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The shifted Hilbert-space normal operator is invertible with inverse norm at most
Statement
Assume the Axiom of Countable Choice. Let and be real or complex Hilbert spaces and let be bounded, with Hilbert adjoint . For , define
Then is bijective and
Facts & Assumptions
Given: The Axiom of Countable Choice, real or complex Hilbert spaces , a bounded linear map , its Hilbert adjoint , and a real .
Hilbert space: A Hilbert space is an inner-product space complete for the norm induced by its inner product.
The Axiom of Countable Choice (): Countable Choice states that every countable family of nonempty sets has a choice function.
The Hilbert-space adjoint of a bounded operator: Under Countable Choice, the adjoint assigns to each the unique vector satisfying .
A bounded linear operator between normed spaces: A linear map is bounded when some finite satisfies for every .
Cauchy–Schwarz: , with equality exactly for dependent pairs: For vectors in a real or complex inner-product space, .
Orthogonal decomposition by a closed subspace: Under Countable Choice, a closed linear subspace of a Hilbert space satisfies .
Real and complex inner product spaces, with the inner product linear in the first argument: The inner product is linear in its first argument, conjugate symmetric, and positive definite.
The operator norm as the least bound and as the unit-sphere or unit-ball supremum: The operator norm is the supremum of over vectors satisfying .
Orthogonality and the orthogonal complement: A vector lies in exactly when for every .
The induced length is a norm: The induced norm satisfies .
Proof
Uniqueness in [F2] gives linearity of : for scalars and , conjugate-linearity of the second slot shows that satisfies the defining identity for , so it equals . For , [F2], [F4] and [F7] give . Dividing when , and treating directly, proves . Thus is bounded, and composition and addition make linear and bounded with . The adjoint identity and conjugate symmetry [F6] give for all . In particular, .
By [F4], . Thus for ; the same inequality is trivial for . Hence is injective. If converges, this inequality applied to makes Cauchy; completeness of and boundedness of show its limit lies in . Therefore the range is closed.
If , [F8] gives for every . By conjugate symmetry [F6] this is , and the symmetry identity from step 1.1 gives for every . Taking and using positive definiteness [F6] yields ; injectivity from step 2.1 then gives . Thus the closed range has zero orthogonal complement.
By [F5] and the Countable Choice assumption [A1], a closed subspace with zero orthogonal complement is all of ; so is surjective as well as injective. For , step 2.1 gives , proving .
Depends on
- Hilbert space
- The Hilbert-space adjoint of a bounded operator
- A bounded linear operator between normed spaces
- Real and complex inner product spaces, with the inner product linear in the first argument
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- The operator norm as the least bound and as the unit-sphere or unit-ball supremum
- Orthogonality and the orthogonal complement
- The induced length is a norm
- Cauchy–Schwarz: $|\langle x,y\rangle|\le\|x\|\,\|y\|$, with equality exactly for dependent pairs
- Orthogonal decomposition by a closed subspace
Used by
- Anchored iteratively regularized Gauss-Newton with finite discrepancy stopping Algorithm
- Exact-residual Morozov discrepancy bracketing and bisection Algorithm
- Unanchored Levenberg–Marquardt step with explicit early stopping Algorithm
- Compact spectral filters, source conditions, and qualification Definition
- An exact normal-equation residual gives an a posteriori solution bound 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
- Hilbert-space Tikhonov amplifies data by at most 1/(2√λ) Lemma
- One-step error recurrence for the assigned nonlinear regularized Gauss–Newton step Lemma
- Real and complex Hilbert source powers and fixed-reference Tikhonov filter bounds Lemma
- Anchored IRGNM local source-condition convergence with ordinary discrepancy stopping Theorem
- Forward discretization enters before inversion and controls the Tikhonov parameter coupling Theorem
- Morozov discrepancy roots exist and converge for nonzero exact data Theorem
- Tikhonov error separates bias, data noise, discretization and solve residual Theorem
Dependency tree · two levels
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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)
- Per Christian Hansen, Introduction to Inverse Problems, Chapter 5: Regularization Parameter Choice (standard reference, not scraped)