merge with default branch

17 files changed, 215 insertions, 1051 deletions

diff --git a/Eigen/SVD b/Eigen/SVD
index 5eee46df5..c3d24286c 100644
--- a/Eigen/SVD
+++ b/Eigen/SVD
@@ -21,6 +21,7 @@
   */
 
 #include "src/misc/Solve.h"
+#include "src/SVD/SVDBase.h"
 #include "src/SVD/JacobiSVD.h"
 #if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
 #include "src/SVD/JacobiSVD_MKL.h"
diff --git a/Eigen/src/Core/arch/NEON/PacketMath.h b/Eigen/src/Core/arch/NEON/PacketMath.h
index 380b76ae9..7c4509585 100644
--- a/Eigen/src/Core/arch/NEON/PacketMath.h
+++ b/Eigen/src/Core/arch/NEON/PacketMath.h
@@ -52,7 +52,7 @@ typedef uint32x4_t  Packet4ui;
 
 // arm64 does have the pld instruction. If available, let's trust the __builtin_prefetch built-in function
 // which available on LLVM and GCC (at least)
-#if (defined(__has_builtin) && __has_builtin(__builtin_prefetch)) || defined(__GNUC__)
+#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || defined(__GNUC__)
   #define EIGEN_ARM_PREFETCH(ADDR) __builtin_prefetch(ADDR);
 #elif defined __pld
   #define EIGEN_ARM_PREFETCH(ADDR) __pld(ADDR)
diff --git a/Eigen/src/Core/util/Macros.h b/Eigen/src/Core/util/Macros.h
index d029e0c6c..2eac86376 100644
--- a/Eigen/src/Core/util/Macros.h
+++ b/Eigen/src/Core/util/Macros.h
@@ -107,6 +107,13 @@
 #define EIGEN_DEFAULT_DENSE_INDEX_TYPE std::ptrdiff_t
 #endif
 
+// Cross compiler wrapper around LLVM's __has_builtin
+#ifdef __has_builtin
+#  define EIGEN_HAS_BUILTIN(x) __has_builtin(x)
+#else
+#  define EIGEN_HAS_BUILTIN(x) 0
+#endif
+
 // A Clang feature extension to determine compiler features.
 // We use it to determine 'cxx_rvalue_references'
 #ifndef __has_feature
diff --git a/Eigen/src/SVD/JacobiSVD.h b/Eigen/src/SVD/JacobiSVD.h
index d1b63b607..3d778516a 100644
--- a/Eigen/src/SVD/JacobiSVD.h
+++ b/Eigen/src/SVD/JacobiSVD.h
@@ -2,6 +2,7 @@
 // for linear algebra.
 //
 // Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2013-2014 Gael Guennebaud <gael.guennebaud@inria.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
@@ -442,6 +443,12 @@ void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
   *j_left  = rot1 * j_right->transpose();
 }
 
+template<typename _MatrixType, int QRPreconditioner> 
+struct traits<JacobiSVD<_MatrixType,QRPreconditioner> >
+{
+  typedef _MatrixType MatrixType;
+};
+
 } // end namespace internal
 
 /** \ingroup SVD_Module
@@ -498,7 +505,9 @@ void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
   * \sa MatrixBase::jacobiSvd()
   */
 template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
+ : public SVDBase<JacobiSVD<_MatrixType,QRPreconditioner> >
 {
+    typedef SVDBase<JacobiSVD> Base;
   public:
 
     typedef _MatrixType MatrixType;
@@ -515,13 +524,10 @@ template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
       MatrixOptions = MatrixType::Options
     };
 
-    typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime,
-                   MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime>
-            MatrixUType;
-    typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime,
-                   MatrixOptions, MaxColsAtCompileTime, MaxColsAtCompileTime>
-            MatrixVType;
-    typedef typename internal::plain_diag_type<MatrixType, RealScalar>::type SingularValuesType;
+    typedef typename Base::MatrixUType MatrixUType;
+    typedef typename Base::MatrixVType MatrixVType;
+    typedef typename Base::SingularValuesType SingularValuesType;
+    
     typedef typename internal::plain_row_type<MatrixType>::type RowType;
     typedef typename internal::plain_col_type<MatrixType>::type ColType;
     typedef Matrix<Scalar, DiagSizeAtCompileTime, DiagSizeAtCompileTime,
@@ -534,11 +540,6 @@ template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
       * perform decompositions via JacobiSVD::compute(const MatrixType&).
       */
     JacobiSVD()
-      : m_isInitialized(false),
-        m_isAllocated(false),
-        m_usePrescribedThreshold(false),
-        m_computationOptions(0),
-        m_rows(-1), m_cols(-1), m_diagSize(0)
     {}
 
 
@@ -549,11 +550,6 @@ template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
       * \sa JacobiSVD()
       */
     JacobiSVD(Index rows, Index cols, unsigned int computationOptions = 0)
-      : m_isInitialized(false),
-        m_isAllocated(false),
-        m_usePrescribedThreshold(false),
-        m_computationOptions(0),
-        m_rows(-1), m_cols(-1)
     {
       allocate(rows, cols, computationOptions);
     }
@@ -569,11 +565,6 @@ template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
      * available with the (non-default) FullPivHouseholderQR preconditioner.
      */
     JacobiSVD(const MatrixType& matrix, unsigned int computationOptions = 0)
-      : m_isInitialized(false),
-        m_isAllocated(false),
-        m_usePrescribedThreshold(false),
-        m_computationOptions(0),
-        m_rows(-1), m_cols(-1)
     {
       compute(matrix, computationOptions);
     }
@@ -601,54 +592,6 @@ template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
       return compute(matrix, m_computationOptions);
     }
 
-    /** \returns the \a U matrix.
-     *
-     * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
-     * the U matrix is n-by-n if you asked for #ComputeFullU, and is n-by-m if you asked for #ComputeThinU.
-     *
-     * The \a m first columns of \a U are the left singular vectors of the matrix being decomposed.
-     *
-     * This method asserts that you asked for \a U to be computed.
-     */
-    const MatrixUType& matrixU() const
-    {
-      eigen_assert(m_isInitialized && "JacobiSVD is not initialized.");
-      eigen_assert(computeU() && "This JacobiSVD decomposition didn't compute U. Did you ask for it?");
-      return m_matrixU;
-    }
-
-    /** \returns the \a V matrix.
-     *
-     * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
-     * the V matrix is p-by-p if you asked for #ComputeFullV, and is p-by-m if you asked for ComputeThinV.
-     *
-     * The \a m first columns of \a V are the right singular vectors of the matrix being decomposed.
-     *
-     * This method asserts that you asked for \a V to be computed.
-     */
-    const MatrixVType& matrixV() const
-    {
-      eigen_assert(m_isInitialized && "JacobiSVD is not initialized.");
-      eigen_assert(computeV() && "This JacobiSVD decomposition didn't compute V. Did you ask for it?");
-      return m_matrixV;
-    }
-
-    /** \returns the vector of singular values.
-     *
-     * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p, the
-     * returned vector has size \a m.  Singular values are always sorted in decreasing order.
-     */
-    const SingularValuesType& singularValues() const
-    {
-      eigen_assert(m_isInitialized && "JacobiSVD is not initialized.");
-      return m_singularValues;
-    }
-
-    /** \returns true if \a U (full or thin) is asked for in this SVD decomposition */
-    inline bool computeU() const { return m_computeFullU || m_computeThinU; }
-    /** \returns true if \a V (full or thin) is asked for in this SVD decomposition */
-    inline bool computeV() const { return m_computeFullV || m_computeThinV; }
-
     /** \returns a (least squares) solution of \f$ A x = b \f$ using the current SVD decomposition of A.
       *
       * \param b the right-hand-side of the equation to solve.
@@ -677,80 +620,13 @@ template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
       return internal::solve_retval<JacobiSVD, Rhs>(*this, b.derived());
     }
 #endif
-
-    /** \returns the number of singular values that are not exactly 0 */
-    Index nonzeroSingularValues() const
-    {
-      eigen_assert(m_isInitialized && "JacobiSVD is not initialized.");
-      return m_nonzeroSingularValues;
-    }
-    
-    /** \returns the rank of the matrix of which \c *this is the SVD.
-      *
-      * \note This method has to determine which singular values should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index rank() const
-    {
-      using std::abs;
-      eigen_assert(m_isInitialized && "JacobiSVD is not initialized.");
-      if(m_singularValues.size()==0) return 0;
-      RealScalar premultiplied_threshold = m_singularValues.coeff(0) * threshold();
-      Index i = m_nonzeroSingularValues-1;
-      while(i>=0 && m_singularValues.coeff(i) < premultiplied_threshold) --i;
-      return i+1;
-    }
     
