C++ MultiVector::clone方法代码示例

LOCA::Hopf::MooreSpence::ExtendedMultiVector::ExtendedMultiVector(
		  const Teuchos::RCP<LOCA::GlobalData>& global_data,
		  const NOX::Abstract::MultiVector& xVec,
		  const NOX::Abstract::MultiVector& realEigenVec,
		  const NOX::Abstract::MultiVector& imagEigenVec,
		  const NOX::Abstract::MultiVector::DenseMatrix& freqs,
		  const NOX::Abstract::MultiVector::DenseMatrix& bifParams) :
  LOCA::Extended::MultiVector(global_data, xVec.numVectors(), 3, 2)
{
  LOCA::Extended::MultiVector::setMultiVectorPtr(0, xVec.clone(NOX::DeepCopy));
  LOCA::Extended::MultiVector::setMultiVectorPtr(1, realEigenVec.clone(NOX::DeepCopy));
  LOCA::Extended::MultiVector::setMultiVectorPtr(2, imagEigenVec.clone(NOX::DeepCopy));
  LOCA::Extended::MultiVector::getScalarRows(1,0)->assign(freqs);
  LOCA::Extended::MultiVector::getScalarRows(1,1)->assign(bifParams);
}

void
LOCA::AnasaziOperator::Cayley2Matrix::apply(const NOX::Abstract::MultiVector& input,
                     NOX::Abstract::MultiVector& output) const
{
  std::string callingFunction =
    "LOCA::AnasaziOperator::Cayley2Matrix::apply()";

  NOX::Abstract::Group::ReturnType finalStatus = NOX::Abstract::Group::Ok;
  NOX::Abstract::Group::ReturnType status;

  // Allocate temporary vector
  if (tmp_r == Teuchos::null || tmp_r->numVectors() != input.numVectors())
    tmp_r = input.clone(NOX::ShapeCopy);

  // Compute J-mu*M -- moved to preProcessSeedVector

  // Compute (J-mu*M)*input
  status = grp->applySecondShiftedMatrixMultiVector(input, *tmp_r);
  finalStatus =
    globalData->locaErrorCheck->combineAndCheckReturnTypes(status,
                               finalStatus,
                               callingFunction);

  // Compute J-sigma*M -- moved to preProcessSeedVector

  // Solve (J-sigma*M)*output = (J-mu*M)*input
  status = grp->applyShiftedMatrixInverseMultiVector(*solverParams, *tmp_r,
                             output);

  finalStatus =
    globalData->locaErrorCheck->combineAndCheckReturnTypes(status,
                               finalStatus,
                               callingFunction);
}

LOCA::MultiContinuation::ExtendedMultiVector::ExtendedMultiVector(
		    const Teuchos::RCP<LOCA::GlobalData>& global_data,
		    const NOX::Abstract::MultiVector& xVec, 
		    int nScalarRows) :
  LOCA::Extended::MultiVector(global_data, xVec.numVectors(), 1, nScalarRows)
{
  LOCA::Extended::MultiVector::setMultiVectorPtr(0, xVec.clone(NOX::DeepCopy));
}

void
LOCA::Epetra::AnasaziOperator::Floquet::apply(const NOX::Abstract::MultiVector& input, 
				     NOX::Abstract::MultiVector& output) const
{

  // Apply first part of monodromy operator on input vector
  Teuchos::RCP<NOX::Abstract::MultiVector> tmpVec =  input.clone();
  for (int i=0; i < input.numVectors(); i++) {
    NOX::Abstract::Vector& nAV = tmpVec->operator[](i);
    NOX::Epetra::Vector& nEV = dynamic_cast<NOX::Epetra::Vector&>(nAV);
    Epetra_Vector& eV = nEV.getEpetraVector();

    xyztInterface->beginFloquetOperatorApplication(eV);
  }
    
  // Now apply the main part of the monodromy matrix
  NOX::Abstract::Group::ReturnType status =
    grp->applyJacobianInverseMultiVector(*solverParams, *(tmpVec.get()), output);
  globalData->locaErrorCheck->checkReturnType(status,
                       "LOCA::Epetra::AnasaziOperator::Floquet::apply()");


  for (int i=0; i < input.numVectors(); i++) {
    NOX::Abstract::Vector& nAV = output.operator[](i);
    NOX::Epetra::Vector& nEV = dynamic_cast<NOX::Epetra::Vector&>(nAV);
    Epetra_Vector& eV = nEV.getEpetraVector();

    xyztInterface->finishFloquetOperatorApplication(eV);
  }

// Was this needed?

