v2010.10.26 - Convex Optimization

v2010.10.26 - Convex Optimization v2010.10.26 - Convex Optimization

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584 CHAPTER 7. PROXIMITY PROBLEMSprojection on a positive semidefinite cone (1336); zeroing those eigenvalueshere in Problem 3 would place an elbow in the projection path (Figure 153)thereby producing a result that is necessarily suboptimal. Problem 3 isinstead a projection on the EDM cone whose associated spectral cone isconsiderably different. (5.11.2.3) Proper choice of spectral cone is demandedby diagonalization of that variable argument to the objective:7.3.3.1 Cayley-Menger formWe use Cayley-Menger composition of the Euclidean distance matrix to solvea problem that is the same as Problem 3 (1383): (5.7.3.0.1)[ ] [ ]∥ minimize0 1T 0 1T ∥∥∥2D∥ −1 −D 1 −H[ ]F0 1T(1401)subject to rank≤ ρ + 21 −DD ∈ EDM Na projection of H on a generally nonconvex subset (when ρ < N −1) of theEuclidean distance matrix cone boundary rel∂EDM N ; id est, projectionfrom the EDM cone interior or exterior on a subset of its relative boundary(6.5, (1179)).Rank of an optimal solution is intrinsically bounded above and below;[ ] 0 1T2 ≤ rank1 −D ⋆ ≤ ρ + 2 ≤ N + 1 (1402)Our proposed strategy ([ for low-rank ]) solution is projection on that subset0 1Tof a spectral cone λ1 −EDM N (5.11.2.3) corresponding to affinedimension not in excess of that ρ desired; id est, spectral projection on⎡ ⎤⎣ Rρ+1 +0 ⎦ ∩ ∂H ⊂ R N+1 (1403)R −where∂H = {λ ∈ R N+1 | 1 T λ = 0} (1112)is a hyperplane through the origin. This pointed polyhedral cone (1403), towhich membership subsumes the rank constraint, is not full-dimensional.

7.3. THIRD PREVALENT PROBLEM: 585Given desired affine dimension 0 ≤ ρ ≤ N −1 and diagonalization (A.5)of unknown EDM D [ ] 0 1T UΥU T ∈ S N+11 −Dh(1404)and given symmetric H in diagonalization[ ] 0 1T QΛQ T ∈ S N+1 (1405)1 −Hhaving eigenvalues arranged in nonincreasing order, then by (1125) problem(1401) is equivalent to∥minimize ∥δ(Υ) − π ( δ(R T ΛR) )∥ ∥ 2Υ , R⎡ ⎤subject to δ(Υ) ∈ ⎣ Rρ+1 +0 ⎦ ∩ ∂H(1406)R −δ(QRΥR T Q T ) = 0R −1 = R Twhere π is the permutation operator from7.1.3 arranging its vectorargument in nonincreasing order, 7.20 whereR Q T U ∈ R N+1×N+1 (1407)in U on the set of orthogonal matrices is a bijection, and where ∂H insuresone negative eigenvalue. Hollowness constraint δ(QRΥR T Q T ) = 0 makesproblem (1406) difficult by making the two variables dependent.Our plan is to instead divide problem (1406) into two and then iteratetheir solution:minimizeΥsubject to δ(Υ) ∈(∥ δ(Υ) − π δ(R T ΛR) )∥ ∥ 2⎡ ⎤⎣ Rρ+1 +0 ⎦ ∩ ∂HR −(a)(1408)minimize ‖R Υ ⋆ R T − Λ‖ 2 FRsubject to δ(QR Υ ⋆ R T Q T ) = 0R −1 = R T(b)7.20 Recall, any permutation matrix is an orthogonal matrix.

7.3. THIRD PREVALENT PROBLEM: 585Given desired affine dimension 0 ≤ ρ ≤ N −1 and diagonalization (A.5)of unknown EDM D [ ] 0 1T UΥU T ∈ S N+11 −Dh(1404)and given symmetric H in diagonalization[ ] 0 1T QΛQ T ∈ S N+1 (1405)1 −Hhaving eigenvalues arranged in nonincreasing order, then by (1125) problem(1401) is equivalent to∥minimize ∥δ(Υ) − π ( δ(R T ΛR) )∥ ∥ 2Υ , R⎡ ⎤subject to δ(Υ) ∈ ⎣ Rρ+1 +0 ⎦ ∩ ∂H(1406)R −δ(QRΥR T Q T ) = 0R −1 = R Twhere π is the permutation operator from7.1.3 arranging its vectorargument in nonincreasing order, 7.20 whereR Q T U ∈ R N+1×N+1 (1407)in U on the set of orthogonal matrices is a bijection, and where ∂H insuresone negative eigenvalue. Hollowness constraint δ(QRΥR T Q T ) = 0 makesproblem (1406) difficult by making the two variables dependent.Our plan is to instead divide problem (1406) into two and then iteratetheir solution:minimizeΥsubject to δ(Υ) ∈(∥ δ(Υ) − π δ(R T ΛR) )∥ ∥ 2⎡ ⎤⎣ Rρ+1 +0 ⎦ ∩ ∂HR −(a)(1408)minimize ‖R Υ ⋆ R T − Λ‖ 2 FRsubject to δ(QR Υ ⋆ R T Q T ) = 0R −1 = R T(b)7.20 Recall, any permutation matrix is an orthogonal matrix.

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