Convex cones in inner product spaces #
We define Set.innerDualCone
to be the cone consisting of all points y
such that for
all points x
in a given set 0 ≤ ⟪ x, y ⟫
.
Main statements #
We prove the following theorems:
ConvexCone.innerDualCone_of_innerDualCone_eq_self
: TheinnerDualCone
of theinnerDualCone
of a nonempty, closed, convex cone is itself.ConvexCone.hyperplane_separation_of_nonempty_of_isClosed_of_nmem
: This variant of the hyperplane separation theorem states that given a nonempty, closed, convex coneK
in a complete, real inner product spaceH
and a pointb
disjoint from it, there is a vectory
which separatesb
fromK
in the sense that for all pointsx
inK
,0 ≤ ⟪x, y⟫_ℝ
and⟪y, b⟫_ℝ < 0
. This is also a geometric interpretation of the Farkas lemma.
The dual cone #
The dual cone is the cone consisting of all points y
such that for
all points x
in a given set 0 ≤ ⟪ x, y ⟫
.
Equations
Instances For
Dual cone of the convex cone {0} is the total space.
Dual cone of the total space is the convex cone {0}.
The inner dual cone of a singleton is given by the preimage of the positive cone under the
linear map fun y ↦ ⟪x, y⟫
.
The dual cone of s
equals the intersection of dual cones of the points in s
.
This is a stronger version of the Hahn-Banach separation theorem for closed convex cones. This is also the geometric interpretation of Farkas' lemma.
The inner dual of inner dual of a non-empty, closed convex cone is itself.