-    /** Allows to prescribe a threshold to be used by certain methods, such as rank() and solve(),
-      * which need to determine when singular values are to be considered nonzero.
-      * This is not used for the SVD decomposition itself.
-      *
-      * When it needs to get the threshold value, Eigen calls threshold().
-      * The default is \c NumTraits<Scalar>::epsilon()
-      *
-      * \param threshold The new value to use as the threshold.
-      *
-      * A singular value will be considered nonzero if its value is strictly greater than
-      *  \f$ \vert singular value \vert \leqslant threshold \times \vert max singular value \vert \f$.
-      *
-      * If you want to come back to the default behavior, call setThreshold(Default_t)
-      */
-    JacobiSVD& setThreshold(const RealScalar& threshold)
-    {
-      m_usePrescribedThreshold = true;
-      m_prescribedThreshold = threshold;
-      return *this;
-    }
-
-    /** Allows to come back to the default behavior, letting Eigen use its default formula for
-      * determining the threshold.
-      *
-      * You should pass the special object Eigen::Default as parameter here.
-      * \code svd.setThreshold(Eigen::Default); \endcode
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    JacobiSVD& setThreshold(Default_t)
-    {
-      m_usePrescribedThreshold = false;
-      return *this;
-    }
+    using Base::computeU;
+    using Base::computeV;
+    using Base::rows;
+    using Base::cols;
+    using Base::rank;
 
-    /** Returns the threshold that will be used by certain methods such as rank().
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    RealScalar threshold() const
-    {
-      eigen_assert(m_isInitialized || m_usePrescribedThreshold);
-      return m_usePrescribedThreshold ? m_prescribedThreshold
-                                      : (std::max<Index>)(1,m_diagSize)*NumTraits<Scalar>::epsilon();
-    }
-
-    inline Index rows() const { return m_rows; }
-    inline Index cols() const { return m_cols; }
-    
     #ifndef EIGEN_PARSED_BY_DOXYGEN
     template<typename RhsType, typename DstType>
     EIGEN_DEVICE_FUNC
@@ -761,16 +637,23 @@ template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
     void allocate(Index rows, Index cols, unsigned int computationOptions);
 
   protected:
-    MatrixUType m_matrixU;
-    MatrixVType m_matrixV;
-    SingularValuesType m_singularValues;
+    using Base::m_matrixU;
+    using Base::m_matrixV;
+    using Base::m_singularValues;
+    using Base::m_isInitialized;
+    using Base::m_isAllocated;
+    using Base::m_usePrescribedThreshold;
+    using Base::m_computeFullU;
+    using Base::m_computeThinU;
+    using Base::m_computeFullV;
+    using Base::m_computeThinV;
+    using Base::m_computationOptions;
+    using Base::m_nonzeroSingularValues;
+    using Base::m_rows;
+    using Base::m_cols;
+    using Base::m_diagSize;
+    using Base::m_prescribedThreshold;
     WorkMatrixType m_workMatrix;
-    bool m_isInitialized, m_isAllocated, m_usePrescribedThreshold;
-    bool m_computeFullU, m_computeThinU;
-    bool m_computeFullV, m_computeThinV;
-    unsigned int m_computationOptions;
-    Index m_nonzeroSingularValues, m_rows, m_cols, m_diagSize;
-    RealScalar m_prescribedThreshold;
 
     template<typename __MatrixType, int _QRPreconditioner, bool _IsComplex>
     friend struct internal::svd_precondition_2x2_block_to_be_real;
diff --git a/unsupported/Eigen/src/SVD/SVDBase.h b/Eigen/src/SVD/SVDBase.h
index fd8af3b8c..61b01fb8a 100644
--- a/unsupported/Eigen/src/SVD/SVDBase.h
+++ b/Eigen/src/SVD/SVDBase.h
@@ -2,6 +2,7 @@
 // for linear algebra.
 //
 // Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
 // Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
 // Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
@@ -12,8 +13,8 @@
 // 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_SVD_H
-#define EIGEN_SVD_H
+#ifndef EIGEN_SVDBASE_H
+#define EIGEN_SVDBASE_H
 
 namespace Eigen {
 /** \ingroup SVD_Module
@@ -21,9 +22,10 @@ namespace Eigen {
  *
  * \class SVDBase
  *
- * \brief Mother class of SVD classes algorithms
+ * \brief Base class of SVD algorithms
+ *
+ * \tparam Derived the type of the actual SVD decomposition
  *
- * \param MatrixType the type of the matrix of which we are computing the SVD decomposition
  * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
  *   \f[ A = U S V^* \f]
  * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal;
@@ -42,12 +44,12 @@ namespace Eigen {
  * terminate in finite (and reasonable) time.
  * \sa MatrixBase::genericSvd()
  */
-template<typename _MatrixType> 
+template<typename Derived>
 class SVDBase
 {
 
 public:
-  typedef _MatrixType MatrixType;
+  typedef typename internal::traits<Derived>::MatrixType MatrixType;
   typedef typename MatrixType::Scalar Scalar;
   typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
   typedef typename MatrixType::Index Index;
@@ -61,46 +63,16 @@ public:
     MatrixOptions = MatrixType::Options
   };
 
-  typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime,
-		 MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime>
-  MatrixUType;
-  typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime,
-		 MatrixOptions, MaxColsAtCompileTime, MaxColsAtCompileTime>
-  MatrixVType;
+  typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixUType;
+  typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime, MatrixOptions, MaxColsAtCompileTime, MaxColsAtCompileTime> MatrixVType;
   typedef typename internal::plain_diag_type<MatrixType, RealScalar>::type SingularValuesType;
-  typedef typename internal::plain_row_type<MatrixType>::type RowType;
-  typedef typename internal::plain_col_type<MatrixType>::type ColType;
-  typedef Matrix<Scalar, DiagSizeAtCompileTime, DiagSizeAtCompileTime,
-		 MatrixOptions, MaxDiagSizeAtCompileTime, MaxDiagSizeAtCompileTime>
-  WorkMatrixType;
-	
-
-
-
-  /** \brief Method performing the decomposition of given matrix using custom options.
-   *
-   * \param matrix the matrix to decompose
-   * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-   *                           By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU,
-   *                           #ComputeFullV, #ComputeThinV.
-   *
-   * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-   * available with the (non-default) FullPivHouseholderQR preconditioner.
-   */
-  SVDBase& compute(const MatrixType& matrix, unsigned int computationOptions);
-
-  /** \brief Method performing the decomposition of given matrix using current options.
-   *
-   * \param matrix the matrix to decompose
-   *
-   * This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
-   */
-  //virtual SVDBase& compute(const MatrixType& matrix) = 0;
-  SVDBase& compute(const MatrixType& matrix);
+  
+  Derived& derived() { return *static_cast<Derived*>(this); }
+  const Derived& derived() const { return *static_cast<const Derived*>(this); }
 
