Sparse-matrix-algorithm-

上三角或下三角矩阵也有大部份的元素不储存值（为0），我们可以将它们使用一维阵列来储存以节省储存空间，而对称矩阵因为对称于对角线，所以可以视为上三角或下三角矩阵来储存。(Upper or lower triangular matrix or a large majority of elements are not stored value (0), we can use them to store a one-dimensional array in order to save storage space, and because the symmetrical diagonal symmetric matrix, it can be regarded as the or lower triangular matrix storage.)

matlab的时频分析工具箱，含全部工具箱程序(Matlab time-frequency analysis toolbox, including all toolbox programs)

最小二乘法数据处理软件，翻遍计算的程序，应用于各种优化算法(math caculateor)

说明：  基于mises屈服准则的随动强化模型的umat子程序(Follow up reinforcement UMAT)

粒子群算法，也称粒子群优化算法或鸟群觅食算法（Particle Swarm Optimization），缩写为 PSO。它具有精度高，收敛快等优点，广泛用于系统辨识、函数优化等领域。文件pso.m是粒子群算法辨识程序，在此基础上，需要自行编写目标函数计算程序以实现不同系统的参数辨识。(Particle swarm optimization (PSO), also known as particle swarm optimization algorithm or bird swarm foraging algorithm (Particle Swarm Optimization), abbreviated as PSO. It has the advantages of high precision and fast convergence. It is widely used in system identification, function optimization and other fields. The document pso.m is a particle swarm algorithm identification program. On this basis, it is necessary to write an object function calculation program to realize the parameter identification of different systems.)

In this section we introduce the idea of Galerkin approximations by considering a simple 1-d boundary value problem

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.)

渗流计算，渗流浸润线的计算，溢出点高程的确定(seepage computation)

常用的积分函数，能够很方便的解决数值积分的问题(Commonly used integral function, can be very convenient to solve the problem of numerical integration)

用SVD奇异值分解法和阻尼最小二乘算法解决地球物理反问题，程序比较简单易懂(Using SVD, singular value decomposition method and damped least-squares algorithm to solve geophysical inverse problem, the procedure is relatively simple to understand)