Home
Convex Optimization
Convex Optimization Group
Calculus of Inequalities
Conic Independence
Convex Cones
Convex, Affine, Conic Hulls
Convex Functions
Convex Geometry
Distance Geometry
Distance Matrix Cone
Dual Cones
Duality Gap
Euclidean Distance Matrices
Elliptope and Fantope
Extreme Directions
Eigenvalues/Eigenvectors
Farkas Lemma
Face Recognition
Fifth Metric Property
Kissing Number
Linear Algebra
Linear Matrix Inequality
Matrix Calculus
Manifold Learning
Molecular Conformation
Positive Semidefinite Cone
Projection
Quasiconvex Functions
Rank Constraint
Semidefinite Programming
Schoenberg Criterion
Sensor Network Localization
Optimization News
SEO Consultant
Video
Wikimization
Zeros of Polynomials
Contact Us
Wikimization     Meboo     Video     News     Contact     See
Felice crystal
Home arrow Convex Cones
Convex Cones

                 Euclidean distance matrix cone

We call a set K a convex cone iff any nonnegative combination of elements from K remains in K.  The set of all convex cones is a proper subset of all cones. The set of convex cones is a narrower but more familiar class of cone, any member of which can be equivalently described as the intersection of a possibly (but not necessarily) infinite number of hyperplanes (through the origin) and halfspaces whose bounding hyperplanes pass through the origin; a halfspace-description.  The interior of a convex cone is possibly empty.

Familiar examples of convex cones include an unbounded ice-cream cone united with its interior (a.k.a: second-order cone, quadratic cone, circular cone, Lorentz cone) and any polyhedral cone; e.g., any orthant generated by Cartesian axes. Esoteric examples of convex cones include the point at the origin, any line through the origin, any ray having the origin as base such as the nonnegative real line in a subspace, any halfspace partially bounded by a hyperplane through the origin, the positive semidefinite cone, the cone of Euclidean distance matrices, any subspace, and Euclidean vector space.

More Euclidean bodies are cones, it seems, than are not.  This class of convex body, the convex cone, is invariant to nonnegative scaling, vector summation, affine and inverse affine transformation, Cartesian product, and intersection, but is not invariant to projection.

Read more...

 

The Course

The Videos

See Inside

Convex Optimization

by Stephen Boyd 

& L. Vandenberghe 

Buy Book



See Figures

See Inside

Dattorro

by Dattorro

Buy Book



The Course

Bertsekas

by Dimitri Bertsekas 

Buy Book



See Inside

Hiriart-Urruty & Lemaréchal

by Hiriart-Urruty

& Lemaréchal

Buy Book



See Inside

Rockafellar

by Rockafellar

Buy Book



Optimization Newsletter
Subscription:

Email:

Receive HTML mailings?
Subscribe Unsubscribe