凸优化convex optimization Stephen Boyd Lieven Vandenberghe著 王书宁等译，电子扫描版《信息技术和电气工程学科国际知名教材中译本系列:凸优化》内容非常丰富。理论部分由4章构成，不仅涵盖了凸优化的所有基本概念和主要结果，还详细介绍了几类基本的凸优化问题以及将特殊的优化问题表述为凸优化问题的变换方法，这些内容对灵活运用凸优化知识解决实际问题非常有用。应用部分由3章构成，分别介绍凸优化在解决逼近与拟合、统计估计和几何关系分析这三类实际问题中的应用。算法部分也由3章构成，依次介绍求解无约束凸优化模型、等式约束凸优化模型以及包含不等式约束的凸优化模型的经典数值方法，以及如何利用凸优化理论分析这些方法的收敛性质。通过阅读《信息技术和电气工程学科国际知名教材中译本系列:凸优化》，能够对凸优化理论和方法建立完整的认识。

SeMuDi是Self-Dual-Minimization的缩写，她能求解带有线性约束，二阶锥约束和半定约束的优化问题。

Numerous control and decision problems in networked systems can be posed as optimization problems. Examples include the framework of network utility maxi-mization for resource allocation in communication networks, multi-agent coordina-tion in robotics, and collaborative estimation in wireless sensor networks (WSNs). In contrast to classical distributed optimization, which focuses on improving compu-tational efficiency and scalability, these new applications require simple mechanisms that can operate under limited communication. In this thesis, we develop several novel mechanisms for distributed optimization under communication constraints, and apply these to several challenging engineering problems

Minghui Zhu和Sonia Martínez关于多智能体系统分布式优化方面的经典教材。

As the main building block of the smart grid systems, microgrid (MG) integrates a number of local distributed generation units, energy storage systems, and local loads to form a small-scale, low- and medium-voltage level power system. In gen- eral, an MG can operate in two modes, i.e., the grid-connected and islanded mode. Recently, in order to standardize its operation and functionality, hierarchical con- trol for islanded MG systems has been proposed. It divides the control structure into three layers, namely, primary, secondary, and tertiary control. The primary control is based on each local distributed generation (DG) controller and is realized in a de- centralized way. In the secondary layer, the frequency and voltage restoration control as well as the power quality enhancement is usually carried out. In the tertiary con- trol, economic dispatch and power flow optimization issues are considered. However, conventionally both the secondary and tertiary control are realized in a centralized way. There are certain drawbacks to such centralized control, such as high compu- tation and communication cost, poor fault tolerance ability, lack of plug-and-play properties, and so on. In order to overcome the above drawbacks, distributed control is proposed in the secondary control and tertiary optimization in this book. In the secondary control, restorations for both voltage and frequency in the droop- controlled inverter-based islanded MG are addressed. A distributed finite-time con- trol approach is used in the voltage restoration which enables the voltages at all the DGs to converge to the reference value in finite time, and thus, the voltage and frequency control design can be separated. Then, a consensus-based distributed fre- quency control is proposed for frequency restoration, subject to certain control input constraints. The proposed control strategy can restore both voltage and frequency to their respective reference values while having accurate real power sharing, under a sufficient local stability condition established. Then the distributed control strategy is also employed in the secondary voltage unbalance compensation to replace the conventional centralized controller. The con- cept of contribution level (CL) for compensation is first proposed for each local DG to indicate its compensation ability. A two-layer secondary compensation architecture consisting of a communication layer and a compensation layer is designed for each xvii xviii Distributed Control and Optimization Technologies in Smart Grid Systems local DG. A totally distributed strategy involving information sharing and exchange is proposed, which is based on finite-time average consensus and newly developed graph discovery algorithm. In the tertiary layer, a distributed economic dispatch (ED) strategy based on pro- jected gradient and finite-time average consensus algorithms is proposed. By de- composing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution for the optimization problem in a distributed manner with limited communication among neighbors. It is shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. Besides, two distributed multi-cluster optimization meth- ods are proposed for a large-scale multi-area power system. We first propose to divide all the generator agents into clusters (groups) and each cluster has a leader to com- municate with the leaders of its neighboring clusters. Then two different schemes are proposed for each agent to iteratively estimate a solution of the optimization prob- lem in a distributed manner. It is theoretically proved that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. In addition, a novel hierarchical decentralized optimization architecture is proposed to solve the ED problem. Similar to distributed algorithms, each local generator only solves its own problem based on its own cost function and generation constraint. An extra co- ordinator agent is employed to coordinate all the local generator agents. Besides, it also takes the responsibility for handling the global demand supply constraint. In this way, different from existing distributed algorithms, the global demand supply con- straint and local generation constraints are handled separately, which would greatly reduce the computational complexity. It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion. A distributed optimal energy scheduling strategy is also proposed in the tertiary layer, which is based on a newly proposed pricing strategy named PD pricing. Con- ventional real-time pricing strategies only depend on the current total energy con- sumption. In contrast to this, our proposed pricing strategy also takes the incremen- tal energy consumption into consideration, which aims to further fill the valley load and shave the peak load. An optimal energy scheduling problem is then formulated by minimizing the total social cost of the overall power system. Two different dis- tributed optimization algorithms with different communication strategies are pro- posed to solve the problem.

matlab地质反演代码这个 Matlab 代码是为论文开发的： Peter C.、Salina Borello E.、Dalman R.、Karamitopoulos P.、Busschers F.、Sacchi Q.、Verga F. (2919) “通过整合地层正向建模改进了沟渠储层的岩性预测：在全新世莱茵河-默兹河-三角洲系统的案例研究"计算机和地球科学。 爱思唯尔（印刷中） 该代码实现了应用于地质盆地反演的邻域算法，基于： Sambridge, M. (1999)。 使用邻域算法进行地球物理反演——搜索参数空间：国际地球物理杂志，138, 479–494, doi:10.1046/j.1365-246X.1999.00876.x。 Sambridge, M. (2001)。 使用邻域算法在非线性逆问题中寻找可接受的模型：Inverse Problems, 17, 387–403, doi:10.1088/0266-5611/17/3/302。 Sambridge, M. 和 K. Mosegaard (2002)。 地球物理反问题中的蒙特卡罗方法：地球物理学评论，40, 1-

Genetic Algorithm Optimization Toolbox matlab遗传算法优化工具包

ansys与matlab代码APDL 优化例程 该代码是作为我的机械工程毕业项目的要求而开发的。 它们基于 K. Liu & A. Tovar 2014 年发表的文章“用 MATLAB 编写的高效 3D 拓扑优化代码”。它使用了 Martin P. Bendsøe，1989 年的 SIMP 方法。SIMP 方法应用于 Ansys 有限元框架并根据优化结果使用 /PREP7 环境对工件进行建模。 有关更多信息，请参阅项目报告及其中的参考资料，或随时通过或您找到的任何其他方式与我联系。

爱立信 无线网络优化 红宝书 Ericsson optimization radio

matlab优化算法代码，混沌序列优化优化粒子群算法，蚁狮优化算法，附带详细解释，非常适合新手学习理解