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718 BIBLIOGRAPHY[124] Peter Gritzmann and Victor Klee. On the complexity of somebasic problems in computational convexity: II. volume and mixedvolumes. In T. Bisztriczky, P. McMullen, R. Schneider, and A. IvićWeiss, editors, Polytopes: Abstract, Convex and Computational, pages373–466. Kluwer Academic Publishers, 1994.[125] L. G. Gubin, B. T. Polyak, and E. V. Raik. The method of projectionsfor finding the common point of convex sets. U.S.S.R. ComputationalMathematics and Mathematical Physics, 7(6):1–24, 1967.[126] Osman Güler and Yinyu Ye. Convergence behavior of interior-pointalgorithms. Mathematical Programming, 60(2):215–228, 1993.[127] P. R. Halmos. Positive approximants of operators. Indiana UniversityMathematics Journal, 21:951–960, 1972.[128] Shih-Ping Han. A successive projection method. MathematicalProgramming, 40:1–14, 1988.[129] Godfrey H. Hardy, John E. Littlewood, and George Pólya. Inequalities.Cambridge University Press, second edition, 1952.[130] Arash Hassibi and Mar Hershenson. Automated optimal design ofswitched-capacitor filters. Design Automation and Test in EuropeConference, 2001.[131] Johan Håstad. Some optimal inapproximability results, 1999.http://citeseer.ist.psu.edu/280448.html[132] Timothy F. Havel and Kurt Wüthrich. An evaluation of the combineduse of nuclear magnetic resonance and distance geometry for thedetermination of protein conformations in solution. Journal ofMolecular Biology, 182:281–294, 1985.[133] Tom L. Hayden and Jim Wells. Approximation by matrices positivesemidefinite on a subspace. Linear Algebra and its Applications,109:115–130, 1988.[134] Tom L. Hayden, Jim Wells, Wei-Min Liu, and Pablo Tarazaga.The cone of distance matrices. Linear Algebra and its Applications,144:153–169, 1991.
BIBLIOGRAPHY 719[135] Uwe Helmke and John B. Moore. Optimization and DynamicalSystems. Springer-Verlag, 1994.[136] Bruce Hendrickson. Conditions for unique graph realizations. SIAMJournal on Computing, 21(1):65–84, February 1992.[137] T. Herrmann, Peter Güntert, and Kurt Wüthrich. Protein NMRstructure determination with automated NOE assignment using thenew software CANDID and the torsion angle dynamics algorithmDYANA. Journal of Molecular Biology, 319(1):209–227, May 2002.[138] Mar Hershenson. Design of pipeline analog-to-digital converters viageometric programming. International Conference on Computer AidedDesign - ICCAD, 2002.[139] Mar Hershenson. Efficient description of the design space of analogcircuits. 40 th Design Automation Conference, 2003.[140] Mar Hershenson, Stephen Boyd, and Thomas Lee. Optimal design ofa CMOS OpAmp via geometric programming. IEEE Transactions onComputer-Aided Design of Integrated Circuits and Systems, 2001.[141] Mar Hershenson, Dave Colleran, Arash Hassibi, and Navraj Nandra.Synthesizable full custom mixed-signal IP. Electronics DesignAutomation Consortium - EDA, 2002.[142] Mar Hershenson, Sunderarajan S. Mohan, Stephen Boyd, and ThomasLee. Optimization of inductor circuits via geometric programming,1999.[143] Mar Hershenson and Xiling Shen. A new analog design flow. Cadence,2002.[144] Nick Higham. Matrix Procrustes problems, 1995.http://www.ma.man.ac.uk/∼higham/talksLecture notes.[145] Richard D. Hill and Steven R. Waters. On the cone of positivesemidefinite matrices. Linear Algebra and its Applications, 90:81–88,1987.
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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 191 Orion
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LIST OF FIGURES 1559 Epigraph . . .
