matlab

fluent udf 壁面热流通量随时间的变化。 调用fluent udf 里面的宏运算。(fluent udf wall heat flux changes with time. Call fluent udf inside the macro operator.)

sod problem solved by ausm format

此程序用最小二乘法拟合椭圆图像，模拟地球的形状(This program fit ellipse images using the least squares method to simulate the Earth' s shape)

通过滑动T检验时间序列的变化趋势，一般多用于气象分析(T-test time series by sliding the trend, generally used for weather analysis)

最小二乘拟合的matlab程序，用于数据处理。简单，易懂(The least squares fitting matlab program for data processing.Simple, easy to understand)

基于配电网络辐射型的前推回代潮流计算程序，包括分层等内容(And backward substitution based on the radiation type of distribution network before flow calculation procedures, including the contents of the stratification)

偏微分方程数值解上机实习 运用古典显式差分格式求解二维扩散方程的初边值问题 运用P-R差分格式求解二维扩散方程的初边值问题

根据给定的等距结点上的函数值与一阶导数值, 用埃尔米特等距插值公式计算指定插值点处的函数值。(According to the given equidistant nodes function value with a first derivative values, with equidistant interpolation Hermite interpolation formula specified function value at the point.)

说明：  共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(Conjugate gradient method Gradient) is a method between the steepest descent method and Newton's method. It only uses the first derivative information, but overcomes the disadvantage of slow convergence of steepest descent method, and avoids the disadvantage of storing and calculating Hesse matrix and solving inverse of Newton's method. Conjugate gradient method is not only one of the most useful methods to solve large-scale linear equations, but also the most effective method to solve large-scale nonlinear optimization One of the algorithms of. Among all kinds of optimization algorithms, conjugate gradient method is very important. It has the advantages of small storage, step convergence, high stability and no external parameters.)

最优化课程中的最速下降法matlab程序，利用了wolfe搜索和armijo线搜索(Matlab program of steepest descent method in optimization course, using Wolfe search and Armijo line search)