Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications / Edition 1

Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications / Edition 1

ISBN-10:
1498776450
ISBN-13:
9781498776455
Pub. Date:
02/01/2017
Publisher:
Taylor & Francis
ISBN-10:
1498776450
ISBN-13:
9781498776455
Pub. Date:
02/01/2017
Publisher:
Taylor & Francis
Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications / Edition 1

Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications / Edition 1

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Overview

Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications provides fundamental background knowledge of convex optimization, while striking a balance between mathematical theory and applications in signal processing and communications.

In addition to comprehensive proofs and perspective interpretations for core convex optimization theory, this book also provides many insightful figures, remarks, illustrative examples, and guided journeys from theory to cutting-edge research explorations, for efficient and in-depth learning, especially for engineering students and professionals.

With the powerful convex optimization theory and tools, this book provides you with a new degree of freedom and the capability of solving challenging real-world'scientific and engineering problems.


Product Details

ISBN-13: 9781498776455
Publisher: Taylor & Francis
Publication date: 02/01/2017
Pages: 446
Product dimensions: 7.00(w) x 10.00(h) x (d)

About the Author

Dr. Chong-Yung Chi is a Professor, Department of Electrical Engineering (since 1989) and the Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan. He got his Ph.D. degree in Electrical Engineering from University of Southern California in 1983.His main research interests include signal processing for wireless communications, convex analysis and optimization for blind source separation, biomedical and hyperspectral image analysis. He has published more than 210 technical papers, including more than 75 journal papers (mostly in IEEE Trans. Signal Processing), 4 book chapters and more than 130 peer-reviewed conference papers, as well as a graduate-level textbook, Blind Equalization and System Identification, Springer-Verlag, 2006. Dr. Chi is a senior member of IEEE. He was an Associate Editor (AE) of IEEE Trans. Signal Processing (5/2001~4/2006), IEEE Trans. Circuits and Systems II (1/2006-12/2007), IEEE Trans. Circuits and Systems I (1/2008-12/2009), AE of IEEE Signal Processing Letters (6/2006~5/2010), and a member of Editorial Board of Elsevier Signal Processing (6/2005~5/2008), and an editor (7/2003~12/2005) as well as a Guest Editor (2006) of EURASIP Journal on Applied Signal Processing. Currently, he is a member of Signal Processing for Communications and Networking Technical Committee (SPCOM-TC) and a member of Sensor Array and Multichannel Technical Committee (SAM-TC), IEEE Signal Processing Society, and an AE of IEEE Trans. Signal Processing.

Wei-Chiang Li received the B.S. degree in electrical engineering from the National Tsing Hua University, Hsinchu, Taiwan, in 2009. He is currently pursuing the Ph.D. degree in communications engineering at the National Tsing Hua University, Hsinchu, Taiwan. His research interests are in optimization methods for wireless communications and signal processing.

Chia-Hsiang Lin received the B.S. degree in electrical engineering from the National Tsing Hua University, Hsinchu, Taiwan, in 2010, where he is currently working toward the Ph.D. degree in communications engineering. He is currently a visiting Doctoral Graduate Research Assistant with Virginia Polytechnic Institute and State University, Arlington, VA, USA. His research interests are convex geometry and optimization, network science, game theory, and blind source separation.

Table of Contents

  • Preface
  • Chapter 1: Mathematical Background
  • Chapter 2: Convex Sets
  • Chapter 3: Convex Functions
  • Chapter 4: Convex Optimization Problems
  • Chapter 5: Geometric Programming
  • Chapter 6: Linear Programming and Quadratic Programming
  • Chapter 7: Second-order Cone Programming
  • Chapter 8: Semidefinite Programming
  • Chapter 9: Duality
  • Chapter 10: Interior-point Methods
  • Appendix: Convex Optimization Solvers
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