umat_temp

利用MATLAB实现了16位快速傅立叶变换，并与自包含函数进行了比较。(The 16-bit Fast Fourier Transform is realized by using MATLAB and compared with the self-contained function.)

牛顿插值法进行多项式逼近，matlab编译成功，直接可用(Newton interpolation polynomial approximation method, matlab compiled successfully, directly available)

用最小二乘法进行电力系统状态估计，包含IEEE30节点的电力系统图(Using least squares power system state estimation, including IEEE30 bus power system diagram)

说明：  重力异常正演代码，请好好使用它。帮助学习。。。。(Gravity anomaly forward code, please use it well. Help learn)

weno格式解决一维黎曼问题的代码，fortran90编写(Fortran code for Riemann problem solver in weno scheme)

vic模型参数率定代码，用Python编写(VIC Model Parameter rate code, written in Python)

多项式计算器，可以用于多项式的各种计算。(Polynomial calculator can be used for a variety of computing polynomials.)

利用abaqus软件建模，然后将inp文件导入flac软件中。(ABAQUS software is used to model, and then the InP file is imported into the FLAC software.)

偏微分方程数值解上机实习 运用古典显式差分格式求解二维扩散方程的初边值问题 运用P-R差分格式求解二维扩散方程的初边值问题

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.)