The DOI matches its Crossref record; 126 of the 126 references it cites were found in a registry or an index.
Convex Optimization in Signal Processing and Communications
Abstract
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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Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Horn, R. A., & Johnson, C. R. (1985) · Cited by 22,203
Linear Matrix Inequalities in System and Control Theory
Boyd, S., El Ghaoui, L., Feron, E., & Balakrishnan, V. (1994) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970777 · Cited by 21,687
Donoho, D. L. (2004) · Cited by 16,703
Nonlinear total variation based noise removal algorithms
Rudin, L. I., Osher, S., & Fatemi, E. (1992) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(92)90242-f · Cited by 15,704
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 13,868
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
Consensus Problems in Networks of Agents With Switching Topology and Time-Delays
Olfati-Saber, R., & Murray, R. M. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.834113 · Cited by 12,788
Cognitive radio: brain-empowered wireless communications
Haykin, S. (2005) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2004.839380 · Cited by 11,916
Cited by
Semidefinite Relaxation of Quadratic Optimization Problems
Luo, Z.-Q., Ma, W.-K., So, A., Ye, Y., & Zhang, S. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.936019 · Cited by 3,715
Joint Transmit Beamforming for Multiuser MIMO Communications and MIMO Radar
Liu, X., Huang, T., Shlezinger, N., Liu, Y., Zhou, J., & Eldar, Y. C. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3004739 · Cited by 992
Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization
Sundhar Ram, S., Nedić, A., & Veeravalli, V. V. (2010) · Journal of Optimization Theory and Applications · doi:10.1007/s10957-010-9737-7 · Cited by 814
Sayed, A. H. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2306253 · Cited by 587
Rank-Constrained Separable Semidefinite Programming With Applications to Optimal Beamforming
Huang, Y., & Palomar, D. P. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2009.2031732 · Cited by 561
Metadata
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2009-12-03
- OpenAlex
- W562372631
- Cited by
- 554
- References
- 126
- FWCI
- 16.15
- Type
- book
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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