   /** \returns the \a U matrix.
    *
-   * For the SVDBase decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
+   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
    * the U matrix is n-by-n if you asked for #ComputeFullU, and is n-by-m if you asked for #ComputeThinU.
    *
    * The \a m first columns of \a U are the left singular vectors of the matrix being decomposed.
@@ -141,26 +113,84 @@ public:
     return m_singularValues;
   }
 
-  
-
   /** \returns the number of singular values that are not exactly 0 */
   Index nonzeroSingularValues() const
   {
     eigen_assert(m_isInitialized && "SVD is not initialized.");
     return m_nonzeroSingularValues;
   }
+  
+  /** \returns the rank of the matrix of which \c *this is the SVD.
+    *
+    * \note This method has to determine which singular values should be considered nonzero.
+    *       For that, it uses the threshold value that you can control by calling
+    *       setThreshold(const RealScalar&).
+    */
+  inline Index rank() const
+  {
+    using std::abs;
+    eigen_assert(m_isInitialized && "JacobiSVD is not initialized.");
+    if(m_singularValues.size()==0) return 0;
+    RealScalar premultiplied_threshold = m_singularValues.coeff(0) * threshold();
+    Index i = m_nonzeroSingularValues-1;
+    while(i>=0 && m_singularValues.coeff(i) < premultiplied_threshold) --i;
+    return i+1;
+  }
+  
+  /** Allows to prescribe a threshold to be used by certain methods, such as rank() and solve(),
+    * which need to determine when singular values are to be considered nonzero.
+    * This is not used for the SVD decomposition itself.
+    *
+    * When it needs to get the threshold value, Eigen calls threshold().
+    * The default is \c NumTraits<Scalar>::epsilon()
+    *
+    * \param threshold The new value to use as the threshold.
+    *
+    * A singular value will be considered nonzero if its value is strictly greater than
+    *  \f$ \vert singular value \vert \leqslant threshold \times \vert max singular value \vert \f$.
+    *
+    * If you want to come back to the default behavior, call setThreshold(Default_t)
+    */
+  Derived& setThreshold(const RealScalar& threshold)
+  {
+    m_usePrescribedThreshold = true;
+    m_prescribedThreshold = threshold;
+    return derived();
+  }
+
+  /** Allows to come back to the default behavior, letting Eigen use its default formula for
+    * determining the threshold.
+    *
+    * You should pass the special object Eigen::Default as parameter here.
+    * \code svd.setThreshold(Eigen::Default); \endcode
+    *
+    * See the documentation of setThreshold(const RealScalar&).
+    */
+  Derived& setThreshold(Default_t)
+  {
+    m_usePrescribedThreshold = false;
+    return derived();
+  }
 
+  /** Returns the threshold that will be used by certain methods such as rank().
+    *
+    * See the documentation of setThreshold(const RealScalar&).
+    */
+  RealScalar threshold() const
+  {
+    eigen_assert(m_isInitialized || m_usePrescribedThreshold);
+    return m_usePrescribedThreshold ? m_prescribedThreshold
+                                    : (std::max<Index>)(1,m_diagSize)*NumTraits<Scalar>::epsilon();
+  }
 
   /** \returns true if \a U (full or thin) is asked for in this SVD decomposition */
   inline bool computeU() const { return m_computeFullU || m_computeThinU; }
   /** \returns true if \a V (full or thin) is asked for in this SVD decomposition */
   inline bool computeV() const { return m_computeFullV || m_computeThinV; }
 
-
   inline Index rows() const { return m_rows; }
   inline Index cols() const { return m_cols; }
 
-
 protected:
   // return true if already allocated
   bool allocate(Index rows, Index cols, unsigned int computationOptions) ;
@@ -168,12 +198,12 @@ protected:
   MatrixUType m_matrixU;
   MatrixVType m_matrixV;
   SingularValuesType m_singularValues;
-  bool m_isInitialized, m_isAllocated;
+  bool m_isInitialized, m_isAllocated, m_usePrescribedThreshold;
   bool m_computeFullU, m_computeThinU;
   bool m_computeFullV, m_computeThinV;
   unsigned int m_computationOptions;
   Index m_nonzeroSingularValues, m_rows, m_cols, m_diagSize;
-
+  RealScalar m_prescribedThreshold;
 
   /** \brief Default Constructor.
    *
@@ -182,8 +212,9 @@ protected:
   SVDBase()
     : m_isInitialized(false),
       m_isAllocated(false),
+      m_usePrescribedThreshold(false),
       m_computationOptions(0),
-      m_rows(-1), m_cols(-1)
+      m_rows(-1), m_cols(-1), m_diagSize(0)
   {}
 
 
@@ -220,17 +251,13 @@ bool SVDBase<MatrixType>::allocate(Index rows, Index cols, unsigned int computat
   m_diagSize = (std::min)(m_rows, m_cols);
   m_singularValues.resize(m_diagSize);
   if(RowsAtCompileTime==Dynamic)
-    m_matrixU.resize(m_rows, m_computeFullU ? m_rows
-		     : m_computeThinU ? m_diagSize
-		     : 0);
+    m_matrixU.resize(m_rows, m_computeFullU ? m_rows : m_computeThinU ? m_diagSize : 0);
   if(ColsAtCompileTime==Dynamic)
-    m_matrixV.resize(m_cols, m_computeFullV ? m_cols
-		     : m_computeThinV ? m_diagSize
-		     : 0);
+    m_matrixV.resize(m_cols, m_computeFullV ? m_cols : m_computeThinV ? m_diagSize : 0);
 
   return false;
 }
 
 }// end namespace
 
-#endif // EIGEN_SVD_H
+#endif // EIGEN_SVDBASE_H
diff --git a/Eigen/src/SVD/UpperBidiagonalization.h b/Eigen/src/SVD/UpperBidiagonalization.h
index 40067682c..225b19e3c 100644
--- a/Eigen/src/SVD/UpperBidiagonalization.h
+++ b/Eigen/src/SVD/UpperBidiagonalization.h
@@ -2,6 +2,7 @@
 // for linear algebra.
 //
 // Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2013-2014 Gael Guennebaud <gael.guennebaud@inria.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
diff --git a/test/main.h b/test/main.h
index b8854a1c3..85f951db4 100644
--- a/test/main.h
+++ b/test/main.h
@@ -17,13 +17,36 @@
 #include <sstream>
 #include <vector>
 #include <typeinfo>
+
+// The following includes of STL headers have to be done _before_ the
+// definition of macros min() and max().  The reason is that many STL
+// implementations will not work properly as the min and max symbols collide
+// with the STL functions std:min() and std::max().  The STL headers may check
+// for the macro definition of min/max and issue a warning or undefine the
+// macros.
+//
+// Still, Windows defines min() and max() in windef.h as part of the regular
+// Windows system interfaces and many other Windows APIs depend on these
+// macros being available.  To prevent the macro expansion of min/max and to
+// make Eigen compatible with the Windows environment all function calls of
+// std::min() and std::max() have to be written with parenthesis around the
+// function name.
+//
+// All STL headers used by Eigen should be included here.  Because main.h is
+// included before any Eigen header and because the STL headers are guarded
+// against multiple inclusions, no STL header will see our own min/max macro
+// definitions.
 #include <limits>
 #include <algorithm>
-#include <sstream>
 #include <complex>
 #include <deque>
 #include <queue>
+#include <list>
 
