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10 years of news and resources for members of the IEEE Signal Processing Society
edited by Daniel P. Palomar and Yonina C. Eldar, Cambridge University Press, 2010
Description from the publisher: 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.
|Meet the 2021 Candidates: IEEE President-Elect||1 October 2021|
|Call for Nominations for Editor-in-Chief, Open Journal of Signal Processing||1 October 2021|
|Election of President-Elect, Regional Directors-at-Large and Members-at-Large||1 October 2021|
|Call for Nominations: SPS Chapter of the Year Award||15 October 2021|
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