#include "../general/sanity.F90"
#include "../general/precision_kinds.F90"
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| subroutine | trans_ev_cpu_MATH_DATATYPE_PRECISION (obj, na, nqc, a_mat, lda, tau, q_mat, ldq, nblk, matrixcols, mpi_comm_rows, mpi_comm_cols, success) |
| | Transforms the eigenvectors of a tridiagonal matrix back to the eigenvectors of the original matrix (like Scalapack Routine PDORMTR)
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◆ trans_ev_cpu_MATH_DATATYPE_PRECISION()
| subroutine trans_ev_cpu_MATH_DATATYPE_PRECISION |
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class(elpa_abstract_impl_t), intent(inout) | obj, |
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integer(kind=ik), intent(in) | na, |
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integer(kind=ik), intent(in) | nqc, |
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dimension(lda,matrixcols), intent(inout) | a_mat, |
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integer(kind=ik), intent(in) | lda, |
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dimension(na), intent(in) | tau, |
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dimension(ldq,matrixcols), intent(inout) | q_mat, |
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integer(kind=ik), intent(in) | ldq, |
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integer(kind=ik), intent(in) | nblk, |
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integer(kind=ik), intent(in) | matrixcols, |
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integer(kind=ik), intent(in) | mpi_comm_rows, |
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integer(kind=ik), intent(in) | mpi_comm_cols, |
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logical | success ) |
Transforms the eigenvectors of a tridiagonal matrix back to the eigenvectors of the original matrix (like Scalapack Routine PDORMTR)
- Parameters
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| na | Order of matrix a_mat, number of rows of matrix q_mat |
| nqc | Number of columns of matrix q_mat |
| a_mat(lda,matrixCols) | Matrix containing the Householder vectors (i.e. matrix a after tridiag_real) Distribution is like in Scalapack. |
| lda | Leading dimension of a_mat |
| tau(na) | Factors of the Householder vectors |
| q_mat | On input: Eigenvectors of tridiagonal matrix On output: Transformed eigenvectors Distribution is like in Scalapack. |
| ldq | Leading dimension of q_mat |
| nblk | blocksize of cyclic distribution, must be the same in both directions! |
| matrixCols | local columns of matrix a_mat and q_mat |
| mpi_comm_rows | MPI-Communicator for rows |
| mpi_comm_cols | MPI-Communicator for columns |
| useGPU | If true, GPU version of the subroutine will be used |
