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Conditional convergence of double-discrepancy composite inversion
Statement
Assume Countable Choice. Let be real or complex Hilbert spaces over one scalar field. Let compact linear maps and have a common singular system indexed by a finite or countable set : , , and are orthonormal bases of , , and , and , , , and with . Put . Let , , , with , and ; set . The common system gives the compatible minimum-norm factorization on this exact datum: and . For , let and . Assume finite constants satisfy , , and for every ; and, for each , for finite , where and . Fix and . Let be decreasing sequences with successive ratios in for one fixed . For noisy data with , choose first indices (when they exist) such that for every , and for every . For any sequence for which both selected indices exist for all sufficiently small , there are constants independent of and such that, for all sufficiently small , In particular, for fixed this two-stage reconstruction converges to . The statement is conditional on existence of both stopping indices and makes no claim that for arbitrary factorizations.
Statement references
Facts & Assumptions
Given: Countable Choice; compact real or complex Hilbert maps and with the common positive singular system in the Statement; ; , , ; the compatible minimum-norm factorization; spectral filters with the stated residual, norm, and qualification bounds; the two geometric first-crossing rules; and noisy data for a sequence .
The Axiom of Countable Choice (): Every sequence of nonempty sets admits a choice function selecting one member from each set.
Composite double regularization: The forward map is and the reconstruction applies first, then .
Compact linear inverse model between Hilbert spaces: A compact Hilbert inverse model has a compact linear forward map and exact data for a truth .
Power source ball: , where ; source elements lie in and are minimum-norm representatives.
Hilbert-space Moore–Penrose inverse: The inverse domain is , and on exact range data it returns the unique minimum-norm solution.
Singular value decomposition for compact operators: A compact Hilbert operator has a positive singular system with orthonormal right and left singular families and the corresponding operator expansion.
The Hilbert-space adjoint of a bounded operator: The adjoint is characterized by .
Compact spectral filters, source conditions, and qualification: For a filter family, the residual multiplier is and qualification bounds positive spectral powers times the absolute residual.
Spectral interpolation for power source balls: If , then .
Proof
Because and are compact, hence bounded, is compact: the image under of any bounded sequence is bounded, and compactness of gives a subsequence whose images under converge. Put and . The adjoint identity [F6] and the singular-vector equations show and : each kernel vector is orthogonal to the indicated family, and conversely a vector orthogonal to the family is orthogonal to the full range of the adjoint, hence is killed by the operator. Since is injective on (its coefficients there are the positive ), ; hence is a basis of . By [F1], . The adjoint identity [F6] gives , so [F5] yields , , and . The vectors span , since the range of is generated by and . Compact spectral calculus and [F3] therefore give and . The source element is perpendicular to , so [F4] gives . Thus lies in and lies in ; since and , the minimum-norm property [F4] gives and . Finally, satisfies and .
Let . On each , has multiplier , whose absolute value is at most because has that same multiplier on . Thus with . Also . The difference between the exact and noisy first residuals is , of norm at most ; the stopping inequality therefore gives . Applying [F8] to proves .
Put . On the common singular system, has multiplier . The bounds on and bound the two factors by ; on the product with is zero. Therefore with and . The same coefficient formula, now on , gives . The exact composite residual decomposes as . These four terms have norms at most , respectively, by the noise bound, the two stopping rules, and the composite operator bound. Since , it follows that . Applying [F8] to proves .
Replace by for ; the qualification bounds remain valid and these constants are positive. Define . Its common-system multiplier on is . Since , qualification at , , and give . On arbitrary noise coordinates, and , so . Let denote the first index of . If , the preceding noisy discrepancy is greater than ; subtracting the noise bound gives . The exact-data bound just proved then yields , and gives the same lower bound with factor . Hence . If , then is fixed. The common system and give , so for all sufficiently small . Thus , and after enlarging .
Let denote the first index of . If , the preceding noisy residual is greater than . Its difference from the exact-data residual is , of norm at most , so . Expanding in the common basis and applying the qualification bound at gives . Thus , and gives the same lower bound for with the extra factor . Consequently , where is independent of . If , then is fixed. Since , we have for all sufficiently small , and hence . Combining this noise bound with step 2.1 and the triangle inequality gives .
The exact/noisy decomposition is . By linearity, , and these terms are bounded by steps 2.1 and 3.1. Combining those estimates with the exact composite bias from step 2.2 and the outer norm bound from step 2.3 gives the two terms in the Statement, with constants independent of and . Since , both powers of are positive, so the error tends to zero for fixed .
Depends on
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- Compact spectral filters, source conditions, and qualification
- The Hilbert-space adjoint of a bounded operator
- Compact linear inverse model between Hilbert spaces
- Composite double regularization
- Power source ball
- Hilbert-space Moore–Penrose inverse
- Spectral interpolation for power source balls
- Singular value decomposition for compact operators
Used by
- Optimal rate for composite double discrepancy Open problem
Dependency tree · two levels
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Sources
- Christina Brandt and Patrick Horn, Regularisation and Parameter Choice Rules for Composite Linear Inverse Problems, Theorem 6 and proof (standard reference, not scraped)
- Christian Clason, Regularization of Inverse Problems, full lecture notes, Theorem 5.11 (standard reference, not scraped)