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814 BIBLIOGRAPHY[361] Lieven Vandenberghe and Stephen Boyd. Applications of semidefinite programming.Applied Numerical Mathematics, 29(3):283–299, March 1999.[362] Lieven Vandenberghe, Stephen Boyd, and Shao-Po Wu. Determinant maximizationwith linear matrix inequality constraints. SIAM Journal on Matrix Analysis andApplications, 19(2):499–533, April 1998.[363] Robert J. Vanderbei. <strong>Convex</strong> optimization: Interior-point methods and applications.Lecture notes, 1999.http://orfe.princeton.edu/~rvdb/pdf/talks/pumath/talk.pdf[364] Richard S. Varga. Geršgorin and His Circles. Springer-Verlag, 2004.[365] Martin Vetterli and Jelena Kovačević. Wavelets and Subband Coding. Prentice-Hall,1995.[366] Martin Vetterli, Pina Marziliano, and Thierry Blu. Sampling signals with finiterate of innovation. IEEE Transactions on Signal Processing, 50(6):1417–1428, June2002.http://bigwww.epfl.ch/publications/vetterli0201.pdfhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.5630[367] È. B. Vinberg. The theory of convex homogeneous cones. Transactions of theMoscow Mathematical Society, 12:340–403, 1963. American Mathematical Societyand London Mathematical Society joint translation, 1965.[368] Marie A. Vitulli. A brief history of linear algebra and matrix theory, 2004.darkwing.uoregon.edu/~vitulli/441.sp04/LinAlgHistory.html[369] John von Neumann. Functional Operators, Volume II: The Geometry of OrthogonalSpaces. Princeton University Press, 1950. Reprinted from mimeographed lecturenotes first distributed in 1933.[370] Michael B. Wakin, Jason N. Laska, Marco F. Duarte, Dror Baron, ShriramSarvotham, Dharmpal Takhar, Kevin F. Kelly, and Richard G. Baraniuk. Anarchitecture for compressive imaging. In Proceedings of the IEEE InternationalConference on Image Processing (ICIP), pages 1273–1276, October 2006.http://www.convexoptimization.com/TOOLS/CSCam-ICIP06.pdf[371] Roger Webster. <strong>Convex</strong>ity. Oxford University Press, 1994.[372] Kilian Q. Weinberger and Lawrence K. Saul. Unsupervised learning of imagemanifolds by semidefinite programming. In Proceedings of the IEEE ComputerSociety Conference on Computer Vision and Pattern Recognition, volume 2, pages988–995, 2004.http://www.cs.ucsd.edu/~saul/papers/sde cvpr04.pdf[373] Eric W. Weisstein. Mathworld – A Wolfram Web Resource, 2007.http://mathworld.wolfram.com/search[374] D. J. White. An analogue derivation of the dual of the general Fermat problem.Management Science, 23(1):92–94, September 1976.http://www.convexoptimization.com/TOOLS/White.pdf
BIBLIOGRAPHY 815[375] Bernard Widrow and Samuel D. Stearns. Adaptive Signal Processing. Prentice-Hall,1985.[376] Norbert Wiener. On factorization of matrices. Commentarii Mathematici Helvetici,29:97–111, 1955.[377] Ami Wiesel, Yonina C. Eldar, and Shlomo Shamai (Shitz). Semidefinite relaxationfor detection of 16-QAM signaling in MIMO channels. IEEE Signal ProcessingLetters, 12(9):653–656, September 2005.[378] Michael P. Williamson, Timothy F. Havel, and Kurt Wüthrich. Solutionconformation of proteinase inhibitor IIA from bull seminal plasma by 1 H nuclearmagnetic resonance and distance geometry. Journal of Molecular Biology,182:295–315, 1985.[379] Willie W. Wong. Cayley-Menger determinant and generalized N-dimensionalPythagorean theorem, November 2003. Application of Linear Algebra: Notes onTalk given to Princeton University Math Club.http://www.convexoptimization.com/TOOLS/gp-r.pdf[380] William Wooton, Edwin F. Beckenbach, and Frank J. Fleming. Modern AnalyticGeometry. Houghton Mifflin, 1975.[381] Margaret H. Wright. The interior-point revolution in optimization: History, recentdevelopments, and lasting consequences. Bulletin of the American MathematicalSociety, 42(1):39–56, January 2005.[382] Stephen J. Wright. Primal-Dual Interior-Point Methods. SIAM, 1997.