Convex cone

In linear algebra, a convex cone is a subset of a vector space over an ordered field that is closed under linear combinations with positive coefficients.

A convex cone (light blue). Inside of it, the light red convex cone consists of all points αx + βy with α, β > 0, for the depicted x and y. The curves on the upper right symbolize that the regions are infinite in extent.

Definition

A subset C of a vector space V is a cone (or sometimes called a linear cone) if for each x in C and positive scalars α, the product αx is in C.

A cone C is a convex cone if αx + βy belongs to C, for any positive scalars α, β, and any x, y in C.

This concept is meaningful for any vector space that allows the concept of "positive" scalar, such as spaces over the rational, algebraic, or (more commonly) the real numbers. Also note that the scalars in the definition are positive meaning that the origin does not have to belong to C. Some authors use a definition that ensures the origin belongs to C. Because of the scaling parameters α and β, cones are infinite in extent and not bounded.

If C is a convex cone, then for any positive scalar α and any x in C the vector $\alpha x={\tfrac {\alpha }{2}}x+{\tfrac {\alpha }{2}}x\in C.$ It follows that a convex cone C is a special case of a linear cone.

It follows from the above property that a convex cone can also be defined as a linear cone that is closed under convex combinations, or just under additions. More succinctly, a set C is a convex cone if and only if αC = C and C + C = C, for any positive scalar α.

Examples

Convex cone that is not a conic combination of finitely many generators.

Convex cone generated by the conic combination of three black vectors.

A cone (the union of two rays) that is not a convex cone.

• For a vector space V, the empty set, the space V, and any linear subspace of V are convex cones.
• The conical combination of a finite or infinite set of vectors in $\mathbb {R} ^{n}$ is a convex cone.
• The tangent cones of a convex set are convex cones.
• The set

$\left\{x\in \mathbb {R} ^{2}\mid x_{2}\geq 0,x_{1}=0\right\}\cup \left\{x\in \mathbb {R} ^{2}\mid x_{1}\geq 0,x_{2}=0\right\}$

is a cone but not a convex cone.

• The norm cone

$\left\{(x,r)\in \mathbb {R} ^{d+1}\mid \|x\|\leq r\right\}$

is a convex cone.

• The intersection of two convex cones in the same vector space is again a convex cone, but their union may fail to be one.
• The class of convex cones is also closed under arbitrary linear maps. In particular, if C is a convex cone, so is its opposite $-C$ and $C\cap -C$ is the largest linear subspace contained in C.
• The set of positive semidefinite matrices.
• The set of nonnegative continuous functions is a convex cone.

Special Examples

Affine convex cones

An affine convex cone is the set resulting from applying an affine transformation to a convex cone. A common example is translating a convex cone by a point p: p+C. Technically, such transformations can produce non-cones. For example, unless p=0, p+C is not a linear cone. However, it is still called an affine convex cone.

Half-spaces

A (linear) hyperplane is a set in the form $\{x\in V\mid f(x)=c\}$ where f is a linear functional on the vector space V. A closed half-space is a set in the form $\{x\in V\mid f(x)\leq c\}$ or $\{x\in V\mid f(x)\geq c\},$ and likewise an open half-space uses strict inequality.

Half-spaces (open or closed) are affine convex cones. Moreover (in finite dimensions), any convex cone C that is not the whole space V must be contained in some closed half-space H of V; this is a special case of Farkas' lemma.

Polyhedral and finitely generated cones

A cone $C$ is called polyhedral if there is some matrix $A$ such that $C=\{x\in \mathbb {R} ^{n}\mid Ax\geq 0\}$, and a cone $C$ is called finitely generated if it is the conic combination of finitely many vectors.

Every polyhedral cone in $R^{n}$can be represented in two different ways; as an intersection of inequalities or the conical hull of vectors. In the inequality description, the polyhedral cone $C$ can be given by a matrix $A$ such that $C=\{x\in \mathbb {R} ^{n}\mid Ax\geq 0\}$. Geometrically, each system of linear inequalities defines a halfspace that passes through the origin, and their intersection defines the cone.

In the conical combination description, $C$ can be represented by a finite set of vectors $v_{1},\ldots ,v_{k}$ such that $C$ is the conical combination of $\{v_{1},\ldots ,v_{k}\}$, or $C=\{a_{1}v_{1}+\cdots +a_{k}v_{k}\mid a_{i}\in \mathbb {R} _{+},v_{i}\in \mathbb {R} ^{n}\}$. Every finitely generated cone is a polyhedral cone, and every polyhedral cone is a finitely generated cone. Every polyhedral cone has a unique representation as a conical hull of its extremal generators, and every polyhedral cone has a unique representation of intersections of halfspaces, given each linear form associated with the halfspaces also define a support hyperplane of a facet.

