Description:
Cambridge University Press, 2009-12-03. Hardcover. Very Good. 2.7919 24.8731 18.0203. Unread. There is some minor wear to the cover.
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Convex Optimization in Signal Processing and Communications Hardcover - 2010 - 1st Edition
by Daniel P. Palomar (Editor); Yonina C. Eldar (Editor)
Details
- Title Convex Optimization in Signal Processing and Communications
- Author Daniel P. Palomar (Editor); Yonina C. Eldar (Editor)
- Binding Hardcover
- Edition number 1st
- Edition 1
- Pages 512
- Volumes 1
- Language ENG
- Publisher Cambridge University Press
- Date 2010-01
- Illustrated Yes
- Features Bibliography, Illustrated, Index, Table of Contents
- ISBN 9780521762229 / 0521762227
- Weight 2.05 lbs (0.93 kg)
- Dimensions 10.1 x 7.1 x 0.8 in (25.65 x 18.03 x 2.03 cm)
-
Themes
- Aspects (Academic): Science/Technology Aspects
- Library of Congress subjects Mathematical optimization, Signal processing
- Library of Congress Catalog Number 2010276013
- Dewey Decimal Code 621.382
Media reviews
Citations
- Scitech Book News, 06/01/2010, Page 30
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Cambridge University Press, 2009-12-03. Hardcover. New. 2.7919 24.8731 18.0203.
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Convex Optimization in Signal Processing and Communications
by Daniel P. Palomar
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Hardback. New. Leading experts provide the theoretical underpinnings of the subject plus tutorials on a wide range of applications, from automatic code generation to robust broadband beamforming. Emphasis on cutting-edge research and formulating problems in convex form make this an ideal textbook for advanced graduate courses and a useful self-study guide.
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Convex Optimization in Signal Processing and Communications
by Palomar, Daniel P. (Editor)/ Eldar, Yonina C. (Editor)
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Cambridge Univ Pr, 2009. Hardcover. New. 1st edition. 520 pages. 7.72x4.57x0.47 inches.
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Convex Optimization in Signal Processing and Communications by Palomar, Daniel P.
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Description:
ISBN: 9780521762229CAMBRIDGE UNIVERSITY PRESS | 25 January 2010
Hardback | 512 pages
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… Read More
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