FFT algorithm for power system improvements, there is a need to look at the Frie...

高性能计算高斯消去解方程组c++程序（复杂度 n^3-high-performance computing solutions Gaussian Elimination equations c (n ^ complex 3

一个介绍最优化方法课件，很详细。从最简单的一维搜索到共轭梯度，最速下降法等等，都有说明-An optimization method introduction courseware, in great detail. From the simplest one-dimensional search to the conjugate gradient, steepest descent method, etc., all indicate

误差分析的方法有多种，例如，威点逊（J. H. Wilkison）针对的计算机的浮点运算提出的“向后误差分析”，这是一种先验估计误差的方法，较以往的“向前误差分析”在矩阵运算的舍入误差估计上有较好的结果，以而使矩阵的误差分析获得了突破性的进展，使不少用向前误差分析难于判定可靠性的数值方法获得新的进展。-error analysis by a number of means, for example, Granville point Morrison (J. H. Wilkison), referring to a computer"s floating-point operations "backward error analysis," This is a priori estimation error method, compared to the previous "Forward Error Analysis" in the matrix computation Rounding error a better estimate on the results of a matrix so the error analysis of a breakthrough in the progress, many with forward error analysis difficult to determine the reliability of numerical methods to achieve new progress.

三维图形学NURBS算法实现源代码，对搞三维图形编程的人有帮助-The graphic model NURBS algorithm realization source code, to the person which does the three dimensional graph programming has helps

一个大整数计算的小程序,可以进行大数的计算,可以对素数进行测试-a large integer calculation procedures for the small, the large numbers can be calculated in the number of test-

资源描述 若矩阵Am´n中的某个元素aij是第i行中的最小值，同时又是第j列中的最大值，则称此元素为该矩阵中的一个马鞍点。试在顺序存储结构或链式存储结构的情况下，编程求出矩阵中所有的马鞍点。

杭州电子科技大学在线系统ACM的1050题，用贪心方法解决-Hangzhou University of Electronic Science and Technology ACM-line system of the 1050 title with a greedy solution

这是在 c + + 中为单链表操作的源代码，表明在此程序中的操作技巧将节点添加到链接列表，从链接列表中删除一个节点和它是如何影响的堆栈指针，计算的当前链接列表中的节点数。这个程序演示了上述函数单个链接列表中的所有。

insertion_sort.cpp #include #include #include #define N 5000 int insertion_sort( int data_insert[] ); void creat_file( char *file_name, int data_file[] ); int main(int argc, char *argv[]){    int data[N];    creat_file( argv[1], data ); 请点击左侧文件开始预览 ！预览只提供20%的代码片段，完整代码需下载后查看 加载中 侵权举报

用仿生模式识别来训练目标，在视频序列中跟踪目标 function [sausage, num] = hypersausage_construct(P) %按照样本给定的顺序去求解超香肠神经元 [~, W] = size(P);%W是训练样例的个数 sausage = []; for i = 2:W     temp = power_two(P(:, i - 1), P(:, i));%构建第i-1个神经元  //temp是一个行向量     %temp = power_three(P(:, i - 2), P(:, i-1),P(:,i));     sausage = [sausage temp]; end sausage = [sausage P(:, end)]; num = (size(sausage, 2) - 1) / (W - 1);