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756 INDEX unique, 56 conic, 64, 135 empty set, 64 convex, 55, 59, 135, 346 empty set, 59 extreme directions, 100 orthogonal matrices, 64 orthonormal matrices, 64 outer product, 59 permutation matrices, 63 positive semidefinite cone, 113 projection matrices, 59 rank-1 matrices, 61, 62 rank-1 symmetric matrices, 113 unique, 59 hyperboloid, 574 hyperbox, 76, 683 hypercube, 76, 201, 683 nonnegative, 266 slice nonnegative, 296, 336 hyperdimensional, 614 hyperdisc, 621 hyperplane, 66–68, 224 hypersphere radius, 69 independent, 79 movement, 69, 82, 203 normal, 68 separating, 77, 155 supporting, 74, 75, 111, 113 nontrivially, 75 polarity, 75 strictly, 75, 93 unique, 228 vertex-description, 71 hypersphere, 61, 69, 361, 409 circumhypersphere, 397 hypograph, 214, 242 idempotent, 643, 647 symmetric, 649, 652 iff, 717 image, 44 inverse, 44, 107, 108, 164 magnetic resonance, 326 indefinite, 416 independence affine, 71, 72, 127 preservation, 72 conic, 25, 126–131 dual cone, 176 preservation, 129 linear, 35, 127, 130, 581 preservation, 35 inequality angle, 350, 437 matrix, 381 Bessel, 651 diagonal, 553 generalized, 25, 95, 140, 152 dual, 152, 160 partial order, 93 Hardy-Littlewood-Pólya, 508 linear, 153 matrix, 25, 161, 164, 254, 255, 258 norm, 263, 720 triangle, 199 spectral, 418 trace, 553 triangle, 347, 379, 400, 401, 436 norm, 199 strict, 402 unique, 435 variation, 157 inertia, 417, 558 complementary, 420, 558 Sylvester’s law, 550 infimum, 243, 595, 649, 670, 717 inflection, 231 injective, 48, 49, 96, 191, 387 non, 49, 50 pseudoinverse, 639, 640 innovation, 30 rate, 325 interior, 37, 38 cone EDM, 447, 462 membership, 153 positive semidefinite, 104, 256 empty, 37 halfspace, 87
INDEX 757 of point, 37 ray, 90 relative, 37, 86 interior-point method, 248, 364, 487 intersection, 42 cone, 92 halfspaces, 68 hyperplane, 79 convex set, 73 line with boundary, 39 nullspaces, 81 planes, 80 positive semidefinite cone affine, 124, 258 geometric center subspace, 573 line, 367 subspaces, 81 tangential, 40 invariance, 382 Gram form, 383 inner-product form, 386 orthogonal, 48, 433 reflection, 384 rotation, 384 set, 415 translation, 382 inversion conic coordinates, 191 Gram form, 389 matrix, 580, 626 operator, 48, 49 invertible, 48 is, 709 isometry, 48, 433, 502 isomorphic, 47, 50, 53, 707 face, 112, 360, 464 isometrically, 39, 47, 108, 160 non, 573 range, 47 isomorphism, 47, 163, 390, 392, 446 isometric, 48, 433 symmetric hollow subspace, 54 symmetric matrix subspace, 51 iterate, 687 iteration alternating projection, 527, 687, 701 convex, see convex Jacobian, 222 K-convexity, 196 Karhunen−Loéve transform, 424 kissing, 145, 201, 202, 303–305, 361, 643 number, 362 KKT conditions, 505 Kronecker product, 108, 540, 606, 614, 632 eigen, 540 inverse, 329 positive semidefinite, 352, 552 projector, 688 pseudoinverse, 641 vector, 616 vectorization, 539 Lagrange multiplier, 157 Lagrangian, 192, 321, 333 largest entries, 204 Lasso, 326 lattice, 283–286, 292, 293 regular, 27, 282 Laurent, 413 law of cosines, 378 least 1-norm, 201, 202, 262, 303 norm, 201, 263, 642 squares, 280, 281, 642 Legendre-Fenchel transform, 518 Lemaréchal, 19 line, 224, 236 tangential, 41 linear algebra, 537 bijection, see bijective complementarity, 185 function, see operator independence, 35, 127, 130, 581 preservation, 35 inequality, 153 matrix, 25, 161, 164, 254, 255, 258 operator, 43, 48, 53, 196, 209, 211, 355, 389, 391, 433, 467, 537, 672
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DATTORRO CONVEX OPTIMIZATION & EUCL
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Convex Optimization & Euclidean Dis
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for Jennie Columba ♦ Antonio ♦
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Prelude The constant demands of my
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Convex Optimization & Euclidean Dis
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CONVEX OPTIMIZATION & EUCLIDEAN DIS
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List of Figures 1 Overview 19 1 Ori
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LIST OF FIGURES 15 3 Geometry of co
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LIST OF FIGURES 17 126 Decomposing
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Chapter 1 Overview Convex Optimizat
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ˇx 4 ˇx 3 ˇx 2 Figure 2: Applica
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23 Figure 4: This coarsely discreti
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ases (biorthogonal expansion). We e
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27 Figure 7: These bees construct a
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that establish its membership to th
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31 appendices Provided so as to be
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Chapter 2 Convex geometry Convexity
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2.1. CONVEX SET 35 2.1.2 linear ind
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2.1. CONVEX SET 37 2.1.6 empty set
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2.1. CONVEX SET 39 2.1.7.1 Line int
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2.1. CONVEX SET 41 (a) R 2 (b) R 3
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2.1. CONVEX SET 43 This theorem in
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2.2. VECTORIZED-MATRIX INNER PRODUC
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2.2. VECTORIZED-MATRIX INNER PRODUC
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2.2. VECTORIZED-MATRIX INNER PRODUC
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2.2. VECTORIZED-MATRIX INNER PRODUC
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2.2. VECTORIZED-MATRIX INNER PRODUC
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2.3. HULLS 55 Figure 16: Convex hul
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2.3. HULLS 57 The affine hull of tw
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2.3. HULLS 59 2.3.2 Convex hull The
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2.3. HULLS 61 In case k = N , the F
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2.3. HULLS 63 2.3.2.0.3 Exercise. C
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2.3. HULLS 65 Figure 20: A simplici
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2.4. HALFSPACE, HYPERPLANE 67 H + a
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2.4. HALFSPACE, HYPERPLANE 69 1 1
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2.4. HALFSPACE, HYPERPLANE 71 Recal
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2.4. HALFSPACE, HYPERPLANE 73 C H
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2.4. HALFSPACE, HYPERPLANE 75 2.4.2
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2.4. HALFSPACE, HYPERPLANE 77 (conf
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2.5. SUBSPACE REPRESENTATIONS 79 Ra
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2.5. SUBSPACE REPRESENTATIONS 81 If
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2.6. EXTREME, EXPOSED 83 2.6 Extrem
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2.6. EXTREME, EXPOSED 85 A B C D Fi
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2.7. CONES 87 2.6.1.3.1 Definition.
