v2007.09.17 - Convex Optimization

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666 APPENDIX F. MATLAB PROGRAMSbreakendendif ~violy(:,k) = v(:,1);elseif isempty(j)disp(’Sturm procedure taking default j’), j = 2; returnend % debugalpha = (-2*(v(:,1)’*A*v(:,j)) + sqrt((2*v(:,1)’*A*v(:,j)).^2 ...-4*(v(:,j)’*A*v(:,j))*(v(:,1)’*A*v(:,1))))/(2*(v(:,j)’*A*v(:,j)));y(:,k) = (v(:,1) + alpha*v(:,j))/sqrt(1+alpha^2);if chop(y(:,k)’*A*y(:,k),tol) ~= 0alpha = (-2*(v(:,1)’*A*v(:,j)) - sqrt((2*v(:,1)’*A*v(:,j)).^2 ...-4*(v(:,j)’*A*v(:,j))*(v(:,1)’*A*v(:,1))))/(2*(v(:,j)’*A*v(:,j)));y(:,k) = (v(:,1) + alpha*v(:,j))/sqrt(1+alpha^2);if chop(y(:,k)’*A*y(:,k),tol) ~= 0disp(’Zero problem in Sturm!’), returnend % debugendendY = Y - y(:,k)*y(:,k)’;rho = rho - 1;endz = zeros(size(y));e = zeros(N,N);for i=1:rz(:,i) = y(:,i)/norm(y(:,i));e(i,i) = norm(y(:,i))^2;endlam = diag(e);[junk id] = sort(real(lam));id = id(length(id):-1:1);z = [z(:,id(1:r)) null(z’)] % Sturme = diag(lam(id))[v,d] = signeig(X) % eigenvalue decompositionX-z*e*z’traceAX = trace(A*X)
F.6. CONVEX ITERATION DEMONSTRATION 667F.6 <strong>Convex</strong> iteration demonstrationWe demonstrate implementation of a rank constraint in a semidefiniteBoolean feasibility problem from4.4.3.0.5. It requires CVX, [117] an intuitiveMatlab interface for interior-point method solvers.There are a finite number 2 N=50 ≈1E15 of binary vectors x . The feasibleset of semidefinite program (668) is the intersection of an elliptope withM =10 halfspaces in vectorized variable G . Size of the optimal rank-1solution set is proportional to the positive factor scaling vector b . Thesmaller that optimal Boolean solution set, the harder this problem is to solve.That scale factor and initial states of random number generators, makingmatrix A and vector b , are selected to demonstrate Boolean solution to oneinstance in about 7 iterations (about 6 seconds), whereas a sequential binarysearch tests 25.7 million vectors (in one hour) before finding one Booleansolution feasible to nonconvex problem (665). (Other parameters can beselected to reverse these timings.)% Discrete optimization problem demo.% -Jon Dattorro, June 4, 2007% Find x\in{-1,1}^N such that Ax
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DattorroCONVEXOPTIMIZATION&EUCLIDEA
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Meboo Publishing USA345 Stanford Sh
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EDM = S h ∩ ( S ⊥ c − S +)
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There is a great race under way to
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10 CONVEX OPTIMIZATION & EUCLIDEAN
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12 CONVEX OPTIMIZATION & EUCLIDEAN
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14 LIST OF FIGURES24 Nonconvex cone
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16 LIST OF FIGURES92 Largest ten ei
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List of Tables2 Convex geometryTabl
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20 CHAPTER 1. OVERVIEWFigure 1: Ori
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22 CHAPTER 1. OVERVIEWFigure 3: [13
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24 CHAPTER 1. OVERVIEWFigure 5: Swi
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26 CHAPTER 1. OVERVIEWoriginalrecon
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28 CHAPTER 1. OVERVIEWFigure 8: Rob
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30 CHAPTER 1. OVERVIEWnoveltyp.120
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32 CHAPTER 1. OVERVIEW
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34 CHAPTER 2. CONVEX GEOMETRY2.1 Co
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36 CHAPTER 2. CONVEX GEOMETRY2.1.3
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38 CHAPTER 2. CONVEX GEOMETRY(a)R(b
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40 CHAPTER 2. CONVEX GEOMETRYwhere
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42 CHAPTER 2. CONVEX GEOMETRYNow le
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46 CHAPTER 2. CONVEX GEOMETRYand wh
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48 CHAPTER 2. CONVEX GEOMETRY2.2.1.
