq)∼log⁡q, ψ(tq)≥c′(log⁡q)−νpaper, and qtq=(log⁡q)−νpaper−1, hence H(q)≥c′′(log⁡q)−νpaper for large q. Therefore H(q)≍(log⁡q)−νpaper. For α≍δp~, 0<p~≤2, this gives αH(c/α)≍δp~(log⁡(1/δ))−νpaper. If p~=2, the ratio to δ2 tends to zero; if p~<2, that ratio tends to infinity. Hence no such pure-power choice satisfies the two-sided sufficient coupling in Theorem 3; this does not decide the separate assertion in Remark 1.

2.1givenA1F4F7F8F9step 1.1step 1.2step 1.3∎

The local interface [F7] verifies the full admitted EIT hypotheses, with the corrected VSC exponent from step 1.1 and the exact quadratic norm conversion from step 1.2. The two local rate theorems therefore give their explicit EIT conclusions from proved suppliers. Step 1.3 explains why strict pure powers require their own scalar argument instead of the sufficient two-sided balanced bracket. At the nominal endpoint, [F8] retains a valid rate under the stated actual-noise/tolerance condition and gives a concrete counterexample to the unrestricted abstract endpoint. Finally [F9] establishes the centered observation space and its necessary affine-data interpretation. These are proved source corrections preserving the full truth regularity classes, rather than unsupported imports of the printed formulas.

4 · Examples, counterexamples and false statements

None yet.

Sources