// TESTING:  Doubling the call to this routine resulted in the
//  squaring of the Floquet multipliers, as they should.
//  Replacing the apply function so the operator is diagonal
//  with entries 1/(i+2) led to the Floquet multipliers.

/*
  std::cout << " Fixing apply so Floquets at 1/2 1/3 1/4 ... " << std::endl;

  Teuchos::RCP<NOX::Abstract::MultiVector> tmpVec =  input.clone();
  for (int i=0; i < input.numVectors(); i++) {
    NOX::Abstract::Vector& nAV = output.operator[](i);
    NOX::Epetra::Vector& nEV = dynamic_cast<NOX::Epetra::Vector&>(nAV);
    Epetra_Vector& oV = nEV.getEpetraVector();

    NOX::Abstract::Vector& nAV2 = tmpVec->operator[](i);
    NOX::Epetra::Vector& nEV2 = dynamic_cast<NOX::Epetra::Vector&>(nAV2);
    Epetra_Vector& iV = nEV2.getEpetraVector();

    for (int j=0; j < iV.MyLength(); j++) {
     oV[j] = iV[j] / (j + 2.0);
    }
  }
*/
}

void
LOCA::AnasaziOperator::ShiftInvert::apply(
				     const NOX::Abstract::MultiVector& input, 
				     NOX::Abstract::MultiVector& output) const
{
  std::string callingFunction = 
    "LOCA::AnasaziOperator::ShiftInvert::apply()";

  NOX::Abstract::Group::ReturnType finalStatus = NOX::Abstract::Group::Ok;
  NOX::Abstract::Group::ReturnType status;

  // Allocate temporary vector
  if (tmp_r == Teuchos::null || tmp_r->numVectors() != input.numVectors())
    tmp_r = input.clone(NOX::ShapeCopy);

  // Compute M
  status = grp->computeShiftedMatrix(0.0, 1.0);
  finalStatus = 
    globalData->locaErrorCheck->combineAndCheckReturnTypes(status, 
							   finalStatus,
							   callingFunction);

  // Compute M*input
  status = grp->applyShiftedMatrixMultiVector(input, *tmp_r);
  finalStatus = 
    globalData->locaErrorCheck->combineAndCheckReturnTypes(status, 
							   finalStatus,
							   callingFunction);

  // Compute J-omega*M
  status = grp->computeShiftedMatrix(1.0, -shift);
  finalStatus = 
    globalData->locaErrorCheck->combineAndCheckReturnTypes(status, 
							   finalStatus,
							   callingFunction);

  // Solve (J-omega*M)*output = M*input
  status = grp->applyShiftedMatrixInverseMultiVector(*solverParams, *tmp_r, 
						     output);
  finalStatus = 
    globalData->locaErrorCheck->combineAndCheckReturnTypes(status, 
							   finalStatus,
							   callingFunction);
}

// Solves turning point equations via classic Salinger bordering
// The first m columns of input_x and input_null store the RHS while
// the last column stores df/dp, d(Jn)/dp respectively.  Note however
// input_param has only m columns (not m+1).  result_x, result_null,
// are result_param have the same dimensions as their input counterparts
NOX::Abstract::Group::ReturnType 
LOCA::TurningPoint::MooreSpence::PhippsBordering::solveTransposeContiguous(
		  Teuchos::ParameterList& params,
		  const NOX::Abstract::MultiVector& input_x,
		  const NOX::Abstract::MultiVector& input_null,
	          const NOX::Abstract::MultiVector::DenseMatrix& input_param,
		  NOX::Abstract::MultiVector& result_x,
		  NOX::Abstract::MultiVector& result_null,
	          NOX::Abstract::MultiVector::DenseMatrix& result_param) const
{
  std::string callingFunction = 
    "LOCA::TurningPoint::MooreSpence::PhippsBordering::solveTransposeContiguous()";
  NOX::Abstract::Group::ReturnType finalStatus = NOX::Abstract::Group::Ok;
  NOX::Abstract::Group::ReturnType status;

