competetree

本程序可用于线性插值,Fortran源码用于插值计算。(Program for linear interpolation.)

包括基本的克里金(Kriging)插值法实现代码，仅实现基本方法部分，不包含扩展克里金方法( kriging uses ordinary kriging to interpolate a variable z measured at locations with the coordinates x and y at unsampled locations xi, yi. The function requires the variable vstruct that contains all necessary information on the variogram. vstruct is the forth output argument of the function variogramfit. This is a rudimentary, but easy to use function to perform a simple kriging interpolation. I call it rudimentary since it always includes ALL observations to estimate values at unsampled locations. This may not be necessary when sample locations are not within the autocorrelation range but would require something like a k nearest neighbor search algorithm or something similar. Thus, the algorithms works best for relatively small numbers of observations (100-500). For larger numbers of observations I recommend the use of GSTAT. Note that kriging fails if there are two or more observa)

说明：  包括我自己写的一些数值分析用到的函数 包括矩阵的LU分解的两种方法, Cholesky分解, 以及线性方程组的迭代解法中的雅克比, 高斯-赛德尔以及线性松弛法的函数(Including myself, used to write some of the numerical analysis functions, including matrix LU decomposition of two methods, Cholesky decomposition, and iterative solution of linear equations in the Jacobi, Gauss- Seidel and linear relaxation method function)

关于常用的FORTRAN算法，该书有关于线性方程组的求解，矩阵特征值的计算，常微分方程组的求解等(FORTRAN on the commonly used algorithms, the book on the linear equations, eigenvalue calculation, solving ordinary differential equations, etc.)

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 (Conjugate Gradient) is between the steepest descent method between the method and Newton' s method, it takes only a first derivative information, but to overcome the steepest descent method convergence slow shortcomings, but also to avoid the Newton method needs to be stored Hesse and disadvantages of computing inverse matrix and the conjugate gradient method is not only one of the most useful methods to solve large linear equations, solution of large-scale nonlinear optimization is one of the most effective algorithm. In various optimization algorithm, conjugate gradient method is a very important one. The advantage is that a small amount of memory required, with step convergence, high stability, and does not require any external parameters.)

无网格伽辽金法中用到的数值积分，此程序，提供多点高斯积分，可任意选择！(element-free Galerkin method used in the numerical integration of this process, provide more Gaussian integral, can be arbitrary choice.)

这是用于将格点数据转化站点数据的fortran程序，比较详细，可以画图(This is a fortran program for the grid data into the site data in more detail, you can draw)

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.)

用于造波 对于从零开始学习UDF，建议还是先看一下UDF中文帮助，我估计大家知道的都是马世虎翻译的那本吧，感觉挺好。(fluent In order to learn UDF from scratch, it is suggested that we first look at UDF Chinese help.)

c++的有限元源码,带说明书和测试数据,是难得的有限元开发资料.(This is the code of finite element method, with manual document and data samples, it is valusble for FEM development)