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52 CHAPTER 2. CONVEX GEOMETRYAny ma
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54 CHAPTER 2. CONVEX GEOMETRYIn par
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58 CHAPTER 2. CONVEX GEOMETRYsvec
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60 CHAPTER 2. CONVEX GEOMETRYFigure
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64 CHAPTER 2. CONVEX GEOMETRY11−1
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66 CHAPTER 2. CONVEX GEOMETRYHyperp
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68 CHAPTER 2. CONVEX GEOMETRYCH −
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70 CHAPTER 2. CONVEX GEOMETRYnonemp
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72 CHAPTER 2. CONVEX GEOMETRYto vec
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78 CHAPTER 2. CONVEX GEOMETRYABCDFi
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82 CHAPTER 2. CONVEX GEOMETRYX(a)00
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84 CHAPTER 2. CONVEX GEOMETRYXXFigu
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86 CHAPTER 2. CONVEX GEOMETRYFamili
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88 CHAPTER 2. CONVEX GEOMETRYC 1C 2
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90 CHAPTER 2. CONVEX GEOMETRYA prop
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92 CHAPTER 2. CONVEX GEOMETRY∂K
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94 CHAPTER 2. CONVEX GEOMETRYWhen t
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96 CHAPTER 2. CONVEX GEOMETRYBCADFi
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98 CHAPTER 2. CONVEX GEOMETRYThe po
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104 CHAPTER 2. CONVEX GEOMETRY√2
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106 CHAPTER 2. CONVEX GEOMETRYwhich
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110 CHAPTER 2. CONVEX GEOMETRYA con
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112 CHAPTER 2. CONVEX GEOMETRY0-1-0
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114 CHAPTER 2. CONVEX GEOMETRYposit
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118 CHAPTER 2. CONVEX GEOMETRYThe c
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120 CHAPTER 2. CONVEX GEOMETRYWhen
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122 CHAPTER 2. CONVEX GEOMETRY2.10.
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124 CHAPTER 2. CONVEX GEOMETRY{extr
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126 CHAPTER 2. CONVEX GEOMETRY2.11
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128 CHAPTER 2. CONVEX GEOMETRYFrom
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130 CHAPTER 2. CONVEX GEOMETRYS = {
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132 CHAPTER 2. CONVEX GEOMETRYFigur
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136 CHAPTER 2. CONVEX GEOMETRYK ∗
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138 CHAPTER 2. CONVEX GEOMETRYKK
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140 CHAPTER 2. CONVEX GEOMETRYthe p
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142 CHAPTER 2. CONVEX GEOMETRY(dual
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146 CHAPTER 2. CONVEX GEOMETRYfor w
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148 CHAPTER 2. CONVEX GEOMETRYBy al
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150 CHAPTER 2. CONVEX GEOMETRYb −
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154 CHAPTER 2. CONVEX GEOMETRYDual
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160 CHAPTER 2. CONVEX GEOMETRYΓ 4
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162 CHAPTER 2. CONVEX GEOMETRYEigen
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164 CHAPTER 2. CONVEX GEOMETRYunder
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166 CHAPTER 2. CONVEX GEOMETRYWhen
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168 CHAPTER 2. CONVEX GEOMETRYFor e
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170 CHAPTER 2. CONVEX GEOMETRY10.80
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172 CHAPTER 2. CONVEX GEOMETRYx 210
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174 CHAPTER 2. CONVEX GEOMETRYwhile
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176 CHAPTER 2. CONVEX GEOMETRYαα
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178 CHAPTER 2. CONVEX GEOMETRYFrom
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182 CHAPTER 2. CONVEX GEOMETRYhavin
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184 CHAPTER 3. GEOMETRY OF CONVEX F
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226 CHAPTER 4. SEMIDEFINITE PROGRAM
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292 CHAPTER 5. EUCLIDEAN DISTANCE M
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390 CHAPTER 6. EDM CONEa resemblanc
