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347 lines
12 KiB
C++
347 lines
12 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2009 Claire Maurice
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// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
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// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
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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 http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_COMPLEX_EIGEN_SOLVER_H
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#define EIGEN_COMPLEX_EIGEN_SOLVER_H
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#include "./ComplexSchur.h"
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namespace Eigen {
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/** \eigenvalues_module \ingroup Eigenvalues_Module
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*
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*
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* \class ComplexEigenSolver
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*
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* \brief Computes eigenvalues and eigenvectors of general complex matrices
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*
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* \tparam _MatrixType the type of the matrix of which we are
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* computing the eigendecomposition; this is expected to be an
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* instantiation of the Matrix class template.
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*
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* The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars
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* \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda v
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* \f$. If \f$ D \f$ is a diagonal matrix with the eigenvalues on
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* the diagonal, and \f$ V \f$ is a matrix with the eigenvectors as
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* its columns, then \f$ A V = V D \f$. The matrix \f$ V \f$ is
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* almost always invertible, in which case we have \f$ A = V D V^{-1}
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* \f$. This is called the eigendecomposition.
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*
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* The main function in this class is compute(), which computes the
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* eigenvalues and eigenvectors of a given function. The
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* documentation for that function contains an example showing the
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* main features of the class.
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*
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* \sa class EigenSolver, class SelfAdjointEigenSolver
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*/
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template<typename _MatrixType> class ComplexEigenSolver
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{
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public:
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/** \brief Synonym for the template parameter \p _MatrixType. */
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typedef _MatrixType MatrixType;
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enum {
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RowsAtCompileTime = MatrixType::RowsAtCompileTime,
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ColsAtCompileTime = MatrixType::ColsAtCompileTime,
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Options = MatrixType::Options,
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MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
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MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
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};
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/** \brief Scalar type for matrices of type #MatrixType. */
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typedef typename MatrixType::Scalar Scalar;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
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/** \brief Complex scalar type for #MatrixType.
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*
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* This is \c std::complex<Scalar> if #Scalar is real (e.g.,
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* \c float or \c double) and just \c Scalar if #Scalar is
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* complex.
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*/
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typedef std::complex<RealScalar> ComplexScalar;
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/** \brief Type for vector of eigenvalues as returned by eigenvalues().
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*
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* This is a column vector with entries of type #ComplexScalar.
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* The length of the vector is the size of #MatrixType.
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*/
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typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options&(~RowMajor), MaxColsAtCompileTime, 1> EigenvalueType;
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/** \brief Type for matrix of eigenvectors as returned by eigenvectors().
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*
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* This is a square matrix with entries of type #ComplexScalar.
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* The size is the same as the size of #MatrixType.
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*/
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typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorType;
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/** \brief Default constructor.
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*
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* The default constructor is useful in cases in which the user intends to
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* perform decompositions via compute().
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*/
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ComplexEigenSolver()
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: m_eivec(),
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m_eivalues(),
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m_schur(),
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m_isInitialized(false),
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m_eigenvectorsOk(false),
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m_matX()
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{}
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/** \brief Default Constructor with memory preallocation
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*
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* Like the default constructor but with preallocation of the internal data
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* according to the specified problem \a size.
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* \sa ComplexEigenSolver()
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*/
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explicit ComplexEigenSolver(Index size)
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: m_eivec(size, size),
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m_eivalues(size),
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m_schur(size),
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m_isInitialized(false),
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m_eigenvectorsOk(false),
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m_matX(size, size)
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{}
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/** \brief Constructor; computes eigendecomposition of given matrix.
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*
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* \param[in] matrix Square matrix whose eigendecomposition is to be computed.
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* \param[in] computeEigenvectors If true, both the eigenvectors and the
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* eigenvalues are computed; if false, only the eigenvalues are
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* computed.
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*
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* This constructor calls compute() to compute the eigendecomposition.
