Convex optimization: in signal processing and communications

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.


Table of Contents

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. Nenadić and A. Ozdaglar;
11. Competitive