v2009.01.01 - Convex Optimization
v2009.01.01 - Convex Optimization v2009.01.01 - Convex Optimization
744 BIBLIOGRAPHY [320] È. B. Vinberg. The theory of convex homogeneous cones. Transactions of the Moscow Mathematical Society, 12:340–403, 1963. American Mathematical Society and London Mathematical Society joint translation, 1965. [321] Marie A. Vitulli. A brief history of linear algebra and matrix theory, 2004. darkwing.uoregon.edu/∼vitulli/441.sp04/LinAlgHistory.html [322] John von Neumann. Functional Operators, Volume II: The Geometry of Orthogonal Spaces. Princeton University Press, 1950. Reprinted from mimeographed lecture notes first distributed in 1933. [323] Michael B. Wakin, Jason N. Laska, Marco F. Duarte, Dror Baron, Shriram Sarvotham, Dharmpal Takhar, Kevin F. Kelly, and Richard G. Baraniuk. An architecture for compressive imaging. In Proceedings of the IEEE International Conference on Image Processing (ICIP), pages 1273–1276, October 2006. http://www.dsp.rice.edu/cs/CSCam-ICIP06.pdf [324] Roger Webster. Convexity. Oxford University Press, 1994. [325] Kilian Q. Weinberger and Lawrence K. Saul. Unsupervised learning of image manifolds by semidefinite programming. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2, pages 988–995, 2004. http://www.cs.ucsd.edu/∼saul/papers/sde cvpr04.pdf [326] Eric W. Weisstein. Mathworld – A Wolfram Web Resource, 2007. http://mathworld.wolfram.com/search [327] Bernard Widrow and Samuel D. Stearns. Adaptive Signal Processing. Prentice-Hall, 1985. [328] Norbert Wiener. On factorization of matrices. Commentarii Mathematici Helvetici, 29:97–111, 1955. [329] Ami Wiesel, Yonina C. Eldar, and Shlomo Shamai (Shitz). Semidefinite relaxation for detection of 16-QAM signaling in MIMO channels. IEEE Signal Processing Letters, 12(9):653–656, September 2005. [330] Michael P. Williamson, Timothy F. Havel, and Kurt Wüthrich. Solution conformation of proteinase inhibitor IIA from bull seminal plasma by 1 H nuclear magnetic resonance and distance geometry. Journal of Molecular Biology, 182:295–315, 1985. [331] Willie W. Wong. Cayley-Menger determinant and generalized N-dimensional Pythagorean theorem, November 2003. Application of Linear Algebra: Notes on Talk given to Princeton University Math Club. http://www.princeton.edu/∼wwong/papers/gp-r.pdf [332] William Wooton, Edwin F. Beckenbach, and Frank J. Fleming. Modern Analytic Geometry. Houghton Mifflin, 1975.
[333] Margaret H. Wright. The interior-point revolution in optimization: History, recent developments, and lasting consequences. Bulletin of the American Mathematical Society, 42(1):39–56, January 2005. [334] Stephen J. Wright. Primal-Dual Interior-Point Methods. SIAM, 1997. [335] Shao-Po Wu. max-det Programming with Applications in Magnitude Filter Design. A dissertation submitted to the department of Electrical Engineering, Stanford University, December 1997. [336] Shao-Po Wu and Stephen Boyd. sdpsol: A parser/solver for semidefinite programs with 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 [337] Shao-Po Wu, Stephen Boyd, and Lieven Vandenberghe. FIR filter design via spectral factorization and convex optimization, 1997. http://www.stanford.edu/∼boyd/papers/magdes.html [338] Naoki Yamamoto and Maryam Fazel. A computational approach to quantum encoder design for purity optimization, 2006. http://arxiv.org/abs/quant-ph/0606106 [339] David D. Yao, Shuzhong Zhang, and Xun Yu Zhou. Stochastic linear-quadratic control via primal-dual semidefinite programming. SIAM Review, 46(1):87–111, March 2004. Erratum: p.209 herein. [340] Yinyu Ye. Semidefinite programming for Euclidean distance geometric optimization. Lecture notes, 2003. http://www.stanford.edu/class/ee392o/EE392o-yinyu-ye.pdf [341] Yinyu Ye. Convergence behavior of central paths for convex homogeneous self-dual cones, 1996. http://www.stanford.edu/∼yyye/yyye/ye.ps [342] Yinyu Ye. Interior Point Algorithms: Theory and Analysis. Wiley, 1997. [343] D. C. Youla. Mathematical theory of image restoration by the method of convex projection. In Henry Stark, editor, Image Recovery: Theory and Application, chapter 2, pages 29–77. Academic Press, 1987. [344] Fuzhen Zhang. Matrix Theory: Basic Results and Techniques. Springer-Verlag, 1999. [345] Günter M. Ziegler. Kissing numbers: Surprises in dimension four. Emissary, pages 4–5, Spring 2004. http://www.msri.org/communications/emissary 2001 Jon Dattorro. CO&EDG version 2009.01.01. All rights reserved. Citation: Jon Dattorro, Convex Optimization & Euclidean Distance Geometry, Meboo Publishing USA, 2005. 745
- Page 693 and 694: E.10. ALTERNATING PROJECTION 693 (a
- Page 695 and 696: E.10. ALTERNATING PROJECTION 695 wh
- Page 697 and 698: E.10. ALTERNATING PROJECTION 697 E.
- Page 699 and 700: E.10. ALTERNATING PROJECTION 699 10
- Page 701 and 702: E.10. ALTERNATING PROJECTION 701 E.
