v2010.10.26 - Convex Optimization
v2010.10.26 - Convex Optimization v2010.10.26 - Convex Optimization
440 CHAPTER 5. EUCLIDEAN DISTANCE MATRIX∈ basis S N⊥hdim S N c = dim S N h = N(N−1)2in R N(N+1)/2dim S N⊥c= dim S N⊥h= N in R N(N+1)/2basis S N c = V {E ij }V (confer (59))S N cbasis S N⊥cS N h∈ basis S N⊥hFigure 129: Orthogonal complements in S N abstractly oriented inisometrically isomorphic R N(N+1)/2 . Case N = 2 accurately illustrated in R 3 .Orthogonal projection of basis for S N⊥h on S N⊥c yields another basis for S N⊥c .(Basis vectors for S N⊥c are illustrated lying in a plane orthogonal to S N c in thisdimension. Basis vectors for each ⊥ space outnumber those for its respectiveorthogonal complement; such is not the case in higher dimension.)
5.6. INJECTIVITY OF D & UNIQUE RECONSTRUCTION 441To prove injectivity of D(G) on S N c : Any matrix Y ∈ S N can bedecomposed into orthogonal components in S N ;Y = V Y V + (Y − V Y V ) (991)where V Y V ∈ S N c and Y −V Y V ∈ S N⊥c (2000). Because of translationinvariance (5.5.1.1) and linearity, D(Y −V Y V )=0 hence N(D)⊇ S N⊥c .It remains only to showD(V Y V ) = 0 ⇔ V Y V = 0 (992)(⇔ Y = u1 T + 1u T for some u∈ R N) . D(V Y V ) will vanish whenever2V Y V = δ(V Y V )1 T + 1δ(V Y V ) T . But this implies R(1) (B.2) were asubset of R(V Y V ) , which is contradictory. Thus we haveN(D) = {Y | D(Y )=0} = {Y | V Y V = 0} = S N⊥c (993)Since G1=0 ⇔ X1=0 (911) simply means list X is geometricallycentered at the origin, and because the Gram-form EDM operator D istranslation invariant and N(D) is the translation-invariant subspace S N⊥c ,then EDM definition D(G) (989) on 5.28 (confer6.5.1,6.6.1,A.7.4.0.1)S N c ∩ S N + = {V Y V ≽ 0 | Y ∈ S N } ≡ {V N AV T Nmust be surjective onto EDM N ; (confer (904))EDM N = { D(G) | G ∈ S N c ∩ S N +5.6.1.1 Gram-form operator D inversion| A∈ S N−1+ } ⊂ S N (994)Define the linear geometric centering operator V ; (confer (912))}(995)V(D) : S N → S N −V DV 1 2(996)[89,4.3] 5.29 This orthogonal projector V has no nullspace onS N h = aff EDM N (1249)5.28 Equivalence ≡ in (994) follows from the fact: Given B = V Y V = V N AVN T ∈ SN + withonly matrix A unknown, then V † †TNBV N= A and A∈ SN−1 + must be positive semidefiniteby positive semidefiniteness of B and Corollary A.3.1.0.5.5.29 Critchley cites Torgerson (1958) [349, ch.11,2] for a history and derivation of (996).
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5.6. INJECTIVITY OF D & UNIQUE RECONSTRUCTION 441To prove injectivity of D(G) on S N c : Any matrix Y ∈ S N can bedecomposed into orthogonal components in S N ;Y = V Y V + (Y − V Y V ) (991)where V Y V ∈ S N c and Y −V Y V ∈ S N⊥c (2000). Because of translationinvariance (5.5.1.1) and linearity, D(Y −V Y V )=0 hence N(D)⊇ S N⊥c .It remains only to showD(V Y V ) = 0 ⇔ V Y V = 0 (992)(⇔ Y = u1 T + 1u T for some u∈ R N) . D(V Y V ) will vanish whenever2V Y V = δ(V Y V )1 T + 1δ(V Y V ) T . But this implies R(1) (B.2) were asubset of R(V Y V ) , which is contradictory. Thus we haveN(D) = {Y | D(Y )=0} = {Y | V Y V = 0} = S N⊥c (993)Since G1=0 ⇔ X1=0 (911) simply means list X is geometricallycentered at the origin, and because the Gram-form EDM operator D istranslation invariant and N(D) is the translation-invariant subspace S N⊥c ,then EDM definition D(G) (989) on 5.28 (confer6.5.1,6.6.1,A.7.4.0.1)S N c ∩ S N + = {V Y V ≽ 0 | Y ∈ S N } ≡ {V N AV T Nmust be surjective onto EDM N ; (confer (904))EDM N = { D(G) | G ∈ S N c ∩ S N +5.6.1.1 Gram-form operator D inversion| A∈ S N−1+ } ⊂ S N (994)Define the linear geometric centering operator V ; (confer (912))}(995)V(D) : S N → S N −V DV 1 2(996)[89,4.3] 5.29 This orthogonal projector V has no nullspace onS N h = aff EDM N (1249)5.28 Equivalence ≡ in (994) follows from the fact: Given B = V Y V = V N AVN T ∈ SN + withonly matrix A unknown, then V † †TNBV N= A and A∈ SN−1 + must be positive semidefiniteby positive semidefiniteness of B and Corollary A.3.1.0.5.5.29 Critchley cites Torgerson (1958) [349, ch.11,2] for a history and derivation of (996).