SelfAdjointEigenSolver.h

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <[email protected]>
// Copyright (C) 2010 Jitse Niesen <[email protected]>
//
// 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_SELFADJOINTEIGENSOLVER_H
#define EIGEN_SELFADJOINTEIGENSOLVER_H
 
#include "./Tridiagonalization.h"
 
// IWYU pragma: private
#include "./InternalHeaderCheck.h"
 
namespace Eigen {
 
template <typename MatrixType_>
class GeneralizedSelfAdjointEigenSolver;
 
namespace internal {
template <typename SolverType, int Size, bool IsComplex>
struct direct_selfadjoint_eigenvalues;
 
template <typename MatrixType, typename DiagType, typename SubDiagType>
EIGEN_DEVICE_FUNC ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag,
                                                              const Index maxIterations, bool computeEigenvectors,
                                                              MatrixType& eivec);
}  // namespace internal
 
template <typename MatrixType_>
class SelfAdjointEigenSolver {
 public:
  typedef MatrixType_ MatrixType;
  enum {
    Size = MatrixType::RowsAtCompileTime,
    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
    Options = internal::traits<MatrixType>::Options,
    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
  };
 
  typedef typename MatrixType::Scalar Scalar;
  typedef Eigen::Index Index;  
 
  typedef Matrix<Scalar, Size, Size, ColMajor, MaxColsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType;
 
  typedef typename NumTraits<Scalar>::Real RealScalar;
 
  friend struct internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver, Size, NumTraits<Scalar>::IsComplex>;
 
  typedef typename internal::plain_col_type<MatrixType, Scalar>::type VectorType;
  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVectorType;
  typedef Tridiagonalization<MatrixType> TridiagonalizationType;
  typedef typename TridiagonalizationType::SubDiagonalType SubDiagonalType;
 
  EIGEN_DEVICE_FUNC SelfAdjointEigenSolver()
      : m_eivec(),
        m_workspace(),
        m_eivalues(),
        m_subdiag(),
        m_hcoeffs(),
        m_info(InvalidInput),
        m_isInitialized(false),
        m_eigenvectorsOk(false) {}
 
  EIGEN_DEVICE_FUNC explicit SelfAdjointEigenSolver(Index size)
      : m_eivec(size, size),
        m_workspace(size),
        m_eivalues(size),
        m_subdiag(size > 1 ? size - 1 : 1),
        m_hcoeffs(size > 1 ? size - 1 : 1),
        m_isInitialized(false),
        m_eigenvectorsOk(false) {}
 
  template <typename InputType>
  EIGEN_DEVICE_FUNC explicit SelfAdjointEigenSolver(const EigenBase<InputType>& matrix,
                                                    int options = ComputeEigenvectors)
      : m_eivec(matrix.rows(), matrix.cols()),
        m_workspace(matrix.cols()),
        m_eivalues(matrix.cols()),
        m_subdiag(matrix.rows() > 1 ? matrix.rows() - 1 : 1),
        m_hcoeffs(matrix.cols() > 1 ? matrix.cols() - 1 : 1),
        m_isInitialized(false),
        m_eigenvectorsOk(false) {
    compute(matrix.derived(), options);
  }
 
  template <typename InputType>
  EIGEN_DEVICE_FUNC SelfAdjointEigenSolver& compute(const EigenBase<InputType>& matrix,
                                                    int options = ComputeEigenvectors);
 
  EIGEN_DEVICE_FUNC SelfAdjointEigenSolver& computeDirect(const MatrixType& matrix, int options = ComputeEigenvectors);
 
  SelfAdjointEigenSolver& computeFromTridiagonal(const RealVectorType& diag, const SubDiagonalType& subdiag,
                                                 int options = ComputeEigenvectors);
 
  EIGEN_DEVICE_FUNC const EigenvectorsType& eigenvectors() const {
    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
    return m_eivec;
  }
 
  EIGEN_DEVICE_FUNC const RealVectorType& eigenvalues() const {
    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
    return m_eivalues;
  }
 
  EIGEN_DEVICE_FUNC MatrixType operatorSqrt() const {
    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
    return m_eivec * m_eivalues.cwiseSqrt().asDiagonal() * m_eivec.adjoint();
  }
 
