本文整理汇总了C++中teuchos::LAPACK::GETRI方法的典型用法代码示例。如果您正苦于以下问题：C++ LAPACK::GETRI方法的具体用法？C++ LAPACK::GETRI怎么用？C++ LAPACK::GETRI使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类teuchos::LAPACK的用法示例。
在下文中一共展示了LAPACK::GETRI方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: BcRow
void Constraint<Scalar, LocalOrdinal, GlobalOrdinal, Node, LocalMatOps>::Setup(const MultiVector& B, const MultiVector& Bc, RCP<const CrsGraph> Ppattern) {
Ppattern_ = Ppattern;
const RCP<const Map> uniqueMap = Ppattern_->getDomainMap();
const RCP<const Map> nonUniqueMap = Ppattern_->getColMap();
RCP<const Import> importer = ImportFactory::Build(uniqueMap, nonUniqueMap);
const size_t NSDim = Bc.getNumVectors();
X_ = MultiVectorFactory::Build(nonUniqueMap, NSDim);
X_->doImport(Bc, *importer, Xpetra::INSERT);
size_t numRows = Ppattern_->getNodeNumRows();
XXtInv_.resize(numRows);
Teuchos::SerialDenseVector<LO,SC> BcRow(NSDim, false);
for (size_t i = 0; i < numRows; i++) {
Teuchos::ArrayView<const LO> indices;
Ppattern_->getLocalRowView(i, indices);
size_t nnz = indices.size();
Teuchos::SerialDenseMatrix<LO,SC> locX(NSDim, nnz, false);
for (size_t j = 0; j < nnz; j++) {
for (size_t k = 0; k < NSDim; k++)
BcRow[k] = X_->getData(k)[indices[j]];
Teuchos::setCol(BcRow, (LO)j, locX);
}
XXtInv_[i] = Teuchos::SerialDenseMatrix<LO,SC>(NSDim, NSDim, false);
Teuchos::BLAS<LO,SC> blas;
blas.GEMM(Teuchos::NO_TRANS, Teuchos::CONJ_TRANS, NSDim, NSDim, nnz,
Teuchos::ScalarTraits<SC>::one(), locX.values(), locX.stride(),
locX.values(), locX.stride(), Teuchos::ScalarTraits<SC>::zero(),
XXtInv_[i].values(), XXtInv_[i].stride());
Teuchos::LAPACK<LO,SC> lapack;
LO info, lwork = 3*NSDim;
ArrayRCP<LO> IPIV(NSDim);
ArrayRCP<SC> WORK(lwork);
lapack.GETRF(NSDim, NSDim, XXtInv_[i].values(), XXtInv_[i].stride(), IPIV.get(), &info);
lapack.GETRI(NSDim, XXtInv_[i].values(), XXtInv_[i].stride(), IPIV.get(), WORK.get(), lwork, &info);
}
}示例2: tagView
Basis_HGRAD_LINE_Cn_FEM<SpT,OT,PT>::
Basis_HGRAD_LINE_Cn_FEM( const ordinal_type order,
const EPointType pointType ) {
this->basisCardinality_ = order+1;
this->basisDegree_ = order;
this->basisCellTopology_ = shards::CellTopology(shards::getCellTopologyData<shards::Line<2> >() );
this->basisType_ = BASIS_FEM_FIAT;
this->basisCoordinates_ = COORDINATES_CARTESIAN;
const ordinal_type card = this->basisCardinality_;
// points are computed in the host and will be copied
Kokkos::DynRankView<typename scalarViewType::value_type,typename SpT::array_layout,Kokkos::HostSpace>
