帕洛马等编写的《信号处理与通信中的凸优化理论》结合当前信号处理和通信应用中的多种不同热点需求，分别自成体系地介绍了凸优化的最新应用发展情况。通过这本书，读者可以看到凸优化在信号处理和通信中的广泛应用，了解这些领域的当前最近进展，学习凸优化问题的建模方法、技巧与解法，掌握将非凸优化问题转化或近似为凸优化问题的思路等。

本书适合信号处理、通信等领域的研究人员和高年级研究生阅读。由于主要面向应用，所以假设读者已经了有一定的凸优化理论基础。本书也可以作为凸优化数学理论教学的一本反映最新工程应用的参考书。相信本书可以使更多的学者和工程师掌握这些基本数学方法并应用到工程实践中。

凸优化理论是信号处理领域具有重要应用价值的理论分析工具，最近二十年一大批的信号处理问题都基于凸优化理论取得了突破进展。帕洛马等编写的这本《信号处理与通信中的凸优化理论》以通信与信号处理中的经典与前沿问题为脉络，深入浅出地介绍了各类凸优化分析的建模方法与基本理论。内容包括图模型理论、基于梯度的信号重建算法、半定松弛(SDP)算法、基于SDP的雷达信号设计、图像处理中的盲信源分离、现代抽样理论，特别是宽带移动通信中的MIMO信号检测、认知无线电中的波束成形理论、分布式多目标优化理论与博弈论等。

《信号处理与通信中的凸优化理论》可作为电子与通信工程等相关领域科研人员、工程技术人员的参考书，也可供相关专业高年级本科生、研究生阅读。

 List of contributors

 Preface

1 Automatic code generation for real.time convex optimization

 Jacob Mattingley and Stephen Boyd

 1.1 Introduction

 1.2 Solvers and specification languages

 1.3 Examples

 1.4 Algorithm considerations

 1.5 Code generation

 1.6 CVXMOD: a preliminary implementation

 1.7 Numerical examples

 1.8 Summary, conclusions, and implications

 Acknowledgments

 References

2 Gradient-based algorithms with applications to signal-recovery problems

 Arnir Beck and Marc Teboulle

 2.1 Introduction

 2.2 The general optimization model

 2.3 Building gradient-based schemes

 2.4 Convergence results tor the proximal-gradient method

 2.5 A fast proximal-gradient method

 2.6 Algorithms for It-based regularization problems

 2.7 TV-based restoration problems

 2.8 The source-localization problem

 2.9 Bibliographic notes

 References

3 Graphical models of autoregressive processes

 Jitkomut Songsiri, Joachim Dahl, and Lieven Vandenberghe

 3.1 Introduction

 3.2 Autoregressive processes

 3.3 Autoregressive graphical models

 3.4 Numerical examples

 3.5 Conclusion

 Acknowledgments

 References

4 SDP relaxation of homogeneous quadratic optimization: approximation bounds and applications Zhi-Quan Luo and Tsung-Hui Chang

 4.1 Introduction

 4.2 Nonconvex QCQPs and SDP relaxation

 4.3 SDP relaxation for separable homogeneous QCQPs

 4.4 SDP relaxation for maximization homogeneous QCQPs

 4.5 SDP relaxation for fractional QCQPs

 4.6 More applications of SDP relaxation

 4.7 Summary and discussion

 Acknowledgments

 References

5 ProbabilisUc analysis of semidefinite relaxation detectors for multiple-input,multiple-output systems

 Anthony Man-Cho So and Yinyu Ye

 5.1 Introduction

 5.2 Problem formulation

 5.3 Analysis of the SDR detector for the MPSK constellations

 5.4 Extension to the QAM constellations

 5.5 Concluding remarks

 Acknowledgments

 References

6 Semidefinite programming, matrix decomposition, and radar code design

 Yongwei Huang, Antonio De Maio, and Shuzhong Zhang

 6.1 Introduction and notation

 6.2 Matrix rank- 1 decomposition

 6.3 Semidefinite programming

 6.4 Quadratically constrained quadratic programming and its SDP relaxation

 6.5 Polynomially solvable QCQP problems

 6.6 The radar code-design problem

 6.7 Performance measures for code design

 6.8 Optimal code design

 6.9 Performance analysis

 6.10 Conclusions

 References

7 Convex analysis for non-negative blind source separation with application in imaging

 Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi, and Yue Wang

 7.1 Introduction

 7.2 Problem statement

 7.3 Review of some concepts in convex analysis

 7.4 Non-negative, blind source-separation criterion via CAMNS

 7.5 Systematic linear-programming method for CAMNS

 7.6 Alternating volume-maximization heuristics for CAMNS

 7.7 Numerical results

 7.8 Summary and discussion

 Acknowledgments

 References

8 Optimization techniques in modern sampling theory

 Tomer Michaeli and Yonina C. Eldar

 8.1 Introduction

 8.2 Notation and mathematical preliminaries

 8.3 Sampling and reconstruction setup

 8.4 Optimization methods

 8.5 Subspace priors

 8.6 Smoothness priors

 8.7 Comparison of the various scenarios

 8.8 Sampling with noise

 8.9 Conclusions

 Acknowledgments

 References

9 Robust broadband adaptive beamforming using convex optimization

 Michael Riibsamen, Amr EI-Keyi, Alex B. Gershman, and Thia Kirubarajan

 9.1 Introduction

 9.2 Background

 9.3 Robust broadband beamformers

 9.4 Simulations

 9.5 Conclusions

 Acknowledgments

 References

10 Cooperative distributed multi-agent optimization

 Angelia Nedic and Asuman Ozdaglar

 10.1 Introduction and motivation

 10.2 Distributed-optimization methods using dual decomposition

 10.3 Distributed-optimization methods using consensus algorithms

 10.4 Extensions

 10.5 Future work

 10.6 Conclusions

 10.7 Problems

 References

11 Competitive optimization of cognitive radio MIMO systems via game theory

 Gesualso Scutari, Daniel P. Palomar, and Sergio Barbarossa

 11.1 Introduction and motivation

 11.2 Strategic non-cooperative games: basic solution concepts and algorithms

 11.3 Opportunistic communications over unlicensed bands

 11.4 Opportunistic communications under individual-interference constraints

 11.5 Opportunistic communications under global-interference constraints

 21.6 Conclusions

 Acknowledgments

 References

12 Nash equilibria: the variational approach

 Francisco Facchinei and Jong-Shi Pang

 12.1 Introduction

 12.2 The Nash-equilibrium problem

 12.3 Existence theory

 12.4 Uniqueness theory

 12.5 Sensitivity analysis

 12.6 Iterative algorithms

 12.7 A communication game

 Acknowledgments

 References

 Afierword

 Index