本文整理汇总了C++中teuchos::SerialDenseMatrix类的典型用法代码示例。如果您正苦于以下问题：C++ SerialDenseMatrix类的具体用法？C++ SerialDenseMatrix怎么用？C++ SerialDenseMatrix使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了SerialDenseMatrix类的20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，您的评价将有助于我们的系统推荐出更棒的C++代码示例。
示例1: runtime_error
/// \brief Verify the result of the "thin" QR factorization \f$A = QR\f$.
///
/// This method returns a list of three magnitudes:
/// - \f$\| A - QR \|_F\f$
/// - \f$\|I - Q^* Q\|_F\f$
/// - \f$\|A\|_F\f$
///
/// The notation $\f\| X \|\f$ denotes the Frobenius norm
/// (square root of sum of squares) of a matrix \f$X\f$.
/// Returning the Frobenius norm of \f$A\f$ allows you to scale
/// or not scale the residual \f$\|A - QR\|\f$ as you prefer.
virtual std::vector< magnitude_type >
verify (const multivector_type& A,
const multivector_type& Q,
const Teuchos::SerialDenseMatrix< local_ordinal_type, scalar_type >& R)
{
using Teuchos::ArrayRCP;
local_ordinal_type nrowsLocal_A, ncols_A, LDA;
local_ordinal_type nrowsLocal_Q, ncols_Q, LDQ;
fetchDims (A, nrowsLocal_A, ncols_A, LDA);
fetchDims (Q, nrowsLocal_Q, ncols_Q, LDQ);
if (nrowsLocal_A != nrowsLocal_Q)
throw std::runtime_error ("A and Q must have same number of rows");
else if (ncols_A != ncols_Q)
throw std::runtime_error ("A and Q must have same number of columns");
else if (ncols_A != R.numCols())
throw std::runtime_error ("A and R must have same number of columns");
else if (R.numRows() < R.numCols())
throw std::runtime_error ("R must have no fewer rows than columns");
// Const views suffice for verification
ArrayRCP< const scalar_type > A_ptr = fetchConstView (A);
ArrayRCP< const scalar_type > Q_ptr = fetchConstView (Q);
return global_verify (nrowsLocal_A, ncols_A, A_ptr.get(), LDA,
Q_ptr.get(), LDQ, R.values(), R.stride(),
pScalarMessenger_.get());
}
开发者ID:，项目名称:，代码行数:38，代码来源:
示例2: assembleIRKState
void assembleIRKState(
const int stageIndex,
const Teuchos::SerialDenseMatrix<int,Scalar> &A_in,
const Scalar dt,
const Thyra::VectorBase<Scalar> &x_base,
const Thyra::ProductVectorBase<Scalar> &x_stage_bar,
Teuchos::Ptr<Thyra::VectorBase<Scalar> > x_out_ptr
)
{
typedef ScalarTraits<Scalar> ST;
const int numStages_in = A_in.numRows();
TEUCHOS_ASSERT_IN_RANGE_UPPER_EXCLUSIVE( stageIndex, 0, numStages_in );
TEUCHOS_ASSERT_EQUALITY( A_in.numRows(), numStages_in );
TEUCHOS_ASSERT_EQUALITY( A_in.numCols(), numStages_in );
TEUCHOS_ASSERT_EQUALITY( x_stage_bar.productSpace()->numBlocks(), numStages_in );
Thyra::VectorBase<Scalar>& x_out = *x_out_ptr;
V_V( outArg(x_out), x_base );
for ( int j = 0; j < numStages_in; ++j ) {
Vp_StV( outArg(x_out), dt * A_in(stageIndex,j), *x_stage_bar.getVectorBlock(j) );
}
}
开发者ID:00liujj，项目名称:trilinos，代码行数:25，代码来源:Rythmos_RKButcherTableauHelpers.hpp
示例3: gather
void
Stokhos::SmolyakPseudoSpectralOperator<ordinal_type,value_type,point_compare_type>::
transformPCE2QP_smolyak(
const value_type& alpha,
const Teuchos::SerialDenseMatrix<ordinal_type,value_type>& input,
