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diff --git a/eigen/Eigen/src/Eigen2Support/SVD.h b/eigen/Eigen/src/Eigen2Support/SVD.h
deleted file mode 100644
index 3d03d22..0000000
--- a/eigen/Eigen/src/Eigen2Support/SVD.h
+++ /dev/null
@@ -1,637 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-//
-// 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 EIGEN2_SVD_H
-#define EIGEN2_SVD_H
-
-namespace Eigen {
-
-/** \ingroup SVD_Module
-  * \nonstableyet
-  *
-  * \class SVD
-  *
-  * \brief Standard SVD decomposition of a matrix and associated features
-  *
-  * \param MatrixType the type of the matrix of which we are computing the SVD decomposition
-  *
-  * This class performs a standard SVD decomposition of a real matrix A of size \c M x \c N
-  * with \c M \>= \c N.
-  *
-  *
-  * \sa MatrixBase::SVD()
-  */
-template<typename MatrixType> class SVD
-{
-  private:
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-
-    enum {
-      PacketSize = internal::packet_traits<Scalar>::size,
-      AlignmentMask = int(PacketSize)-1,
-      MinSize = EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime)
-    };
-
-    typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVector;
-    typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> RowVector;
-
-    typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MinSize> MatrixUType;
-    typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> MatrixVType;
-    typedef Matrix<Scalar, MinSize, 1> SingularValuesType;
-
-  public:
-
-    SVD() {} // a user who relied on compiler-generated default compiler reported problems with MSVC in 2.0.7
-    
-    SVD(const MatrixType& matrix)
-      : m_matU(matrix.rows(), (std::min)(matrix.rows(), matrix.cols())),
-        m_matV(matrix.cols(),matrix.cols()),
-        m_sigma((std::min)(matrix.rows(),matrix.cols()))
-    {
-      compute(matrix);
-    }
-
-    template<typename OtherDerived, typename ResultType>
-    bool solve(const MatrixBase<OtherDerived> &b, ResultType* result) const;
-
-    const MatrixUType& matrixU() const { return m_matU; }
-    const SingularValuesType& singularValues() const { return m_sigma; }
-    const MatrixVType& matrixV() const { return m_matV; }
-
-    void compute(const MatrixType& matrix);
-    SVD& sort();
-
-    template<typename UnitaryType, typename PositiveType>
-    void computeUnitaryPositive(UnitaryType *unitary, PositiveType *positive) const;
-    template<typename PositiveType, typename UnitaryType>
-    void computePositiveUnitary(PositiveType *positive, UnitaryType *unitary) const;
-    template<typename RotationType, typename ScalingType>
-    void computeRotationScaling(RotationType *unitary, ScalingType *positive) const;
-    template<typename ScalingType, typename RotationType>
-    void computeScalingRotation(ScalingType *positive, RotationType *unitary) const;
-
-  protected:
-    /** \internal */
-    MatrixUType m_matU;
-    /** \internal */
-    MatrixVType m_matV;
-    /** \internal */
-    SingularValuesType m_sigma;
-};
-
-/** Computes / recomputes the SVD decomposition A = U S V^* of \a matrix
-  *
-  * \note this code has been adapted from JAMA (public domain)
-  */
-template<typename MatrixType>
-void SVD<MatrixType>::compute(const MatrixType& matrix)
-{
-  const int m = matrix.rows();
-  const int n = matrix.cols();
-  const int nu = (std::min)(m,n);
-  ei_assert(m>=n && "In Eigen 2.0, SVD only works for MxN matrices with M>=N. Sorry!");
-  ei_assert(m>1 && "In Eigen 2.0, SVD doesn't work on 1x1 matrices");
-
-  m_matU.resize(m, nu);
-  m_matU.setZero();
-  m_sigma.resize((std::min)(m,n));
-  m_matV.resize(n,n);
-
-  RowVector e(n);
-  ColVector work(m);
-  MatrixType matA(matrix);
-  const bool wantu = true;
-  const bool wantv = true;
-  int i=0, j=0, k=0;
-
-  // Reduce A to bidiagonal form, storing the diagonal elements
-  // in s and the super-diagonal elements in e.
-  int nct = (std::min)(m-1,n);
-  int nrt = (std::max)(0,(std::min)(n-2,m));
-  for (k = 0; k < (std::max)(nct,nrt); ++k)
-  {
-    if (k < nct)
-    {
-      // Compute the transformation for the k-th column and
-      // place the k-th diagonal in m_sigma[k].
