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经典R-K法，通过它可以计算一些简单的常微分方程的数值解(Classical RK method, which can be calculated by simple numerical solution of ordinary differential equations)

单元刚度矩阵，用于计算三单元刚度矩阵，输出刚度矩阵(Element stiffness matrix is ​ ​ used to calculate the three-element stiffness matrix, the output stiffness matrix)

随机生成裂缝网格，由于地下水文学模拟，及裂缝网格连通性计算。(Randomly generated grid of cracks, due to subsurface hydrology simulation and calculation of cracks grid connectivity.)

数值计算各算法的程序,包括:牛顿迭代法,超松驰迭代法,微分法等.对于初学数值分析这门课程的人有很大的帮助.(Numerical calculation procedure of the algorithm, including: Newton iteration, ultra-relaxation iteration method, differential method and so on. Beginner numerical analysis for this course were very helpful.)

说明：  微分进化算法中的其中两个子程序，还有未上传(Differential evolution algorithm)

利用c++编写了一个外点惩罚函数，用于对于对目标值的推算与尝试。(Use c++ to write a point outside the penalty function for the target value for the calculation and try.)

本程序是贝叶斯网络结构学习的K2算法程序，可获取离散变量的贝叶斯网络(This procedure is K2 Bayesian network structure learning algorithm program, available discrete variables Bayesian Network)

这是利用FFT分析相位差的程序，具有一定的参考价值！(This is done using FFT analysis phase of the program, has a certain reference value!)

说明：  Galileo单频定位程序，能基于rinex文件实现高精度定位，供编写时参考(Galileo single-frequency positioning program can achieve high-precision positioning based on RINEX file for reference when writing.)

％ 奇异值分解 (sigular value decomposition,SVD) 是另一种正交矩阵分解法；SVD是最可靠的分解法， ％ 但是它比QR 分解法要花上近十倍的计算时间。[U,S,V]=svd(A)，其中U和V代表二个相互正交矩阵， ％ 而S代表一对角矩阵。 和QR分解法相同者， 原矩阵A不必为正方矩阵。 ％ 使用SVD分解法的用途是解最小平方误差法和数据压缩。用svd分解法解线性方程组，在Quke2中就用这个来计算图形信息，性能相当的好。在计算线性方程组时，一些不能分解的矩阵或者严重病态矩阵的线性方程都能很好的得到解( Singular value decomposition (sigular value decomposition, SVD) is another orthogonal matrix decomposition method SVD decomposition is the most reliable method, but it takes more than QR decomposition near ten times the computing time. [U, S, V] = svd (A), in which U and V on behalf of two mutually orthogonal matrix, and the S on behalf of a diagonal matrix. And QR decomposition are the same, the original matrix A is no need for the square matrix. The use of SVD decomposition method are used as a solution of least squares error method and data compression. Using SVD decomposition solution of linear equations, in Quke2 on to use this information to calculate the graphics performance quite good. In the calculation of linear equations, some indecomposable matrix or serious pathological matrix of linear equations can be a very good solution)