  int m = input_x.numVectors()-2;
  std::vector<int> index_input(m);
  std::vector<int> index_input_dp(m+1);
  std::vector<int> index_null(1);
  std::vector<int> index_dp(1);
  for (int i=0; i<m; i++) {
    index_input[i] = i;
    index_input_dp[i] = i;
  }
  index_input_dp[m] = m;
  index_dp[0] = m;
  index_null[0] = m+1;

  NOX::Abstract::MultiVector::DenseMatrix tmp_mat_1(1, m+1);
  NOX::Abstract::MultiVector::DenseMatrix tmp_mat_2(1, m+2);

  // Create view of first m+1 columns of input_null, result_null
  Teuchos::RCP<NOX::Abstract::MultiVector> input_null_view = 
      input_null.subView(index_input_dp);
  Teuchos::RCP<NOX::Abstract::MultiVector> result_null_view = 
      result_null.subView(index_input_dp);

  // verify underlying Jacobian is valid
  if (!group->isJacobian()) {
    status = group->computeJacobian();
    finalStatus = 
      globalData->locaErrorCheck->combineAndCheckReturnTypes(status, 
							     finalStatus,
							     callingFunction);
  }

  // Solve  |J^T v||A B| = |G -phi|
  //        |u^T 0||a b|   |0   0 |
  status =
    transposeBorderedSolver->applyInverseTranspose(params, 
						   input_null_view.get(), 
						   NULL, 
						   *result_null_view, 
						   tmp_mat_1);
  finalStatus = 
    globalData->locaErrorCheck->combineAndCheckReturnTypes(status, finalStatus,
							   callingFunction);
  Teuchos::RCP<NOX::Abstract::MultiVector> A = 
    result_null.subView(index_input);
  Teuchos::RCP<NOX::Abstract::MultiVector> B = 
    result_null.subView(index_dp);
  double b = tmp_mat_1(0,m);

  // compute (Jv)_x^T[A B u]
  result_null[m+1] = *uVector;
  Teuchos::RCP<NOX::Abstract::MultiVector> tmp = 
    result_null.clone(NOX::ShapeCopy);
  status = group->computeDwtJnDxMulti(result_null, *nullVector, *tmp);
  finalStatus = 
    globalData->locaErrorCheck->combineAndCheckReturnTypes(status, 
							   finalStatus,
							   callingFunction);

  // compute [F 0 0] - (Jv)_x^T[A B u]
  tmp->update(1.0, input_x, -1.0);

  // verify underlying Jacobian is valid
  if (!group->isJacobian()) {
    status = group->computeJacobian();
    finalStatus = 
      globalData->locaErrorCheck->combineAndCheckReturnTypes(status, 
							     finalStatus,
							     callingFunction);
  }

  // Solve  |J^T v||C D E| = |F - (Jv)_x^T A  -(Jv)_x^T B  -(Jv)_x^T u|
  //        |u^T 0||c d e|   |         0             0            0   |
  status = 
    transposeBorderedSolver->applyInverseTranspose(params, 
						   tmp.get(), 
						   NULL, 
						   result_x,
						   tmp_mat_2);
  finalStatus = 
    globalData->locaErrorCheck->combineAndCheckReturnTypes(status, finalStatus,
//.........这里部分代码省略.........

