Scilab Function
Last update : 7/2/2005
pcg - precondioned conjugate gradient
Calling Sequence
-
[x, flag, err, iter, res] = pcg(A,
b, tol, maxit, M, x)
Parameters
-
A
: symmetric positive definite matrix or function
returning
A*x
-
b
: right hand side vector (size: n)
-
tol
: error tolerance (default: 1000*%eps)
-
maxi
: maximum number of iterations (default: n)
-
M
: preconditioner: matrix or function returning
M*x
(default: none)
-
x0
: initial guess vector (default: zeros(n,1))
-
x
: solution vector
-
flag
: 0 if
pcg
converged to the desired
tolerance within
maxi
iterations, 1 else
-
err
: final relative norm of the residual
-
iter
: number of iterations performed
-
res
: vector of the residual norms
Description
Solves the linear system
Ax=b
using the conjugate
gradient method with preconditioning.
The
A
matrix must be a symmetric positive definite
matrix.
Examples
See Also
pcg
,
Author
Sage Group (IRISA, 2004)