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:	Signal processing. Mathematical optimization. Convex functions. Convex functions. Mathematical optimization. Signal processing. Electronic book.
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

Table of 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.

Convex optimization in signal processing and communications /

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