信号处理与通信中的凸优化理论 英文版2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载

信号处理与通信中的凸优化理论 英文版PDF格式电子书版下载

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1 Automatic code generation for real-time convex optimization&Jacob Mattingley and Stephen Boyd1

1.1 Introduction1

1.2 Solvers and specification languages6

1.3 Examples12

1.4 Algorithm considerations22

1.5 Code generation26

1.6 CVXMOD:a preliminary implementation28

1.7 Numerical examples29

1.8 Summary,conclusions,and implications33

Acknowledgments35

References35

2 Gradient-based algorithms with applications to signal-recovery problems&Amir Beck and Marc Teboulle42

2.1 Introduction42

2.2 The general optimization model43

2.3 Building gradient-based schemes46

2.4 Convergence results for the proximal-gradient method53

2.5 A fast proximal-gradient method62

2.6 Algorithms for l1-based regularization problems67

2.7 TV-based restoration problems71

2.8 The source-localization problem77

2.9 Bibliographic notes83

References85

3 Graphical models of autoregressive processes&Jitkomut Songsiri,Joachim Dahl,and Lieven Vandenberghe89

3.1 Introduction89

3.2 Autoregressive processes92

3.3 Autoregressive graphical models98

3.4 Numerical examples104

3.5 Conclusion113

Acknowledgments114

References114

4 SDP relaxation of homogeneous quadratic optimization:approximation bounds and applications&Zhi-Quan Luo and Tsung-Hui Chang117

4.1 Introduction117

4.2 Nonconvex QCQPs and SDP relaxation118

4.3 SDP relaxation for separable homogeneous QCQPs123

4.4 SDP relaxation for maximization homogeneous QCQPs137

4.5 SDP relaxation for fractional QCQPs143

4.6 More applications of SDP relaxation156

4.7 Summary and discussion161

Acknowledgments162

References162

5 Probabilistic analysis of semidefinite relaxation detectors for multiple-input,multiple-output systems&Anthony Man-Cho So and Yinyu Ye166

5.1 Introduction166

5.2 Problem formulation169

5.3 Analysis of the SDR detector for the MPSK constellations172

5.4 Extension to the QAM constellations179

5.5 Concluding remarks182

Acknowledgments182

References189

6 Semidefinite programming,matrix decomposition,and radar code design&Yongwei Huang,Antonio De Maio,and Shuzhong Zhang192

6.1 Introduction and notation192

6.2 Matrix rank-1 decomposition194

6.3 Semidefinite programming200

6.4 Quadratically constrained quadratic programming and its SDP relaxation201

6.5 Polynomially solvable QCQP problems203

6.6 The radar code-design problem208

6.7 Performance measures for code design211

6.8 Optimal code design214

6.9 Performance analysis218

6.10 Conclusions223

References226

7 Convex analysis for non-negative blind source separation with application in imaging&Wing-Kin Ma,Tsung-Han Chart,Chong-Yung Chi,and Yue Wang229

7.1 Introduction229

7.2 Problem statement231

7.3 Review of some concepts in convex analysis236

7.4 Non-negative,blind source-separation criterion via CAMNS238

7.5 Systematic linear-programming method for CAMNS245

7.6 Alternating volume-maximization heuristics for CAMNS248

7.7 Numerical results252

7.8 Summary and discussion257

Acknowledgments263

References263

8 Optimization techniques in modern sampling theory&Tomer Michaeli and Yonina C.Eldar266

8.1 Introduction266

8.2 Notation and mathematical preliminaries268

8.3 Sampling and reconstruction setup270

8.4 Optimization methods278

8.5 Subspace priors280

8.6 Smoothness priors290

8.7 Comparison of the various scenarios300

8.8 Sampling with noise302

8.9 Conclusions310

Acknowledgments311

References311

9 Robust broadband adaptive beamforming using convex optimization&Michael Rübsamen,Amr EI-Keyi,Alex B.Gershman,and Thia Kirubarajan315

9.1 Introduction315

9.2 Background317

9.3 Robust broadband beamformers321

9.4 Simulations330

9.5 Conclusions337

Acknowledgments337

References337

10 Cooperative distributed multi-agent optimization&Angelia Nedi？ and Asuman Ozdaglar340

10.1 Introduction and motivation340

10.2 Distributed-optimization methods using dual decomposition343

10.3 Distributed-optimization methods using consensus algorithms358

10.4 Extensions372

10.5 Future work378

10.6 Conclusions380

10.7 Problems381

References384

11 Competitive optimization of cognitive radio MIMO systems via game theory&Gesualso Scutari,Daniel P.Palornar,and Sergio Barbarossa387

11.1 Introduction and motivation387

11.2 Strategic non-cooperative games:basic solution concepts and algorithms393

11.3 Opportunistic communications over unlicensed bands400

11.4 Opportunistic communications under individual-interference constraints415

11.5 Opportunistic communications under global-interference constraints431

11.6 Conclusions438

Acknowledgments439

References439

12 Nash equilibria:the variational approach&Francisco Facchinei and Jong-Shi Pang443

12.1 Introduction443

12.2 The Nash-equilibrium problem444

12.3 Existence theory455

12.4 Uniqueness theory466

12.5 Sensitivity analysis472

12.6 Iterative algorithms478

12.7 Acommunication game483

Acknowledgments490

References491

Afterword494

Index495