bool EigenLinearSolver::solve(EigenMatrix &A, EigenVector& b, EigenVector &x)
{
INFO("------------------------------------------------------------------");
INFO("*** Eigen solver computation");
#ifdef USE_EIGEN_UNSUPPORTED
std::unique_ptr<Eigen::IterScaling<EigenMatrix::RawMatrixType>> scal;
if (_option.scaling)
{
INFO("-> scale");
scal.reset(new Eigen::IterScaling<EigenMatrix::RawMatrixType>());
scal->computeRef(A.getRawMatrix());
b.getRawVector() = scal->LeftScaling().cwiseProduct(b.getRawVector());
}
#endif
auto const success = _solver->solve(A.getRawMatrix(), b.getRawVector(),
x.getRawVector(), _option);
#ifdef USE_EIGEN_UNSUPPORTED
if (scal)
x.getRawVector() = scal->RightScaling().cwiseProduct(x.getRawVector());
#endif
INFO("------------------------------------------------------------------");
return success;
}
bool EigenLisLinearSolver::solve(EigenMatrix &A_, EigenVector& b_,
EigenVector &x_)
{
static_assert(EigenMatrix::RawMatrixType::IsRowMajor,
"Sparse matrix is required to be in row major storage.");
auto &A = A_.getRawMatrix();
auto &b = b_.getRawVector();
auto &x = x_.getRawVector();
if (!A.isCompressed())
A.makeCompressed();
int nnz = A.nonZeros();
int* ptr = A.outerIndexPtr();
int* col = A.innerIndexPtr();
double* data = A.valuePtr();
LisMatrix lisA(A_.getNumberOfRows(), nnz, ptr, col, data);
LisVector lisb(b.rows(), b.data());
LisVector lisx(x.rows(), x.data());
LisLinearSolver lissol; // TODO not always creat Lis solver here
lissol.setOption(_lis_option);
lissol.solve(lisA, lisb, lisx);
for (std::size_t i=0; i<lisx.size(); i++)
x[i] = lisx[i];
return true; // TODO implement checks
}
EigenLinearSolver::EigenLinearSolver(EigenMatrix &A,
const std::string /*solver_name*/,
BaseLib::ConfigTree const*const option)
{
if (option)
setOption(*option);
if (!A.getRawMatrix().isCompressed())
A.getRawMatrix().makeCompressed();
if (_option.solver_type==EigenOption::SolverType::SparseLU) {
using SolverType = Eigen::SparseLU<EigenMatrix::RawMatrixType, Eigen::COLAMDOrdering<int>>;
_solver = new details::EigenDirectLinearSolver<SolverType, IEigenSolver>(A.getRawMatrix());
} else if (_option.solver_type==EigenOption::SolverType::BiCGSTAB) {
using SolverType = Eigen::BiCGSTAB<EigenMatrix::RawMatrixType, Eigen::DiagonalPreconditioner<double>>;
_solver = new details::EigenIterativeLinearSolver<SolverType, IEigenSolver>(A.getRawMatrix());
} else if (_option.solver_type==EigenOption::SolverType::CG) {
using SolverType = Eigen::ConjugateGradient<EigenMatrix::RawMatrixType, Eigen::Lower, Eigen::DiagonalPreconditioner<double>>;
_solver = new details::EigenIterativeLinearSolver<SolverType, IEigenSolver>(A.getRawMatrix());
}
}
void addToMatrix(EigenMatrix& m,
std::initializer_list<double> values)
{
auto const rows = m.getNumberOfRows();
auto const cols = m.getNumberOfColumns();
assert(static_cast<EigenMatrix::IndexType>(values.size()) == rows*cols);
Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> tmp(rows, cols);
auto it = values.begin();
for (GlobalIndexType r=0; r<rows; ++r) {
for (GlobalIndexType c=0; c<cols; ++c) {
tmp(r, c) = *(it++);
}
}
m.getRawMatrix() += tmp.sparseView();
}
void setMatrix(EigenMatrix& m, Eigen::MatrixXd const& tmp)
{
m.getRawMatrix() = tmp.sparseView();
}
