英文版《凸优化》课程作业的答案； 自己整理，部分习题可能没有，但大部分习题都整理出来了 对应的课程为西交《系统优化与调度》作业，

凸优化方面的经典著作 Here　is　a　book　devoted　to　well-structured　and　thus　efficiently　solvable　convex　optimization　problems,　with　emphasis　on　conic　quadratic　and　semidefinite　programming.　The　authors　present　the　basic　theory　underlying　these　problems　as　well　as　their　numerous　applications　in　engineering,　including　synthesis　of　filters,　Lyapunov　stability　analysis,　and　structural　design.　The　authors　also　discuss　the　complexity　issues　and　provide　an　overview　of　the　basic　theory　of　state-of-the-art　polynomial　time　interior　point　methods　for　linear,　conic　quadratic,　and　semidefinite　programming.　The　book's　focus　on　well-structured　convex　problems　in　conic　form　allows　for　unified　theoretical　and　algorithmical　treatment　of　a　wide　spectrum　of　important　optimization　problems　arising　in　applications.

凸优化理论（convex optimization）在信号处理，图像处理，通信中有很广泛的应用，并受到越来越多的关注。这本书是由这个领域里的专家合写的，对理论研究和工程应用都有很大的启迪。此外，这本书中还包含了凸优化理论在压缩感知（compressed sensing）中的应用。

课后作业

优化理论方面的经典之作，Stephen Boyd的凸优化理论，英文原版。

讲述了凸集、凸函数、凸优化对偶等基础理论，还有凸优化算法的应用以及带约束以及不带约束的优化问题

凸优化 使用cvx工具箱的SDP用于传感器网络本地化 在论文[2]中，公式（34）中的方程M_ik矩阵可能不正确。 e_k项应为负。 成本函数是随机选择的，可能不合适

一本写给工程师看的凸优化书籍。作者本身是这方面的大牛！

convex optimization中文版和相关资料 Stephen Boyd, Stanford University, California, Lieven Vandenberghe, University of California, Los Angeles Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems.

Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.