# rocsolver_cheevd_strided_batched Interface Reference#

HIPFORT API Reference: hipfort_rocsolver::rocsolver_cheevd_strided_batched Interface Reference
hipfort_rocsolver::rocsolver_cheevd_strided_batched Interface Reference

HEEVD_STRIDED_BATCHED computes the eigenvalues and optionally the eigenvectors of a batch of Hermitian matrices A_j. More...

## Public Member Functions

integer(kind(rocblas_status_success)) function rocsolver_cheevd_strided_batched_ (handle, evect, uplo, n, A, lda, strideA, D, strideD, E, strideE, myInfo, batch_count)

integer(kind(rocblas_status_success)) function rocsolver_cheevd_strided_batched_full_rank (handle, evect, uplo, n, A, lda, strideA, D, strideD, E, strideE, myInfo, batch_count)

integer(kind(rocblas_status_success)) function rocsolver_cheevd_strided_batched_rank_0 (handle, evect, uplo, n, A, lda, strideA, D, strideD, E, strideE, myInfo, batch_count)

integer(kind(rocblas_status_success)) function rocsolver_cheevd_strided_batched_rank_1 (handle, evect, uplo, n, A, lda, strideA, D, strideD, E, strideE, myInfo, batch_count)

## Detailed Description

HEEVD_STRIDED_BATCHED computes the eigenvalues and optionally the eigenvectors of a batch of Hermitian matrices A_j.

The eigenvalues are returned in ascending order. The eigenvectors are computed using a divide-and-conquer algorithm, depending on the value of evect. The computed eigenvectors are orthonormal.

Parameters
 [in] handle rocblas_handle. [in] evect rocblas_evect. Specifies whether the eigenvectors are to be computed. If evect is rocblas_evect_original, then the eigenvectors are computed. rocblas_evect_tridiagonal is not supported. [in] uplo rocblas_fill. Specifies whether the upper or lower part of the Hermitian matrices A_j is stored. If uplo indicates lower (or upper), then the upper (or lower) part of A_j is not used. [in] n rocblas_int. n >= 0. Number of rows and columns of matrices A_j. [in,out] A pointer to type. Array on the GPU (the size depends on the value of strideA). On entry, the matrices A_j. On exit, the eigenvectors of A_j if they were computed and the algorithm converged; otherwise the contents of A_j are destroyed. [in] lda rocblas_int. lda >= n. Specifies the leading dimension of matrices A_j. [in] strideA rocblas_stride. Stride from the start of one matrix A_j to the next one A_(j+1). There is no restriction for the value of strideA. Normal use case is strideA >= lda*n. [out] D pointer to real type. Array on the GPU (the size depends on the value of strideD). The eigenvalues of A_j in increasing order. [in] strideD rocblas_stride. Stride from the start of one vector D_j to the next one D_(j+1). There is no restriction for the value of strideD. Normal use case is strideD >= n. [out] E pointer to real type. Array on the GPU (the size depends on the value of strideE). This array is used to work internally with the tridiagonal matrix T_j associated with A_j. On exit, if info[j] > 0, E_j contains the unconverged off-diagonal elements of T_j (or properly speaking, a tridiagonal matrix equivalent to T_j). The diagonal elements of this matrix are in D_j; those that converged correspond to a subset of the eigenvalues of A_j (not necessarily ordered). [in] strideE rocblas_stride. Stride from the start of one vector E_j to the next one E_(j+1). There is no restriction for the value of strideE. Normal use case is strideE >= n. [out] info pointer to rocblas_int. Array of batch_count integers on the GPU. If info[j] = 0, successful exit for matrix A_j. If info[j] = i > 0 and evect is rocblas_evect_none, the algorithm did not converge. i elements of E_j did not converge to zero. If info[j] = i > 0 and evect is rocblas_evect_original, the algorithm failed to compute an eigenvalue in the submatrix from [i/(n+1), i/(n+1)] to [i%(n+1), i%(n+1)]. [in] batch_count rocblas_int. batch_count >= 0. Number of matrices in the batch.

## ◆ rocsolver_cheevd_strided_batched_()

 integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_cheevd_strided_batched::rocsolver_cheevd_strided_batched_ ( type(c_ptr), value handle, integer(kind(rocblas_evect_original)), value evect, integer(kind(rocblas_fill_upper)), value uplo, integer(c_int), value n, type(c_ptr), value A, integer(c_int), value lda, integer(c_int64_t), value strideA, type(c_ptr), value D, integer(c_int64_t), value strideD, type(c_ptr), value E, integer(c_int64_t), value strideE, integer(c_int) myInfo, integer(c_int), value batch_count )

## ◆ rocsolver_cheevd_strided_batched_full_rank()

 integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_cheevd_strided_batched::rocsolver_cheevd_strided_batched_full_rank ( type(c_ptr) handle, integer(kind(rocblas_evect_original)) evect, integer(kind(rocblas_fill_upper)) uplo, integer(c_int) n, complex(c_float_complex), dimension(:,:), target A, integer(c_int) lda, integer(c_int64_t) strideA, real(c_float), dimension(:), target D, integer(c_int64_t) strideD, real(c_float), dimension(:), target E, integer(c_int64_t) strideE, integer(c_int) myInfo, integer(c_int) batch_count )

## ◆ rocsolver_cheevd_strided_batched_rank_0()

 integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_cheevd_strided_batched::rocsolver_cheevd_strided_batched_rank_0 ( type(c_ptr) handle, integer(kind(rocblas_evect_original)) evect, integer(kind(rocblas_fill_upper)) uplo, integer(c_int) n, complex(c_float_complex), target A, integer(c_int) lda, integer(c_int64_t) strideA, real(c_float), target D, integer(c_int64_t) strideD, real(c_float), target E, integer(c_int64_t) strideE, integer(c_int) myInfo, integer(c_int) batch_count )

## ◆ rocsolver_cheevd_strided_batched_rank_1()

 integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_cheevd_strided_batched::rocsolver_cheevd_strided_batched_rank_1 ( type(c_ptr) handle, integer(kind(rocblas_evect_original)) evect, integer(kind(rocblas_fill_upper)) uplo, integer(c_int) n, complex(c_float_complex), dimension(:), target A, integer(c_int) lda, integer(c_int64_t) strideA, real(c_float), dimension(:), target D, integer(c_int64_t) strideD, real(c_float), dimension(:), target E, integer(c_int64_t) strideE, integer(c_int) myInfo, integer(c_int) batch_count )

The documentation for this interface was generated from the following file: