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## Matrix Algorithms in MATLAB. Tongru Huo

Matrix.Algorithms.in.MATLAB.pdf
ISBN: 9780128038048 | 750 pages | 19 Mb

Matrix Algorithms in MATLAB Tongru Huo
Publisher: Elsevier Science

[4] describe and compare many algorithms for computing a matrix exponential. This paper describes the results of a project to interface MATLAB with a parallel Interfacing MATLAB with a parallel virtual processor for matrix algorithms. Starting from a random array X with rank 20, try a few iterations at several replicates using the multiplicative algorithm:. This MATLAB function solves the system of linear equations A*x = B. Det computes the determinant from the triangular factors obtained by Gaussian elimination with the lu function. This example shows how to use the genetic algorithm to minimize a function Given the list of city locations, we can calculate the distance matrix for all the For example, a custom data type can be specified using a MATLAB® cell array. Std2 computes the standard deviation of the array A using std(A(:)) . Try MATLAB, Simulink, and Other Products. Higham, "A Schur-Parlett algorithm for computing matrix functions," SIAM J. I know that there are some clever algorithms to exploit the fact that the matrix is sparse "Never" invert a matrix, particularly a large sparse one. Function C = strassen(A, B, nmin) %STRASSEN Strassen's fast matrix multiplication algorithm. The algorithm expm uses is described in [1] and [2]. Using a matrix from the MATLAB Gallery collection as an example, this article discusses three variants of the QR algorithm available in MATLAB. Ziggurat algorithm generates normally distributed random numbers. It uses block algorithms, which operate on several columns of a matrix at a time. This function computes the fundamental matrix using the normalized eight-point algorithm [1] a fundamental matrix, f, by using the normalized 8-point algorithm.