dofCoords("Hgrad::Line::Cn::dofCoords", card, 1);
switch (pointType) {
case POINTTYPE_EQUISPACED:
case POINTTYPE_WARPBLEND: {
// lattice ordering
{
const ordinal_type offset = 0;
PointTools::getLattice( dofCoords,
this->basisCellTopology_,
order, offset,
pointType );
}
// topological order
// {
// // two vertices
// dofCoords(0,0) = -1.0;
// dofCoords(1,0) = 1.0;
// // internal points
// typedef Kokkos::pair<ordinal_type,ordinal_type> range_type;
// auto pts = Kokkos::subview(dofCoords, range_type(2, card), Kokkos::ALL());
// const auto offset = 1;
// PointTools::getLattice( pts,
// this->basisCellTopology_,
// order, offset,
// pointType );
// }
break;
}
case POINTTYPE_GAUSS: {
// internal points only
PointTools::getGaussPoints( dofCoords,
order );
break;
}
default: {
INTREPID2_TEST_FOR_EXCEPTION( !isValidPointType(pointType),
std::invalid_argument ,
">>> ERROR: (Intrepid2::Basis_HGRAD_LINE_Cn_FEM) invalid pointType." );
}
}
this->dofCoords_ = Kokkos::create_mirror_view(typename SpT::memory_space(), dofCoords);
Kokkos::deep_copy(this->dofCoords_, dofCoords);
// form Vandermonde matrix; actually, this is the transpose of the VDM,
// this matrix is used in LAPACK so it should be column major and left layout
const ordinal_type lwork = card*card;
Kokkos::DynRankView<typename scalarViewType::value_type,Kokkos::LayoutLeft,Kokkos::HostSpace>
vmat("Hgrad::Line::Cn::vmat", card, card),
work("Hgrad::Line::Cn::work", lwork),
ipiv("Hgrad::Line::Cn::ipiv", card);
const double alpha = 0.0, beta = 0.0;
Impl::Basis_HGRAD_LINE_Cn_FEM_JACOBI::
getValues<Kokkos::HostSpace::execution_space,Parameters::MaxNumPtsPerBasisEval>
(vmat, dofCoords, order, alpha, beta, OPERATOR_VALUE);
ordinal_type info = 0;
Teuchos::LAPACK<ordinal_type,typename scalarViewType::value_type> lapack;
lapack.GETRF(card, card,
vmat.data(), vmat.stride_1(),
(ordinal_type*)ipiv.data(),
&info);
INTREPID2_TEST_FOR_EXCEPTION( info != 0,
std::runtime_error ,
">>> ERROR: (Intrepid2::Basis_HGRAD_LINE_Cn_FEM) lapack.GETRF returns nonzero info." );
lapack.GETRI(card,
vmat.data(), vmat.stride_1(),
(ordinal_type*)ipiv.data(),
work.data(), lwork,
&info);
INTREPID2_TEST_FOR_EXCEPTION( info != 0,
std::runtime_error ,
">>> ERROR: (Intrepid2::Basis_HGRAD_LINE_Cn_FEM) lapack.GETRI returns nonzero info." );
// create host mirror
Kokkos::DynRankView<typename scalarViewType::value_type,typename SpT::array_layout,Kokkos::HostSpace>
vinv("Hgrad::Line::Cn::vinv", card, card);
//.........这里部分代码省略.........
