CUDAMatrixMul

MFC实现快速傅里叶变换（FFT）MFC实现快速傅里叶变换（FFT）(MFC MFC Fast Fourier Transform (FFT) fast Fourier transform (FFT))

计算机流体理论方面的文献，动网格的划分和计算(Literature, of the theory of computational fluid dynamic division and calculation of the grid)

一个短时傅里叶变换的C语言实现，对3万个数据进行加窗、FFT处理(A short time Fourier transform of C language, for 30,000 data windowing, FFT processing)

适用于大型稀疏矩阵 矩阵转置 c语言 来自于有限元方法编程(From the finite element method suitable for large-scale sparse matrix matrix transpose c language programming)

高斯-赛德尔迭代法是解线性方程组的常规算法，本文是C语言的高斯-赛德尔计算代码(Gauss-Seidel C program)

Orthogonal Polynomials Approximation 数值分析，计算正交基多项式的系数 (Given a function f and a set of m >0 distinct points . You are supposed to write a function to approximate f by an orthogonal polynomial using the exact function values at the given m points with a weight assigned to each point . The total error must be no larger than a given tolerance. Format of function int OPA( double (*f)(double t), int m, double x[], double w[], double c[], double*eps ) where the function pointer double (*f)(double t) defines the function f int m is the number of points double x[] contains points double w[] contains the values of a weight function at the given points x[] double c[] contains the coefficients of the approximation polynomial double*eps is passed into the function as the tolerance for the error, and is supposed to be returned as the value of error. The function OPA is supposed to return the degree of the approximation polynomial. Note: a constant Max_n is defined so that if the total error is still not small enough when n = Ma)

复数的四则运算完整版，包括加减乘除，阶乘，开方等。。。(The plural arithmetic full version,, including addition, subtraction, multiplication, and division factorial, prescribing, etc...)

数值分析中用C 语言验证验证Gauss列主元消去法。(Numerical analysis using the C language to verify verify the Gauss column principal component elimination.)

说明：  Optics聚类算法 OPTICS没有显示地产生一个数据集合簇，它为自动和交互地聚类分析计算一个簇次序。这个次序代表了数据基于密度地聚类结构。它包含地信息，等同于从一个宽广地参数设置范围所获得的基于密度的聚类(Optics do not show clustering algorithm OPTICS to produce a collection of data clusters, it is automatically and interactively computing cluster analysis a cluster order. This order represents the data to cluster based on the density structure. It contains in information from a broadly equivalent range of parameters obtained by density-based clustering)

music高阶谱分析算法，IDW距离反比加权方法，BP神经网络用于函数拟合与模式识别。( music higher order spectral analysis algorithm, IDW inverse distance weighting method, BP neural network function fitting and pattern recognition.)