Corollary 18.5.1. Let C be a closed bounded convex set. Then C is the convex hull of its
extreme points.
Extreme points are monotone with respect to set inclusion.
conv (closure (exposedPoints)) is closed and contained in the set.
An extreme point outside the closure of exposed points is not in conv (closure ...).
Given a compact convex set C, a closed convex subset D, and a point x ∈ C \ D, there is
a nonempty exposed face of C disjoint from D.
An extreme point outside the closure of exposed points yields a disjoint exposed face.
If vectorSpan has dimension zero, the set is a singleton.
Uniform-gap perturbation: if l has a positive gap away from l.toExposed C and g is
bounded on C, then for small positive ε, every maximizer of l + ε g lies in l.toExposed C.
On any closed subset of C disjoint from l.toExposed C, the exposing functional is
uniformly below its maximal value by a positive gap.
Compact lexicographic perturbation (singleton target): if z is the unique g-maximizer on
l.toExposed C and l has a uniform positive gap away from l.toExposed C, then for a small
positive perturbation l + ε g, the exposed set on C is exactly {z}.
Quantitative lexicographic perturbation:
with a uniform gap away from l.toExposed C and a bound on g over C, a small perturbation
realizes exactly g.toExposed (l.toExposed C).
A nonempty exposed face of a compact set contains an extreme point of the ambient set.
Bridge-to-ambient step: once the compact lexicographic singleton data is available on an
exposed face l.toExposed C, the selected point is an exposed point of C.
Perturbation-limit bridge:
if exposed points p k are selected from perturbed maximizer sets (l + εₖ g).toExposed C,
converge to z, and the perturbation terms vanish in the limit, then z lies in the exposed face
l.toExposed C and in closure (C.exposedPoints ℝ).
Compact extraction for perturbed exposed selectors.
Vanishing perturbation terms from a vanishing scale and bounded functional values on C.
Choice form: nonempty intersections of exposed points with perturbed exposed faces give a selector sequence.
Theorem 18.6. Every extreme point lies in the closure of the exposed points (bounded case).
The closure mixed convex hull is closed and convex.
The closure mixed convex hull recedes along extreme directions.
Exposed points are contained in the mixed convex hull, hence their closure lies in K.
Extreme points lie in the closure mixed convex hull (bounded Straszewicz step).
Use Theorem 18.5 to show C ⊆ K once extreme points lie in K.
Theorem 18.7. A closed bounded convex set with no lines is the closure of the mixed convex hull of its exposed points and extreme directions.
The continuous linear functional x ↦ dotProduct x xStar.
Equations
- dotProductCLM xStar = LinearMap.toContinuousLinearMap ((dotProductBilin ℝ ℝ).flip xStar)
Instances For
For each xStar, there is an extreme-point maximizer of y ↦ dotProduct y xStar.
A maximizer in an exposed face realizes the support function value.
Theorem 18.8 (extreme-point form). A closed bounded convex set is the intersection of its tangent half-spaces at extreme points.