  EIGEN_DEVICE_FUNC MatrixType operatorInverseSqrt() const {
    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
    return m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint();
  }
 
  EIGEN_DEVICE_FUNC ComputationInfo info() const {
    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
    return m_info;
  }
 
  static const int m_maxIterations = 30;
 
 protected:
  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
 
  EigenvectorsType m_eivec;
  VectorType m_workspace;
  RealVectorType m_eivalues;
  typename TridiagonalizationType::SubDiagonalType m_subdiag;
  typename TridiagonalizationType::CoeffVectorType m_hcoeffs;
  ComputationInfo m_info;
  bool m_isInitialized;
  bool m_eigenvectorsOk;
};
 
namespace internal {
template <int StorageOrder, typename RealScalar, typename Scalar, typename Index>
EIGEN_DEVICE_FUNC static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end,
                                                  Scalar* matrixQ, Index n);
}  // namespace internal
 
template <typename MatrixType>
template <typename InputType>
EIGEN_DEVICE_FUNC SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::compute(
    const EigenBase<InputType>& a_matrix, int options) {
  const InputType& matrix(a_matrix.derived());
 
  EIGEN_USING_STD(abs);
  eigen_assert(matrix.cols() == matrix.rows());
  eigen_assert((options & ~(EigVecMask | GenEigMask)) == 0 && (options & EigVecMask) != EigVecMask &&
               "invalid option parameter");
  bool computeEigenvectors = (options & ComputeEigenvectors) == ComputeEigenvectors;
  Index n = matrix.cols();
  m_eivalues.resize(n, 1);
 
  if (n == 1) {
    m_eivec = matrix;
    m_eivalues.coeffRef(0, 0) = numext::real(m_eivec.coeff(0, 0));
    if (computeEigenvectors) m_eivec.setOnes(n, n);
    m_info = Success;
    m_isInitialized = true;
    m_eigenvectorsOk = computeEigenvectors;
    return *this;
  }
 
  // declare some aliases
  RealVectorType& diag = m_eivalues;
  EigenvectorsType& mat = m_eivec;
 
  // map the matrix coefficients to [-1:1] to avoid over- and underflow.
  mat = matrix.template triangularView<Lower>();
  RealScalar scale = mat.cwiseAbs().maxCoeff();
  if (numext::is_exactly_zero(scale)) scale = RealScalar(1);
  mat.template triangularView<Lower>() /= scale;
  m_subdiag.resize(n - 1);
  m_hcoeffs.resize(n - 1);
  internal::tridiagonalization_inplace(mat, diag, m_subdiag, m_hcoeffs, m_workspace, computeEigenvectors);
 
  m_info = internal::computeFromTridiagonal_impl(diag, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);
 
  // scale back the eigen values
  m_eivalues *= scale;
 
  m_isInitialized = true;
  m_eigenvectorsOk = computeEigenvectors;
  return *this;
}
 
template <typename MatrixType>
SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::computeFromTridiagonal(
    const RealVectorType& diag, const SubDiagonalType& subdiag, int options) {
  // TODO : Add an option to scale the values beforehand
  bool computeEigenvectors = (options & ComputeEigenvectors) == ComputeEigenvectors;
 
  m_eivalues = diag;
  m_subdiag = subdiag;
  if (computeEigenvectors) {
    m_eivec.setIdentity(diag.size(), diag.size());
  }
  m_info = internal::computeFromTridiagonal_impl(m_eivalues, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);
 
  m_isInitialized = true;
  m_eigenvectorsOk = computeEigenvectors;
  return *this;
}
 
namespace internal {
template <typename MatrixType, typename DiagType, typename SubDiagType>
EIGEN_DEVICE_FUNC ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag,
                                                              const Index maxIterations, bool computeEigenvectors,
                                                              MatrixType& eivec) {
  ComputationInfo info;
  typedef typename MatrixType::Scalar Scalar;
 
