v2007.09.13 - Convex Optimization
v2007.09.13 - Convex Optimization v2007.09.13 - Convex Optimization
350 CHAPTER 5. EUCLIDEAN DISTANCE MATRIX0Figure 87: Elliptope E 2 in isometrically isomorphic R 3 is a line segmentillustrated interior to positive semidefinite cone S 2 + (Figure 31).5.9.1 Geometric arguments5.9.1.0.1 Definition. Elliptope: [173] [170,2.3] [77,31.5]a unique bounded immutable convex Euclidean body in S n ; intersection ofpositive semidefinite cone S n + with that set of n hyperplanes defined by unitymain diagonal;E n ∆ = S n + ∩ {Φ∈ S n | δ(Φ)=1} (880)a.k.a, the set of all correlation matrices of dimensiondim E n = n(n −1)/2 in R n(n+1)/2 (881)An elliptope E n is not a polyhedron, in general, but has some polyhedral facesand an infinity of vertices. 5.41 Of those, 2 n−1 vertices are extreme directionsyy T of the positive semidefinite cone where the entries of vector y ∈ R n eachbelong to {±1} while the vector exercises every combination. Each of theremaining vertices has rank belonging to the set {k>0 | k(k + 1)/2 ≤ n}.Each and every face of an elliptope is exposed.△5.41 Laurent defines vertex distinctly from the sense herein (2.6.1.0.1); she defines vertexas a point with full-dimensional (nonempty interior) normal cone (E.10.3.2.1). Herdefinition excludes point C in Figure 21, for example.
5.9. BRIDGE: CONVEX POLYHEDRA TO EDMS 351The elliptope for dimension n = 2 is a line segment in isometricallyisomorphic R n(n+1)/2 (Figure 87). Obviously, cone(E n )≠ S n + . The elliptopefor dimension n = 3 is realized in Figure 86.5.9.1.0.2 Lemma. Hypersphere. [15,4] (confer bullet p.302)Matrix A = [A ij ]∈ S N belongs to the elliptope in S N iff there exist N pointsp on the boundary of a hypersphere having radius 1 in R rank A such that‖p i − p j ‖ = √ 2 √ 1 − A ij , i,j=1... N (882)There is a similar theorem for Euclidean distance matrices:We derive matrix criteria for D to be an EDM, validating (724) usingsimple geometry; distance to the polyhedron formed by the convex hull of alist of points (65) in Euclidean space R n .⋄5.9.1.0.3 EDM assertion.D is a Euclidean distance matrix if and only if D ∈ S N h and distances-squarefrom the origin{‖p(y)‖ 2 = −y T V T NDV N y | y ∈ S − β} (883)correspond to points p in some bounded convex polyhedronP − α = {p(y) | y ∈ S − β} (884)having N or fewer vertices embedded in an r-dimensional subspace A − αof R n , where α ∈ A = aff P and where the domain of linear surjection p(y)is the unit simplex S ⊂ R N−1+ shifted such that its vertex at the origin istranslated to −β in R N−1 . When β = 0, then α = x 1 .⋄In terms of V N , the unit simplex (253) in R N−1 has an equivalentrepresentation:S = {s ∈ R N−1 | √ 2V N s ≽ −e 1 } (885)where e 1 is as in (721). Incidental to the EDM assertion, shifting theunit-simplex domain in R N−1 translates the polyhedron P in R n . Indeed,
- Page 299 and 300: 5.4. EDM DEFINITION 2995.4.2 Gram-f
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- Page 305 and 306: 5.4. EDM DEFINITION 305ten affine e
- Page 307 and 308: 5.4. EDM DEFINITION 307spheres:Then
- Page 309 and 310: 5.4. EDM DEFINITION 309By eliminati
- Page 311 and 312: 5.4. EDM DEFINITION 311whereΦ ij =
- Page 313 and 314: 5.4. EDM DEFINITION 3135.4.2.2.5 De
- Page 315 and 316: 5.4. EDM DEFINITION 315105ˇx 4ˇx
- Page 317 and 318: 5.4. EDM DEFINITION 317corrected by
- Page 319 and 320: 5.4. EDM DEFINITION 319aptly be app
- Page 321 and 322: 5.4. EDM DEFINITION 321As before, a
- Page 323 and 324: 5.4. EDM DEFINITION 323where ([√t
- Page 325 and 326: 5.4. EDM DEFINITION 325because (A.3
- Page 327 and 328: 5.5. INVARIANCE 3275.5.1.0.1 Exampl
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- Page 361 and 362: 5.11. EDM INDEFINITENESS 361(confer
- Page 363 and 364: 5.11. EDM INDEFINITENESS 363we have
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5.9. BRIDGE: CONVEX POLYHEDRA TO EDMS 351The elliptope for dimension n = 2 is a line segment in isometricallyisomorphic R n(n+1)/2 (Figure 87). Obviously, cone(E n )≠ S n + . The elliptopefor dimension n = 3 is realized in Figure 86.5.9.1.0.2 Lemma. Hypersphere. [15,4] (confer bullet p.302)Matrix A = [A ij ]∈ S N belongs to the elliptope in S N iff there exist N pointsp on the boundary of a hypersphere having radius 1 in R rank A such that‖p i − p j ‖ = √ 2 √ 1 − A ij , i,j=1... N (882)There is a similar theorem for Euclidean distance matrices:We derive matrix criteria for D to be an EDM, validating (724) usingsimple geometry; distance to the polyhedron formed by the convex hull of alist of points (65) in Euclidean space R n .⋄5.9.1.0.3 EDM assertion.D is a Euclidean distance matrix if and only if D ∈ S N h and distances-squarefrom the origin{‖p(y)‖ 2 = −y T V T NDV N y | y ∈ S − β} (883)correspond to points p in some bounded convex polyhedronP − α = {p(y) | y ∈ S − β} (884)having N or fewer vertices embedded in an r-dimensional subspace A − αof R n , where α ∈ A = aff P and where the domain of linear surjection p(y)is the unit simplex S ⊂ R N−1+ shifted such that its vertex at the origin istranslated to −β in R N−1 . When β = 0, then α = x 1 .⋄In terms of V N , the unit simplex (253) in R N−1 has an equivalentrepresentation:S = {s ∈ R N−1 | √ 2V N s ≽ −e 1 } (885)where e 1 is as in (721). Incidental to the EDM assertion, shifting theunit-simplex domain in R N−1 translates the polyhedron P in R n . Indeed,