CalcActivationCode

QR分解法是目前求一般矩阵全部特征值的最有效并广泛应用的方法，一般矩阵先经过正交相似变化成为Hessenberg矩阵，然后再应用QR方法求特征值和特征向量。它是将矩阵分解成一个正规正交矩阵Q与上三角形矩阵R，所以称为QR分解法,(QR decomposition )

通过具有随机结构参数的四分之一车辆模型研究了具有不确定性结构参数的车辆在受到来自道路的随 机激励作用下的振动响应问题。将簧上质量、簧下质量、悬挂阻尼、悬挂刚度以及轮胎刚度均认为是随机变量。将路面的不平整引起的对车辆的激励看作高斯随机过程并通过简单指数功率谱密度来建立力学模型。(By a quarter vehicle model with random parameters studied vehicle structure uncertain structural parameters of the vibration by the random excitation the road response under question. The sprung mass, unsprung mass, suspension damping, suspension stiffness and rigidity of the tire are considered to be random variables. The excitation caused by uneven road vehicle regarded as Gaussian random process and to create a mechanical model of a simple exponential power spectral density.)

在Z2[X]中判断一个多项式是否本原多项式，并能对给定的多项式求逆。 (in Z2 [X] to judge whether a polynomial primitive polynomial, and the right to the inverse polynomials.)

fluent多相流DPM模型中使用用户自定义函数对颗粒进行统计(DPM fluent multiphase flow model using user-defined functions for particle statistics)

关于汉诺塔的代码,中间是用迭代的方法实现的(About Hanio Tower)

单纯形法，求解线性规划问题的通用方法。单纯形是美国数学家G.B.丹齐克于1947年首先提出来的。它的理论根据是：线性规划问题的可行域是 n维向量空间Rn中的多面凸集，其最优值如果存在必在该凸集的某顶点处达到。顶点所对应的可行解称为基本可行解。单纯形法的基本思想是：先找出一个基本可行解，对它进行鉴别，看是否是最优解；若不是，则按照一定法则转换到另一改进的基本可行解,再鉴别；若仍不是，则再转换,按此重复进行。因基本可行解的个数有限，故经有限次转换必能得出问题的最优解。如果问题无最优解也可用此法判别。 (Simplex method for the minimum)

cluster in quest聚类算法是基于密度和网格的聚类算法。对于大型数据库的高维数据聚类集合。(cluster in quest clustering algorithm is based on the density of the grid and clustering algorithm. For large database of high-dimensional data clustering pool.)

包括牛顿法，阻尼牛顿法，变尺度算法，最速下降法的优化算法程序。(Newton' s method, damped Newton method, variable metric algorithm, the steepest descent method optimization algorithm.)

This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers using the K-means algorithm. Output is directed to the screen. Usage for KMEANS is: KMEANS SOURCEFILE <enter> The format of the source file is: NPat - Number of patterns (int) SizeVect - Size of vector (int) NClust - Number of cluster centers(int) vect[1,1] ... vect[1,SizeVect] - vector 1 (real) vect[2,1] ... vect[2,SizeVect] - vector 2 (real) . . . . . . vect[NPat,1] ... vect[NClust,SizeVector] - vector N (real) To compile: ICC KMEANS.CPP <enter> (This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers using the K-means algorithm. Output is directed to the screen. Usage for KMEANS is: KMEANS SOURCEFILE <enter> The format of the source file is: NPat - Number of patterns (int) SizeVect - Size of vector (int) NClust - Number of cluster centers(int) vect[1,1] ... vect[1,SizeVect] - vector 1 (real) vect[2,1] ... vect[2,SizeVect] - vector 2 (real) . . . . . . vect[NPat,1] ... vect[NClust,SizeVector]- vector N (real) To compile: ICC KMEANS.CPP <enter> )

fft code in visual c++