v2010.10.26 - Convex Optimization
v2010.10.26 - Convex Optimization v2010.10.26 - Convex Optimization
78 CHAPTER 2. CONVEX GEOMETRYA 1A 2A 30Figure 27: Any one particular point of three points illustrated does not belongto affine hull A i (i∈1, 2, 3, each drawn truncated) of points remaining.Three corresponding vectors in R 2 are, therefore, affinely independent (butneither linearly or conically independent).whereR(A) = {Ax | ∀x} (142)2.4.2.3 Affine independence, minimal setFor any particular affine set, a minimal set of points constituting itsvertex-description is an affinely independent generating set and vice versa.Arbitrary given points {x i ∈ R n , i=1... N} are affinely independent(a.i.) if and only if, over all ζ ∈ R N ζ T 1=1, ζ k = 0 (confer2.1.2)x i ζ i + · · · + x j ζ j − x k = 0, i≠ · · · ≠j ≠k = 1... N (123)has no solution ζ ; in words, iff no point from the given set can be expressedas an affine combination of those remaining. We deducel.i. ⇒ a.i. (124)Consequently, {x i , i=1... N} is an affinely independent set if and only if{x i −x 1 , i=2... N} is a linearly independent (l.i.) set. [[205,3] ] (Figure 27)XThis is equivalent to the property that the columns of1 T (for X ∈ R n×Nas in (76)) form a linearly independent set. [199,A.1.3]
2.4. HALFSPACE, HYPERPLANE 792.4.2.4 Preservation of affine independenceIndependence in the linear (2.1.2.1), affine, and conic (2.10.1) senses canbe preserved under linear transformation. Suppose a matrix X ∈ R n×N (76)holds an affinely independent set in its columns. Consider a transformationon the domain of such matricesT(X) : R n×N → R n×N XY (125)where fixed matrix Y [y 1 y 2 · · · y N ]∈ R N×N represents linear operator T .Affine independence of {Xy i ∈ R n , i=1... N} demands (by definition (123))there exist no solution ζ ∈ R N ζ T 1=1, ζ k = 0, toXy i ζ i + · · · + Xy j ζ j − Xy k = 0, i≠ · · · ≠j ≠k = 1... N (126)By factoring out X , we see that is ensured by affine independence of{y i ∈ R N } and by R(Y )∩ N(X) = 0 where2.4.2.5 Affine mapsN(A) = {x | Ax=0} (143)Affine transformations preserve affine hulls. Given any affine mapping T ofvector spaces and some arbitrary set C [307, p.8]aff(T C) = T(aff C) (127)2.4.2.6 PRINCIPLE 2: Supporting hyperplaneThe second most fundamental principle of convex geometry also follows fromthe geometric Hahn-Banach theorem [250,5.12] [18,1] that guaranteesexistence of at least one hyperplane in R n supporting a full-dimensionalconvex set 2.18 at each point on its boundary.The partial boundary ∂H of a halfspace that contains arbitrary set Y iscalled a supporting hyperplane ∂H to Y when the hyperplane contains atleast one point of Y . [307,11]2.18 It is customary to speak of a hyperplane supporting set C but not containing C ;called nontrivial support. [307, p.100] Hyperplanes in support of lower-dimensional bodiesare admitted.
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78 CHAPTER 2. CONVEX GEOMETRYA 1A 2A 30Figure 27: Any one particular point of three points illustrated does not belongto affine hull A i (i∈1, 2, 3, each drawn truncated) of points remaining.Three corresponding vectors in R 2 are, therefore, affinely independent (butneither linearly or conically independent).whereR(A) = {Ax | ∀x} (142)2.4.2.3 Affine independence, minimal setFor any particular affine set, a minimal set of points constituting itsvertex-description is an affinely independent generating set and vice versa.Arbitrary given points {x i ∈ R n , i=1... N} are affinely independent(a.i.) if and only if, over all ζ ∈ R N ζ T 1=1, ζ k = 0 (confer2.1.2)x i ζ i + · · · + x j ζ j − x k = 0, i≠ · · · ≠j ≠k = 1... N (123)has no solution ζ ; in words, iff no point from the given set can be expressedas an affine combination of those remaining. We deducel.i. ⇒ a.i. (124)Consequently, {x i , i=1... N} is an affinely independent set if and only if{x i −x 1 , i=2... N} is a linearly independent (l.i.) set. [[205,3] ] (Figure 27)XThis is equivalent to the property that the columns of1 T (for X ∈ R n×Nas in (76)) form a linearly independent set. [199,A.1.3]