Teuchos::SerialDenseMatrix<ordinal_type,value_type>& result,
const value_type& beta,
bool trans) const {
Teuchos::SerialDenseMatrix<ordinal_type,value_type> op_input, op_result;
result.scale(beta);
for (ordinal_type i=0; i<operators.size(); i++) {
Teuchos::RCP<operator_type> op = operators[i];
if (trans) {
op_input.reshape(input.numRows(), op->coeff_size());
op_result.reshape(result.numRows(), op->point_size());
}
else {
op_input.reshape(op->coeff_size(), input.numCols());
op_result.reshape(op->point_size(), result.numCols());
}
gather(scatter_maps[i], input, trans, op_input);
op->transformPCE2QP(smolyak_coeffs[i], op_input, op_result, 0.0, trans);
scatter(gather_maps[i], op_result, trans, result);
}
}
开发者ID:，项目名称:，代码行数:27，代码来源:
示例4: ApplyInverse
virtual ordinal_type ApplyInverse(
const Teuchos::SerialDenseMatrix<ordinal_type, value_type>& Input,
Teuchos::SerialDenseMatrix<ordinal_type, value_type>& Result,
ordinal_type m) const {
ordinal_type n=Input.numRows();
Teuchos::SerialDenseMatrix<ordinal_type, value_type> G(A);
Teuchos::SerialDenseMatrix<ordinal_type, value_type> z(n,1);
for (ordinal_type j=0; j<m; j++){
if (j==0){ // Compute z=D-1r
for (ordinal_type i=0; i<n; i++)
z(i,0)=Input(i,0)/A(i,i);
}
else {
//Compute G=invD(-L-U)=I-inv(D)A
for (ordinal_type i=0; i<n; i++){
for (ordinal_type j=0; j<n; j++){
if (j==i)
G(i,j)=0;
else
G(i,j)=-A(i,j)/A(i,i);
}
}
Result.assign(z);
//z=Gz+inv(D)r
Result.multiply(Teuchos::NO_TRANS,Teuchos::NO_TRANS,1.0, G, z, 1.0);
}
}
return 0;
}
开发者ID:00liujj，项目名称:trilinos，代码行数:32，代码来源:Stokhos_JacobiPreconditioner.hpp
示例5: MvTimesMatAddMv
void EpetraOpMultiVec::MvTimesMatAddMv ( double alpha, const MultiVec<double>& A,
const Teuchos::SerialDenseMatrix<int,double>& B, double beta )
{
Epetra_LocalMap LocalMap(B.numRows(), 0, Epetra_MV->Map().Comm());
Epetra_MultiVector B_Pvec(Epetra_DataAccess::View, LocalMap, B.values(), B.stride(), B.numCols());
EpetraOpMultiVec *A_vec = dynamic_cast<EpetraOpMultiVec *>(&const_cast<MultiVec<double> &>(A));
TEUCHOS_TEST_FOR_EXCEPTION( A_vec==NULL, std::invalid_argument, "Anasazi::EpetraOpMultiVec::SetBlocks() cast of MultiVec<double> to EpetraOpMultiVec failed.");
TEUCHOS_TEST_FOR_EXCEPTION(
Epetra_MV->Multiply( 'N', 'N', alpha, *(A_vec->GetEpetraMultiVector()), B_Pvec, beta ) != 0,
EpetraSpecializedMultiVecFailure, "Anasazi::EpetraOpMultiVec::MvTimesMatAddMv() call to Epetra_MultiVec::Multiply() returned a nonzero value.");
}
开发者ID:Tech-XCorp，项目名称:Trilinos，代码行数:13，代码来源:AnasaziSpecializedEpetraAdapter.cpp
示例6: MvTimesMatAddMv
// Update *this with alpha * A * B + beta * (*this).
void MvTimesMatAddMv (ScalarType alpha, const Anasazi::MultiVec<ScalarType> &A,
const Teuchos::SerialDenseMatrix<int, ScalarType> &B,
ScalarType beta)
{
assert (Length_ == A.GetVecLength());
assert (B.numRows() == A.GetNumberVecs());
assert (B.numCols() <= NumberVecs_);
MyMultiVec* MyA;
MyA = dynamic_cast<MyMultiVec*>(&const_cast<Anasazi::MultiVec<ScalarType> &>(A));
assert(MyA!=NULL);
if ((*this)[0] == (*MyA)[0]) {
// If this == A, then need additional storage ...
