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最速下降法就是梯度下降法,可以用于求解最小二乘问题(线性和非线性都可以)。可以说是求解机器算法的最古老、最经典的模型，另一种常用的方法是最小二乘法。(The steepest descent method is the gradient descent method, which can be used to solve the least squares problem (both linear and non-linear). It can be said that it is the oldest and most classical model for solving machine algorithm. Another commonly used method is the least square method.)

使用奇异值分解来帮助求解最小二乘问题，特别是在方程系数矩阵不满秩的情况下。(SGELSD computes the minimum-norm solution to a real linear least * squares problem: * minimize 2-norm(| b- A*x |) * using the singular value decomposition (SVD) of A. A is an M-by-N * matrix which may be rank-deficient.)

用于解决投入产出问题的RAS算法，已知矩阵的行和和列和，可以求出矩阵的具体值。(For the RAS algorithm to solve the problem of input and output. We know the rows and columns of the matrix,then the specific value of the matrix can be calculated.)

自己编写的一个正弦函数频谱的估计，利用FFT函数实现，有详细注释，方便理解，附带仿真图一张(A sine function spectrum estimate their preparation, the use of FFT function implementation, detailed notes, easy to understand, with a simulation map)

程序包中含有Lagrange插值、Newton插值、Hermite插值、jacobi迭代、gauss迭代、 超松弛迭代、cholesky分解(Package contains Lagrange interpolation, Newton interpolation, Hermite interpolation, jacobi iteration, gauss iteration, ultra-relaxation iteration, cholesky decomposition)

室内无线定位技术，基于两次最小二乘法的matlab仿真程序(Indoor wireless positioning technology, based on the two least-squares method matlab simulation program)

偏微分方程数值解法的程序包，包括拉普拉斯方程、对流方程和扩散方程的各种解法(Numerical Solution of Partial Differential Equations package, including the Laplace equation, convection-diffusion equation equation and the various solution)

计算器编程代码，便于需要编程计算器的学员查询相关代码(Calculator programming code, to facilitate students in need of programming calculator related code)

RPCA的求解方法(利用拉格朗日乘子法，进行迭代)(the solution of RPCA called ALM)

用C语言程序通过先随机选取K个对象作为初始的聚类中心。然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心。聚类中心以及分配给它们的对象就代表一个聚类。一旦全部对象都被分配了，每个聚类的聚类中心会根据聚类中现有的对象被重新计算。(C Programming Language by first randomly selected the K object as initial cluster centers. And then calculate the distance between each object and each seed cluster center, and assign each object to its nearest cluster center. Cluster centers, and the object assigned to them on behalf of a clustering. Once all the objects are assigned to each cluster cluster centers will be based on the existing object clustering is recalculated.)