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Banach range invariance and EIT rates
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Absolute Continuity and the Sharp Fundamental Theorem of Calculus
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- Arc Length and Rectifiable Curves
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- The Exponential Function
- The Fundamental Theorems of Calculus
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- The Lebesgue and Riemann Integrals Compared
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- The Logarithm and General Powers
- The Lᵖ Spaces Holder Minkowski and Riesz Fischer
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- Weak Derivatives and Sobolev Spaces
2 · Logical flowchart
3 · Definitions, theorems and proofs
Besov smoothness gives the exact collared Sobolev penalty a cutoff source bound
Statement
Assume Countable Choice. Put , fix , and let and . Use the following precise intrinsic Hilbert norm on . If with integer and , set For integer use just the derivative sum with . Denote the corresponding real inner product by . On use the unitary Fourier transform and . Let have norm , where and for ; the restriction space on consists of restrictions of members of this whole-space Besov space.
Let be real and equal to almost everywhere on . Then there is , depending on , such that for every real also equal to there and every integer , Consequently, suppose on a specified admissible sublevel a forward map satisfies with and . For the exact regularizer , its exact Bregman distance is , and In particular, an stability modulus of order yields a VSC source term of order with . The conditional stability estimate is a hypothesis here, not a conclusion.
Statement references
Facts & Assumptions
Given: Countable Choice and the intrinsic penalty, exponents, collar and truth in the Statement.
Plancherel supplies the unitary transform. Its extension to tempered distributions, agreement with the 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
Smooth dyadic Fourier blocks characterize the Bessel Hilbert Sobolev norms. Littlewood-Paley characterisation of the Hilbert-Sobolev spaces
Young's convolution inequality in the case bounds convolution by an kernel on . Young's convolution inequality under Countable Choice
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
Countable Choice is used at the Fourier and Hilbert-Sobolev interfaces. The Axiom of Countable Choice ()
Proof
Use the angular-frequency convention . The -frequency results in [F1] transfer by , with the same change of variables on distributional tests; hence 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 . For , has Fourier kernel . If , Young's convolution inequality [F3] bounds its operator norm by a constant times . If , then on these supports. Repeated integration by parts in the Fourier integral of gives . Cauchy–Schwarz in the two frequency variables therefore bounds the operator norm by . For any real , multiply these bounds by and sum over . Choosing makes both off-diagonal geometric sums uniformly bounded in , while the near-diagonal sum has at most five terms. Hence . 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.
The sharp annular definition agrees, up to constants, with any usual smooth inhomogeneous dyadic definition at . Indeed a sharp annulus meets at most a fixed number of the smooth supports at neighboring scales. On that annulus, the identity and Cauchy–Schwarz give its norm at most the sum of those finitely many smooth-block norms; conversely each smooth symbol has magnitude at most one and support in finitely many sharp annuli. The weights 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 , consistently with [F2].
Choose equal to one near , and choose equal to one near . Let be one whole-space extension, whose existence is part of the restriction-space hypothesis. The compactly supported distribution is in by step 1.1 (the constant term multiplied by is smooth). It equals on and vanishes outside , since the conductivity equals one in the collar. Thus is a compactly supported extension of . In particular : the squared block sum is bounded by a constant times .
For , let be its zero extension. Its support lies in . Zero extension creates no boundary derivatives because vanishes on an open collar: local distributional tests near see zero, and interior tests give exactly the zero extensions of the weak derivatives. For noninteger, the whole-space top-order difference integral equals the integral over plus . Where can be nonzero, the inner integral is bounded by . The whole-space difference norm is therefore bounded by , and the integer case is immediate. The translation identity follows by substitution for Schwartz functions and then by density and unitarity for every . Thus [F1] and Tonelli [F4] give . The inner integral equals by rotation and dilation; follows by the bounds . Summing derivative multipliers shows that this whole-space difference norm is equivalent to . Thus , while exactly .
Let be the whole-space intrinsic bilinear form associated with the derivative and difference norm in step 3.2. Its Fourier multiplier equals in the noninteger case, and just the first polynomial in the integer case. Thus , and belongs to by the annular multiplier bound and step 3.1. Its distributional pairing with is . The pairing is defined by convergent Fourier blocks: high blocks have the product bound , a summable sequence. This also justifies the bilinear identity by truncation and limits, without assuming classical derivatives of order .
In the integer case the whole-space and domain pairings are identical, since every derivative of is supported in ; take . For noninteger , their difference is where and . To obtain the formula, split each whole-space double integral into , and its transpose; is zero outside and there. On any fixed neighborhood of compact in , every derivative of is bounded, by differentiating the separated kernel and using its integrable tail. Choose equal to one on that neighborhood. The domain pairing is therefore the pairing of with By step 1.1, its correction terms lie in : differentiation lowers the block regularity by the derivative order, multiplication by the smooth compact coefficient preserves it, and is smooth and compactly supported. Since , they also lie in . Hence in every case, and . This treats the exact domain penalty pairing rather than replacing its norm by an equivalent one.
Put and . Let . By step 5.1 and the Hilbert block norm formula, . Similarly . Weighted Fourier Cauchy–Schwarz and step 3.2 now give , the first claimed estimate.
Expansion of the quadratic form gives exactly . Set . The conditional stability hypothesis and applied to the high-frequency term in step 6.1 yield . If , stability gives . For , take the integer with . Then and . Since , this proves the asserted source inequality with coefficient on the exact Bregman distance. Substitution of the stated logarithmic stability exponent gives by multiplying exponents.
Positive-Sobolev CGO remainders from a shifted Fourier lattice
Statement
Assume Countable Choice. Fix , and . If has support in and norm at most , there are constants , depending only on , such that for every with and , there is for which 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.
Statement references
Facts & Assumptions
Given: The choice assumption, exponents, support, potential and null phase in the Statement.
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
Whole-space Fourier transformation is unitary on . Plancherel theorem
Countable Choice is assumed at [F1]–[F2]. The Axiom of Countable Choice ()
Proof
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 for the corresponding blocks. A shell contains at most lattice points, so Cauchy–Schwarz gives . For and , these estimates prove . For low against high , use and square-sum . For high against low , when use , and when use the uniform bound by ; the critical case has an extra factor , absorbed by . The resulting weighted terms are bounded by or by . For comparable high input shells that produce an output shell , use , obtained from the bound on the product and the number of output frequencies. Multiplication by and summation over gives a summable geometric kernel, since and ; at this case is not being used. Cauchy–Schwarz for the two input shell sequences and the geometric kernel completes the bound.
Rotate so that , where , and choose a fixed cube containing . Put and use the shifted orthonormal basis . For , , its symbol is , with . For , this gives ; for , its real part has magnitude at least . Hence for every real and the coefficient multiplier satisfies for . The Sobolev spaces here are defined by the shifted weighted coefficient norms. Parseval proves these bounds and in distributions inside , first on finite sums and then by convergence.