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LIST OF FIGURES 17120 Four fundamen
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Chapter 1OverviewConvex Optimizatio
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ˇx 4ˇx 3ˇx 2Figure 2: Applicatio
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23Figure 4: This coarsely discretiz
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ases (biorthogonal expansion). We e
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27Figure 7: These bees construct a
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its membership to the EDM cone. The
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31appendicesProvided so as to be mo
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Chapter 2Convex geometryConvexity h
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2.1. CONVEX SET 35Figure 11: A slab
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2.1. CONVEX SET 372.1.6 empty set v
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2.1. CONVEX SET 392.1.7.1 Line inte
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2.1. CONVEX SET 41(a)R 2(b)R 3(c)(d
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2.1. CONVEX SET 43This theorem in c
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2.2. VECTORIZED-MATRIX INNER PRODUC
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2.3. HULLS 53Figure 14: Convex hull
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2.3. HULLS 55Aaffine hull (drawn tr
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2.3. HULLS 57The union of relative
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2.4. HALFSPACE, HYPERPLANE 59of dim
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2.4. HALFSPACE, HYPERPLANE 61H +ay
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2.4. HALFSPACE, HYPERPLANE 63Inters
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2.4. HALFSPACE, HYPERPLANE 65Conver
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2.4. HALFSPACE, HYPERPLANE 67A 1A 2
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2.4. HALFSPACE, HYPERPLANE 69tradit
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2.4. HALFSPACE, HYPERPLANE 71There
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2.5. SUBSPACE REPRESENTATIONS 73nor
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2.5. SUBSPACE REPRESENTATIONS 752.5
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2.6. EXTREME, EXPOSED 77In other wo
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2.6. EXTREME, EXPOSED 792.6.1 Expos
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2.7. CONES 812.6.1.3.1 Definition.
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2.7. CONES 830Figure 26: Boundary o
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2.7. CONES 852.7.2 Convex coneWe ca
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2.7. CONES 87Thus the simplest and
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2.7. CONES 89nomenclature generaliz
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2.8. CONE BOUNDARY 91Proper cone {0
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2.8. CONE BOUNDARY 93the same extre
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2.8. CONE BOUNDARY 95For a proper c
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2.9. POSITIVE SEMIDEFINITE (PSD) CO
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2.10. CONIC INDEPENDENCE (C.I.) 121
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2.12. CONVEX POLYHEDRA 127It follow
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2.12. CONVEX POLYHEDRA 129Coefficie
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2.12. CONVEX POLYHEDRA 1312.12.3 Un
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2.12. CONVEX POLYHEDRA 133
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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 185f 1 (x)f 2
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3.1. CONVEX FUNCTION 1873.1.3 norm
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3.1. CONVEX FUNCTION 189n∑π(x) i
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3.1. CONVEX FUNCTION 191This means
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3.1. CONVEX FUNCTION 1933.1.5.3 pos
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3.1. CONVEX FUNCTION 1953.1.6.0.1 E
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3.1. CONVEX FUNCTION 1973.1.7 epigr
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3.1. CONVEX FUNCTION 201Although th
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3.1. CONVEX FUNCTION 203minimize tX
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3.1. CONVEX FUNCTION 2053.1.8 gradi
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3.1. CONVEX FUNCTION 209f(Y )[ ∇f
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3.1. CONVEX FUNCTION 211g is convex
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3.1. CONVEX FUNCTION 213αβα ≥
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3.1. CONVEX FUNCTION 2153.1.11 seco
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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 221matrices in the
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3.3. QUASICONVEX 223on R M + . Alth
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Chapter 4Semidefinite programmingPr
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4.1. CONIC PROBLEM 227where K is a
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4.1. CONIC PROBLEM 229C0PΓ 1Γ 2S+
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4.1. CONIC PROBLEM 231faces of S 3
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4.1. CONIC PROBLEM 2334.1.1.3 Previ
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4.2. FRAMEWORK 235Equivalently, pri
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4.2. FRAMEWORK 237is positive semid
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4.2. FRAMEWORK 239Optimal value of
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4.2. FRAMEWORK 2414.2.3.1.1 Example
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4.2. FRAMEWORK 243where δ is the m
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4.2. FRAMEWORK 2454.2.3.1.2 Example
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4.3. RANK REDUCTION 2474.3 Rank red
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4.3. RANK REDUCTION 249A rank-reduc
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4.3. RANK REDUCTION 251(t ⋆ i)
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4.3. RANK REDUCTION 2534.3.3.0.1 Ex
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4.3. RANK REDUCTION 2554.3.3.0.2 Ex
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4.4. RANK-CONSTRAINED SEMIDEFINITE
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4.5. CONSTRAINING CARDINALITY 2734.
