Squashed 'third_party/eigen/' content from commit 61d72f6
Change-Id: Iccc90fa0b55ab44037f018046d2fcffd90d9d025
git-subtree-dir: third_party/eigen
git-subtree-split: 61d72f6383cfa842868c53e30e087b0258177257
diff --git a/Eigen/src/Eigenvalues/Tridiagonalization.h b/Eigen/src/Eigenvalues/Tridiagonalization.h
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+++ b/Eigen/src/Eigenvalues/Tridiagonalization.h
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 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_TRIDIAGONALIZATION_H
+#define EIGEN_TRIDIAGONALIZATION_H
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename MatrixType> struct TridiagonalizationMatrixTReturnType;
+template<typename MatrixType>
+struct traits<TridiagonalizationMatrixTReturnType<MatrixType> >
+{
+ typedef typename MatrixType::PlainObject ReturnType;
+};
+
+template<typename MatrixType, typename CoeffVectorType>
+void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs);
+}
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class Tridiagonalization
+ *
+ * \brief Tridiagonal decomposition of a selfadjoint matrix
+ *
+ * \tparam _MatrixType the type of the matrix of which we are computing the
+ * tridiagonal decomposition; this is expected to be an instantiation of the
+ * Matrix class template.
+ *
+ * This class performs a tridiagonal decomposition of a selfadjoint matrix \f$ A \f$ such that:
+ * \f$ A = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real symmetric tridiagonal matrix.
+ *
+ * A tridiagonal matrix is a matrix which has nonzero elements only on the
+ * main diagonal and the first diagonal below and above it. The Hessenberg
+ * decomposition of a selfadjoint matrix is in fact a tridiagonal
+ * decomposition. This class is used in SelfAdjointEigenSolver to compute the
+ * eigenvalues and eigenvectors of a selfadjoint matrix.
+ *
+ * Call the function compute() to compute the tridiagonal decomposition of a
+ * given matrix. Alternatively, you can use the Tridiagonalization(const MatrixType&)
+ * constructor which computes the tridiagonal Schur decomposition at
+ * construction time. Once the decomposition is computed, you can use the
+ * matrixQ() and matrixT() functions to retrieve the matrices Q and T in the
+ * decomposition.
+ *
+ * The documentation of Tridiagonalization(const MatrixType&) contains an
+ * example of the typical use of this class.
+ *
+ * \sa class HessenbergDecomposition, class SelfAdjointEigenSolver
+ */
+template<typename _MatrixType> class Tridiagonalization
+{
+ public:
+
+ /** \brief Synonym for the template parameter \p _MatrixType. */
+ typedef _MatrixType MatrixType;
+
+ typedef typename MatrixType::Scalar Scalar;
+ typedef typename NumTraits<Scalar>::Real RealScalar;
+ typedef typename MatrixType::Index Index;
+
+ enum {
+ Size = MatrixType::RowsAtCompileTime,
+ SizeMinusOne = Size == Dynamic ? Dynamic : (Size > 1 ? Size - 1 : 1),
+ Options = MatrixType::Options,
+ MaxSize = MatrixType::MaxRowsAtCompileTime,
+ MaxSizeMinusOne = MaxSize == Dynamic ? Dynamic : (MaxSize > 1 ? MaxSize - 1 : 1)
+ };
+
+ typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
+ typedef typename internal::plain_col_type<MatrixType, RealScalar>::type DiagonalType;
+ typedef Matrix<RealScalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> SubDiagonalType;
+ typedef typename internal::remove_all<typename MatrixType::RealReturnType>::type MatrixTypeRealView;
+ typedef internal::TridiagonalizationMatrixTReturnType<MatrixTypeRealView> MatrixTReturnType;
+
+ typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
+ typename internal::add_const_on_value_type<typename Diagonal<const MatrixType>::RealReturnType>::type,
+ const Diagonal<const MatrixType>
+ >::type DiagonalReturnType;
+
+ typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
+ typename internal::add_const_on_value_type<typename Diagonal<
+ Block<const MatrixType,SizeMinusOne,SizeMinusOne> >::RealReturnType>::type,
+ const Diagonal<
+ Block<const MatrixType,SizeMinusOne,SizeMinusOne> >
+ >::type SubDiagonalReturnType;
+
+ /** \brief Return type of matrixQ() */
+ typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename CoeffVectorType::ConjugateReturnType>::type> HouseholderSequenceType;
+
+ /** \brief Default constructor.
