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paperback. Etat : New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback. Pub Date: 2013 01 of Pages: 510 in Publisher: Science Press convex optimization theory is the field of signal processing theory has important application value analysis tools. the last two decades. a large number of signal processing based on convex optimization theory made breakthroughs. The outstanding books abroad of Information Science and Technology Series Electronics and Communications Technology: signal processing and communication of convex optimization theory (the English version). classic and cutting-edge issues in the communication and signal processing for context for visitors to learn all kinds of convex optimization analysis modeling methods and basic theory. Content. including the diagram model theory. based on the gradient of the signal reconstruction algorithm. semidefinite relaxation (SDP) algorithm based on the SDP's radar signal design. image processing. blind letter source separation. modern sampling theory. especially in broadband mobile communication MIMO signal detection . the beamforming theory in cognitive radio. distributed multi-objective optimization theory and game theory. The book can serve as researchers in the field of electronics and communication engineering. engineering and technical personnel. reference books. are also available for professional high grade undergraduate. graduate read. Contents: List of contributorPreface1 Automatic code generation for real.time convex optimizationJacob Mattingley and Stephen Boyd1.1 Introduction1.2 Solver and specification languages1.3 Examples1.4 Algorithm corideratior1.5 Code generation1.6 CVXMOD: a preliminary implementation1.7 Numerical examples1. 8 Summary. conclusior. and implicatiorAcknowledgmentsReferences2 Gradient-based algorithms with applicatior to signal-recovery problemsArnir Beck and Marc Teboulle2.1 Introduction2.2 The general optimization model2.3 Building gradient-based schemes2.4 Convergence results tor the proximal-gradient method2.5 A fast proximal-gradient method2.6 Algorithms for It-based regularization problems2.7 TV-based restoration problems2.8 The source-localization problem2.9 Bibliographic notesReferences3 Graphical models of autoregressive processesJitkomut Songsiri. Joachim Dahl. and Lieven Vandenberghe3.1 Introduction3. 2 Autoregressive processes3.3 Autoregressive graphical models3.4 Numerical examples3.5 ConclusionAcknowledgmentsReferences4 SDP relaxation of homogeneous quadratic optimization: approximation bounds and applicatior Zhi-Quan Luo and Tsung-Hui Chang4.1 Introduction4.2 Nonconvex QCQPs and SDP relaxation4.3 SDP relaxation for separable homogeneous QCQPs4.4 SDP relaxation for maximization homogeneous QCQPs4.5 SDP relaxation for fractional QCQPs4.6 More applicatior of SDP relaxation4.7 Summary and discussionAcknowledgmentsReferences5 ProbabilisUc analysis of semidefinite relaxation detector for multiple-input. multiple-output systemsAnthony Man-Cho So and Yinyu Ye5.1 Introduction5.2 Problem formulation5.3 Analysis of the SDR detector for the MPSK cortellatior5.4 Exterion to the QAM cortellatior5.5 Concluding remarksAcknowledgmentsReferences6 Semidefinite programming. matrix decomposition. and radar code designYongwei Huang. Antonio De Maio. and Shuzhong Zhang6 .1 Introduction and notation6.2 Matrix rank-1 decomposition6.3 Semidefinite programming6.4 Quadratically cortrained quadratic programming and its SDP relaxation6.5 Polynomially solvable QCQP problems6.6 The radar code-design problem6.7 Performance measures for code design6.8 Optimal code design6.9 Performance analysis6.10 ConclusiorReferences7 Convex analysis for non-negative blind source separation with application in imagingWing-Kin Ma. Tsung-Han Chan. Chong-Yung Chi. and Yue Wang7.1 Introduction7.2 Problem statement7.3 Review of some concepts in convex analysis7.4 Non-negative. blind source-separation criterion via CAMNS7.5 Systematic linear-p.