- Page 703 and 704: E.10. ALTERNATING PROJECTION 703 E
- Page 705 and 706: Appendix F Notation and a few defin
- Page 707 and 708: 707 a.i. c.i. l.i. w.r.t affinely i
- Page 709 and 710: 709 is or ← → t → 0 + as in
- Page 711 and 712: 711 ∑ π(γ) Ξ Π ∏ ψ(Z) D D
- Page 713 and 714: 713 R m×n Euclidean vector space o
- Page 715 and 716: 715 H − H + ∂H ∂H ∂H −
- Page 717 and 718: 717 O O sort-index matrix order of
- Page 719 and 720: (x,y) angle between vectors x and y
- Page 721 and 722: Bibliography [1] Suliman Al-Homidan
- Page 723 and 724: BIBLIOGRAPHY 723 [24] Alexander I.
- Page 725 and 726: BIBLIOGRAPHY 725 [52] Stephen Boyd,
- Page 727 and 728: BIBLIOGRAPHY 727 [78] Frank Critchl
- Page 729 and 730: BIBLIOGRAPHY 729 [105] Richard L. D
- Page 731 and 732: BIBLIOGRAPHY 731 [132] Michel X. Go
- Page 733 and 734: BIBLIOGRAPHY 733 [162] T. Herrmann,
- Page 735 and 736: BIBLIOGRAPHY 735 [191] Mark Kahrs a
- Page 737 and 738: BIBLIOGRAPHY 737 [220] K. V. Mardia
- Page 739 and 740: BIBLIOGRAPHY 739 [250] Pythagoras P
- Page 741 and 742: BIBLIOGRAPHY 741 [277] Anthony Man-
- Page 743: BIBLIOGRAPHY 743 [306] Michael W. T
- Page 747 and 748: Index 0-norm, 203, 261, 294, 296, 2
- Page 749 and 750: INDEX 749 product, 43, 92, 147, 254
- Page 751 and 752: INDEX 751 coordinates, 140, 170, 17
- Page 753 and 754: INDEX 753 affine dimension, 485 fea
- Page 755 and 756: INDEX 755 affine, 209 nonlinear, 19
- Page 757 and 758: INDEX 757 of point, 37 ray, 90 rela
- Page 759 and 760: INDEX 759 normal, 47, 548, 563 norm
- Page 761 and 762: INDEX 761 strictly, 515, 520 functi
- Page 763 and 764: INDEX 763 vector, 45, 241, 248, 325
- Page 765 and 766: INDEX 765 convex envelope, see conv
- Page 767 and 768: INDEX 767 cone, 418, 420, 507 dual,
- Page 769 and 770: INDEX 769 trilateration, 21, 42, 36
- Page 772: Convex Optimization & Euclidean Dis
[333] Margaret H. Wright. The interior-point revolution in optimization: History, recent<br />
developments, and lasting consequences. Bulletin of the American Mathematical<br />
Society, 42(1):39–56, January 2005.<br />
[334] Stephen J. Wright. Primal-Dual Interior-Point Methods. SIAM, 1997.<br />
[335] Shao-Po Wu. max-det Programming with Applications in Magnitude Filter Design.<br />
A dissertation submitted to the department of Electrical Engineering, Stanford<br />
University, December 1997.<br />
[336] Shao-Po Wu and Stephen Boyd. sdpsol: A parser/solver for semidefinite programs<br />
with matrix structure. In Laurent El Ghaoui and Silviu-Iulian Niculescu, editors,<br />
Advances in Linear Matrix Inequality Methods in Control, chapter 4, pages 79–91.<br />
SIAM, 2000.<br />
http://www.stanford.edu/∼boyd/sdpsol.html<br />
[337] Shao-Po Wu, Stephen Boyd, and Lieven Vandenberghe. FIR filter design via<br />
spectral factorization and convex optimization, 1997.<br />
http://www.stanford.edu/∼boyd/papers/magdes.html<br />
[338] Naoki Yamamoto and Maryam Fazel. A computational approach to quantum<br />
encoder design for purity optimization, 2006.<br />
http://arxiv.org/abs/quant-ph/0606106<br />
[339] David D. Yao, Shuzhong Zhang, and Xun Yu Zhou. Stochastic linear-quadratic<br />
control via primal-dual semidefinite programming. SIAM Review, 46(1):87–111,<br />
March 2004. Erratum: p.209 herein.<br />
[340] Yinyu Ye. Semidefinite programming for Euclidean distance geometric optimization.<br />
Lecture notes, 2003.<br />
http://www.stanford.edu/class/ee392o/EE392o-yinyu-ye.pdf<br />
[341] Yinyu Ye. Convergence behavior of central paths for convex homogeneous self-dual<br />
cones, 1996.<br />
http://www.stanford.edu/∼yyye/yyye/ye.ps<br />
[342] Yinyu Ye. Interior Point Algorithms: Theory and Analysis. Wiley, 1997.<br />
[343] D. C. Youla. Mathematical theory of image restoration by the method of convex<br />
projection. In Henry Stark, editor, Image Recovery: Theory and Application,<br />
chapter 2, pages 29–77. Academic Press, 1987.<br />
[344] Fuzhen Zhang. Matrix Theory: Basic Results and Techniques. Springer-Verlag,<br />
1999.<br />
[345] Günter M. Ziegler. Kissing numbers: Surprises in dimension four. Emissary, pages<br />
4–5, Spring 2004.<br />
http://www.msri.org/communications/emissary<br />
2001 Jon Dattorro. CO&EDG version 2009.01.01. All rights reserved.<br />
Citation: Jon Dattorro, <strong>Convex</strong> <strong>Optimization</strong> & Euclidean Distance Geometry,<br />
Meboo Publishing USA, 2005.<br />
745