void PetscMatrix<T>::add_block_matrix(const DenseMatrix<T>& dm,
const std::vector<numeric_index_type>& brows,
const std::vector<numeric_index_type>& bcols)
{
libmesh_assert (this->initialized());
const numeric_index_type n_rows = dm.m();
const numeric_index_type n_cols = dm.n();
const numeric_index_type n_brows = brows.size();
const numeric_index_type n_bcols = bcols.size();
const numeric_index_type blocksize = n_rows / n_brows;
libmesh_assert_equal_to (n_cols / n_bcols, blocksize);
libmesh_assert_equal_to (blocksize*n_brows, n_rows);
libmesh_assert_equal_to (blocksize*n_bcols, n_cols);
PetscErrorCode ierr=0;
#ifndef NDEBUG
PetscInt petsc_blocksize;
ierr = MatGetBlockSize(_mat, &petsc_blocksize);
LIBMESH_CHKERRABORT(ierr);
libmesh_assert_equal_to (blocksize, static_cast<numeric_index_type>(petsc_blocksize));
#endif
// These casts are required for PETSc <= 2.1.5
ierr = MatSetValuesBlocked(_mat,
n_brows, (PetscInt*) &brows[0],
n_bcols, (PetscInt*) &bcols[0],
(PetscScalar*) &dm.get_values()[0],
ADD_VALUES);
LIBMESH_CHKERRABORT(ierr);
}
void EpetraMatrix<T>::add_matrix(const DenseMatrix<T>& dm,
const std::vector<unsigned int>& rows,
const std::vector<unsigned int>& cols)
{
libmesh_assert (this->initialized());
const unsigned int m = dm.m();
const unsigned int n = dm.n();
libmesh_assert (rows.size() == m);
libmesh_assert (cols.size() == n);
_mat->SumIntoGlobalValues(m, (int *)&rows[0], n, (int *)&cols[0], &dm.get_values()[0]);
}
void EpetraMatrix<T>::add_matrix(const DenseMatrix<T>& dm,
const std::vector<numeric_index_type>& rows,
const std::vector<numeric_index_type>& cols)
{
libmesh_assert (this->initialized());
const numeric_index_type m = dm.m();
const numeric_index_type n = dm.n();
libmesh_assert_equal_to (rows.size(), m);
libmesh_assert_equal_to (cols.size(), n);
_mat->SumIntoGlobalValues(m, (int *)&rows[0], n, (int *)&cols[0], &dm.get_values()[0]);
}
void PetscMatrix<T>::add_matrix(const DenseMatrix<T>& dm,
const std::vector<numeric_index_type>& rows,
const std::vector<numeric_index_type>& cols)
{
libmesh_assert (this->initialized());
const numeric_index_type n_rows = dm.m();
const numeric_index_type n_cols = dm.n();
libmesh_assert_equal_to (rows.size(), n_rows);
libmesh_assert_equal_to (cols.size(), n_cols);
PetscErrorCode ierr=0;
// These casts are required for PETSc <= 2.1.5
ierr = MatSetValues(_mat,
n_rows, (PetscInt*) &rows[0],
n_cols, (PetscInt*) &cols[0],
(PetscScalar*) &dm.get_values()[0],
ADD_VALUES);
LIBMESH_CHKERRABORT(ierr);
}
void DenseMatrix<T>::_svd_lapack (DenseVector<Real> & sigma,
DenseMatrix<Number> & U,
DenseMatrix<Number> & VT)
{
// The calling sequence for dgetrf is:
// DGESVD( JOBU, JOBVT, M, N, A, LDA, S, U, LDU, VT, LDVT, WORK, LWORK, INFO )
// JOBU (input) CHARACTER*1
// Specifies options for computing all or part of the matrix U:
// = 'A': all M columns of U are returned in array U:
// = 'S': the first min(m,n) columns of U (the left singular
// vectors) are returned in the array U;
// = 'O': the first min(m,n) columns of U (the left singular
// vectors) are overwritten on the array A;
// = 'N': no columns of U (no left singular vectors) are
// computed.
char JOBU = 'S';
// JOBVT (input) CHARACTER*1
// Specifies options for computing all or part of the matrix
// V**T:
// = 'A': all N rows of V**T are returned in the array VT;
// = 'S': the first min(m,n) rows of V**T (the right singular
// vectors) are returned in the array VT;
// = 'O': the first min(m,n) rows of V**T (the right singular
// vectors) are overwritten on the array A;
// = 'N': no rows of V**T (no right singular vectors) are
// computed.
char JOBVT = 'S';
// Note: Lapack is going to compute the singular values of A^T. If
// A=U * S * V^T, then A^T = V * S * U^T, which means that the
// values returned in the "U_val" array actually correspond to the
// entries of the V matrix, and the values returned in the VT_val
// array actually correspond to the entries of U^T. Therefore, we
// pass VT in the place of U and U in the place of VT below!
std::vector<Real> sigma_val;
int M = this->n();
int N = this->m();
int min_MN = (M < N) ? M : N;
// Size user-provided storage appropriately. Inside svd_helper:
// U_val is sized to (M x min_MN)
// VT_val is sized to (min_MN x N)
// So, we set up U to have the shape of "VT_val^T", and VT to
// have the shape of "U_val^T".
//
// Finally, since the results are stored in column-major order by
// Lapack, but we actually want the transpose of what Lapack
// returns, this means (conveniently) that we don't even have to
// copy anything after the call to _svd_helper, it should already be
// in the correct order!
U.resize(N, min_MN);
VT.resize(min_MN, M);
_svd_helper(JOBU, JOBVT, sigma_val, VT.get_values(), U.get_values());
// Copy the singular values into sigma.
sigma.resize(cast_int<unsigned int>(sigma_val.size()));
for (unsigned int i=0; i<sigma.size(); i++)
sigma(i) = sigma_val[i];
}