// This situation is a bit peculiar but it may be required by
// certain algorithms.
std::vector<ScalarType> tmp(NumberVecs_);
for (int i = 0 ; i < Length_ ; ++i) {
for (int v = 0; v < A.GetNumberVecs() ; ++v) {
tmp[v] = (*MyA)(i, v);
}
for (int v = 0 ; v < B.numCols() ; ++v) {
(*this)(i, v) *= beta;
ScalarType res = Teuchos::ScalarTraits<ScalarType>::zero();
for (int j = 0 ; j < A.GetNumberVecs() ; ++j) {
res += tmp[j] * B(j, v);
}
(*this)(i, v) += alpha * res;
}
}
}
else {
for (int i = 0 ; i < Length_ ; ++i) {
for (int v = 0 ; v < B.numCols() ; ++v) {
(*this)(i, v) *= beta;
ScalarType res = 0.0;
for (int j = 0 ; j < A.GetNumberVecs() ; ++j) {
res += (*MyA)(i, j) * B(j, v);
}
(*this)(i, v) += alpha * res;
}
}
}
}
开发者ID:00liujj，项目名称:trilinos，代码行数:52，代码来源:MyMultiVec.hpp
示例7: MvTransMv
void EpetraMultiVec::MvTransMv ( const double alpha, const MultiVec<double>& A,
Teuchos::SerialDenseMatrix<int,double>& B) const
{
EpetraMultiVec *A_vec = dynamic_cast<EpetraMultiVec *>(&const_cast<MultiVec<double> &>(A));
if (A_vec) {
Epetra_LocalMap LocalMap(B.numRows(), 0, Map().Comm());
Epetra_MultiVector B_Pvec(View, LocalMap, B.values(), B.stride(), B.numCols());
int info = B_Pvec.Multiply( 'T', 'N', alpha, *A_vec, *this, 0.0 );
TEST_FOR_EXCEPTION(info!=0, EpetraMultiVecFailure,
"Belos::EpetraMultiVec::MvTransMv call to Multiply() returned a nonzero value.");
}
}
开发者ID:haripandey，项目名称:trilinos，代码行数:14，代码来源:BelosEpetraAdapter.cpp
示例8: gemm
// Generic BLAS level 3 matrix multiplication
// \f$\text{this}\leftarrow \alpha A B+\beta\text{this}\f$
void gemm(const Real alpha,
const MV& A,
const Teuchos::SerialDenseMatrix<int,Real> &B,
const Real beta) {
// Scale this by beta
this->scale(beta);
for(int i=0;i<B.numRows();++i) {
for(int j=0;j<B.numCols();++j) {
mvec_[j]->axpy(alpha*B(i,j),*A.getVector(i));
}
}
}
开发者ID:Russell-Jones-OxPhys，项目名称:Trilinos，代码行数:16，代码来源:ROL_MultiVectorDefault.hpp
示例9: MvTimesMatAddMv
void EpetraMultiVec::MvTimesMatAddMv ( const double alpha, const MultiVec<double>& A,
const Teuchos::SerialDenseMatrix<int,double>& B, const double beta )
{
Epetra_LocalMap LocalMap(B.numRows(), 0, Map().Comm());
Epetra_MultiVector B_Pvec(View, LocalMap, B.values(), B.stride(), B.numCols());
EpetraMultiVec *A_vec = dynamic_cast<EpetraMultiVec *>(&const_cast<MultiVec<double> &>(A));
TEST_FOR_EXCEPTION(A_vec==NULL, EpetraMultiVecFailure,
"Belos::EpetraMultiVec::MvTimesMatAddMv cast from Belos::MultiVec<> to Belos::EpetraMultiVec failed.");
int info = Multiply( 'N', 'N', alpha, *A_vec, B_Pvec, beta );
TEST_FOR_EXCEPTION(info!=0, EpetraMultiVecFailure,
"Belos::EpetraMultiVec::MvTimesMatAddMv call to Multiply() returned a nonzero value.");
}
开发者ID:haripandey，项目名称:trilinos，代码行数:15，代码来源:BelosEpetraAdapter.cpp
示例10: U
LocalOrdinal
revealRank (Kokkos::MultiVector<Scalar, NodeType>& Q,
Teuchos::SerialDenseMatrix<LocalOrdinal, Scalar>& R,
const magnitude_type& tol,
const bool contiguousCacheBlocks = false) const
{
typedef Kokkos::MultiVector<Scalar, NodeType> KMV;
const LocalOrdinal nrows = static_cast<LocalOrdinal> (Q.getNumRows());
const LocalOrdinal ncols = static_cast<LocalOrdinal> (Q.getNumCols());
const LocalOrdinal ldq = static_cast<LocalOrdinal> (Q.getStride());
Teuchos::ArrayRCP<Scalar> Q_ptr = Q.getValuesNonConst();
// Take the easy exit if available.
