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A compact Hilbert-space Gram operator is positive and has the forward kernel
Statement
Assume Countable Choice. Let be real or complex Hilbert spaces and let be compact and bounded, with adjoint . Then both and are compact, self-adjoint and positive, and
Facts & Assumptions
Given: Countable Choice, real or complex Hilbert spaces , a compact bounded linear operator , and its Hilbert adjoint .
The Axiom of Countable Choice (): Countable Choice is sufficient for the Hilbert adjoint used here; the proof does not assume full Choice.
The Hilbert-space adjoint of a bounded operator: The adjoint satisfies .
Compact linear operator: A compact operator maps bounded sets to sets with compact closure.
Compositions with a compact operator are compact: A composition of a compact operator with a bounded operator is compact.
Hilbert-adjoint identities: The adjoint of a composition reverses order, and .
Self-adjoint, positive, unitary and normal operators: Self-adjoint means ; positive means for every .
Real and complex inner product spaces, with the inner product linear in the first argument: The inner product is positive definite.
Proof
Since is compact and is bounded, [F3] makes compact. The same lemma makes compact because it is the composition of the compact map after the bounded map . By [F4], and , so both are self-adjoint.
For every , [F1] and [F7] give . For every , the same identity gives . Thus both operators are positive.
If , then . Conversely, if , step 1.2 implies , so positive definiteness [F6] gives . Hence . The same argument with in place of proves .
Depends on
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- The Hilbert-space adjoint of a bounded operator
- Self-adjoint, positive, unitary and normal operators
- A bounded linear operator between normed spaces
- Compact linear operator
- Real and complex inner product spaces, with the inner product linear in the first argument
- The induced length is a norm
- Hilbert-adjoint identities
- Compositions with a compact operator are compact
Used by
- Landweber discrepancy stopping converges under a source condition Corollary
- Compact spectral filters, source conditions, and qualification Definition
- 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
- Landweber converges for exact data and balances source bias with noise Theorem
- Morozov discrepancy roots exist and converge for nonzero exact data Theorem
Dependency tree · two levels
40 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)