  Index n = diag.size();
  Index end = n - 1;
  Index start = 0;
  Index iter = 0;  // total number of iterations
 
  typedef typename DiagType::RealScalar RealScalar;
  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
  const RealScalar precision_inv = RealScalar(1) / NumTraits<RealScalar>::epsilon();
  while (end > 0) {
    for (Index i = start; i < end; ++i) {
      if (numext::abs(subdiag[i]) < considerAsZero) {
        subdiag[i] = RealScalar(0);
      } else {
        // abs(subdiag[i]) <= epsilon * sqrt(abs(diag[i]) + abs(diag[i+1]))
        // Scaled to prevent underflows.
        const RealScalar scaled_subdiag = precision_inv * subdiag[i];
        if (scaled_subdiag * scaled_subdiag <= (numext::abs(diag[i]) + numext::abs(diag[i + 1]))) {
          subdiag[i] = RealScalar(0);
        }
      }
    }
 
    // find the largest unreduced block at the end of the matrix.
    while (end > 0 && numext::is_exactly_zero(subdiag[end - 1])) {
      end--;
    }
    if (end <= 0) break;
 
    // if we spent too many iterations, we give up
    iter++;
    if (iter > maxIterations * n) break;
 
    start = end - 1;
    while (start > 0 && !numext::is_exactly_zero(subdiag[start - 1])) start--;
 
    internal::tridiagonal_qr_step<MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor>(
        diag.data(), subdiag.data(), start, end, computeEigenvectors ? eivec.data() : (Scalar*)0, n);
  }
  if (iter <= maxIterations * n)
    info = Success;
  else
    info = NoConvergence;
 
  // Sort eigenvalues and corresponding vectors.
  // TODO make the sort optional ?
  // TODO use a better sort algorithm !!
  if (info == Success) {
    for (Index i = 0; i < n - 1; ++i) {
      Index k;
      diag.segment(i, n - i).minCoeff(&k);
      if (k > 0) {
        numext::swap(diag[i], diag[k + i]);
        if (computeEigenvectors) eivec.col(i).swap(eivec.col(k + i));
      }
    }
  }
  return info;
}
 
template <typename SolverType, int Size, bool IsComplex>
struct direct_selfadjoint_eigenvalues {
  EIGEN_DEVICE_FUNC static inline void run(SolverType& eig, const typename SolverType::MatrixType& A, int options) {
    eig.compute(A, options);
  }
};
 
template <typename SolverType>
struct direct_selfadjoint_eigenvalues<SolverType, 3, false> {
  typedef typename SolverType::MatrixType MatrixType;
  typedef typename SolverType::RealVectorType VectorType;
  typedef typename SolverType::Scalar Scalar;
  typedef typename SolverType::EigenvectorsType EigenvectorsType;
 
  EIGEN_DEVICE_FUNC static inline void computeRoots(const MatrixType& m, VectorType& roots) {
    EIGEN_USING_STD(sqrt)
    EIGEN_USING_STD(atan2)
    EIGEN_USING_STD(cos)
    EIGEN_USING_STD(sin)
    const Scalar s_inv3 = Scalar(1) / Scalar(3);
    const Scalar s_sqrt3 = sqrt(Scalar(3));
 
    // The characteristic equation is x^3 - c2*x^2 + c1*x - c0 = 0.  The
    // eigenvalues are the roots to this equation, all guaranteed to be
    // real-valued, because the matrix is symmetric.
    Scalar c0 = m(0, 0) * m(1, 1) * m(2, 2) + Scalar(2) * m(1, 0) * m(2, 0) * m(2, 1) - m(0, 0) * m(2, 1) * m(2, 1) -
                m(1, 1) * m(2, 0) * m(2, 0) - m(2, 2) * m(1, 0) * m(1, 0);
    Scalar c1 = m(0, 0) * m(1, 1) - m(1, 0) * m(1, 0) + m(0, 0) * m(2, 2) - m(2, 0) * m(2, 0) + m(1, 1) * m(2, 2) -
                m(2, 1) * m(2, 1);
    Scalar c2 = m(0, 0) + m(1, 1) + m(2, 2);
 
    // Construct the parameters used in classifying the roots of the equation
    // and in solving the equation for the roots in closed form.
    Scalar c2_over_3 = c2 * s_inv3;
    Scalar a_over_3 = (c2 * c2_over_3 - c1) * s_inv3;
    a_over_3 = numext::maxi(a_over_3, Scalar(0));
 