if (ncols == 0)
return 0;
//
// FIXME (mfh 16 Jul 2010) We _should_ compute the SVD of R (as
// the copy B) on Proc 0 only. This would ensure that all
// processors get the same SVD and rank (esp. in a heterogeneous
// computing environment). For now, we just do this computation
// redundantly, and hope that all the returned rank values are
// the same.
//
matrix_type U (ncols, ncols, STS::zero());
const ordinal_type rank =
reveal_R_rank (ncols, R.values(), R.stride(),
U.get(), U.lda(), tol);
if (rank < ncols)
{
// cerr << ">>> Rank of R: " << rank << " < ncols=" << ncols << endl;
// cerr << ">>> Resulting U:" << endl;
// print_local_matrix (cerr, ncols, ncols, R, ldr);
// cerr << endl;
// If R is not full rank: reveal_R_rank() already computed
// the SVD \f$R = U \Sigma V^*\f$ of (the input) R, and
// overwrote R with \f$\Sigma V^*\f$. Now, we compute \f$Q
// := Q \cdot U\f$, respecting cache blocks of Q.
Q_times_B (nrows, ncols, Q_ptr.getRawPtr(), ldq,
U.get(), U.lda(), contiguousCacheBlocks);
}
return rank;
}
开发者ID:，项目名称:，代码行数:45，代码来源:
示例11: MvTransMv
void EpetraMultiVec::MvTransMv ( double alpha, const MultiVec<double>& A,
Teuchos::SerialDenseMatrix<int,double>& B
#ifdef HAVE_ANASAZI_EXPERIMENTAL
, ConjType conj
#endif
) const
{
EpetraMultiVec *A_vec = dynamic_cast<EpetraMultiVec *>(&const_cast<MultiVec<double> &>(A));
if (A_vec) {
Epetra_LocalMap LocalMap(B.numRows(), 0, Map().Comm());
Epetra_MultiVector B_Pvec(View, LocalMap, B.values(), B.stride(), B.numCols());
TEUCHOS_TEST_FOR_EXCEPTION(
B_Pvec.Multiply( 'T', 'N', alpha, *A_vec, *this, 0.0 ) != 0,
EpetraMultiVecFailure, "Anasazi::EpetraMultiVec::MvTransMv() call to Epetra_MultiVec::Multiply() returned a nonzero value.");
}
}
开发者ID:cakeisalie，项目名称:oomphlib_003，代码行数:18，代码来源:AnasaziEpetraAdapter.cpp
示例12: input
void
Stokhos::SmolyakPseudoSpectralOperator<ordinal_type,value_type,point_compare_type>::
scatter(
const Teuchos::Array<ordinal_type>& map,
const Teuchos::SerialDenseMatrix<ordinal_type,value_type>& input,
bool trans,
Teuchos::SerialDenseMatrix<ordinal_type,value_type>& result) const {
if (trans) {
for (ordinal_type j=0; j<map.size(); j++)
for (ordinal_type i=0; i<input.numRows(); i++)
result(i,map[j]) += input(i,j);
}
else {
for (ordinal_type j=0; j<input.numCols(); j++)
for (ordinal_type i=0; i<map.size(); i++)
result(map[i],j) += input(i,j);
}
}
开发者ID:，项目名称:，代码行数:18，代码来源:
示例13: MvTransMv
// Compute a dense matrix B through the matrix-matrix multiply alpha * A^H * (*this).
