v2009.01.01 - Convex Optimization

v2009.01.01 - Convex Optimization v2009.01.01 - Convex Optimization

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420 CHAPTER 5. EUCLIDEAN DISTANCE MATRIX (confer (958)) D ∈ EDM N ⇔ [ ][ ] 0 1 1 −D 2:N,2:N − [1 −D 2:N,1 ] T = −2VN 1 0 −D TDV N ≽ 0 1,2:N and (1015) D ∈ S N h Positive semidefiniteness of that Schur complement insures nonnegativity (D ∈ R N×N + ,5.8.1), whereas complementary inertia (1417) insures existence of that lone negative eigenvalue of the Cayley-Menger form. Now we apply results from chapter 2 with regard to polyhedral cones and their duals. 5.11.2.2 Ordered eigenspectra Conditions (1012) specify eigenvalue [ membership ] to the smallest pointed 0 1 T polyhedral spectral cone for 1 −EDM N : K ∆ λ = {ζ ∈ R N+1 | ζ 1 ≥ ζ 2 ≥ · · · ≥ ζ N ≥ 0 ≥ ζ N+1 , 1 T ζ = 0} [ ] R N + = K M ∩ ∩ ∂H R − ([ ]) 0 1 T = λ 1 −EDM N (1016) where ∂H ∆ = {ζ ∈ R N+1 | 1 T ζ = 0} (1017) is a hyperplane through the origin, and K M is the monotone cone (2.13.9.4.3, implying ordered eigenspectra) which has nonempty interior but is not pointed; K M = {ζ ∈ R N+1 | ζ 1 ≥ ζ 2 ≥ · · · ≥ ζ N+1 } (390) K ∗ M = { [e 1 − e 2 e 2 −e 3 · · · e N −e N+1 ]a | a ≽ 0 } ⊂ R N+1 (391)

5.11. EDM INDEFINITENESS 421 So because of the hyperplane, indicating K λ has empty interior. Defining A ∆ = ⎡ ⎢ ⎣ e T 1 − e T 2 e T 2 − e T 3 . e T N − eT N+1 we have the halfspace-description: ⎤ dim aff K λ = dim ∂H = N (1018) ⎥ ⎦ ∈ RN×N+1 , B ∆ = ⎡ ⎢ ⎣ e T 1 e T2 . e T N −e T N+1 ⎤ ⎥ ⎦ ∈ RN+1×N+1 (1019) K λ = {ζ ∈ R N+1 | Aζ ≽ 0, Bζ ≽ 0, 1 T ζ = 0} (1020) From this and (398) we get a vertex-description for a pointed spectral cone having empty interior: { ([ ] ) † } Â K λ = V N V N b | b ≽ 0 (1021) ˆB where V N ∈ R N+1×N , and where [sic] ˆB = e T N ∈ R 1×N+1 (1022) and ⎡ e T 1 − e T ⎤ 2 Â = ⎢ e T 2 − e T 3 ⎥ ⎣ . ⎦ ∈ RN−1×N+1 (1023) e T N−1 − eT N hold [ ] those rows of A and B corresponding to conically independent rows in A V B N . Conditions (1012) can be equivalently restated in terms of a spectral cone for Euclidean distance matrices: ⎧ ([ ]) [ ] ⎪⎨ 0 1 T R N + λ ∈ K D ∈ EDM N ⇔ 1 −D M ∩ ∩ ∂H R − (1024) ⎪⎩ D ∈ S N h

5.11. EDM INDEFINITENESS 421<br />

So because of the hyperplane,<br />

indicating K λ has empty interior. Defining<br />

A ∆ =<br />

⎡<br />

⎢<br />

⎣<br />

e T 1 − e T 2<br />

e T 2 − e T 3<br />

.<br />

e T N − eT N+1<br />

we have the halfspace-description:<br />

⎤<br />

dim aff K λ = dim ∂H = N (1018)<br />

⎥<br />

⎦ ∈ RN×N+1 , B ∆ =<br />

⎡<br />

⎢<br />

⎣<br />

e T 1<br />

e T2 .<br />

e T N<br />

−e T N+1<br />

⎤<br />

⎥<br />

⎦ ∈ RN+1×N+1 (1019)<br />

K λ = {ζ ∈ R N+1 | Aζ ≽ 0, Bζ ≽ 0, 1 T ζ = 0} (1020)<br />

From this and (398) we get a vertex-description for a pointed spectral cone<br />

having empty interior:<br />

{ ([ ] ) †<br />

}<br />

Â<br />

K λ = V N V N b | b ≽ 0<br />

(1021)<br />

ˆB<br />

where V N ∈ R N+1×N , and where [sic]<br />

ˆB = e T N ∈ R 1×N+1 (1022)<br />

and<br />

⎡<br />

e T 1 − e T ⎤<br />

2<br />

 = ⎢<br />

e T 2 − e T 3 ⎥<br />

⎣ . ⎦ ∈ RN−1×N+1 (1023)<br />

e T N−1 − eT N<br />

hold [ ] those rows of A and B corresponding to conically independent rows in<br />

A<br />

V<br />

B N .<br />

Conditions (1012) can be equivalently restated in terms of a spectral cone<br />

for Euclidean distance matrices:<br />

⎧ ([ ]) [ ]<br />

⎪⎨ 0 1<br />

T<br />

R<br />

N<br />

+<br />

λ<br />

∈ K<br />

D ∈ EDM N ⇔ 1 −D<br />

M ∩ ∩ ∂H<br />

R − (1024)<br />

⎪⎩<br />

D ∈ S N h

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