+// To test that all calls from Eigen code to std::min() and std::max() are
+// protected by parenthesis against macro expansion, the min()/max() macros
+// are defined here and any not-parenthesized min/max call will cause a
+// compiler error.
 #define min(A,B) please_protect_your_min_with_parentheses
 #define max(A,B) please_protect_your_max_with_parentheses
 
diff --git a/unsupported/Eigen/BDCSVD b/unsupported/Eigen/BDCSVD
new file mode 100644
index 000000000..44649dbd0
--- /dev/null
+++ b/unsupported/Eigen/BDCSVD
@@ -0,0 +1,26 @@
+#ifndef EIGEN_BDCSVD_MODULE_H
+#define EIGEN_BDCSVD_MODULE_H
+
+#include <Eigen/SVD>
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup BDCSVD_Module BDCSVD module
+  *
+  *
+  *
+  * This module provides Divide & Conquer SVD decomposition for matrices (both real and complex).
+  * This decomposition is accessible via the following MatrixBase method:
+  *  - MatrixBase::bdcSvd()
+  *
+  * \code
+  * #include <Eigen/BDCSVD>
+  * \endcode
+  */
+
+#include "src/BDCSVD/BDCSVD.h"
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_BDCSVD_MODULE_H
+/* vim: set filetype=cpp et sw=2 ts=2 ai: */
diff --git a/unsupported/Eigen/SVD b/unsupported/Eigen/SVD
deleted file mode 100644
index 900a8aa60..000000000
--- a/unsupported/Eigen/SVD
+++ /dev/null
@@ -1,35 +0,0 @@
-#ifndef EIGEN_SVD_MODULE_H
-#define EIGEN_SVD_MODULE_H
-
-#include <Eigen/QR>
-#include <Eigen/Householder>
-#include <Eigen/Jacobi>
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup SVD_Module SVD module
-  *
-  *
-  *
-  * This module provides SVD decomposition for matrices (both real and complex).
-  * This decomposition is accessible via the following MatrixBase method:
-  *  - MatrixBase::jacobiSvd()
-  *
-  * \code
-  * #include <Eigen/SVD>
-  * \endcode
-  */
-
-#include "../../Eigen/src/misc/Solve.h"
-#include "../../Eigen/src/SVD/UpperBidiagonalization.h"
-#include "src/SVD/SVDBase.h"
-#include "src/SVD/JacobiSVD.h"
-#include "src/SVD/BDCSVD.h"
-#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
-#include "../../Eigen/src/SVD/JacobiSVD_MKL.h"
-#endif
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SVD_MODULE_H
-/* vim: set filetype=cpp et sw=2 ts=2 ai: */
diff --git a/unsupported/Eigen/src/SVD/BDCSVD.h b/unsupported/Eigen/src/BDCSVD/BDCSVD.h
index 80006fd60..a7c369633 100644
--- a/unsupported/Eigen/src/SVD/BDCSVD.h
+++ b/unsupported/Eigen/src/BDCSVD/BDCSVD.h
@@ -24,6 +24,20 @@
 #define ALGOSWAP 16
 
 namespace Eigen {
+
+template<typename _MatrixType> class BDCSVD;
+
+namespace internal {
+
+template<typename _MatrixType> 
+struct traits<BDCSVD<_MatrixType> >
+{
+  typedef _MatrixType MatrixType;
+};  
+
+} // end namespace internal
+  
+  
 /** \ingroup SVD_Module
  *
  *
@@ -36,9 +50,9 @@ namespace Eigen {
  * It should be used to speed up the calcul of SVD for big matrices. 
  */
 template<typename _MatrixType> 
-class BDCSVD : public SVDBase<_MatrixType>
+class BDCSVD : public SVDBase<BDCSVD<_MatrixType> >
 {
-  typedef SVDBase<_MatrixType> Base;
+  typedef SVDBase<BDCSVD> Base;
     
 public:
   using Base::rows;
@@ -77,9 +91,7 @@ public:
    * The default constructor is useful in cases in which the user intends to
    * perform decompositions via BDCSVD::compute(const MatrixType&).
    */
-  BDCSVD()
-    : SVDBase<_MatrixType>::SVDBase(), 
-      algoswap(ALGOSWAP), m_numIters(0)
+  BDCSVD() : algoswap(ALGOSWAP), m_numIters(0)
   {}
 
 
@@ -90,8 +102,7 @@ public:
    * \sa BDCSVD()
    */
   BDCSVD(Index rows, Index cols, unsigned int computationOptions = 0)
-    : SVDBase<_MatrixType>::SVDBase(), 
-      algoswap(ALGOSWAP), m_numIters(0)
+    : algoswap(ALGOSWAP), m_numIters(0)
   {
     allocate(rows, cols, computationOptions);
   }
@@ -107,8 +118,7 @@ public:
    * available with the (non - default) FullPivHouseholderQR preconditioner.
    */
   BDCSVD(const MatrixType& matrix, unsigned int computationOptions = 0)
-    : SVDBase<_MatrixType>::SVDBase(), 
-      algoswap(ALGOSWAP), m_numIters(0)
+    : algoswap(ALGOSWAP), m_numIters(0)
   {
     compute(matrix, computationOptions);
   }
@@ -116,6 +126,7 @@ public:
   ~BDCSVD() 
   {
   }
+  
   /** \brief Method performing the decomposition of given matrix using custom options.
    *
    * \param matrix the matrix to decompose
@@ -126,7 +137,7 @@ public:
    * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
    * available with the (non - default) FullPivHouseholderQR preconditioner.
    */
-  SVDBase<MatrixType>& compute(const MatrixType& matrix, unsigned int computationOptions);
+  BDCSVD& compute(const MatrixType& matrix, unsigned int computationOptions);
 
   /** \brief Method performing the decomposition of given matrix using current options.
    *
@@ -134,7 +145,7 @@ public:
    *
    * This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
    */
-  SVDBase<MatrixType>& compute(const MatrixType& matrix)
+  BDCSVD& compute(const MatrixType& matrix)
   {
     return compute(matrix, this->m_computationOptions);
   }
@@ -160,8 +171,8 @@ public:
   solve(const MatrixBase<Rhs>& b) const
   {
     eigen_assert(this->m_isInitialized && "BDCSVD is not initialized.");
-    eigen_assert(SVDBase<_MatrixType>::computeU() && SVDBase<_MatrixType>::computeV() && 
-		 "BDCSVD::solve() requires both unitaries U and V to be computed (thin unitaries suffice).");
+    eigen_assert(computeU() && computeV() && 
+                 "BDCSVD::solve() requires both unitaries U and V to be computed (thin unitaries suffice).");
     return internal::solve_retval<BDCSVD, Rhs>(*this, b.derived());
   }
 
@@ -195,6 +206,9 @@ public:
       return this->m_matrixV;
     }
   }
+  
+  using Base::computeU;
+  using Base::computeV;
  
 private:
   void allocate(Index rows, Index cols, unsigned int computationOptions);
@@ -229,7 +243,7 @@ template<typename MatrixType>
 void BDCSVD<MatrixType>::allocate(Index rows, Index cols, unsigned int computationOptions)
 {
   isTranspose = (cols > rows);
-  if (SVDBase<MatrixType>::allocate(rows, cols, computationOptions)) return;
+  if (Base::allocate(rows, cols, computationOptions)) return;
   m_computed = MatrixXr::Zero(this->m_diagSize + 1, this->m_diagSize );
   if (isTranspose){
     compU = this->computeU();
@@ -262,8 +276,7 @@ void BDCSVD<MatrixType>::allocate(Index rows, Index cols, unsigned int computati
 