A second bound is for every . Here the low-high and high-low estimates from step 1.1, with output weight , have the additional decay . For comparable high shells, the output estimate becomes , with and . The geometric kernel has finite sum, and Cauchy–Schwarz bounds the product sequence in by . 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 , , 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.
The periodic and shifted Sobolev norms of the compactly supported potential are bounded by . For completeness, choose a fixed smooth cutoff equal to one on and supported in . Since it multiplies without changing it, each Fourier coefficient of 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 and the corresponding integrals are finite for , and polynomial Sobolev weights are absorbed by increasing . The reverse local bound needed later follows in the same way by multiplying the Fourier series by a smooth cutoff supported inside and equal to one on , then estimating the resulting convolution sum in whole-space weighted . The cutoffs can be chosen radial before rotation, so all constants are independent of the phase direction.
Put and . Split the periodic potential into by retaining frequencies of size at most in . Then , whereas has bounded Sobolev norms of every order with bounds depending only on . Choose , depending only on , so that as a multiplication-composition operator has norm at most both on shifted and on shifted . On this follows from the product bound in step 1.1, with multiplier and , and the bound of step 1.2. On it follows from step 2.1 with and the same bound. Next choose large enough, depending only on , that has norm at most on both spaces, using the smooth multiplier bound from step 1.1 and the bound from step 1.2. Thus has inverse given by a convergent geometric series, of norm at most two, on both spaces.
Define in shifted . The forcing in the last position is the actual compactly supported function on , represented in the shifted basis; the multiplication operators use the periodic representation of the same function. These agree pointwise inside . Step 1.2 with and gives . Since , weighted Cauchy–Schwarz for its coefficients gives . This solution is also the unique shifted solution, by the second invertibility statement in step 3.1; using step 1.2 with gives . Finally, its equation is ; since and , the bound from gives , enlarging the threshold to keep .
Applying to the equation in step 4.1 gives in the interior of . Direct conjugation and show this is exactly . The local comparison in step 2.2 transfers the fractional bounds to restriction norms on , and restriction gives the integer bound there. Pulling back the initial rotation preserves the whole-space potential norm, the support ball, and the Euclidean PDE. Since , 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.
Sharp weighted absorption of modulated CGO remainders
Statement
Assume Countable Choice. Write and use unitary Fourier transformation on . There is an absolute constant such that for every and , Consequently, if , and is any family with for , its modulated bilinear distributional remainders satisfy whenever the scalar family is measurable. No continuity or derivative bound in is required.
Statement references
Facts & Assumptions
Given: Countable Choice and the whole-space Sobolev norms, radius, potential difference, remainder bounds in the Statement.
Countable Choice is assumed for the Fourier isometry in [F1]. The Axiom of Countable Choice ()
Under [A1], the unitary Fourier transform is an isometry on and defines the Fourier Sobolev norms by . Plancherel theorem
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
The positive-Sobolev CGO construction supplies remainders with norm and uniformly bounded norm for the phases used in the application. Positive-Sobolev CGO remainders from a shifted Fourier lattice
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
Define . For , throughout the integration ball, so . Polar integration gives for . Hence in this region.
If , Cauchy–Schwarz gives . The first integral is at most by polar integration. In the second, the substitution places its domain inside , so it is also at most . Thus . Since , this is at most . Together with step 1.1 this proves for every .
Fourier translation gives . 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 . Tonelli [F2] and step 2.1 now give .
For the bilinear duality pairing, weighted Fourier Cauchy–Schwarz gives . The absence of complex conjugation changes the second Fourier factor to its reflection and does not change its Sobolev norm. Squaring, integrating with weight , using the bound , and applying step 3.1 prove the remainder estimate.
Application to the conductivity remainder
For , set . Translation, Plancherel and Tonelli [F1]–[F2] give
where rotation and dilation give the inner integral, and follows from . The identity holds with values in , since Plancherel applies to each translate difference and Tonelli to its nonnegative squared integrand. Thus exactly when , and this quantity is equivalent to . Cutoff remainders are whole-space functions by [F4], so this identity applies directly to them. The identity therefore yields
for bounded cutoff remainders. For the exact CGO identity, take , with a fixed smooth cutoff equal to one on the support of . For phases with , [F3] gives and uniformly bounded norms. Insert another cutoff equal to one on the support of to apply the product estimate to . Consequently , uniformly in . 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 , 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 , and uses the integrated kernel estimate in steps 1.1–3.1. No independent proof review or native workflow acceptance is asserted.
Boundary trace quotient and chart norm equivalence
Statement
Assume the Axiom of Choice and Countable Choice. Let and let be a nonempty bounded connected open set whose boundary is covered by finitely many graph charts. Give its Sobolev norm, let be the -closure of , and let have the finite-chart Slobodeckij norm. Define with its quotient norm. The trace induces a bounded linear bijection 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 , with equivalent quotient norms. A continuous linear functional on descends to the constant quotient exactly when it annihilates constants, and its descended functional has the quotient dual norm.
Statement references
Facts & Assumptions
Given: The Axiom of Choice and Countable Choice; the bounded domain; the trace operator ; and the quotient spaces in the statement.
The trace is bounded from into and is onto. (The sharp trace theorem: boundedness and range in the fractional space)
The kernel of the trace is exactly , 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)
There is a bounded right inverse with . (A bounded right inverse of the trace, supported in a prescribed collar)
The trace-page boundary norm is a finite-chart Slobodeckij norm on almost-everywhere boundary classes. (The fractional Sobolev space on a compact boundary)
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 ())
Proof
Given: The data in the statement.
If , then [F2] gives , so is well-defined on . By [F1], for every representative ; taking the infimum over its coset gives . If , [F2] gives , hence ; thus the induced map is injective.
By [F1], every is for some , so is onto. More quantitatively, choose the lift from [F3]. Then and , proving that the inverse is bounded.
The trace of a constant function is that same constant, so maps the closed constant subspace onto the constant subspace of . 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.
If is a continuous linear functional on that annihilates constants, then is independent of the representative because for every constant . By the quotient-norm definition, , so is continuous with quotient dual norm at most . Conversely, any functional that factors as vanishes on constants because ; and because the quotient map has norm at most one. Hence , while the first inequality gives for the induced functional. This proves the equivalence and equality of dual norms.
Centered Neumann voltage sequences on the three-dimensional ball
Statement
Assume the Axiom of Choice, Countable Choice and Dependent Choice. Let , fix and , and let be the closed real subspace of consisting of functions vanishing almost everywhere outside . Set and . Choose an orthonormal spherical-harmonic basis of mean-zero , with and . For each basis current there is a unique weak solution with boundary mean zero such that The centered sequence belongs to . The map is Fréchet differentiable, and both and are Lipschitz on , with constants depending only on and the ball. Its derivative in direction is the trace sequence of the unique boundary-mean-zero solutions The uncentered sequence does not belong to . Thus absolute voltage data in this convention lie in an affine translate of , whereas data differences belong to . All complex spaces here may be regarded as real Banach spaces when taking derivatives with respect to real conductivities.