-      m_sigma[k] = matA.col(k).end(m-k).norm();
-      if (m_sigma[k] != 0.0) // FIXME
-      {
-        if (matA(k,k) < 0.0)
-          m_sigma[k] = -m_sigma[k];
-        matA.col(k).end(m-k) /= m_sigma[k];
-        matA(k,k) += 1.0;
-      }
-      m_sigma[k] = -m_sigma[k];
-    }
-
-    for (j = k+1; j < n; ++j)
-    {
-      if ((k < nct) && (m_sigma[k] != 0.0))
-      {
-        // Apply the transformation.
-        Scalar t = matA.col(k).end(m-k).eigen2_dot(matA.col(j).end(m-k)); // FIXME dot product or cwise prod + .sum() ??
-        t = -t/matA(k,k);
-        matA.col(j).end(m-k) += t * matA.col(k).end(m-k);
-      }
-
-      // Place the k-th row of A into e for the
-      // subsequent calculation of the row transformation.
-      e[j] = matA(k,j);
-    }
-
-    // Place the transformation in U for subsequent back multiplication.
-    if (wantu & (k < nct))
-      m_matU.col(k).end(m-k) = matA.col(k).end(m-k);
-
-    if (k < nrt)
-    {
-      // Compute the k-th row transformation and place the
-      // k-th super-diagonal in e[k].
-      e[k] = e.end(n-k-1).norm();
-      if (e[k] != 0.0)
-      {
-          if (e[k+1] < 0.0)
-            e[k] = -e[k];
-          e.end(n-k-1) /= e[k];
-          e[k+1] += 1.0;
-      }
-      e[k] = -e[k];
-      if ((k+1 < m) & (e[k] != 0.0))
-      {
-        // Apply the transformation.
-        work.end(m-k-1) = matA.corner(BottomRight,m-k-1,n-k-1) * e.end(n-k-1);
-        for (j = k+1; j < n; ++j)
-          matA.col(j).end(m-k-1) += (-e[j]/e[k+1]) * work.end(m-k-1);
-      }
-
-      // Place the transformation in V for subsequent back multiplication.
-      if (wantv)
-        m_matV.col(k).end(n-k-1) = e.end(n-k-1);
-    }
-  }
-
-
-  // Set up the final bidiagonal matrix or order p.
-  int p = (std::min)(n,m+1);
-  if (nct < n)
-    m_sigma[nct] = matA(nct,nct);
-  if (m < p)
-    m_sigma[p-1] = 0.0;
-  if (nrt+1 < p)
-    e[nrt] = matA(nrt,p-1);
-  e[p-1] = 0.0;
-
-  // If required, generate U.
-  if (wantu)
-  {
-    for (j = nct; j < nu; ++j)
-    {
-      m_matU.col(j).setZero();
-      m_matU(j,j) = 1.0;
-    }
-    for (k = nct-1; k >= 0; k--)
-    {
-      if (m_sigma[k] != 0.0)
-      {
-        for (j = k+1; j < nu; ++j)
-        {
-          Scalar t = m_matU.col(k).end(m-k).eigen2_dot(m_matU.col(j).end(m-k)); // FIXME is it really a dot product we want ?
-          t = -t/m_matU(k,k);
-          m_matU.col(j).end(m-k) += t * m_matU.col(k).end(m-k);
-        }
-        m_matU.col(k).end(m-k) = - m_matU.col(k).end(m-k);
-        m_matU(k,k) = Scalar(1) + m_matU(k,k);
-        if (k-1>0)
-          m_matU.col(k).start(k-1).setZero();
-      }
-      else
-      {
-        m_matU.col(k).setZero();
-        m_matU(k,k) = 1.0;
-      }
-    }
-  }
-
-  // If required, generate V.
-  if (wantv)
-  {
-    for (k = n-1; k >= 0; k--)
-    {
-      if ((k < nrt) & (e[k] != 0.0))
-      {
-        for (j = k+1; j < nu; ++j)
-        {
-          Scalar t = m_matV.col(k).end(n-k-1).eigen2_dot(m_matV.col(j).end(n-k-1)); // FIXME is it really a dot product we want ?
-          t = -t/m_matV(k+1,k);
-          m_matV.col(j).end(n-k-1) += t * m_matV.col(k).end(n-k-1);
-        }
-      }
-      m_matV.col(k).setZero();
-      m_matV(k,k) = 1.0;
-    }
-  }
-
-  // Main iteration loop for the singular values.
-  int pp = p-1;
-  int iter = 0;
-  Scalar eps = ei_pow(Scalar(2),ei_is_same_type<Scalar,float>::ret ? Scalar(-23) : Scalar(-52));
-  while (p > 0)
-  {
-    int k=0;
-    int kase=0;
-
-    // Here is where a test for too many iterations would go.
