v2010.10.26 - Convex Optimization

v2010.10.26 - Convex Optimization v2010.10.26 - Convex Optimization

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146 CHAPTER 2. CONVEX GEOMETRY{x l ∈ R n , l=1... n + 1} be affinely independent (we want vector x n+1 notparallel to ∂H), thenH = ⋃ (ζ x n+1 + ∂H)ζ ≥0= {ζ x n+1 + cone{x l ∈ R n , l=1... n} | ζ ≥0}= cone{x l ∈ R n , l=1... n + 1}(285)a union of parallel hyperplanes. Cardinality is one step beyond dimension ofthe ambient space, but {x l ∀l} is a minimal set of generators for this convexcone H which has no extreme elements.2.10.2.0.2 Exercise. Enumerating conically independent directions.Describe a nonpointed polyhedral cone in three dimensions having more than8 conically independent generators. (confer Table 2.10.0.0.1) 2.10.3 Utility of conic independencePerhaps the most useful application of conic independence is determinationof the intersection of closed convex cones from their halfspace-descriptions,or representation of the sum of closed convex cones from theirvertex-descriptions.⋂Ki∑KiA halfspace-description for the intersection of any number of closedconvex cones K i can be acquired by pruning normals; specifically,only the conically independent normals from the aggregate of all thehalfspace-descriptions need be retained.Generators for the sum of any number of closed convex cones K i canbe determined by retaining only the conically independent generatorsfrom the aggregate of all the vertex-descriptions.Such conically independent sets are not necessarily unique or minimal.2.11 When extreme means exposedFor any convex full-dimensional polyhedral set in R n , distinction betweenthe terms extreme and exposed vanishes [330,2.4] [113,2.2] for faces of

2.12. CONVEX POLYHEDRA 147all dimensions except n ; their meanings become equivalent as we saw inFigure 20 (discussed in2.6.1.2). In other words, each and every face of anypolyhedral set (except the set itself) can be exposed by a hyperplane, andvice versa; e.g., Figure 24.Lewis [246,6] [215,2.3.4] claims nonempty extreme proper subsets andthe exposed subsets coincide for S n + ; id est, each and every face of the positivesemidefinite cone, whose dimension is less than dimension of the cone, isexposed. A more general discussion of cones having this property can befound in [341]; e.g., Lorentz cone (178) [20,II.A].2.12 Convex polyhedraEvery polyhedron, such as the convex hull (86) of a bounded list X , canbe expressed as the solution set of a finite system of linear equalities andinequalities, and vice versa. [113,2.2]2.12.0.0.1 Definition. Convex polyhedra, halfspace-description.A convex polyhedron is the intersection of a finite number of halfspaces andhyperplanes;P = {y | Ay ≽ b, Cy = d} ⊆ R n (286)where coefficients A and C generally denote matrices. Each row of C is avector normal to a hyperplane, while each row of A is a vector inward-normalto a hyperplane partially bounding a halfspace.△By the halfspaces theorem in2.4.1.1.1, a polyhedron thus described isa closed convex set possibly not full-dimensional; e.g., Figure 20. Convexpolyhedra 2.53 are finite-dimensional comprising all affine sets (2.3.1),polyhedral cones, line segments, rays, halfspaces, convex polygons, solids[223, def.104/6 p.343], polychora, polytopes, 2.54 etcetera.It follows from definition (286) by exposure that each face of a convexpolyhedron is a convex polyhedron.Projection of any polyhedron on a subspace remains a polyhedron. Moregenerally, image and inverse image of a convex polyhedron under any linear2.53 We consider only convex polyhedra throughout, but acknowledge the existence ofconcave polyhedra. [373, Kepler-Poinsot Solid]2.54 Some authors distinguish bounded polyhedra via the designation polytope. [113,2.2]

2.12. CONVEX POLYHEDRA 147all dimensions except n ; their meanings become equivalent as we saw inFigure 20 (discussed in2.6.1.2). In other words, each and every face of anypolyhedral set (except the set itself) can be exposed by a hyperplane, andvice versa; e.g., Figure 24.Lewis [246,6] [215,2.3.4] claims nonempty extreme proper subsets andthe exposed subsets coincide for S n + ; id est, each and every face of the positivesemidefinite cone, whose dimension is less than dimension of the cone, isexposed. A more general discussion of cones having this property can befound in [341]; e.g., Lorentz cone (178) [20,II.A].2.12 <strong>Convex</strong> polyhedraEvery polyhedron, such as the convex hull (86) of a bounded list X , canbe expressed as the solution set of a finite system of linear equalities andinequalities, and vice versa. [113,2.2]2.12.0.0.1 Definition. <strong>Convex</strong> polyhedra, halfspace-description.A convex polyhedron is the intersection of a finite number of halfspaces andhyperplanes;P = {y | Ay ≽ b, Cy = d} ⊆ R n (286)where coefficients A and C generally denote matrices. Each row of C is avector normal to a hyperplane, while each row of A is a vector inward-normalto a hyperplane partially bounding a halfspace.△By the halfspaces theorem in2.4.1.1.1, a polyhedron thus described isa closed convex set possibly not full-dimensional; e.g., Figure 20. <strong>Convex</strong>polyhedra 2.53 are finite-dimensional comprising all affine sets (2.3.1),polyhedral cones, line segments, rays, halfspaces, convex polygons, solids[223, def.104/6 p.343], polychora, polytopes, 2.54 etcetera.It follows from definition (286) by exposure that each face of a convexpolyhedron is a convex polyhedron.Projection of any polyhedron on a subspace remains a polyhedron. Moregenerally, image and inverse image of a convex polyhedron under any linear2.53 We consider only convex polyhedra throughout, but acknowledge the existence ofconcave polyhedra. [373, Kepler-Poinsot Solid]2.54 Some authors distinguish bounded polyhedra via the designation polytope. [113,2.2]

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