资源列表
fredholm
- 第二类Fredholm积分方程的数值解。 所谓积分方程就是积分号内有未知函数的方程-Second numerical solution of Fredholm integral equation
paiduilun
- 模拟顾客服务过程，顾客到达为泊松分布，时间间隔为指数分布。一个服务窗口。-Simulation of the process of customer service, customers arrive for the Poisson distribution, the time interval for the exponential distribution. A service window.
suntime
- 根据当地的经纬度，准确计算出日出日落的时间，可以用于各个领域。-Sunset time calculated based on latitude and longitude
optics-VCPP
- Optics聚类算法 OPTICS没有显示地产生一个数据集合簇，它为自动和交互地聚类分析计算一个簇次序。这个次序代表了数据基于密度地聚类结构。它包含地信息，等同于从一个宽广地参数设置范围所获得的基于密度的聚类-Optics do not show clustering algorithm OPTICS to produce a collection of data clusters, it is automatically and interactively computing cluster
2007511145848565_136Z_Com
- 四阶龙格库塔法求解微分方程，Visual C++ 环境下编译-4RK typical numerical analysis procedures, with four bands Runge- Kutta method to solve initial value problems
1113melkman
- 1113 melkman 凸包算法，O(n),前提是有拓补结构，是一种在线算法-1113 melkman convex hull algorithm, O (n), the premise is expanding premium structure, is an online algorithm
bingreedy
- 此源程序为解决一维集装箱装载问题，但是为二维和三维算法提供了很好的思路。-This source code to solve one-dimensional container loading problem, but for the two-dimensional and three-dimensional algorithm provides a very good idea.
FFTfilterbank
- 一個音樂分頻程式，以c++寫成。內含一個將音樂轉成純數字的源碼，一個分頻源碼(用FFT的overlap-and-add技術寫成)，一個把不同頻率加起來的源碼(確認分頻正確)。-A filter bank using FFT overlap-and-add with a transformer that transfer the wav file into txt file and a checker that ensure the filter is correct.
Matrix
- 关于矩阵运算的各种数值算法，包括实（复）矩阵求逆，对称正定矩阵与托伯利兹矩阵的求逆，线性方程组的常用解法，矩阵的各种分解方法，特征向量与特征值的求解等等。-Matrix operations on a variety of numerical algorithms, including the real (complex) matrix inversion,托伯利兹symmetric positive definite matrix and matrix inversion, linear eq
Dam_thurey
- LBM method for calculating free-surface flow
DDA
- DDA颗粒生成，能够生成二维的随机颗粒，很好的与DC程序融合在一起-DDA particle generate
multifit
- 功能：离散试验数据点的多项式曲线拟合 调用格式：A=multifit(x,y,m) 其中：x: 试验数据点的x坐标向量 Y: 试验数据点的y坐标向量 m: 拟合多项式的次数 -Functions: discrete experimental data points, the polynomial curve fitting call format: A = multifit (x, y, m) where: x: experimental data points, x