sod ausm

此程序用于计算矩形截面的弯矩曲率特性以及不同极限状态的识别(This program is used to calculate the moment curvature characteristic of the rectangular cross-section, and the identification of different limit state)

子空间学习方法工具箱，包含PCA、LDA、LLE、ISOMAP等经典子空间分析方法(Subspace learning method toolbox, including classical subspace analysis methods such as PCA, LDA, LLE, ISOMAP and so on)

再生混凝土细观损伤分析程序，采用随机骨料模型与双折线模型。(Recycled concrete meso-damage analysis procedures, using random aggregate model with two-line model.)

NSGA-II is a popular non-domination based genetic algorithm for multi- objective optimization.

new robot search algorithms

使用Matlab软件计算Normal Copula,Student t Copula,Clayton Copula,Gumbel Copula的程序(Calculated Normal Copula, Student t Copula, Clayton Copula, Gumbel Copula using Matlab software )

C-means in 2 types. C=2

fft的文件，fortran环境下运行！欢迎指正！(fft documents, fortran environment running! Please correct me!)

说明：  这是一个ACM试题中的算法，叫分而治之算法，上面有PPT还有实例代码，希望喜欢ACM的同学可以喜欢(This is a Question in the ACM algorithm, called divide and conquer algorithms, there are examples of the code above, there are PPT hope that students like to be like the ACM)

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.)