两目标pso算法

包括基本概念，数学规划的解析法，基本构件优化设计，线性规划，桁架优化设计，连续梁和钢架的优化设计，非线性规划在结构优化设计中的应用，动态规划，几何规划，结构动力优化-Including basic concepts, the analytic method of mathematical programming, the basic components to optimize the design, linear programming, optimization of truss design, steel beams and continuous optimization of the design, non-linear programming in structural optimization and design, dynamic programming, geometric programming, structural dynamics Optimization

高数矩阵运算代码-high matrix operation code

点云配准的经典算法，最邻近点迭代，iterative closest point输入为模型与待配准点云，输出为两块点云之间的旋转、平移以及中误差。

用于计算G－P关联维；和一些相关参数。并能确定最佳的嵌入维数；不是很成熟；希望各位指正；-G-P used to calculate the correlation dimension and a number of relevant parameters. And to determine the optimal embedding dimension not very mature hope that correction

this is an example demonstrating the Radial Basis Function,if you select a RBF that supports it (Gausian, or 1st or 3rd order polyharmonic spline), this also calculates a line integral between twopoints.

基于MFC对IGES文件进行解析并分流存储，采用数学算法完成NURBS模型统一建立，最后利用OpenGL完成NURBS模型重绘并进行交互操作。

独立于AVL库的存储媒体 虽现在有不少可用的AVL树库，但几乎都是工作在RAM中，当数据量小的时候，这种方式可行，但当树结构变大时时，读取和存储的时间就会大大变长，任务效率大大降低。能不能直接用磁盘进行树的操作呢？若可以，就不必进行频繁的磁盘内存交换了，本程序就实现了这个功能。实际上，这个程序无论是磁盘还是内存，都很好用。-independent of the AVL storage media is now available in a number of the AVL tree, But almost all work in the RAM, when a small amount of data, this approach feasible, but big tree structure constant, read and storage time will be much longer and the task efficiency has been greatly reduced. Can directly use the disk tree operation? If so, there is no need for frequent exchange of disk memory, the process to achieve this function. In fact, this procedure is disk or memory, are very handy.

数值计算,表达式求值, 如计算3 + 2 * ( 3 + 2) / 5 + 8 + Sin( pi() / 180 * 30 ) + 1 + 2 ^ 10, 可自定义函数, 采用递归算法-numerical calculation, in the expression evaluates, as calculated 3 2* (2)/5 8 Sin (pi ()/180* 30) 1 2 ^ 10,- defined functions using recursive algorithm

fft算法的C代码，经过验证可用。速度较快。-fft algorithm C code can be used after authentication. Faster.

Svd Feature包含一个很灵活的Matrix Factorization推荐框架，能方便的实现SVD、SVD++等方法, 是单模型推荐算法中精度最高的一种。SVDFeature代码精炼，可以用相对较少的内存实现较大规模的单机版矩阵分解运算。 另外含有Logistic regression的model，可以很方便的用来进行ensemble运算