| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2012-2016 Gael Guennebaud <gael.guennebaud@inria.fr> |
| // Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk> |
| // Copyright (C) 2016 Tobias Wood <tobias@spinicist.org.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" |
| |
| // IWYU pragma: private |
| #include "./InternalHeaderCheck.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 = internal::traits<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 Eigen::Index Index; ///< \deprecated since Eigen 3.3 |
| |
| /** \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 alphas(). |
| * |
| * 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_computeEigenvectors(false), m_isInitialized(false), m_realQZ() {} |
| |
| /** \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() |
| */ |
| explicit GeneralizedEigenSolver(Index size) |
| : m_eivec(size, size), |
| m_alphas(size), |
| m_betas(size), |
| m_computeEigenvectors(false), |
| m_isInitialized(false), |
| m_realQZ(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_computeEigenvectors(false), |
| m_isInitialized(false), |
| m_realQZ(A.cols()), |
| m_tmp(A.cols()) { |
| compute(A, B, computeEigenvectors); |
| } |
| |
| /* \brief Returns the computed generalized eigenvectors. |
| * |
| * \returns %Matrix whose columns are the (possibly complex) right eigenvectors. |
| * i.e. the eigenvectors that solve (A - l*B)x = 0. The ordering matches the eigenvalues. |
| * |
| * \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). |
| * |
| * \sa eigenvalues() |
| */ |
| EigenvectorsType eigenvectors() const { |
| eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute eigenvectors"); |
| eigen_assert(m_computeEigenvectors && "Eigenvectors for GeneralizedEigenSolver were not calculated"); |
| return m_eivec; |
| } |
| |
| /** \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(info() == Success && "GeneralizedEigenSolver failed to compute eigenvalues."); |
| 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() */ |
| const ComplexVectorType& alphas() const { |
| eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute alphas."); |
| 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() */ |
| const VectorType& betas() const { |
| eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute betas."); |
| 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: |
| EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar) |
| EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL) |
| |
| EigenvectorsType m_eivec; |
| ComplexVectorType m_alphas; |
| VectorType m_betas; |
| bool m_computeEigenvectors; |
| bool m_isInitialized; |
| RealQZ<MatrixType> m_realQZ; |
| ComplexVectorType m_tmp; |
| }; |
| |
| template <typename MatrixType> |
| GeneralizedEigenSolver<MatrixType>& GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, |
| const MatrixType& B, |
| bool computeEigenvectors) { |
| using std::abs; |
| using std::sqrt; |
| eigen_assert(A.cols() == A.rows() && B.cols() == A.rows() && B.cols() == B.rows()); |
| Index size = A.cols(); |
| // 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) { |
| // Resize storage |
| m_alphas.resize(size); |
| m_betas.resize(size); |
| if (computeEigenvectors) { |
| m_eivec.resize(size, size); |
| m_tmp.resize(size); |
| } |
| |
| // Aliases: |
| Map<VectorType> v(reinterpret_cast<Scalar*>(m_tmp.data()), size); |
| ComplexVectorType& cv = m_tmp; |
| const MatrixType& mS = m_realQZ.matrixS(); |
| const MatrixType& mT = m_realQZ.matrixT(); |
| |
| Index i = 0; |
| while (i < size) { |
| if (i == size - 1 || mS.coeff(i + 1, i) == Scalar(0)) { |
| // Real eigenvalue |
| m_alphas.coeffRef(i) = mS.diagonal().coeff(i); |
| m_betas.coeffRef(i) = mT.diagonal().coeff(i); |
| if (computeEigenvectors) { |
| v.setConstant(Scalar(0.0)); |
| v.coeffRef(i) = Scalar(1.0); |
