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.
The proximal map of an indicator is Euclidean projection
Statement
Let be nonempty, closed and convex. Its indicator is proper, closed and convex in the convention of Proper closed convex extended-real functions on . For every and , is the singleton consisting of the unique nearest point in to .
Facts & Assumptions
Given: The set , scalar , and point in the Statement.
Extended-real indicator of a set: is on and off .
A convex subset of contains every line segment between two of its points: every convex combination of two points of belongs to .
Proper closed convex extended-real functions on : properness means nonempty effective domain; closedness means a closed epigraph.
Every point has a unique nearest point in a nonempty closed Euclidean convex set: for , every point has a unique Euclidean nearest point in a nonempty closed convex set.
Proximal mapping: is the set of global minimizers of .
The Euclidean inner product on : is the singleton zero vector space, and its norm and inner product are zero.
Proof
Since is nonempty, some has , so the indicator is proper. If , convexity of puts every convex combination in , where the indicator is zero; if either endpoint is outside , the convexity inequality is automatic for an interior coefficient, and at endpoints it is equality. Thus is convex.
Its epigraph is exactly : for the inequality is required, and for no real satisfies . This product is closed because both factors are closed, so is closed.
If , then by [F6], and nonempty is . The proximal objective has this sole point as its unique minimizer, and it is also the unique nearest point in to . Hence the claim holds for .
Now assume . The proximal objective is finite exactly on , where it equals . Since , minimizing it over all is equivalent to minimizing distance from over . By [F4] the latter has a unique minimizer, which is therefore exactly the singleton in [F5].
Depends on
- Proper closed convex extended-real functions on $\mathbb R^n$
- A convex subset of $\mathbb{R}^m$ contains every line segment between two of its points
- Proximal mapping
- Extended-real indicator of a set
- The Euclidean inner product $\langle x,y\rangle = \sum_{k<n} x_k y_k$ on $\mathbb{R}^n$
- Every point has a unique nearest point in a nonempty closed Euclidean convex set
Used by
- Projected stochastic-gradient update Definition
- Proximal-gradient mapping and projected-gradient mapping Definition
- Projected gradient on the nonnegative orthant Example
- Diagonal AdaGrad subproblem has a unique minimizer Lemma
- The convex normal-cone operator is maximal monotone and its resolvent is projection Lemma
- Finite-time projected SGD bound for a convex objective Theorem
- Projected SGD converges almost surely on a strongly convex objective Theorem
- Projected-gradient fixed points are normal-cone certificates Theorem
Dependency tree · two levels
27 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
- Neal Parikh and Stephen Boyd, Proximal Algorithms, §2.4.2 (standard reference, not scraped)
- Stephen Boyd and Lieven Vandenberghe, Convex Optimization, §8.1.1 (standard reference, not scraped)