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二维有限元源代码，为位势问题基本代码，具有一定实用性(Two-dimensional finite element source code for potential problems basic code, with some practical)

PCA算法程序设计步骤： 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量 (PCA algorithm programming steps: 1 to mean 2, calculation of covariance matrix and its eigenvalues and eigenvectors 3, the calculation of covariance matrix eigenvalues greater than the threshold number of 4, in descending order of eigenvalue 5, remove the smaller eigenvalue 6, remove the larger eigenvalue (usually not this step) 7, the combined choice of eigenvalues 8, select the appropriate eigenvalues and eigenvectors 9, computing whitening matrix 10, principal component extraction)

说明：  LDL分解。作为矩阵方程数值解法最基础的矩阵分解算法，LDL算法可以高效分解对称矩阵。(LDL decomposition. Numerical Solution of matrix equation as the most basic matrix factorization algorithm, LDL decomposition algorithm can be efficient symmetric matrix.)

weno格式解决一维黎曼问题的代码，fortran90编写(Fortran code for Riemann problem solver in weno scheme)

最小二乘法拟合形如f(x)=(a+bx)/(1+cx)的函数（9.2.2(1))，并解决人口预测问题(9.2.2(2))(Least squares fit of the form f (x) = (a+bx)/(1+cx) function (9.2.2 (1)), and to solve the population prediction problem (9.2.2 (2)))

求解KZK方程的VISUAL C++源代码 具有计算速度快，计算精度高等优点。(source code solution of KZK equation visual c++)

牛顿法计算方程，能计算非线性方程/方程程组，有需要的能试试，主要是数学方法的实现 (Newton method to calculate the equation to calculate the nonlinear equation/equation of equations, needs to try, mainly the implementation of the mathematical methods)

电商的数据集,可用于做数据分析 平台搭建(The data set of e-commerce can be used to build data analysis platform.)

说明：  主要用于Fluent 中风剖面、湍流剖面以及耗散率剖面的开发，应用的是日本的规范。(It is mainly used for the development of fluent stroke profile, turbulence profile and dissipation rate profile, and it is applied to the specifications of Japan.)

based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. While the importance of sparsity is much emphasized in SRC and many related works, the use of collaborative representation (CR) in SRC is ignored by most literature.