Closed Convex Set in terms of Bounded Linear Functionals
Theorem
Let $X$ be a normed vector space over $\R$.
Let $X^\ast$ be the normed dual of $X$.
Let $C$ be a closed convex subset of $X$.
Then:
- $\ds C = \bigcap_{f \in X^\ast} \set {x \in X : \map f x \le \sup_{c \in C} \map f c}$
Proof
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Clearly if $x \in C$ then:
- $\ds \map f x \le \sup_{c \in C} \map f c$
for all $f \in X^\ast$, by the definition of supremum.
That is:
- $\ds C \subseteq \bigcap_{f \in X^\ast} \set {x \in X : \map f x \le \sup_{c \in C} \map f c}$
To show that:
- $\ds \bigcap_{f \in X^\ast} \set {x \in X : \map f x \le \sup_{c \in C} \map f c} \subseteq C$
we prove that if $x_0 \not \in C$ then:
- $\ds x_0 \not \in \bigcap_{f \in X^\ast} \set {x \in X : \map f x \le \sup_{c \in C} \map f c}$
The Rule of Transposition will then give us the result.
Let $x_0 \not \in C$.
From Finite Topological Space is Compact, we have that $\set {x_0}$ is compact.
Clearly $\set {x_0}$ is disjoint from $C$.
We can therefore apply Hahn-Banach Separation Theorem: Compact Convex Set and Closed Convex Set, which gives the existence of $f \in X^\ast$, $c \in \R$ and $\epsilon > 0$ such that:
- $\map f {x_0} \le c - \epsilon < c + \epsilon \le \map f x$ for each $x \in C$.
From the definition of infimum, we therefore have:
- $\ds c + \epsilon \le \inf_{x \in C} \map f x$
so that:
- $\ds \map f {x_0} \le c - \epsilon < c + \epsilon \le \inf_{x \in C} \map f x$
Then, from Negative of Supremum is Infimum of Negatives, we have:
- $\ds -\map f {x_0} \ge \epsilon - c > -\paren {c + \epsilon} \ge \sup_{x \in C} \paren {-\map f x}$
Setting $g = -f \in X^\ast$ we have:
- $\ds \map g {x_0} > \sup_{x \in C} \map g x$
So:
- $\ds x_0 \not \in \bigcap_{f \in X^\ast} \set {x \in X : \map f x \le \sup_{c \in C} \map f c}$
as required.
Hence we obtain:
- $\ds \bigcap_{f \in X^\ast} \set {x \in X : \map f x \le \sup_{c \in C} \map f c} \subseteq C$
and:
- $\ds C = \bigcap_{f \in X^\ast} \set {x \in X : \map f x \le \sup_{c \in C} \map f c}$
$\blacksquare$