By Daniel P. Palomar, Yonina C. Eldar

During the last twenty years there were major advances within the box of optimization. particularly, convex optimization has emerged as a robust sign processing device, and the diversity of functions maintains to develop quickly. This ebook, written by means of a crew of major specialists, units out the theoretical underpinnings of the topic and gives tutorials on a variety of convex optimization functions. Emphasis all through is on state-of-the-art learn and on formulating difficulties in convex shape, making this a fantastic textbook for complex graduate classes and an invaluable self-study advisor. subject matters lined variety from computerized code new release, graphical types, and gradient-based algorithms for sign restoration, to semidefinite programming (SDP) leisure and radar waveform layout through SDP. it is also blind resource separation for snapshot processing, powerful broadband beamforming, disbursed multi-agent optimization for networked structures, cognitive radio platforms through online game thought, and the variational inequality process for Nash equilibrium suggestions.

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Karger, and M. J. Wainwright, “LP decoding,” in Proceedings, Annual Allerton Conference on Communication Control and Computing, vol. 41, no. 2, pp. 951–60, 2003. [10] J. Feldman, “Decoding error-correcting codes via linear programming,” PhD dissertation, Massachusetts Institute of Technology, 2003. [11] J. Jalden, C. Martin, and B. Ottersten, “Semidefinite programming for detection in linear systems—optimality conditions and space-time decoding,” IEEE International Conference on Acoustics, Speech, and Signal Processing, vol.

4151–61, 2007. -K. Ma, T. N. Davidson, K. M. -Q. -C. Ching, “Quasi-maximumlikelihood multiuser detection using semi-definite relaxation with application to synchronous CDMA,” IEEE Transactions on Signal Processing, vol. 50, pp. 912–22, 2002. 38 Automatic code generation for real-time convex optimization [44] F. P. Kelly, A. K. Maulloo, and D. K. H. Tan, “Rate control for communication networks: shadow prices, proportional fairness and stability,” Journal of Operational Research Society, vol. 49, no.

C. Ching, “Quasi-maximumlikelihood multiuser detection using semi-definite relaxation with application to synchronous CDMA,” IEEE Transactions on Signal Processing, vol. 50, pp. 912–22, 2002. 38 Automatic code generation for real-time convex optimization [44] F. P. Kelly, A. K. Maulloo, and D. K. H. Tan, “Rate control for communication networks: shadow prices, proportional fairness and stability,” Journal of Operational Research Society, vol. 49, no. 3, pp. 237–52, 1998. [45] D. X. Wei, C.

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