How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
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Strong separation of a closed and a compact convex set
Statement
Let be disjoint nonempty convex sets, where is closed and is compact. Then they are strongly separated by a nonzero functional in .
Facts & Assumptions
Given: Disjoint nonempty convex , with closed and compact.
Distance to a fixed nonempty set is continuous (indeed -Lipschitz) (, so the distance to a fixed nonempty set is -Lipschitz).
A continuous real-valued function on a compact metric space attains its minimum (A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value).
Disjoint convex sets, one open, are strictly separated by a nonzero continuous functional (Separation of disjoint convex sets when one is open).
Proof
By [F1]--[F2], has a minimum on . It is positive: a zero minimum would put some in the closed set . Choose .
The thickening is open and convex and is disjoint from . Apply [F3] to to obtain nonzero with for .
For , take the supremum over in the inequalities from step 2.1. Since , this gives for every . Hence , a positive gap.
Depends on
- Separation of disjoint convex sets when one is open
- Weak, strict, and strong separation
- $|d(x,A) - d(y,A)| \le d(x,y)$, so the distance to a fixed nonempty set is $1$-Lipschitz
- A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value
- Open cover, subcover, compact metric space, and compact subset of a metric space
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
36 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Gerald Teschl, Topics in Real and Functional Analysis, Corollary 5.4 (standard reference, not scraped)