generalJacobianMatrix

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

matlab计算码头堆场集装箱的操作，集卡，场桥，岸桥(matlab container)

一个短时傅里叶变换的C语言实现，对3万个数据进行加窗、FFT处理(A short time Fourier transform of C language, for 30,000 data windowing, FFT processing)

可以对高温状态下的混凝土材料进行准确的塑型模型构建(It can be used to construct accurate plastic model of concrete under high temperature)

矩阵求逆，使用fortran语言编写，希望能对有需要的朋友有所帮助(Matrix inversion, written in FORTRAN language, hoping to help friends in need)

说明：  ABAQUS 的二次开发,详细注释说明，自学ABAQUS的二次开发(ABAQUS secondary development, detailed notes, self-study ABAQUS secondary development)

简单的滤波代码，包含高通滤波，低通滤波，带通滤波和带阻滤波。( U7B80 u5E5 u6E4 u6E4 u6E1 u6E1 u6E2 U5E26 u963B u6EE4 u6CE2 u3002)

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.)

说明：  用C语言编写的Fluent UDF实例，包含Adjust宏，profile宏等八个实例教程，快速上手(The UDF is for computational fluid dynamics software Fluent, it concludes the Profile.c,the Adjust.c)

这是一个实现曲线拟合的matlab程序，在数字预失真技术里面用到。(this is a matlab program for accomplishing the consolidation function,it always be used in digital predistortion technology.)