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Hilbert-valued extension with a concave modulus
Statement
Assume the Axiom of Choice. Let be real Hilbert spaces, let , and let . If a continuous, nondecreasing, concave function satisfies and then the Hilbert-valued same-modulus extension theorem asserts that there is extending and satisfying the same inequality for every .
For the EIT inverse map, take the kernel input space to be viewed as a real Hilbert space with its usual complex norm. If IP-45 gives for , and the NTD kernel estimate transfers , then after decreasing and, if necessary, enlarging the concave modulus is an admissible modulus for that inverse map and can be extended by the Hilbert same-modulus theorem to the whole underlying real Hilbert space of complex-valued kernels.
Statement references
Facts & Assumptions
Given: the real Hilbert spaces, subset, map and modulus in the Statement, and the EIT class data when applying the second paragraph.
IP-45 states with a saturated logarithmic modulus and . (Logarithmic L2 stability for Calderón conductivity classes)
For any concave nondecreasing modulus vanishing continuously at zero, source points and target points satisfying have for every additional source point . This includes arbitrary index sets and zero-radius balls. (Concave-modulus intersection of Hilbert balls)
The actual kernel comparison is , where is the kernel distance. (NtD smoothing and Hilbert–Schmidt kernel bounds)
Under the Axiom of Choice, a nonempty poset whose every chain has an upper bound has a maximal element. (Zorn's lemma)
The Axiom of Choice supplies Zorn's lemma and implies the Countable Choice used by the Hilbert-ball and EIT interfaces. (The Axiom of Choice, The Axiom of Countable Choice ())
Proof
Given: the data in the Statement.
Consider the set of pairs where and extends and satisfies for all . Order it by graph inclusion. It is nonempty, since is in it. Every nonempty chain has as upper bound the union of its graphs: every two of its domain points occur together in one member of the chain, so the same modulus inequality holds for their union. The empty chain has as an upper bound. By [F4] and [A1] there is a maximal pair .
For the EIT application first arrange . If the original IP-45 constants have a larger cutoff, decrease it and enlarge if needed: the uniform class diameter is at most , so taking covers all DN distances above the new cutoff, while increasing preserves the old small-distance estimate. For , put and . Then and . At the cutoff it joins continuously to a constant and its derivative drops to zero; at zero it tends to zero. Thus the displayed is continuous, nondecreasing and concave on the entire nonnegative half-line.
If , fix . Apply [F2] to the source family and target family . It gives a point satisfying for every . Adding gives a strictly larger admissible graph, contradicting maximality. This also covers , since [F2]'s empty intersection is , which contains zero. Thus and proves the full same-modulus extension theorem for arbitrary real Hilbert spaces and arbitrary subsets.
For an exact conductivity pair put . By [F1], [F3] and monotonicity, when ; if , [F3] gives zero DN distance and [F1] gives equal conductivities. The operators and in the smoothed observation commute with complex conjugation: the weak Neumann problem has real coefficients and unique mean-zero representatives, while the real boundary Laplacian has conjugation-invariant eigenspaces and the spectral multiplier is real and constant on each eigenspace. Thus commutes with conjugation. Let be its unique complex kernel from the Hilbert–Schmidt kernel theorem. The kernel also represents , since its integral operator sends to . Uniqueness of the kernel gives almost everywhere. In particular, the exact kernel image lies in the real-valued subspace, while it is still regarded as a subset of the underlying real Hilbert space of complex-valued kernels. Therefore the exact-data inverse is well-defined and has this modulus on that real Hilbert space. Step 2.1 applies there and in the real conductivity Hilbert space, with concavity verified in step 1.2, to give its whole-space extension with exactly the same modulus.
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Sources
- Yoav Benyamini and Joram Lindenstrauss, Geometric Nonlinear Functional Analysis, Volume 1 (standard reference, not scraped)
- Maarten V. de Hoop, Nikola B. Kovachki, Matti Lassas and Nicholas H. Nelsen, Extension and neural operator approximation of the electrical impedance tomography inverse map (standard reference, not scraped)