SparseMatrix.tar

一个挺好用的源程序打包器，北京联高软件公司的，fortran代码转换成chm文档。(A useful tool for converting fortran source files to chm document.)

说明：  GM（1，1）灰色模型，便于了解其混沌特性，程序很短，由matlab编译，(GM (1,1) gray model to facilitate understanding of their chaotic behavior, a very short procedure, Matlab Compiler,)

采用相互垂直的两个振子构成圆极化天线的FDTD程序．可以计算其圆极化波瓣宽度等天线指标 (perpendicular to each other using the two oscillator constitute a circularly polarized antenna FDTD procedures. Can calculate its lobed circular polarization antennas width indicators)

采用时域数值仿真算法较为精确的仿真铣削加工过程动态力(By using numerical simulation algorithm is more accurate simulation of milling process dynamic force)

电弧炉对电网影响仿真软件，希望对大家有所帮助(EAF impact on the power grid simulation software, want to help everyone)

ANSYS车桥耦合瞬态分析代码,实现车辆移动荷载加载(ANSYS-vehicle bridge coupled)

本程序是基于非等距节点的正交多项式的最小二乘法拟合算法，该算法已经在vc++6.0下调试通过，经多次验证，本算法的拟合误差较小。(This procedure is based on equidistant nodes of non-orthogonal polynomial least squares fit algorithm, which has been in vc++6.0 through debugging, after repeated verification, the algorithm of fitting error is small.)

使用正交多项式完成数据拟合。程序对读入的gps采样点完成曲线拟合。()

book of document including test

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.)