-
-    // This section of the program inspects for
-    // negligible elements in the s and e arrays.  On
-    // completion the variables kase and k are set as follows.
-
-    // kase = 1     if s(p) and e[k-1] are negligible and k<p
-    // kase = 2     if s(k) is negligible and k<p
-    // kase = 3     if e[k-1] is negligible, k<p, and
-    //              s(k), ..., s(p) are not negligible (qr step).
-    // kase = 4     if e(p-1) is negligible (convergence).
-
-    for (k = p-2; k >= -1; --k)
-    {
-      if (k == -1)
-          break;
-      if (ei_abs(e[k]) <= eps*(ei_abs(m_sigma[k]) + ei_abs(m_sigma[k+1])))
-      {
-          e[k] = 0.0;
-          break;
-      }
-    }
-    if (k == p-2)
-    {
-      kase = 4;
-    }
-    else
-    {
-      int ks;
-      for (ks = p-1; ks >= k; --ks)
-      {
-        if (ks == k)
-          break;
-        Scalar t = (ks != p ? ei_abs(e[ks]) : Scalar(0)) + (ks != k+1 ? ei_abs(e[ks-1]) : Scalar(0));
-        if (ei_abs(m_sigma[ks]) <= eps*t)
-        {
-          m_sigma[ks] = 0.0;
-          break;
-        }
-      }
-      if (ks == k)
-      {
-        kase = 3;
-      }
-      else if (ks == p-1)
-      {
-        kase = 1;
-      }
-      else
-      {
-        kase = 2;
-        k = ks;
-      }
-    }
-    ++k;
-
-    // Perform the task indicated by kase.
-    switch (kase)
-    {
-
-      // Deflate negligible s(p).
-      case 1:
-      {
-        Scalar f(e[p-2]);
-        e[p-2] = 0.0;
-        for (j = p-2; j >= k; --j)
-        {
-          Scalar t(numext::hypot(m_sigma[j],f));
-          Scalar cs(m_sigma[j]/t);
-          Scalar sn(f/t);
-          m_sigma[j] = t;
-          if (j != k)
-          {
-            f = -sn*e[j-1];
-            e[j-1] = cs*e[j-1];
-          }
-          if (wantv)
-          {
-            for (i = 0; i < n; ++i)
-            {
-              t = cs*m_matV(i,j) + sn*m_matV(i,p-1);
-              m_matV(i,p-1) = -sn*m_matV(i,j) + cs*m_matV(i,p-1);
-              m_matV(i,j) = t;
-            }
-          }
-        }
-      }
-      break;
-
-      // Split at negligible s(k).
-      case 2:
-      {
-        Scalar f(e[k-1]);
-        e[k-1] = 0.0;
-        for (j = k; j < p; ++j)
-        {
-          Scalar t(numext::hypot(m_sigma[j],f));
-          Scalar cs( m_sigma[j]/t);
-          Scalar sn(f/t);
-          m_sigma[j] = t;
-          f = -sn*e[j];
-          e[j] = cs*e[j];
-          if (wantu)
-          {
-            for (i = 0; i < m; ++i)
-            {
-              t = cs*m_matU(i,j) + sn*m_matU(i,k-1);
-              m_matU(i,k-1) = -sn*m_matU(i,j) + cs*m_matU(i,k-1);
-              m_matU(i,j) = t;
-            }
-          }
-        }
-      }
-      break;
-
-      // Perform one qr step.
-      case 3:
-      {
-        // Calculate the shift.
-        Scalar scale = (std::max)((std::max)((std::max)((std::max)(
-                        ei_abs(m_sigma[p-1]),ei_abs(m_sigma[p-2])),ei_abs(e[p-2])),
-                        ei_abs(m_sigma[k])),ei_abs(e[k]));
-        Scalar sp = m_sigma[p-1]/scale;
-        Scalar spm1 = m_sigma[p-2]/scale;
-        Scalar epm1 = e[p-2]/scale;
-        Scalar sk = m_sigma[k]/scale;
-        Scalar ek = e[k]/scale;
-        Scalar b = ((spm1 + sp)*(spm1 - sp) + epm1*epm1)/Scalar(2);
-        Scalar c = (sp*epm1)*(sp*epm1);
-        Scalar shift(0);
-        if ((b != 0.0) || (c != 0.0))
-        {
-          shift = ei_sqrt(b*b + c);
-          if (b < 0.0)
-            shift = -shift;
-          shift = c/(b + shift);
-        }
-        Scalar f = (sk + sp)*(sk - sp) + shift;
-        Scalar g = sk*ek;
-
-        // Chase zeros.
