Convex Optimization in Signal Processing and Communications
1st Edition
Published on 15. June 2010
978-0-511-68750-1 (ISBN)
Description
Over the past two decades there have been significant advances in the field of optimization. In particular, convex optimization has emerged as a powerful signal processing tool, and the variety of applications continues to grow rapidly. This book, written by a team of leading experts, sets out the theoretical underpinnings of the subject and provides tutorials on a wide range of convex optimization applications. Emphasis throughout is on cutting-edge research and on formulating problems in convex form, making this an ideal textbook for advanced graduate courses and a useful self-study guide. Topics covered range from automatic code generation, graphical models, and gradient-based algorithms for signal recovery, to semidefinite programming (SDP) relaxation and radar waveform design via SDP. It also includes blind source separation for image processing, robust broadband beamforming, distributed multi-agent optimization for networked systems, cognitive radio systems via game theory, and the variational inequality approach for Nash equilibrium solutions.
More details
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Cambridge University Press
Target group
Professional and scholarly
Illustrations
Worked examples or Exercises; 16 Tables, unspecified; 14 Halftones, unspecified; 81 Line drawings, unspecified
File size
4,38 MB
ISBN-13
978-0-511-68750-1 (9780511687501)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Daniel P. Palomar | Yonina C. Eldar
Convex Optimization in Signal Processing and Communications
Book
12/2009
Cambridge University Press
€121.32
Article not available at the moment
Persons
Daniel P. Palomar is an Assistant Professor in the Department of Electronic and Computer Engineering at Hong Kong University of Science and Technology. He received his Ph.D. from the Technical University of Catalonia (UPC), Spain, in 2003 and has since received numerous awards including the Best Doctoral Thesis in Advanced Mobile Communications by the Vodafone Foundation and COIT (2004). His current research interests include applications of convex optimization theory, game theory, and variational inequality theory to signal processing and communications. Yonina C. Eldar is a Professor in the Department of Engineering at Technion, Israel University of Technology, and is also a Research Affiliate with the Research Laboratory of Electronics at MIT. She received her Ph.D. from the Massachusetts Institute of Technology (MIT) in 2001. She has received many awards, including, in 2008, the Hershel Rich Innovation Award, the Award for Women with Distinguished Contributions and the Muriel & David Jacknow Award for Excellence in Teaching.
Content
1. Automatic code generation for real-time convex optimization J. Mattingley and S. Boyd; 2. Gradient-based algorithms with applications to signal recovery problems A. Beck and M. Teboulle; 3. Graphical models of autoregressive processes J. Songsiri, J. Dahl and L. Vandenberghe; 4. SDP relaxation of homogeneous quadratic optimization Z. Q. Luo and T. H. Chang; 5. Probabilistic analysis of SDR detectors for MIMO systems A. Man-Cho So and Y. Ye; 6. Semidefinite programming, matrix decomposition, and radar code design Y. Huang, A. De Maio and S. Zhang; 7. Convex analysis for non-negative blind source separation with application in imaging W. K. Ma, T. H. Chan, C. Y. Chi and Y. Wang; 8. Optimization techniques in modern sampling theory T. Michaeli and Y. C. Eldar; 9. Robust broadband adaptive beamforming using convex optimization M. Ruebsamen, A. El-Keyi, A. B. Gershman and T. Kirubarajan; 10. Cooperative distributed multi-agent optimization A. Nenadic and A. Ozdaglar; 11. Competitive optimization of cognitive radio MIMO systems via game theory G. Scutari, D. P. Palomar and S. Barbarossa; 12. Nash equilibria: the variational approach F. Facchinei and J. S. Pang.