 // Methode which compute the BDCSVD for the int
 template<>
-SVDBase<Matrix<int, Dynamic, Dynamic> >&
-BDCSVD<Matrix<int, Dynamic, Dynamic> >::compute(const MatrixType& matrix, unsigned int computationOptions) {
+BDCSVD<Matrix<int, Dynamic, Dynamic> >& BDCSVD<Matrix<int, Dynamic, Dynamic> >::compute(const MatrixType& matrix, unsigned int computationOptions) {
   allocate(matrix.rows(), matrix.cols(), computationOptions);
   this->m_nonzeroSingularValues = 0;
   m_computed = Matrix<int, Dynamic, Dynamic>::Zero(rows(), cols());
@@ -279,8 +292,7 @@ BDCSVD<Matrix<int, Dynamic, Dynamic> >::compute(const MatrixType& matrix, unsign
 
 // Methode which compute the BDCSVD
 template<typename MatrixType>
-SVDBase<MatrixType>&
-BDCSVD<MatrixType>::compute(const MatrixType& matrix, unsigned int computationOptions) 
+BDCSVD<MatrixType>& BDCSVD<MatrixType>::compute(const MatrixType& matrix, unsigned int computationOptions) 
 {
   allocate(matrix.rows(), matrix.cols(), computationOptions);
   using std::abs;
diff --git a/unsupported/Eigen/src/BDCSVD/CMakeLists.txt b/unsupported/Eigen/src/BDCSVD/CMakeLists.txt
new file mode 100644
index 000000000..73b89ea18
--- /dev/null
+++ b/unsupported/Eigen/src/BDCSVD/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_BDCSVD_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_BDCSVD_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}unsupported/Eigen/src/BDCSVD COMPONENT Devel
+  )
diff --git a/unsupported/Eigen/src/SVD/TODOBdcsvd.txt b/unsupported/Eigen/src/BDCSVD/TODOBdcsvd.txt
index 0bc9a46e6..0bc9a46e6 100644
--- a/unsupported/Eigen/src/SVD/TODOBdcsvd.txt
+++ b/unsupported/Eigen/src/BDCSVD/TODOBdcsvd.txt
diff --git a/unsupported/Eigen/src/SVD/doneInBDCSVD.txt b/unsupported/Eigen/src/BDCSVD/doneInBDCSVD.txt
index 8563ddab8..8563ddab8 100644
--- a/unsupported/Eigen/src/SVD/doneInBDCSVD.txt
+++ b/unsupported/Eigen/src/BDCSVD/doneInBDCSVD.txt
diff --git a/unsupported/Eigen/src/CMakeLists.txt b/unsupported/Eigen/src/CMakeLists.txt
index 8eb2808e3..654a2327f 100644
--- a/unsupported/Eigen/src/CMakeLists.txt
+++ b/unsupported/Eigen/src/CMakeLists.txt
@@ -12,3 +12,4 @@ ADD_SUBDIRECTORY(Skyline)
 ADD_SUBDIRECTORY(SparseExtra)
 ADD_SUBDIRECTORY(KroneckerProduct)
 ADD_SUBDIRECTORY(Splines)
+ADD_SUBDIRECTORY(BDCSVD)
diff --git a/unsupported/Eigen/src/SVD/CMakeLists.txt b/unsupported/Eigen/src/SVD/CMakeLists.txt
deleted file mode 100644
index b40baf092..000000000
--- a/unsupported/Eigen/src/SVD/CMakeLists.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-FILE(GLOB Eigen_SVD_SRCS "*.h")
-
-INSTALL(FILES
-  ${Eigen_SVD_SRCS}
-  DESTINATION ${INCLUDE_INSTALL_DIR}unsupported/Eigen/src/SVD COMPONENT Devel
-  )
diff --git a/unsupported/Eigen/src/SVD/JacobiSVD.h b/unsupported/Eigen/src/SVD/JacobiSVD.h
deleted file mode 100644
index 02fac409e..000000000
--- a/unsupported/Eigen/src/SVD/JacobiSVD.h
+++ /dev/null
@@ -1,782 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// 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_JACOBISVD_H
-#define EIGEN_JACOBISVD_H
-
-namespace Eigen { 
-
-namespace internal {
-// forward declaration (needed by ICC)
-// the empty body is required by MSVC
-template<typename MatrixType, int QRPreconditioner,
-         bool IsComplex = NumTraits<typename MatrixType::Scalar>::IsComplex>
-struct svd_precondition_2x2_block_to_be_real {};
-
-/*** QR preconditioners (R-SVD)
- ***
- *** Their role is to reduce the problem of computing the SVD to the case of a square matrix.
- *** This approach, known as R-SVD, is an optimization for rectangular-enough matrices, and is a requirement for
- *** JacobiSVD which by itself is only able to work on square matrices.
- ***/
-
-enum { PreconditionIfMoreColsThanRows, PreconditionIfMoreRowsThanCols };
-
-template<typename MatrixType, int QRPreconditioner, int Case>
-struct qr_preconditioner_should_do_anything
-{
-  enum { a = MatrixType::RowsAtCompileTime != Dynamic &&
-             MatrixType::ColsAtCompileTime != Dynamic &&
-             MatrixType::ColsAtCompileTime <= MatrixType::RowsAtCompileTime,
-         b = MatrixType::RowsAtCompileTime != Dynamic &&
-             MatrixType::ColsAtCompileTime != Dynamic &&
-             MatrixType::RowsAtCompileTime <= MatrixType::ColsAtCompileTime,
-         ret = !( (QRPreconditioner == NoQRPreconditioner) ||
-                  (Case == PreconditionIfMoreColsThanRows && bool(a)) ||
-                  (Case == PreconditionIfMoreRowsThanCols && bool(b)) )
-  };
-};
-
-template<typename MatrixType, int QRPreconditioner, int Case,
-         bool DoAnything = qr_preconditioner_should_do_anything<MatrixType, QRPreconditioner, Case>::ret
-> struct qr_preconditioner_impl {};
-
-template<typename MatrixType, int QRPreconditioner, int Case>
-class qr_preconditioner_impl<MatrixType, QRPreconditioner, Case, false>
-{
-public:
-  typedef typename MatrixType::Index Index;
-  void allocate(const JacobiSVD<MatrixType, QRPreconditioner>&) {}
-  bool run(JacobiSVD<MatrixType, QRPreconditioner>&, const MatrixType&)
-  {
-    return false;
-  }
-};
-
-/*** preconditioner using FullPivHouseholderQR ***/
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, FullPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
-{
-public:
-  typedef typename MatrixType::Index Index;
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime
-  };
-  typedef Matrix<Scalar, 1, RowsAtCompileTime, RowMajor, 1, MaxRowsAtCompileTime> WorkspaceType;
-
-  void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.rows(), svd.cols());
-    }
-    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
-  }
-
-  bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.rows() > matrix.cols())
-    {
-      m_qr.compute(matrix);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
-      if(svd.m_computeFullU) m_qr.matrixQ().evalTo(svd.m_matrixU, m_workspace);
-      if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
-      return true;
-    }
-    return false;
-  }
-private:
-  typedef FullPivHouseholderQR<MatrixType> QRType;
-  QRType m_qr;
-  WorkspaceType m_workspace;
-};
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, FullPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
-{
-public:
-  typedef typename MatrixType::Index Index;
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    Options = MatrixType::Options
-  };
-  typedef Matrix<Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime>
-          TransposeTypeWithSameStorageOrder;
-
-  void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.cols(), svd.rows());
-    }
-    m_adjoint.resize(svd.cols(), svd.rows());
-    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
-  }
-
-  bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.cols() > matrix.rows())
-    {
-      m_adjoint = matrix.adjoint();
-      m_qr.compute(m_adjoint);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
-      if(svd.m_computeFullV) m_qr.matrixQ().evalTo(svd.m_matrixV, m_workspace);
-      if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
-      return true;
-    }
-    else return false;
-  }
-private:
-  typedef FullPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
-  QRType m_qr;
-  TransposeTypeWithSameStorageOrder m_adjoint;
-  typename internal::plain_row_type<MatrixType>::type m_workspace;
-};
-
-/*** preconditioner using ColPivHouseholderQR ***/
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, ColPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
-{
-public:
-  typedef typename MatrixType::Index Index;
-
-  void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.rows(), svd.cols());
-    }
-    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
-    else if (svd.m_computeThinU) m_workspace.resize(svd.cols());
-  }
-
