Convex optimization in signal processing and communications /

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Bibliographic Details
Imprint:Cambridge ; New York : Cambridge University Press, ©2010.
Description:1 online resource (xiv, 498 pages) : illustrations
Language:English
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11825674
Hidden Bibliographic Details
Other authors / contributors:Palomar, Daniel P.
Eldar, Yonina C.
ISBN:9780511691232
0511691238
9780511692352
0511692358
9780511804458
0511804458
9780521762229
0521762227
1107208122
9781107208124
1282653261
9781282653269
9786612653261
6612653264
0511689756
9780511689758
0511690495
9780511690495
0511689004
9780511689000
Notes:Includes bibliographical references and index.
English.
Print version record.
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.
Other form:Print version: Convex optimization in signal processing and communications. Cambridge, UK ; New York : Cambridge University Press, 2010 9780521762229
Standard no.:9786612653261

MARC

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245 0 0 |a Convex optimization in signal processing and communications /  |c edited by Daniel P. Palomar and Yonina C. Eldar. 
260 |a Cambridge ;  |a New York :  |b Cambridge University Press,  |c ©2010. 
300 |a 1 online resource (xiv, 498 pages) :  |b illustrations 
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504 |a Includes bibliographical references and index. 
505 0 |a 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. 
588 0 |a Print version record. 
520 |a 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. 
546 |a English. 
650 0 |a Signal processing.  |0 http://id.loc.gov/authorities/subjects/sh85122397 
650 0 |a Mathematical optimization.  |0 http://id.loc.gov/authorities/subjects/sh85082127 
650 0 |a Convex functions.  |0 http://id.loc.gov/authorities/subjects/sh85031728 
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650 7 |a TECHNOLOGY & ENGINEERING  |x Signals & Signal Processing.  |2 bisacsh 
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650 7 |a Signal processing.  |2 fast  |0 (OCoLC)fst01118281 
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700 1 |a Palomar, Daniel P.  |0 http://id.loc.gov/authorities/names/no2007146184 
700 1 |a Eldar, Yonina C.  |0 http://id.loc.gov/authorities/names/no2010062198 
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