| Description |
1 online resource (xiv, 498 pages) : illustrations |
| Contents |
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 |
| Summary |
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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
|
Print version record |
| Subject |
Signal processing.
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Mathematical optimization.
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Convex functions.
|
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COMPUTERS -- Information Theory.
|
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TECHNOLOGY & ENGINEERING -- Signals & Signal Processing.
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Convex functions
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Mathematical optimization
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Signal processing
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| Form |
Electronic book
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| Author |
Palomar, Daniel P.
|
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Eldar, Yonina C.
|
| LC no. |
2010276013 |
| ISBN |
9780511691232 |
|
0511691238 |
|
9780511692352 |
|
0511692358 |
|
9780511804458 |
|
0511804458 |
|
1107208122 |
|
9781107208124 |
|
1282653261 |
|
9781282653269 |
|
9786612653261 |
|
6612653264 |
|
0511689756 |
|
9780511689758 |
|
0511690495 |
|
9780511690495 |
|
0511689004 |
|
9780511689000 |
|
