Developer Reference for Intel® oneAPI Math Kernel Library for C
mkl_sparse_?_symgs_mv
Computes a single step of the symmetric Gauss-Seidel preconditioner followed by a matrix-vector multiplication.
Syntax
sparse_status_t mkl_sparse_s_symgs_mv ( const sparse_operation_t op,
const sparse_matrix_t A,
const struct matrix_descr descr,
const float alpha,
const float *b,
float *x,
float *y );sparse_status_t mkl_sparse_d_symgs_mv ( const sparse_operation_t op,
const sparse_matrix_t A,
const struct matrix_descr descr,
const double alpha,
const double *b,
double *x,
double *y );sparse_status_t mkl_sparse_c_symgs_mv ( const sparse_operation_t op,
const sparse_matrix_t A,
const struct matrix_descr descr,
const MKL_Complex8 alpha,
const MKL_Complex8 *b,
MKL_Complex8 *x,
MKL_Complex8 *y );sparse_status_t mkl_sparse_z_symgs_mv ( const sparse_operation_t op,
const sparse_matrix_t A,
const struct matrix_descr descr,
const MKL_Complex16 alpha,
const MKL_Complex16 *b,
MKL_Complex16 *x,
MKL_Complex16 *y );Include Files
mkl_spblas.h
Description
The mkl_sparse_?_symgs_mv routine performs this series of triangular solve and matrix-vector multiplication operations:
\[x^{0} &\leftarrow \alpha \cdot x \\ (L + D)\cdot x^{1} &= b - U\cdot x^{0} \\ (U + D)\cdot x &= b - L\cdot x^{1} \\ y &\leftarrow A\cdot x\]
where \(A = L + D + U\) is a decomposition of \(A\) into strictly lower, diagonal and upper triangular parts.
Input Parameters
op
Specifies operation on input matrix \(A\).
operation |
Description |
|---|---|
SPARSE_OPERATION_NON_TRANSPOSE |
\(op(A) = A\) |
operation |
Description |
|---|---|
SPARSE_OPERATION_TRANSPOSE |
\(op(A) = A^{T}\) |
SPARSE_OPERATION_CONJUGATE_TRANSPOSE |
\(op(A) = A^{H}\) |
A
Handle which contains the sparse matrix \(A\) .
alpha
Specifies the scalar, \(\alpha\) .
descr
Descriptor specifying sparse matrix properties.
sparse_matrix_type_t type |
Specifies the type of a sparse matrix:
|
sparse_fill_mode_t mode |
Specifies the triangular matrix part for symmetric, Hermitian, triangular, and block-triangular matrices:
|
sparse_diag_type_t diag |
Specifies diagonal type for non-general matrices:
|
x
Array of size at least \(m\), where \(m\) is the number of rows of matrix \(A\) . On entry, the array x must contain the vector x.
b
Array of size at least \(m\), where \(m\) is the number of rows of matrix \(A\) . On entry, the array b must contain the vector b .
Output Parameters
- x
-
Overwritten by the computed vector \(x\).
y
Array of size at least \(m\), where \(m\) is the number of rows of matrix \(A\) . Overwritten by the computed vector \(y\).
Return Values
The function returns a value indicating whether the operation was successful or not, and why.
SPARSE_STATUS_SUCCESS |
The operation was successful. |
SPARSE_STATUS_NOT_INITIALIZED |
The routine encountered an empty handle or matrix array. |
SPARSE_STATUS_ALLOC_FAILED |
Internal memory allocation failed. |
SPARSE_STATUS_INVALID_VALUE |
The input parameters contain an invalid value. |
SPARSE_STATUS_EXECUTION_FAILED |
Execution failed. |
SPARSE_STATUS_INTERNAL_ERROR |
An error in algorithm implementation occurred. |
SPARSE_STATUS_NOT_SUPPORTED |
The requested operation is not supported. |