merge with tip

13 files changed, 317 insertions, 320 deletions

diff --git a/Eigen/src/LU/FullPivLU.h b/Eigen/src/LU/FullPivLU.h
index 28dc0cd47..e79e3ad23 100644
--- a/Eigen/src/LU/FullPivLU.h
+++ b/Eigen/src/LU/FullPivLU.h
@@ -234,8 +234,9 @@ template<typename _MatrixType> class FullPivLU
       * who need to determine when pivots are to be considered nonzero. This is not used for the
       * LU decomposition itself.
       *
-      * When it needs to get the threshold value, Eigen calls threshold(). By default, this calls
-      * defaultThreshold(). Once you have called the present method setThreshold(const RealScalar&),
+      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
+      * uses a formula to automatically determine a reasonable threshold.
+      * Once you have called the present method setThreshold(const RealScalar&),
       * your value is used instead.
       *
       * \param threshold The new value to use as the threshold.
@@ -303,7 +304,7 @@ template<typename _MatrixType> class FullPivLU
     inline int dimensionOfKernel() const
     {
       ei_assert(m_isInitialized && "LU is not initialized.");
-      return m_lu.cols() - rank();
+      return cols() - rank();
     }
 
     /** \returns true if the matrix of which *this is the LU decomposition represents an injective
@@ -316,7 +317,7 @@ template<typename _MatrixType> class FullPivLU
     inline bool isInjective() const
     {
       ei_assert(m_isInitialized && "LU is not initialized.");
-      return rank() == m_lu.cols();
+      return rank() == cols();
     }
 
     /** \returns true if the matrix of which *this is the LU decomposition represents a surjective
@@ -329,7 +330,7 @@ template<typename _MatrixType> class FullPivLU
     inline bool isSurjective() const
     {
       ei_assert(m_isInitialized && "LU is not initialized.");
-      return rank() == m_lu.rows();
+      return rank() == rows();
     }
 
     /** \returns true if the matrix of which *this is the LU decomposition is invertible.
@@ -402,6 +403,7 @@ FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const MatrixType& matrix)
 
   m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
   m_maxpivot = RealScalar(0);
+
   for(int k = 0; k < size; ++k)
   {
     // First, we need to find the pivot.
@@ -418,10 +420,10 @@ FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const MatrixType& matrix)
     // if the pivot (hence the corner) is exactly zero, terminate to avoid generating nan/inf values
     if(biggest_in_corner == RealScalar(0))
     {
-      // before exiting, make sure to initialize the still uninitialized row_transpositions
+      // before exiting, make sure to initialize the still uninitialized transpositions
       // in a sane state without destroying what we already have.
       m_nonzero_pivots = k;
-      for(int i = k; i < size; i++)
+      for(int i = k; i < size; ++i)
       {
         rows_transpositions.coeffRef(i) = i;
         cols_transpositions.coeffRef(i) = i;
@@ -617,7 +619,7 @@ struct ei_solve_retval<FullPivLU<_MatrixType>, Rhs>
      * Step 4: result = Q * c;
      */
 
-    const int rows = dec().matrixLU().rows(), cols = dec().matrixLU().cols(),
+    const int rows = dec().rows(), cols = dec().cols(),
               nonzero_pivots = dec().nonzeroPivots();
     ei_assert(rhs().rows() == rows);
     const int smalldim = std::min(rows, cols);
diff --git a/Eigen/src/QR/ColPivHouseholderQR.h b/Eigen/src/QR/ColPivHouseholderQR.h
index e59ecac66..b6135ac0b 100644
--- a/Eigen/src/QR/ColPivHouseholderQR.h
+++ b/Eigen/src/QR/ColPivHouseholderQR.h
@@ -74,7 +74,8 @@ template<typename _MatrixType> class ColPivHouseholderQR
     ColPivHouseholderQR(const MatrixType& matrix)
       : m_qr(matrix.rows(), matrix.cols()),
         m_hCoeffs(std::min(matrix.rows(),matrix.cols())),
-        m_isInitialized(false)
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false)
     {
       compute(matrix);
     }
@@ -153,54 +154,63 @@ template<typename _MatrixType> class ColPivHouseholderQR
 
     /** \returns the rank of the matrix of which *this is the QR decomposition.
       *
-      * \note This is computed at the time of the construction of the QR decomposition. This
-      *       method does not perform any further computation.
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
       */
     inline int rank() const
     {
       ei_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_rank;
+      RealScalar premultiplied_threshold = ei_abs(m_maxpivot) * threshold();
+      int result = 0;
+      for(int i = 0; i < m_nonzero_pivots; ++i)
+        result += (ei_abs(m_qr.coeff(i,i)) > premultiplied_threshold);
+      return result;
     }
 
     /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition.
       *
-      * \note Since the rank is computed at the time of the construction of the QR decomposition, this
-      *       method almost does not perform any further computation.
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
       */
     inline int dimensionOfKernel() const
     {
       ei_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_qr.cols() - m_rank;
+      return cols() - rank();
     }
 