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2.7. CONES 89 0 Figure 30: Boundary
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2.7. CONES 91 2.7.2 Convex cone We
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2.7. CONES 93 Then a pointed closed
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2.7. CONES 95 A pointed closed conv
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2.8. CONE BOUNDARY 97 So the ray th
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2.8. CONE BOUNDARY 99 2.8.1.1 extre
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2.8. CONE BOUNDARY 101 2.8.2 Expose
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2.8. CONE BOUNDARY 103 From Theorem
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.10. CONIC INDEPENDENCE (C.I.) 127
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2.10. CONIC INDEPENDENCE (C.I.) 129
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2.10. CONIC INDEPENDENCE (C.I.) 131
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2.12. CONVEX POLYHEDRA 133 It follo
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2.12. CONVEX POLYHEDRA 135 Coeffici
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2.12. CONVEX POLYHEDRA 137 2.12.3 U
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2.12. CONVEX POLYHEDRA 139
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2.13. DUAL CONE & GENERALIZED INEQU
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2.13. DUAL CONE & GENERALIZED INEQU
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2.13. DUAL CONE & GENERALIZED INEQU
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2.13. DUAL CONE & GENERALIZED INEQU
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Chapter 3 Geometry of convex functi
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3.1. CONVEX FUNCTION 197 f 1 (x) f
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3.1. CONVEX FUNCTION 199 3.1.3 norm
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3.1. CONVEX FUNCTION 201 A B 1 Figu
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3.1. CONVEX FUNCTION 203 k/m 1 0.9
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k∑ i=1 3.1. CONVEX FUNCTION 205 S
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3.1. CONVEX FUNCTION 207 rather x >
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3.1. CONVEX FUNCTION 209 rather ] x
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3.1. CONVEX FUNCTION 211 3.1.6.0.2
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3.1. CONVEX FUNCTION 213 q(x) f(x)
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3.1. CONVEX FUNCTION 215 3.1.7.0.2
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3.1. CONVEX FUNCTION 217 We learned
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3.1. CONVEX FUNCTION 219 Since opti
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3.1. CONVEX FUNCTION 221 2 1.5 1 0.
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3.1. CONVEX FUNCTION 223 Setting th
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3.1. CONVEX FUNCTION 225 Similarly,
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3.1. CONVEX FUNCTION 227 For vector
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3.1. CONVEX FUNCTION 229 This means
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3.1. CONVEX FUNCTION 231 f(Y ) −
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3.2. MATRIX-VALUED CONVEX FUNCTION
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3.2. MATRIX-VALUED CONVEX FUNCTION
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3.2. MATRIX-VALUED CONVEX FUNCTION
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3.3. QUASICONVEX 239 exponential al
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3.3. QUASICONVEX 241 Unlike convex
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3.4. SALIENT PROPERTIES 243 6. (af
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Chapter 4 Semidefinite programming
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4.1. CONIC PROBLEM 247 where K is a
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4.1. CONIC PROBLEM 249 4.1.1.2 Redu
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4.1. CONIC PROBLEM 251 In any SDP f
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4.1. CONIC PROBLEM 253 Proposition
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4.2. FRAMEWORK 255 sets are closed
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4.2. FRAMEWORK 257 4.2.1.1.3 Exampl
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4.2. FRAMEWORK 259 4.2.2 Duals The
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4.2. FRAMEWORK 261 When equality is
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4.2. FRAMEWORK 263 The pseudoinvers
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4.2. FRAMEWORK 265 For the data giv
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4.2. FRAMEWORK 267 minimizes an aff
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4.3. RANK REDUCTION 269 whose rank
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4.3. RANK REDUCTION 271 and where m
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4.3. RANK REDUCTION 273 4.3.3 Optim
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4.3. RANK REDUCTION 275 Initialize:
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4.4. RANK-CONSTRAINED SEMIDEFINITE
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4.4. RANK-CONSTRAINED SEMIDEFINITE
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4.4. RANK-CONSTRAINED SEMIDEFINITE
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4.4. RANK-CONSTRAINED SEMIDEFINITE
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4.4. RANK-CONSTRAINED SEMIDEFINITE
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4.4. RANK-CONSTRAINED SEMIDEFINITE
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4.4. RANK-CONSTRAINED SEMIDEFINITE
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4.4. RANK-CONSTRAINED SEMIDEFINITE
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4.4. RANK-CONSTRAINED SEMIDEFINITE
