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《最优化理论与方法》书籍中的乘子法的源程序，该书中的很多案例都用此方法试验过，本代码是一个小案例，将目标函数和约束函数按自己的需求换掉就能进行所期望的运算(" Optimization Theory and Methods" books multiplier method of the source, the book' s many cases are tested using this method, the code is a small case, the objective function and constraints to replace it according to their needs can perform the desired operation)

book on markov models and techniques

基于前推回代法的电力系统潮流计算程序。 需要输入支路阻抗和节点功率矩阵。包括支路号、首节点号、尾节点号、支路自阻抗、尾节点给定功率。 输出各节点的电压幅值、相角和各支路电流。(Power flow calculation program based on the forward and backward substitution method. Need input the branch impedance node power matrix. Including branch number, node number, the tail node number, branch self-impedance tail node given power. The output node voltage amplitude, phase angle and current of each branch.)

包括回归分析和概率统计，有借鉴意义哦，基于chebyshev的水声信号分析。( Including regression analysis and probability and statistics, There are reference Oh, Based chebyshev underwater acoustic signal analysis.)

大型上用有限元软件ABAQUS的用户自定义材料子程序UMAT，用于计算材料低周疲劳极限和损伤下的应力应变关系(calculating material low cycle fatigue limit)

数值代数通常也称为矩阵计算，是以计算机为工具来求解各种数学模型的主要课程，同时也是计算方法课程的延续和深入(Numerical algebra also commonly called matrix calculation is based on the computer as a tool to solve a variety of mathematical models of the main course, but also the calculation method of curriculum continuity and depth)

加汉宁窗fft插值算法，计算谐波幅值、频率、相位，需输入采样频率、fft长度(hanning windows Harmonic analysis)

说明：  球形粒子Mie 散射用Matlab编写的Mie theory计算程序。在光子学中运用广泛，使用非常方便(Mie scattering of spherical particles prepared by using Matlab program Mie theory. In the use of photonics in a wide range of very convenient to use)

多机电力系统仿真及其潮流计算，图像的光流法计算的matlab程序,与理论分析结果相比。( Multi-machine power system simulation and flow calculation, Image optical flow calculation matlab program, Compared with the results of theoretical analysis.)

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)