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A proper closed convex function has an affine minorant
Statement
Let and let be proper, closed and convex in the epigraph convention of Proper closed convex extended-real functions on . Then there are and such that
Facts & Assumptions
Given: The function in the Statement, with effective domain and epigraph .
Proper closed convex extended-real functions on : is nonempty and convex, and is a nonempty closed convex subset of .
Every point has a unique nearest point in a nonempty closed Euclidean convex set: every point of a nonempty closed convex Euclidean set has a unique nearest point.
Metric projection onto a closed convex set satisfies the variational inequality: if is the nearest point of a closed convex set to , then for every point in that set.
For every bounded sequence in has a convergent subsequence: every bounded sequence in a positive-dimensional Euclidean space has a convergent subsequence; this corollary records a choice-free proof.
Proof
Let be the dimension of the direction space of . If , then is a singleton and the constant affine function is a minorant. If , choose affinely independent points spanning and let be their barycenter. Iterating the two-point convexity condition shows their simplex lies in . In affine barycentric coordinates, this simplex is defined by and ; at every . These affine coordinate functions on the finite-dimensional space are continuous, so they remain positive on a relative neighborhood of , which therefore lies in the simplex and in . Thus is a relative-interior point of .
Put and for integers . Each lies outside and tends to . Let be its unique nearest point in , supplied by [F1]. Since , . For each coordinate , , because each squared coordinate is a term of the sum defining the squared Euclidean norm. As coordinatewise, this gives coordinatewise and hence in Euclidean norm, since there are finitely many coordinates.
Normalize , which is defined because and is closed. The vectors have norm one, so their range lies in the Euclidean ball . By [F3], choose a subsequence converging in to . Since each coordinate difference is bounded by the Euclidean norm, this convergence gives coordinate convergence; the finite-sum formula for the squared norm then gives . Both and have horizontal coordinate in , so each horizontal component of lies in the direction space of . This finite-dimensional subspace is closed: after extending a basis of to one of , it is the common zero set of the remaining continuous coordinate functionals. Hence . For each fixed , [F2] gives . Along the chosen subsequence, coordinate convergence and the finite-sum formula for the inner product show , so .
The upward vertical ray lies in for all , so step 2.1 forces . If , the relative neighborhood from step 1.1 lets each direction be tested at and for sufficiently small . Applying step 2.1 to the corresponding epigraph points gives both and , so for every . Taking gives , hence , contradicting . Thus .
For any , apply step 2.1 to to get . Dividing by reverses the inequality and yields . Set and . This is the desired affine bound on , and outside the value is , so the bound holds on all of .
Remarks
The proof constructs a support normal using nearest points to a sequence below the epigraph. It uses the choice-free finite-dimensional Bolzano–Weierstrass supplier [F3], not the separate boundary-support theorem whose published statement assumes Countable Choice and the Axiom of Choice.
Depends on
- Proper closed convex extended-real functions on $\mathbb R^n$
- 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
- Metric projection onto a closed convex set satisfies the variational inequality
- For $n \ge 1$ every bounded sequence in $\mathbb{R}^n$ has a convergent subsequence
Used by
- The Fenchel conjugate of a proper closed convex function is proper closed convex Lemma
- Existence, resolvent characterization, and firm nonexpansiveness of the proximal map Theorem
- Proximal coordinate contraction from strong convexity of the composite Theorem
- Strongly convex smooth-part proximal coordinate contraction Theorem
Dependency tree · two levels
45 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, §1.1 (standard reference, not scraped)
- Amir Beck, First-Order Methods in Optimization, SIAM, 2017, Chapter 6 (standard reference, not scraped)