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.
Every point has a unique nearest point in a nonempty closed Euclidean convex set
Statement
Let , let be nonempty, closed, and convex, and let . Then there is a unique such that for every .
Facts & Assumptions
Given: The set and point in the Statement. Closedness and convexity have the meanings of Interior, closure, boundary, limit point, isolated point and dense subset of a metric space and A convex subset of contains every line segment between two of its points, and distances come from The Euclidean inner product on .
For , a subset of is compact if and only if it is closed and bounded (Heine-Borel in : with the Euclidean metric a subset of is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line).
A continuous real function on a nonempty compact metric space attains a maximum and a minimum (A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value).
The Euclidean norm satisfies the parallelogram law (Cauchy-Schwarz with its equality case, the triangle inequality for , the parallelogram law and polarisation).
Proof
Choose and put . The set is nonempty, closed, and bounded, hence compact by [L1]. The continuous distance attains a minimum at some by [L2]. Points of have distance greater than , so minimizes distance over all of .
Suppose distinct both minimize the squared distance at . Convexity puts in , while [L3] gives contradicting minimality. Thus the nearest point is unique.
Depends on
- A convex subset of $\mathbb{R}^m$ contains every line segment between two of its points
- A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value
- Heine-Borel in $\mathbb{R}^n$: with the Euclidean metric a subset of $\mathbb{R}^n$ is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line
- The Euclidean inner product $\langle x,y\rangle = \sum_{k<n} x_k y_k$ on $\mathbb{R}^n$
- Cauchy-Schwarz $\lvert\langle x,y\rangle\rvert \le \lVert x\rVert_2\lVert y\rVert_2$ with its equality case, the triangle inequality for $\lVert\cdot\rVert_2$, the parallelogram law and polarisation
- Interior, closure, boundary, limit point, isolated point and dense subset of a metric space
Used by
Dependency tree · two levels
53 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
- S. Boyd and L. Vandenberghe, Convex Optimization, §2.5.1 (standard reference, not scraped)
- D. Bertsekas, MIT 6.253 Convex Analysis and Optimization, Lecture 6 (standard reference, not scraped)