finity2D

基于信任域算法求解rosenberg函数的极小值问题(Trust region algorithm for solving the rosenberg function minimum value)

book of document including test

一个简单的计算块的运动矢量源码，使用四步搜索算法(A simple calculation of the block motion vector source, use the four-step search algorithm)

ABAQUS Fortran学习的一个简单例子

说明：  自己编写的期望最大化（EM）算法的MATLAB实现，里面有较为运行方法和程序说明，对新手有很好的帮助( I have written the expectation-maximization (EM) algorithm in MATLAB, which has run more methods and procedures described, there is a good help for the novice)

％ 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)

final积分，一种用于计算积分的快速有效的fortran方法。(final integrate)

水质模型，用于计算河流下游及河口水环境容量(use for water quality)

这是非均匀B样条曲线构造方法代码，如果有问题可以邮件联系：wuxiaod1987@163.com(NUBUS Spline Build)

基于全周傅立叶的实时阻抗测量 基于全周傅立叶的实时阻抗测量(Impedance measurement)