bingtaixitong

TGM.m \传统的Galerkin方法 MD.m \时滞惯性流形方法 brownian.m \演示布朗运动 randomwalk.m \ 演示随机游走 tumor.m \ 演示tumor演化(TGM.m traditional Galerkin method MD.m Delays inertial manifold method brownian.m demo Brownian motion randomwalk.m demo random walk tumor.m demo tumor evolution)

kaerman 滤波MATLAB工具 以及程序(kaerman MATLAB TOOL)

实现了测井数据的fft变化分析，可以用于查看测井数据的频谱特征(Realize the logging of data changes fft analysis, can be used to view the logging data of spectrum characteristics of)

Hansen最新（2008）修订后的关于反演正则化方法的电子书与相应Matlab程序(Hansen the latest (2008) as amended on the inversion method is the e-book with the corresponding Matlab program)

Fortran常用算法程序集-徐士良著,本书讲解了各种数值计算方法及其程序实现,此为程序第一部分,(Fortran commonly used algorithm assembly-Xu Shiliang, this book explains various numerical calculation methods and their program implementation, this is the first part of the program.)

内点惩罚函数法C语言程序，适用于机械行业机械优化设计(SUMT)

说明：  共轭梯度法求解线性方程组,主要采用FR共轭梯度法(solve liner solutions from CG)

说明：  abaqus子程序学习资料，欢迎大家下载学习，共同讨论~(ABAQUS subroutine learning materials)

汽车油耗计算（C实现）对于家用经济型小轿车来说，统计油耗是经常做的一件事。本题目要求根据汽油单价、每次加油所用钱数，以及该次加的油所跑里程数计算出该车的百公里油耗，并进行保存，根据需要给出统计数据。(Vehicle fuel consumption calculation (C implementation) for the household economy cars, the fuel consumption statistics is often one of the things. The questions asked based on gasoline price, amount of money each fuel used, and the second oil added to calculate the mileage run car 100 km fuel consumption, and save, according to the need to give statistics.)

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.)