Convex Optimization in Signal Processing and Communications

by Edited by Daniel P. Palomar , Yonina C. Eldar

• ISBN13: 9780521762229
• ISBN10: 0521762227
• Edition: 1st
• Format: Hardcover
• Copyright: 2010-01-25
• Publisher: Cambridge University Press

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.

Table of Contents

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

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