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4.5. CONSTRAINING CARDINALITY 275Th
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4.5. CONSTRAINING CARDINALITY 277wh
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4.5. CONSTRAINING CARDINALITY 279f
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4.5. CONSTRAINING CARDINALITY 281al
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4.6. CARDINALITY AND RANK CONSTRAIN
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4.7. CONVEX ITERATION RANK-1 301whi
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4.7. CONVEX ITERATION RANK-1 303the
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Chapter 5Euclidean Distance MatrixT
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5.2. FIRST METRIC PROPERTIES 307cor
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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 313The collecti
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5.4. EDM DEFINITION 3155.4.2 Gram-f
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5.4. EDM DEFINITION 317D ∈ EDM N
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5.4. EDM DEFINITION 3195.4.2.2.1 Ex
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5.4. EDM DEFINITION 321ten affine e
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5.4. EDM DEFINITION 323spheres:Then
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5.4. EDM DEFINITION 325By eliminati
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5.4. EDM DEFINITION 327whereΦ ij =
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5.4. EDM DEFINITION 3295.4.2.2.5 De
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5.4. EDM DEFINITION 331105ˇx 4ˇx
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5.4. EDM DEFINITION 333corrected by
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5.4. EDM DEFINITION 335by translate
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5.4. EDM DEFINITION 337Crippen & Ha
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5.4. EDM DEFINITION 339where ([√t
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5.4. EDM DEFINITION 341because (A.3
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5.5. INVARIANCE 3435.5.1.0.1 Exampl
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5.6. INJECTIVITY OF D & UNIQUE RECO
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5.7. EMBEDDING IN AFFINE HULL 3515.
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5.7. EMBEDDING IN AFFINE HULL 353Fo
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5.7. EMBEDDING IN AFFINE HULL 3555.
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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 373(ii)
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5.11. EDM INDEFINITENESS 3755.11.1
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5.11. EDM INDEFINITENESS 377(confer
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5.11. EDM INDEFINITENESS 379we have
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5.11. EDM INDEFINITENESS 381For pre
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5.12. LIST RECONSTRUCTION 383where
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5.12. LIST RECONSTRUCTION 385(a)(c)
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5.13. RECONSTRUCTION EXAMPLES 387D
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5.13. RECONSTRUCTION EXAMPLES 389Th
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5.13. RECONSTRUCTION EXAMPLES 391wh
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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 4056.1 Defin
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6.2. POLYHEDRAL BOUNDS 407This cone
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6.3.√EDM CONE IS NOT CONVEX 409N
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6.4. A GEOMETRY OF COMPLETION 4116.
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6.4. A GEOMETRY OF COMPLETION 413(a
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6.4. A GEOMETRY OF COMPLETION 415Fi
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6.5. EDM DEFINITION IN 11 T 417and
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6.5. EDM DEFINITION IN 11 T 419then
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6.5. EDM DEFINITION IN 11 T 4216.5.
