void DislocationDensity<EvalT, Traits>::
evaluateFields(typename Traits::EvalData workset)
{
Teuchos::SerialDenseMatrix<int, double> A;
Teuchos::SerialDenseMatrix<int, double> X;
Teuchos::SerialDenseMatrix<int, double> B;
Teuchos::SerialDenseSolver<int, double> solver;
A.shape(numNodes,numNodes);
X.shape(numNodes,numNodes);
B.shape(numNodes,numNodes);
// construct Identity for RHS
for (int i = 0; i < numNodes; ++i)
B(i,i) = 1.0;
for (int i=0; i < G.size() ; i++) G[i] = 0.0;
// construct the node --> point operator
for (std::size_t cell=0; cell < workset.numCells; ++cell)
{
for (std::size_t node=0; node < numNodes; ++node)
for (std::size_t qp=0; qp < numQPs; ++qp)
A(qp,node) = BF(cell,node,qp);
X = 0.0;
solver.setMatrix( Teuchos::rcp( &A, false) );
solver.setVectors( Teuchos::rcp( &X, false ), Teuchos::rcp( &B, false ) );
// Solve the system A X = B to find A_inverse
int status = 0;
status = solver.factor();
status = solver.solve();
// compute nodal Fp
nodalFp.initialize(0.0);
for (std::size_t node=0; node < numNodes; ++node)
for (std::size_t qp=0; qp < numQPs; ++qp)
for (std::size_t i=0; i < numDims; ++i)
for (std::size_t j=0; j < numDims; ++j)
nodalFp(node,i,j) += X(node,qp) * Fp(cell,qp,i,j);
// compute the curl using nodalFp
curlFp.initialize(0.0);
for (std::size_t node=0; node < numNodes; ++node)
{
for (std::size_t qp=0; qp < numQPs; ++qp)
{
curlFp(qp,0,0) += nodalFp(node,0,2) * GradBF(cell,node,qp,1) - nodalFp(node,0,1) * GradBF(cell,node,qp,2);
curlFp(qp,0,1) += nodalFp(node,1,2) * GradBF(cell,node,qp,1) - nodalFp(node,1,1) * GradBF(cell,node,qp,2);
curlFp(qp,0,2) += nodalFp(node,2,2) * GradBF(cell,node,qp,1) - nodalFp(node,2,1) * GradBF(cell,node,qp,2);
curlFp(qp,1,0) += nodalFp(node,0,0) * GradBF(cell,node,qp,2) - nodalFp(node,0,2) * GradBF(cell,node,qp,0);
curlFp(qp,1,1) += nodalFp(node,1,0) * GradBF(cell,node,qp,2) - nodalFp(node,1,2) * GradBF(cell,node,qp,0);
curlFp(qp,1,2) += nodalFp(node,2,0) * GradBF(cell,node,qp,2) - nodalFp(node,2,2) * GradBF(cell,node,qp,0);
curlFp(qp,2,0) += nodalFp(node,0,1) * GradBF(cell,node,qp,0) - nodalFp(node,0,0) * GradBF(cell,node,qp,1);
curlFp(qp,2,1) += nodalFp(node,1,1) * GradBF(cell,node,qp,0) - nodalFp(node,1,0) * GradBF(cell,node,qp,1);
curlFp(qp,2,2) += nodalFp(node,2,1) * GradBF(cell,node,qp,0) - nodalFp(node,2,0) * GradBF(cell,node,qp,1);
}
}
for (std::size_t qp=0; qp < numQPs; ++qp)
for (std::size_t i=0; i < numDims; ++i)
for (std::size_t j=0; j < numDims; ++j)
for (std::size_t k=0; k < numDims; ++k)
G(cell,qp,i,j) += Fp(cell,qp,i,k) * curlFp(qp,k,j);
}
}
void SaddleOperator<ScalarType, MV, OP>::Apply(const SaddleContainer<ScalarType,MV>& X, SaddleContainer<ScalarType,MV>& Y) const
{
RCP<SerialDenseMatrix> Xlower = X.getLower();
RCP<SerialDenseMatrix> Ylower = Y.getLower();
if(pt_ == NO_PREC)
{
// trans does literally nothing, because the operator is symmetric
// Y.bottom = B'X.top
MVT::MvTransMv(1., *B_, *(X.upper_), *Ylower);
// Y.top = A*X.top+B*X.bottom
A_->Apply(*(X.upper_), *(Y.upper_));
MVT::MvTimesMatAddMv(1., *B_, *Xlower, 1., *(Y.upper_));
}
else if(pt_ == NONSYM)
{
// Y.bottom = -B'X.top
MVT::MvTransMv(-1., *B_, *(X.upper_), *Ylower);
// Y.top = A*X.top+B*X.bottom
A_->Apply(*(X.upper_), *(Y.upper_));
MVT::MvTimesMatAddMv(1., *B_, *Xlower, 1., *(Y.upper_));
}
else if(pt_ == BD_PREC)
{
Teuchos::SerialDenseSolver<int,ScalarType> MySolver;
// Solve A Y.X = X.X
A_->Apply(*(X.upper_),*(Y.upper_));
// So, let me tell you a funny story about how the SerialDenseSolver destroys the original matrix...
Teuchos::RCP<SerialDenseMatrix> localSchur = Teuchos::rcp(new SerialDenseMatrix(*Schur_));
// Solve the small system
MySolver.setMatrix(localSchur);
MySolver.setVectors(Ylower, Xlower);
MySolver.solve();
}
// Hermitian-Skew Hermitian splitting has some extra requirements
// We need B'B = I, which is true for standard eigenvalue problems, but not generalized
// We also need to use gmres, because our operator is no longer symmetric
else if(pt_ == HSS_PREC)
{
// std::cout << "applying preconditioner to";
// X.MvPrint(std::cout);
// Solve (H + alpha I) Y1 = X
// 1. Apply preconditioner
A_->Apply(*(X.upper_),*(Y.upper_));
// 2. Scale by 1/alpha
*Ylower = *Xlower;
Ylower->scale(1./alpha_);
// std::cout << "H preconditioning produced";
// Y.setLower(Ylower);
// Y.MvPrint(std::cout);
// Solve (S + alpha I) Y = Y1
// 1. Y_lower = (B' Y1_upper + alpha Y1_lower) / (1 + alpha^2)
Teuchos::RCP<SerialDenseMatrix> Y1_lower = Teuchos::rcp(new SerialDenseMatrix(*Ylower));
MVT::MvTransMv(1,*B_,*(Y.upper_),*Ylower);
// std::cout << "Y'b1 " << *Ylower;
Y1_lower->scale(alpha_);
// std::cout << "alpha b2 " << *Y1_lower;
*Ylower += *Y1_lower;
// std::cout << "alpha b2 + Y'b1 " << *Ylower;
Ylower->scale(1/(1+alpha_*alpha_));
// 2. Y_upper = (Y1_upper - B Y_lower) / alpha
MVT::MvTimesMatAddMv(-1/alpha_,*B_,*Ylower,1/alpha_,*(Y.upper_));
// std::cout << "preconditioning produced";
// Y.setLower(Ylower);
// Y.MvPrint(std::cout);
}
else
{
std::cout << "Not a valid preconditioner type\n";
}
Y.setLower(Ylower);
// std::cout << "result of applying operator";
// Y.MvPrint(std::cout);
}