+ *
+ * \param [in] size Positive integer, size of the matrix whose tridiagonal
+ * decomposition will be computed.
+ *
+ * The default constructor is useful in cases in which 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.
+ */
+ Tridiagonalization(Index size = Size==Dynamic ? 2 : Size)
+ : m_matrix(size,size),
+ m_hCoeffs(size > 1 ? size-1 : 1),
+ m_isInitialized(false)
+ {}
+
+ /** \brief Constructor; computes tridiagonal decomposition of given matrix.
+ *
+ * \param[in] matrix Selfadjoint matrix whose tridiagonal decomposition
+ * is to be computed.
+ *
+ * This constructor calls compute() to compute the tridiagonal decomposition.
+ *
+ * Example: \include Tridiagonalization_Tridiagonalization_MatrixType.cpp
+ * Output: \verbinclude Tridiagonalization_Tridiagonalization_MatrixType.out
+ */
+ Tridiagonalization(const MatrixType& matrix)
+ : m_matrix(matrix),
+ m_hCoeffs(matrix.cols() > 1 ? matrix.cols()-1 : 1),
+ m_isInitialized(false)
+ {
+ internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
+ m_isInitialized = true;
+ }
+
+ /** \brief Computes tridiagonal decomposition of given matrix.
+ *
+ * \param[in] matrix Selfadjoint matrix whose tridiagonal decomposition
+ * is to be computed.
+ * \returns Reference to \c *this
+ *
+ * The tridiagonal decomposition is computed by bringing the columns of
+ * the matrix successively in the required form using Householder
+ * reflections. The cost is \f$ 4n^3/3 \f$ flops, where \f$ n \f$ denotes
+ * the size of the given matrix.
+ *
+ * This method reuses of the allocated data in the Tridiagonalization
+ * object, if the size of the matrix does not change.
+ *
+ * Example: \include Tridiagonalization_compute.cpp
+ * Output: \verbinclude Tridiagonalization_compute.out
+ */
+ Tridiagonalization& compute(const MatrixType& matrix)
+ {
+ m_matrix = matrix;
+ m_hCoeffs.resize(matrix.rows()-1, 1);
+ internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
+ m_isInitialized = true;
+ return *this;
+ }
+
+ /** \brief Returns the Householder coefficients.
+ *
+ * \returns a const reference to the vector of Householder coefficients
+ *
+ * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+ * the member function compute(const MatrixType&) has been called before
+ * to compute the tridiagonal decomposition of a matrix.
+ *
+ * The Householder coefficients allow the reconstruction of the matrix
+ * \f$ Q \f$ in the tridiagonal decomposition from the packed data.
+ *
+ * Example: \include Tridiagonalization_householderCoefficients.cpp
+ * Output: \verbinclude Tridiagonalization_householderCoefficients.out
+ *
+ * \sa packedMatrix(), \ref Householder_Module "Householder module"
+ */
+ inline CoeffVectorType householderCoefficients() const
+ {
+ eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+ return m_hCoeffs;
+ }
+
+ /** \brief Returns the internal representation of the decomposition
+ *
+ * \returns a const reference to a matrix with the internal representation
+ * of the decomposition.
+ *
+ * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+ * the member function compute(const MatrixType&) has been called before
+ * to compute the tridiagonal decomposition of a matrix.
+ *
+ * The returned matrix contains the following information:
+ * - the strict upper triangular part is equal to the input matrix A.
+ * - the diagonal and lower sub-diagonal represent the real tridiagonal
+ * symmetric matrix T.
+ * - the rest of the lower part contains the Householder vectors that,
+ * combined with Householder coefficients returned by
+ * householderCoefficients(), allows to reconstruct the matrix Q as
+ * \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
+ * Here, the matrices \f$ H_i \f$ are the Householder transformations
+ * \f$ H_i = (I - h_i v_i v_i^T) \f$
+ * where \f$ h_i \f$ is the \f$ i \f$th Householder coefficient and
+ * \f$ v_i \f$ is the Householder vector defined by
+ * \f$ v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \f$
+ * with M the matrix returned by this function.
+ *
+ * See LAPACK for further details on this packed storage.
+ *
+ * Example: \include Tridiagonalization_packedMatrix.cpp
+ * Output: \verbinclude Tridiagonalization_packedMatrix.out
+ *
+ * \sa householderCoefficients()
+ */
+ inline const MatrixType& packedMatrix() const
+ {
+ eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+ return m_matrix;
+ }
+
+ /** \brief Returns the unitary matrix Q in the decomposition
+ *
+ * \returns object representing the matrix Q
+ *
+ * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+ * the member function compute(const MatrixType&) has been called before
+ * to compute the tridiagonal decomposition of a matrix.
