Convex optimization in signal processing and communications / edited by Daniel P. Palomar and Yonina C. Eldar.

Format:

Book

Contributor:

Palomar, Daniel P., editor.

Eldar, Yonina C., editor.

Language:

English

Subjects (All):

Signal processing.

Mathematical optimization.

Convex functions.

Physical Description:

1 online resource (xiv, 498 pages) : digital, PDF file(s).

Other Title:

Convex Optimization in Signal Processing & Communications

Place of Publication:

Cambridge : Cambridge University Press, 2010.

Language Note:

English

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.

Contents:

Cover; Half-title; Title; Copyright; Contents; List of contributors; Preface; 1 Automatic code generation for real-time convex optimization; 2 Gradient-based algorithms with applications to signal-recovery problems; 3 Graphical models of autoregressive processes; 4 SDP relaxation of homogeneous quadratic optimization: approximation bounds and applications; 5 Probabilistic analysis of semidefinite relaxation detectors for multiple-input, multiple-output systems; 6 Semidefinite programming, matrix decomposition, and radar code design

7 Convex analysis for non-negative blind source separation with application in imaging8 Optimization techniques in modern sampling theory; 9 Robust broadband adaptive beamforming using convex optimization; 10 Cooperative distributed multi-agent optimization; 11 Competitive optimization of cognitive radio MIMO systems via game theory; 12 Nash equilibria: the variational approach; Afterword; Index

Notes:

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Includes bibliographical references and index.

ISBN:

1-107-20812-2

1-282-65326-1

9786612653261

0-511-68975-6

0-511-69235-8

0-511-69123-8

0-511-69049-5

0-511-80445-8

0-511-68900-4

OCLC:

642661103