newton

说明：  埃特金不等距插值，有例子，检验过，很顺利(Aitken non-equidistant interpolation, there are examples of tests, it is smooth)

C + +程序，根据用户的输入可以产生特定的Gauss -1型切比雪夫正交规则，(C++ program which can generate a specific Gauss-Chebyshev type 1 quadrature rule, based on user input)

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)

Dot Matrix 5 x 7 sample progmam , hope you enjoy

基于D2Q9模型的LBM程序，用于计算方腔驱动流，对应何亚玲《格子Boltzmann方法的理论及应用》一书的源码(LBM D2Q9 model-based program used to calculate the square cavity driven flow, which corresponds He Yaling " Lattice Boltzmann Method Theory and Applications," a source book)

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> )

FDTD Solutions 是一款三维麦克斯韦方程求解软件，可以分析紫外、可见、红外至太赫兹和微波频率段电磁波与具有亚波长典型尺寸复杂结构的相互作用。 FDTD Solutions 使得设计师能够从容地面对光子设计复杂的挑战问题。快速原型制造和高精度仿真大大降低对费用高昂的原型试验的依赖，因而可以对设计概念进行快速评价，进而缩短产品研发周期，降低费用。 FDTD Solutions 可以帮助你成功应对各种复杂的应用，从基础光子学研究，到目前工业界领先应用领域如成像、照明、生物光子学、光伏、以及众多其他应用。(FDTD Solutions is a three-dimensional software for solving Maxwell' s equations, we can analyze the ultraviolet, visible, infrared and microwave to terahertz frequencies of electromagnetic waves having a typical size of subwavelength interaction of complex structures. FDTD Solutions allows designers to calmly face the complex photonic design challenges. Rapid prototyping and simulation precision greatly reduce dependence on costly prototype testing, which can quickly uate design concepts, and thus shorten the product development cycle and reduce costs. FDTD Solutions can help you successfully deal with a variety of complex applications, leading application areas such as imaging, lighting, biophotonics, photovoltaics, and many other applications basic photonics research, the current industry.)

c语言程序编写在经验模态分解，包含三次样条(c language programming in the empirical mode decomposition, contains the cubic spline)

复杂网络的结构检测室近几年的研究热点，目前最快的算法是标签传播！(Community detection is hot topic in recent years. The most fast algorithm for community detection is Label Propagation Algorithm(LPA),and this is the program!)

针对传统的算法如遗传算法、粒子群算法等在TSP问题上求解精确性和求解规模上都还有一定的不足，本文提出了一种基于动态规划思想的粒子群优化算法。该算法用动态规划的方法实现粒子间的信息交互和粒子的进化，并且将粒子群中的粒子按无标度信息指导网络拓扑图的方式进行连接。仿真结果表明该方法能有效地减小误差率，提高解的精确，同时还保持了较低的计算复杂度，具有良好的稳健性。(TSP problem solving for the traditional algorithms such as genetic algorithms, particle swarm optimization accuracy and solving the scale, there is a certain lack of a dynamic programming algorithm-based particle swarm optimization algorithm. The algorithm using dynamic programming method to achieve the evolution of information exchange between the particles and the particles, and particle swarm particle scale-free information guide network topology map to connect. The simulation results show that this method can effectively reduce the error rate and improve the solution precision, while maintaining a low computational complexity, good soundness.)