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diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h b/third_party/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h deleted file mode 100644 index dc240e13e1..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h +++ /dev/null @@ -1,341 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// 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 http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_GENERALIZEDEIGENSOLVER_H -#define EIGEN_GENERALIZEDEIGENSOLVER_H - -#include "./RealQZ.h" - -namespace Eigen { - -/** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \class GeneralizedEigenSolver - * - * \brief Computes the generalized eigenvalues and eigenvectors of a pair of general matrices - * - * \tparam _MatrixType the type of the matrices of which we are computing the - * eigen-decomposition; this is expected to be an instantiation of the Matrix - * class template. Currently, only real matrices are supported. - * - * The generalized eigenvalues and eigenvectors of a matrix pair \f$ A \f$ and \f$ B \f$ are scalars - * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda Bv \f$. If - * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and - * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V = - * B V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we - * have \f$ A = B V D V^{-1} \f$. This is called the generalized eigen-decomposition. - * - * The generalized eigenvalues and eigenvectors of a matrix pair may be complex, even when the - * matrices are real. Moreover, the generalized eigenvalue might be infinite if the matrix B is - * singular. To workaround this difficulty, the eigenvalues are provided as a pair of complex \f$ \alpha \f$ - * and real \f$ \beta \f$ such that: \f$ \lambda_i = \alpha_i / \beta_i \f$. If \f$ \beta_i \f$ is (nearly) zero, - * then one can consider the well defined left eigenvalue \f$ \mu = \beta_i / \alpha_i\f$ such that: - * \f$ \mu_i A v_i = B v_i \f$, or even \f$ \mu_i u_i^T A = u_i^T B \f$ where \f$ u_i \f$ is - * called the left eigenvector. - * - * Call the function compute() to compute the generalized eigenvalues and eigenvectors of - * a given matrix pair. Alternatively, you can use the - * GeneralizedEigenSolver(const MatrixType&, const MatrixType&, bool) constructor which computes the - * eigenvalues and eigenvectors at construction time. Once the eigenvalue and - * eigenvectors are computed, they can be retrieved with the eigenvalues() and - * eigenvectors() functions. - * - * Here is an usage example of this class: - * Example: \include GeneralizedEigenSolver.cpp - * Output: \verbinclude GeneralizedEigenSolver.out - * - * \sa MatrixBase::eigenvalues(), class ComplexEigenSolver, class SelfAdjointEigenSolver - */ -template<typename _MatrixType> class GeneralizedEigenSolver -{ - public: - - /** \brief Synonym for the template parameter \p _MatrixType. */ - typedef _MatrixType MatrixType; - - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - - /** \brief Scalar type for matrices of type #MatrixType. */ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef typename MatrixType::Index Index; - - /** \brief Complex scalar type for #MatrixType. - * - * This is \c std::complex<Scalar> if #Scalar is real (e.g., - * \c float or \c double) and just \c Scalar if #Scalar is - * complex. - */ - typedef std::complex<RealScalar> ComplexScalar; - - /** \brief Type for vector of real scalar values eigenvalues as returned by betas(). - * - * This is a column vector with entries of type #Scalar. - * The length of the vector is the size of #MatrixType. - */ - typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> VectorType; - - /** \brief Type for vector of complex scalar values eigenvalues as returned by betas(). - * - * This is a column vector with entries of type #ComplexScalar. - * The length of the vector is the size of #MatrixType. - */ - typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ComplexVectorType; - - /** \brief Expression type for the