Read more
Informationen zum Autor 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. Klappentext Leading experts provide the theoretical underpinnings of the subject and tutorials on a wide variety of key applications. Zusammenfassung Leading experts provide the theoretical underpinnings of the subject plus tutorials on a wide range of applications! from automatic code generation to robust broadband beamforming. Emphasis on cutting-edge research and formulating problems in convex form make this an ideal textbook for advanced graduate courses and a useful self-study guide. Inhaltsverzeichnis 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. Rübsamen, A. El-Keyi, A. B. Gershman and T. Kirubarajan; 10. Cooperative distributed multi-agent optimization A. Nenadi¿ 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....
