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Scilab Help >> UMFPACK Interface (sparse) > rafiter

# rafiter

Iterative refinement for a s.p.d. linear system. This function is obsolete.

### 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 factorization of a sparse s.p.d. matrix

### Comments

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