eigenvalues as returned by eigenvalues(). - */ - typedef CwiseBinaryOp<internal::scalar_quotient_op<ComplexScalar,Scalar>,ComplexVectorType,VectorType> EigenvalueType; - - /** \brief Type for matrix of eigenvectors as returned by eigenvectors(). - * - * This is a square matrix with entries of type #ComplexScalar. - * The size is the same as the size of #MatrixType. - */ - typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType; - - /** \brief Default constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via EigenSolver::compute(const MatrixType&, bool). - * - * \sa compute() for an example. - */ - GeneralizedEigenSolver() : m_eivec(), m_alphas(), m_betas(), m_isInitialized(false), m_realQZ(), m_matS(), m_tmp() {} - - /** \brief Default constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem \a size. - * \sa GeneralizedEigenSolver() - */ - GeneralizedEigenSolver(Index size) - : m_eivec(size, size), - m_alphas(size), - m_betas(size), - m_isInitialized(false), - m_eigenvectorsOk(false), - m_realQZ(size), - m_matS(size, size), - m_tmp(size) - {} - - /** \brief Constructor; computes the generalized eigendecomposition of given matrix pair. - * - * \param[in] A Square matrix whose eigendecomposition is to be computed. - * \param[in] B Square matrix whose eigendecomposition is to be computed. - * \param[in] computeEigenvectors If true, both the eigenvectors and the - * eigenvalues are computed; if false, only the eigenvalues are computed. - * - * This constructor calls compute() to compute the generalized eigenvalues - * and eigenvectors. - * - * \sa compute() - */ - GeneralizedEigenSolver(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true) - : m_eivec(A.rows(), A.cols()), - m_alphas(A.cols()), - m_betas(A.cols()), - m_isInitialized(false), - m_eigenvectorsOk(false), - m_realQZ(A.cols()), - m_matS(A.rows(), A.cols()), - m_tmp(A.cols()) - { - compute(A, B, computeEigenvectors); - } - - /* \brief Returns the computed generalized eigenvectors. - * - * \returns %Matrix whose columns are the (possibly complex) eigenvectors. - * - * \pre Either the constructor - * GeneralizedEigenSolver(const MatrixType&,const MatrixType&, bool) or the member function - * compute(const MatrixType&, const MatrixType& bool) has been called before, and - * \p computeEigenvectors was set to true (the default). - * - * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding - * to eigenvalue number \f$ k \f$ as returned by eigenvalues(). The - * eigenvectors are normalized to have (Euclidean) norm equal to one. The - * matrix returned by this function is the matrix \f$ V \f$ in the - * generalized eigendecomposition \f$ A = B V D V^{-1} \f$, if it exists. - * - * \sa eigenvalues() - */ -// EigenvectorsType eigenvectors() const; - - /** \brief Returns an expression of the computed generalized eigenvalues. - * - * \returns An expression of the column vector containing the eigenvalues. - * - * It is a shortcut for \code this->alphas().cwiseQuotient(this->betas()); \endcode - * Not that betas might contain zeros. It is therefore not recommended to use this function, - * but rather directly deal with the alphas and betas vectors. - * - * \pre Either the constructor - * GeneralizedEigenSolver(const MatrixType&,const MatrixType&,bool) or the member function - * compute(const MatrixType&,const MatrixType&,bool) has been called before. - * - * The eigenvalues are repeated according to their algebraic multiplicity, - * so there are as many eigenvalues as rows in the matrix. The eigenvalues - * are not sorted in any particular order. - * - * \sa alphas(), betas(), eigenvectors() - */ - EigenvalueType eigenvalues() const - { - eigen_assert(m_isInitialized && "GeneralizedEigenSolver is not initialized."); - return EigenvalueType(m_alphas,m_betas); - } - - /** \returns A const reference to the vectors containing the alpha values - * - * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j). - * - * \sa betas(), eigenvalues() */ - ComplexVectorType alphas() const - { - eigen_assert(m_isInitialized && "GeneralizedEigenSolver is not initialized."); - return m_alphas; - } - - /** \returns A const reference to the vectors containing the beta values - * - * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j). - * - * \sa alphas(), eigenvalues() */ - VectorType betas() const - { - eigen_assert(m_isInitialized && "GeneralizedEigenSolver is not initialized."); - return m_betas; - } - - /** \brief Computes generalized eigendecomposition of given matrix. - * - * \param[in] A Square matrix whose eigendecomposition is to be computed. - * \param[in] B Square matrix whose eigendecomposition is to be computed. - * \param[in] computeEigenvectors If true, both the eigenvectors and the - * eigenvalues are computed; if false, only the eigenvalues are - * computed. - * \returns Reference to \c *this - * - * This function computes the eigenvalues of the real matrix \p matrix. - * The eigenvalues() function can be used to retrieve them. If - * \p computeEigenvectors is true, then the eigenvectors are also computed - * and can be retrieved by calling eigenvectors(). - * - * The matrix is first reduced to real generalized Schur form using the RealQZ - * class. The generalized Schur decomposition is then used to compute the eigenvalues - * and eigenvectors. - * - * The cost of the computation is dominated by the cost of the - * generalized Schur decomposition. - * - * This method reuses of the allocated data in the GeneralizedEigenSolver object. - */ - GeneralizedEigenSolver& compute(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true); - - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "EigenSolver is not initialized."); - return m_realQZ.info(); - } - - /** Sets the maximal number of iterations allowed. - */ - GeneralizedEigenSolver& setMaxIterations(Index maxIters) - { - m_realQZ.setMaxIterations(maxIters); - return *this; - } - - protected: - MatrixType m_eivec; - ComplexVectorType m_alphas; - VectorType m_betas; - bool m_isInitialized; - bool m_eigenvectorsOk; - RealQZ<MatrixType> m_realQZ; - MatrixType m_matS; - - typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType; - ColumnVectorType m_tmp; -}; - -//template<typename MatrixType> -//typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType GeneralizedEigenSolver<MatrixType>::eigenvectors() const -//{ -// eigen_assert(m_isInitialized && "EigenSolver is not initialized."); -// eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues."); -// Index n = m_eivec.cols(); -// EigenvectorsType matV(n,n); -// // TODO -// return matV; -//} - -template<typename MatrixType> -GeneralizedEigenSolver<MatrixType>& -GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, const MatrixType& B, bool computeEigenvectors) -{ - using std::sqrt; - using std::abs; - eigen_assert(A.cols() == A.rows() && B.cols() == A.rows() && B.cols() == B.rows()); - - // Reduce to generalized real Schur form: - // A = Q S Z and B = Q T Z - m_realQZ.compute(A, B, computeEigenvectors); - - if (m_realQZ.info() == Success) - { - m_matS = m_realQZ.matrixS(); - if (computeEigenvectors) - m_eivec = m_realQZ.matrixZ().transpose(); - - // Compute eigenvalues from matS - m_alphas.resize(A.cols()); - m_betas.resize(A.cols()); - Index i = 0; - while (i < A.cols()) - { - if (i == A.cols() - 1 || m_matS.coeff(i+1, i) == Scalar(0)) - { - m_alphas.coeffRef(i) = m_matS.coeff(i, i); - m_betas.coeffRef(i) = m_realQZ.matrixT().coeff(i,i); - ++i; - } - else - { - Scalar p = Scalar(0.5) * (m_matS.coeff(i, i) - m_matS.coeff(i+1, i+1)); - Scalar z = sqrt(abs(p * p + m_matS.coeff(i+1, i) * m_matS.coeff(i, i+1))); - m_alphas.coeffRef(i) = ComplexScalar(m_matS.coeff(i+1, i+1) + p, z); - m_alphas.coeffRef(i+1) = ComplexScalar(m_matS.coeff(i+1, i+1) + p, -z); - - m_betas.coeffRef(i) = m_realQZ.matrixT().coeff(i,i); - m_betas.coeffRef(i+1) = m_realQZ.matrixT().coeff(i,i); - i += 2; - } - } - } - - m_isInitialized = true; - m_eigenvectorsOk = false;//computeEigenvectors; - - return *this; -} - -} // end namespace Eigen - -#endif // EIGEN_GENERALIZEDEIGENSOLVER_H |