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diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h b/third_party/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
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--- a/third_party/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
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@@ -1,160 +0,0 @@
-// 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_MATRIXBASEEIGENVALUES_H
-#define EIGEN_MATRIXBASEEIGENVALUES_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
-
-/** \brief Computes the eigenvalues of a matrix 
-  * \returns Column vector containing the eigenvalues.
-  *
-  * \eigenvalues_module
-  * This function computes the eigenvalues with the help of the EigenSolver
-  * class (for real matrices) or the ComplexEigenSolver class (for complex
-  * matrices). 
-  *
-  * The eigenvalues are repeated according to their algebraic multiplicity,
-  * so there are as many eigenvalues as rows in the matrix.
-  *
-  * The SelfAdjointView class provides a better algorithm for selfadjoint
-  * matrices.
-  *
-  * Example: \include MatrixBase_eigenvalues.cpp
-  * Output: \verbinclude MatrixBase_eigenvalues.out
-  *
-  * \sa EigenSolver::eigenvalues(), ComplexEigenSolver::eigenvalues(),
-  *     SelfAdjointView::eigenvalues()
-  */
-template<typename Derived>
-inline typename MatrixBase<Derived>::EigenvaluesReturnType
-MatrixBase<Derived>::eigenvalues() const
-{
-  typedef typename internal::traits<Derived>::Scalar Scalar;
-  return internal::eigenvalues_selector<Derived, NumTraits<Scalar>::IsComplex>::run(derived());
-}
-
-/** \brief Computes the eigenvalues of a matrix
-  * \returns Column vector containing the eigenvalues.
-  *
-  * \eigenvalues_module
-  * This function computes the eigenvalues with the help of the
-  * SelfAdjointEigenSolver class.  The eigenvalues are repeated according to
-  * their algebraic multiplicity, so there are as many eigenvalues as rows in
-  * the matrix.
-  *
-  * Example: \include SelfAdjointView_eigenvalues.cpp
-  * Output: \verbinclude SelfAdjointView_eigenvalues.out
-  *
-  * \sa SelfAdjointEigenSolver::eigenvalues(), MatrixBase::eigenvalues()
-  */
-template<typename MatrixType, unsigned int UpLo> 
-inline typename SelfAdjointView<MatrixType, UpLo>::EigenvaluesReturnType
-SelfAdjointView<MatrixType, UpLo>::eigenvalues() const
-{
-  typedef typename SelfAdjointView<MatrixType, UpLo>::PlainObject PlainObject;
-  PlainObject thisAsMatrix(*this);
-  return SelfAdjointEigenSolver<PlainObject>(thisAsMatrix, false).eigenvalues();
-}
-
-
-
-/** \brief Computes the L2 operator norm
-  * \returns Operator norm of the matrix.
-  *
-  * \eigenvalues_module
-  * This function computes the L2 operator norm of a matrix, which is also
-  * known as the spectral norm. The norm of a matrix \f$ A \f$ is defined to be
-  * \f[ \|A\|_2 = \max_x \frac{\|Ax\|_2}{\|x\|_2} \f]
-  * where the maximum is over all vectors and the norm on the right is the
-  * Euclidean vector norm. The norm equals the largest singular value, which is
-  * the square root of the largest eigenvalue of the positive semi-definite
-  * matrix \f$ A^*A \f$.
-  *
-  * The current implementation uses the eigenvalues of \f$ A^*A \f$, as computed
-  * by SelfAdjointView::eigenvalues(), to compute the operator norm of a
-  * matrix.  The SelfAdjointView class provides a better algorithm for
-  * selfadjoint matrices.
-  *
-  * Example: \include MatrixBase_operatorNorm.cpp
-  * Output: \verbinclude MatrixBase_operatorNorm.out
-  *
-  * \sa SelfAdjointView::eigenvalues(), SelfAdjointView::operatorNorm()
-  */
-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()
-		 );
-}
-
-/** \brief Computes the L2 operator norm
-  * \returns Operator norm of the matrix.
-  *
-  * \eigenvalues_module
-  * This function computes the L2 operator norm of a self-adjoint matrix. For a
-  * self-adjoint matrix, the operator norm is the largest eigenvalue.
-  *
-  * The current implementation uses the eigenvalues of the matrix, as computed
-  * by eigenvalues(), to compute the operator norm of the matrix.
-  *
-  * Example: \include SelfAdjointView_operatorNorm.cpp
-  * Output: \verbinclude SelfAdjointView_operatorNorm.out
-  *
-  * \sa eigenvalues(), MatrixBase::operatorNorm()
-  */
-template<typename MatrixType, unsigned int UpLo>
-inline typename SelfAdjointView<MatrixType, UpLo>::RealScalar
-SelfAdjointView<MatrixType, UpLo>::operatorNorm() const
-{
-  return eigenvalues().cwiseAbs().maxCoeff();
-}
-
-} // end namespace Eigen
-
-#endif