Please note that the recommended version of Scilab is 2023.1.0. This page might be outdated.
However, this page did not exist in the previous stable version.
Iterative refinement for a s.p.d. linear system. This function is obsolete.
[xn, rn] = rafiter(A, C_ptr, b, x0, [, nb_iter, verb])
a real symmetric positive definite sparse matrix
a pointer to a Cholesky factorization (got with taucs_chfact)
column vector (r.h.s of the linear system) but "matrix" (multiple r.h.s.) are allowed.
first solution obtained with taucs_chsolve(C_ptr, b)
(optional) number of raffinement iterations (default 2)
(optional) boolean, must be %t for displaying the intermediary results, and %f (default) if you do not want.
new refined solution
A*xn - b)
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.
Currently there is no verification for the input parameters !
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