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: TheinnerDualConeof theinnerDualConeof 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 coneKin a complete, real inner product spaceHand a pointbdisjoint from it, there is a vectorywhich separatesbfromKin the sense that for all pointsxinK,0 ≤ ⟪x, y⟫_ℝand⟪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
- Set.innerDualCone s = { carrier := {y : H | ∀ x ∈ s, 0 ≤ ⟪x, y⟫_ℝ}, smul_mem' := ⋯, add_mem' := ⋯ }
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.