GeneralizedEigenSolver.h

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012-2016 Gael Guennebaud <[email protected]>
// Copyright (C) 2010,2012 Jitse Niesen <[email protected]>
// Copyright (C) 2016 Tobias Wood <[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_GENERALIZEDEIGENSOLVER_H
#define EIGEN_GENERALIZEDEIGENSOLVER_H
 
#include "./RealQZ.h"
 
// IWYU pragma: private
#include "./InternalHeaderCheck.h"
 
namespace Eigen {
 
template <typename MatrixType_>
class GeneralizedEigenSolver {
 public:
  typedef MatrixType_ MatrixType;
 
  enum {
    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
    Options = internal::traits<MatrixType>::Options,
    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
  };
 
  typedef typename MatrixType::Scalar Scalar;
  typedef typename NumTraits<Scalar>::Real RealScalar;
  typedef Eigen::Index Index;  
 
  typedef std::complex<RealScalar> ComplexScalar;
 
  typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> VectorType;
 
  typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ComplexVectorType;
 
  typedef CwiseBinaryOp<internal::scalar_quotient_op<ComplexScalar, Scalar>, ComplexVectorType, VectorType>
      EigenvalueType;
 
  typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime,
                 MaxColsAtCompileTime>
      EigenvectorsType;
 
  GeneralizedEigenSolver()
      : m_eivec(), m_alphas(), m_betas(), m_computeEigenvectors(false), m_isInitialized(false), m_realQZ() {}
 
  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) {}
 
  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;
  }
 
  EigenvalueType eigenvalues() const {
    eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute eigenvalues.");
    return EigenvalueType(m_alphas, m_betas);
  }
 
  const ComplexVectorType& alphas() const {
    eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute alphas.");
    return m_alphas;
  }
 
  const VectorType& betas() const {
    eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute betas.");
    return m_betas;
  }
 
  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();
  }
 
  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