-  bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.rows() > matrix.cols())
-    {
-      m_qr.compute(matrix);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
-      if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
-      else if(svd.m_computeThinU)
-      {
-        svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
-      }
-      if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
-      return true;
-    }
-    return false;
-  }
-
-private:
-  typedef ColPivHouseholderQR<MatrixType> QRType;
-  QRType m_qr;
-  typename internal::plain_col_type<MatrixType>::type m_workspace;
-};
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, ColPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
-{
-public:
-  typedef typename MatrixType::Index Index;
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    Options = MatrixType::Options
-  };
-
-  typedef Matrix<Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime>
-          TransposeTypeWithSameStorageOrder;
-
-  void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.cols(), svd.rows());
-    }
-    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
-    else if (svd.m_computeThinV) m_workspace.resize(svd.rows());
-    m_adjoint.resize(svd.cols(), svd.rows());
-  }
-
-  bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.cols() > matrix.rows())
-    {
-      m_adjoint = matrix.adjoint();
-      m_qr.compute(m_adjoint);
-
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
-      if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
-      else if(svd.m_computeThinV)
-      {
-        svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
-      }
-      if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
-      return true;
-    }
-    else return false;
-  }
-
-private:
-  typedef ColPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
-  QRType m_qr;
-  TransposeTypeWithSameStorageOrder m_adjoint;
-  typename internal::plain_row_type<MatrixType>::type m_workspace;
-};
-
-/*** preconditioner using HouseholderQR ***/
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
-{
-public:
-  typedef typename MatrixType::Index Index;
-
-  void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd)
-  {
-    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.rows(), svd.cols());
-    }
-    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
-    else if (svd.m_computeThinU) m_workspace.resize(svd.cols());
-  }
-
-  bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.rows() > matrix.cols())
-    {
-      m_qr.compute(matrix);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
-      if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
-      else if(svd.m_computeThinU)
-      {
-        svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
-      }
-      if(svd.computeV()) svd.m_matrixV.setIdentity(matrix.cols(), matrix.cols());
-      return true;
-    }
-    return false;
-  }
-private:
-  typedef HouseholderQR<MatrixType> QRType;
-  QRType m_qr;
-  typename internal::plain_col_type<MatrixType>::type m_workspace;
-};
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
-{
-public:
-  typedef typename MatrixType::Index Index;
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    Options = MatrixType::Options
-  };
-
-  typedef Matrix<Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime>
-          TransposeTypeWithSameStorageOrder;
-
-  void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd)
-  {
-    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.cols(), svd.rows());
-    }
-    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
-    else if (svd.m_computeThinV) m_workspace.resize(svd.rows());
-    m_adjoint.resize(svd.cols(), svd.rows());
-  }
-
-  bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.cols() > matrix.rows())
-    {
-      m_adjoint = matrix.adjoint();
-      m_qr.compute(m_adjoint);
-
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
-      if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
-      else if(svd.m_computeThinV)
-      {
-        svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
-      }
-      if(svd.computeU()) svd.m_matrixU.setIdentity(matrix.rows(), matrix.rows());
-      return true;
-    }
-    else return false;
-  }
-
-private:
-  typedef HouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
-  QRType m_qr;
-  TransposeTypeWithSameStorageOrder m_adjoint;
-  typename internal::plain_row_type<MatrixType>::type m_workspace;
-};
-
-/*** 2x2 SVD implementation
- ***
- *** JacobiSVD consists in performing a series of 2x2 SVD subproblems
- ***/
-
-template<typename MatrixType, int QRPreconditioner>
-struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, false>
-{
-  typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
-  typedef typename SVD::Index Index;
-  static void run(typename SVD::WorkMatrixType&, SVD&, Index, Index) {}
-};
-
-template<typename MatrixType, int QRPreconditioner>
-struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, true>
-{
-  typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef typename SVD::Index Index;
-  static void run(typename SVD::WorkMatrixType& work_matrix, SVD& svd, Index p, Index q)
-  {
-    using std::sqrt;
-    Scalar z;
-    JacobiRotation<Scalar> rot;
-    RealScalar n = sqrt(numext::abs2(work_matrix.coeff(p,p)) + numext::abs2(work_matrix.coeff(q,p)));
-    if(n==0)
-    {
-      z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q);
-      work_matrix.row(p) *= z;
-      if(svd.computeU()) svd.m_matrixU.col(p) *= conj(z);
-      z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q);
-      work_matrix.row(q) *= z;
-      if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
-    }
-    else
-    {
-      rot.c() = conj(work_matrix.coeff(p,p)) / n;
-      rot.s() = work_matrix.coeff(q,p) / n;
-      work_matrix.applyOnTheLeft(p,q,rot);
-      if(svd.computeU()) svd.m_matrixU.applyOnTheRight(p,q,rot.adjoint());
-      if(work_matrix.coeff(p,q) != Scalar(0))
-      {
-        Scalar z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q);
-        work_matrix.col(q) *= z;
-        if(svd.computeV()) svd.m_matrixV.col(q) *= z;
-      }
-      if(work_matrix.coeff(q,q) != Scalar(0))
-      {
-        z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q);
-        work_matrix.row(q) *= z;
-        if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
-      }
-    }
-  }
-};
-
-template<typename MatrixType, typename RealScalar, typename Index>
-void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
-                            JacobiRotation<RealScalar> *j_left,
-                            JacobiRotation<RealScalar> *j_right)
-{
-  using std::sqrt;
-  Matrix<RealScalar,2,2> m;
-  m << numext::real(matrix.coeff(p,p)), numext::real(matrix.coeff(p,q)),
-       numext::real(matrix.coeff(q,p)), numext::real(matrix.coeff(q,q));
-  JacobiRotation<RealScalar> rot1;
-  RealScalar t = m.coeff(0,0) + m.coeff(1,1);
-  RealScalar d = m.coeff(1,0) - m.coeff(0,1);
-  if(t == RealScalar(0))
-  {
-    rot1.c() = RealScalar(0);
-    rot1.s() = d > RealScalar(0) ? RealScalar(1) : RealScalar(-1);
-  }
-  else
-  {
-    RealScalar u = d / t;
-    rot1.c() = RealScalar(1) / sqrt(RealScalar(1) + numext::abs2(u));
-    rot1.s() = rot1.c() * u;
-  }
-  m.applyOnTheLeft(0,1,rot1);
-  j_right->makeJacobi(m,0,1);
-  *j_left  = rot1 * j_right->transpose();
-}
-
-} // end namespace internal
-
-/** \ingroup SVD_Module
-  *
-  *
-  * \class JacobiSVD
-  *
-  * \brief Two-sided Jacobi SVD decomposition of a rectangular matrix
-  *
-  * \param MatrixType the type of the matrix of which we are computing the SVD decomposition
-  * \param QRPreconditioner this optional parameter allows to specify the type of QR decomposition that will be used internally