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*/
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template<typename InputType>
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explicit ComplexEigenSolver(const EigenBase<InputType>& matrix, bool computeEigenvectors = true)
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: m_eivec(matrix.rows(),matrix.cols()),
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m_eivalues(matrix.cols()),
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m_schur(matrix.rows()),
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m_isInitialized(false),
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m_eigenvectorsOk(false),
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m_matX(matrix.rows(),matrix.cols())
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{
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compute(matrix.derived(), computeEigenvectors);
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}
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/** \brief Returns the eigenvectors of given matrix.
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*
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* \returns A const reference to the matrix whose columns are the eigenvectors.
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*
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* \pre Either the constructor
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* ComplexEigenSolver(const MatrixType& matrix, bool) or the member
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* function compute(const MatrixType& matrix, bool) has been called before
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* to compute the eigendecomposition of a matrix, and
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* \p computeEigenvectors was set to true (the default).
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*
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* This function returns a matrix whose columns are the eigenvectors. Column
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* \f$ k \f$ is an eigenvector corresponding to eigenvalue number \f$ k
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* \f$ as returned by eigenvalues(). The eigenvectors are normalized to
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* have (Euclidean) norm equal to one. The matrix returned by this
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* function is the matrix \f$ V \f$ in the eigendecomposition \f$ A = V D
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* V^{-1} \f$, if it exists.
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*
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* Example: \include ComplexEigenSolver_eigenvectors.cpp
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* Output: \verbinclude ComplexEigenSolver_eigenvectors.out
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*/
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const EigenvectorType& eigenvectors() const
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{
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eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
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eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
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return m_eivec;
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}
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/** \brief Returns the eigenvalues of given matrix.
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*
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* \returns A const reference to the column vector containing the eigenvalues.
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*
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* \pre Either the constructor
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* ComplexEigenSolver(const MatrixType& matrix, bool) or the member
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* function compute(const MatrixType& matrix, bool) has been called before
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* to compute the eigendecomposition of a matrix.
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*
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* This function returns a column vector containing the
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* eigenvalues. Eigenvalues are repeated according to their
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* algebraic multiplicity, so there are as many eigenvalues as
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* rows in the matrix. The eigenvalues are not sorted in any particular
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* order.
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*
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* Example: \include ComplexEigenSolver_eigenvalues.cpp
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* Output: \verbinclude ComplexEigenSolver_eigenvalues.out
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*/
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const EigenvalueType& eigenvalues() const
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{
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eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
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return m_eivalues;
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}
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/** \brief Computes eigendecomposition of given matrix.
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*
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* \param[in] matrix Square matrix whose eigendecomposition is to be computed.
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* \param[in] computeEigenvectors If true, both the eigenvectors and the
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* eigenvalues are computed; if false, only the eigenvalues are
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* computed.
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* \returns Reference to \c *this
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*
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* This function computes the eigenvalues of the complex matrix \p matrix.
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* The eigenvalues() function can be used to retrieve them. If
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* \p computeEigenvectors is true, then the eigenvectors are also computed
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* and can be retrieved by calling eigenvectors().
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*
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* The matrix is first reduced to Schur form using the
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* ComplexSchur class. The Schur decomposition is then used to
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* compute the eigenvalues and eigenvectors.
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*
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* The cost of the computation is dominated by the cost of the
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* Schur decomposition, which is \f$ O(n^3) \f$ where \f$ n \f$
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* is the size of the matrix.
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*
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* Example: \include ComplexEigenSolver_compute.cpp
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* Output: \verbinclude ComplexEigenSolver_compute.out
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*/
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template<typename InputType>
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ComplexEigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
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/** \brief Reports whether previous computation was successful.
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*
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* \returns \c Success if computation was successful, \c NoConvergence otherwise.