[383] Shao-Po Wu. max-det Programming with Applications in Magnitude Filter Design.A dissertation submitted to the department of Electrical Engineering, StanfordUniversity, December 1997.[384] Shao-Po Wu and Stephen Boyd. sdpsol: A parser/solver for semidefiniteprogramming and determinant maximization problems with matrix structure, May1996.http://www.stanford.edu/~boyd/old software/SDPSOL.html[385] Shao-Po Wu and Stephen Boyd. sdpsol: A parser/solver for semidefinite programswith matrix structure. In Laurent El Ghaoui and Silviu-Iulian Niculescu, editors,Advances in Linear Matrix Inequality Methods in Control, chapter 4, pages 79–91.SIAM, 2000.http://www.stanford.edu/~boyd/sdpsol.html[386] Shao-Po Wu, Stephen Boyd, and Lieven Vandenberghe. FIR filter design viaspectral factorization and convex optimization. In Biswa Nath Datta, editor,Applied and Computational Control, Signals, and Circuits, volume 1, chapter 5,pages 215–246. Birkhäuser, 1998.http://www.stanford.edu/~boyd/papers/magdes.html
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DATTORROCONVEXOPTIMIZATION&EUCLIDEA
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Convex Optimization&Euclidean Dista
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for Jennie Columba♦Antonio♦♦&
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PreludeThe constant demands of my d
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Convex Optimization&Euclidean Dista
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CONVEX OPTIMIZATION & EUCLIDEAN DIS
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List of Figures1 Overview 211 Orion
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LIST OF FIGURES 1562 Shrouded polyh
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LIST OF FIGURES 17130 Elliptope E 3
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Chapter 1OverviewConvex Optimizatio
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ˇx 4ˇx 3ˇx 2Figure 2: Applicatio
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25Figure 4: This coarsely discretiz
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(biorthogonal expansion) is examine
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29cardinality Boolean solution to a
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31Figure 8: Robotic vehicles in con
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an elaborate exposition offering in
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Chapter 2Convex geometryConvexity h
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2.1. CONVEX SET 372.1.2 linear inde
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2.1. CONVEX SET 392.1.6 empty set v
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2.1. CONVEX SET 41(a)R(b)R 2(c)R 3F
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2.1. CONVEX SET 43where Q∈ R 3×3
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2.1. CONVEX SET 45Now let’s move
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2.1. CONVEX SET 47By additive inver
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2.1. CONVEX SET 49R nR mR(A T )x px
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2.2. VECTORIZED-MATRIX INNER PRODUC
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2.3. HULLS 61Figure 20: Convex hull
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2.3. HULLS 63Aaffine hull (drawn tr
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2.3. HULLS 65subset of the affine h
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2.3. HULLS 672.3.2.0.2 Example. Nuc
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2.3. HULLS 692.3.2.0.3 Exercise. Co
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2.3. HULLS 71Figure 24: A simplicia
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2.4. HALFSPACE, HYPERPLANE 73H + =
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2.4. HALFSPACE, HYPERPLANE 7511−1
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2.4. HALFSPACE, HYPERPLANE 772.4.2.
