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Please note that the recommended version of Scilab is 6.0.0. This page might be outdated.
See the recommended documentation of this function

Справка Scilab >> UMFPACK Interface (sparse) > res_with_prec


computes the residual r = Ax-b with precision

Calling Sequence

[r,norm2_r] = res_with_prec(A, x, b)



real or complex sparse matrix (m x n)


column vector (n x 1) or matrix (n x p)


column vector (m x 1) or matrix (m x p)


column vector (m x 1) or matrix (m x p)


scalar or vector (1 x p) when b is a m x p matrix


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).


[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)
norm(res_with_prec(A, x0, b))

See Also

  • rafiter — Iterative refinement for a s.p.d. linear system. This function is obsolete.
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Last updated:
Thu Oct 02 14:01:07 CEST 2014