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Compact spectral filters, source conditions, and qualification
Definition
Assume Countable Choice. Let be real or complex Hilbert spaces and let be compact and bounded. Put . By A compact Hilbert-space Gram operator is positive and has the forward kernel, is compact, positive, and self-adjoint, with . Let be the set of nonzero eigenvalues of and the orthogonal projection onto the eigenspace of ; let be the orthogonal projection onto . The compact spectral multiplier of a bounded scalar function is where the finite-subset net converges in norm by Bounded multipliers on compact self-adjoint eigenspaces define bounded operators. For , define by the multiplier and for . This multiplier is bounded: if and is a unit vector, then , so .
For , the Tikhonov filter and residual filter are The Tikhonov solution for data is Indeed, the spectral decomposition gives the multiplier on each and on , so is its inverse, whose existence is also supplied by The shifted Hilbert-space normal operator is invertible with inverse norm at most . The source condition of order and radius means that for some with . A family of filters with residual filters has qualification at least when, for each , some finite constant satisfies When , this condition is vacuous.
Remarks
The parameter here is the coefficient of in . Hansen's discrete slides write this coefficient as , so . Clason writes the source condition as with ; here . Thus Tikhonov's qualification-one range in the present convention is .
Depends on
- A compact Hilbert-space Gram operator is positive and has the forward kernel
- Bounded multipliers on compact self-adjoint eigenspaces define bounded operators
- Spectral theorem for compact self adjoint operators
- The shifted Hilbert-space normal operator is invertible with inverse norm at most $1/\lambda$
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- The Hilbert-space adjoint of a bounded operator
- A bounded linear operator between normed spaces
- The operator norm as the least bound and as the unit-sphere or unit-ball supremum
Used by
- Exact-residual Morozov discrepancy bracketing and bisection Algorithm
- Landweber discrepancy stopping converges under a source condition Corollary
- A compact diagonal model displays the Tikhonov bias-noise balance Example
- Underestimated noise can put the Morozov target below every residual Example
- Compact-operator Tikhonov bias vanishes on the minimum-norm solution Lemma
- Compact-operator Tikhonov residuals are continuous and increase from range distance to data norm Lemma
- Landweber iterates and residuals are spectral-filter iterates Lemma
- Tikhonov has qualification one for source powers of the Gram operator Lemma
- Forward discretization enters before inversion and controls the Tikhonov parameter coupling Theorem
- Landweber converges for exact data and balances source bias with noise Theorem
- Morozov discrepancy roots exist and converge for nonzero exact data Theorem
- Tikhonov source-condition rates balance noise at qualification one Theorem
- Vanishing Tikhonov bias and controlled noise amplification imply convergence Theorem
Dependency tree · two levels
61 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Guido Clason, Regularization of Inverse Problems, full lecture notes (standard reference, not scraped)
- Per Christian Hansen, Introduction to Inverse Problems, Chapter 4: Regularization Methods (standard reference, not scraped)