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Scilab help >> Sparse Matrix > qmr

qmr

quasi minimal resiqual method with preconditioning

Calling Sequence

[x,flag,err,iter,res] = qmr(A,b,x0,M1,M1p,M2,M2p,maxi,tol)

Arguments

A

matrix of size n-by-n or function returning A*x

b

right hand side vector

x0

initial guess vector (default: zeros(n,1))

M1

left preconditioner: matrix or function returning M1*x (In the first case, default: eye(n,n))

M1p

must only be provided when M1 is a function. In this case M1p is the function which returns M1'*x

M2

right preconditioner: matrix or function returning M2*x (In the first case, default: eye(n,n))

M2p

must only be provided when M2 is a function. In this case M2p is the function which returns M2'*x

maxi

maximum number of iterations (default: n)

tol

error tolerance (default: 1000*%eps)

x

solution vector

flag
0 =

gmres converged to the desired tolerance within maxi iterations

1 =

no convergence given maxi

res

residual vector

err

final residual norm

iter

number of iterations performed

Description

Solves the linear system Ax=b using the Quasi Minimal Residual Method with preconditioning.

See Also

  • gmres — Generalized Minimum RESidual method

Authors

SAGE Group, IRISA 2005

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Last updated:
Wed Oct 05 12:09:57 CEST 2011