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4.5. CONSTRAINING CARDINALITY 295 m
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4.5. CONSTRAINING CARDINALITY 297 m
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4.5. CONSTRAINING CARDINALITY 299 a
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4.5. CONSTRAINING CARDINALITY 301 f
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4.5. CONSTRAINING CARDINALITY 303 n
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4.5. CONSTRAINING CARDINALITY 305 W
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4.5. CONSTRAINING CARDINALITY 307 t
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4.6. CARDINALITY AND RANK CONSTRAIN
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4.6. CARDINALITY AND RANK CONSTRAIN
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4.6. CARDINALITY AND RANK CONSTRAIN
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4.7. CONVEX ITERATION RANK-1 341 fi
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4.7. CONVEX ITERATION RANK-1 343 Gi
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Chapter 5 Euclidean Distance Matrix
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5.2. FIRST METRIC PROPERTIES 347 co
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5.3. ∃ FIFTH EUCLIDEAN METRIC PRO
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5.3. ∃ FIFTH EUCLIDEAN METRIC PRO
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5.4. EDM DEFINITION 353 The collect
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5.4. EDM DEFINITION 355 5.4.2 Gram-
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5.4. EDM DEFINITION 357 D ∈ EDM N
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5.4. EDM DEFINITION 359 5.4.2.2.1 E
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5.4. EDM DEFINITION 361 ten affine
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5.4. EDM DEFINITION 363 spheres: Th
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5.4. EDM DEFINITION 365 By eliminat
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5.4. EDM DEFINITION 367 where Φ ij
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5.4. EDM DEFINITION 369 5.4.2.2.6 D
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5.4. EDM DEFINITION 371 10 5 ˇx 4
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5.4. EDM DEFINITION 373 corrected b
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5.4. EDM DEFINITION 375 by translat
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5.4. EDM DEFINITION 377 Crippen & H
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5.4. EDM DEFINITION 379 where ([√
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5.4. EDM DEFINITION 381 because (A.
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5.5. INVARIANCE 383 5.5.1.0.1 Examp
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5.5. INVARIANCE 385 x 2 x 2 x 3 x 1
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5.6. INJECTIVITY OF D & UNIQUE RECO
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5.6. INJECTIVITY OF D & UNIQUE RECO
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5.6. INJECTIVITY OF D & UNIQUE RECO
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5.7. EMBEDDING IN AFFINE HULL 393 5
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5.7. EMBEDDING IN AFFINE HULL 395 F
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5.7. EMBEDDING IN AFFINE HULL 397 5
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5.8. EUCLIDEAN METRIC VERSUS MATRIX
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5.8. EUCLIDEAN METRIC VERSUS MATRIX
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5.8. EUCLIDEAN METRIC VERSUS MATRIX
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5.8. EUCLIDEAN METRIC VERSUS MATRIX
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5.9. BRIDGE: CONVEX POLYHEDRA TO ED
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5.9. BRIDGE: CONVEX POLYHEDRA TO ED
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5.9. BRIDGE: CONVEX POLYHEDRA TO ED
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5.10. EDM-ENTRY COMPOSITION 413 of
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5.10. EDM-ENTRY COMPOSITION 415 The
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5.11. EDM INDEFINITENESS 417 5.11.1
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5.11. EDM INDEFINITENESS 419 we hav
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5.11. EDM INDEFINITENESS 421 So bec
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5.11. EDM INDEFINITENESS 423 where
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5.12. LIST RECONSTRUCTION 425 where
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5.12. LIST RECONSTRUCTION 427 (a) (
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5.13. RECONSTRUCTION EXAMPLES 429 D
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5.13. RECONSTRUCTION EXAMPLES 431 T
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5.13. RECONSTRUCTION EXAMPLES 433 w
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5.14. FIFTH PROPERTY OF EUCLIDEAN M
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5.14. FIFTH PROPERTY OF EUCLIDEAN M
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5.14. FIFTH PROPERTY OF EUCLIDEAN M
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5.14. FIFTH PROPERTY OF EUCLIDEAN M
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5.14. FIFTH PROPERTY OF EUCLIDEAN M
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Chapter 6 Cone of distance matrices
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6.1. DEFINING EDM CONE 447 6.1 Defi
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6.2. POLYHEDRAL BOUNDS 449 This con
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6.3. √ EDM CONE IS NOT CONVEX 451
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6.4. A GEOMETRY OF COMPLETION 453 (
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6.4. A GEOMETRY OF COMPLETION 455 (