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2.4. HALFSPACE, HYPERPLANE 81tradit
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2.5. SUBSPACE REPRESENTATIONS 852.5
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2.5. SUBSPACE REPRESENTATIONS 87(Ex
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2.5. SUBSPACE REPRESENTATIONS 89(a)
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2.5. SUBSPACE REPRESENTATIONS 91are
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2.6. EXTREME, EXPOSED 93A one-dimen
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2.6. EXTREME, EXPOSED 952.6.1.1 Den
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2.7. CONES 97X(a)00(b)XFigure 33: (
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2.7. CONES 99XXFigure 37: Truncated
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2.7. CONES 101Figure 39: Not a cone
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2.7. CONES 103cone that is a halfli
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2.7. CONES 105A pointed closed conv
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2.8. CONE BOUNDARY 107That means th
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2.8. CONE BOUNDARY 1092.8.1.1 extre
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2.8. CONE BOUNDARY 1112.8.2 Exposed
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.10. CONIC INDEPENDENCE (C.I.) 141
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2.12. CONVEX POLYHEDRA 147all dimen
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2.12. CONVEX POLYHEDRA 149convex po
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2.12. CONVEX POLYHEDRA 1512.12.2.2
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2.13. DUAL CONE & GENERALIZED INEQU
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Chapter 3Geometry of convex functio
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3.1. CONVEX FUNCTION 221f 1 (x)f 2
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3.1. CONVEX FUNCTION 223Rf(b)f(X
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3.2. PRACTICAL NORM FUNCTIONS, ABSO
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3.3. INVERTED FUNCTIONS AND ROOTS 2
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3.4. AFFINE FUNCTION 237rather]x >
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3.4. AFFINE FUNCTION 239f(z)Az 2z 1
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3.5. EPIGRAPH, SUBLEVEL SET 241{a T
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3.5. EPIGRAPH, SUBLEVEL SET 243Subl
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3.5. EPIGRAPH, SUBLEVEL SET 245wher
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3.5. EPIGRAPH, SUBLEVEL SET 247part
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3.5. EPIGRAPH, SUBLEVEL SET 249that
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3.6. GRADIENT 251respect to its vec
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3.6. GRADIENT 253Invertibility is g
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3.6. GRADIENT 2553.6.1.0.2 Theorem.
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3.6. GRADIENT 257f(Y )[ ∇f(X)−1
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3.6. GRADIENT 259αβα ≥ β ≥
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3.6. GRADIENT 2613.6.4 second-order
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3.7. CONVEX MATRIX-VALUED FUNCTION
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3.8. QUASICONVEX 269exponential alw
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3.9. SALIENT PROPERTIES 2713.8.0.0.
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Chapter 4Semidefinite programmingPr
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4.1. CONIC PROBLEM 275(confer p.162
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4.1. CONIC PROBLEM 277PCsemidefinit
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4.1. CONIC PROBLEM 279is the affine
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4.1. CONIC PROBLEM 281faces of S 3
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4.1. CONIC PROBLEM 2834.1.2.3 Previ
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4.2. FRAMEWORK 285Semidefinite Fark
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4.2. FRAMEWORK 287On the other hand
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4.2. FRAMEWORK 2894.2.2.1 Dual prob
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4.2. FRAMEWORK 291For symmetric pos
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4.2. FRAMEWORK 293has norm ‖x ⋆
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4.2. FRAMEWORK 295minimize 1 TˆxX
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4.2. FRAMEWORK 297asminimize ‖ỹ
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4.3. RANK REDUCTION 2994.3 Rank red
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4.3. RANK REDUCTION 301A rank-reduc
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4.3. RANK REDUCTION 303(t ⋆ i)
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4.3. RANK REDUCTION 3054.3.3.0.1 Ex
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4.3. RANK REDUCTION 3074.3.3.0.2 Ex
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4.4. RANK-CONSTRAINED SEMIDEFINITE
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4.5. CONSTRAINING CARDINALITY 333mi
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4.5. CONSTRAINING CARDINALITY 3350R
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4.5. CONSTRAINING CARDINALITY 337it
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4.5. CONSTRAINING CARDINALITY 339m/
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4.5. CONSTRAINING CARDINALITY 341we
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4.5. CONSTRAINING CARDINALITY 343fl
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4.5. CONSTRAINING CARDINALITY 345We
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4.5. CONSTRAINING CARDINALITY 3474.
