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A finite-dimensional convex value function has a support at relative-interior points
Statement
Let be a finite-dimensional Euclidean space and let be convex, never , and finite somewhere. If and , then there is a linear functional such that
Facts & Assumptions
Given: The finite-dimensional Euclidean space, convex value function, relative-interior point, and finite value in the Statement. The conventions for domains, affine hull, relative interior and epigraph are those of Finite-dimensional convex program, Lagrangian dual, and Slater condition.
A finite convex real-valued function on an open convex subset of a finite-dimensional Euclidean space is locally Lipschitz and hence continuous (A convex function on an open convex set is locally Lipschitz).
Every point has a nearest point in a nonempty closed convex Euclidean set (Every point has a unique nearest point in a nonempty closed Euclidean convex set), and the projection satisfies for every (Metric projection onto a closed convex set satisfies the variational inequality).
The unit sphere in a finite-dimensional normed space is sequentially compact, including the choice-free finite-dimensional case (Unit spheres in finite-dimensional normed spaces are sequentially compact).
Proof
Let . Since belongs to the domain, is a linear subspace; relative interior gives a ball about in contained in . If , take . Otherwise identify with a Euclidean coordinate space. The restriction of to that open ball is finite and convex, so [F1] makes it continuous at .
In , let and let be its closure. The set is convex, , and is nonempty, closed and convex. Continuity from step 1.1 shows that for every : choose so whenever , while a sufficiently small open ball about has and , so it misses . Thus is a boundary point of .
For each positive integer , project onto , obtaining . Since , the projection distance is at most , so . The unit vectors have a convergent subsequence by [F3], with unit limit . For each , [F2] gives ; passing along that subsequence gives .
The epigraph contains every upward vertical ray, so the support inequality forces . If , points for all sufficiently small and in the ball from step 1.1 force both and . Hence , contradicting . Therefore . Setting , the support inequality at yields for in the domain; outside the domain the inequality is automatic because .
Depends on
- Finite-dimensional convex program, Lagrangian dual, and Slater condition
- A convex function on an open convex set is locally Lipschitz
- 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
- Unit spheres in finite-dimensional normed spaces are sequentially compact
Used by
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Sources
- Boyd and Vandenberghe, Convex Optimization (standard reference, not scraped)
- Zhiyuan Li, Convex Optimization, Lecture 5: Lagrange Multipliers and KKT (standard reference, not scraped)