From: Tao Jin Date: Sun, 18 Jun 2023 00:58:17 +0000 (-0400) Subject: Remove error output in lapack_full_matrix.cc X-Git-Tag: v9.5.0-rc1~83^2 X-Git-Url: https://gitweb.dealii.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=284bb5ba3663d188cd24109e5a22275033249c0c;p=dealii.git Remove error output in lapack_full_matrix.cc Based on the error values provided by LAPACK, provide detailed error messages via the AssertThrow mechanism. --- diff --git a/source/lac/lapack_full_matrix.cc b/source/lac/lapack_full_matrix.cc index 24aa99b831..692caff4bb 100644 --- a/source/lac/lapack_full_matrix.cc +++ b/source/lac/lapack_full_matrix.cc @@ -1974,9 +1974,22 @@ LAPACKFullMatrix::compute_eigenvalues(const bool right, const bool left) info); Assert(info >= 0, ExcInternalError()); - // TODO:[GK] What if the QR method fails? - if (info != 0) - std::cerr << "LAPACK error in geev" << std::endl; + if (info < 0) + { + AssertThrow(info == 0, + ExcMessage("Lapack error in geev: the " + + std::to_string(-info) + + "-th" + " parameter had an illegal value.")); + } + else + { + AssertThrow( + info == 0, + ExcMessage( + "Lapack error in geev: the QR algorithm failed to compute " + "all the eigenvalues, and no eigenvectors have been computed.")); + } state = LAPACKSupport::State(LAPACKSupport::eigenvalues | unusable); } @@ -2178,8 +2191,28 @@ LAPACKFullMatrix::compute_eigenvalues_symmetric( // Negative return value implies a wrong argument. This should be internal. Assert(info >= 0, ExcInternalError()); - if (info != 0) - std::cerr << "LAPACK error in syevx" << std::endl; + if (info < 0) + { + AssertThrow(info == 0, + ExcMessage("Lapack error in syevx: the " + + std::to_string(-info) + + "-th" + " parameter had an illegal value.")); + } + else if ((info > 0) && (info <= nn)) + { + AssertThrow(info == 0, + ExcMessage( + "Lapack error in syevx: " + std::to_string(info) + + " eigenvectors failed to converge." + " (You may need to scale the abs_accuracy according" + " to your matrix norm.)")); + } + else + { + AssertThrow(info == 0, + ExcMessage("Lapack error in syevx: unknown error.")); + } eigenvalues.reinit(n_eigenpairs); eigenvectors.reinit(nn, n_eigenpairs, true); @@ -2303,8 +2336,40 @@ LAPACKFullMatrix::compute_generalized_eigenvalues_symmetric( // Negative return value implies a wrong argument. This should be internal. Assert(info >= 0, ExcInternalError()); - if (info != 0) - std::cerr << "LAPACK error in sygvx" << std::endl; + if (info < 0) + { + AssertThrow(info == 0, + ExcMessage("Lapack error in sygvx: the " + + std::to_string(-info) + + "-th" + " parameter had an illegal value.")); + } + else if ((info > 0) && (info <= nn)) + { + AssertThrow( + info == 0, + ExcMessage( + "Lapack error in sygvx: ssyevx/dsyevx failed to converge, and " + + std::to_string(info) + + " eigenvectors failed to converge." + " (You may need to scale the abs_accuracy" + " according to the norms of matrices A and B.)")); + } + else if ((info > nn) && (info <= 2 * nn)) + { + AssertThrow(info == 0, + ExcMessage( + "Lapack error in sygvx: the leading minor of order " + + std::to_string(info - nn) + + " of matrix B is not positive-definite." + " The factorization of B could not be completed and" + " no eigenvalues or eigenvectors were computed.")); + } + else + { + AssertThrow(info == 0, + ExcMessage("Lapack error in sygvx: unknown error.")); + } eigenvalues.reinit(n_eigenpairs); eigenvectors.resize(n_eigenpairs); @@ -2401,8 +2466,41 @@ LAPACKFullMatrix::compute_generalized_eigenvalues_symmetric( // Negative return value implies a wrong argument. This should be internal. Assert(info >= 0, ExcInternalError()); - if (info != 0) - std::cerr << "LAPACK error in sygv" << std::endl; + if (info < 0) + { + AssertThrow(info == 0, + ExcMessage("Lapack error in sygv: the " + + std::to_string(-info) + + "-th" + " parameter had an illegal value.")); + } + else if ((info > 0) && (info <= nn)) + { + AssertThrow( + info == 0, + ExcMessage( + "Lapack error in sygv: ssyev/dsyev failed to converge, and " + + std::to_string(info) + + " off-diagonal elements of an intermediate " + " tridiagonal did not converge to zero." + " (You may need to scale the abs_accuracy" + " according to the norms of matrices A and B.)")); + } + else if ((info > nn) && (info <= 2 * nn)) + { + AssertThrow(info == 0, + ExcMessage( + "Lapack error in sygv: the leading minor of order " + + std::to_string(info - nn) + + " of matrix B is not positive-definite." + " The factorization of B could not be completed and" + " no eigenvalues or eigenvectors were computed.")); + } + else + { + AssertThrow(info == 0, + ExcMessage("Lapack error in sygv: unknown error.")); + } for (size_type i = 0; i < eigenvectors.size(); ++i) {