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4.5. CONSTRAINING CARDINALITY 349R
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4.5. CONSTRAINING CARDINALITY 351pe
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4.6. CARDINALITY AND RANK CONSTRAIN
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4.7. CONSTRAINING RANK OF INDEFINIT
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4.8. CONVEX ITERATION RANK-1 391whi
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4.8. CONVEX ITERATION RANK-1 393the
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Chapter 5Euclidean Distance MatrixT
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5.2. FIRST METRIC PROPERTIES 397to
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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 403The collecti
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5.4. EDM DEFINITION 4055.4.2 Gram-f
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5.4. EDM DEFINITION 407We provide a
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5.4. EDM DEFINITION 4095.4.2.3.1 Ex
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5.4. EDM DEFINITION 411is the fact:
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5.4. EDM DEFINITION 413Figure 120:
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5.4. EDM DEFINITION 415is found fro
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5.4. EDM DEFINITION 417one less dim
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5.4. EDM DEFINITION 419equality con
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5.4. EDM DEFINITION 421How much dis
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5.4. EDM DEFINITION 423105ˇx 4ˇx
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5.4. EDM DEFINITION 425now implicit
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5.4. EDM DEFINITION 427by translate
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5.4. EDM DEFINITION 429Crippen & Ha
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5.4. EDM DEFINITION 431where ([√t
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5.4. EDM DEFINITION 433because (A.3
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5.5. INVARIANCE 4355.5.1.0.1 Exampl
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5.5. INVARIANCE 437x 2 x 2x 3 x 1 x
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5.6. INJECTIVITY OF D & UNIQUE RECO
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5.7. EMBEDDING IN AFFINE HULL 4455.
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5.7. EMBEDDING IN AFFINE HULL 447Fo
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5.7. EMBEDDING IN AFFINE HULL 4495.
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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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5.10. EDM-ENTRY COMPOSITION 465of a
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5.10. EDM-ENTRY COMPOSITION 467Then
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5.11. EDM INDEFINITENESS 4695.11.1
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5.11. EDM INDEFINITENESS 471we have
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5.11. EDM INDEFINITENESS 473So beca
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5.11. EDM INDEFINITENESS 475holds o
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5.12. LIST RECONSTRUCTION 477where
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5.12. LIST RECONSTRUCTION 479(a)(c)
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5.13. RECONSTRUCTION EXAMPLES 481Wi
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5.13. RECONSTRUCTION EXAMPLES 483d
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5.13. RECONSTRUCTION EXAMPLES 485Th
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5.14. FIFTH PROPERTY OF EUCLIDEAN M
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Chapter 6Cone of distance matricesF
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6.1. DEFINING EDM CONE 4996.1 Defin
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6.2. POLYHEDRAL BOUNDS 501This cone
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6.4. EDM DEFINITION IN 11 T 503That
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6.4. EDM DEFINITION IN 11 T 505N(1
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6.4. EDM DEFINITION IN 11 T 507Then
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6.4. EDM DEFINITION IN 11 T 5096.4.
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6.5. CORRESPONDENCE TO PSD CONE S N
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6.6. VECTORIZATION & PROJECTION INT
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6.7. A GEOMETRY OF COMPLETION 523(b
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6.7. A GEOMETRY OF COMPLETION 525[3
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6.7. A GEOMETRY OF COMPLETION 527wh
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6.8. DUAL EDM CONE 529to the geomet
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6.8. DUAL EDM CONE 531Proof. First,
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6.8. DUAL EDM CONE 533EDM 2 = S 2 h
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6.8. DUAL EDM CONE 535therefore the
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6.8. DUAL EDM CONE 537Elegance of t
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6.8. DUAL EDM CONE 5396.8.1.5 Affin
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6.8. DUAL EDM CONE 5416.8.1.7 Schoe
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6.8. DUAL EDM CONE 5430dvec rel ∂
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6.10. POSTSCRIPT 5456.10 Postscript
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Chapter 7Proximity problemsIn the
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549project on the subspace, then pr
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551HS N h0EDM NK = S N h ∩ R N×N
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5537.0.3 Problem approachProblems t
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7.1. FIRST PREVALENT PROBLEM: 555fi
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7.1. FIRST PREVALENT PROBLEM: 5577.
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7.1. FIRST PREVALENT PROBLEM: 559di
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7.1. FIRST PREVALENT PROBLEM: 5617.
