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Convex Optimization in Signal Processing and Communications

$118.00 (P)

J. Mattingley, S. Boyd, A. Beck, M. Teboulle, J. Songsiri, J. Dahl, L. Vandenberghe, Z. Q. Luo, T. H. Chang, A. Man-Cho So, Y. Ye, Y. Huang, A. De Maio, S. Zhang, W. K. Ma, T. H. Chan, C. Y. Chi, Y. Wang, T. Michaeli, Y. C. Eldar, M. Rübsamen, A. El-Keyi, A. B. Gershman, T. Kirubarajan, A. Nenadić, A. Ozdaglar, G. Scutari, D. P. Palomar, S. Barbarossa, F. Facchinei, J. S. Pang
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  • Date Published: January 2010
  • availability: In stock
  • format: Hardback
  • isbn: 9780521762229

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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.

    • A team of leading experts provide tutorials on a wide range on convex optimization applications
    • Covers the theoretical underpinnings of the subject
    • Emphasizes cutting-edge research and formulating problems in convex form
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    Product details

    • Date Published: January 2010
    • format: Hardback
    • isbn: 9780521762229
    • length: 512 pages
    • dimensions: 255 x 185 x 29 mm
    • weight: 1.14kg
    • contains: 95 b/w illus. 16 tables 5 exercises
    • availability: In stock
  • Table of Contents

    1. Automatic code generation for real-time convex optimization J. Mattingley and S. Boyd
    2. Gradient-based algorithms with applications to signal recovery problems A. Beck and M. Teboulle
    3. Graphical models of autoregressive processes J. Songsiri, J. Dahl and L. Vandenberghe
    4. SDP relaxation of homogeneous quadratic optimization Z. Q. Luo and T. H. Chang
    5. Probabilistic analysis of SDR detectors for MIMO systems A. Man-Cho So and Y. Ye
    6. Semidefinite programming, matrix decomposition, and radar code design Y. Huang, A. De Maio and S. Zhang
    7. Convex analysis for non-negative blind source separation with application in imaging W. K. Ma, T. H. Chan, C. Y. Chi and Y. Wang
    8. Optimization techniques in modern sampling theory T. Michaeli and Y. C. Eldar
    9. Robust broadband adaptive beamforming using convex optimization M. Rübsamen, A. El-Keyi, A. B. Gershman and T. Kirubarajan
    10. Cooperative distributed multi-agent optimization A. Nenadić and A. Ozdaglar
    11. Competitive optimization of cognitive radio MIMO systems via game theory G. Scutari, D. P. Palomar and S. Barbarossa
    12. Nash equilibria: the variational approach F. Facchinei and J. S. Pang.

  • Editors

    Daniel P. Palomar, Hong Kong University of Science and Technology
    Daniel P. Palomar is an Assistant Professor in the Department of Electronic and Computer Engineering at Hong Kong University of Science and Technology. He received his Ph.D. from the Technical University of Catalonia (UPC), Spain, in 2003 and has since received numerous awards including the Best Doctoral Thesis in Advanced Mobile Communications by the Vodafone Foundation and COIT (2004). His current research interests include applications of convex optimization theory, game theory, and variational inequality theory to signal processing and communications.

    Yonina C. Eldar, Technion - Israel Institute of Technology, Haifa
    Yonina C. Eldar is a Professor in the Department of Engineering at Technion, Israel University of Technology, and is also a Research Affiliate with the Research Laboratory of Electronics at MIT. She received her Ph.D. from the Massachusetts Institute of Technology (MIT) in 2001. She has received many awards, including, in 2008, the Hershel Rich Innovation Award, the Award for Women with Distinguished Contributions and the Muriel & David Jacknow Award for Excellence in Teaching.

    Contributors

    J. Mattingley, S. Boyd, A. Beck, M. Teboulle, J. Songsiri, J. Dahl, L. Vandenberghe, Z. Q. Luo, T. H. Chang, A. Man-Cho So, Y. Ye, Y. Huang, A. De Maio, S. Zhang, W. K. Ma, T. H. Chan, C. Y. Chi, Y. Wang, T. Michaeli, Y. C. Eldar, M. Rübsamen, A. El-Keyi, A. B. Gershman, T. Kirubarajan, A. Nenadić, A. Ozdaglar, G. Scutari, D. P. Palomar, S. Barbarossa, F. Facchinei, J. S. Pang

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