-
-        for (j = k; j < p-1; ++j)
-        {
-          Scalar t = numext::hypot(f,g);
-          Scalar cs = f/t;
-          Scalar sn = g/t;
-          if (j != k)
-            e[j-1] = t;
-          f = cs*m_sigma[j] + sn*e[j];
-          e[j] = cs*e[j] - sn*m_sigma[j];
-          g = sn*m_sigma[j+1];
-          m_sigma[j+1] = cs*m_sigma[j+1];
-          if (wantv)
-          {
-            for (i = 0; i < n; ++i)
-            {
-              t = cs*m_matV(i,j) + sn*m_matV(i,j+1);
-              m_matV(i,j+1) = -sn*m_matV(i,j) + cs*m_matV(i,j+1);
-              m_matV(i,j) = t;
-            }
-          }
-          t = numext::hypot(f,g);
-          cs = f/t;
-          sn = g/t;
-          m_sigma[j] = t;
-          f = cs*e[j] + sn*m_sigma[j+1];
-          m_sigma[j+1] = -sn*e[j] + cs*m_sigma[j+1];
-          g = sn*e[j+1];
-          e[j+1] = cs*e[j+1];
-          if (wantu && (j < m-1))
-          {
-            for (i = 0; i < m; ++i)
-            {
-              t = cs*m_matU(i,j) + sn*m_matU(i,j+1);
-              m_matU(i,j+1) = -sn*m_matU(i,j) + cs*m_matU(i,j+1);
-              m_matU(i,j) = t;
-            }
-          }
-        }
-        e[p-2] = f;
-        iter = iter + 1;
-      }
-      break;
-
-      // Convergence.
-      case 4:
-      {
-        // Make the singular values positive.
-        if (m_sigma[k] <= 0.0)
-        {
-          m_sigma[k] = m_sigma[k] < Scalar(0) ? -m_sigma[k] : Scalar(0);
-          if (wantv)
-            m_matV.col(k).start(pp+1) = -m_matV.col(k).start(pp+1);
-        }
-
-        // Order the singular values.
-        while (k < pp)
-        {
-          if (m_sigma[k] >= m_sigma[k+1])
-            break;
-          Scalar t = m_sigma[k];
-          m_sigma[k] = m_sigma[k+1];
-          m_sigma[k+1] = t;
-          if (wantv && (k < n-1))
-            m_matV.col(k).swap(m_matV.col(k+1));
-          if (wantu && (k < m-1))
-            m_matU.col(k).swap(m_matU.col(k+1));
-          ++k;
-        }
-        iter = 0;
-        p--;
-      }
-      break;
-    } // end big switch
-  } // end iterations
-}
-
-template<typename MatrixType>
-SVD<MatrixType>& SVD<MatrixType>::sort()
-{
-  int mu = m_matU.rows();
-  int mv = m_matV.rows();
-  int n  = m_matU.cols();
-
-  for (int i=0; i<n; ++i)
-  {
-    int  k = i;
-    Scalar p = m_sigma.coeff(i);
-
-    for (int j=i+1; j<n; ++j)
-    {
-      if (m_sigma.coeff(j) > p)
-      {
-        k = j;
-        p = m_sigma.coeff(j);
-      }
-    }
-    if (k != i)
-    {
-      m_sigma.coeffRef(k) = m_sigma.coeff(i);  // i.e.
-      m_sigma.coeffRef(i) = p;                 // swaps the i-th and the k-th elements
-
-      int j = mu;
-      for(int s=0; j!=0; ++s, --j)
-        std::swap(m_matU.coeffRef(s,i), m_matU.coeffRef(s,k));
-
-      j = mv;
-      for (int s=0; j!=0; ++s, --j)
-        std::swap(m_matV.coeffRef(s,i), m_matV.coeffRef(s,k));
-    }
-  }
-  return *this;
-}
-
-/** \returns the solution of \f$ A x = b \f$ using the current SVD decomposition of A.
-  * The parts of the solution corresponding to zero singular values are ignored.
