Convex optimization in signal processing and communications

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.;1. Automatic code generation for real-time convex optimization / Jacob Mattingley and Stephen Boyd -- 2. Gradient-based algorithmswith applications to signal-recovery problems / Amir Beck and Marc Teboulle -- 3. Graphical models of autoregressive processes / Jitkomut Songsiri, Joachim Dahl and Lieven Vandenberghe -- 4. SDP relaxation of homogeneous quadratic optimization: approximation bounds and applications / Zhi-Quan Luo and Tsung-Hui Chang -- 5. Probabilistic analysis of semidefinite relaxation detectors for multiple-input, multiple-output systems / Anthony Man-Cho So and Yinyu Ye -- 6. Semidefinite programming, matrix decomposition, and radar code design / Yongwei Huang, Antonio De Maio and Shuzhong Zhang -- 7. Convex analysis for non-negative blind source separation with application in imaging / Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi and Vue Wang -- 8. Optimization techniques in modern sampling theory / Tomer Michaeli and Yonina C. Eldar -- 9. Robust broadband adaptive beamforming using convex optimization / Michael Rubsamen, Amr El-Keyi, Alex B. Gershman and Thia Kirubarajan -- 10. Cooperative distributed multi-agentoptimization / Angelia Nedic and Asuman Ozdaglar -- 11. Competitive optimization of cognitive radio MIMO systems via game theory / Gesualso Scutari, Daniel P. Palomar and Sergio Barbarossa -- 12. Nash equilibria: the variational approach / Francisco Facchinei and Jong-Shi Pang.