v2009.01.01 - Convex Optimization

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

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532 CHAPTER 7. PROXIMITY PROBLEMS dimension not in excess of that ρ desired; id est, spectral projection on ⎡ ⎤ ⎣ Rρ+1 + 0 ⎦ ∩ ∂H ⊂ R N+1 (1306) R − where ∂H = {λ ∈ R N+1 | 1 T λ = 0} (1017) is a hyperplane through the origin. This pointed polyhedral cone (1306), to which membership subsumes the rank constraint, has empty interior. Given desired affine dimension 0 ≤ ρ ≤ N −1 and diagonalization (A.5) of unknown EDM D [ 0 1 T 1 −D ] ∆ = UΥU T ∈ S N+1 h (1307) and given symmetric H in diagonalization [ ] 0 1 T ∆ = QΛQ T ∈ S N+1 (1308) 1 −H having eigenvalues arranged in nonincreasing order, then by (1030) problem (1304) is equivalent to ∥ minimize ∥δ(Υ) − π ( δ(R T ΛR) )∥ ∥ 2 Υ , R ⎡ ⎤ subject to δ(Υ) ∈ ⎣ Rρ+1 + 0 ⎦ ∩ ∂H (1309) R − δ(QRΥR T Q T ) = 0 R −1 = R T where π is the permutation operator from7.1.3 arranging its vector argument in nonincreasing order, 7.18 where R ∆ = Q T U ∈ R N+1×N+1 (1310) in U on the set of orthogonal matrices is a bijection, and where ∂H insures one negative eigenvalue. Hollowness constraint δ(QRΥR T Q T ) = 0 makes problem (1309) difficult by making the two variables dependent. 7.18 Recall, any permutation matrix is an orthogonal matrix.

7.3. THIRD PREVALENT PROBLEM: 533 Our plan is to instead divide problem (1309) into two and then iterate their solution: ∥ minimize ∥δ(Υ) − π ( δ(R T ΛR) )∥ ∥ 2 Υ ⎡ ⎤ subject to δ(Υ) ∈ ⎣ Rρ+1 + (a) 0 ⎦ ∩ ∂H R − (1311) minimize ‖R Υ ⋆ R T − Λ‖ 2 F R subject to δ(QR Υ ⋆ R T Q T ) = 0 R −1 = R T (b) Proof. We justify disappearance of the hollowness constraint in convex optimization problem (1311a): From the arguments in7.1.3 with regard ⎡ to⎤π the permutation operator, cone membership constraint δ(Υ) ∈⎣ Rρ+1 + 0 ⎦∩ ∂H from (1311a) is equivalent to R − ⎡ ⎤ δ(Υ) ∈ ⎣ Rρ+1 + 0 ⎦ ∩ ∂H ∩ K M (1312) R − where K M is the monotone cone (2.13.9.4.3). Membership of δ(Υ) to the polyhedral cone of majorization (Theorem A.1.2.0.1) K ∗ λδ = ∂H ∩ K ∗ M+ (1329) where K ∗ M+ is the dual monotone nonnegative cone (2.13.9.4.2), is a condition (in absence of a hollowness [ constraint) ] that would insure existence 0 1 T of a symmetric hollow matrix . Curiously, intersection of 1 −D ⎡ ⎤ this feasible superset ⎣ Rρ+1 + 0 ⎦∩ ∂H ∩ K M from (1312) with the cone of majorization K ∗ λδ R − is a benign operation; id est, ∂H ∩ K ∗ M+ ∩ K M = ∂H ∩ K M (1313)

7.3. THIRD PREVALENT PROBLEM: 533<br />

Our plan is to instead divide problem (1309) into two and then iterate<br />

their solution:<br />

∥<br />

minimize ∥δ(Υ) − π ( δ(R T ΛR) )∥ ∥ 2<br />

Υ ⎡ ⎤<br />

subject to δ(Υ) ∈ ⎣ Rρ+1 +<br />

(a)<br />

0 ⎦ ∩ ∂H<br />

R −<br />

(1311)<br />

minimize ‖R Υ ⋆ R T − Λ‖ 2 F<br />

R<br />

subject to δ(QR Υ ⋆ R T Q T ) = 0<br />

R −1 = R T<br />

(b)<br />

Proof. We justify disappearance of the hollowness constraint in<br />

convex optimization problem (1311a): From the arguments in7.1.3<br />

with regard ⎡ to⎤π the permutation operator, cone membership constraint<br />

δ(Υ) ∈⎣ Rρ+1 +<br />

0 ⎦∩ ∂H from (1311a) is equivalent to<br />

R −<br />

⎡ ⎤<br />

δ(Υ) ∈ ⎣ Rρ+1 +<br />

0 ⎦ ∩ ∂H ∩ K M (1312)<br />

R −<br />

where K M is the monotone cone (2.13.9.4.3). Membership of δ(Υ) to the<br />

polyhedral cone of majorization (Theorem A.1.2.0.1)<br />

K ∗ λδ = ∂H ∩ K ∗ M+ (1329)<br />

where K ∗ M+ is the dual monotone nonnegative cone (2.13.9.4.2), is a<br />

condition (in absence of a hollowness [ constraint) ] that would insure existence<br />

0 1<br />

T<br />

of a symmetric hollow matrix . Curiously, intersection of<br />

1 −D<br />

⎡ ⎤<br />

this feasible superset ⎣ Rρ+1 +<br />

0 ⎦∩ ∂H ∩ K M from (1312) with the cone of<br />

majorization K ∗ λδ<br />

R −<br />

is a benign operation; id est,<br />

∂H ∩ K ∗ M+ ∩ K M = ∂H ∩ K M (1313)

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