-  *
-  * \sa MatrixBase::svd(), LU::solve(), LLT::solve()
-  */
-template<typename MatrixType>
-template<typename OtherDerived, typename ResultType>
-bool SVD<MatrixType>::solve(const MatrixBase<OtherDerived> &b, ResultType* result) const
-{
-  ei_assert(b.rows() == m_matU.rows());
-
-  Scalar maxVal = m_sigma.cwise().abs().maxCoeff();
-  for (int j=0; j<b.cols(); ++j)
-  {
-    Matrix<Scalar,MatrixUType::RowsAtCompileTime,1> aux = m_matU.transpose() * b.col(j);
-
-    for (int i = 0; i <m_matU.cols(); ++i)
-    {
-      Scalar si = m_sigma.coeff(i);
-      if (ei_isMuchSmallerThan(ei_abs(si),maxVal))
-        aux.coeffRef(i) = 0;
-      else
-        aux.coeffRef(i) /= si;
-    }
-
-    result->col(j) = m_matV * aux;
-  }
-  return true;
-}
-
-/** Computes the polar decomposition of the matrix, as a product unitary x positive.
-  *
-  * If either pointer is zero, the corresponding computation is skipped.
-  *
-  * Only for square matrices.
-  *
-  * \sa computePositiveUnitary(), computeRotationScaling()
-  */
-template<typename MatrixType>
-template<typename UnitaryType, typename PositiveType>
-void SVD<MatrixType>::computeUnitaryPositive(UnitaryType *unitary,
-                                             PositiveType *positive) const
-{
-  ei_assert(m_matU.cols() == m_matV.cols() && "Polar decomposition is only for square matrices");
-  if(unitary) *unitary = m_matU * m_matV.adjoint();
-  if(positive) *positive = m_matV * m_sigma.asDiagonal() * m_matV.adjoint();
-}
-
-/** Computes the polar decomposition of the matrix, as a product positive x unitary.
-  *
-  * If either pointer is zero, the corresponding computation is skipped.
-  *
-  * Only for square matrices.
-  *
-  * \sa computeUnitaryPositive(), computeRotationScaling()
-  */
-template<typename MatrixType>
-template<typename UnitaryType, typename PositiveType>
-void SVD<MatrixType>::computePositiveUnitary(UnitaryType *positive,
-                                             PositiveType *unitary) const
-{
-  ei_assert(m_matU.rows() == m_matV.rows() && "Polar decomposition is only for square matrices");
-  if(unitary) *unitary = m_matU * m_matV.adjoint();
-  if(positive) *positive = m_matU * m_sigma.asDiagonal() * m_matU.adjoint();
-}
-
-/** decomposes the matrix as a product rotation x scaling, the scaling being
-  * not necessarily positive.
-  *
-  * If either pointer is zero, the corresponding computation is skipped.
-  *
-  * This method requires the Geometry module.
-  *
-  * \sa computeScalingRotation(), computeUnitaryPositive()
-  */
-template<typename MatrixType>
-template<typename RotationType, typename ScalingType>
-void SVD<MatrixType>::computeRotationScaling(RotationType *rotation, ScalingType *scaling) const
-{
-  ei_assert(m_matU.rows() == m_matV.rows() && "Polar decomposition is only for square matrices");
-  Scalar x = (m_matU * m_matV.adjoint()).determinant(); // so x has absolute value 1
-  Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> sv(m_sigma);
-  sv.coeffRef(0) *= x;
-  if(scaling) scaling->lazyAssign(m_matV * sv.asDiagonal() * m_matV.adjoint());
-  if(rotation)
-  {
-    MatrixType m(m_matU);
-    m.col(0) /= x;
-    rotation->lazyAssign(m * m_matV.adjoint());
-  }
-}
-
-/** decomposes the matrix as a product scaling x rotation, the scaling being
-  * not necessarily positive.
-  *
-  * If either pointer is zero, the corresponding computation is skipped.
-  *
-  * This method requires the Geometry module.
-  *
-  * \sa computeRotationScaling(), computeUnitaryPositive()
-  */
-template<typename MatrixType>
-template<typename ScalingType, typename RotationType>
-void SVD<MatrixType>::computeScalingRotation(ScalingType *scaling, RotationType *rotation) const
-{
-  ei_assert(m_matU.rows() == m_matV.rows() && "Polar decomposition is only for square matrices");
-  Scalar x = (m_matU * m_matV.adjoint()).determinant(); // so x has absolute value 1
-  Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> sv(m_sigma);
-  sv.coeffRef(0) *= x;
-  if(scaling) scaling->lazyAssign(m_matU * sv.asDiagonal() * m_matU.adjoint());
-  if(rotation)
-  {
-    MatrixType m(m_matU);
-    m.col(0) /= x;
-    rotation->lazyAssign(m * m_matV.adjoint());
-  }
-}
-
-
-/** \svd_module
-  * \returns the SVD decomposition of \c *this
-  */
-template<typename Derived>
-inline SVD<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::svd() const
-{
-  return SVD<PlainObject>(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN2_SVD_H