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说明：  an introduction to programming and methods (an introduction to programming and methods)

稀疏和低秩矩阵分解。 This paper focuses on the algorithmic improvement for the sparse and low-rank recovery.( Sparse and Low-Rank Matrix Decomposition Via Alternating Direction Methods.The problem of recovering the sparse and low-rank components of a matrix captures a broad spectrum of applications. )

matlab教案，详细地讲解课程，数学建模学习资料(matlab lesson plans, explain in detail the curriculum, learning materials mathematical modeling)

排队论模型仿真程序。用MATLAB实现 。欢迎大家一起讨论(Queuing theory model simulation program. Use MATLAB to achieve. Welcome to discuss with)

一个简单的风场模型，适用一般的仿真应用，程序运行准确(A simple wind field model for simulation of general application, the program runs exactly)

关于利用matlab进行系统建模与仿真的全面资料，并附有多个仿真参考实例(On the use of matlab for system modeling and simulation of the full information with reference to instances of multiple simulation)

基于RSSI的测距定位算法，可以直接自己写demo然后进行测试，十分的方便(RSSI-based distance positioning algorithm can write your own demo and then test, very convenient)

系统系统辨识文件夹内是产生高斯白噪声，m序列和最小二乘法的一次完成算法，递推算法，限定记忆法的程序。系统仿真文件夹内是对系统线性和非线性的建模和仿真程序。(System folder on system identification is to generate Gaussian white noise, m sequence and a complete least-squares method algorithm, recursive algorithm, limited memory method procedures. System Simulation System folder is a linear and nonlinear modeling and simulation program.)

Function to perform Principle Component Analysis over a set of training vectors passed as a concatenated matrix. Usage:- [V,D,M] = pca(X,n) [V,D] = pca(X,aM,n) where:- <input> X = concatenated set of column vectors aM = assume that the mean is aM n = number of principal components to extract (optional) <output> V = ensemble of column eigen-vectors D = vector of eigen-values M = mean of X (optional)

Solving several BVP s (1D example, 2D wave equation, 2D heat equation, 3D cantilever beam deflection (linear & quadratic elements) with use of very basic FEM techniques.