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" Convex optimization in signal processing and communications / "
edited by Daniel P. Palomar and Yonina C. Eldar.
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BL
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| Record Number
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1011681
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| Doc. No
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b766051
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| Title & Author
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Convex optimization in signal processing and communications /\ edited by Daniel P. Palomar and Yonina C. Eldar.
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| Publication Statement
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Cambridge ;New York :: Cambridge University Press,, ©2010.
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| Page. NO
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1 online resource (xiv, 498 pages) :: illustrations
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| ISBN
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0511689004
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: 0511689756
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: 0511690495
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: 0511691238
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: 0511692358
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: 0511804458
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: 1107208122
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: 1282653261
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: 6612653264
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: 9780511689000
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: 9780511689758
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: 9780511690495
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: 9780511691232
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: 9780511692352
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: 9780511804458
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: 9781107208124
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: 9781282653269
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: 9786612653261
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0521762227
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9780521762229
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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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.
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| Abstract
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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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| Subject
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Convex functions.
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Mathematical optimization.
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Signal processing.
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COMPUTERS-- Information Theory.
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Convex functions.
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Mathematical optimization.
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Signal processing.
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TECHNOLOGY ENGINEERING-- Signals Signal Processing.
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| Dewey Classification
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621.3822015196
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| LC Classification
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QA402.5.C66 2010eb
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| Added Entry
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Eldar, Yonina C.
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Palomar, Daniel P.
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