Convex cones #
In an R-module M, we define a convex cone as a set s such that a • x + b • y ∈ s whenever
x, y ∈ s and a, b > 0. We prove that convex cones form a CompleteLattice, and define their
images (ConvexCone.map) and preimages (ConvexCone.comap) under linear maps.
We define pointed, blunt, flat and salient cones, and prove the correspondence between convex cones and ordered modules.
We define Convex.toCone to be the minimal cone that includes a given convex set.
Main statements #
In Mathlib/Analysis/Convex/Cone/Extension.lean we prove
the M. Riesz extension theorem and a form of the Hahn-Banach theorem.
In Mathlib/Analysis/Convex/Cone/Dual.lean we prove
a variant of the hyperplane separation theorem.
Implementation notes #
While Convex R is a predicate on sets, ConvexCone R M is a bundled convex cone.
References #
Definition of ConvexCone and basic properties #
A convex cone is a subset s of an R-module such that a • x + b • y ∈ s whenever a, b > 0
and x, y ∈ s.
- carrier : Set M
The carrier set underlying this cone: the set of points contained in it
Instances For
Equations
- ConvexCone.instSetLike = { coe := ConvexCone.carrier, coe_injective := ⋯ }
Equations
Two ConvexCones are equal if they have the same elements.
Copy of a convex cone with a new carrier equal to the old one. Useful to fix definitional
equalities.
Instances For
Equations
- ConvexCone.instInfSet = { sInf := fun (S : Set (ConvexCone R M)) => { carrier := ⋂ C ∈ S, ↑C, smul_mem' := ⋯, add_mem' := ⋯ } }
Equations
- ConvexCone.instCompleteSemilatticeInf = { toPartialOrder := ConvexCone.instPartialOrder, toInfSet := ConvexCone.instInfSet, isGLB_sInf := ⋯ }
The cone hull of a set. The smallest convex cone containing that set.
Equations
- ConvexCone.hull R s = sInf {C : ConvexCone R M | s ⊆ ↑C}
Instances For
Galois insertion between ConvexCone and SetLike.coe.
Equations
- ConvexCone.gi = { choice := fun (s : Set M) (hs : ↑(ConvexCone.hull R s) ⊆ s) => (ConvexCone.hull R s).copy s ⋯, gc := ⋯, le_l_u := ⋯, choice_eq := ⋯ }
Instances For
Equations
- One or more equations did not get rendered due to their size.
Equations
- ConvexCone.instInhabited = { default := ⊥ }
The image of a convex cone under an R-linear map is a convex cone.
Equations
- ConvexCone.map f C = { carrier := ⇑f '' ↑C, smul_mem' := ⋯, add_mem' := ⋯ }
Instances For
The preimage of a convex cone under an R-linear map is a convex cone.
Equations
- ConvexCone.comap f C = { carrier := ⇑f ⁻¹' ↑C, smul_mem' := ⋯, add_mem' := ⋯ }
Instances For
Convex cones with extra properties #
A convex cone is pointed if it includes 0.
Instances For
A convex cone is blunt if it doesn't include 0.
Instances For
A convex cone is flat if it contains some nonzero vector x and its opposite -x.
Instances For
A convex cone is salient if it doesn't include x and -x for any nonzero x.
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A flat cone is always pointed (contains 0).
A blunt cone (one not containing 0) is always salient.
A pointed convex cone defines a preorder.
Equations
Instances For
A pointed and salient cone defines a partial order.
Equations
- C.toPartialOrder h₁ h₂ = { toPreorder := C.toPreorder h₁, le_antisymm := ⋯ }
Instances For
A pointed and salient cone defines an IsOrderedAddMonoid.
Equations
- ConvexCone.instAdd = { add := fun (C₁ C₂ : ConvexCone R M) => { carrier := ↑C₁ + ↑C₂, smul_mem' := ⋯, add_mem' := ⋯ } }
Equations
- ConvexCone.instAddZeroClass = { toZero := ConvexCone.instZero, toAdd := ConvexCone.instAdd, zero_add := ⋯, add_zero := ⋯ }
Equations
- ConvexCone.instAddCommSemigroup = { toAdd := ConvexCone.instAdd, add_assoc := ⋯, add_comm := ⋯ }
A convex cone is reproducing if its set of element differences equals the entire module,
i.e., every element of M can be written as a difference of two elements of C.
Equations
- C.IsReproducing = (↑C - ↑C = Set.univ)
Instances For
A sufficient criterion for a convex cone C to be reproducing is that Set.univ is a subset
of C - C.
The set difference of a reproducing cone with itself equals Set.univ.
The cone hull of a convex set is simply the union of the open halflines through that set.
The cone hull of a convex set is simply the union of the open halflines through that set.
Submodules are cones #
Every submodule is trivially a convex cone.
Equations
- C.toConvexCone = { carrier := ↑C, smul_mem' := ⋯, add_mem' := ⋯ }
Instances For
Positive cone of an ordered module #
The positive cone is the convex cone formed by the set of nonnegative elements in an ordered module.
Equations
- ConvexCone.positive R M = { carrier := Set.Ici 0, smul_mem' := ⋯, add_mem' := ⋯ }
Instances For
The positive cone of an ordered module is always salient.
The positive cone of an ordered module is always pointed.
The cone of strictly positive elements.
Note that this naming diverges from the mathlib convention of pos and nonneg due to "positive
cone" (ConvexCone.positive) being established terminology for the non-negative elements.
Equations
- ConvexCone.strictlyPositive R M = { carrier := Set.Ioi 0, smul_mem' := ⋯, add_mem' := ⋯ }
Instances For
The strictly positive cone of an ordered module is always salient.
The strictly positive cone of an ordered module is always blunt.