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.
Nearest-point maps to convex sets need not be linear
Statement refuted
The nearest-point map of a nonempty closed convex set in a Hilbert space is linear.
Facts & Assumptions
with the pairing is a real inner-product space whose induced norm is the absolute value (Real and complex inner-product spaces and their induced length, A norm on a real vector space, the induced metric, and the dictionary with the metric axioms), and it is complete, hence a Hilbert space (Complete metric space: every Cauchy sequence converges in the space, Banach space, Hilbert space).
A set is convex when for all and , and the nearest point of a nonempty closed convex subset of a Hilbert space is the point minimising the distance (Convex sets and continuous real-hyperplane separation in a normed space, Hilbert space).
Counterexample
Given: The Hilbert space of [A1] and the closed convex set .
is closed and convex, and is nonempty with .
For the point lies in with , so ; for and any one has with equality exactly at , so .
Hence ; this map is not additive, since , and it is not homogeneous either, since .
Therefore the nearest-point map of a nonempty closed convex set in a Hilbert space need not be linear, so the statement refuted is false.
Depends on
- Hilbert space
- Convex sets and continuous real-hyperplane separation in a normed space
- Real and complex inner-product spaces and their induced length
- Complete metric space: every Cauchy sequence converges in the space
- Banach space
- A norm on a real vector space, the induced metric, and the dictionary with the metric axioms
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
29 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
- Theo Bühler and Dietmar Salamon, Functional Analysis, Theorem 1.44, pp.40–41 (standard reference, not scraped)
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Theorem 178 (standard reference, not scraped)