Statement references
Facts & Assumptions
Given: The choices, fixed collar radius, uniformly elliptic conductivities and mean-zero current basis in the Statement.
The degree- harmonic space has dimension , and these spaces form a complete orthogonal expansion on . Spherical harmonics form a complete basis with the addition formula
The ball is an extension domain, satisfies the mean-zero Poincaré inequality, and has a bounded trace . 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
Bounded coercive forms have unique solutions for bounded conjugate-linear functionals. The Lax--Milgram theorem
The divergence theorem on a bounded domain gives Green's identity for smooth functions; the complex form follows by real and imaginary parts. Divergence on a bounded C1 Euclidean domain
Restrictions of ambient smooth functions are dense in , so identities proved for smooth tests pass to all tests. Ambient smooth restrictions are dense on bounded C^k domains
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 (), The axiom of dependent choice: a relation in which every element is related to something admits an -indexed chain
Proof
Let . This is closed by [F2]. For , write for its volume mean. Poincaré gives , and the trace bound and boundary-mean condition give . Thus on . The form is bounded and coercive there, uniformly for . By [F3], any functional with has a unique solution in , of norm at most .
The background solution is . It is a harmonic polynomial with normal derivative 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 test. The same integration by parts on gives . In particular , since .
Apply step 1.1 with , supported in , to obtain satisfying . The sum solves the required Neumann equation, first for and then for all 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 . The trace bound proves . Moreover , uniformly over , since .
For , subtract the weak equations to obtain . By step 1.1 and the summed interior bound in step 3.1, the sequence of differences has norm at most . For , the same estimate constructs the asserted derivative sequence with norm at most . Subtracting its equation from the difference equation for shows that the remainder has forcing and hence norm at most . Taking traces proves Fréchet differentiability. Finally, subtract the derivative equations at : . The established estimates bound its sequence norm by , proving the operator-norm Lipschitz derivative bound and the asserted Lipschitz bound for .
By step 2.1, . Its squared sequence norm is , which diverges since each summand is at least . Thus the absolute background voltage sequence is not in , while every centered difference is in by step 3.1. This establishes the affine-data distinction without changing the set of measured currents.
Logarithmic source indices and their admissible parameter scales
Statement
Fix and . Define , for , and for . Then is continuous, nondecreasing and concave, and For each fixed , one consequently has . For , fixed and nonnegative errors satisfying the balanced choice gives For a pure-power choice with , the same error rate follows, with residual bound . No assertion for is made. If , the corresponding norm rate has exponent .
Statement references
Facts & Assumptions
Given: The constants, index, scalar inequality and parameter choices in the Statement.
A conjugate bound for residual and Bregman errors: the scalar inequality implies and .
Proof
On write . Direct differentiation gives and . At 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 ; taking limits at zero extends the chord inequalities there. Continuity and monotonicity are immediate from the formula. In particular is finite because , and by testing .
Put and take sufficiently large. For , one has , so . For , one has , so the expression is negative. This bounds above by . Testing gives , proving the matching lower bound. Replacing by changes by the fixed constant , hence .
For the balanced scale, , so step 2.1 gives both conjugate values in [F1] of order . Moreover has this same order, and . Substitution in [F1] proves the balanced error and residual bounds.
If with , step 2.1 again gives the two conjugate values of order . Since (put and compare the exponential with its power-series term of integer degree greater than ), is bounded by the desired logarithmic order. Also . Apply [F1] for the two asserted bounds. Finally, gives the norm conclusion by taking square roots.
Collared Sobolev conductivities have regular Liouville potentials and H1 recovery
Statement
Assume Countable Choice. Fix , , and . Suppose on , where is real, supported in , , and , for . Then and belong to , with supported in and uniformly bounded in and . The real Liouville potentials belong to , have support in , and have norms bounded in terms of . Moreover, with the same dependence. In particular the right side may be replaced by when that stronger norm is used. The Fourier and intrinsic norms for collared variations are those of the collared Sobolev source-functional lemma.
Statement references
Facts & Assumptions
Given: The choice assumption and collared positive conductivities, Sobolev exponent, and uniform norm bounds in the Statement.
Whole-space Fourier transformation is unitary on . 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: , so . Weighted Sobolev pairings then follow by Fourier Cauchy–Schwarz.
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.
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
A nonnegative unit-mass smooth bump generates a compactly supported approximate identity. The mollifier family generated by a unit-mass smooth bump, An approximate identity on
Mollification commutes with weak derivatives on the whole space, and approximate identities converge in finite . Interior mollification commutes with weak derivatives, Every approximate identity converges to the identity in for
Tonelli's theorem interchanges the nonnegative measurable fractional-difference integrals below. Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
Countable Choice is used at the Fourier interfaces. The Axiom of Countable Choice ()
Proof
Weighted Fourier Cauchy–Schwarz gives because for . The Fourier integrals for and its first derivatives converge absolutely and are continuous by dominated convergence, so these bounds hold for a representative. We shall also use when and . Here is a direct justification: split Fourier space into dyadic annuli, use for low-high products, and when comparable high shells produce a shell . The low-high sum is bounded by since . For high-low products, sum the low blocks with bound if , a square-root logarithmic factor if , or if ; the excess makes the resulting geometric sum converge. For high-high products, weighted summation over has geometric kernel and excess factor ; 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.
For and , Plancherel and [F6] give . The translation formula follows by substitution on Schwartz functions and then density. Rotation and dilation make the inner integral , where because . Adding proves equivalence with the Fourier norm. Applying this identity to the weak derivatives of order and using [F1] proves the analogous whole-space intrinsic/Fourier comparison at order ; at integer orders use the derivative rule alone. For , multiplication by a bounded function is bounded on . Indeed . In the last term use ; integrating the kernel near and far from the diagonal gives a finite constant times . Apply the identity just proved to get the Fourier Sobolev bound. For this is just the multiplier bound.
If is any smooth scalar function on a neighborhood of , then belongs to with a uniform bound over this class. To verify the fractional highest derivative directly, write with or and . For , the chain rule gives terms and . The first term is in by step 2.1, since is bounded uniformly; for the second, with , so the algebra bound in step 1.1 and then step 2.1 apply. For , the terms are , , and . Here is a multiplier on by step 1.1, so ; the remaining products follow from the same algebra and step 2.1. Lower derivatives have the corresponding easier estimates; the function itself has an bound by the mean value theorem and vanishes wherever . These calculations may first be made for smooth approximations: convolve with a nonnegative mollifier, which preserves and uniform and bounds, and then use convergence in lower Sobolev orders and weak compactness in the Hilbert 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.