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392 CHAPTER 6. EDM CONEdvec rel∂E
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394 CHAPTER 6. EDM CONE(b)(c)dvec r
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396 CHAPTER 6. EDM CONE(a)2 nearest
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398 CHAPTER 6. EDM CONEthe graph. T
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400 CHAPTER 6. EDM CONEwhere e i
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402 CHAPTER 6. EDM CONE6.5 EDM defi
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404 CHAPTER 6. EDM CONEN(1 T )δ(V
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406 CHAPTER 6. EDM CONE10(a)-110-1V
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408 CHAPTER 6. EDM CONE6.5.3 Faces
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410 CHAPTER 6. EDM CONE6.5.3.2 Smal
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412 CHAPTER 6. EDM CONE6.6.0.0.1 Pr
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414 CHAPTER 6. EDM CONEdvec rel∂E
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416 CHAPTER 6. EDM CONE6.7 Vectoriz
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418 CHAPTER 6. EDM CONEsvec ∂ S 2
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420 CHAPTER 6. EDM CONEIn fact, the
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422 CHAPTER 6. EDM CONEThe ordinary
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424 CHAPTER 6. EDM CONEEDM 2 = S 2
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426 CHAPTER 6. EDM CONEFrom the res
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428 CHAPTER 6. EDM CONE6.8.1.3 Dual
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430 CHAPTER 6. EDM CONED ◦ = δ(D
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432 CHAPTER 6. EDM CONE6.8.1.6 EDM
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434 CHAPTER 6. EDM CONEBecause 〈
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436 CHAPTER 6. EDM CONE0dvec rel∂
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438 CHAPTER 6. EDM CONE
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440 CHAPTER 7. PROXIMITY PROBLEMS7.
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442 CHAPTER 7. PROXIMITY PROBLEMS..
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444 CHAPTER 7. PROXIMITY PROBLEMSTh
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446 CHAPTER 7. PROXIMITY PROBLEMSwh
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448 CHAPTER 7. PROXIMITY PROBLEMSpo
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452 CHAPTER 7. PROXIMITY PROBLEMSof
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456 CHAPTER 7. PROXIMITY PROBLEMSR
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458 CHAPTER 7. PROXIMITY PROBLEMSwh
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462 CHAPTER 7. PROXIMITY PROBLEMSCo
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466 CHAPTER 7. PROXIMITY PROBLEMSTo
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468 CHAPTER 7. PROXIMITY PROBLEMSth
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470 CHAPTER 7. PROXIMITY PROBLEMSco
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472 CHAPTER 7. PROXIMITY PROBLEMSto
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476 CHAPTER 7. PROXIMITY PROBLEMSdi
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478 CHAPTER 7. PROXIMITY PROBLEMSve
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480 CHAPTER 7. PROXIMITY PROBLEMSth
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482 APPENDIX A. LINEAR ALGEBRAA.1.1
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486 APPENDIX A. LINEAR ALGEBRAonly
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488 APPENDIX A. LINEAR ALGEBRA(AB)
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492 APPENDIX A. LINEAR ALGEBRAFor A
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494 APPENDIX A. LINEAR ALGEBRADiago
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496 APPENDIX A. LINEAR ALGEBRAFor A
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500 APPENDIX A. LINEAR ALGEBRAA.4 S
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504 APPENDIX A. LINEAR ALGEBRAA.5 e
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506 APPENDIX A. LINEAR ALGEBRAs i w
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510 APPENDIX A. LINEAR ALGEBRAΣq 2
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512 APPENDIX A. LINEAR ALGEBRAA.7 Z
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514 APPENDIX A. LINEAR ALGEBRAThere
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518 APPENDIX A. LINEAR ALGEBRA
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520 APPENDIX B. SIMPLE MATRICESB.1
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522 APPENDIX B. SIMPLE MATRICESProo
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524 APPENDIX B. SIMPLE MATRICESB.1.
