v2007.09.13 - Convex Optimization

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700 BIBLIOGRAPHYWeiss, editors, Polytopes: Abstract, Convex and Computational, pages373–466. Kluwer Academic Publishers, 1994.[124] 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.[125] Osman Güler and Yinyu Ye. Convergence behavior of interior-pointalgorithms. Mathematical Programming, 60(2):215–228, 1993.[126] P. R. Halmos. Positive approximants of operators. Indiana UniversityMathematics Journal, 21:951–960, 1972.[127] Shih-Ping Han. A successive projection method. MathematicalProgramming, 40:1–14, 1988.[128] Godfrey H. Hardy, John E. Littlewood, and George Pólya. Inequalities.Cambridge University Press, second edition, 1952.[129] Arash Hassibi and Mar Hershenson. Automated optimal design ofswitched-capacitor filters. Design Automation and Test in EuropeConference, 2001.[130] Johan Håstad. Some optimal inapproximability results, 1999.http://citeseer.ist.psu.edu/280448.html[131] 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.[132] Tom L. Hayden and Jim Wells. Approximation by matrices positivesemidefinite on a subspace. Linear Algebra and its Applications,109:115–130, 1988.[133] 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.[134] Uwe Helmke and John B. Moore. Optimization and DynamicalSystems. Springer-Verlag, 1994.
BIBLIOGRAPHY 701[135] Bruce Hendrickson. Conditions for unique graph realizations. SIAMJournal on Computing, 21(1):65–84, February 1992.[136] 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.[137] Mar Hershenson. Design of pipeline analog-to-digital converters viageometric programming. International Conference on Computer AidedDesign - ICCAD, 2002.[138] Mar Hershenson. Efficient description of the design space of analogcircuits. 40 th Design Automation Conference, 2003.[139] 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.[140] Mar Hershenson, Dave Colleran, Arash Hassibi, and Navraj Nandra.Synthesizable full custom mixed-signal IP. Electronics DesignAutomation Consortium - EDA, 2002.[141] Mar Hershenson, Sunderarajan S. Mohan, Stephen Boyd, and ThomasLee. Optimization of inductor circuits via geometric programming,1999.[142] Mar Hershenson and Xiling Shen. A new analog design flow. Cadence,2002.[143] Nick Higham. Matrix Procrustes problems, 1995.http://www.ma.man.ac.uk/∼higham/talksLecture notes.[144] Richard D. Hill and Steven R. Waters. On the cone of positivesemidefinite matrices. Linear Algebra and its Applications, 90:81–88,1987.[145] Jean-Baptiste Hiriart-Urruty. Ensembles de Tchebychev vs. ensemblesconvexes: l’état de la situation vu via l’analyse convexe non lisse.Annales des Sciences Mathématiques du Québec, 22(1):47–62, 1998.
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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 Quadratic func
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LIST OF FIGURES 17E Projection 5791
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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 9: 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 12: 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 732.5
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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 24: 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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96 CHAPTER 2. CONVEX GEOMETRYBCADFi
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98 CHAPTER 2. CONVEX GEOMETRYThe po
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100 CHAPTER 2. CONVEX GEOMETRY2.9.0
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102 CHAPTER 2. CONVEX GEOMETRYwhere
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104 CHAPTER 2. CONVEX GEOMETRY√2
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106 CHAPTER 2. CONVEX GEOMETRYwhich
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108 CHAPTER 2. CONVEX GEOMETRY2.9.2
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110 CHAPTER 2. CONVEX GEOMETRYA con
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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 189where the n
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3.1. CONVEX FUNCTION 191where k ∈
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3.1. CONVEX FUNCTION 193f(z)Az 2z 1
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3.1. CONVEX FUNCTION 195{a T z 1 +
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3.1. CONVEX FUNCTION 197When an epi
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3.1. CONVEX FUNCTION 199orthonormal
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3.1. CONVEX FUNCTION 201[30,1.1] Ex
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3.1. CONVEX FUNCTION 20321.510.5Y 2
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3.1. CONVEX FUNCTION 2053.1.8.0.1 E
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3.1. CONVEX FUNCTION 207This equiva
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3.1. CONVEX FUNCTION 2093.1.8.1 mon
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3.1. CONVEX FUNCTION 211[ Yt]∈ ep
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3.1. CONVEX FUNCTION 213→Y −Xwh
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3.2. MATRIX-VALUED CONVEX FUNCTION
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3.3. QUASICONVEX 221A quasiconcave
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3.4. SALIENT PROPERTIES 2236.A nonn
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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 2414.2.3.0.2 Example