void MvTransMv (ScalarType alpha, const Anasazi::MultiVec<ScalarType>& A,
Teuchos::SerialDenseMatrix< int, ScalarType >& B
#ifdef HAVE_ANASAZI_EXPERIMENTAL
, Anasazi::ConjType conj
#endif
) const
{
MyMultiVec* MyA;
MyA = dynamic_cast<MyMultiVec*>(&const_cast<Anasazi::MultiVec<ScalarType> &>(A));
assert (MyA != 0);
assert (A.GetVecLength() == Length_);
assert (NumberVecs_ <= B.numCols());
assert (A.GetNumberVecs() <= B.numRows());
#ifdef HAVE_ANASAZI_EXPERIMENTAL
if (conj == Anasazi::CONJ) {
#endif
for (int v = 0 ; v < A.GetNumberVecs() ; ++v) {
for (int w = 0 ; w < NumberVecs_ ; ++w) {
ScalarType value = 0.0;
for (int i = 0 ; i < Length_ ; ++i) {
value += Teuchos::ScalarTraits<ScalarType>::conjugate((*MyA)(i, v)) * (*this)(i, w);
}
B(v, w) = alpha * value;
}
}
#ifdef HAVE_ANASAZI_EXPERIMENTAL
} else {
for (int v = 0 ; v < A.GetNumberVecs() ; ++v) {
for (int w = 0 ; w < NumberVecs_ ; ++w) {
ScalarType value = 0.0;
for (int i = 0 ; i < Length_ ; ++i) {
value += (*MyA)(i, v) * (*this)(i, w);
}
B(v, w) = alpha * value;
}
}
}
#endif
}
开发者ID:00liujj，项目名称:trilinos，代码行数:42，代码来源:MyMultiVec.hpp
示例14: MvTransMv
// Compute a dense matrix B through the matrix-matrix multiply alpha * A^H * (*this).
void MvTransMv (const ScalarType alpha, const Belos::MultiVec<ScalarType>& A,
Teuchos::SerialDenseMatrix< int, ScalarType >& B) const
{
MyMultiVec* MyA;
MyA = dynamic_cast<MyMultiVec*>(&const_cast<Belos::MultiVec<ScalarType> &>(A));
TEUCHOS_ASSERT(MyA != NULL);
assert (A.GetGlobalLength() == Length_);
assert (NumberVecs_ <= B.numCols());
assert (A.GetNumberVecs() <= B.numRows());
for (int v = 0 ; v < A.GetNumberVecs() ; ++v) {
for (int w = 0 ; w < NumberVecs_ ; ++w) {
ScalarType value = 0.0;
for (int i = 0 ; i < Length_ ; ++i) {
value += Teuchos::ScalarTraits<ScalarType>::conjugate((*MyA)(i, v)) * (*this)(i, w);
}
B(v, w) = alpha * value;
}
}
}
开发者ID:KineticTheory，项目名称:Trilinos，代码行数:22，代码来源:MyMultiVec.hpp
示例15: MvTransMv
void EpetraOpMultiVec::MvTransMv ( double alpha, const MultiVec<double>& A,
Teuchos::SerialDenseMatrix<int,double>& B
#ifdef HAVE_ANASAZI_EXPERIMENTAL
, ConjType conj
#endif
) const
{
EpetraOpMultiVec *A_vec = dynamic_cast<EpetraOpMultiVec *>(&const_cast<MultiVec<double> &>(A));
if (A_vec) {
Epetra_LocalMap LocalMap(B.numRows(), 0, Epetra_MV->Map().Comm());
Epetra_MultiVector B_Pvec(Epetra_DataAccess::View, LocalMap, B.values(), B.stride(), B.numCols());
int info = Epetra_OP->Apply( *Epetra_MV, *Epetra_MV_Temp );
TEUCHOS_TEST_FOR_EXCEPTION( info != 0, EpetraSpecializedMultiVecFailure,
"Anasazi::EpetraOpMultiVec::MvTransMv(): Error returned from Epetra_Operator::Apply()" );
TEUCHOS_TEST_FOR_EXCEPTION(
B_Pvec.Multiply( 'T', 'N', alpha, *(A_vec->GetEpetraMultiVector()), *Epetra_MV_Temp, 0.0 ) != 0,
EpetraSpecializedMultiVecFailure, "Anasazi::EpetraOpMultiVec::MvTransMv() call to Epetra_MultiVector::Multiply() returned a nonzero value.");
}
}
开发者ID:Tech-XCorp，项目名称:Trilinos，代码行数:22，代码来源:AnasaziSpecializedEpetraAdapter.cpp
示例16: updateGuess
void updateGuess(Teuchos::SerialDenseVector<int, std::complex<double> >& myCurrentGuess,
Teuchos::SerialDenseVector<int, std::complex<double> >& myTargetsCalculated,
Teuchos::SerialDenseMatrix<int, std::complex<double> >& myJacobian,
Teuchos::LAPACK<int, std::complex<double> >& myLAPACK
)
{
//v = J(inverse) * (-F(x))
//new guess = v + old guess
myTargetsCalculated *= -1.0;
//Perform an LU factorization of this matrix.
