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Leading experts provide the theoretical underpinnings of the subject and tutorials on a wide variety of key applications.
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Daniel P. Palomar is an Assistant Professor in the Department of Electronic and Computer Engineering at Hong Kong University of Science and Technology. He received his Ph.D. from the Technical University of Catalonia (UPC), Spain, in 2003 and has since received numerous awards including the Best Doctoral Thesis in Advanced Mobile Communications by the Vodafone Foundation and COIT (2004). His current research interests include applications of convex optimization theory, game theory, and variational inequality theory to signal processing and communications.
Yonina C. Eldar is a Professor in the Department of Engineering at Technion, Israel University of Technology, and is also a Research Affiliate with the Research Laboratory of Electronics at MIT. She received her Ph.D. from the Massachusetts Institute of Technology (MIT) in 2001. She has received many awards, including, in 2008, the Hershel Rich Innovation Award, the Award for Women with Distinguished Contributions and the Muriel & David Jacknow Award for Excellence in Teaching.
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Convex Optimization in Signal Processing and Communications
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Cambridge University Press, 2009
ISBN 10: 0521762227
ISBN 13: 9780521762229
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Convex Optimization in Signal Processing and Communications
Editore:
Cambridge University Press, 2010
ISBN 10: 0521762227
ISBN 13: 9780521762229
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Convex Optimization in Signal Processing and Communications (Hardcover)
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Cambridge University Press, Cambridge, 2009
ISBN 10: 0521762227
ISBN 13: 9780521762229
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Hardcover. Condizione: new. Hardcover. 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9780521762229
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Convex Optimization in Signal Processing and Communications
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Convex Optimization in Signal Processing and Communications (Hardcover)
Editore:
Cambridge University Press, Cambridge, 2009
ISBN 10: 0521762227
ISBN 13: 9780521762229
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Hardcover. Condizione: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780521762229
Quantità: 1 disponibili