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4.2. FRAMEWORK 243where δ is the m
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4.2. FRAMEWORK 2454.2.3.0.3 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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Chapter 5Euclidean Distance MatrixT
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5.2. FIRST METRIC PROPERTIES 291cor
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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 297The collecti
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5.4. EDM DEFINITION 2995.4.2 Gram-f
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5.4. EDM DEFINITION 301D ∈ EDM N
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5.4. EDM DEFINITION 3035.4.2.2.1 Ex
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5.4. EDM DEFINITION 305ten affine e
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5.4. EDM DEFINITION 307spheres:Then
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5.4. EDM DEFINITION 309By eliminati
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5.4. EDM DEFINITION 311whereΦ ij =
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5.4. EDM DEFINITION 3135.4.2.2.5 De
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5.4. EDM DEFINITION 315105ˇx 4ˇx
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5.4. EDM DEFINITION 317corrected by
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5.4. EDM DEFINITION 319aptly be app
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5.4. EDM DEFINITION 321As before, a
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5.4. EDM DEFINITION 323where ([√t
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5.4. EDM DEFINITION 325because (A.3
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5.5. INVARIANCE 3275.5.1.0.1 Exampl
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5.6. INJECTIVITY OF D & UNIQUE RECO
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5.7. EMBEDDING IN AFFINE HULL 3355.
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5.7. EMBEDDING IN AFFINE HULL 337Fo
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5.7. EMBEDDING IN AFFINE HULL 3395.
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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 357(ii)
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5.11. EDM INDEFINITENESS 3595.11.1
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5.11. EDM INDEFINITENESS 361(confer
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5.11. EDM INDEFINITENESS 363we have
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5.11. EDM INDEFINITENESS 365For pre
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5.12. LIST RECONSTRUCTION 367where
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5.12. LIST RECONSTRUCTION 369(a)(c)
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5.13. RECONSTRUCTION EXAMPLES 371D
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5.13. RECONSTRUCTION EXAMPLES 373Th
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5.13. RECONSTRUCTION EXAMPLES 375wh
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5.14. FIFTH PROPERTY OF EUCLIDEAN M
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Chapter 6EDM coneFor N > 3, the con
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6.1. DEFINING EDM CONE 3896.1 Defin
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6.2. POLYHEDRAL BOUNDS 391This cone
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6.3.√EDM CONE IS NOT CONVEX 393N
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6.4. A GEOMETRY OF COMPLETION 3956.
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6.4. A GEOMETRY OF COMPLETION 397(a
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6.4. A GEOMETRY OF COMPLETION 399Fi
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6.5. EDM DEFINITION IN 11 T 401and
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6.5. EDM DEFINITION IN 11 T 403then
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6.5. EDM DEFINITION IN 11 T 4056.5.
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6.5. EDM DEFINITION IN 11 T 407D =
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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 419When the Fins
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6.8. DUAL EDM CONE 421Proof. First,
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6.8. DUAL EDM CONE 423EDM 2 = S 2 h
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6.8. DUAL EDM CONE 425whose veracit
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6.8. DUAL EDM CONE 4276.8.1.3.1 Exe
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6.8. DUAL EDM CONE 429has dual affi
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6.8. DUAL EDM CONE 4316.8.1.7 Schoe
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6.9. THEOREM OF THE ALTERNATIVE 433
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6.10. POSTSCRIPT 435When 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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441HS N h0EDM NK = S N h ∩ R N×N
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4437.0.3 Problem approachProblems t
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7.1. FIRST PREVALENT PROBLEM: 445fi
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7.1. FIRST PREVALENT PROBLEM: 4477.
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7.1. FIRST PREVALENT PROBLEM: 449di
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7.1. FIRST PREVALENT PROBLEM: 4517.