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6.4. A GEOMETRY OF COMPLETION 457 F
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6.5. EDM DEFINITION IN 11 T 459 by
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6.5. EDM DEFINITION IN 11 T 461 6.5
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6.5. EDM DEFINITION IN 11 T 463 1 0
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6.5. EDM DEFINITION IN 11 T 465 6.5
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6.6. CORRESPONDENCE TO PSD CONE S N
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6.6. CORRESPONDENCE TO PSD CONE S N
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6.6. CORRESPONDENCE TO PSD CONE S N
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6.7. VECTORIZATION & PROJECTION INT
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6.7. VECTORIZATION & PROJECTION INT
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6.8. DUAL EDM CONE 477 When the Fin
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6.8. DUAL EDM CONE 479 Proof. First
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6.8. DUAL EDM CONE 481 EDM 2 = S 2
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6.8. DUAL EDM CONE 483 whose veraci
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6.8. DUAL EDM CONE 485 6.8.1.3.1 Ex
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6.8. DUAL EDM CONE 487 has dual aff
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6.8. DUAL EDM CONE 489 6.8.1.7 Scho
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6.9. THEOREM OF THE ALTERNATIVE 491
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6.10. POSTSCRIPT 493 When D is an E
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Chapter 7 Proximity problems In sum
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497 project on the subspace, then p
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499 H S N h 0 EDM N K = S N h ∩ R
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501 7.0.3 Problem approach Problems
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7.1. FIRST PREVALENT PROBLEM: 503 f
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7.1. FIRST PREVALENT PROBLEM: 505 7
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7.1. FIRST PREVALENT PROBLEM: 507 d
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7.1. FIRST PREVALENT PROBLEM: 509 7
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7.1. FIRST PREVALENT PROBLEM: 511 w
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7.1. FIRST PREVALENT PROBLEM: 513 T
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7.2. SECOND PREVALENT PROBLEM: 515
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7.2. SECOND PREVALENT PROBLEM: 517
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7.2. SECOND PREVALENT PROBLEM: 519
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7.2. SECOND PREVALENT PROBLEM: 521
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7.2. SECOND PREVALENT PROBLEM: 523
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7.3. THIRD PREVALENT PROBLEM: 525 g
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7.3. THIRD PREVALENT PROBLEM: 527 w
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7.3. THIRD PREVALENT PROBLEM: 529 7
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7.3. THIRD PREVALENT PROBLEM: 531 7
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7.3. THIRD PREVALENT PROBLEM: 533 O
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7.4. CONCLUSION 535 The rank constr
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Appendix A Linear algebra A.1 Main-
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A.1. MAIN-DIAGONAL δ OPERATOR, λ
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Page 541 and 542:
A.1. MAIN-DIAGONAL δ OPERATOR, λ
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Page 543 and 544:
A.2. SEMIDEFINITENESS: DOMAIN OF TE
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Page 545 and 546:
A.3. PROPER STATEMENTS 545 (AB) T
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Page 547 and 548:
A.3. PROPER STATEMENTS 547 A.3.1 Se
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A.3. PROPER STATEMENTS 549 For A di
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A.3. PROPER STATEMENTS 551 Diagonal
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A.3. PROPER STATEMENTS 553 For A,B
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A.3. PROPER STATEMENTS 555 A.3.1.0.
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A.4. SCHUR COMPLEMENT 557 A.4 Schur
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A.4. SCHUR COMPLEMENT 559 A.4.0.0.2
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A.5. EIGEN DECOMPOSITION 561 When B
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A.5. EIGEN DECOMPOSITION 563 dim N(
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A.6. SINGULAR VALUE DECOMPOSITION,
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A.6. SINGULAR VALUE DECOMPOSITION,
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A.7. ZEROS 569 Given symmetric matr
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A.7. ZEROS 571 (TRANSPOSE.) Likewis
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A.7. ZEROS 573 For X,A∈ S M + [31
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A.7. ZEROS 575 A.7.5.0.1 Propositio
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Appendix B Simple matrices Mathemat
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Page 579 and 580:
B.1. RANK-ONE MATRIX (DYAD) 579 R(v
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Page 581 and 582:
B.1. RANK-ONE MATRIX (DYAD) 581 ran
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Page 583 and 584:
B.2. DOUBLET 583 R([u v ]) R(Π)= R
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B.3. ELEMENTARY MATRIX 585 If λ
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B.4. AUXILIARY V -MATRICES 587 the
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Page 589 and 590:
B.4. AUXILIARY V -MATRICES 589 18.
