Numerical algorithm for Complex numbers

这是一篇很好的代码，希望可以帮助他人，而且我也收益颇多，所以我把这代码上传上来，希望可以解决更多人的问题

数值积分算法实现对一阶微分方程组的计算：通过编写程序语言，运用欧拉，预报校正，龙格库塔的方法实现对x1微分=x2 x2微分=x3 x3微分=-800*x1-80*x2-24*x3+sin(t）； y=800x1 的运算-Numerical integration algorithm for the calculation of first-order differential equations: through the preparation of programming language, the use of Euler and forecast correction, Runge-Kutta method of achieving x1 differential = x2 x2 differential = x3 x3 differential =- 800* x1-80* x2-24* x3+ sin (t) y = 800x1 computing

在学习数字信号处理算法程序中用VC编写的几个通用算法程序。 卷积计算/DFT与FFT实现/-In the learning process of digital signal processing algorithms using VC General Algorithm for the preparation of several procedures. Convolution calculation/DFT and FFT realization /

这是我们数据结构与算法设计的一次作业，是一个外部排序的算法实现，希望对刚刚学数据结构的同学有一点帮助。

数值分析中的雅可比方法问题，含输入输出界面。-Numerical analysis of Jacobi methods, including input and output interface.

数理公式，所有的数学公式都可以直接得到，非常方便-mathematical formula, all the mathematical formula can be obtained directly, a very convenient

一维伽辽金型无网格法MATLAB程序 无网格方法采用基于点的近似，可以彻底或部分地消除网格，不需要网格的初始划分和重构，不仅可以保证计算的精度，而且可以大大减小计算的难度。然而，由于目前的无网格近似一般没有解析表达式，且大都基于伽辽金原理，因此计算量很大，要超出传统的有限元法；另外，无网格近似大都是拟合，因此对于位移边界的处理比较困难，多采用拉格朗日乘子法处理。-1维伽Galerkin-type meshless method MATLAB procedures meshless method based on the point of approximation, can be completely or partially eliminate the grid, the grid does not require the initial division and reconstruction, not only can guarantee the accuracy of the calculation, and can greatly reduce the difficulty of calculation. However, due to the current meshless approximation there is no analytic expression in general, and mostly based on the Galerkin principle, the calculation of a large volume, it is necessary to go beyond the traditional finite element method In addition, the meshless approximation are fitted, so displacement of the border more difficult to deal with, the use of Lagrange law.

无和集问题求解算法-no set of issues and algorithm

数码管仿真实验 动态刷新法   右到左  依次显示12345678  并做无限循环处理

应用背景Implement k-means clustering on a training data set  to predict classification values of a test data set.关键技术The experiment intended to classify test data values using the data   mining strategy known as k-means clustering.   The 6 variables in the training data are used to plot the data and   centroids are placed to locate clumps of the 255, classified training   data. Once located, a classification value can be assigned to the 100,   test data.