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7.1. FIRST PREVALENT PROBLEM: 453wh
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7.1. FIRST PREVALENT PROBLEM: 455Th
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7.2. SECOND PREVALENT PROBLEM: 457O
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7.2. SECOND PREVALENT PROBLEM: 459S
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7.2. SECOND PREVALENT PROBLEM: 461r
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7.2. SECOND PREVALENT PROBLEM: 463c
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7.2. SECOND PREVALENT PROBLEM: 4657
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7.3. THIRD PREVALENT PROBLEM: 467fo
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7.3. THIRD PREVALENT PROBLEM: 469a
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7.3. THIRD PREVALENT PROBLEM: 4717.
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7.3. THIRD PREVALENT PROBLEM: 4737.
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7.3. THIRD PREVALENT PROBLEM: 475Ou
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478 CHAPTER 7. PROXIMITY PROBLEMSth
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480 APPENDIX A. LINEAR ALGEBRAA.1.1
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484 APPENDIX A. LINEAR ALGEBRAonly
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486 APPENDIX A. LINEAR ALGEBRA(AB)
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490 APPENDIX A. LINEAR ALGEBRAFor A
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492 APPENDIX A. LINEAR ALGEBRADiago
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494 APPENDIX A. LINEAR ALGEBRAFor A
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498 APPENDIX A. LINEAR ALGEBRAA.4 S
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502 APPENDIX A. LINEAR ALGEBRAA.5 e
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504 APPENDIX A. LINEAR ALGEBRAs i w
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508 APPENDIX A. LINEAR ALGEBRAΣq 2
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510 APPENDIX A. LINEAR ALGEBRAA.7 Z
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512 APPENDIX A. LINEAR ALGEBRAThere
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516 APPENDIX A. LINEAR ALGEBRA
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518 APPENDIX B. SIMPLE MATRICESB.1
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520 APPENDIX B. SIMPLE MATRICESProo
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522 APPENDIX B. SIMPLE MATRICESB.1.
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524 APPENDIX B. SIMPLE MATRICESN(u
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526 APPENDIX B. SIMPLE MATRICESDue
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530 APPENDIX B. SIMPLE MATRICEShas
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532 APPENDIX B. SIMPLE MATRICESFigu
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Page 536 and 537:
536 APPENDIX C. SOME ANALYTICAL OPT
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550 APPENDIX D. MATRIX CALCULUSThe
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552 APPENDIX D. MATRIX CALCULUSGrad
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554 APPENDIX D. MATRIX CALCULUSBeca
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556 APPENDIX D. MATRIX CALCULUSwhic
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558 APPENDIX D. MATRIX CALCULUS⎡
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560 APPENDIX D. MATRIX CALCULUS→Y
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562 APPENDIX D. MATRIX CALCULUSD.1.
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564 APPENDIX D. MATRIX CALCULUSwhic
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566 APPENDIX D. MATRIX CALCULUSIn t
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570 APPENDIX D. MATRIX CALCULUSD.2
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572 APPENDIX D. MATRIX CALCULUSalge
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574 APPENDIX D. MATRIX CALCULUStrac
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578 APPENDIX D. MATRIX CALCULUS
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580 APPENDIX E. PROJECTIONThe follo
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582 APPENDIX E. PROJECTIONFor matri
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584 APPENDIX E. PROJECTION(⇐) To
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586 APPENDIX E. PROJECTIONNonorthog
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588 APPENDIX E. PROJECTIONE.2.0.0.1
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590 APPENDIX E. PROJECTIONE.3.2Orth
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592 APPENDIX E. PROJECTIONE.3.5Unif
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594 APPENDIX E. PROJECTIONE.4 Algeb
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596 APPENDIX E. PROJECTIONa ∗ 2K
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598 APPENDIX E. PROJECTIONwhere Y =
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600 APPENDIX E. PROJECTION(B.4.2).