+ *
+ * This function returns a light-weight object of template class
+ * HouseholderSequence. You can either apply it directly to a matrix or
+ * you can convert it to a matrix of type #MatrixType.
+ *
+ * \sa Tridiagonalization(const MatrixType&) for an example,
+ * matrixT(), class HouseholderSequence
+ */
+ HouseholderSequenceType matrixQ() const
+ {
+ eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+ return HouseholderSequenceType(m_matrix, m_hCoeffs.conjugate())
+ .setLength(m_matrix.rows() - 1)
+ .setShift(1);
+ }
+
+ /** \brief Returns an expression of the tridiagonal matrix T in the decomposition
+ *
+ * \returns expression object representing the matrix T
+ *
+ * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+ * the member function compute(const MatrixType&) has been called before
+ * to compute the tridiagonal decomposition of a matrix.
+ *
+ * Currently, this function can be used to extract the matrix T from internal
+ * data and copy it to a dense matrix object. In most cases, it may be
+ * sufficient to directly use the packed matrix or the vector expressions
+ * returned by diagonal() and subDiagonal() instead of creating a new
+ * dense copy matrix with this function.
+ *
+ * \sa Tridiagonalization(const MatrixType&) for an example,
+ * matrixQ(), packedMatrix(), diagonal(), subDiagonal()
+ */
+ MatrixTReturnType matrixT() const
+ {
+ eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+ return MatrixTReturnType(m_matrix.real());
+ }
+
+ /** \brief Returns the diagonal of the tridiagonal matrix T in the decomposition.
+ *
+ * \returns expression representing the diagonal of T
+ *
+ * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+ * the member function compute(const MatrixType&) has been called before
+ * to compute the tridiagonal decomposition of a matrix.
+ *
+ * Example: \include Tridiagonalization_diagonal.cpp
+ * Output: \verbinclude Tridiagonalization_diagonal.out
+ *
+ * \sa matrixT(), subDiagonal()
+ */
+ DiagonalReturnType diagonal() const;
+
+ /** \brief Returns the subdiagonal of the tridiagonal matrix T in the decomposition.
+ *
+ * \returns expression representing the subdiagonal of T
+ *
+ * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+ * the member function compute(const MatrixType&) has been called before
+ * to compute the tridiagonal decomposition of a matrix.
+ *
+ * \sa diagonal() for an example, matrixT()
+ */
+ SubDiagonalReturnType subDiagonal() const;
+
+ protected:
+
+ MatrixType m_matrix;
+ CoeffVectorType m_hCoeffs;
+ bool m_isInitialized;
+};
+
+template<typename MatrixType>
+typename Tridiagonalization<MatrixType>::DiagonalReturnType
+Tridiagonalization<MatrixType>::diagonal() const
+{
+ eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+ return m_matrix.diagonal();
+}
+
+template<typename MatrixType>
+typename Tridiagonalization<MatrixType>::SubDiagonalReturnType
+Tridiagonalization<MatrixType>::subDiagonal() const
+{
+ eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+ Index n = m_matrix.rows();
+ return Block<const MatrixType,SizeMinusOne,SizeMinusOne>(m_matrix, 1, 0, n-1,n-1).diagonal();
+}
+
+namespace internal {
+
+/** \internal
+ * Performs a tridiagonal decomposition of the selfadjoint matrix \a matA in-place.
+ *
+ * \param[in,out] matA On input the selfadjoint matrix. Only the \b lower triangular part is referenced.
+ * On output, the strict upper part is left unchanged, and the lower triangular part
+ * represents the T and Q matrices in packed format has detailed below.
+ * \param[out] hCoeffs returned Householder coefficients (see below)
+ *
+ * On output, the tridiagonal selfadjoint matrix T is stored in the diagonal
+ * and lower sub-diagonal of the matrix \a matA.
+ * The unitary matrix Q is represented in a compact way as a product of
+ * Householder reflectors \f$ H_i \f$ such that:
+ * \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
+ * The Householder reflectors are defined as
+ * \f$ H_i = (I - h_i v_i v_i^T) \f$
+ * where \f$ h_i = hCoeffs[i]\f$ is the \f$ i \f$th Householder coefficient and
+ * \f$ v_i \f$ is the Householder vector defined by
+ * \f$ v_i = [ 0, \ldots, 0, 1, matA(i+2,i), \ldots, matA(N-1,i) ]^T \f$.
