FEM

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.)

麻省理工大学k.j.bathe 教授开发的adina代码,欢迎大家下载,(adina sources by k.j.bathe MIT please download)

说明：  A numerical method for analysis of Korres2003

该程序实现了基于罗德里格矩阵的三维坐标转换，该算法简单快速，无需迭代而具有较高的精度(The program implemented based on three-dimensional coordinate transformation matrix Rodriguez, the algorithm is simple and fast, without iteration and with high precision)

本例是研究断裂问题的ABAQUS 子程序代码，使用Fortran语言编辑，研究扩展有限元的同志们可以借鉴一下。(This example is the study of fracture problems ABAQUS subroutine code, Fortran language editing, extended finite element study comrades can learn it.)

大地测量学软件，可以计算各种物理量，包括重力梯度等(geodesy software)

迭代最邻近点算法: 计算两组点云之间的最佳匹配效果(Iterative closest point (ICP) algorithm: seaching the best overlapping result of two point clouds)

MATLAB编写的非结构化网格生成程序，不算很长(MATLAB program written in unstructured mesh generation, not very long)

TDMA solver for tridiagonal matrix

％ 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)