    Scalar half_b = Scalar(0.5) * (c0 + c2_over_3 * (Scalar(2) * c2_over_3 * c2_over_3 - c1));
 
    Scalar q = a_over_3 * a_over_3 * a_over_3 - half_b * half_b;
    q = numext::maxi(q, Scalar(0));
 
    // Compute the eigenvalues by solving for the roots of the polynomial.
    Scalar rho = sqrt(a_over_3);
    Scalar theta = atan2(sqrt(q), half_b) * s_inv3;  // since sqrt(q) > 0, atan2 is in [0, pi] and theta is in [0, pi/3]
    Scalar cos_theta = cos(theta);
    Scalar sin_theta = sin(theta);
    // roots are already sorted, since cos is monotonically decreasing on [0, pi]
    roots(0) = c2_over_3 - rho * (cos_theta + s_sqrt3 * sin_theta);  // == 2*rho*cos(theta+2pi/3)
    roots(1) = c2_over_3 - rho * (cos_theta - s_sqrt3 * sin_theta);  // == 2*rho*cos(theta+ pi/3)
    roots(2) = c2_over_3 + Scalar(2) * rho * cos_theta;
  }
 
  EIGEN_DEVICE_FUNC static inline bool extract_kernel(MatrixType& mat, Ref<VectorType> res,
                                                      Ref<VectorType> representative) {
    EIGEN_USING_STD(abs);
    EIGEN_USING_STD(sqrt);
    Index i0;
    // Find non-zero column i0 (by construction, there must exist a non zero coefficient on the diagonal):
    mat.diagonal().cwiseAbs().maxCoeff(&i0);
    // mat.col(i0) is a good candidate for an orthogonal vector to the current eigenvector,
    // so let's save it:
    representative = mat.col(i0);
    Scalar n0, n1;
    VectorType c0, c1;
    n0 = (c0 = representative.cross(mat.col((i0 + 1) % 3))).squaredNorm();
    n1 = (c1 = representative.cross(mat.col((i0 + 2) % 3))).squaredNorm();
    if (n0 > n1)
      res = c0 / sqrt(n0);
    else
      res = c1 / sqrt(n1);
 
    return true;
  }
 
  EIGEN_DEVICE_FUNC static inline void run(SolverType& solver, const MatrixType& mat, int options) {
    eigen_assert(mat.cols() == 3 && mat.cols() == mat.rows());
    eigen_assert((options & ~(EigVecMask | GenEigMask)) == 0 && (options & EigVecMask) != EigVecMask &&
                 "invalid option parameter");
    bool computeEigenvectors = (options & ComputeEigenvectors) == ComputeEigenvectors;
 
    EigenvectorsType& eivecs = solver.m_eivec;
    VectorType& eivals = solver.m_eivalues;
 
    // Shift the matrix to the mean eigenvalue and map the matrix coefficients to [-1:1] to avoid over- and underflow.
    Scalar shift = mat.trace() / Scalar(3);
    // TODO Avoid this copy. Currently it is necessary to suppress bogus values when determining maxCoeff and for
    // computing the eigenvectors later
    MatrixType scaledMat = mat.template selfadjointView<Lower>();
    scaledMat.diagonal().array() -= shift;
    Scalar scale = scaledMat.cwiseAbs().maxCoeff();
    if (scale > 0) scaledMat /= scale;  // TODO for scale==0 we could save the remaining operations
 
    // compute the eigenvalues
    computeRoots(scaledMat, eivals);
 
    // compute the eigenvectors
    if (computeEigenvectors) {
      if ((eivals(2) - eivals(0)) <= Eigen::NumTraits<Scalar>::epsilon()) {
        // All three eigenvalues are numerically the same
        eivecs.setIdentity();
      } else {
        MatrixType tmp;
        tmp = scaledMat;
 
        // Compute the eigenvector of the most distinct eigenvalue
        Scalar d0 = eivals(2) - eivals(1);
        Scalar d1 = eivals(1) - eivals(0);
        Index k(0), l(2);
        if (d0 > d1) {
          numext::swap(k, l);
          d0 = d1;
        }
 