| // For singular eigenvalues do nothing more |
| if (abs(m_betas.coeffRef(i)) >= (std::numeric_limits<RealScalar>::min)()) { |
| // Non-singular eigenvalue |
| const Scalar alpha = real(m_alphas.coeffRef(i)); |
| const Scalar beta = m_betas.coeffRef(i); |
| for (Index j = i - 1; j >= 0; j--) { |
| const Index st = j + 1; |
| const Index sz = i - j; |
| if (j > 0 && mS.coeff(j, j - 1) != Scalar(0)) { |
| // 2x2 block |
| Matrix<Scalar, 2, 1> rhs = (alpha * mT.template block<2, Dynamic>(j - 1, st, 2, sz) - |
| beta * mS.template block<2, Dynamic>(j - 1, st, 2, sz)) |
| .lazyProduct(v.segment(st, sz)); |
| Matrix<Scalar, 2, 2> lhs = |
| beta * mS.template block<2, 2>(j - 1, j - 1) - alpha * mT.template block<2, 2>(j - 1, j - 1); |
| v.template segment<2>(j - 1) = lhs.partialPivLu().solve(rhs); |
| j--; |
| } else { |
| v.coeffRef(j) = -v.segment(st, sz) |
| .transpose() |
| .cwiseProduct(beta * mS.block(j, st, 1, sz) - alpha * mT.block(j, st, 1, sz)) |
| .sum() / |
| (beta * mS.coeffRef(j, j) - alpha * mT.coeffRef(j, j)); |
| } |
| } |
| } |
| m_eivec.col(i).real().noalias() = m_realQZ.matrixZ().transpose() * v; |
| m_eivec.col(i).real().normalize(); |
| m_eivec.col(i).imag().setConstant(0); |
| } |
| ++i; |
| } else { |
| // We need to extract the generalized eigenvalues of the pair of a general 2x2 block S and a positive diagonal |
| // 2x2 block T Then taking beta=T_00*T_11, we can avoid any division, and alpha is the eigenvalues of A = (U^-1 |
| // * S * U) * diag(T_11,T_00): |
| |
| // T = [a 0] |
| // [0 b] |
| RealScalar a = mT.diagonal().coeff(i), b = mT.diagonal().coeff(i + 1); |
| const RealScalar beta = m_betas.coeffRef(i) = m_betas.coeffRef(i + 1) = a * b; |
| |
| // ^^ NOTE: using diagonal()(i) instead of coeff(i,i) workarounds a MSVC bug. |
| Matrix<RealScalar, 2, 2> S2 = mS.template block<2, 2>(i, i) * Matrix<Scalar, 2, 1>(b, a).asDiagonal(); |
| |
| Scalar p = Scalar(0.5) * (S2.coeff(0, 0) - S2.coeff(1, 1)); |
| Scalar z = sqrt(abs(p * p + S2.coeff(1, 0) * S2.coeff(0, 1))); |
| const ComplexScalar alpha = ComplexScalar(S2.coeff(1, 1) + p, (beta > 0) ? z : -z); |
| m_alphas.coeffRef(i) = conj(alpha); |
| m_alphas.coeffRef(i + 1) = alpha; |
| |
| if (computeEigenvectors) { |
| // Compute eigenvector in position (i+1) and then position (i) is just the conjugate |
| cv.setZero(); |
| cv.coeffRef(i + 1) = Scalar(1.0); |
| // here, the "static_cast" workaound expression template issues. |
| cv.coeffRef(i) = -(static_cast<Scalar>(beta * mS.coeffRef(i, i + 1)) - alpha * mT.coeffRef(i, i + 1)) / |
| (static_cast<Scalar>(beta * mS.coeffRef(i, i)) - alpha * mT.coeffRef(i, i)); |
| for (Index j = i - 1; j >= 0; j--) { |
| const Index st = j + 1; |
| const Index sz = i + 1 - j; |
| if (j > 0 && mS.coeff(j, j - 1) != Scalar(0)) { |
| // 2x2 block |
| Matrix<ComplexScalar, 2, 1> rhs = (alpha * mT.template block<2, Dynamic>(j - 1, st, 2, sz) - |
| beta * mS.template block<2, Dynamic>(j - 1, st, 2, sz)) |
| .lazyProduct(cv.segment(st, sz)); |
| Matrix<ComplexScalar, 2, 2> lhs = |
| beta * mS.template block<2, 2>(j - 1, j - 1) - alpha * mT.template block<2, 2>(j - 1, j - 1); |
| cv.template segment<2>(j - 1) = lhs.partialPivLu().solve(rhs); |
| j--; |
| } else { |
| cv.coeffRef(j) = cv.segment(st, sz) |
| .transpose() |
| .cwiseProduct(beta * mS.block(j, st, 1, sz) - alpha * mT.block(j, st, 1, sz)) |
| .sum() / |
| (alpha * mT.coeffRef(j, j) - static_cast<Scalar>(beta * mS.coeffRef(j, j))); |
| } |
| } |
| m_eivec.col(i + 1).noalias() = (m_realQZ.matrixZ().transpose() * cv); |
| m_eivec.col(i + 1).normalize(); |
| m_eivec.col(i) = m_eivec.col(i + 1).conjugate(); |
| } |
| i += 2; |
| } |
| } |
| } |
| m_computeEigenvectors = computeEigenvectors; |
| m_isInitialized = true; |
| return *this; |
| } |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_GENERALIZEDEIGENSOLVER_H |