// Solves Hopf equations via classic Salinger bordering
// The first m columns of input_x, input_y, input_z store the RHS, the
// next column stores df/dp, (Jy-wBz)_p and (Jz+wBy)_p respectively, the
// last column of input_y and input_z store Bz and -By respectively.  Note 
// input_x has m+1 columns, input_y and input_z have m+2, and input_w and
// input_p have m columns.  result_x, result_y, result_z, result_w and 
// result_param have the same dimensions as their input counterparts
NOX::Abstract::Group::ReturnType 
LOCA::Hopf::MooreSpence::SalingerBordering::solveContiguous(
		      Teuchos::ParameterList& params,
		      const NOX::Abstract::MultiVector& input_x,
		      const NOX::Abstract::MultiVector& input_y,
		      const NOX::Abstract::MultiVector& input_z,
		      const NOX::Abstract::MultiVector::DenseMatrix& input_w,
		      const NOX::Abstract::MultiVector::DenseMatrix& input_p,
		      NOX::Abstract::MultiVector& result_x,
		      NOX::Abstract::MultiVector& result_y,
		      NOX::Abstract::MultiVector& result_z,
		      NOX::Abstract::MultiVector::DenseMatrix& result_w,
	              NOX::Abstract::MultiVector::DenseMatrix& result_p) const
{
  std::string callingFunction = 
    "LOCA::Hopf::MooreSpence::SalingerBordering::solveContiguous()";
  NOX::Abstract::Group::ReturnType finalStatus = NOX::Abstract::Group::Ok;
  NOX::Abstract::Group::ReturnType status;

  int m = input_x.numVectors()-1;
  std::vector<int> index_input(m);
  std::vector<int> index_dp(1);
  std::vector<int> index_B(1);
  std::vector<int> index_ip(m+1);
  for (int i=0; i<m; i++) {
    index_input[i] = i;
    index_ip[i] = i;
  }
  index_ip[m] = m;
  index_dp[0] = m;
  index_B[0] = m+1;

  // verify underlying Jacobian is valid
  if (!group->isJacobian()) {
    status = group->computeJacobian();
    finalStatus = 
      globalData->locaErrorCheck->combineAndCheckReturnTypes(status, 
							     finalStatus,
							     callingFunction);
  }
  
  // compute [A b] = J^-1 [F df/dp]
  status = group->applyJacobianInverseMultiVector(params, input_x, result_x);
  finalStatus = 
    globalData->locaErrorCheck->combineAndCheckReturnTypes(status, finalStatus,
							   callingFunction);
  Teuchos::RCP<NOX::Abstract::MultiVector> A = 
    result_x.subView(index_input);
  Teuchos::RCP<NOX::Abstract::MultiVector> b = 
    result_x.subView(index_dp);

  // verify underlying complex matrix is valid
   if (!group->isComplex()) {
    status = group->computeComplex(w);
    finalStatus = 
      globalData->locaErrorCheck->combineAndCheckReturnTypes(status, 
							     finalStatus,
							     callingFunction);
  }

  // compute (J+iwB)(y+iz)_x [A b]
  Teuchos::RCP<NOX::Abstract::MultiVector> tmp_real = 
    result_y.clone(NOX::ShapeCopy);
  Teuchos::RCP<NOX::Abstract::MultiVector> tmp_real_sub =
    tmp_real->subView(index_ip);
  Teuchos::RCP<NOX::Abstract::MultiVector> tmp_imag = 
    result_y.clone(NOX::ShapeCopy);
  Teuchos::RCP<NOX::Abstract::MultiVector> tmp_imag_sub =
    tmp_imag->subView(index_ip);
  tmp_real->init(0.0);
  tmp_imag->init(0.0);
  status = group->computeDCeDxa(*yVector, *zVector, w, result_x,
				*CeRealVector, *CeImagVector, *tmp_real_sub,
				*tmp_imag_sub);
  finalStatus = 
    globalData->locaErrorCheck->combineAndCheckReturnTypes(status, finalStatus,
							   callingFunction);

  // compute [G+iH d(J+iwB)(y+iz)/dp iB(y+iz)] - [(J+iwB)_x[A b] 0+i0]
  tmp_real->update(1.0, input_y, -1.0);
  tmp_imag->update(1.0, input_z, -1.0);

