/* eigen/genherm.c * * Copyright (C) 2007 Patrick Alken * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3 of the License, or (at * your option) any later version. * * This program is distributed in the hope that it will be useful, but * WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU * General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include #include #include #include #include #include #include #include #include #include /* * This module computes the eigenvalues of a complex generalized * hermitian-definite eigensystem A x = \lambda B x, where A and * B are hermitian, and B is positive-definite. */ /* gsl_eigen_genherm_alloc() Allocate a workspace for solving the generalized hermitian-definite eigenvalue problem. The size of this workspace is O(3n). Inputs: n - size of matrices Return: pointer to workspace */ gsl_eigen_genherm_workspace * gsl_eigen_genherm_alloc(const size_t n) { gsl_eigen_genherm_workspace *w; if (n == 0) { GSL_ERROR_NULL ("matrix dimension must be positive integer", GSL_EINVAL); } w = (gsl_eigen_genherm_workspace *) calloc (1, sizeof (gsl_eigen_genherm_workspace)); if (w == 0) { GSL_ERROR_NULL ("failed to allocate space for workspace", GSL_ENOMEM); } w->size = n; w->herm_workspace_p = gsl_eigen_herm_alloc(n); if (!w->herm_workspace_p) { gsl_eigen_genherm_free(w); GSL_ERROR_NULL("failed to allocate space for herm workspace", GSL_ENOMEM); } return (w); } /* gsl_eigen_genherm_alloc() */ /* gsl_eigen_genherm_free() Free workspace w */ void gsl_eigen_genherm_free (gsl_eigen_genherm_workspace * w) { RETURN_IF_NULL (w); if (w->herm_workspace_p) gsl_eigen_herm_free(w->herm_workspace_p); free(w); } /* gsl_eigen_genherm_free() */ /* gsl_eigen_genherm() Solve the generalized hermitian-definite eigenvalue problem A x = \lambda B x for the eigenvalues \lambda. Inputs: A - complex hermitian matrix B - complex hermitian and positive definite matrix eval - where to store eigenvalues w - workspace Return: success or error */ int gsl_eigen_genherm (gsl_matrix_complex * A, gsl_matrix_complex * B, gsl_vector * eval, gsl_eigen_genherm_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 ((N != B->size1) || (N != B->size2)) { GSL_ERROR ("B matrix dimensions must match A", GSL_EBADLEN); } else if (eval->size != N) { GSL_ERROR ("eigenvalue vector must match matrix size", GSL_EBADLEN); } else if (w->size != N) { GSL_ERROR ("matrix size does not match workspace", GSL_EBADLEN); } else { int s; /* compute Cholesky factorization of B */ s = gsl_linalg_complex_cholesky_decomp(B); if (s != GSL_SUCCESS) return s; /* B is not positive definite */ /* transform to standard hermitian eigenvalue problem */ gsl_eigen_genherm_standardize(A, B); s = gsl_eigen_herm(A, eval, w->herm_workspace_p); return s; } } /* gsl_eigen_genherm() */ /* gsl_eigen_genherm_standardize() Reduce the generalized hermitian-definite eigenproblem to the standard hermitian eigenproblem by computing C = L^{-1} A L^{-H} where L L^H is the Cholesky decomposition of B Inputs: A - (input/output) complex hermitian matrix B - complex hermitian, positive definite matrix in Cholesky form Return: success Notes: A is overwritten by L^{-1} A L^{-H} */ int gsl_eigen_genherm_standardize(gsl_matrix_complex *A, const gsl_matrix_complex *B) { const size_t N = A->size1; size_t i; double a, b; gsl_complex y, z; GSL_SET_IMAG(&z, 0.0); for (i = 0; i < N; ++i) { /* update lower triangle of A(i:n, i:n) */ y = gsl_matrix_complex_get(A, i, i); a = GSL_REAL(y); y = gsl_matrix_complex_get(B, i, i); b = GSL_REAL(y); a /= b * b; GSL_SET_REAL(&z, a); gsl_matrix_complex_set(A, i, i, z); if (i < N - 1) { gsl_vector_complex_view ai = gsl_matrix_complex_subcolumn(A, i, i + 1, N - i - 1); gsl_matrix_complex_view ma = gsl_matrix_complex_submatrix(A, i + 1, i + 1, N - i - 1, N - i - 1); gsl_vector_complex_const_view bi = gsl_matrix_complex_const_subcolumn(B, i, i + 1, N - i - 1); gsl_matrix_complex_const_view mb = gsl_matrix_complex_const_submatrix(B, i + 1, i + 1, N - i - 1, N - i - 1); gsl_blas_zdscal(1.0 / b, &ai.vector); GSL_SET_REAL(&z, -0.5 * a); gsl_blas_zaxpy(z, &bi.vector, &ai.vector); gsl_blas_zher2(CblasLower, GSL_COMPLEX_NEGONE, &ai.vector, &bi.vector, &ma.matrix); gsl_blas_zaxpy(z, &bi.vector, &ai.vector); gsl_blas_ztrsv(CblasLower, CblasNoTrans, CblasNonUnit, &mb.matrix, &ai.vector); } } return GSL_SUCCESS; } /* gsl_eigen_genherm_standardize() */