Supporting functional

In convex analysis and mathematical optimization, the supporting functional is a generalization of the supporting hyperplane of a set.

Mathematical definition[edit]

Let X be a locally convex topological space, and $C\subset X$ be a convex set, then the continuous linear functional $\phi :X\to \mathbb {R}$ is a supporting functional of C at the point $x_{0}$ if $\phi \not =0$ and $\phi (x)\leq \phi (x_{0})$ for every $x\in C$ .^[1]

Relation to support function[edit]

If $h_{C}:X^{*}\to \mathbb {R}$ (where $X^{*}$ is the dual space of $X$ ) is a support function of the set C, then if $h_{C}\left(x^{*}\right)=x^{*}\left(x_{0}\right)$ , it follows that $h_{C}$ defines a supporting functional $\phi :X\to \mathbb {R}$ of C at the point $x_{0}$ such that $\phi (x)=x^{*}(x)$ for any $x\in X$ .

Relation to supporting hyperplane[edit]

If $\phi$ is a supporting functional of the convex set C at the point $x_{0}\in C$ such that

\phi \left(x_{0}\right)=\sigma =\sup _{x\in C}\phi (x)>\inf _{x\in C}\phi (x)

then $H=\phi ^{-1}(\sigma )$ defines a supporting hyperplane to C at $x_{0}$ .^[2]

References[edit]

^ Pallaschke, Diethard; Rolewicz, Stefan (1997). Foundations of mathematical optimization: convex analysis without linearity. Springer. p. 323. ISBN 978-0-7923-4424-7.
^ Borwein, Jonathan; Lewis, Adrian (2006). Convex Analysis and Nonlinear Optimization: Theory and Examples (2 ed.). Springer. p. 240. ISBN 978-0-387-29570-1.

[1] Pallaschke, Diethard; Rolewicz, Stefan (1997). Foundations of mathematical optimization: convex analysis without linearity. Springer. p. 323. ISBN 978-0-7923-4424-7.

[2] Borwein, Jonathan; Lewis, Adrian (2006). Convex Analysis and Nonlinear Optimization: Theory and Examples (2 ed.). Springer. p. 240. ISBN 978-0-387-29570-1.

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