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The Fenchel conjugate of a proper closed convex function is proper closed convex
Statement
If is proper, closed and convex, then its conjugate from Fenchel conjugate of an extended-real function is also proper, closed and convex. The affine-minorant result cited below is used only to prove properness; closedness and convexity follow directly from the supremum formula.
Facts & Assumptions
Given: Proper closed convex on .
The function is proper, closed and convex.
A proper closed convex function has an affine minorant: some satisfy for every .
Proper closed convex extended-real functions on : has a nonempty effective domain, is convex, and has a closed epigraph.
The Euclidean inner product on : the pairing is bilinear and finite on all vectors.
Proof
Choose by [F3]. The affine minorant [F2] gives for every with finite , so . The term at gives . Thus is proper.
If , [F4] gives the singleton and properness makes finite, so is proper, closed and convex directly. Hence assume . For each and , [F5] gives . Thus is finite continuous affine. A pointwise supremum of these affine functions is convex: apply each affine function to , bound by , then take the supremum.
The epigraph of is the intersection, over , of the closed half-spaces Each half-space is closed because its defining affine function is continuous by step 1.2, and any intersection of closed sets is closed. Hence is closed in the epigraph convention of [F3].
Depends on
- Fenchel conjugate of an extended-real function
- A proper closed convex function has an affine minorant
- 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
- Proper closed convex extended-real functions on $\mathbb R^n$
Used by
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Sources
- Stephen Boyd and Lieven Vandenberghe, Convex Optimization, §§3.3.1 and 3.3.2 (standard reference, not scraped)
- Neal Parikh and Stephen Boyd, Proximal Algorithms, §1.2 (standard reference, not scraped)