Convex Optimization in Signal Processing and Communications

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Cambridge University PressEdition: Illustrated, 12/3/2009EAN 9780521762229, ISBN10: 0521762227Hardcover, 512 pages, 25.4 x 18.5 x 2.8 cmLanguage: EnglishOriginally published in Eng lishOver 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.1. Automatic code generation for real-time convex optimization J. Mattingley and S. Boyd2. Gradient-based algorithms with applications to signal recovery problems A. Beck and M. Teboulle3. Graphical models of autoregressive processes J. Songsiri, J. Dahl and L. Vandenberghe4. SDP relaxation of homogeneous quadratic optimization Z. Q. Luo and T. H. Chang5. Probabilistic analysis of SDR detectors for MIMO systems A. Man-Cho So and Y. Ye6. Semidefinite programming, matrix decomposition, and radar code design Y. Huang, A. De Maio and S. Zhang7. Convex analysis for non-negative blind source separation with application in imaging W. K. Ma, T. H. Chan, C. Y. Chi and Y. Wang8. Optimization techniques in modern sampling theory T. Michaeli and Y. C. Eldar9. Robust broadband adaptive beamforming using convex optimization M. Rübsamen, A. El-Keyi, A. B. Gershman and T. Kirubarajan10. Cooperative distributed multi-agent optimization A. Nenadić and A. Ozdaglar11. Competitive optimization of cognitive radio MIMO systems via game theory G. Scutari, D. P. Palomar and S. Barbarossa12. Nash equilibriathe variational approach F. Facchinei and J. S. Pang. Show more