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hyperplane separation
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(Theorem)
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Let $X$ be a vector space, and $\Phi$ be any subspace of linear functionals on $X$ . Impose on $X$ the weak topology generated by $\Phi$ .
Theorem 1 (Hyperplane Separation Theorem I) Given a weakly closed convex subset $S \subset X$ , and $a \in X \setminus S$ . there is $\phi \in \Phi$ such that $$ \phi(a) < \inf_{x \in S} \phi(x)\,. $$
Proof. The weak topology on $X$ can be generated by the semi-norms $x \mapsto \abs{ p(x) }$ for $p \in \Phi$ . A subbasis for the weak topology consists of neigborhoods of the form $\{ x \in X \colon \abs{p(x-y)} < \epsilon \}$ for $y \in X$ , $p \in \Phi$ and $\epsilon > 0$ . Since $X \setminus S$ is weakly open, there exist
 and $\epsilon > 0$ such that
 for all $i=1, &cdots#dotsc;, n$ implies 
In other words, if $x \in S$ then at least one of $\abs{f_i(x) - f_i(a)}$ is $\geq \epsilon$ .
Define a map $F\colon X \to \real^n$ by
. The set $\overline{F(S)}$ is evidently closed and convex in $\real^n$ , a Hilbert space under the standard inner product. So there is a point $b \in \overline{F(S)}$ that minimizes the norm $\norm{b - F(a)}$ .
It follows that $\ip{y-b}{b-F(a)} \geq 0$ for all $y \in \overline{F(S)}$ ; for otherwise we can attain a smaller value of the norm by moving from the point $b$ along a line towards $y$ . (Formally, we have $0 \leq \left.\frac{d}{dt}\right|_{t=0} \norm{ty + (1-t)b - F(a)}^2 = 2\ip{y-b}{b-F(a)}$ .)
Take $\phi = \sum_{i=1}^n \lambda_i f_i$ where $\lambda = b-F(a)$ . Then we find, for all $x \in S$ ,

Theorem 2 (Hyperplane Separation Theorem II) Let $S \subset X$ be a weakly closed convex subset, and $K \subset X$ a compact convex subset, that do not intersect each other. Then there exists $\phi \in \Phi$ such that $$ \sup_{y \in K} \phi(y) < \inf_{x \in S} \phi(x)\,. $$
Proof. We show that $S - K = \{ x - y \colon x \in S\,, y \in K\}$ is weakly closed in $X$ . Let $\{ z_\alpha = x_\alpha - y_\alpha\} \subseteq A$ be a net convergent to $z$ . Since $K$ is compact, $\{ y_\alpha \}$ has a subnet $\{ y_{\alpha(\beta)} \}$ convergent to $y \in K$ . Then the subnet $x_{\alpha(\beta)} = z_{\alpha(\beta)} + y_{\alpha(\beta)}$ is convergent to $x = z + y$ . The point $x$ is in $S$ since $S$ is closed; therefore $z = x-y$ is in $S - K$ .
Also, $S-K$ is convex since $S$ and $K$ are. Noting that $0 \notin S-K$ (otherwise $S$ and $K$ would have a common point), we apply the previous theorem to obtain a $\phi \in \Phi$ such that $$ 0 = \phi(0) < \inf_{z \in S-K} \phi(z) \leq \phi(x-y) \,, \text{ for all $x \in S$ and $y \in K$. } $$ The desired conclusion follows at once. 
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"hyperplane separation" is owned by stevecheng.
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Cross-references: conclusion, subnet, convergent, net, intersect, compact, line, norm, point, inner product, Hilbert space, convex, map, open, subbasis, semi-norms, convex subset, closed, theorem, separation, generated by, weak topology, linear functionals, subspace, vector space
There is 1 reference to this entry.
This is version 1 of hyperplane separation, born on 2007-06-24.
Object id is 9667, canonical name is HyperplaneSeparation.
Accessed 2522 times total.
Classification:
| AMS MSC: | 46A55 (Functional analysis :: Topological linear spaces and related structures :: Convex sets in topological linear spaces; Choquet theory) | | | 49J27 (Calculus of variations and optimal control; optimization :: Existence theories :: Problems in abstract spaces) | | | 46A20 (Functional analysis :: Topological linear spaces and related structures :: Duality theory) |
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Pending Errata and Addenda
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