Apply step 3.1 to , , and to obtain the stated regularity and support of , , and . To bound in , for use step 2.1 with and the uniform norm of ; for use the multiplier bound in step 1.1 with and . Split for that whole-space bound. The support statement follows from outside the collar ball and from the local definition of weak derivatives.
Put , and . Since , distributional differentiation gives . 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 are and . The functions and its gradient vanish outside . By [F3], extend by zero to . Choose a nonnegative unit-mass smooth mollifier from [F4]. For small , its convolution with the zero extension is supported in ; by [F5], the mollifications converge to in . Thus the distributional identity may be tested against : and multiplication by the bounded function is bounded on . It gives .
A compactly supported function in obeys : for a smooth approximation supported in the same larger cube, write , apply Cauchy–Schwarz, and integrate in ; passing to limits gives the inequality. Since , step 5.1 and the Fourier dual bound yield . Poincaré in the first sentence then implies , dividing by its norm if nonzero. Finally ; the uniform bounds for , and , the mean value theorem, and the ordinary weak product rule give . Also by the Fourier weights. This proves all recovery claims.
A logarithmic endpoint rate with explicit effective noise and tolerance control
Statement
Fix and let be a continuous nondecreasing concave source index with . Suppose its conjugate satisfies for all sufficiently large . Let be a nominal parameter, and put . Suppose nonnegative errors satisfy Then For the joint objective estimate with and actual data noise , the hypothesis on is supplied by . This condition explicitly strengthens the usual nominal bound , at the endpoint. If , the norm rate is .
The universal assertion that yields a vanishing norm error for every deterministic noise family bounded by is false, even for an exact quadratic Tikhonov minimizer and a truth satisfying every nonnegative variational source inequality: on real , take , , , , , and objective . Its unique minimizer is . This counterexample refutes the universal abstract endpoint assertion; it makes no assertion that these data arise from an EIT conductivity map.
Statement references
Facts & Assumptions
Given: The scalar comparison, conjugate bound, nominal endpoint parameter, and effective noise hypothesis in the Statement.
Proof
Put . The scalar inequality remains true with on its right, and . Since , one has for either fixed or . Thus both conjugate terms in [F1] are , while . Substitution into [F1] proves both error estimates. If the joint approximate-minimum comparison is made with actual noise and , its comparison term is ; 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.
For the asserted counterexample, complete squares coordinatewise in . Every coordinate has unique minimizer zero. The th coordinate has unique minimizer . All quadratic coefficients are positive and the penalty is strictly positive definite, so this point is the unique global minimizer on . Its norm error is for every . 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 , 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.
Centered NtD data control the exact CGO identity with its modulation factor
Statement
Assume the Axiom of Choice, Countable Choice and Dependent Choice. Let , and let be positive real conductivities with supported in , , , , and , for fixed positive bounds. Put , , , and . Let be the boundary-mean-zero Neumann-to-Dirichlet map for on , and define the actual data difference by the centered voltage sequences This quantity is finite. If are the solutions from the positive-Sobolev CGO lemma, then and the fluxes have mean zero. The boundary pairing is bilinear, without complex conjugation. In particular, there are depending only on the fixed class such that for and one can choose null phases with , , and obtain the rigorous estimate The factor is part of this conclusion; this lemma does not assert the sharper stability estimate that would follow from dropping it.
Statement references
Facts & Assumptions
Given: The choice assumptions, conductivity class, potentials, data norm and CGO solutions in the Statement.
The centered voltage sequences are square summable, and the boundary-mean-zero variational Neumann solutions obey uniform energy estimates. Centered Neumann voltage sequences on the three-dimensional ball
The potentials belong uniformly to and have compact support in ; the conductivities and their square roots and inverses have uniformly bounded norms. Collared Sobolev conductivities have regular Liouville potentials and H1 recovery
The CGO remainders have norm , uniformly bounded norm, and norm on . Positive-Sobolev CGO remainders from a shifted Fourier lattice
The boundary trace is bounded from into . Boundary trace quotient and chart norm equivalence
The divergence theorem on a bounded 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
Ambient smooth restrictions are dense in , so Green identities for smooth approximants pass to the CGO solutions using the trace bound [F4]. Ambient smooth restrictions are dense on bounded C^k domains
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
Compactly supported Sobolev classes extend by zero with their weak derivatives unchanged. Compactly supported Sobolev functions extend by zero in every integer order
All three choice assumptions are retained from [F1]–[F8]. The Axiom of Choice, The Axiom of Countable Choice (), The axiom of dependent choice: a relation in which every element is related to something admits an -indexed chain
Proof
The energy argument in [F1] applies to every mean-zero : the functional is bounded on the boundary-mean-zero subspace by [F4], and its uniformly coercive conductivity form has a unique solution with norm at most . This defines a bounded map . Its centered sequence columns are those in [F1], so their difference has finite squared sum. For a finite current expansion , Cauchy–Schwarz gives . Density and boundedness extend this to every . Finally the bilinear symmetry follows by testing the two weak equations against one another, using real coefficients and complex bilinear extension.
Put . The weak Liouville identity gives : expand , whose divergence is . Products are legitimate by [F2]–[F3]; one may also verify the equality first on smooth approximations and pass in distributions. Since on a full collar of , the boundary normal flux of is exactly . It belongs to because 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 CGO solutions on . On the collar is harmonic and the coefficient is identically one. Taking the test function one shows . Thus for a constant .
Subtract the two bilinear Schrödinger Green identities [F5]–[F6], testing the equation against and the equation against . Their gradient terms cancel, giving . Since on the boundary, step 2.1 identifies these traces with . 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 and ; boundedness follows, for example, by weighted Fourier Cauchy–Schwarz after multiplication by an interior cutoff.
If , [F3] and differentiation of the exponential give for . The trace bound on first derivatives then gives , increasing to absorb the polynomial. The operator-norm bound in step 1.1 and Cauchy–Schwarz in the bilinear boundary pairing yield .
For smooth compactly supported put . Translation, Plancherel and Tonelli [F7] give where rotation and dilation give the inner integral, and follows from . Thus . Restrict each remainder to and use [F6] to approximate it there by ambient smooth functions; multiplication by a cutoff supported in and [F8] extend the products to , so the norm comparison extends to these cutoff remainders. For bounded compactly supported factors the identity then yields . For , choose real orthonormal perpendicular to and set , ; for choose any orthonormal pair. Direct expansion gives , and . Hence . Choose cutoffs with on and on . Applying the product estimate to and , and [F3], gives . Fourier translation and then give .
Extract the Fourier coefficient of from the exact product identity in step 5.1. Its remainder is precisely the signed pairing of with , not with the unmodulated remainder. Weighted Fourier Cauchy–Schwarz bounds its absolute value by . Combine this with step 4.1 and absorb the fixed unitary Fourier factor into . This proves the stated estimate with its modulation factor retained.
