Tridiagonalization.h

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 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_TRIDIAGONALIZATION_H
#define EIGEN_TRIDIAGONALIZATION_H
 
// IWYU pragma: private
#include "./InternalHeaderCheck.h"
 
namespace Eigen {
 
namespace internal {
 
template <typename MatrixType>
struct TridiagonalizationMatrixTReturnType;
template <typename MatrixType>
struct traits<TridiagonalizationMatrixTReturnType<MatrixType>> : public traits<typename MatrixType::PlainObject> {
  typedef typename MatrixType::PlainObject ReturnType;  // FIXME shall it be a BandMatrix?
  enum { Flags = 0 };
};
 
template <typename MatrixType, typename CoeffVectorType>
EIGEN_DEVICE_FUNC void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs);
}  // namespace internal
 
template <typename MatrixType_>
class Tridiagonalization {
 public:
  typedef MatrixType_ MatrixType;
 
  typedef typename MatrixType::Scalar Scalar;
  typedef typename NumTraits<Scalar>::Real RealScalar;
  typedef Eigen::Index Index;  
 
  enum {
    Size = MatrixType::RowsAtCompileTime,
    SizeMinusOne = Size == Dynamic ? Dynamic : (Size > 1 ? Size - 1 : 1),
    Options = internal::traits<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 internal::remove_all_t<typename MatrixType::RealReturnType> MatrixTypeRealView;
  typedef internal::TridiagonalizationMatrixTReturnType<MatrixTypeRealView> MatrixTReturnType;
 
  typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
                             internal::add_const_on_value_type_t<typename Diagonal<const MatrixType>::RealReturnType>,
                             const Diagonal<const MatrixType>>
      DiagonalReturnType;
 
  typedef std::conditional_t<
      NumTraits<Scalar>::IsComplex,
      internal::add_const_on_value_type_t<typename Diagonal<const MatrixType, -1>::RealReturnType>,
      const Diagonal<const MatrixType, -1>>
      SubDiagonalReturnType;
 
  typedef HouseholderSequence<MatrixType, internal::remove_all_t<typename CoeffVectorType::ConjugateReturnType>>
      HouseholderSequenceType;
 
  explicit Tridiagonalization(Index size = Size == Dynamic ? 2 : Size)
      : m_matrix(size, size), m_hCoeffs(size > 1 ? size - 1 : 1), m_isInitialized(false) {}
 
  template <typename InputType>
  explicit Tridiagonalization(const EigenBase<InputType>& matrix)
      : m_matrix(matrix.derived()), m_hCoeffs(matrix.cols() > 1 ? matrix.cols() - 1 : 1), m_isInitialized(false) {
    internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
    m_isInitialized = true;
  }
 
  template <typename InputType>
  Tridiagonalization& compute(const EigenBase<InputType>& matrix) {
    m_matrix = matrix.derived();
    m_hCoeffs.resize(matrix.rows() - 1, 1);
    internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
    m_isInitialized = true;
    return *this;
  }
 
  inline CoeffVectorType householderCoefficients() const {
    eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
    return m_hCoeffs;
  }
 
  inline const MatrixType& packedMatrix() const {
    eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
    return m_matrix;
  }
 
  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);
  }
 
  MatrixTReturnType matrixT() const {
    eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
    return MatrixTReturnType(m_matrix.real());
  }
 
  DiagonalReturnType diagonal() const;
 
  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().real();
}
 
template <typename MatrixType>
typename Tridiagonalization<MatrixType>::SubDiagonalReturnType Tridiagonalization<MatrixType>::subDiagonal() const {
  eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
  return m_matrix.template diagonal<-1>().real();
}
 
namespace internal {
 
template <typename MatrixType, typename CoeffVectorType>
EIGEN_DEVICE_FUNC void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs) {
  using numext::conj;
  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) * RealScalar(-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), Scalar(-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;
 
template <typename MatrixType, typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType,
          typename WorkSpaceType>
EIGEN_DEVICE_FUNC void tridiagonalization_inplace(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag,
                                                  CoeffVectorType& hcoeffs, WorkSpaceType& workspace, 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, hcoeffs, workspace, extractQ);
}
 
template <typename MatrixType, int Size, bool IsComplex>
struct tridiagonalization_inplace_selector {
  typedef typename Tridiagonalization<MatrixType>::HouseholderSequenceType HouseholderSequenceType;
  template <typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType, typename WorkSpaceType>
  static EIGEN_DEVICE_FUNC void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag,
                                    CoeffVectorType& hCoeffs, WorkSpaceType& workspace, bool extractQ) {
    tridiagonalization_inplace(mat, hCoeffs);
    diag = mat.diagonal().real();
    subdiag = mat.template diagonal<-1>().real();
    if (extractQ) {
      HouseholderSequenceType(mat, hCoeffs.conjugate()).setLength(mat.rows() - 1).setShift(1).evalTo(mat, workspace);
    }
  }
};
 
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, typename CoeffVectorType, typename WorkSpaceType>
  static EIGEN_DEVICE_FUNC void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, CoeffVectorType&,
                                    WorkSpaceType&, bool extractQ) {
    using std::sqrt;
    const RealScalar tol = (std::numeric_limits<RealScalar>::min)();
    diag[0] = mat(0, 0);
    RealScalar v1norm2 = numext::abs2(mat(2, 0));
    if (v1norm2 <= tol) {
      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;
      }
    }
  }
};
 
template <typename MatrixType, bool IsComplex>
struct tridiagonalization_inplace_selector<MatrixType, 1, IsComplex> {
  typedef typename MatrixType::Scalar Scalar;
 
  template <typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType, typename WorkSpaceType>
  static EIGEN_DEVICE_FUNC void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType&, CoeffVectorType&,
                                    WorkSpaceType&, bool extractQ) {
    diag(0, 0) = numext::real(mat(0, 0));
    if (extractQ) mat(0, 0) = Scalar(1);
  }
};
 
template <typename MatrixType>
struct TridiagonalizationMatrixTReturnType : public ReturnByValue<TridiagonalizationMatrixTReturnType<MatrixType>> {
 public:
  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>();
  }
 
  EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
  EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
 
 protected:
  typename MatrixType::Nested m_matrix;
};
 
}  // end namespace internal
 
}  // end namespace Eigen
 
#endif  // EIGEN_TRIDIAGONALIZATION_H