MatrixBaseEigenvalues.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_MATRIXBASEEIGENVALUES_H
#define EIGEN_MATRIXBASEEIGENVALUES_H
 
// IWYU pragma: private
#include "./InternalHeaderCheck.h"
 
namespace Eigen {
 
namespace internal {
 
template <typename Derived, bool IsComplex>
struct eigenvalues_selector {
  // this is the implementation for the case IsComplex = true
  static inline typename MatrixBase<Derived>::EigenvaluesReturnType const run(const MatrixBase<Derived>& m) {
    typedef typename Derived::PlainObject PlainObject;
    PlainObject m_eval(m);
    return ComplexEigenSolver<PlainObject>(m_eval, false).eigenvalues();
  }
};
 
template <typename Derived>
struct eigenvalues_selector<Derived, false> {
  static inline typename MatrixBase<Derived>::EigenvaluesReturnType const run(const MatrixBase<Derived>& m) {
    typedef typename Derived::PlainObject PlainObject;
    PlainObject m_eval(m);
    return EigenSolver<PlainObject>(m_eval, false).eigenvalues();
  }
};
 
}  // end namespace internal
 
template <typename Derived>
inline typename MatrixBase<Derived>::EigenvaluesReturnType MatrixBase<Derived>::eigenvalues() const {
  return internal::eigenvalues_selector<Derived, NumTraits<Scalar>::IsComplex>::run(derived());
}
 
template <typename MatrixType, unsigned int UpLo>
EIGEN_DEVICE_FUNC inline typename SelfAdjointView<MatrixType, UpLo>::EigenvaluesReturnType
SelfAdjointView<MatrixType, UpLo>::eigenvalues() const {
  PlainObject thisAsMatrix(*this);
  return SelfAdjointEigenSolver<PlainObject>(thisAsMatrix, false).eigenvalues();
}
 
template <typename Derived>
inline typename MatrixBase<Derived>::RealScalar MatrixBase<Derived>::operatorNorm() const {
  using std::sqrt;
  typename Derived::PlainObject m_eval(derived());
  // FIXME if it is really guaranteed that the eigenvalues are already sorted,
  // then we don't need to compute a maxCoeff() here, comparing the 1st and last ones is enough.
  return sqrt((m_eval * m_eval.adjoint()).eval().template selfadjointView<Lower>().eigenvalues().maxCoeff());
}
 
template <typename MatrixType, unsigned int UpLo>
EIGEN_DEVICE_FUNC inline typename SelfAdjointView<MatrixType, UpLo>::RealScalar
SelfAdjointView<MatrixType, UpLo>::operatorNorm() const {
  return eigenvalues().cwiseAbs().maxCoeff();
}
 
}  // end namespace Eigen
 
#endif