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mpibingxing
- 介绍行列划分算法和矩阵相乘并行算法M P I 程序, 给出基于矩阵相乘并行算法的M P I 实现, 分析和讨 论处理器数目、复杂性、矩阵划分、B 子块传递、死锁避免和矩阵数据的获取等问题.-Introduction into the ranks of the matrix multiplication algorithm and parallel algorithm for MPI procedures, give parallel algorithm based on matrix mul
c
- 本子程序根据所给的支路导纳及有关信息,形成结点--导纳矩阵,如打印参数K=1,则输出电导矩阵G和电纳矩B -Procedures based on the book of the slip road to the admittance and related information, the formation of node** admittance matrix, such as print parameters K = 1, the output conductance matrix G
solver
- solves any equation of matrix Ax=b
qili
- 实现矩阵三角分解的乔利斯基方法及用此方法解Ax=b的方程。-Triangular matrix decomposition to achieve乔利斯基and use this method to solve the equation Ax = b.
1989xishujuzheng
- 稀疏矩阵采用三元组表示。（1）求两个具有相同行列数的稀疏矩阵A和B的相加矩阵C，并输出C。（2）求出C的转置矩阵D，输出D。-The use of sparse matrix triple that. (1) for the ranks of the two with the same number of sparse matrix A and B the sum of matrix C, and output C. (2) calculated C matrix transpose of D,
fisher_classify
- function [clusters,c,F]=fisher_classify(A,B,data) fisher判别法程序 输入A、B为已知类别样本的样本-变量矩阵，data为待分类样本 输出C为判别系数向量 -function [clusters, c, F] = fisher_classify (A, B, data) fisher discriminant method procedures input A, B for a sample of known typ
duichengjuzhen
- 对称矩阵相乘：2. A和B是两个n×n阶的对称矩阵，以行为主序输入对称矩阵的下三角元素，压缩存储存入一维数组A和B，编写一个算法计算对称矩阵A和B的乘积，结果存入二维数组C。-Symmetric matrices: 2. A and B are two n × n symmetric band matrix, in order to conduct the main sequence of input symmetric matrix elements of lower triangular,
liehuanweifa
- 行列换位法采用密钥，假设密钥是5，明文是I am a Chinese boy那么就会以5位列数排成一个矩阵。 I a m a c h I n e s e b o y 那么密文就是ihe aib mno aey cs× ，最后一行不足的用随机字母填充，（×代表那个随机字母）！-The ranks of the use of key transposition law, assuming that key is 5, is clearly I am a Chinese boy then
ww
- Modify the Matlab Gauss Elimination routine given in lectures so that it (a) performs implicit complete pivoting, and (b) handles m right hand sides at once by performing an LU decomposition of the matrix A first and then doing forward substitu
5[1][1].4
- 两个稀疏矩阵的三元组a,b输出，转置，相加及相乘-Two sparse matrix triple a, b output, transpose, add and multiply
cannon
- cannon 并行程序 Matrix multiplication with the Cannon Algorithm: The matrices A and B are stored in files. The file names have to be specified as parameter. The root process reads the matrices and distributes the respective values to all processes,
jacobi
- Write an MPI program that solves a set of linear equations Ax = b with the 并行计算 Jacobi method. The root process reads the matrix A and the vector b from files. The file names have to be specified by the user as parameters.-Write an MPI p
canon2
- 主要还是让A(i,j) B(i,j)进行移动A(i,i+j),B(i+j,j)，然后进能直接相乘。具体的内容可以看并行算法导论，-Matrix multiplication with the Cannon Algorithm: The Cannon Algorithm for matrix multiplication was presented in the course “Parallel and Distributed Algorithms “by Dr. Klauck. Sh
work
- 建立一个矩阵，进行一些运算，求得XA=B中X的值-matrix caculation
Data
- 1998年数学建模B组题的求解与分析的数据，即各乡镇间的带权邻接矩阵-Mathematical Modeling 1998 Group B to solve the problem of data and analysis, that is, between the township weighted adjacency matrix
Close3B
- 采用拟三对角矩阵反算出过一定点（大于等于3个）的控制顶点，再由这些控制顶点计算出3次B样条曲线，由于曲线封闭的，所以无需引入边界条件即可求得曲线。-Used to be anti-tridiagonal matrix calculated over a certain point (greater than or equal to 3) of the control points and then calculate the control points of these 3 B-spline
xianshipin
- 四个显示字符数据表放在50H-6FH单元内，字符用8*8点阵，R4（30H）用于 控制显示静止字的时间，R5（31H）静止字显示跳转地址步距，B内放显示首址-Table 4 shows data on the character 50H-6FH unit, character 8* 8 Dot Matrix, R4 (30H) for control shows the time the word static, R5 (31H) characters display a static
maseidel
- 用Gauss-Seidel迭代法解线性方程组Ax=b， A为系数矩阵，b为右端向量-Using Gauss-Seidel iteration method for solving linear equations Ax = b, A as the coefficient matrix, b is the right end of the vector
majacobi
- 用Jacobi迭代法解线性方程组Ax=b，A为系数矩阵，b为右端向量-Solution using Jacobi iterative method of linear equations Ax = b, A as the coefficient matrix, b is the right end of the vector
mexSparseLogical0Diag
- Because of memory constraints, it is often impossible to change by subscr ipt all the elements of a large sparse matrix to zero. This leads to changing the elements in a loop, which is horrendously slow. This mex solves that problem. Usage: B =