Convex Optimization in Signal Processing and Communications - Daniel P. Palomar

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Convex Optimization in Signal Processing and Communications

By: Daniel P. Palomar (Editor), Yonina C. Eldar (Editor)

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Hardcover | 3 December 2009

At a Glance

Hardcover
512 Pages

Dimensions(cm)
25.9 x 18.2 x 2.9

Hardcover

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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.

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You Can Find This Book In

Non-Fiction Engineering & Technology Electronics & Communications Engineering Communications Engineering & Telecommunications Mathematics Optimisation Applied Mathematics Electronics Engineering Energy Technology & Engineering Electrical Engineering Computing & I.T.Computer Networking & Communications

Automatic code generation for real-time convex optimization
Gradient-based algorithms with applications to signal recovery problems
Graphical models of autoregressive processes
SDP relaxation of homogeneous quadratic optimization
Probabilistic analysis of SDR detectors for MIMO systems
Semidefinite programming, matrix decomposition, and radar code design
Convex analysis for non-negative blind source separation with application in imaging
Optimization techniques in modern sampling theory
Robust broadband adaptive beamforming using convex optimization
Cooperative distributed multi-agent optimization
Competitive optimization of cognitive radio MIMO systems via game theory
Nash equilibria: the variational approach
Table of Contents provided by Publisher. All Rights Reserved.

Convex Optimization in Signal Processing and Communications

At a Glance

Hardcover

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How to return your order

Defective items

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