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.
Error bound for a finite simplex and affine slice
Statement
Let , let , let be linear, and let be nonempty. There is a finite such that
Facts & Assumptions
Given: The finite-dimensional simplex, linear map, and nonempty affine slice in the Statement.
Every point has a unique nearest point in a nonempty closed Euclidean convex set: every point of Euclidean space has a unique nearest point in a nonempty closed convex set.
Metric projection onto a closed convex set satisfies the variational inequality: if is the nearest point in a closed convex set to , then for every point in that set.
For a nonempty subset of with , compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent: closed bounded subsets of finite-dimensional Euclidean space are compact, and continuous real functions attain minima on nonempty compact sets.
Proof
For each support set realized as by some , define This is a closed cone, being an intersection of closed linear constraints and closed half-spaces. If and , then is an allowed in its defining inequality, hence and .
The set is nonempty, closed, and convex: in coordinates it is given by finitely many closed linear inequalities and affine equations. Thus [F1] applies. Suppose and let be its nearest point in . Put and . Then and outside , because there.
For each realized , the set is compact by [F3]. If nonempty, the continuous function has a positive minimum : a zero would contradict step 1.1. There are finitely many , so the minimum of these positive is positive whenever any such unit section is nonempty. If all are empty, suppose and take its nearest point by [F1]. With and , every vector in the tangent conditions defining has for small . [F2] then gives , so and , a contradiction. Hence and the claim is immediate.
If satisfies the linear and sign conditions in the definition of , then for all sufficiently small : the affine equations are preserved, coordinates outside remain nonnegative, and the finitely many positive coordinates of on remain positive. By [F2], , so and .
For the nontrivial case of step 2.1, homogeneity and its constant give . Taking proves the result for ; for the left side is zero. In the empty-unit-section case from step 2.1, and any works.
Depends on
- Every point has a unique nearest point in a nonempty closed Euclidean convex set
- Metric projection onto a closed convex set satisfies the variational inequality
- For a nonempty subset of $\mathbb{R}^n$ with $n\ge1$, compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent
Used by
Dependency tree · two levels
14 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
- D. Bertsekas, MIT 6.253 Convex Analysis and Optimization, Lecture 6 (standard reference, not scraped)