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6.5. EDM DEFINITION IN 11 T 423D =
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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 435When the Fins
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6.8. DUAL EDM CONE 437Proof. First,
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6.8. DUAL EDM CONE 439EDM 2 = S 2 h
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6.8. DUAL EDM CONE 441whose veracit
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6.8. DUAL EDM CONE 4436.8.1.3.1 Exe
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6.8. DUAL EDM CONE 445has dual affi
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6.8. DUAL EDM CONE 4476.8.1.7 Schoe
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6.9. THEOREM OF THE ALTERNATIVE 449
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6.10. POSTSCRIPT 451When D is an ED
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Chapter 7Proximity problemsIn summa
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In contrast, order of projection on
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457HS N h0EDM NK = S N h ∩ R N×N
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4597.0.3 Problem approachProblems t
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7.1. FIRST PREVALENT PROBLEM: 461fi
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7.1. FIRST PREVALENT PROBLEM: 4637.
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7.1. FIRST PREVALENT PROBLEM: 465di
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7.1. FIRST PREVALENT PROBLEM: 4677.
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7.1. FIRST PREVALENT PROBLEM: 469wh
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7.1. FIRST PREVALENT PROBLEM: 471Th
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7.2. SECOND PREVALENT PROBLEM: 473O
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7.2. SECOND PREVALENT PROBLEM: 475S
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7.2. SECOND PREVALENT PROBLEM: 477r
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7.2. SECOND PREVALENT PROBLEM: 479c
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7.2. SECOND PREVALENT PROBLEM: 4817
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7.3. THIRD PREVALENT PROBLEM: 483fo
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7.3. THIRD PREVALENT PROBLEM: 485a
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7.3. THIRD PREVALENT PROBLEM: 4877.
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7.3. THIRD PREVALENT PROBLEM: 4897.
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7.3. THIRD PREVALENT PROBLEM: 491Ou
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7.4. CONCLUSION 493The rank constra
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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.2. SEMIDEFINITENESS: DOMAIN OF TE
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A.2. SEMIDEFINITENESS: DOMAIN OF TE
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A.3. PROPER STATEMENTS 503A.3.0.0.1
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A.3. PROPER STATEMENTS 505By simila
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A.3. PROPER STATEMENTS 507Because R
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For A,B ∈ R n×n x T Ax ≥ x T B
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A.3. PROPER STATEMENTS 511A.3.1.0.2
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A.3. PROPER STATEMENTS 513We can de
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A.4. SCHUR COMPLEMENT 515Origin of
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A.4. SCHUR COMPLEMENT 517Schur-form
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A.5. EIGEN DECOMPOSITION 519A.5.0.1
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A.6. SINGULAR VALUE DECOMPOSITION,
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A.7. ZEROS 527For diagonalizable ma
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A.7. ZEROS 529A.7.4For X,A∈ S M +
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A.7. ZEROS 531A.7.5.0.1 Proposition
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Appendix BSimple matricesMathematic
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B.1. RANK-ONE MATRIX (DYAD) 535R(v)
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B.1. RANK-ONE MATRIX (DYAD) 537rang
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B.2. DOUBLET 539R([u v ])R(Π)= R([
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B.3. ELEMENTARY MATRIX 541If λ ≠
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B.4. AUXILIARY V -MATRICES 543the n
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B.4. AUXILIARY V -MATRICES 54518. V
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B.5. ORTHOGONAL MATRIX 547B.5 Ortho
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B.5. ORTHOGONAL MATRIX 549B.5.3.0.1
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Appendix CSome analytical optimal r
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C.2. DIAGONAL, TRACE, SINGULAR AND
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C.3. ORTHOGONAL PROCRUSTES PROBLEM
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C.4. TWO-SIDED ORTHOGONAL PROCRUSTE
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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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597Equivalent to the corresponding
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E.1. IDEMPOTENT MATRICES 599where R
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E.1. IDEMPOTENT MATRICES 601TxT ⊥
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E.2. I − P , PROJECTION ON ALGEBR
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E.3. SYMMETRIC IDEMPOTENT MATRICES
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E.5. PROJECTION EXAMPLES 611E.5.0.0
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E.5. PROJECTION EXAMPLES 613of rela
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E.5. PROJECTION EXAMPLES 615E.5.0.0
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E.6. VECTORIZATION INTERPRETATION,
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E.7. ON VECTORIZED MATRICES OF HIGH
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E.7. ON VECTORIZED MATRICES OF HIGH
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E.9. PROJECTION ON CONVEX SET 629E.
