/**
* C++ version of gsl_linalg_balance_matrix().
* @param A A matrix
* @param D A vector
* @return Error code on failure
*/
inline int balance_matrix( matrix& A, vector& D ){
return gsl_linalg_balance_matrix( A.get(), D.get() ); }
int
gsl_eigen_nonsymm (gsl_matrix * A, gsl_vector_complex * eval,
gsl_eigen_nonsymm_workspace * w)
{
const size_t N = A->size1;
/* check matrix and vector sizes */
if (N != A->size2)
{
GSL_ERROR ("matrix must be square to compute eigenvalues", GSL_ENOTSQR);
}
else if (eval->size != N)
{
GSL_ERROR ("eigenvalue vector must match matrix size", GSL_EBADLEN);
}
else
{
int s;
if (w->do_balance)
{
/* balance the matrix */
gsl_linalg_balance_matrix(A, w->diag);
}
/* compute the Hessenberg reduction of A */
gsl_linalg_hessenberg_decomp(A, w->tau);
if (w->Z)
{
/*
* initialize the matrix Z to U, which is the matrix used
* to construct the Hessenberg reduction.
*/
/* compute U and store it in Z */
gsl_linalg_hessenberg_unpack(A, w->tau, w->Z);
/* find the eigenvalues and Schur vectors */
s = gsl_eigen_francis_Z(A, eval, w->Z, w->francis_workspace_p);
if (w->do_balance)
{
/*
* The Schur vectors in Z are the vectors for the balanced
* matrix. We now must undo the balancing to get the
* vectors for the original matrix A.
*/
gsl_linalg_balance_accum(w->Z, w->diag);
}
}
else
{
/* find the eigenvalues only */
s = gsl_eigen_francis(A, eval, w->francis_workspace_p);
}
w->n_evals = w->francis_workspace_p->n_evals;
return s;
}
} /* gsl_eigen_nonsymm() */
