| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> |
| // Copyright (C) 2010 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_GENERALIZEDSELFADJOINTEIGENSOLVER_H |
| #define EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H |
| |
| #include "./Tridiagonalization.h" |
| |
| namespace Eigen { |
| |
| /** \eigenvalues_module \ingroup Eigenvalues_Module |
| * |
| * |
| * \class GeneralizedSelfAdjointEigenSolver |
| * |
| * \brief Computes eigenvalues and eigenvectors of the generalized selfadjoint eigen problem |
| * |
| * \tparam _MatrixType the type of the matrix of which we are computing the |
| * eigendecomposition; this is expected to be an instantiation of the Matrix |
| * class template. |
| * |
| * This class solves the generalized eigenvalue problem |
| * \f$ Av = \lambda Bv \f$. In this case, the matrix \f$ A \f$ should be |
| * selfadjoint and the matrix \f$ B \f$ should be positive definite. |
| * |
| * Only the \b lower \b triangular \b part of the input matrix is referenced. |
| * |
| * Call the function compute() to compute the eigenvalues and eigenvectors of |
| * a given matrix. Alternatively, you can use the |
| * GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int) |
| * 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. |
| * |
| * The documentation for GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int) |
| * contains an example of the typical use of this class. |
| * |
| * \sa class SelfAdjointEigenSolver, class EigenSolver, class ComplexEigenSolver |
| */ |
| template<typename _MatrixType> |
| class GeneralizedSelfAdjointEigenSolver : public SelfAdjointEigenSolver<_MatrixType> |
| { |
| typedef SelfAdjointEigenSolver<_MatrixType> Base; |
| public: |
| |
| typedef _MatrixType MatrixType; |
| |
| /** \brief Default constructor for fixed-size matrices. |
| * |
| * The default constructor is useful in cases in which the user intends to |
| * perform decompositions via compute(). This constructor |
| * can only be used if \p _MatrixType is a fixed-size matrix; use |
| * GeneralizedSelfAdjointEigenSolver(Index) for dynamic-size matrices. |
| */ |
| GeneralizedSelfAdjointEigenSolver() : Base() {} |
| |
| /** \brief Constructor, pre-allocates memory for dynamic-size matrices. |
| * |
| * \param [in] size Positive integer, size of the matrix whose |
| * eigenvalues and eigenvectors will be computed. |
| * |
| * This constructor is useful for dynamic-size matrices, when the user |
| * intends to perform decompositions via compute(). The \p size |
| * parameter is only used as a hint. It is not an error to give a wrong |
| * \p size, but it may impair performance. |
| * |
| * \sa compute() for an example |
| */ |
| explicit GeneralizedSelfAdjointEigenSolver(Index size) |
| : Base(size) |
| {} |
| |
| /** \brief Constructor; computes generalized eigendecomposition of given matrix pencil. |
| * |
| * \param[in] matA Selfadjoint matrix in matrix pencil. |
| * Only the lower triangular part of the matrix is referenced. |
| * \param[in] matB Positive-definite matrix in matrix pencil. |
| * Only the lower triangular part of the matrix is referenced. |
| * \param[in] options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}. |
| * Default is #ComputeEigenvectors|#Ax_lBx. |
| * |
| * This constructor calls compute(const MatrixType&, const MatrixType&, int) |
| * to compute the eigenvalues and (if requested) the eigenvectors of the |
| * generalized eigenproblem \f$ Ax = \lambda B x \f$ with \a matA the |
| * selfadjoint matrix \f$ A \f$ and \a matB the positive definite matrix |
| * \f$ B \f$. Each eigenvector \f$ x \f$ satisfies the property |
| * \f$ x^* B x = 1 \f$. The eigenvectors are computed if |
| * \a options contains ComputeEigenvectors. |
| * |
| * In addition, the two following variants can be solved via \p options: |
| * - \c ABx_lx: \f$ ABx = \lambda x \f$ |
| * - \c BAx_lx: \f$ BAx = \lambda x \f$ |
| * |
| * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.cpp |
| * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.out |
| * |
| * \sa compute(const MatrixType&, const MatrixType&, int) |
| */ |
| GeneralizedSelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB, |
| int options = ComputeEigenvectors|Ax_lBx) |
| : Base(matA.cols()) |
| { |
| compute(matA, matB, options); |
| } |
| |
| /** \brief Computes generalized eigendecomposition of given matrix pencil. |