Geometric parameters satisfy the logarithmic frozen stopping and growth conditions
Statement
Let , and . Suppose is the conjugate of a truncated logarithmic source index with as . Starting sufficiently far down the geometric sequence, relabel it as , , and put and . Then one can choose and , fixed independently of noise, such that The positive sequence decreases strictly to zero. For any positive target , let be the last index with . For sufficiently small this index exists and is at least two, and In particular gives and . The nominal endpoint target gives the same and . In that endpoint case, an actual noise bound and tolerances give exactly the noise/tolerance scale required for applying a frozen recursion at this stopping index.
Statement references
Facts & Assumptions
Given: The source index, fixed geometric sequence, contraction number and target in the Statement.
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
Positivity follows directly by testing the supremum at a sufficiently small : the logarithmic index has , so for any fixed . Finiteness and monotonicity follow from boundedness of and from its defining supremum. By [F1], , so . Fix any and choose large enough that all ratios in the relabeled tail are at most . Since decreases strictly and is positive and nonincreasing, decreases strictly; its asymptotic is a positive constant times , hence it tends to zero and its consecutive ratio is at most .
For sufficiently small , , and strict decrease to zero gives a unique last index . It satisfies , so . In particular as . Taking logarithms in the geometric asymptotic gives . Since , it follows that . Substituting in yields , and gives the asserted parameter scale.
For , , giving the first specialization. For , , and the same formulas give and . If , use the positive noise upper bound , increasing if necessary. The stopping bracket then has fixed constants and , while every earlier and the stated tolerance bound is already . Thus all parameter/noise/tolerance conditions hold at that upper-bound scale.
Differential range invariance for Banach-space maps
Definition
Let , , and be real Banach spaces, let be nonempty, let , and let be a map. A differential range-invariance factorization at on consists of a bounded linear map and a map such that
Thus every data increment lies in the range of the fixed operator . The factorization alone does not assert that is injective, that is unique, or that a tangential-cone inequality holds. If is open and and are Fréchet differentiable, differentiating this identity gives at every ; no converse is asserted without further hypotheses.
H1 logarithmic stability from centered three-dimensional NtD data
Statement
Assume the Axiom of Choice, Countable Choice and Dependent Choice. Fix , , and . Let be real conductivities on , with supported in , , and , for . Let be their boundary-mean-zero NtD maps on , and let Then there is depending only on such that If , the conductivities are equal. The zero-noise modulus is therefore defined to be zero. Restricting the left side to gives the same norm because the difference is supported in .
Statement references
Facts & Assumptions
Given: The choice assumptions and fixed collared Sobolev conductivity class in the Statement.
With and , one has , and . Collared Sobolev conductivities have regular Liouville potentials and H1 recovery
The exact centered NtD/CGO identity has boundary term at most . For each , its remaining Fourier term is the bilinear pairing of with a cutoff remainder of norm at most , uniformly in phase direction. Centered NtD data control the exact CGO identity with its modulation factor
For every , . Sharp weighted absorption of modulated CGO remainders
All choice assumptions are retained from the actual suppliers [F1]–[F3]. The Axiom of Choice, The Axiom of Countable Choice (), The axiom of dependent choice: a relation in which every element is related to something admits an -indexed chain
Proof
Put and . For , the high-frequency contribution satisfies . Indeed the ratio of the two Fourier weights is .
For , [F2] gives the pointwise inequality for . 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 , multiply by and integrate. Since , [F3] yields a low-frequency bound , after increasing the exponential constant. Combining with step 1.1 gives . This uses the integrated modulated estimate, not an unmodulated pointwise Sobolev pairing.
Choose a fixed large enough that for the last constant in step 2.1, and set . Absorb that term into the left side and take square roots. Increasing fixed constants and using for gives for some depending only on the fixed class. If , let to obtain . If , take . Then , which is bounded by a constant times because is bounded for . Hence for all sufficiently small positive .
By [F1] and the Fourier weight inequality, . This proves the claimed small-noise rate and, if , equality of the conductivities. For larger positive , the uniform bound and the positive lower bound for on that region prove the same displayed estimate after increasing . On a sufficiently small noise interval, is comparable to , so the two small-noise formulations agree. All constants came from the fixed class.
Joint and frozen-Newton objectives for range-invariant relaxations
Definition
Let be real Banach spaces, let be a closed linear subspace of , and let contain and a nonempty set . Fix , a map , a bounded linear map , and a map such that for every . Assume that is a bijection from onto a set , and that for every admitted below. Here is the identity on and is a bounded linear projection with , , and . Let be a proper convex regularizer, let be a nonnegative penalty, and let satisfy and exactly when . For , set
For , data , and parameters , the joint relaxed objective is
The joint source-condition objective takes . The term ties the relaxed range variable to the physical variable, while penalizes the component outside .
If is Gâteaux differentiable at a current iterate , the frozen-Newton objective with parameters is
Here is the bounded linear Gâteaux derivative used to form the affine range proxy; no minimizer existence or uniqueness is part of either definition.
The joint relaxed objective controls Bregman and range errors
Statement
Let . Let be real Banach spaces, a closed linear subspace, a domain containing and , and . Let , , and satisfy on , with bijective and Lipschitz with constant on . Let be a bounded linear projection such that , , and for an exact solution ; put . Assume the projected points belong to for all admitted .
Let be proper and convex, choose , and suppose that for some , , and continuous nondecreasing concave with , the variational source inequality
holds whenever and , where . Set in the joint objective. Let satisfy and exactly when , and assume there is a constant such that
For , let satisfy . Let , , and let be an -minimizer of the joint objective, meaning
Assume lies in the stated VSC sublevel. Define
and
Then
Statement references
Facts & Assumptions
Given: The Banach spaces, range factorization, projection, regularizer, VSC, compatibility inequality, exact datum, noise bound, and approximate minimizer in the Statement.
The candidate projected point belongs to and satisfies , so the VSC applies to it.
The noisy datum and approximate-minimizer tolerance satisfy the bounds in the Statement.
The projection-coupling bound holds for all range variables in the Statement.
Differential range invariance for Banach-space maps: for every in the range-factorization domain.
Joint and frozen-Newton objectives for range-invariant relaxations: with , the joint objective is .
Banach-space convex subgradients and dual pairings: means for every .
Variational source conditions on a regularizer sublevel: on the named regularizer sublevel, .
Proof
Compare the approximate-minimum inequality with . By [F1], , while and . Since and , the comparison regularizer is , so its objective is at most . Writing the estimated data residual as , the triangle inequality gives . For the power inequality is equality; for , has for and is continuous at zero, so it is convex on ; midpoint convexity gives for . Rearranging yields . Combining this with the comparison and gives .
By [F3], is a subgradient at , and [F4] gives . The VSC [F5] therefore implies . Substituting this lower bound in step 1.1 yields .
Put , so . Since , assumption [A3] and the triangle inequality give . Lipschitz continuity of gives . Applying [F1] at and , then applying twice, yields . Monotonicity of gives .
Substitute the last bound from step 2.2 into the inequality from step 2.1. This gives , as claimed.
