Convex Optimization in Signal Processing and Communications

doi:10.1017/CBO9780511804458

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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Contents

Frontmatter
pp i-iv
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Contents
pp v-viii
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List of contributors
pp ix-x
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Preface
pp xi-xiv
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1 - Automatic code generation for real-time convex optimization
pp 1-41
- By Jacob Mattingley, Stanford University, Stephen Boyd, Stanford University
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2 - Gradient-based algorithms with applications to signal-recovery problems
pp 42-88
- By Amir Beck, Technion – Israel Institute of Technology, Marc Teboulle, Tel-Aviv University
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3 - Graphical models of autoregressive processes
pp 89-116
- By Jitkomut Songsiri, University of California Los Angeles, Joachim Dahl, Anybody Technology A/S Denmark, Lieven Vandenberghe, University of California Los Angeles
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4 - SDP relaxation of homogeneous quadratic optimization: approximation bounds and applications
pp 117-165
- By Zhi-Quan Luo, University of Minnesota, Tsung-Hui Chang, National Tsing Hua University Hsinchu
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5 - Probabilistic analysis of semidefinite relaxation detectors for multiple-input, multiple-output systems
pp 166-191
- By Anthony Man-Cho So, Chinese University of Hong Kong, Yinyu Ye, Stanford University
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6 - Semidefinite programming, matrix decomposition, and radar code design
pp 192-228
- By Yongwei Huang, Hong Kong University of Science and Technology, Antonio De Maio, Università degli Studi di Napoli – Federico II, Shuzhong Zhang, Chinese University of Hong Kong
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7 - Convex analysis for non-negative blind source separation with application in imaging
pp 229-265
- By Wing-Kin Ma, Chinese University of Hong Kong, Tsung-Han Chan, National Tsing Hua University, Chong-Yung Chi, National Tsing Hua University, Yue Wang, Virginia Polytechnic Institute and State University
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8 - Optimization techniques in modern sampling theory
pp 266-314
- By Tomer Michaeli, Technion – Israel Institute of Technology, Yonina C. Eldar, Technion – Israel Institute of Technology
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9 - Robust broadband adaptive beamforming using convex optimization
pp 315-339
- By Michael Rübsamen, Darmstadt University of Technology, Amr El-Keyi, Alexandria University, Alex B. Gershman, Darmstadt University of Technology, Thia Kirubarajan, McMaster University
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10 - Cooperative distributed multi-agent optimization
pp 340-386
- By Angelia Nedić, University of Illinois at Urbana-Champaign, Asuman Ozdaglar, Massachusetts Institute of Technology
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11 - Competitive optimization of cognitive radio MIMO systems via game theory
pp 387-442
- By Gesualso Scutari, Hong Kong University of Science and Technology, Daniel P. Palomar, Hong Kong University of Science and Technology, Sergio Barbarossa, University of Rome – La Sapienza
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12 - Nash equilibria: the variational approach
pp 443-493
- By Francisco Facchinei, University of Rome – La Sapienza, Jong-Shi Pang, University of Illinois at Urbana-Champaign
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Afterword
pp 494-494
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Index
pp 495-498
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Convex Optimization in Signal Processing and Communications

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Contents

Frontmatter
pp i-iv

Contents
pp v-viii

List of contributors
pp ix-x

Preface
pp xi-xiv

1 - Automatic code generation for real-time convex optimization
pp 1-41

2 - Gradient-based algorithms with applications to signal-recovery problems
pp 42-88

3 - Graphical models of autoregressive processes
pp 89-116

4 - SDP relaxation of homogeneous quadratic optimization: approximation bounds and applications
pp 117-165

5 - Probabilistic analysis of semidefinite relaxation detectors for multiple-input, multiple-output systems
pp 166-191

6 - Semidefinite programming, matrix decomposition, and radar code design
pp 192-228

7 - Convex analysis for non-negative blind source separation with application in imaging
pp 229-265

8 - Optimization techniques in modern sampling theory
pp 266-314

9 - Robust broadband adaptive beamforming using convex optimization
pp 315-339

10 - Cooperative distributed multi-agent optimization
pp 340-386

11 - Competitive optimization of cognitive radio MIMO systems via game theory
pp 387-442

12 - Nash equilibria: the variational approach
pp 443-493

Afterword
pp 494-494

Index
pp 495-498

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Convex Optimization in Signal Processing and Communications

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