  // verify underlying complex matrix is valid
  if (!group->isComplex()) {
    status = group->computeComplex(w);
    finalStatus = 
      globalData->locaErrorCheck->combineAndCheckReturnTypes(status, 
							     finalStatus,
							     callingFunction);
  }

  // compute [C+iD e+if g+ih] = (J+iwB)^-1 (tmp_real + i tmp_imag)
  status = group->applyComplexInverseMultiVector(params, *tmp_real, *tmp_imag,
//.........这里部分代码省略.........

void
LOCA::BorderedSolver::HouseholderQR::computeQR(
                const NOX::Abstract::MultiVector::DenseMatrix& C,
                const NOX::Abstract::MultiVector& B,
                bool use_c_transpose,
                NOX::Abstract::MultiVector::DenseMatrix& Y1,
                NOX::Abstract::MultiVector& Y2,
                NOX::Abstract::MultiVector::DenseMatrix& T,
                NOX::Abstract::MultiVector::DenseMatrix& R)
{
  double beta;
  int m = B.numVectors();

  // Initialize
  Y1.putScalar(0.0);
  T.putScalar(0.0);
  Y2 = B;
  if (use_c_transpose) {
    for (int i=0; i<m; i++)
      for (int j=0; j<m; j++)
    R(i,j) = C(j,i);        // Copy transpose of C into R
  }
  else
    R.assign(C);

  // A temporary vector
  Teuchos::RCP<NOX::Abstract::MultiVector> v2 = Y2.clone(1);

  Teuchos::RCP<NOX::Abstract::MultiVector::DenseMatrix> v1;
  Teuchos::RCP<NOX::Abstract::MultiVector> h2;
  Teuchos::RCP<NOX::Abstract::MultiVector::DenseMatrix> h1;
  Teuchos::RCP<NOX::Abstract::MultiVector> y2;
  Teuchos::RCP<NOX::Abstract::MultiVector::DenseMatrix> y1;
  Teuchos::RCP<NOX::Abstract::MultiVector::DenseMatrix> z;
  std::vector<int> h_idx;
  std::vector<int> y_idx;
  y_idx.reserve(m);

  for (int i=0; i<m; i++) {

    // Create view of column i of Y1 starting at row i
    v1 =
      Teuchos::rcp(new NOX::Abstract::MultiVector::DenseMatrix(Teuchos::View,
                                   Y1,
                                   m-i,
                                   1, i, i));

    // Create view of columns i through m-1 of Y2
    h_idx.resize(m-i);
    for (unsigned int j=0; j<h_idx.size(); j++)
      h_idx[j] = i+j;
    h2 = Y2.subView(h_idx);

    // Create view of columns i thru m-1 of R, starting at row i
    h1 =
      Teuchos::rcp(new NOX::Abstract::MultiVector::DenseMatrix(Teuchos::View,
                                   R,
                                   m-i,
                                   m-i,
                                   i, i));

    if (i > 0) {

      // Create view of columns 0 through i-1 of Y2
      y_idx.push_back(i-1);
      y2 = Y2.subView(y_idx);

      // Create view of columns 0 through i-1 of Y1, starting at row i
      y1 =
    Teuchos::rcp(new NOX::Abstract::MultiVector::DenseMatrix(Teuchos::View,
                                 Y1,
                                 m-i,
                                 i, i, 0));

      // Create view of column i, row 0 through i-1 of T
      z =
    Teuchos::rcp(new NOX::Abstract::MultiVector::DenseMatrix(Teuchos::View,
                                 T,
                                 i,
                                 1,
                                 0, i));
    }

    // Compute Householder Vector
    computeHouseholderVector(i, R, Y2, *v1, *v2, beta);

    // Apply Householder reflection
    applyHouseholderVector(*v1, *v2, beta, *h1, *h2);

    // Copy v2 into Y2
    Y2[i] = (*v2)[0];

    T(i,i) = -beta;

    if (i > 0) {

      // Compute z = y2^T * v2
      v2->multiply(1.0, *y2, *z);

      // Compute z = -beta * (z + y1^T * v1)
//.........这里部分代码省略.........