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B.5. ORTHOGONAL MATRIX 591 B.5 Orth
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B.5. ORTHOGONAL MATRIX 593 Figure 1
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Page 595 and 596:
Appendix C Some analytical optimal
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Page 597 and 598:
C.2. TRACE, SINGULAR AND EIGEN VALU
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Page 599 and 600:
C.2. TRACE, SINGULAR AND EIGEN VALU
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Page 601 and 602:
C.2. TRACE, SINGULAR AND EIGEN VALU
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Page 603 and 604:
C.3. ORTHOGONAL PROCRUSTES PROBLEM
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Page 605 and 606:
C.4. TWO-SIDED ORTHOGONAL PROCRUSTE
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Page 607 and 608:
C.4. TWO-SIDED ORTHOGONAL PROCRUSTE
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Page 609 and 610:
Appendix D Matrix calculus From too
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Page 611 and 612:
D.1. DIRECTIONAL DERIVATIVE, TAYLOR
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Page 613 and 614:
D.1. DIRECTIONAL DERIVATIVE, TAYLOR
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Page 615 and 616:
D.1. DIRECTIONAL DERIVATIVE, TAYLOR
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Page 617 and 618:
D.1. DIRECTIONAL DERIVATIVE, TAYLOR
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Page 619 and 620:
D.1. DIRECTIONAL DERIVATIVE, TAYLOR
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Page 621 and 622:
D.1. DIRECTIONAL DERIVATIVE, TAYLOR
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Page 623 and 624:
D.1. DIRECTIONAL DERIVATIVE, TAYLOR
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Page 625 and 626:
D.1. DIRECTIONAL DERIVATIVE, TAYLOR
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Page 627 and 628:
D.1. DIRECTIONAL DERIVATIVE, TAYLOR
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Page 629 and 630:
D.1. DIRECTIONAL DERIVATIVE, TAYLOR
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Page 631 and 632:
D.2. TABLES OF GRADIENTS AND DERIVA
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Page 633 and 634:
D.2. TABLES OF GRADIENTS AND DERIVA
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Page 635 and 636:
D.2. TABLES OF GRADIENTS AND DERIVA
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Page 637 and 638:
D.2. TABLES OF GRADIENTS AND DERIVA
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Page 639 and 640:
Appendix E Projection For any A∈
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Page 641 and 642:
641 U T = U † for orthonormal (in
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Page 643 and 644:
E.1. IDEMPOTENT MATRICES 643 where
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Page 645 and 646:
E.1. IDEMPOTENT MATRICES 645 order,
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Page 647 and 648:
E.1. IDEMPOTENT MATRICES 647 When t
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Page 649 and 650:
E.3. SYMMETRIC IDEMPOTENT MATRICES
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Page 651 and 652:
E.3. SYMMETRIC IDEMPOTENT MATRICES
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Page 653 and 654:
E.3. SYMMETRIC IDEMPOTENT MATRICES
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Page 655 and 656:
E.5. PROJECTION EXAMPLES 655 E.4.0.
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Page 657 and 658:
E.5. PROJECTION EXAMPLES 657 a ∗
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Page 659 and 660:
E.5. PROJECTION EXAMPLES 659 E.5.0.