int ipiv[NUMDIMENSIONS], info;
char TRANS = 'N';
myLAPACK.GETRF( NUMDIMENSIONS, NUMDIMENSIONS, myJacobian.values(), myJacobian.stride(), ipiv, &info );
// Solve the linear system.
myLAPACK.GETRS( TRANS, NUMDIMENSIONS, 1, myJacobian.values(), myJacobian.stride(),
ipiv, myTargetsCalculated.values(), myTargetsCalculated.stride(), &info );
//We have overwritten myTargetsCalculated with guess update values
//myBLAS.AXPY(NUMDIMENSIONS, 1.0, myGuessAdjustment.values(), 1, myCurrentGuess.values(), 1);
myCurrentGuess += myTargetsCalculated;
}
开发者ID:MDBrothers，项目名称:NewtonRaphsonExamples，代码行数:23，代码来源:complex_step.cpp
示例17: MvTimesMatAddMv
/*! \brief Update \c mv with \f$ \alpha A B + \beta mv \f$.
*/
static void MvTimesMatAddMv( const double alpha, const _MV & A,
const Teuchos::SerialDenseMatrix<int,double>& B,
const double beta, _MV & mv )
{
// Out::os() << "MvTimesMatAddMv()" << endl;
int n = B.numCols();
// Out::os() << "B.numCols()=" << n << endl;
TEST_FOR_EXCEPT(mv.size() != n);
for (int j=0; j<mv.size(); j++)
{
Vector<double> tmp;
if (beta==one())
{
tmp = mv[j].copy();
}
else if (beta==zero())
{
tmp = mv[j].copy();
tmp.setToConstant(zero());
}
else
{
tmp = beta * mv[j];
}
if (alpha != zero())
{
for (int i=0; i<A.size(); i++)
{
tmp = tmp + alpha*B(i,j)*A[i];
}
}
mv[j].acceptCopyOf(tmp);
}
}
开发者ID:coyigg，项目名称:trilinos，代码行数:38，代码来源:TSFAnasaziAdapter.hpp
示例18: if
ordinal_type
Stokhos::CGDivisionExpansionStrategy<ordinal_type,value_type,node_type>::
CG(const Teuchos::SerialDenseMatrix<ordinal_type, value_type> & A,
Teuchos::SerialDenseMatrix<ordinal_type,value_type> & X,
const Teuchos::SerialDenseMatrix<ordinal_type,value_type> & B,
ordinal_type max_iter,
value_type tolerance,
ordinal_type prec_iter,
ordinal_type order ,
ordinal_type m,
ordinal_type PrecNum,
const Teuchos::SerialDenseMatrix<ordinal_type, value_type> & M,
ordinal_type diag)
{
ordinal_type n = A.numRows();
ordinal_type k=0;
value_type resid;
Teuchos::SerialDenseMatrix<ordinal_type, value_type> Ax(n,1);
Ax.multiply(Teuchos::NO_TRANS,Teuchos::NO_TRANS,1.0, A, X, 0.0);
Teuchos::SerialDenseMatrix<ordinal_type, value_type> r(Teuchos::Copy,B);
r-=Ax;
resid=r.normFrobenius();
Teuchos::SerialDenseMatrix<ordinal_type, value_type> p(r);
Teuchos::SerialDenseMatrix<ordinal_type, value_type> rho(1,1);
Teuchos::SerialDenseMatrix<ordinal_type, value_type> oldrho(1,1);
Teuchos::SerialDenseMatrix<ordinal_type, value_type> pAp(1,1);
Teuchos::SerialDenseMatrix<ordinal_type, value_type> Ap(n,1);
value_type b;
value_type a;
while (resid > tolerance && k < max_iter){
Teuchos::SerialDenseMatrix<ordinal_type, value_type> z(r);