A frozen Newton step yields a controlled error inequality
Statement
Let . Let be real Banach spaces, let be a closed linear subspace of , and let . Let , let , , and satisfy for all . Assume is a bijection, is Lipschitz on with constant , and has bounded linear Gâteaux derivatives on . Let be a bounded linear projection with , , and . Assume and for each admitted .
Let be proper and convex, choose , and write
Suppose that, on the stated sublevel , the variational source inequality
holds for some and . Assume that is continuous, nondecreasing and concave, with , and that for a constant ,
Let , and assume the values of used below are finite. Let , , and . Let satisfy and
For each admitted , put and . Assume the compatibility bound
Choose with , and set
Assume and , and for all assume
Assume the candidate projected point satisfies .
Let . Fix , with , and , with . Suppose satisfies, for some ,
where is the frozen objective in the Joint and frozen-Newton objectives definition, with regularizer and distance .
Define
and
Then
Consequently,
Statement references
Facts & Assumptions
Given: All spaces, maps, penalties, parameters, source inequalities, compatibility inequalities, and the approximate frozen step satisfy the hypotheses in the Statement.
The projected point belongs to the regularizer sublevel where the VSC holds.
Differential range invariance for Banach-space maps: on the specified domain, and is invertible onto .
Joint and frozen-Newton objectives for range-invariant relaxations: the frozen objective has data term , regularizer , coupling , and penalty , where .
Banach-space convex subgradients and dual pairings: means for every .
Variational source conditions on a regularizer sublevel: on the stated sublevel.
A conjugate bound for residual and Bregman errors: if , then and .
Proof
Write , , , and . Compare the approximate-minimum inequality with . The competitor has data residual at most , penalty , and . If , then ; convexity of gives . Thus the comparison gives . The remainder hypothesis at and imply .
Let , , and . The triangle inequality gives , hence . Applying the same power inequality to gives . The Taylor hypothesis and another triangle-power estimate give . Therefore , with and .
Set . Since , the factorization and power inequality give . By the VSC [F5], the Bregman identity [F4], the penalty lower bound, and , we obtain . Indeed, the VSC yields coefficient on the Bregman term, and the penalty leaves coefficient on the penalty-index term; their minimum is .
Substituting steps 1.1–1.3 into the objective comparison and applying the compatibility inequality gives , where . By [F3], , so . Since and , the left regularizer term absorbs the displayed compatibility term and a second copy of it, leaving ; in particular it bounds another . The resulting inequality is .
Divide step 2.1 by and use . Since , we have . Monotonicity of gives . Applying [F6] with , , , and proves both conjugate bounds.
The full collared EIT class satisfies the centered range and source interfaces
Statement
Assume the Axiom of Choice, Countable Choice and Dependent Choice. Let , , , and . Use the exact intrinsic Hilbert norm from the collared source-functional lemma, and put Let , and assume for . Set , and choose . Write for the centered mean-zero Neumann voltage sequence space, regarded as a real Hilbert space, and give the sum norm. Let , , and .
There is a globally Lipschitz, continuously Fréchet differentiable map with globally Lipschitz derivative, agreeing for with the centered physical observation . Define Then on , and its inverse are globally Lipschitz, and these facts hold on the admitted set The projected points belong to for .
On the ambient linear space define the proper convex regularizer where the indicator is zero on and elsewhere. On every admitted physical coefficient this is exactly the original quadratic Sobolev penalty. The functional is a subgradient at , and its Bregman distance on is exactly . On every fixed sublevel containing the truth, the VSC holds with and a continuous nondecreasing concave truncated logarithmic index continued constantly above a sufficiently small . Its subadditivity constant may be taken as .
Put and let . For , the projection-coupling constant may be taken as , and For the frozen method choose , and . Then the required compatibility bound holds with In particular, the structural hypotheses of the joint and frozen one-step estimates hold with , the displayed exact regularizer, for the joint method, and the displayed for the frozen method. For frozen rates additionally require , choose , 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.
Statement references
Centered Neumann voltage sequences on the three-dimensional ball
H1 logarithmic stability from centered three-dimensional NtD data
Besov smoothness gives the exact collared Sobolev penalty a cutoff source bound
Joint and frozen-Newton objectives for range-invariant relaxations
The joint relaxed objective controls Bregman and range errors
Facts & Assumptions
Given: The choice assumptions and full conductivity, Sobolev, Besov, collar and local-admission data in the Statement.
The centered physical map on uniformly elliptic collared 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
Uniform collared classes satisfy stability with modulus in their centered sequence distance. H1 logarithmic stability from centered three-dimensional NtD data
For the exact intrinsic quadratic penalty, the modulus and truth imply a VSC with coefficient and logarithmic exponent in the Statement. The proof treats the exact domain inner product. Besov smoothness gives the exact collared Sobolev penalty a cutoff source bound
Truncated logarithmic indices are concave when the truncation point is small enough, and their conjugates satisfy . Logarithmic source indices and their admissible parameter scales
All three choice assumptions are retained from [F1]–[F4]. The Axiom of Choice, The Axiom of Countable Choice (), The axiom of dependent choice: a relation in which every element is related to something admits an -indexed chain
Proof
Domains for the generic estimates. The canonical maps , , and , , are linear isometries onto and . In the generic one-step estimates, transport the regularizer and subgradient by and the frozen penalty by . The map preserves the dual pairing, so the subgradient inequality verified in step 3.2 transports to . It also preserves the Bregman distance: The penalty is a functional on under and has the required norm there. These are the domains used by the generic joint and frozen estimates.
The space is closed in the intrinsic Sobolev Hilbert space, since convergence there implies convergence and preserves vanishing on the collar. For its elements, zero extension is bounded into by [F3]. Fourier Cauchy–Schwarz gives because . Thus embeds continuously into the bounded collared coefficient space. The set is convex and closed in : convexity preserves the pointwise bounds, and uniform convergence from this embedding preserves them under a norm limit.
Choose a smooth scalar function with , equal to one on and zero outside , and set . Then on , , and globally. All its first and second derivatives are bounded. Put . Since outside and , for every . Moreover, is globally Lipschitz from into and continuously Fréchet differentiable, with derivative and globally Lipschitz derivative; its Taylor remainder is bounded by . Use the centered map [F1] with fixed strict ellipticity bounds and , and denote it by . Since , every is admissible for that map. Define explicitly The chain rule gives . The bounded and Lipschitz derivatives of and the Lipschitz derivative of the centered supplier give all the asserted global bounds for and . For , , so this formula agrees with the centered physical observation.
The identities for and the inverse follow by direct substitution. The map is globally Lipschitz with , so both and have Lipschitz constant at most in the sum norm. In particular . The restriction of a bijection to is a bijection onto ; its projected inverse point has the same coefficient and zero artificial voltage, hence remains in . The extension to all of supplies the global factorization domain required by the original generic hypotheses without changing the physical forward map on any admitted point.