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Page 661 and 662:
E.6. VECTORIZATION INTERPRETATION,
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Page 663 and 664:
E.6. VECTORIZATION INTERPRETATION,
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Page 665 and 666:
E.6. VECTORIZATION INTERPRETATION,
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Page 667 and 668:
E.6. VECTORIZATION INTERPRETATION,
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Page 669 and 670:
E.7. ON VECTORIZED MATRICES OF HIGH
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Page 671 and 672:
E.7. ON VECTORIZED MATRICES OF HIGH
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Page 673 and 674:
E.8. RANGE/ROWSPACE INTERPRETATION
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Page 675 and 676:
E.9. PROJECTION ON CONVEX SET 675 A
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Page 677 and 678:
E.9. PROJECTION ON CONVEX SET 677 W
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Page 679 and 680:
E.9. PROJECTION ON CONVEX SET 679 P
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Page 681 and 682:
E.9. PROJECTION ON CONVEX SET 681 E
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Page 683 and 684:
E.9. PROJECTION ON CONVEX SET 683 T
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Page 685 and 686:
E.9. PROJECTION ON CONVEX SET 685
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Page 687 and 688:
E.10. ALTERNATING PROJECTION 687 E.
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Page 689 and 690:
E.10. ALTERNATING PROJECTION 689 b
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Page 691 and 692:
E.10. ALTERNATING PROJECTION 691 a
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Page 693 and 694:
E.10. ALTERNATING PROJECTION 693 (a
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Page 695 and 696:
E.10. ALTERNATING PROJECTION 695 wh
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Page 697 and 698:
E.10. ALTERNATING PROJECTION 697 E.
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Page 699 and 700:
E.10. ALTERNATING PROJECTION 699 10
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Page 701 and 702:
E.10. ALTERNATING PROJECTION 701 E.
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Page 703 and 704:
E.10. ALTERNATING PROJECTION 703 E
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Page 705 and 706:
Appendix F Notation and a few defin
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Page 707 and 708:
707 a.i. c.i. l.i. w.r.t affinely i
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Page 709 and 710:
709 is or ← → t → 0 + as in
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Page 711 and 712:
711 ∑ π(γ) Ξ Π ∏ ψ(Z) D D
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Page 713 and 714:
713 R m×n Euclidean vector space o
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Page 715 and 716:
715 H − H + ∂H ∂H ∂H −
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Page 717 and 718:
717 O O sort-index matrix order of
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Page 719 and 720:
(x,y) angle between vectors x and y
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Page 721 and 722:
Bibliography [1] Suliman Al-Homidan
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Page 723 and 724:
BIBLIOGRAPHY 723 [24] Alexander I.
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Page 725 and 726:
BIBLIOGRAPHY 725 [52] Stephen Boyd,
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Page 727 and 728:
BIBLIOGRAPHY 727 [78] Frank Critchl
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Page 729 and 730:
BIBLIOGRAPHY 729 [105] Richard L. D
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Page 731 and 732:
BIBLIOGRAPHY 731 [132] Michel X. Go
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Page 733 and 734:
BIBLIOGRAPHY 733 [162] T. Herrmann,
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Page 735 and 736:
BIBLIOGRAPHY 735 [191] Mark Kahrs a
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Page 737 and 738:
BIBLIOGRAPHY 737 [220] K. V. Mardia
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Page 739 and 740:
BIBLIOGRAPHY 739 [250] Pythagoras P
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Page 741 and 742:
BIBLIOGRAPHY 741 [277] Anthony Man-
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Page 743 and 744:
BIBLIOGRAPHY 743 [306] Michael W. T
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Page 745 and 746:
[333] Margaret H. Wright. The inter
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Page 747 and 748:
Index 0-norm, 203, 261, 294, 296, 2
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Page 749 and 750:
INDEX 749 product, 43, 92, 147, 254
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Page 751 and 752:
INDEX 751 coordinates, 140, 170, 17
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Page 753 and 754:
INDEX 753 affine dimension, 485 fea
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Page 755:
INDEX 755 affine, 209 nonlinear, 19
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Page 759 and 760:
INDEX 759 normal, 47, 548, 563 norm
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Page 761 and 762:
INDEX 761 strictly, 515, 520 functi
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Page 763 and 764:
INDEX 763 vector, 45, 241, 248, 325
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Page 765 and 766:
INDEX 765 convex envelope, see conv
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Page 767 and 768:
INDEX 767 cone, 418, 420, 507 dual,
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Page 769 and 770:
INDEX 769 trilateration, 21, 42, 36
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Page 772:
Convex Optimization & Euclidean Dis
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