+ *
+ * Implemented from Golub's "Matrix Computations", algorithm 8.3.1.
+ *
+ * \sa Tridiagonalization::packedMatrix()
+ */
+template<typename MatrixType, typename CoeffVectorType>
+void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs)
+{
+ using numext::conj;
+ typedef typename MatrixType::Index Index;
+ typedef typename MatrixType::Scalar Scalar;
+ typedef typename MatrixType::RealScalar RealScalar;
+ Index n = matA.rows();
+ eigen_assert(n==matA.cols());
+ eigen_assert(n==hCoeffs.size()+1 || n==1);
+
+ for (Index i = 0; i<n-1; ++i)
+ {
+ Index remainingSize = n-i-1;
+ RealScalar beta;
+ Scalar h;
+ matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta);
+
+ // Apply similarity transformation to remaining columns,
+ // i.e., A = H A H' where H = I - h v v' and v = matA.col(i).tail(n-i-1)
+ matA.col(i).coeffRef(i+1) = 1;
+
+ hCoeffs.tail(n-i-1).noalias() = (matA.bottomRightCorner(remainingSize,remainingSize).template selfadjointView<Lower>()
+ * (conj(h) * matA.col(i).tail(remainingSize)));
+
+ hCoeffs.tail(n-i-1) += (conj(h)*Scalar(-0.5)*(hCoeffs.tail(remainingSize).dot(matA.col(i).tail(remainingSize)))) * matA.col(i).tail(n-i-1);
+
+ matA.bottomRightCorner(remainingSize, remainingSize).template selfadjointView<Lower>()
+ .rankUpdate(matA.col(i).tail(remainingSize), hCoeffs.tail(remainingSize), -1);
+
+ matA.col(i).coeffRef(i+1) = beta;
+ hCoeffs.coeffRef(i) = h;
+ }
+}
+
+// forward declaration, implementation at the end of this file
+template<typename MatrixType,
+ int Size=MatrixType::ColsAtCompileTime,
+ bool IsComplex=NumTraits<typename MatrixType::Scalar>::IsComplex>
+struct tridiagonalization_inplace_selector;
+
+/** \brief Performs a full tridiagonalization in place
+ *
+ * \param[in,out] mat On input, the selfadjoint matrix whose tridiagonal
+ * decomposition is to be computed. Only the lower triangular part referenced.
+ * The rest is left unchanged. On output, the orthogonal matrix Q
+ * in the decomposition if \p extractQ is true.
+ * \param[out] diag The diagonal of the tridiagonal matrix T in the
+ * decomposition.
+ * \param[out] subdiag The subdiagonal of the tridiagonal matrix T in
+ * the decomposition.
+ * \param[in] extractQ If true, the orthogonal matrix Q in the
+ * decomposition is computed and stored in \p mat.
+ *
+ * Computes the tridiagonal decomposition of the selfadjoint matrix \p mat in place
+ * such that \f$ mat = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real
+ * symmetric tridiagonal matrix.
+ *
+ * The tridiagonal matrix T is passed to the output parameters \p diag and \p subdiag. If
+ * \p extractQ is true, then the orthogonal matrix Q is passed to \p mat. Otherwise the lower
+ * part of the matrix \p mat is destroyed.
+ *
+ * The vectors \p diag and \p subdiag are not resized. The function
+ * assumes that they are already of the correct size. The length of the
+ * vector \p diag should equal the number of rows in \p mat, and the
+ * length of the vector \p subdiag should be one left.
+ *
+ * This implementation contains an optimized path for 3-by-3 matrices
+ * which is especially useful for plane fitting.
+ *
+ * \note Currently, it requires two temporary vectors to hold the intermediate
+ * Householder coefficients, and to reconstruct the matrix Q from the Householder
+ * reflectors.
