文件名称:ssi
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krylov子空间法 krylov子空间方法的程序实现,用于解高阶矩阵-Matlab Codes for Krylov subspace methods
1. MINRES method by Paige and Saunders implementation
function [ x, istop, itn, rnorm, Arnorm, Anorm, Acond, ynorm ] = ...
minres( A, b, M, shift, show, check, itnlim, rtol )
[ x, istop, itn, rnorm, Arnorm, Anorm, Acond, ynorm ] = ...
minres( A, b, M, shift, show, check, itnlim, rtol )
minres solves the n x n system of linear equations Ax = b
or the n x n least squares problem min ||Ax- b||_2^2,
where A is a symmetric matrix (possibly indefinite or singular)
and b is a given vector. The dimension n is defined by length(b).
INPUT:
A may be a dense or sparse matrix (preferably sparse!)
or a function handle such that y = A(x) returns the product
y = A*x for any given n-vector x.
If M = [], preconditioning is not used. Otherwise,
M defines a positive-definite preconditioner M = C*C .
M may be a dense or sparse matrix (preferably sparse!)
or a function handle such that y = M(x) solves th
1. MINRES method by Paige and Saunders implementation
function [ x, istop, itn, rnorm, Arnorm, Anorm, Acond, ynorm ] = ...
minres( A, b, M, shift, show, check, itnlim, rtol )
[ x, istop, itn, rnorm, Arnorm, Anorm, Acond, ynorm ] = ...
minres( A, b, M, shift, show, check, itnlim, rtol )
minres solves the n x n system of linear equations Ax = b
or the n x n least squares problem min ||Ax- b||_2^2,
where A is a symmetric matrix (possibly indefinite or singular)
and b is a given vector. The dimension n is defined by length(b).
INPUT:
A may be a dense or sparse matrix (preferably sparse!)
or a function handle such that y = A(x) returns the product
y = A*x for any given n-vector x.
If M = [], preconditioning is not used. Otherwise,
M defines a positive-definite preconditioner M = C*C .
M may be a dense or sparse matrix (preferably sparse!)
or a function handle such that y = M(x) solves th
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ssi.txt