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7.1. FIRST PREVALENT PROBLEM: 563wh
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7.1. FIRST PREVALENT PROBLEM: 565Th
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7.2. SECOND PREVALENT PROBLEM: 567O
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7.2. SECOND PREVALENT PROBLEM: 569S
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7.2. SECOND PREVALENT PROBLEM: 571r
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7.2. SECOND PREVALENT PROBLEM: 573w
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7.2. SECOND PREVALENT PROBLEM: 5757
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7.2. SECOND PREVALENT PROBLEM: 577a
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7.3. THIRD PREVALENT PROBLEM: 579is
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7.3. THIRD PREVALENT PROBLEM: 581We
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7.3. THIRD PREVALENT PROBLEM: 583su
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7.3. THIRD PREVALENT PROBLEM: 585Gi
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7.3. THIRD PREVALENT PROBLEM: 587Op
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7.4. CONCLUSION 589filtering, multi
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Appendix ALinear algebraA.1 Main-di
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A.1. MAIN-DIAGONAL δ OPERATOR, λ
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A.1. MAIN-DIAGONAL δ OPERATOR, λ
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A.2. SEMIDEFINITENESS: DOMAIN OF TE
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A.3. PROPER STATEMENTS 599(AB) T
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A.3. PROPER STATEMENTS 601A.3.1Semi
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A.3. PROPER STATEMENTS 603For A dia
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A.3. PROPER STATEMENTS 605Diagonali
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A.3. PROPER STATEMENTS 607For A,B
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A.3. PROPER STATEMENTS 609When B is
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A.4. SCHUR COMPLEMENT 611A.4 Schur
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A.4. SCHUR COMPLEMENT 613A.4.0.0.3
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A.4. SCHUR COMPLEMENT 615From Corol
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A.5. EIGENVALUE DECOMPOSITION 617wh
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A.5. EIGENVALUE DECOMPOSITION 619A.
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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 625A.6.5SVD of symmetric
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A.7. ZEROS 627(Transpose.)Likewise,
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A.7. ZEROS 629For X,A∈ S M +[34,2
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A.7. ZEROS 631A.7.5.0.1 Proposition
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Appendix BSimple matricesMathematic
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B.1. RANK-ONE MATRIX (DYAD) 635R(v)
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B.1. RANK-ONE MATRIX (DYAD) 637B.1.
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B.2. DOUBLET 639R([u v ])R(Π)= R([
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B.3. ELEMENTARY MATRIX 641has N −
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B.4. AUXILIARY V -MATRICES 643is an
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B.4. AUXILIARY V -MATRICES 64514. [
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B.5. ORTHOGONAL MATRIX 647Given X
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B.5. ORTHOGONAL MATRIX 649Figure 15
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B.5. ORTHOGONAL MATRIX 651which is
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Appendix CSome analytical optimal r
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C.2. TRACE, SINGULAR AND EIGEN VALU
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C.3. ORTHOGONAL PROCRUSTES PROBLEM
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C.4. TWO-SIDED ORTHOGONAL PROCRUSTE
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Appendix DMatrix calculusFrom too m
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D.1. DIRECTIONAL DERIVATIVE, TAYLOR
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D.2. TABLES OF GRADIENTS AND DERIVA
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Appendix EProjectionFor any A∈ R
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701U T = U † for orthonormal (inc
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E.1. IDEMPOTENT MATRICES 703where A
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E.1. IDEMPOTENT MATRICES 705order,
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E.1. IDEMPOTENT MATRICES 707are lin
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E.3. SYMMETRIC IDEMPOTENT MATRICES
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E.4. ALGEBRA OF PROJECTION ON AFFIN
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E.5. PROJECTION EXAMPLES 717a ∗ 2
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E.5. PROJECTION EXAMPLES 719where Y
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E.5. PROJECTION EXAMPLES 721(B.4.2)
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E.6. VECTORIZATION INTERPRETATION,
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E.7. PROJECTION ON MATRIX SUBSPACES
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E.7. PROJECTION ON MATRIX SUBSPACES
Page 733 and 734:
E.8. RANGE/ROWSPACE INTERPRETATION
Page 735 and 736:
E.9. PROJECTION ON CONVEX SET 735As
Page 737 and 738:
E.9. PROJECTION ON CONVEX SET 737Wi
Page 739 and 740:
E.9. PROJECTION ON CONVEX SET 739R(
Page 741 and 742:
E.9. PROJECTION ON CONVEX SET 741E.
