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jacobi
- A: aij, 1<= i,j<=n b: 1<=i<=n x0: intial guess, 1<=i<=n tol: tolerance N maximum number of iterations k: number count of iteration Xm(:,k): X in each k iteration-Jacobi s method: In numerical linear algebra, the
GJDN
- 全选主元高斯-约当消去法同时求解系数矩阵相同而右端具有m组常数向量的线性代数方程组AX=B的全部解-QuanXuan primary gaussian-about when elimination technique and then the coefficient matrix is the same and the right side of the constant vector with m linear algebra equations AX = B of all solutions
10102130203
- 与矩阵连乘最小乘法数问题类似，虽然不需要记录加括号的方式，但是需要纪录下从i乘到j的结果中为a、b、c的个数，以便在后续的计算中可以用到之前的数据，避免重复计算。所以就需要用三维数组来存储（当然用struct结构体数据类型也很方便）或者用两个数组。-With a matrix with minimal multiplication problem similar, although need not record bracketed style, but need to record from
zoj1094
- zoj094 Matrix multiplication problem is a typical example of dynamical programming. Suppose you have to evaluate an expression like A*B*C*D*E where A,B,C,D and E are matrices. Since matrix multiplication is associative, the order in which
fisher
- fisher判别，输入两类样本，根据类间相似度和类内相似度矩阵求解权值w和偏值b-fisher discriminant, enter the two types of samples, according to the similarity between class and class similarity matrix to solve the weights w and the partial value of b
2-DFFT
- 该实验的目的是开发一个 2-D FFT程序包。要求程序能完成下面的功能: (a) 用因子 (-1)x+y 乘以输入图像，以实现滤波的中心化变换； (b) 用一个实矩阵乘以一个复数矩阵，即用实矩阵中的元素同时乘以复数矩阵对应位置上的复数的实部与虚部。 可以通过调用两个图像的乘法程序来实现对应元素的相乘； (c) 计算反付立叶变换； (d) 结果乘以 (-1)x+y ，并取其实部； (e) 计算频谱。 -The purpose of this ex
string-data-encryption
- Data Encryption by Matrix Multiplication We will only use numbers 0 to 26 for the encryptive mathematics operation. Numbers from 1 to 26 are for letters from A to Z and 0 is for all non-letter characters. To minimize usage of numbers, all letters
LSQR
- LSQR方法 求解大型稀疏矩阵的稳定可靠方法 A[M][N]·x[N]=b[M]-LSQR method for stable and reliable method for solving large sparse matrix A [M] [N] x [N] = b [M]
aluxian
- 这是2011年求数学建模b题中距离矩阵的程序，请大家参考-This is 2011 years for mathematical modeling b topic distance matrix of the program, please everybody reference
qiux
- 这段代码是用来求Ax=b线性方程的，采用的是列主元消元法，经过很苛刻的矩阵验证，其精度可以和matlab中的求逆再求方程的结果媲美-This code is used to seek Ax = b of linear equations, the main-element elimination method, after a very demanding Matrix verify its accuracy and inversion in matlab and then seek compa
C
- .请编写函数fun,该函数的功能是：实现B=A+A ，即把矩阵A加上A的转置，存放在矩阵B中。计算结果在main函数中输出-. Please write the function fun, the function of the functions are: to achieve B = A+ A ' , ie the matrix A with A, transpose, stored in the matrix B,. The results in the main function
[M]
- 原始单纯形法（大M法，无需给出初始基变量） Programmed by Liyang(faruto s Studio~!) BNU MATH Email:liyangbnu@mail.bnu.edu.cn QQ:516667408 last modified 2008.4.27 求解标准型线性规划:max C*X s.t. A*X=b (b>=0) X>=0 输入：C是n维行向量，A是m*n的系数矩阵，b是m维列向量- Of the origina
RK4
- 1.用四阶 Runge-Kutta 法求数值积分的函数为 RK4 2.此函数的入口参数为：系统维数 dimension,仿真时间 tspan 。 3.tspan 的格式为 [a:h:b] . 其中 a 表示起始时间，h 表示步长，b 表示终止时间。 4.仿真模型的状态方程、输出方程的系数矩阵以及系统初值均存放于文件 input information.txt 中。 5.仿真结果存放于文件 result.txt 中。 6.运行举例： 将 M 文件 RK4.m 和 inpu
Numerical-method-solve-equations
- 数值方法解方程组AX=B（LU分解），过程分3步，首先，分解系数矩阵A=LU，然后，解LY=B，最后再求UX=Y 另一种是直接求解的（2.1）-Numerical method to solve the equations AX = B (LU decomposition), 3 step process, first of all, the decomposition of the coefficient matrix A = LU, then, Xie LY = B, and finally
lusolvec
- lusolvec.f Numerical solution of a set of linear C *** Equations / a matrix equation A * x = b C *** using LU decomposition, matrix A and C *** vectors b and x being double complex, C *** and inversion of A.-3-D FDTD code with PEC
GaussianElimination
- x=GaussianElimination(A,b) GaussianElimination solves system of linear equations of the form Ax = b using Gaussian Elimination method. A = n x n square matrix with the coefficients of the system of equations as the elements of matrix A b
Gauss
- 高斯列主元解方程组，子程序，对应矩阵A，b，即可求解x-The Gaussian main-element solution of equations, subroutine, the corresponding matrix A, b, to solve for x
G-S
- 本程序为G-S迭代法，若系数矩阵满足 1.G-S迭代矩阵谱半径小于一 2.jacobi迭代矩阵一范数或无穷范数小于一 3.系数矩阵A正定 4.系数矩阵A严格对角占优或不可约对角占优 则可返回A*x=b的解。算法迭代次数比 x=M*x+g形式的标准化迭代次数多，但所用时间少很多。-The program for the G-S iteration method, if the coefficient matrix 1.G-S iterative matrix spect
originalsimple
- 原始单纯形法（需直接给出初始的基变量） 求解标准型线性规划:max C*X s.t. A*X=b (b>=0) X>=0 输入：C是n维行向量，A是m*n的系数矩阵，b是m维列向量，XB承装初始基变量的下标。输出：x最优解（如果有的话），fval最优值，flag解的状态说明，interation求解时的循环次数。-Original simplex method (to be given directly to the initial base variable) Solvin
vector_x_in_upper_matrix_mpi
- calculate vector x in Ax=b equation of the upper triangle matrix using pipeline mpi