示例3: Xval
void Constraint<Scalar, LocalOrdinal, GlobalOrdinal, Node>::Setup(const MultiVector& B, const MultiVector& Bc, RCP<const CrsGraph> Ppattern) {
const size_t NSDim = Bc.getNumVectors();
Ppattern_ = Ppattern;
size_t numRows = Ppattern_->getNodeNumRows();
XXtInv_.resize(numRows);
RCP<const Import> importer = Ppattern_->getImporter();
X_ = MultiVectorFactory::Build(Ppattern_->getColMap(), NSDim);
if (!importer.is_null())
X_->doImport(Bc, *importer, Xpetra::INSERT);
else
*X_ = Bc;
std::vector<const SC*> Xval(NSDim);
for (size_t j = 0; j < NSDim; j++)
Xval[j] = X_->getData(j).get();
SC zero = Teuchos::ScalarTraits<SC>::zero();
SC one = Teuchos::ScalarTraits<SC>::one();
Teuchos::BLAS <LO,SC> blas;
Teuchos::LAPACK<LO,SC> lapack;
LO lwork = 3*NSDim;
ArrayRCP<LO> IPIV(NSDim);
ArrayRCP<SC> WORK(lwork);
for (size_t i = 0; i < numRows; i++) {
Teuchos::ArrayView<const LO> indices;
Ppattern_->getLocalRowView(i, indices);
size_t nnz = indices.size();
XXtInv_[i] = Teuchos::SerialDenseMatrix<LO,SC>(NSDim, NSDim, false/*zeroOut*/);
Teuchos::SerialDenseMatrix<LO,SC>& XXtInv = XXtInv_[i];
if (NSDim == 1) {
SC d = zero;
for (size_t j = 0; j < nnz; j++)
d += Xval[0][indices[j]] * Xval[0][indices[j]];
XXtInv(0,0) = one/d;
} else {
Teuchos::SerialDenseMatrix<LO,SC> locX(NSDim, nnz, false/*zeroOut*/);
for (size_t j = 0; j < nnz; j++)
for (size_t k = 0; k < NSDim; k++)
locX(k,j) = Xval[k][indices[j]];
// XXtInv_ = (locX*locX^T)^{-1}
blas.GEMM(Teuchos::NO_TRANS, Teuchos::CONJ_TRANS, NSDim, NSDim, nnz,
one, locX.values(), locX.stride(),
locX.values(), locX.stride(),
zero, XXtInv.values(), XXtInv.stride());
LO info;
// Compute LU factorization using partial pivoting with row exchanges
lapack.GETRF(NSDim, NSDim, XXtInv.values(), XXtInv.stride(), IPIV.get(), &info);
// Use the computed factorization to compute the inverse
lapack.GETRI(NSDim, XXtInv.values(), XXtInv.stride(), IPIV.get(), WORK.get(), lwork, &info);
}
}
}示例4: block
//---------------------------------------------------------------------------//
// Tests.
//---------------------------------------------------------------------------//
TEUCHOS_UNIT_TEST( LAPACK, block_inversion )
{
// Build a 4x4 block.
int m = 4;
int n = 4;
Teuchos::SerialDenseMatrix<int,double> block( m, n );
block(0,0) = 3.2;
block(0,1) = -1.43;
block(0,2) = 2.98;
block(0,3) = 0.32;
block(1,0) = -4.12;
block(1,1) = -7.53;
block(1,2) = 1.44;
block(1,3) = -3.72;
block(2,0) = 4.24;
block(2,1) = -6.42;
block(2,2) = 1.82;
block(2,3) = 2.67;
block(3,0) = -0.23;
block(3,1) = 5.8;
block(3,2) = 1.13;
block(3,3) = -3.73;
// Make a LAPACK object.
Teuchos::LAPACK<int,double> lapack;
// Compute the LU-factorization of the block.
Teuchos::ArrayRCP<int> ipiv( block.numRows() );
int info = 0;
int lda = m;
lapack.GETRF( m, n, block.values(), lda, ipiv.getRawPtr(), &info );
TEST_EQUALITY( info, 0 );
// Compute the inverse of the block from the LU-factorization.
Teuchos::ArrayRCP<double> work( m );
lapack.GETRI( n, block.values(), lda, ipiv.getRawPtr(),
work.getRawPtr(), work.size(), &info );
TEST_EQUALITY( info, 0 );
TEST_EQUALITY( work[0], m );
// Check the inversion against matlab.
TEST_FLOATING_EQUALITY( block(0,0), -0.461356423424245, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(0,1), -0.060920073472551, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(0,2), 0.547244760641934, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(0,3), 0.412904055961420, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(1,0), 0.154767451798665, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(1,1), -0.056225122550555, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(1,2), -0.174451348828054, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(1,3), -0.055523340725809, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(2,0), 0.848746201780808, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(2,1), 0.045927762119214, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(2,2), -0.618485718805259, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(2,3), -0.415712965073367, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(3,0), 0.526232280383953, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(3,1), -0.069757566407458, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(3,2), -0.492378815120724, 1.0e-14 );
TEST_FLOATING_EQUALITY( block(3,3), -0.505833501236923, 1.0e-14 );
}