//Solve Mz=r
if (PrecNum != 0){
if (PrecNum == 1){
Stokhos::DiagPreconditioner<ordinal_type, value_type> precond(M);
precond.ApplyInverse(r,z,prec_iter);
}
else if (PrecNum == 2){
Stokhos::JacobiPreconditioner<ordinal_type, value_type> precond(M);
precond.ApplyInverse(r,z,2);
}
else if (PrecNum == 3){
Stokhos::GSPreconditioner<ordinal_type, value_type> precond(M,0);
precond.ApplyInverse(r,z,1);
}
else if (PrecNum == 4){
Stokhos::SchurPreconditioner<ordinal_type, value_type> precond(M, order, m, diag);
precond.ApplyInverse(r,z,prec_iter);
}
}
rho.multiply(Teuchos::TRANS,Teuchos::NO_TRANS,1.0, r, z, 0.0);
if (k==0){
p.assign(z);
rho.multiply(Teuchos::TRANS, Teuchos::NO_TRANS, 1.0, r, z, 0.0);
}
else {
b=rho(0,0)/oldrho(0,0);
p.scale(b);
p+=z;
}
Ap.multiply(Teuchos::NO_TRANS,Teuchos::NO_TRANS,1.0, A, p, 0.0);
pAp.multiply(Teuchos::TRANS,Teuchos::NO_TRANS,1.0, p, Ap, 0.0);
a=rho(0,0)/pAp(0,0);
Teuchos::SerialDenseMatrix<ordinal_type, value_type> scalep(p);
scalep.scale(a);
X+=scalep;
Ap.scale(a);
r-=Ap;
oldrho.assign(rho);
resid=r.normFrobenius();
k++;
}
//std::cout << "iteration count " << k << std::endl;
return 0;
}
开发者ID:agrippa，项目名称:Trilinos，代码行数:79，代码来源:Stokhos_CGDivisionExpansionStrategy.hpp
示例19: pregmres
//Mean-Based Preconditioned GMRES
int pregmres(const Teuchos::SerialDenseMatrix<int, double> & A, const Teuchos::SerialDenseMatrix<int,double> & X,const Teuchos::SerialDenseMatrix<int,double> & B, int max_iter, double tolerance)
{
int n;
int k;
double resid;
k=1;
n=A.numRows();
std::cout << A << std::endl;
Teuchos::SerialDenseMatrix<int, double> D(n,1);
//Get diagonal entries of A
for (int i=0; i<n; i++){
D(i,0)=A(i,i);
}
Teuchos::SerialDenseMatrix<int, double> Ax(n,1);
Ax.multiply(Teuchos::NO_TRANS,Teuchos::NO_TRANS,1.0, A, X, 0.0);
Teuchos::SerialDenseMatrix<int, double> r0(B);
r0-=Ax;
resid=r0.normFrobenius();
//define vector v=r/norm(r) where r=b-Ax
Teuchos::SerialDenseMatrix<int, double> v(n,1);
r0.scale(1/resid);
Teuchos::SerialDenseMatrix<int, double> h(1,1);
//Matrix of orthog basis vectors V
Teuchos::SerialDenseMatrix<int, double> V(n,1);
//Set v=r0/norm(r0) to be 1st col of V
for (int i=0; i<n; i++){
V(i,0)=r0(i,0);
}
//right hand side
Teuchos::SerialDenseMatrix<int, double> bb(1,1);
bb(0,0)=resid;
Teuchos::SerialDenseMatrix<int, double> w(n,1);
Teuchos::SerialDenseMatrix<int, double> c;
Teuchos::SerialDenseMatrix<int, double> s;
while (resid > tolerance && k < max_iter){
std::cout << "k = " << k << std::endl;
h.reshape(k+1,k);
//Arnoldi iteration(Gram-Schmidt )
V.reshape(n,k+1);
//set vk to be kth col of V