-  *                        for the R-SVD step for non-square matrices. See discussion of possible values below.
-  *
-  * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
-  *   \f[ A = U S V^* \f]
-  * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal;
-  * the diagonal entries of S are known as the \em singular \em values of \a A and the columns of \a U and \a V are known as the left
-  * and right \em singular \em vectors of \a A respectively.
-  *
-  * Singular values are always sorted in decreasing order.
-  *
-  * This JacobiSVD decomposition computes only the singular values by default. If you want \a U or \a V, you need to ask for them explicitly.
-  *
-  * You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p matrix, letting \a m be the
-  * smaller value among \a n and \a p, there are only \a m singular vectors; the remaining columns of \a U and \a V do not correspond to actual
-  * singular vectors. Asking for \em thin \a U or \a V means asking for only their \a m first columns to be formed. So \a U is then a n-by-m matrix,
-  * and \a V is then a p-by-m matrix. Notice that thin \a U and \a V are all you need for (least squares) solving.
-  *
-  * Here's an example demonstrating basic usage:
-  * \include JacobiSVD_basic.cpp
-  * Output: \verbinclude JacobiSVD_basic.out
-  *
-  * This JacobiSVD class is a two-sided Jacobi R-SVD decomposition, ensuring optimal reliability and accuracy. The downside is that it's slower than
-  * bidiagonalizing SVD algorithms for large square matrices; however its complexity is still \f$ O(n^2p) \f$ where \a n is the smaller dimension and
-  * \a p is the greater dimension, meaning that it is still of the same order of complexity as the faster bidiagonalizing R-SVD algorithms.
-  * In particular, like any R-SVD, it takes advantage of non-squareness in that its complexity is only linear in the greater dimension.
-  *
-  * If the input matrix has inf or nan coefficients, the result of the computation is undefined, but the computation is guaranteed to
-  * terminate in finite (and reasonable) time.
-  *
-  * The possible values for QRPreconditioner are:
-  * \li ColPivHouseholderQRPreconditioner is the default. In practice it's very safe. It uses column-pivoting QR.
-  * \li FullPivHouseholderQRPreconditioner, is the safest and slowest. It uses full-pivoting QR.
-  *     Contrary to other QRs, it doesn't allow computing thin unitaries.
-  * \li HouseholderQRPreconditioner is the fastest, and less safe and accurate than the pivoting variants. It uses non-pivoting QR.
-  *     This is very similar in safety and accuracy to the bidiagonalization process used by bidiagonalizing SVD algorithms (since bidiagonalization
-  *     is inherently non-pivoting). However the resulting SVD is still more reliable than bidiagonalizing SVDs because the Jacobi-based iterarive
-  *     process is more reliable than the optimized bidiagonal SVD iterations.
-  * \li NoQRPreconditioner allows not to use a QR preconditioner at all. This is useful if you know that you will only be computing
-  *     JacobiSVD decompositions of square matrices. Non-square matrices require a QR preconditioner. Using this option will result in
-  *     faster compilation and smaller executable code. It won't significantly speed up computation, since JacobiSVD is always checking
-  *     if QR preconditioning is needed before applying it anyway.
-  *
-  * \sa MatrixBase::jacobiSvd()
-  */
-template<typename _MatrixType, int QRPreconditioner> 
-class JacobiSVD : public SVDBase<_MatrixType>
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-    typedef typename MatrixType::Index Index;
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime),
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-      MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime,MaxColsAtCompileTime),
-      MatrixOptions = MatrixType::Options
-    };
-
-    typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime,
-                   MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime>
-            MatrixUType;
-    typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime,
-                   MatrixOptions, MaxColsAtCompileTime, MaxColsAtCompileTime>
-            MatrixVType;
-    typedef typename internal::plain_diag_type<MatrixType, RealScalar>::type SingularValuesType;
-    typedef typename internal::plain_row_type<MatrixType>::type RowType;
-    typedef typename internal::plain_col_type<MatrixType>::type ColType;
-    typedef Matrix<Scalar, DiagSizeAtCompileTime, DiagSizeAtCompileTime,
-                   MatrixOptions, MaxDiagSizeAtCompileTime, MaxDiagSizeAtCompileTime>
-            WorkMatrixType;
-
-    /** \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via JacobiSVD::compute(const MatrixType&).
-      */
-    JacobiSVD()
-      : SVDBase<_MatrixType>::SVDBase()
-    {}
-
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem size.
-      * \sa JacobiSVD()
-      */
-    JacobiSVD(Index rows, Index cols, unsigned int computationOptions = 0)
-      : SVDBase<_MatrixType>::SVDBase() 
-    {
-      allocate(rows, cols, computationOptions);
-    }
-
-    /** \brief Constructor performing the decomposition of given matrix.
-     *
-     * \param matrix the matrix to decompose
-     * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-     *                           By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU,
-     *                           #ComputeFullV, #ComputeThinV.
-     *
-     * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-     * available with the (non-default) FullPivHouseholderQR preconditioner.
-     */
-    JacobiSVD(const MatrixType& matrix, unsigned int computationOptions = 0)
-      : SVDBase<_MatrixType>::SVDBase()
-    {
-      compute(matrix, computationOptions);
-    }
-
-    /** \brief Method performing the decomposition of given matrix using custom options.
-     *
-     * \param matrix the matrix to decompose
-     * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-     *                           By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU,
-     *                           #ComputeFullV, #ComputeThinV.
-     *
-     * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-     * available with the (non-default) FullPivHouseholderQR preconditioner.
-     */
-    SVDBase<MatrixType>& compute(const MatrixType& matrix, unsigned int computationOptions);
-
-    /** \brief Method performing the decomposition of given matrix using current options.
-     *
-     * \param matrix the matrix to decompose
-     *
-     * This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
-     */
-    SVDBase<MatrixType>& compute(const MatrixType& matrix)
-    {
-      return compute(matrix, this->m_computationOptions);
-    }
-    
-    /** \returns a (least squares) solution of \f$ A x = b \f$ using the current SVD decomposition of A.
-      *
-      * \param b the right-hand-side of the equation to solve.
-      *
-      * \note Solving requires both U and V to be computed. Thin U and V are enough, there is no need for full U or V.
-      *
-      * \note SVD solving is implicitly least-squares. Thus, this method serves both purposes of exact solving and least-squares solving.
-      * In other words, the returned solution is guaranteed to minimize the Euclidean norm \f$ \Vert A x - b \Vert \f$.
-      */
-    template<typename Rhs>
-    inline const internal::solve_retval<JacobiSVD, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
-    {
-      eigen_assert(this->m_isInitialized && "JacobiSVD is not initialized.");
-      eigen_assert(SVDBase<MatrixType>::computeU() && SVDBase<MatrixType>::computeV() && "JacobiSVD::solve() requires both unitaries U and V to be computed (thin unitaries suffice).");
