GMM模型

随机子空间算法实现 matlab函数 分两步求取 来源于网络(Stochastic Subspace Realization Algorithm Tohru Katayama, "Subspace Methods for System Identification", Springer)

说明：  matlab调用cplex12.1的例子，这些例子对matlab的cplex接口函数做了非常详细的说明，适用于cplex-matlab的初学者(matlab call cplex12.1 the examples that the cplex interface function of matlab to do a very detailed explanation for beginners cplex-matlab)

MFC编写的计算器，基本功能都已实现，加速键没有实现(Written in MFC calculator, basic functions have been achieved, not achieved accelerator)

说明：  自己编写的期望最大化（EM）算法的MATLAB实现，里面有较为运行方法和程序说明，对新手有很好的帮助( I have written the expectation-maximization (EM) algorithm in MATLAB, which has run more methods and procedures described, there is a good help for the novice)

用蒙特卡洛模拟的方法计算美式期权的价格以及基本描述,实现了图像的加水印，去噪，加噪声等功能，包括最小二乘法、SVM、神经网络、1_k近邻法。( Monte Carlo simulation method of calculating the American option price and basic description, Realize image watermarking, de-noising, plus noise and other functions, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.)

使用正交多项式完成数据拟合。程序对读入的gps采样点完成曲线拟合。()

说明：  数值分析matlab作业答案，介绍，matlab编程，数值分析计算(Numerical analysis matlab homework answers)

最小二乘递推算法及其matlab实现（计算估计值）(least squares method)

汪德灌教授那本 计算水力学书后的 perisiman 隐式差分法源代码的 c++版本，我是在fortran代码中改过来的，书中的fortran代码有错误， 请较高人才得以突破 这个东西应该比较有用的吧， 尤其对刚接触这方面知识的 注释很详细哦 而且有输入文件(Professor Wang Deguan that the calculation of hydraulics perisiman after the implicit difference method source code c++ Version, I was changed from fortran code, and the book fortran code error, please talents to break with this higher things you should be more useful, especially for刚接触this regard very detailed knowledge of the Notes and the input file Oh)

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(Conjugate gradient method (Conjugate Gradient) is between the steepest descent method and Newton' s method between a method that takes only a first derivative information, but to overcome the slow convergence of the steepest descent method shortcomings, but also avoid the need to store Newton and computing the inverse Hesse matrix and disadvantages, conjugate gradient method is not only to solve large linear equations of the most useful methods, large-scale nonlinear optimization solution is the most efficient algorithms)