naca0012

由美国SANDIA实验室一名研究员编写的用于计算半导体量子纳米线的一系列matlab文件。(SANDIA laboratory by the American researcher prepared a semiconductor quantum nanowire used to calculate a series of matlab file.)

自制的计算器，可实现加减运算，清除，后退等功能(Homemade calculator, can realize the addition and subtraction, clear, back, etc)

C-N解偏微分方程的程序。解的是定步长的抛物型偏微分方程： du/dx - a * d2u/dx2 = 0 在程序中可以更改 a 的值以实现不同系数的解。在循环中改变 a 的值以实现变系数. 该说明在压缩包是也有(CN solution of partial differential equations procedures. Solution is scheduled to step parabolic partial differential equation: du/dx- a* d2u/dx2 = 0 in the procedure can be changed in order to realize the value of a different coefficient of the solution. Change in the cycle in order to realize the value of a variable coefficient. The note in the compressed packet is also)

matlab Newmark法 用于求解二阶常系数微分方程组(matlab Newmark method for solving second-order differential equations with constant coefficients)

静力缩聚法求缩聚后的结构刚度、质量矩阵，以及频率(Static condensation method for structural stiffness after condensation, mass matrix, and the frequency)

利用下载的goce原始数据，计算得出含有时间/经纬度/重力梯度格式的程序(Use downloaded goce raw data, calculated with the time/latitude/gravity gradient program formats)

通过最小二乘辨识系统模型，并进行必要的分析，是上系统辨识课的实验内容，有程序和报告(By least squares identification system model and the necessary analysis on System Identification lesson experimental content, procedures and reporting)

这是另一个改进的关于数字预失真功能仿真的matlab程序，效果不错，希望对大家有所帮助。(This is a improved matlab program about function estimation of digital estimation,and it has a good performance,really hope it can help you.)

随机数生成程序，该随机数呈zipf分布，就是大家常说对于内容的访问遵循80/20原则，也就是20 的内容，会占有80 的访问量。(Random number generator, the zipf was distributed random numbers, that is, we often say that the access to the content to follow the 80/20 principle, which is 20 of the content will occupy 80 of visits.)

基于经典的增广拉格朗日乘子法, 对求解一类带有特定结构（主要是针对凸规划）的非光滑等式约束优化问题, 我们提出、分析并测试了一个新算法. 在极小化增广拉格朗日函数的每一步迭代中, 该算法有效结合了带有非单调线性搜索的交替方向技术, 我们建立了算法的收敛性, 并用它来求解在带有全变差正则化的图像恢复问题.(Based on the classic augmented Lagrangian multiplier method, we propose, analyze and test an algorithm for solving a class of equality-constrained nonsmooth optimization problems (chiefly but not necessarily convex programs) with a particular structure. The algorithm effectively combines an alternating direction technique with a nonmonotone line search to minimize the augmented Lagrangian function at each iteration. We establish convergence for this algorithm, and apply it to solving problems in image reconstruction with total variation regularization.)