Polyhedral cones play a central role in the representation theory of polyhedra. For instance, the decomposition theorem for polyhedra states that every polyhedron can be written as the Minkowski sum of a convex polytope and a polyhedral cone. Polyhedral cones also play an important part in proving the related Finite Basis Theorem for polytopes which shows that every polytope is a polyhedron and every bounded polyhedron is a polytope.

Blunt, pointed, flat, salient, and proper cones

According to the above definition, if C is a convex cone, then C ∪ {0} is a convex cone, too. A convex cone is said to be pointed if 0 is in C, and blunt if 0 is not in C. Blunt cones can be excluded from the definition of convex cone by substituting "non-negative" for "positive" in the condition of α, β.

A cone is called flat if it contains some nonzero vector x and its opposite -x, meaning C contains a linear subspace of dimension at least one, and salient otherwise. A blunt convex cone is necessarily salient, but the converse is not necessarily true. A convex cone C is salient if and only if C ∩ −C ⊆ {0}.

Some authors require salient cones to be pointed. The term "pointed" is also often used to refer to a closed cone that contains no complete line (i.e., no nontrivial subspace of the ambient vector space V, or what is called a salient cone). The term proper (convex) cone is variously defined, depending on the context and author. It often means a cone that satisfies other properties like being convex, closed, pointed, salient, and full-dimensional. Because of these varying definitions, the context or source should be consulted for the definition of these terms.

Rational Cones

A type of cone of particular interest to pure mathematicians is the partially ordered set of rational cones. "Rational cones are important objects in toric algebraic geometry, combinatorial commutative algebra, geometric combinatorics, integer programming." . This object arises when we study cones in $\mathbb {R} ^{d}$ together with the lattice $\mathbb {Z} ^{d}$. A cone is called rational (here we assume "pointed", as defined above) whenever its generators all have integer coordinates, i.e., if $C$ is a rational cone, then $C=\{a_{1}v_{1}+\cdots +a_{k}v_{k}\mid a_{i}\in \mathbb {R} _{+},v_{i}\in \mathbb {Z} ^{d}\}$.

Dual cone

Main article: dual cone

Let C ⊂ V be a set, not necessary a convex set, in a real vector space V equipped with an inner product. The (continuous or topological) dual cone to C is the set

$C^{*}=\{v\in V\mid \forall w\in C,\langle w,v\rangle \geq 0\},$

which is always a convex cone.

More generally, the (algebraic) dual cone to C ⊂ V in a linear space V is a subset of the dual space V* defined by:

$C^{*}:=\left\{v\in V^{*}\mid \forall w\in C,v(w)\geq 0\right\}.$

In other words, if V* is the algebraic dual space of V, it is the set of linear functionals that are nonnegative on the primal cone C. If we take V* to be the continuous dual space then it is the set of continuous linear functionals nonnegative on C. This notion does not require the specification of an inner product on V.

In finite dimensions, the two notions of dual cone are essentially the same because every finite dimensional linear functional is continuous, and every continuous linear functional in a inner product space induces a linear isomorphism (nonsingular linear map) from V* to V, and this isomorphism will take the dual cone given by the second definition, in V*, onto the one given by the first definition; see the Riesz representation theorem.

If C is equal to its dual cone, then C is called self-dual. A cone can be said to be self-dual without reference to any given inner product, if there exists an inner product with respect to which it is equal to its dual by the first definition.

Partial order defined by a convex cone

A pointed and salient convex cone C induces a partial ordering "≤" on V, defined so that $x\leq y$ if and only if $y-x\in C.$ (If the cone is flat, the same definition gives merely a preorder.) Sums and positive scalar multiples of valid inequalities with respect to this order remain valid inequalities. A vector space with such an order is called an ordered vector space. Examples include the product order on real-valued vectors, $\mathbb {R} ^{n},$ and the Loewner order on positive semidefinite matrices. Such an ordering is commonly found in positive semidefinite programming.

See also

• Cone (disambiguation)
  • Cone (geometry)
  • Cone (topology)
  • Cone (linear algebra)
• Farkas' lemma
• Bipolar theorem
• Linear combination

References

• Bourbaki, Nicolas (1987). Topological Vector Spaces. Elements of Mathematics. Berlin, New York: Springer-Verlag. ISBN 978-3-540-13627-9.
• Rockafellar, R. T. (1997) [1970]. Convex Analysis. Princeton, NJ: Princeton University Press. ISBN 1-4008-7317-7.
• Zălinescu, C. (2002). Convex Analysis in General Vector Spaces. River Edge, NJ,: World Scientific. ISBN 981-238-067-1. MR 1921556.