Page 743 and 744:
E.9. PROJECTION ON CONVEX SET 743E.
Page 745 and 746:
E.9. PROJECTION ON CONVEX SET 745Un
Page 747 and 748:
E.9. PROJECTION ON CONVEX SET 747ac
Page 749 and 750:
E.10. ALTERNATING PROJECTION 749bC
Page 751 and 752:
E.10. ALTERNATING PROJECTION 7510
Page 753 and 754:
E.10. ALTERNATING PROJECTION 753E.1
Page 755 and 756:
E.10. ALTERNATING PROJECTION 755y 2
Page 757 and 758:
E.10. ALTERNATING PROJECTION 757Def
Page 759 and 760:
E.10. ALTERNATING PROJECTION 759Dis
Page 761 and 762:
E.10. ALTERNATING PROJECTION 761mat
Page 763 and 764: E.10. ALTERNATING PROJECTION 763K
Page 765 and 766: E.10. ALTERNATING PROJECTION 765E.1
Page 767 and 768: E.10. ALTERNATING PROJECTION 767E.1
Page 769 and 770: Appendix FNotation and a few defini
Page 771 and 772: 771A ij or A(i, j) , ij th entry of
Page 773 and 774: 773⊞orthogonal vector sum of sets
Page 775 and 776: 775x +vector x whose negative entri
Page 777 and 778: 777X point list ((76) having cardin
Page 779 and 780: 779SDPSVDSNRdBEDMS n 1S n hS n⊥hS
Page 781 and 782: 781vectorentrycubixquartixfeasible
Page 783 and 784: 783Oorder of magnitude information
Page 785 and 786: 785cofmatrix of cofactors correspon
Page 787 and 788: Bibliography[1] Edwin A. Abbott. Fl
Page 789 and 790: BIBLIOGRAPHY 789[23] Dror Baron, Mi
Page 791 and 792: BIBLIOGRAPHY 791[49] Leonard M. Blu
Page 793 and 794: BIBLIOGRAPHY 793[74] Yves Chabrilla
Page 795 and 796: BIBLIOGRAPHY 795[102] Etienne de Kl
Page 797 and 798: BIBLIOGRAPHY 797[129] Carl Eckart a
Page 799 and 800: BIBLIOGRAPHY 799[154] James Gleik.
Page 801 and 802: BIBLIOGRAPHY 801[182] Johan Håstad
Page 803 and 804: BIBLIOGRAPHY 803[212] Viren Jain an
Page 805 and 806: BIBLIOGRAPHY 805[237] Monique Laure
Page 807 and 808: BIBLIOGRAPHY 807[265] Sunderarajan
Page 809 and 810: BIBLIOGRAPHY 809Notes in Computer S
Page 811 and 812: BIBLIOGRAPHY 811[319] Anthony Man-C
Page 813: BIBLIOGRAPHY 813[346] Pham Dinh Tao
Page 817 and 818: Index∅, see empty set0-norm, 229,
Page 819 and 820: INDEX 819bees, 30, 413Bellman, 276b
Page 821 and 822: INDEX 821circular, 130construction,
Page 823 and 824: INDEX 823measure, 295convex, 21, 36
Page 825 and 826: INDEX 825duality, 160, 410gap, 161,
Page 827 and 828: INDEX 827positive semidefinite, 284
Page 829 and 830: INDEX 829point, 39, 62positive semi
Page 831 and 832: INDEX 831isomorphic, 52, 56, 59, 77
Page 833 and 834: INDEX 833nonsymmetric, 703range, 70
Page 835 and 836: INDEX 835Muller, 624multidimensiona
Page 837 and 838: INDEX 837projection on, 739, 745tra
Page 839 and 840: INDEX 839pseudoinverse, 701trace, 5
Page 841 and 842: INDEX 841coordinate system, 156line
Page 843 and 844: INDEX 843nonconvex, 40, 97-101nulls
Page 845 and 846: INDEX 845faces, 106intersection, 10
Page 847 and 848: INDEX 847projection, 517symmetric,
Page 849 and 850: 849
Page 852: Convex Optimization & Euclidean Dis
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