Please note that the recommended version of Scilab is 2025.0.0. This page might be outdated.
However, this page did not exist in the previous stable version.
rafiter
(obsolete) iterative refinement for a s.p.d. linear system
Calling Sequence
[xn, rn] = rafiter(A, C_ptr, b, x0, [, nb_iter, verb])
Arguments
- A
a real symmetric positive definite sparse matrix
- C_ptr
a pointer to a Cholesky factorization (got with taucs_chfact)
- b
column vector (r.h.s of the linear system) but "matrix" (multiple r.h.s.) are allowed.
- x0
first solution obtained with taucs_chsolve(C_ptr, b)
- nb_iter
(optional) number of raffinement iterations (default 2)
- verb
(optional) boolean, must be %t for displaying the intermediary results, and %f (default) if you do not want.
- xn
new refined solution
- rn
residual (
A*xn - b
)
Description
This function is somewhat obsolete, use x = taucs_chsolve(C_ptr,b,A)
(see taucs_chsolve) which do one iterative refinement step.
To use if you want to improve a little the solution got with taucs_chsolve. Note that with verb=%t the displayed internal steps are essentially meaningful in the case where b is a column vector.
Caution
Currently there is no verification for the input parameters !
Examples
[A] = ReadHBSparse(SCI+"/modules/umfpack/examples/bcsstk24.rsa"); C_ptr = taucs_chfact(A); b = rand(size(A,1),1); x0 = taucs_chsolve(C_ptr, b); norm(A*x0 - b) [xn, rn] = rafiter(A, C_ptr, b, x0, verb=%t); norm(A*xn - b) taucs_chdel(C_ptr)
See Also
- taucs_chsolve — solve a linear sparse (s.p.d.) system given the Cholesky factors
- taucs_chfact — cholesky factorisation of a sparse s.p.d. matrix
Authors
Bruno Pincon <Bruno.Pincon@iecn.u-nancy.fr>
<< condestsp | Interfaces com UMFPACK (sparse) | res_with_prec >> |