Convex Optimization in Signal Processing and Communications (eBook, PDF)

Redaktion: Palomar, Daniel P.

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 3. Dezember 2009
• Englisch
• ISBN-13: 9780511687501
• Artikelnr.: 38215093

Inhaltsangabe

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.