SparseMatrix.tar

国外比较好用的高斯-厄米特数值积分源代码(the code of gauss-hermite )

计算各种气象物理量，包括散度、涡度、螺旋度、垂直速度、水汽通量、比湿、假相当位温、k指数等的grads 程序，(Calculate various meteorological physical quantity, including divergence, vorticity, helicity, vertical velocity, water vapor flux, specific humidity, pseudo-equivalent temperature, k index grads program,)

ansys或ABAQUS二次材料属性的开发,实现材料非线性.(ansys or ABAQUS attributes the development of secondary materials, the realization of material nonlinearity.)

此程序碼為計算inverse-preisach之解析法計算(This program code for calculating inverse-preisach of analytical method)

实现了声波波动方程有限差分正演模拟，交错网格下，时间2阶空间10阶(Achieve the acoustic wave equation finite difference forward modeling, staggered under the grid, the time second-order space 10 order)

说明：  有限体积法求解方腔流动。有限体积法求解方腔流动有限体积法求解方腔流动(Finite volume method to solve the cavity flow. Finite volume method to solve the cavity flow finite volume method to solve the cavity flow)

本程序包括：DFT（离散傅里叶变换），FFT（快速傅里叶变换），IFFT（快速傅里叶逆变换），conv（卷积：圆周卷积、线性卷积），主要应用于信号处理，既可以对实序列做上述操作，也可以对复序列。(This process includes: DFT (Discrete Fourier Transform), FFT (fast Fourier transform), IFFT (inverse fast Fourier transform), conv (convolution: circular convolution, linear convolution), mainly used signal processing, either on the actual sequence to do the operation, can also be complex sequence.)

说明：  二阶曲线拟合，最小二乘法，此程序是在c++ builder6.0环境下开发的(second curve fitting, least squares method, this procedure is in c builder6.0 development of the environment)

将字符串合并的时候要注意中间的空格，否则可能在合并时出错。我调试了好久才发现(combine the string,essential to focus the length of string)

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.)