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E.9. PROJECTION ON CONVEX SET 631
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E.9. PROJECTION ON CONVEX SET 633Pr
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E.9. PROJECTION ON CONVEX SET 637wh
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E.9. PROJECTION ON CONVEX SET 639Un
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E.10. ALTERNATING PROJECTION 643bC
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E.10. ALTERNATING PROJECTION 6450
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E.10. ALTERNATING PROJECTION 647E.1
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E.10. ALTERNATING PROJECTION 649y 2
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E.10. ALTERNATING PROJECTION 651By
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E.10. ALTERNATING PROJECTION 653Bar
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E.10. ALTERNATING PROJECTION 655bH
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E.10. ALTERNATING PROJECTION 657K
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E.10. ALTERNATING PROJECTION 659Whe
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Appendix FMatlab programsMade by Th
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F.1. ISEDM() 663% is nonnegativeif
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F.1. ISEDM() 665F.1.1Subroutines fo
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Page 671 and 672: F.3. MAP OF THE USA 671% plot origi
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Page 677 and 678: F.4. RANK REDUCTION SUBROUTINE, RRF
Page 679 and 680: F.5. STURM’S PROCEDURE 679F.5 Stu
Page 681 and 682: F.6. CONVEX ITERATION DEMONSTRATION
Page 683 and 684: F.6. CONVEX ITERATION DEMONSTRATION
Page 685 and 686: F.7. FAST MAX CUT 685endoldtrace =
Page 687 and 688: F.8. SIGNAL DROPOUT PROBLEM 687whil
Page 689 and 690: Appendix GNotation and a few defini
Page 691 and 692: 691l.i.w.r.tlinearly independentwit
Page 693 and 694: 693t → 0 +t goes to 0 from above;
Page 695 and 696: 695ψ(Z)DDD T (X)D(X) TD −1 (X)D(
Page 697 and 698: 697R n −or R n×n−S nS n⊥S n
Page 699 and 700: 699∂H −∂H +da supporting hype
Page 701 and 702: 701maximizexargsup Xarg supf(x)subj
Page 703 and 704: 703⊁≥not positive definitescala
Page 705 and 706: Bibliography[1] Suliman Al-Homidan
Page 707 and 708: BIBLIOGRAPHY 707[16] Keith Ball. An
Page 709 and 710: BIBLIOGRAPHY 709[38] A. W. Bojanczy
Page 711 and 712: BIBLIOGRAPHY 711[57] Steven Chu. Au
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Page 715 and 716: BIBLIOGRAPHY 715(AACC), June 2004.h
Page 717: BIBLIOGRAPHY 717[114] John Clifford
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Page 729 and 730: BIBLIOGRAPHY 729[240] Joshua A. Sin
Page 731 and 732: BIBLIOGRAPHY 731[264] Warren S. Tor
Page 733 and 734: BIBLIOGRAPHY 733[285] Bernard Widro
Page 735 and 736: Index0-norm, 241, 273, 275, 2941-no
Page 737 and 738: INDEX 737clipping, 190, 454, 638, 6
Page 739 and 740: INDEX 739hull, 53, 56, 306of outer
Page 741 and 742: INDEX 741unique, 519elbow, 457, 458
Page 743 and 744: INDEX 743polarity, 62handoff, 335ov
Page 745 and 746: INDEX 745manifold, 23, 24, 105, 410
Page 747 and 748: INDEX 747objectivebilinear, 256, 27
Page 749 and 750: INDEX 749Hadamard, 46, 472, 571, 69
Page 751 and 752: INDEX 751trace, 477, 478heuristic,
Page 753 and 754: INDEX 753symmetric hollow, 51tangen
Page 755 and 756: INDEX 755matrix, 65slack, 227vec, 4
Page 757 and 758: 757
Page 760: Convex Optimization & Euclidean Dis
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v2007.11.26 - Convex Optimization

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