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

This doc describes a new extensible query optimization framework that resolves many of the shortcomings of the EXODUS and Volcano optimizer generators. In addition to extensibility, dynamic programming, and memorization based on and extended from the EXODUS and Volcano prototypes, this new optimizer provides (i) manipulation of operator arguments using rules or functions, (ii) operators that are both logical and physical for predicates etc., (iii) schema-specific rules for materialized views,

SPO_BPNN_PID：基于粒子群优化的神经网络PID控制

1. 该资源是MATLAB代码，混沌博弈优化算法Chaos Game Optimization (CGO)，该算法基于混沌理论的原理，利用分形和混沌博弈的基本概念进行模型构建，该算法于2020年提出。Reference: Talatahari, S., Azizi, M., Optimization of Constrained Mathematical and Engineering Design Problems Using Chaos Game Optimization, Computers & Industrial Engineering (2020). 2. 该资源是混沌博弈优化算法的MATLAB代码，可直接运行，目标函数包括Sphere测试函数等23组基准测试函数。运行结果包括最优解和最佳适应度值，以及收敛曲线的图像。 3. 适合算法研发人群使用。 4.如果您对资源的使用有疑问，请在CSDN私信作者

PyTorch最小化 Pytorch-minimize表示一组实用程序，用于最小化PyTorch中一个或多个变量的标量函数。 它受到SciPy的optimize模块和MATLAB的极大启发。 与SciPy和MATLAB不同，无需提供jacobian和hessian函数来进行pytorch最小化求解器，并且永远不会使用数值近似。 CPU和CUDA均受支持。 目前，仅实现了不受约束的最小化例程。 作者：鲁本·费曼（Reuben Feinman） 乍看上去： import torch from fmin import minimize def rosen ( x ): return torch . sum ( 100 * ( x [..., 1 :] - x [..., : - 1 ] ** 2 ) ** 2 + ( 1 - x [...

Unity平面网格划分器 现已可用。 一个简单的工具，可让您将任何网格划分为较小的子网格。 最初，它被设计为与导入的Tiled2Unity地形一起使用，但是我将其重写为可以处理的所有东西。 这个工具的目的是什么？ 假设您在一个网格中有一个巨大的地形。 渲染时，Unity将处理整个网格（即使在相机前面只有一小部分可见）。 使用此工具，您可以将较大的网格划分为较小的子网格，这要归功于内置的“截头锥体”剔除（将仅渲染可见的网格），从而可以大大提高性能。 产品特点 简单而快速。 定制： 网格尺寸 多轴（任意组合） 产生对撞机的能力 ... 支持所有顶点数据： 法线 色彩 多个紫外线通道

最优化的神书,尤其是凸规划方面，这本书可以帮助你解决很多这方面的问题。

The purpose of this book is to provide students of electric power system management the basic knowledge of different optimization problems in the power system operation of utilities as well as the electricity market. The contents of the book are based on recent studies specializing in the application of artificial intelligence such as particle swarm optimization, evolutionary programming, fuzzy logic, and augmented Lagrange Hopfield network to power system optimization problems.

Optimization of Power System Operation applies the latest applications of new technologies to power system operation and analysis, including several new and important areas that are not covered in existing books: uncertainty analysis in power systems; steady-state security region analysis; optimal load shedding; and optimal reconfiguration of electric distribution networks. The book covers both traditional and modern technologies, including power flow analysis, steady-state security region analysis, security constrained economic dispatch, multi-area system economic dispatch, unit commitment, optimal power flow, smart grid operation, optimal load shed, optimal reconfiguration of distribution network, power system uncertainty analysis, power system sensitivity analysis, analytic hierarchical process, neural network, fuzzy theory, genetic algorithm, evolutionary programming, and particle swarm optimization, among others. New topics such as the wheeling model, multi-area wheeling, the total transfer capability computation in multiple areas, are also addressed. The application of renewable energy and operation of smart grid is also included in the book

SSO算法模拟蜘蛛群集运动规律实现寻优过程，将整个搜索空间视为蜘蛛运动所依附的蜘蛛网，蜘蛛位置对应于优化问题的可能借，相应权值对应于评价个体好坏的适应度值。