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diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h b/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 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_MATRIX_FUNCTION_ATOMIC
+#define EIGEN_MATRIX_FUNCTION_ATOMIC
+
+namespace Eigen { 
+
+/** \ingroup MatrixFunctions_Module
+  * \class MatrixFunctionAtomic
+  * \brief Helper class for computing matrix functions of atomic matrices.
+  *
+  * \internal
+  * Here, an atomic matrix is a triangular matrix whose diagonal
+  * entries are close to each other.
+  */
+template <typename MatrixType>
+class MatrixFunctionAtomic
+{
+  public:
+
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::Index Index;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef typename internal::stem_function<Scalar>::type StemFunction;
+    typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
+
+    /** \brief Constructor
+      * \param[in]  f  matrix function to compute.
+      */
+    MatrixFunctionAtomic(StemFunction f) : m_f(f) { }
+
+    /** \brief Compute matrix function of atomic matrix
+      * \param[in]  A  argument of matrix function, should be upper triangular and atomic
+      * \returns  f(A), the matrix function evaluated at the given matrix
+      */
+    MatrixType compute(const MatrixType& A);
+
+  private:
+
+    // Prevent copying
+    MatrixFunctionAtomic(const MatrixFunctionAtomic&);
+    MatrixFunctionAtomic& operator=(const MatrixFunctionAtomic&);
+
+    void computeMu();
+    bool taylorConverged(Index s, const MatrixType& F, const MatrixType& Fincr, const MatrixType& P);
+
+    /** \brief Pointer to scalar function */
+    StemFunction* m_f;
+
+    /** \brief Size of matrix function */
+    Index m_Arows;
+
+    /** \brief Mean of eigenvalues */
+    Scalar m_avgEival;
+
+    /** \brief Argument shifted by mean of eigenvalues */
+    MatrixType m_Ashifted;
+
+    /** \brief Constant used to determine whether Taylor series has converged */
+    RealScalar m_mu;
+};
+
+template <typename MatrixType>
+MatrixType MatrixFunctionAtomic<MatrixType>::compute(const MatrixType& A)
+{
+  // TODO: Use that A is upper triangular
+  m_Arows = A.rows();
+  m_avgEival = A.trace() / Scalar(RealScalar(m_Arows));
+  m_Ashifted = A - m_avgEival * MatrixType::Identity(m_Arows, m_Arows);
+  computeMu();
+  MatrixType F = m_f(m_avgEival, 0) * MatrixType::Identity(m_Arows, m_Arows);
+  MatrixType P = m_Ashifted;
+  MatrixType Fincr;
+  for (Index s = 1; s < 1.1 * m_Arows + 10; s++) { // upper limit is fairly arbitrary
+    Fincr = m_f(m_avgEival, static_cast<int>(s)) * P;
+    F += Fincr;
+    P = Scalar(RealScalar(1.0/(s + 1))) * P * m_Ashifted;
+    if (taylorConverged(s, F, Fincr, P)) {
+      return F;
+    }
+  }
+  eigen_assert("Taylor series does not converge" && 0);
+  return F;
+}
+
+/** \brief Compute \c m_mu. */
+template <typename MatrixType>
+void MatrixFunctionAtomic<MatrixType>::computeMu()
+{
+  const MatrixType N = MatrixType::Identity(m_Arows, m_Arows) - m_Ashifted;
+  VectorType e = VectorType::Ones(m_Arows);
+  N.template triangularView<Upper>().solveInPlace(e);
+  m_mu = e.cwiseAbs().maxCoeff();
+}
+
+/** \brief Determine whether Taylor series has converged */
+template <typename MatrixType>
+bool MatrixFunctionAtomic<MatrixType>::taylorConverged(Index s, const MatrixType& F,
+						       const MatrixType& Fincr, const MatrixType& P)
+{
+  const Index n = F.rows();
+  const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff();
+  const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff();
+  if (Fincr_norm < NumTraits<Scalar>::epsilon() * F_norm) {
+    RealScalar delta = 0;
+    RealScalar rfactorial = 1;
+    for (Index r = 0; r < n; r++) {
+      RealScalar mx = 0;
+      for (Index i = 0; i < n; i++)
+        mx = (std::max)(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, static_cast<int>(s+r))));
+      if (r != 0)
+        rfactorial *= RealScalar(r);
+      delta = (std::max)(delta, mx / rfactorial);
+    }
+    const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
+    if (m_mu * delta * P_norm < NumTraits<Scalar>::epsilon() * F_norm)
+      return true;
+  }
+  return false;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_FUNCTION_ATOMIC