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602 APPENDIX E. PROJECTIONis a nono
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604 APPENDIX E. PROJECTIONE.6.4.1Or
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606 APPENDIX E. PROJECTIONq i q T i
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608 APPENDIX E. PROJECTIONThe test
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610 APPENDIX E. PROJECTIONPerpendic
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612 APPENDIX E. PROJECTIONE.8 Range
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614 APPENDIX E. PROJECTIONAs for su
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616 APPENDIX E. PROJECTIONWith refe
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618 APPENDIX E. PROJECTIONProjectio
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620 APPENDIX E. PROJECTIONE.9.2.2.2
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622 APPENDIX E. PROJECTIONThe foreg
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624 APPENDIX E. PROJECTION❇❇❇
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626 APPENDIX E. PROJECTIONE.10 Alte
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628 APPENDIX E. PROJECTIONbH 1H 2P
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630 APPENDIX E. PROJECTIONa(a){y |
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632 APPENDIX E. PROJECTION(a feasib
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634 APPENDIX E. PROJECTIONwhile, th
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636 APPENDIX E. PROJECTIONE.10.2.1.
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638 APPENDIX E. PROJECTION10 0dist(
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640 APPENDIX E. PROJECTIONE.10.3.1D
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642 APPENDIX E. PROJECTIONE 3K ⊥
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644 APPENDIX E. PROJECTION
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646 APPENDIX F. MATLAB PROGRAMSif n
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648 APPENDIX F. MATLAB PROGRAMSend%
Page 650 and 651: 650 APPENDIX F. MATLAB PROGRAMSF.1.
Page 652 and 653: 652 APPENDIX F. MATLAB PROGRAMScoun
Page 654 and 655: 654 APPENDIX F. MATLAB PROGRAMSF.3
Page 658 and 659: 658 APPENDIX F. MATLAB PROGRAMS% so
Page 660 and 661: 660 APPENDIX F. MATLAB PROGRAMS% tr
Page 664 and 665: 664 APPENDIX F. MATLAB PROGRAMSbrea
Page 666 and 667: 666 APPENDIX F. MATLAB PROGRAMSwhil
Page 668 and 669: 668 APPENDIX F. MATLAB PROGRAMSF.7
Page 670 and 671: 670 APPENDIX F. MATLAB PROGRAMS
Page 672 and 673: 672 APPENDIX G. NOTATION AND A FEW
Page 688 and 689: 688 BIBLIOGRAPHY[7] Abdo Y. Alfakih
Page 690 and 691: 690 BIBLIOGRAPHY[27] Aharon Ben-Tal
Page 692 and 693: 692 BIBLIOGRAPHY[48] Lev M. Brègma
Page 694 and 695: 694 BIBLIOGRAPHY[67] Joel Dawson, S
Page 696 and 697: 696 BIBLIOGRAPHY[85] Alan Edelman,
Page 698 and 699: 698 BIBLIOGRAPHY[102] Philip E. Gil
Page 702 and 703: 702 BIBLIOGRAPHY[146] Jean-Baptiste
Page 704 and 705: 704 BIBLIOGRAPHY[168] Jean B. Lasse
Page 706 and 707: 706 BIBLIOGRAPHY[189] Rudolf Mathar
Page 708 and 709: 708 BIBLIOGRAPHY[211] M. L. Overton
Page 710 and 711: 710 BIBLIOGRAPHY[229] C. K. Rushfor
Page 712 and 713: 712 BIBLIOGRAPHY[252] Jos F. Sturm
Page 714 and 715: 714 BIBLIOGRAPHY[274] È. B. Vinber
Page 716 and 717: [294] Yinyu Ye. Semidefinite progra
Page 718 and 719: 718 INDEXobtuse, 62positive semidef
Page 720 and 721: 720 INDEXelliptope, 642orthant, 177
Page 722 and 723: 722 INDEXdistancegeometry, 20, 317m
Page 724 and 725: 724 INDEX-dimensional, 37, 89rank,
Page 726 and 727: 726 INDEXGram form, 331is, 674isedm
Page 728 and 729: 728 INDEXdiscretized, 152, 431in su
Page 730 and 731: 730 INDEXboundary, 115dimension, 10
Page 732 and 733: 732 INDEXlinear operator, 587, 591,
Page 734 and 735: 734 INDEXlargest entries, 188monoto
Page 736: 736 INDEXsimilarity, 606translation
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