+ *
+ * Example (this uses the same matrix as the example in
+ * Tridiagonalization::Tridiagonalization(const MatrixType&)):
+ * \include Tridiagonalization_decomposeInPlace.cpp
+ * Output: \verbinclude Tridiagonalization_decomposeInPlace.out
+ *
+ * \sa class Tridiagonalization
+ */
+template<typename MatrixType, typename DiagonalType, typename SubDiagonalType>
+void tridiagonalization_inplace(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, bool extractQ)
+{
+ eigen_assert(mat.cols()==mat.rows() && diag.size()==mat.rows() && subdiag.size()==mat.rows()-1);
+ tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);
+}
+
+/** \internal
+ * General full tridiagonalization
+ */
+template<typename MatrixType, int Size, bool IsComplex>
+struct tridiagonalization_inplace_selector
+{
+ typedef typename Tridiagonalization<MatrixType>::CoeffVectorType CoeffVectorType;
+ typedef typename Tridiagonalization<MatrixType>::HouseholderSequenceType HouseholderSequenceType;
+ typedef typename MatrixType::Index Index;
+ template<typename DiagonalType, typename SubDiagonalType>
+ static void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, bool extractQ)
+ {
+ CoeffVectorType hCoeffs(mat.cols()-1);
+ tridiagonalization_inplace(mat,hCoeffs);
+ diag = mat.diagonal().real();
+ subdiag = mat.template diagonal<-1>().real();
+ if(extractQ)
+ mat = HouseholderSequenceType(mat, hCoeffs.conjugate())
+ .setLength(mat.rows() - 1)
+ .setShift(1);
+ }
+};
+
+/** \internal
+ * Specialization for 3x3 real matrices.
+ * Especially useful for plane fitting.
+ */
+template<typename MatrixType>
+struct tridiagonalization_inplace_selector<MatrixType,3,false>
+{
+ typedef typename MatrixType::Scalar Scalar;
+ typedef typename MatrixType::RealScalar RealScalar;
+
+ template<typename DiagonalType, typename SubDiagonalType>
+ static void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, bool extractQ)
+ {
+ using std::sqrt;
+ diag[0] = mat(0,0);
+ RealScalar v1norm2 = numext::abs2(mat(2,0));
+ if(v1norm2 == RealScalar(0))
+ {
+ diag[1] = mat(1,1);
+ diag[2] = mat(2,2);
+ subdiag[0] = mat(1,0);
+ subdiag[1] = mat(2,1);
+ if (extractQ)
+ mat.setIdentity();
+ }
+ else
+ {
+ RealScalar beta = sqrt(numext::abs2(mat(1,0)) + v1norm2);
+ RealScalar invBeta = RealScalar(1)/beta;
+ Scalar m01 = mat(1,0) * invBeta;
+ Scalar m02 = mat(2,0) * invBeta;
+ Scalar q = RealScalar(2)*m01*mat(2,1) + m02*(mat(2,2) - mat(1,1));
+ diag[1] = mat(1,1) + m02*q;
+ diag[2] = mat(2,2) - m02*q;
+ subdiag[0] = beta;
+ subdiag[1] = mat(2,1) - m01 * q;
+ if (extractQ)
+ {
+ mat << 1, 0, 0,
+ 0, m01, m02,
+ 0, m02, -m01;
+ }
+ }
+ }
+};
+
+/** \internal
+ * Trivial specialization for 1x1 matrices
+ */
+template<typename MatrixType, bool IsComplex>
+struct tridiagonalization_inplace_selector<MatrixType,1,IsComplex>
+{
+ typedef typename MatrixType::Scalar Scalar;
+
+ template<typename DiagonalType, typename SubDiagonalType>
+ static void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType&, bool extractQ)
+ {
+ diag(0,0) = numext::real(mat(0,0));
+ if(extractQ)
+ mat(0,0) = Scalar(1);
+ }
+};
+
+/** \internal
+ * \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ * \brief Expression type for return value of Tridiagonalization::matrixT()
+ *
+ * \tparam MatrixType type of underlying dense matrix
+ */
+template<typename MatrixType> struct TridiagonalizationMatrixTReturnType
+: public ReturnByValue<TridiagonalizationMatrixTReturnType<MatrixType> >
+{
+ typedef typename MatrixType::Index Index;
+ public:
+ /** \brief Constructor.
+ *
+ * \param[in] mat The underlying dense matrix
+ */
+ TridiagonalizationMatrixTReturnType(const MatrixType& mat) : m_matrix(mat) { }
+
+ template <typename ResultType>
+ inline void evalTo(ResultType& result) const
+ {
+ result.setZero();
+ result.template diagonal<1>() = m_matrix.template diagonal<-1>().conjugate();
+ result.diagonal() = m_matrix.diagonal();
+ result.template diagonal<-1>() = m_matrix.template diagonal<-1>();
+ }
+
+ Index rows() const { return m_matrix.rows(); }
+ Index cols() const { return m_matrix.cols(); }
+
+ protected:
+ typename MatrixType::Nested m_matrix;
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIDIAGONALIZATION_H