-      return internal::solve_retval<JacobiSVD, Rhs>(*this, b.derived());
-    }
-
-    
-
-  private:
-    void allocate(Index rows, Index cols, unsigned int computationOptions);
-
-  protected:
-    WorkMatrixType m_workMatrix;
-   
-    template<typename __MatrixType, int _QRPreconditioner, bool _IsComplex>
-    friend struct internal::svd_precondition_2x2_block_to_be_real;
-    template<typename __MatrixType, int _QRPreconditioner, int _Case, bool _DoAnything>
-    friend struct internal::qr_preconditioner_impl;
-
-    internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreColsThanRows> m_qr_precond_morecols;
-    internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreRowsThanCols> m_qr_precond_morerows;
-};
-
-template<typename MatrixType, int QRPreconditioner>
-void JacobiSVD<MatrixType, QRPreconditioner>::allocate(Index rows, Index cols, unsigned int computationOptions)
-{
-  if (SVDBase<MatrixType>::allocate(rows, cols, computationOptions)) return;
-
-  if (QRPreconditioner == FullPivHouseholderQRPreconditioner)
-  {
-      eigen_assert(!(this->m_computeThinU || this->m_computeThinV) &&
-              "JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
-              "Use the ColPivHouseholderQR preconditioner instead.");
-  }
-
-  m_workMatrix.resize(this->m_diagSize, this->m_diagSize);
-  
-  if(this->m_cols>this->m_rows) m_qr_precond_morecols.allocate(*this);
-  if(this->m_rows>this->m_cols) m_qr_precond_morerows.allocate(*this);
-}
-
-template<typename MatrixType, int QRPreconditioner>
-SVDBase<MatrixType>&
-JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsigned int computationOptions)
-{
-  using std::abs;
-  allocate(matrix.rows(), matrix.cols(), computationOptions);
-
-  // currently we stop when we reach precision 2*epsilon as the last bit of precision can require an unreasonable number of iterations,
-  // only worsening the precision of U and V as we accumulate more rotations
-  const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();
-
-  // limit for very small denormal numbers to be considered zero in order to avoid infinite loops (see bug 286)
-  const RealScalar considerAsZero = RealScalar(2) * std::numeric_limits<RealScalar>::denorm_min();
-
-  /*** step 1. The R-SVD step: we use a QR decomposition to reduce to the case of a square matrix */
-
-  if(!m_qr_precond_morecols.run(*this, matrix) && !m_qr_precond_morerows.run(*this, matrix))
-  {
-    m_workMatrix = matrix.block(0,0,this->m_diagSize,this->m_diagSize);
-    if(this->m_computeFullU) this->m_matrixU.setIdentity(this->m_rows,this->m_rows);
-    if(this->m_computeThinU) this->m_matrixU.setIdentity(this->m_rows,this->m_diagSize);
-    if(this->m_computeFullV) this->m_matrixV.setIdentity(this->m_cols,this->m_cols);
-    if(this->m_computeThinV) this->m_matrixV.setIdentity(this->m_cols, this->m_diagSize);
-  }
-
-  /*** step 2. The main Jacobi SVD iteration. ***/
-
-  bool finished = false;
-  while(!finished)
-  {
-    finished = true;
-
-    // do a sweep: for all index pairs (p,q), perform SVD of the corresponding 2x2 sub-matrix
-
-    for(Index p = 1; p < this->m_diagSize; ++p)
-    {
-      for(Index q = 0; q < p; ++q)
-      {
-        // if this 2x2 sub-matrix is not diagonal already...
-        // notice that this comparison will evaluate to false if any NaN is involved, ensuring that NaN's don't
-        // keep us iterating forever. Similarly, small denormal numbers are considered zero.
-        using std::max;
-        RealScalar threshold = (max)(considerAsZero, precision * (max)(abs(m_workMatrix.coeff(p,p)),
-                                                                       abs(m_workMatrix.coeff(q,q))));
-        if((max)(abs(m_workMatrix.coeff(p,q)),abs(m_workMatrix.coeff(q,p))) > threshold)
-        {
-          finished = false;
-
-          // perform SVD decomposition of 2x2 sub-matrix corresponding to indices p,q to make it diagonal
-          internal::svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner>::run(m_workMatrix, *this, p, q);
-          JacobiRotation<RealScalar> j_left, j_right;
-          internal::real_2x2_jacobi_svd(m_workMatrix, p, q, &j_left, &j_right);
-
-          // accumulate resulting Jacobi rotations
-          m_workMatrix.applyOnTheLeft(p,q,j_left);
-          if(SVDBase<MatrixType>::computeU()) this->m_matrixU.applyOnTheRight(p,q,j_left.transpose());
-
-          m_workMatrix.applyOnTheRight(p,q,j_right);
-          if(SVDBase<MatrixType>::computeV()) this->m_matrixV.applyOnTheRight(p,q,j_right);
-        }
-      }
-    }
-  }
-
-  /*** step 3. The work matrix is now diagonal, so ensure it's positive so its diagonal entries are the singular values ***/
-
-  for(Index i = 0; i < this->m_diagSize; ++i)
-  {
-    RealScalar a = abs(m_workMatrix.coeff(i,i));
-    this->m_singularValues.coeffRef(i) = a;
-    if(SVDBase<MatrixType>::computeU() && (a!=RealScalar(0))) this->m_matrixU.col(i) *= this->m_workMatrix.coeff(i,i)/a;
-  }
-
-  /*** step 4. Sort singular values in descending order and compute the number of nonzero singular values ***/
-
-  this->m_nonzeroSingularValues = this->m_diagSize;
-  for(Index i = 0; i < this->m_diagSize; i++)
-  {
-    Index pos;
-    RealScalar maxRemainingSingularValue = this->m_singularValues.tail(this->m_diagSize-i).maxCoeff(&pos);
-    if(maxRemainingSingularValue == RealScalar(0))
-    {
-      this->m_nonzeroSingularValues = i;
-      break;
-    }
-    if(pos)
-    {
-      pos += i;
-      std::swap(this->m_singularValues.coeffRef(i), this->m_singularValues.coeffRef(pos));
-      if(SVDBase<MatrixType>::computeU()) this->m_matrixU.col(pos).swap(this->m_matrixU.col(i));
-      if(SVDBase<MatrixType>::computeV()) this->m_matrixV.col(pos).swap(this->m_matrixV.col(i));
-    }
-  }
-
-  this->m_isInitialized = true;
-  return *this;
-}
-
-namespace internal {
-template<typename _MatrixType, int QRPreconditioner, typename Rhs>
-struct solve_retval<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
-  : solve_retval_base<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
-{
-  typedef JacobiSVD<_MatrixType, QRPreconditioner> JacobiSVDType;
-  EIGEN_MAKE_SOLVE_HELPERS(JacobiSVDType,Rhs)
-
-  template<typename Dest> void evalTo(Dest& dst) const
-  {
-    eigen_assert(rhs().rows() == dec().rows());
-
-    // A = U S V^*
-    // So A^{-1} = V S^{-1} U^*
-
-    Index diagSize = (std::min)(dec().rows(), dec().cols());
-    typename JacobiSVDType::SingularValuesType invertedSingVals(diagSize);
-
-    Index nonzeroSingVals = dec().nonzeroSingularValues();
-    invertedSingVals.head(nonzeroSingVals) = dec().singularValues().head(nonzeroSingVals).array().inverse();
-    invertedSingVals.tail(diagSize - nonzeroSingVals).setZero();
-
-    dst = dec().matrixV().leftCols(diagSize)
-        * invertedSingVals.asDiagonal()
-        * dec().matrixU().leftCols(diagSize).adjoint()
-        * rhs();
-  }
-};
-} // end namespace internal
-
-/** \svd_module
-  *
-  * \return the singular value decomposition of \c *this computed by two-sided
-  * Jacobi transformations.
-  *
-  * \sa class JacobiSVD
-  */
-template<typename Derived>
-JacobiSVD<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::jacobiSvd(unsigned int computationOptions) const
-{
-  return JacobiSVD<PlainObject>(*this, computationOptions);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_JACOBISVD_H
diff --git a/unsupported/test/svd_common.h b/unsupported/test/svd_common.h
index b40c23a2b..6a96e7c74 100644
--- a/unsupported/test/svd_common.h
+++ b/unsupported/test/svd_common.h
@@ -18,7 +18,7 @@
 #define EIGEN_RUNTIME_NO_MALLOC
 
 #include "main.h"
-#include <unsupported/Eigen/SVD>
+#include <unsupported/Eigen/BDCSVD>
 #include <Eigen/LU>