88 lines
2.5 KiB
C++
88 lines
2.5 KiB
C++
// Copyright (C) 2016-2022 Yixuan Qiu <yixuan.qiu@cos.name>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at https://mozilla.org/MPL/2.0/.
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#ifndef SPECTRA_SYM_GEIGS_CHOLESKY_OP_H
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#define SPECTRA_SYM_GEIGS_CHOLESKY_OP_H
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#include <Eigen/Core>
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#include "../DenseSymMatProd.h"
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#include "../DenseCholesky.h"
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namespace Spectra {
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///
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/// \ingroup Operators
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///
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/// This class defines the matrix operation for generalized eigen solver in the
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/// Cholesky decomposition mode. It calculates \f$y=L^{-1}A(L')^{-1}x\f$ for any
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/// vector \f$x\f$, where \f$L\f$ is the Cholesky decomposition of \f$B\f$.
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/// This class is intended for internal use.
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///
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template <typename OpType = DenseSymMatProd<double>,
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typename BOpType = DenseCholesky<double>>
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class SymGEigsCholeskyOp
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{
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public:
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using Scalar = typename OpType::Scalar;
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private:
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using Index = Eigen::Index;
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using Vector = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;
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const OpType& m_op;
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const BOpType& m_Bop;
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mutable Vector m_cache; // temporary working space
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public:
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///
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/// Constructor to create the matrix operation object.
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///
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/// \param op The \f$A\f$ matrix operation object.
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/// \param Bop The \f$B\f$ matrix operation object.
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///
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SymGEigsCholeskyOp(const OpType& op, const BOpType& Bop) :
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m_op(op), m_Bop(Bop), m_cache(op.rows())
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{}
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///
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/// Move constructor.
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///
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SymGEigsCholeskyOp(SymGEigsCholeskyOp&& other) :
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m_op(other.m_op), m_Bop(other.m_Bop)
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{
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// We emulate the move constructor for Vector using Vector::swap()
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m_cache.swap(other.m_cache);
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}
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///
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/// Return the number of rows of the underlying matrix.
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///
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Index rows() const { return m_Bop.rows(); }
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///
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/// Return the number of columns of the underlying matrix.
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///
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Index cols() const { return m_Bop.rows(); }
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///
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/// Perform the matrix operation \f$y=L^{-1}A(L')^{-1}x\f$.
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///
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/// \param x_in Pointer to the \f$x\f$ vector.
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/// \param y_out Pointer to the \f$y\f$ vector.
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///
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// y_out = inv(L) * A * inv(L') * x_in
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void perform_op(const Scalar* x_in, Scalar* y_out) const
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{
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m_Bop.upper_triangular_solve(x_in, y_out);
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m_op.perform_op(y_out, m_cache.data());
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m_Bop.lower_triangular_solve(m_cache.data(), y_out);
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}
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};
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} // namespace Spectra
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#endif // SPECTRA_SYM_GEIGS_CHOLESKY_OP_H
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