The indicator of the convex set in step 1.1 is convex, and adding the quadratic functional makes proper and nonnegative. For any admitted , expansion of its square gives ; 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 , the coefficients have bounded intrinsic 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 for small data differences. Replacing the norm argument by its square changes only a fixed constant in the logarithm. Choose small enough and then enlarge 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.
Concavity and imply subadditivity: for positive , and ; summing gives . Hence . The derivative remainder of has only the second component . Integrating the Lipschitz derivative along the segment gives norm at most . Since and the first component of is , this is at most . No differentiability of the boundary of is needed, since the map was differentiated on the full ambient space.
If , then . Squaring gives a bound by . Because , concavity gives . The displayed 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 has the required lower bound with equality and vanishes at zero.
All projected coefficients in have , so one fixed covers their regularizer values and the truth. The preceding steps verify every structural range, domain, Lipschitz, projection, subgradient, VSC, compatibility and penalty hypothesis. With and , the frozen constants are , , and . The bound makes and . The displayed choice of is exactly the required linked-parameter bound because and . 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.
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 , and with in the joint objective. Define
Assume and are finite, , , , and there are constants independent of such that
Then the approximate joint reconstructions satisfy, as ,
where is the scalar residual, coupling, and projection error defined in the joint range-invariance VSC estimate lemma. In particular,
and the data residual is .
For the following three-dimensional EIT specialization, assume the Axiom of Choice, Countable Choice and Dependent Choice. Let , let the known background be outside , and impose fixed bounds and . Let , use the intrinsic quadratic penalty on this admissible class, and let with . The Sobolev norm is the exact intrinsic Hilbert norm in the EIT compatibility lemma. Use centered mean-zero Neumann voltage data in , its observation-augmentation range coordinates, and admitted approximate joint minimizers in the set specified by that lemma. The artificial voltage is and the projection is . Put
For fixed , noise bounded by , and , the balanced choice gives
Every pure power with gives the same conductivity norm rate and , hence . At the nominal endpoint , the same conductivity norm rate holds under the explicit actual-noise and tolerance condition
then and . In each case the coupling distance and relaxed data residual have order . In particular the sum of the conductivity norm error and the artificial-voltage norm is in all three stated regimes.
Statement references
Joint objective error inequality
Christian Clason, Regularization of Inverse Problems, Theorem 10.7
The Axiom of Countable Choice ()
The full collared EIT class satisfies the centered range and source interfaces
Logarithmic source indices and their admissible parameter scales
A logarithmic endpoint rate with explicit effective noise and tolerance control
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.
For the generic assertion, the parameter coupling satisfies .
For the generic assertion, the conjugate values at and are finite, and .
A conjugate bound for residual and Bregman errors: if , then and .
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.
The full collared EIT class satisfies the centered range and source interfaces verifies the centered EIT structural hypotheses with , exact quadratic Bregman distance, , and logarithmic source exponent displayed in the Statement.
Logarithmic source indices and their admissible parameter scales gives and the balanced and strict-power scalar comparison rates.
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
Apply [F2] to [F1] with , , , , , and . The first bound gives . By [A1], , hence .
The second bound from [F2] gives . By [A1]–[A2], , so .
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 , where is the first range coordinate translated by . The first-coordinate identity of the range map gives . Thus a Bregman rate and the coupling rate control the actual physical conductivity, without confusing the relaxed coefficient with the admitted output coefficient.
Each nonnegative summand defining is bounded by . Since is fixed and positive, the definitions of and imply the three displayed bounds. Moreover, by [F3]; the triangle inequality and the noise bound then give the data-residual estimate.
For with , apply the strict-power scalar conclusion of [F5] directly to the joint comparison [F1], with . This yields and . The fixed positive coupling coefficient and nonnegative summands of give the claimed artificial-voltage and coupling bounds. Step 1.3 then gives the same physical conductivity norm rate because .
By [F5], for the values and are both of order , their ratio is bounded, and . Hence the generic theorem assumptions apply and steps 1.1–2.1 give , , 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.
At the endpoint use the actual noise in the approximate-minimum comparison, so its scalar right-side constant is . The explicit endpoint assumption and [F6] give and . 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 , exactly as in step 2.1. This completes every stated EIT regime with its explicit noise assumptions.
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 , where is the basepoint from the range-invariance factorization. At step , freeze the objective at the previous iterate , use parameters and , and let satisfy the resulting approximate-minimum inequality. Let and be the error and residual quantities from the one-step lemma for this step, with its input iterate relabeled and output iterate relabeled . Define . The parameter sequence and the associated are fixed independently of . Write
where are as in that lemma. Let decrease strictly to zero, suppose is finite and positive, decreases strictly to zero, and assume for some that
Assume each step through satisfies the one-step lemma, has tolerance , and the initial error is finite. Assume also that for every sufficiently small there is an index such that
for constants independent of . This bounded crossing condition is satisfied, for example, if is the last index with and for all .
Then the stopping iterate satisfies
and therefore
If has a strictly increasing inverse on the relevant range, then
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 , let the known conductivity be outside , impose fixed positive upper and lower conductivity bounds, use the exact intrinsic quadratic penalty with , and assume for . Use the centered mean-zero Neumann voltage map in and the explicit observation augmentation, admitted neighborhood , penalty , and linked-parameter constants in the EIT compatibility lemma. Choose the initial conductivity sufficiently close to the truth that its radius condition holds, and choose a fixed initial range variable in , independently of noise. Put
Take a fixed geometric parameter sequence, starting sufficiently far down its tail that the conjugate growth condition above holds, set , and use admitted approximate steps with . For a positive target , stop at the last index with . If the actual squared data noise is , then
in each of the following regimes: for the balanced choice and ordinary noise bounded by ; for every , giving under the same ordinary noise bound; and for the nominal endpoint , under the explicit stronger actual-noise condition . All tolerances remain bounded by through the stopping index. Thus the total conductivity and artificial-voltage norm error has logarithmic exponent in every stated regime.
Statement references
Frozen-step error and residual inequality
Christian Clason, Regularization of Inverse Problems, Theorem 10.7
The Axiom of Countable Choice ()
The full collared EIT class satisfies the centered range and source interfaces
Logarithmic source indices and their admissible parameter scales
Geometric parameters satisfy the logarithmic frozen stopping and growth conditions
Facts & Assumptions
Given: For the generic assertion, every 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.
for and .
The tolerances satisfy .
and .
is independent of .
A frozen Newton step yields a controlled error inequality: and , where .
The EIT clauses use the Axiom of Choice, Countable Choice and Dependent Choice at their actual elliptic, trace, Fourier and Hilbert suppliers only.
The full collared EIT class satisfies the centered range and source interfaces verifies the full EIT frozen structural hypotheses with , , , the exact intrinsic Sobolev quadratic penalty, and source exponent .
