/* To support gsl-1.9 interface: DEPRECATED */ int gsl_linalg_hessenberg(gsl_matrix *A, gsl_vector *tau) { return gsl_linalg_hessenberg_decomp(A, tau); }
/** * C++ version of gsl_linalg_hessenberg_decomp(). * @param A A matrix * @param tau A vector * @return Error code on failure */ inline int hessenberg_decomp( matrix& A, vector& tau ){ return gsl_linalg_hessenberg_decomp( A.get(), tau.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() */