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526 APPENDIX B. SIMPLE MATRICESN(u
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528 APPENDIX B. SIMPLE MATRICESDue
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532 APPENDIX B. SIMPLE MATRICEShas
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534 APPENDIX B. SIMPLE MATRICESFigu
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538 APPENDIX C. SOME ANALYTICAL OPT
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552 APPENDIX D. MATRIX CALCULUSThe
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554 APPENDIX D. MATRIX CALCULUSGrad
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556 APPENDIX D. MATRIX CALCULUSBeca
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558 APPENDIX D. MATRIX CALCULUSwhic
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560 APPENDIX D. MATRIX CALCULUS⎡
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562 APPENDIX D. MATRIX CALCULUS→Y
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564 APPENDIX D. MATRIX CALCULUSD.1.
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566 APPENDIX D. MATRIX CALCULUSwhic
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568 APPENDIX D. MATRIX CALCULUSIn t
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572 APPENDIX D. MATRIX CALCULUSD.2
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574 APPENDIX D. MATRIX CALCULUSalge
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576 APPENDIX D. MATRIX CALCULUStrac
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580 APPENDIX D. MATRIX CALCULUS
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582 APPENDIX E. PROJECTIONThe follo
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584 APPENDIX E. PROJECTIONFor matri
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586 APPENDIX E. PROJECTION(⇐) To
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588 APPENDIX E. PROJECTIONNonorthog
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590 APPENDIX E. PROJECTIONE.2.0.0.1
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592 APPENDIX E. PROJECTIONE.3.2Orth
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594 APPENDIX E. PROJECTIONE.3.5Unif
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596 APPENDIX E. PROJECTIONE.4 Algeb
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598 APPENDIX E. PROJECTIONa ∗ 2K
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600 APPENDIX E. PROJECTIONwhere Y =
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602 APPENDIX E. PROJECTION(B.4.2).
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604 APPENDIX E. PROJECTIONis a nono
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606 APPENDIX E. PROJECTIONE.6.4.1Or
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608 APPENDIX E. PROJECTIONq i q T i
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610 APPENDIX E. PROJECTIONThe test
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612 APPENDIX E. PROJECTIONPerpendic
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614 APPENDIX E. PROJECTIONE.8 Range
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Page 648 and 649: 648 APPENDIX F. MATLAB PROGRAMSif n
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Page 672 and 673: 672 APPENDIX F. MATLAB PROGRAMS
Page 674 and 675: 674 APPENDIX G. NOTATION AND A FEW
Page 690 and 691: 690 BIBLIOGRAPHY[7] Abdo Y. Alfakih
Page 692 and 693: 692 BIBLIOGRAPHY[27] Aharon Ben-Tal
Page 694 and 695: 694 BIBLIOGRAPHY[48] Lev M. Brègma
Page 696 and 697: 696 BIBLIOGRAPHY[67] Joel Dawson, S
Page 698 and 699: 698 BIBLIOGRAPHY[85] Carl Eckart an
Page 700 and 701: 700 BIBLIOGRAPHY[102] Laurent El Gh
Page 702 and 703: 702 BIBLIOGRAPHY[124] Peter Gritzma
Page 704 and 705: 704 BIBLIOGRAPHY[146] Jean-Baptiste
Page 706 and 707: 706 BIBLIOGRAPHY[168] Jean B. Lasse
Page 708 and 709: 708 BIBLIOGRAPHY[190] Rudolf Mathar
Page 710 and 711: 710 BIBLIOGRAPHY[212] M. L. Overton
Page 712 and 713: 712 BIBLIOGRAPHY[230] R. Tyrrell Ro
Page 714 and 715: 714 BIBLIOGRAPHY[254] Jos F. Sturm
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716 BIBLIOGRAPHY[276] È. B. Vinber
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[295] Naoki Yamamoto and Maryam Faz
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720 INDEXobtuse, 62positive semidef
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722 INDEXnormal, 175, 415, 622, 642
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724 INDEXdiscretization, 152, 185,
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726 INDEXminimization, 206, 611full
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728 INDEXinvariant set, 359inversio
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730 INDEXunitary, 533maximalcomplem
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732 INDEXdifference, 118Farkas’ l
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734 INDEXcommutative, 426, 628direc
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736 INDEXset, 683unique, 42, 185, 1
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738 INDEXaffine, 44, 68, 86, 101, 2
show all

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