Logarithmic source indices and their admissible parameter scales gives the logarithmic conjugate asymptotic.
Geometric parameters satisfy the logarithmic frozen stopping and growth conditions verifies the fixed geometric schedule, growth, bounded crossing and parameter-index asymptotics.
Proof
Since and is nonincreasing in its positive argument, [F1] and the definitions of and give . The first term in that contains the previous nonlinear-range error is at most , because contains and . Also, follows from [A1], and follows from [A2]. Therefore , where .
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 , [F4] supplies a fixed tail of any geometric sequence and a constant with . The initial pair is fixed and lies in the bounded admitted set, so is finite and independent of noise.
Iterating the recurrence from step 1.1 gives . For , repeated use of [A3] yields , so the sum is at most . Also , so , with constant independent of . The same estimate holds with replaced by any , so throughout the stopped segment. Thus .
The definition of , the inequality , and step 2.1 with show that . By [A3], ; by [A1]–[A2], and . Therefore , and the residual estimate in [F1] gives .
Since is a positive constant multiple of , its bound controls both displayed range errors. The definition in [F1] gives . Each nonnegative summand is at most , and is fixed, giving the two estimates. Finally, , so the data residual is . If is strictly increasing, gives ; monotonicity of then gives the stated index bound for .
In each displayed regime, and . By [F3]–[F4], the selected last-crossing index satisfies , , and . This gives exactly the three displayed parameter scales. For , the ordinary squared noise is because . The same bound is immediate in the balanced regime and is the explicit additional assumption at the endpoint. Hence a fixed positive multiple of 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 and .
The first component of is . Its norm is therefore bounded by the controlled range norm in , so gives the stated conductivity error after taking square roots. The projection norm is exactly , and the residual argument in step 4.1 uses the actual noise upper bound , giving both remaining estimates. Since every positive power of times a fixed power of its logarithm tends to zero, in all three regimes, proving the total norm-rate assertion.
Source corrections for three-dimensional EIT rates
Remark
Kaltenbacher's 2024 paper treats the three-dimensional unit ball with background conductivity , quadratic penalty , , and truth for . Its Theorem 10 reports a logarithmic VSC exponent
with , and Corollary 11 reports a conductivity-norm exponent for the joint, split, and frozen-Newton methods. The source fixes and separately uses with ; 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 gives denominator in the corresponding logarithmic exponent. In addition, the quadratic Bregman distance is one half the squared norm error, so a Bregman rate with logarithmic exponent yields a norm exponent ; this quadratic norm conversion agrees with the divisor 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 for a fixed . For a logarithmic index with , no pure-power choice with 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 , retain the entire stated Sobolev/Besov truth range, and prove the conductivity norm exponent . They give balanced parameters, every strict pure power , and the nominal endpoint with an explicit stronger actual-noise condition. The universal abstract endpoint assertion under arbitrary deterministic noise bounded only by 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 in , or equivalently the corresponding affine translate for absolute voltages. This centering is necessary: at the background conductivity , the uncentered voltage columns have norms with multiplicity , so their squared sum diverges. The centered Neumann-map differentiability, 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
Published conductivity Dirichlet-to-Neumann interface ↗
Finite-dimensional elliptic state-map differentiability
The full collared EIT class satisfies the centered range and source interfaces
A logarithmic endpoint rate with explicit effective noise and tolerance control
Centered Neumann voltage sequences on the three-dimensional ball
Facts & Assumptions
Given: The source assertions, comparison formulas, and quadratic regularizer are those stated in the Remark.
[Kaltenbacher, Theorem 10]: the paper gives exponent and cites Weidling Theorem 6.15 with .
Weidling, Theorem 6.15: its proof sets , , and .
Joint regularization rates under differential range invariance: the generic joint rate theorem assumes and , where .
Kaltenbacher, Theorem 10 and Corollary 11 (printed p. 24): the stated VSC uses . Corollary 11 fixes and reports the norm exponent , separately from the parameter choice , .
Kaltenbacher, equation (1.32b): the logarithmic index is for constants .
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.
The full collared EIT class satisfies the centered range and source interfaces supplies the full EIT source/range/penalty interface with the corrected exponent and exact quadratic Bregman distance. Its local stability and function-space source suppliers are fully proved.
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 minimizer.
Centered Neumann voltage sequences on the three-dimensional ball proves the centered sequence map and computes the divergent uncentered background norm.
Verification
Substituting into [F2] gives and . The logarithmic VSC exponent contributed by the source Sobolev gap is therefore . This differs from [F1]'s denominator because the allowed range has ; the cited theorem does not directly yield the exponent printed in Kaltenbacher's Theorem 10.
For , the Hilbert-space subgradient is under the inner product. Expanding the square gives . Therefore implies only by taking square roots; it does not imply exponent .
By [F6], for sufficiently small positive , and is bounded because of the truncation at . For large, if then ; if then . Thus . Conversely, choosing gives , , and , hence for large . Therefore . For , , this gives . If , the ratio to tends to zero; if , 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.
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
- Frederic Weidling, Variational Source Conditions and Conditional Stability Estimates for Inverse Problems in PDEs
- Mikko Salo, Calderon problem, lecture notes
- Petru Mironescu, Fine properties of functions: an introduction (author-hosted 89-page edition)
- Pedro Caro, Andoni García and Juan Manuel Reyes, Stability of the Calderón problem for less regular conductivities
- Barbara Kaltenbacher, Convergence rates under a range invariance condition with application to electrical impedance tomography
- Barbara Kaltenbacher, Convergence rates under a range invariance condition with application to electrical impedance tomography, equation (1.6)
- Barbara Kaltenbacher, Convergence rates under a range invariance condition with application to electrical impedance tomography, equations (1.8), (1.11), (2.7), (2.20), and (2.35)
- Barbara Kaltenbacher, Convergence rates under a range invariance condition with application to electrical impedance tomography, equations (2.10)–(2.14)
- Barbara Kaltenbacher, Convergence rates under a range invariance condition with application to electrical impedance tomography, equations (2.31)–(2.41)
- Frederic Weidling, Variational Source Conditions and Conditional Stability Estimates for Inverse Problems in PDEs, Theorem 2.20
- Barbara Kaltenbacher, Convergence rates under a range invariance condition with application to electrical impedance tomography, Theorem 3
- Christian Clason, Regularization of Inverse Problems, Theorem 10.7
- Barbara Kaltenbacher, Convergence rates under a range invariance condition with application to electrical impedance tomography, Theorem 7, equations (2.31)–(2.41)
- Barbara Kaltenbacher, Convergence rates under a range invariance condition with application to electrical impedance tomography, Section 3, Theorem 10 and Corollary 11
- Frederic Weidling, Variational Source Conditions and Conditional Stability Estimates for Inverse Problems in PDEs, Chapter 6, Theorems 6.10–6.15
- Christian Clason, Regularization of Inverse Problems, Chapter 10, Theorem 10.7