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Aurora/thirdparty/include/Spectra/MatOp/DenseCholesky.h
2023-06-02 10:49:02 +08:00

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// Copyright (C) 2016-2022 Yixuan Qiu <yixuan.qiu@cos.name>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at https://mozilla.org/MPL/2.0/.
#ifndef SPECTRA_DENSE_CHOLESKY_H
#define SPECTRA_DENSE_CHOLESKY_H
#include <Eigen/Core>
#include <Eigen/Cholesky>
#include <stdexcept>
#include "../Util/CompInfo.h"
namespace Spectra {
///
/// \ingroup MatOp
///
/// This class defines the operations related to Cholesky decomposition on a
/// positive definite matrix, \f$B=LL'\f$, where \f$L\f$ is a lower triangular
/// matrix. It is mainly used in the SymGEigsSolver generalized eigen solver
/// in the Cholesky decomposition mode.
///
/// \tparam Scalar_ The element type of the matrix, for example,
/// `float`, `double`, and `long double`.
/// \tparam Uplo Either `Eigen::Lower` or `Eigen::Upper`, indicating which
/// triangular part of the matrix is used.
/// \tparam Flags Either `Eigen::ColMajor` or `Eigen::RowMajor`, indicating
/// the storage format of the input matrix.
///
template <typename Scalar_, int Uplo = Eigen::Lower, int Flags = Eigen::ColMajor>
class DenseCholesky
{
public:
///
/// Element type of the matrix.
///
using Scalar = Scalar_;
private:
using Index = Eigen::Index;
using Matrix = Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flags>;
using Vector = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;
using MapConstVec = Eigen::Map<const Vector>;
using MapVec = Eigen::Map<Vector>;
const Index m_n;
Eigen::LLT<Matrix, Uplo> m_decomp;
CompInfo m_info; // status of the decomposition
public:
///
/// Constructor to create the matrix operation object.
///
/// \param mat An **Eigen** matrix object, whose type can be
/// `Eigen::Matrix<Scalar, ...>` (e.g. `Eigen::MatrixXd` and
/// `Eigen::MatrixXf`), or its mapped version
/// (e.g. `Eigen::Map<Eigen::MatrixXd>`).
///
template <typename Derived>
DenseCholesky(const Eigen::MatrixBase<Derived>& mat) :
m_n(mat.rows()), m_info(CompInfo::NotComputed)
{
static_assert(
static_cast<int>(Derived::PlainObject::IsRowMajor) == static_cast<int>(Matrix::IsRowMajor),
"DenseCholesky: the \"Flags\" template parameter does not match the input matrix (Eigen::ColMajor/Eigen::RowMajor)");
if (m_n != mat.cols())
throw std::invalid_argument("DenseCholesky: matrix must be square");
m_decomp.compute(mat);
m_info = (m_decomp.info() == Eigen::Success) ?
CompInfo::Successful :
CompInfo::NumericalIssue;
}
///
/// Returns the number of rows of the underlying matrix.
///
Index rows() const { return m_n; }
///
/// Returns the number of columns of the underlying matrix.
///
Index cols() const { return m_n; }
///
/// Returns the status of the computation.
/// The full list of enumeration values can be found in \ref Enumerations.
///
CompInfo info() const { return m_info; }
///
/// Performs the lower triangular solving operation \f$y=L^{-1}x\f$.
///
/// \param x_in Pointer to the \f$x\f$ vector.
/// \param y_out Pointer to the \f$y\f$ vector.
///
// y_out = inv(L) * x_in
void lower_triangular_solve(const Scalar* x_in, Scalar* y_out) const
{
MapConstVec x(x_in, m_n);
MapVec y(y_out, m_n);
y.noalias() = m_decomp.matrixL().solve(x);
}
///
/// Performs the upper triangular solving operation \f$y=(L')^{-1}x\f$.
///
/// \param x_in Pointer to the \f$x\f$ vector.
/// \param y_out Pointer to the \f$y\f$ vector.
///
// y_out = inv(L') * x_in
void upper_triangular_solve(const Scalar* x_in, Scalar* y_out) const
{
MapConstVec x(x_in, m_n);
MapVec y(y_out, m_n);
y.noalias() = m_decomp.matrixU().solve(x);
}
};
} // namespace Spectra
#endif // SPECTRA_DENSE_CHOLESKY_H