sin_fft

实现了对文档的数据读入，实现了文档的数据读入(The realization of the document data read into, The realization of the document data read into, )

本人编写的半导体光放大器物理模型仿真程序。(I prepared the physical model of semiconductor optical amplifier simulation program.)

计算球体RCS，输入球直接获取球的雷达散射截面(Sphere radar cross section calculation)

求矩阵奇异分解svd算法，共计4种实现方式，(for SVD matrix decomposition algorithm, for a total of four kinds of achievement,)

采用dsp产生lorenz信号，以用于混沌保密通信，程序在ccs下调试并通过(using dsp have lorenz signal to be used in chaotic secure communications, Under procedures in ccs through debugging and)

回归分析，该支持向量机算法可用于预测，电力负荷、风力发电预测等(Regression analysis, the support vector machine algorithm can be used to forecast the power load, wind power prediction)

edem接触程序，用于计算各种模型的接触力(edem modle)

滤波程序，从同事那里要的，大家一起交流学习(BADNELL & BURGESS D.A.M.T.P. CAMBRIDGE C C DIAGONALIZATION OF REAL SYMMETRIC N-BY-N MATRIX Z. C C METHOD: HOUSEHOLDER REDUCTION TO TRI-DIAGONAL FORM AND SHIFTED C QL ALGORITHM TO DETERMINE THE E-VALUES AND E-VECTORS. C C BASED ON MARTIN, REINSCH & WILKINSON: NUM. MATH. 11, 181-95 (1968). C C INPUT REQUIRED. N, IUP AND Z. ONLY LOWER TRIANGLE OF Z NEED BE SUPPLIED. C MATRIX Z OVERWRITTEN BY EIGENVECTORS OF Z. C IUP=1/-1 ASC/DESCENDING SORT, 0 NO SORT. C MXMAT, IS THE ROW DIMENSION OF Z IN THE CALLING ROUTINE. C C OUTPUT. Z AND D, WHERE Z CONSISTS OF COLUMN EIGENVECTORS C AND D CONSISTS OF CORRESPONDING EIGENVALUES. C C NOTE: E IS A WORKING ARRAY. C)

c++算法源代码，主要是POJ上面的算法，根据标号可以找到。(c++ algorithm source code, POJ above algorithm, based on the label can be found.)

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。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(Conjugate gradient method (Conjugate Gradient) is between the steepest descent method between the method and Newton' s method, it takes only a first derivative information, but to overcome the steepest descent method convergence slow shortcomings, but also to avoid the Newton method needs to be stored Hesse and disadvantages of computing inverse matrix and the conjugate gradient method is not only one of the most useful methods to solve large linear equations, solution of large-scale nonlinear optimization is one of the most effective algorithm. In various optimization algorithm, conjugate gradient method is a very important one. The advantage is that a small amount of memory required, with step convergence, high stability, and does not require any external parameters.)