        // Compute the eigenvector of index k
        {
          tmp.diagonal().array() -= eivals(k);
          // By construction, 'tmp' is of rank 2, and its kernel corresponds to the respective eigenvector.
          extract_kernel(tmp, eivecs.col(k), eivecs.col(l));
        }
 
        // Compute eigenvector of index l
        if (d0 <= 2 * Eigen::NumTraits<Scalar>::epsilon() * d1) {
          // If d0 is too small, then the two other eigenvalues are numerically the same,
          // and thus we only have to ortho-normalize the near orthogonal vector we saved above.
          eivecs.col(l) -= eivecs.col(k).dot(eivecs.col(l)) * eivecs.col(l);
          eivecs.col(l).normalize();
        } else {
          tmp = scaledMat;
          tmp.diagonal().array() -= eivals(l);
 
          VectorType dummy;
          extract_kernel(tmp, eivecs.col(l), dummy);
        }
 
        // Compute last eigenvector from the other two
        eivecs.col(1) = eivecs.col(2).cross(eivecs.col(0)).normalized();
      }
    }
 
    // Rescale back to the original size.
    eivals *= scale;
    eivals.array() += shift;
 
    solver.m_info = Success;
    solver.m_isInitialized = true;
    solver.m_eigenvectorsOk = computeEigenvectors;
  }
};
 
// 2x2 direct eigenvalues decomposition, code from Hauke Heibel
template <typename SolverType>
struct direct_selfadjoint_eigenvalues<SolverType, 2, false> {
  typedef typename SolverType::MatrixType MatrixType;
  typedef typename SolverType::RealVectorType VectorType;
  typedef typename SolverType::Scalar Scalar;
  typedef typename SolverType::EigenvectorsType EigenvectorsType;
 
  EIGEN_DEVICE_FUNC static inline void computeRoots(const MatrixType& m, VectorType& roots) {
    EIGEN_USING_STD(sqrt);
    const Scalar t0 = Scalar(0.5) * sqrt(numext::abs2(m(0, 0) - m(1, 1)) + Scalar(4) * numext::abs2(m(1, 0)));
    const Scalar t1 = Scalar(0.5) * (m(0, 0) + m(1, 1));
    roots(0) = t1 - t0;
    roots(1) = t1 + t0;
  }
 
  EIGEN_DEVICE_FUNC static inline void run(SolverType& solver, const MatrixType& mat, int options) {
    EIGEN_USING_STD(sqrt);
    EIGEN_USING_STD(abs);
 
    eigen_assert(mat.cols() == 2 && mat.cols() == mat.rows());
    eigen_assert((options & ~(EigVecMask | GenEigMask)) == 0 && (options & EigVecMask) != EigVecMask &&
                 "invalid option parameter");
    bool computeEigenvectors = (options & ComputeEigenvectors) == ComputeEigenvectors;
 
    EigenvectorsType& eivecs = solver.m_eivec;
    VectorType& eivals = solver.m_eivalues;
 
    // Shift the matrix to the mean eigenvalue and map the matrix coefficients to [-1:1] to avoid over- and underflow.
    Scalar shift = mat.trace() / Scalar(2);
    MatrixType scaledMat = mat;
    scaledMat.coeffRef(0, 1) = mat.coeff(1, 0);
    scaledMat.diagonal().array() -= shift;
    Scalar scale = scaledMat.cwiseAbs().maxCoeff();
    if (scale > Scalar(0)) scaledMat /= scale;
 
    // Compute the eigenvalues
    computeRoots(scaledMat, eivals);
 