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*/
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ComputationInfo info() const
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{
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eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
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return m_schur.info();
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}
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/** \brief Sets the maximum number of iterations allowed. */
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ComplexEigenSolver& setMaxIterations(Index maxIters)
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{
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m_schur.setMaxIterations(maxIters);
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return *this;
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}
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/** \brief Returns the maximum number of iterations. */
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Index getMaxIterations()
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{
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return m_schur.getMaxIterations();
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}
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protected:
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static void check_template_parameters()
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{
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EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
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}
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EigenvectorType m_eivec;
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EigenvalueType m_eivalues;
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ComplexSchur<MatrixType> m_schur;
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bool m_isInitialized;
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bool m_eigenvectorsOk;
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EigenvectorType m_matX;
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private:
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void doComputeEigenvectors(RealScalar matrixnorm);
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void sortEigenvalues(bool computeEigenvectors);
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};
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template<typename MatrixType>
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template<typename InputType>
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ComplexEigenSolver<MatrixType>&
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ComplexEigenSolver<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeEigenvectors)
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{
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check_template_parameters();
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// this code is inspired from Jampack
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eigen_assert(matrix.cols() == matrix.rows());
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// Do a complex Schur decomposition, A = U T U^*
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// The eigenvalues are on the diagonal of T.
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m_schur.compute(matrix.derived(), computeEigenvectors);
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if(m_schur.info() == Success)
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{
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m_eivalues = m_schur.matrixT().diagonal();
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if(computeEigenvectors)
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doComputeEigenvectors(m_schur.matrixT().norm());
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sortEigenvalues(computeEigenvectors);
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}
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m_isInitialized = true;
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m_eigenvectorsOk = computeEigenvectors;
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return *this;
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}
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template<typename MatrixType>
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void ComplexEigenSolver<MatrixType>::doComputeEigenvectors(RealScalar matrixnorm)
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{
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const Index n = m_eivalues.size();
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matrixnorm = numext::maxi(matrixnorm,(std::numeric_limits<RealScalar>::min)());
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// Compute X such that T = X D X^(-1), where D is the diagonal of T.
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// The matrix X is unit triangular.
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m_matX = EigenvectorType::Zero(n, n);
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for(Index k=n-1 ; k>=0 ; k--)
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{
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m_matX.coeffRef(k,k) = ComplexScalar(1.0,0.0);
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// Compute X(i,k) using the (i,k) entry of the equation X T = D X
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for(Index i=k-1 ; i>=0 ; i--)
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{
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m_matX.coeffRef(i,k) = -m_schur.matrixT().coeff(i,k);
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if(k-i-1>0)
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m_matX.coeffRef(i,k) -= (m_schur.matrixT().row(i).segment(i+1,k-i-1) * m_matX.col(k).segment(i+1,k-i-1)).value();
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ComplexScalar z = m_schur.matrixT().coeff(i,i) - m_schur.matrixT().coeff(k,k);
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if(z==ComplexScalar(0))
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{
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// If the i-th and k-th eigenvalue are equal, then z equals 0.
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// Use a small value instead, to prevent division by zero.
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numext::real_ref(z) = NumTraits<RealScalar>::epsilon() * matrixnorm;
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}
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m_matX.coeffRef(i,k) = m_matX.coeff(i,k) / z;
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}
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}
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// Compute V as V = U X; now A = U T U^* = U X D X^(-1) U^* = V D V^(-1)
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m_eivec.noalias() = m_schur.matrixU() * m_matX;
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// .. and normalize the eigenvectors
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for(Index k=0 ; k<n ; k++)
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{
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m_eivec.col(k).normalize();
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}
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}
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template<typename MatrixType>
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void ComplexEigenSolver<MatrixType>::sortEigenvalues(bool computeEigenvectors)
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{
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const Index n = m_eivalues.size();
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for (Index i=0; i<n; i++)
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{
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Index k;
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m_eivalues.cwiseAbs().tail(n-i).minCoeff(&k);
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if (k != 0)
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{
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k += i;
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std::swap(m_eivalues[k],m_eivalues[i]);
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if(computeEigenvectors)
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m_eivec.col(i).swap(m_eivec.col(k));
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}
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}
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}
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} // end namespace Eigen
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#endif // EIGEN_COMPLEX_EIGEN_SOLVER_H
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