Teuchos::SerialDenseMatrix<int, double> vk(Teuchos::Copy, V, n,1,0,k-1);
//Preconditioning step w=AMj(-1)vj
w.multiply(Teuchos::NO_TRANS, Teuchos::NO_TRANS, 1/D(k-1,0), A, vk, 0.0);
Teuchos::SerialDenseMatrix<int, double> vi(n,1);
Teuchos::SerialDenseMatrix<int, double> ip(1,1);
for (int i=0; i<k; i++){
//set vi to be ith col of V
Teuchos::SerialDenseMatrix<int, double> vi(Teuchos::Copy, V, n,1,0,i);
//Calculate inner product
ip.multiply(Teuchos::TRANS, Teuchos::NO_TRANS, 1.0, vi, w, 0.0);
h(i,k-1)= ip(0,0);
//scale vi by h(i,k-1)
vi.scale(ip(0,0));
w-=vi;
}
h(k,k-1)=w.normFrobenius();
w.scale(1.0/w.normFrobenius());
//add column vk+1=w to V
for (int i=0; i<n; i++){
V(i,k)=w(i,0);
}
//Solve upper hessenberg least squares problem via Givens rotations
//Compute previous Givens rotations
for (int i=0; i<k-1; i++){
h(i,k-1)=c(i,0)*h(i,k-1)+s(i,0)*h(i+1,k-1);
h(i+1,k-1)=-s(i,0)*h(i,k-1)+c(i,0)*h(i+1,k-1);
}
//Compute next Givens rotations
c.reshape(k,1);
s.reshape(k,1);
bb.reshape(k+1,1);
double l = sqrt(h(k-1,k-1)*h(k-1,k-1)+h(k,k-1)*h(k,k-1));
c(k-1,0)=h(k-1,k-1)/l;
s(k-1,0)=h(k,k-1)/l;
std::cout <<" h(k,k-1)= " << h(k,k-1) << std::endl;
// Givens rotation on h and bb
h(k-1,k-1)=l;
h(k,k-1)=0;
bb(k-1,0)=c(k-1,0)*bb(k-1,0);
bb(k,0)=-s(k-1,0)*bb(k-1,0);
//Determine residual
resid = fabs(bb(k,0));
//.........这里部分代码省略.........
开发者ID:，项目名称:，代码行数:101，代码来源:
示例20:
void
Stokhos::SmolyakPseudoSpectralOperator<ordinal_type,value_type,point_compare_type>::
apply_direct(
const Teuchos::SerialDenseMatrix<ordinal_type,value_type>& A,
const value_type& alpha,
const Teuchos::SerialDenseMatrix<ordinal_type,value_type>& input,
Teuchos::SerialDenseMatrix<ordinal_type,value_type>& result,
const value_type& beta,
bool trans) const {
if (trans) {
TEUCHOS_ASSERT(input.numCols() <= A.numCols());
TEUCHOS_ASSERT(result.numCols() == A.numRows());
TEUCHOS_ASSERT(result.numRows() == input.numRows());
blas.GEMM(Teuchos::NO_TRANS, Teuchos::TRANS, input.numRows(),
A.numRows(), input.numCols(), alpha, input.values(),
input.stride(), A.values(), A.stride(), beta,
result.values(), result.stride());
}
else {
TEUCHOS_ASSERT(input.numRows() <= A.numCols());
TEUCHOS_ASSERT(result.numRows() == A.numRows());
TEUCHOS_ASSERT(result.numCols() == input.numCols());
blas.GEMM(Teuchos::NO_TRANS, Teuchos::NO_TRANS, A.numRows(),
input.numCols(), input.numRows(), alpha, A.values(),
A.stride(), input.values(), input.stride(), beta,
result.values(), result.stride());
}
}
开发者ID:，项目名称:，代码行数:28，代码来源:
注：本文中的teuchos::SerialDenseMatrix类示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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