    // compute the eigen vectors
    if (computeEigenvectors) {
      if ((eivals(1) - eivals(0)) <= abs(eivals(1)) * Eigen::NumTraits<Scalar>::epsilon()) {
        eivecs.setIdentity();
      } else {
        scaledMat.diagonal().array() -= eivals(1);
        Scalar a2 = numext::abs2(scaledMat(0, 0));
        Scalar c2 = numext::abs2(scaledMat(1, 1));
        Scalar b2 = numext::abs2(scaledMat(1, 0));
        if (a2 > c2) {
          eivecs.col(1) << -scaledMat(1, 0), scaledMat(0, 0);
          eivecs.col(1) /= sqrt(a2 + b2);
        } else {
          eivecs.col(1) << -scaledMat(1, 1), scaledMat(1, 0);
          eivecs.col(1) /= sqrt(c2 + b2);
        }
 
        eivecs.col(0) << eivecs.col(1).unitOrthogonal();
      }
    }
 
    // Rescale back to the original size.
    eivals *= scale;
    eivals.array() += shift;
 
    solver.m_info = Success;
    solver.m_isInitialized = true;
    solver.m_eigenvectorsOk = computeEigenvectors;
  }
};
 
}  // namespace internal
 
template <typename MatrixType>
EIGEN_DEVICE_FUNC SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::computeDirect(
    const MatrixType& matrix, int options) {
  internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver, Size, NumTraits<Scalar>::IsComplex>::run(
      *this, matrix, options);
  return *this;
}
 
namespace internal {
 
// Francis implicit QR step.
template <int StorageOrder, typename RealScalar, typename Scalar, typename Index>
EIGEN_DEVICE_FUNC static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end,
                                                  Scalar* matrixQ, Index n) {
  // Wilkinson Shift.
  RealScalar td = (diag[end - 1] - diag[end]) * RealScalar(0.5);
  RealScalar e = subdiag[end - 1];
  // Note that thanks to scaling, e^2 or td^2 cannot overflow, however they can still
  // underflow thus leading to inf/NaN values when using the following commented code:
  //   RealScalar e2 = numext::abs2(subdiag[end-1]);
  //   RealScalar mu = diag[end] - e2 / (td + (td>0 ? 1 : -1) * sqrt(td*td + e2));
  // This explain the following, somewhat more complicated, version:
  RealScalar mu = diag[end];
  if (numext::is_exactly_zero(td)) {
    mu -= numext::abs(e);
  } else if (!numext::is_exactly_zero(e)) {
    const RealScalar e2 = numext::abs2(e);
    const RealScalar h = numext::hypot(td, e);
    if (numext::is_exactly_zero(e2)) {
      mu -= e / ((td + (td > RealScalar(0) ? h : -h)) / e);
    } else {
      mu -= e2 / (td + (td > RealScalar(0) ? h : -h));
    }
  }
 
  RealScalar x = diag[start] - mu;
  RealScalar z = subdiag[start];
  // If z ever becomes zero, the Givens rotation will be the identity and
  // z will stay zero for all future iterations.
  for (Index k = start; k < end && !numext::is_exactly_zero(z); ++k) {
    JacobiRotation<RealScalar> rot;
    rot.makeGivens(x, z);
 
    // do T = G' T G
    RealScalar sdk = rot.s() * diag[k] + rot.c() * subdiag[k];
    RealScalar dkp1 = rot.s() * subdiag[k] + rot.c() * diag[k + 1];
 
    diag[k] =
        rot.c() * (rot.c() * diag[k] - rot.s() * subdiag[k]) - rot.s() * (rot.c() * subdiag[k] - rot.s() * diag[k + 1]);
    diag[k + 1] = rot.s() * sdk + rot.c() * dkp1;
    subdiag[k] = rot.c() * sdk - rot.s() * dkp1;
 
    if (k > start) subdiag[k - 1] = rot.c() * subdiag[k - 1] - rot.s() * z;
 
    // "Chasing the bulge" to return to triangular form.
    x = subdiag[k];
    if (k < end - 1) {
      z = -rot.s() * subdiag[k + 1];
      subdiag[k + 1] = rot.c() * subdiag[k + 1];
    }
 
    // apply the givens rotation to the unit matrix Q = Q * G
    if (matrixQ) {
      // FIXME if StorageOrder == RowMajor this operation is not very efficient
      Map<Matrix<Scalar, Dynamic, Dynamic, StorageOrder> > q(matrixQ, n, n);
      q.applyOnTheRight(k, k + 1, rot);
    }
  }
}
 
}  // end namespace internal
 
}  // end namespace Eigen
 
#endif  // EIGEN_SELFADJOINTEIGENSOLVER_H