How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Weakly closed nonlinear forward maps on Hilbert spaces
Definition
Let and be real Hilbert spaces, let be nonempty, and let be a forward map. The domain is weakly closed when it is closed in the weak topology , the coarsest topology making every bounded real-linear functional on continuous. The graph of is sequentially weak-to-weak closed when, for every sequence in and every and , the conditions and imply and ; here weak convergence means convergence under every bounded real-linear functional on the corresponding Hilbert space. We call a weakly closed forward map when its domain is weakly closed and its graph is sequentially weak-to-weak closed. No linearity, norm continuity, Fréchet differentiability, or convexity is assumed; linear maps are allowed as special cases.
Depends on
Used by
- x₀-minimum-norm solutions on weakly closed domains Definition
- A Bochner-integral forward map with weakly closed graph Example
- A nonlinear Tikhonov functional can have two global minimizers Example
- The exact solution set of a weakly closed map is weakly sequentially closed Lemma
- Existence of a global nonlinear Tikhonov minimizer Theorem
- Existence of an x₀-minimum-norm exact solution Theorem
- Vanishing-noise convergence to an x₀-minimum-norm exact solution Theorem
- Weak-cluster stability for fixed-parameter nonlinear Tikhonov regularization Theorem
Dependency tree · two levels
13 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
- Christian Clason, Regularization of Inverse Problems, Chapter 10 (standard reference, not scraped)
- Benedikt Wirth, Inverse Problems, Chapter 10 (standard reference, not scraped)