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