res_with_prec
computes the residual r = Ax-b with precision
Syntax
[r,norm2_r] = res_with_prec(A, x, b)
Arguments
- A
- real or complex sparse matrix (m x n) 
- x
- column vector (n x 1) or matrix (n x p) 
- b
- column vector (m x 1) or matrix (m x p) 
- r
- column vector (m x 1) or matrix (m x p) 
- norm2_r
- scalar or vector (1 x p) when b is a m x p matrix 
Description
This function computes the residual of a linear system r = Ax - b (together
            with its 2-norm) with the additional precision provided on "Intel like"
            FPU (80 bits in place of 64) if the compiler translate "long double" to
            use it. Else one must get the same than using A*x - b at the scilab level.
            In both cases using [r, nr] = res_with_prec(A,x,b) is
            faster than  r = A*x - b (and faster than r = A*x - b; nr = norm(r)).
When p > 1, norm2_r(i) is the 2-norm of the vector r(:,i).
Examples
[A] = ReadHBSparse(SCI+"/modules/umfpack/demos/bcsstk24.rsa"); C_ptr = taucs_chfact(A); b = rand(size(A,1),1); x0 = taucs_chsolve(C_ptr, b); norm(A*x0 - b) norm(res_with_prec(A, x0, b))
See also
- taucs_chsolve — solves a linear s.p.d. system A*X = B from Cholesky factors of the sparse A
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