| * |
| * \param[in] matA Selfadjoint matrix in matrix pencil. |
| * Only the lower triangular part of the matrix is referenced. |
| * \param[in] matB Positive-definite matrix in matrix pencil. |
| * Only the lower triangular part of the matrix is referenced. |
| * \param[in] options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}. |
| * Default is #ComputeEigenvectors|#Ax_lBx. |
| * |
| * \returns Reference to \c *this |
| * |
| * Accoring to \p options, this function computes eigenvalues and (if requested) |
| * the eigenvectors of one of the following three generalized eigenproblems: |
| * - \c Ax_lBx: \f$ Ax = \lambda B x \f$ |
| * - \c ABx_lx: \f$ ABx = \lambda x \f$ |
| * - \c BAx_lx: \f$ BAx = \lambda x \f$ |
| * with \a matA the selfadjoint matrix \f$ A \f$ and \a matB the positive definite |
| * matrix \f$ B \f$. |
| * In addition, each eigenvector \f$ x \f$ satisfies the property \f$ x^* B x = 1 \f$. |
| * |
| * The eigenvalues() function can be used to retrieve |
| * the eigenvalues. If \p options contains ComputeEigenvectors, then the |
| * eigenvectors are also computed and can be retrieved by calling |
| * eigenvectors(). |
| * |
| * The implementation uses LLT to compute the Cholesky decomposition |
| * \f$ B = LL^* \f$ and computes the classical eigendecomposition |
| * of the selfadjoint matrix \f$ L^{-1} A (L^*)^{-1} \f$ if \p options contains Ax_lBx |
| * and of \f$ L^{*} A L \f$ otherwise. This solves the |
| * generalized eigenproblem, because any solution of the generalized |
| * eigenproblem \f$ Ax = \lambda B x \f$ corresponds to a solution |
| * \f$ L^{-1} A (L^*)^{-1} (L^* x) = \lambda (L^* x) \f$ of the |
| * eigenproblem for \f$ L^{-1} A (L^*)^{-1} \f$. Similar statements |
| * can be made for the two other variants. |
| * |
| * Example: \include SelfAdjointEigenSolver_compute_MatrixType2.cpp |
| * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType2.out |
| * |
| * \sa GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int) |
| */ |
| GeneralizedSelfAdjointEigenSolver& compute(const MatrixType& matA, const MatrixType& matB, |
| int options = ComputeEigenvectors|Ax_lBx); |
| |
| protected: |
| |
| }; |
| |
| |
| template<typename MatrixType> |
| GeneralizedSelfAdjointEigenSolver<MatrixType>& GeneralizedSelfAdjointEigenSolver<MatrixType>:: |
| compute(const MatrixType& matA, const MatrixType& matB, int options) |
| { |
| eigen_assert(matA.cols()==matA.rows() && matB.rows()==matA.rows() && matB.cols()==matB.rows()); |
| eigen_assert((options&~(EigVecMask|GenEigMask))==0 |
| && (options&EigVecMask)!=EigVecMask |
| && ((options&GenEigMask)==0 || (options&GenEigMask)==Ax_lBx |
| || (options&GenEigMask)==ABx_lx || (options&GenEigMask)==BAx_lx) |
| && "invalid option parameter"); |
| |
| bool computeEigVecs = ((options&EigVecMask)==0) || ((options&EigVecMask)==ComputeEigenvectors); |
| |
| // Compute the cholesky decomposition of matB = L L' = U'U |
| LLT<MatrixType> cholB(matB); |
| |
| int type = (options&GenEigMask); |
| if(type==0) |
| type = Ax_lBx; |
| |
| if(type==Ax_lBx) |
| { |
| // compute C = inv(L) A inv(L') |
| MatrixType matC = matA.template selfadjointView<Lower>(); |
| cholB.matrixL().template solveInPlace<OnTheLeft>(matC); |
| cholB.matrixU().template solveInPlace<OnTheRight>(matC); |
| |
| Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly ); |
| |
| // transform back the eigen vectors: evecs = inv(U) * evecs |
| if(computeEigVecs) |
| cholB.matrixU().solveInPlace(Base::m_eivec); |
| } |
| else if(type==ABx_lx) |
| { |
| // compute C = L' A L |
| MatrixType matC = matA.template selfadjointView<Lower>(); |
| matC = matC * cholB.matrixL(); |
| matC = cholB.matrixU() * matC; |
| |
| Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly); |
| |
| // transform back the eigen vectors: evecs = inv(U) * evecs |
| if(computeEigVecs) |
| cholB.matrixU().solveInPlace(Base::m_eivec); |
| } |
| else if(type==BAx_lx) |
| { |
| // compute C = L' A L |
| MatrixType matC = matA.template selfadjointView<Lower>(); |
| matC = matC * cholB.matrixL(); |
| matC = cholB.matrixU() * matC; |
| |
| Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly); |
| |
| // transform back the eigen vectors: evecs = L * evecs |
| if(computeEigVecs) |
| Base::m_eivec = cholB.matrixL() * Base::m_eivec; |
| } |
| |
| return *this; |
| } |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H |