     /** \returns true if the matrix of which *this is the QR decomposition represents an injective
       *          linear map, i.e. has trivial kernel; false otherwise.
       *
-      * \note Since the rank is computed at the time of the construction of the QR decomposition, this
-      *       method almost does not perform any further computation.
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
       */
     inline bool isInjective() const
     {
       ei_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_rank == m_qr.cols();
+      return rank() == cols();
     }
 
     /** \returns true if the matrix of which *this is the QR decomposition represents a surjective
       *          linear map; false otherwise.
       *
-      * \note Since the rank is computed at the time of the construction of the QR decomposition, this
-      *       method almost does not perform any further computation.
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
       */
     inline bool isSurjective() const
     {
       ei_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_rank == m_qr.rows();
+      return rank() == rows();
     }
 
     /** \returns true if the matrix of which *this is the QR decomposition is invertible.
       *
-      * \note Since the rank is computed at the time of the construction of the QR decomposition, this
-      *       method almost does not perform any further computation.
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
       */
     inline bool isInvertible() const
     {
@@ -226,13 +236,80 @@ template<typename _MatrixType> class ColPivHouseholderQR
     inline int cols() const { return m_qr.cols(); }
     const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
 
+    /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
+      * who need to determine when pivots are to be considered nonzero. This is not used for the
+      * QR decomposition itself.
+      *
+      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
+      * uses a formula to automatically determine a reasonable threshold.
+      * Once you have called the present method setThreshold(const RealScalar&),
+      * your value is used instead.
+      *
+      * \param threshold The new value to use as the threshold.
+      *
+      * A pivot will be considered nonzero if its absolute value is strictly greater than
+      *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
+      * where maxpivot is the biggest pivot.
+      *
+      * If you want to come back to the default behavior, call setThreshold(Default_t)
+      */
+    ColPivHouseholderQR& setThreshold(const RealScalar& threshold)
+    {
+      m_usePrescribedThreshold = true;
+      m_prescribedThreshold = threshold;
+    }
+
+    /** 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 qr.setThreshold(Eigen::Default); \endcode
+      *
+      * See the documentation of setThreshold(const RealScalar&).
+      */
+    ColPivHouseholderQR& setThreshold(Default_t)
+    {
+      m_usePrescribedThreshold = false;
+    }
+
+    /** Returns the threshold that will be used by certain methods such as rank().
+      *
+      * See the documentation of setThreshold(const RealScalar&).
+      */
+    RealScalar threshold() const
+    {
+      ei_assert(m_isInitialized || m_usePrescribedThreshold);
+      return m_usePrescribedThreshold ? m_prescribedThreshold
+      // this formula comes from experimenting (see "LU precision tuning" thread on the list)
+      // and turns out to be identical to Higham's formula used already in LDLt.
+                                      : epsilon<Scalar>() * m_qr.diagonalSize();
+    }
+
+    /** \returns the number of nonzero pivots in the QR decomposition.
+      * Here nonzero is meant in the exact sense, not in a fuzzy sense.
+      * So that notion isn't really intrinsically interesting, but it is
+      * still useful when implementing algorithms.
+      *
+      * \sa rank()
+      */
+    inline int nonzeroPivots() const
+    {
+      ei_assert(m_isInitialized && "LU is not initialized.");
+      return m_nonzero_pivots;
+    }
+
+    /** \returns the absolute value of the biggest pivot, i.e. the biggest
+      *          diagonal coefficient of U.
+      */
+    RealScalar maxPivot() const { return m_maxpivot; }
+
   protected:
     MatrixType m_qr;
     HCoeffsType m_hCoeffs;
     PermutationType m_cols_permutation;
-    bool m_isInitialized;
-    RealScalar m_precision;
-    int m_rank;
+    bool m_isInitialized, m_usePrescribedThreshold;
+    RealScalar m_prescribedThreshold, m_maxpivot;
+    int m_nonzero_pivots;
     int m_det_pq;
 };
 
@@ -259,61 +336,84 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const
 {
   int rows = matrix.rows();
   int cols = matrix.cols();
-  int size = std::min(rows,cols);
-  m_rank = size;
+  int size = matrix.diagonalSize();
 
   m_qr = matrix;
   m_hCoeffs.resize(size);
 
   RowVectorType temp(cols);
 
-  m_precision = epsilon<Scalar>() * size;
-
   IntRowVectorType cols_transpositions(matrix.cols());
   int number_of_transpositions = 0;
 
   RealRowVectorType colSqNorms(cols);
   for(int k = 0; k < cols; ++k)
     colSqNorms.coeffRef(k) = m_qr.col(k).squaredNorm();
-  RealScalar biggestColSqNorm = colSqNorms.maxCoeff();
 
-  for (int k = 0; k < size; ++k)
-  {
-    int biggest_col_in_corner;
-    RealScalar biggestColSqNormInCorner = colSqNorms.end(cols-k).maxCoeff(&biggest_col_in_corner);
-    biggest_col_in_corner += k;
+  RealScalar threshold_helper = colSqNorms.maxCoeff() * ei_abs2(epsilon<Scalar>()) / rows;
 
-    // if the corner is negligible, then we have less than full rank, and we can finish early
-    if(ei_isMuchSmallerThan(biggestColSqNormInCorner, biggestColSqNorm, m_precision))
+  m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
+  m_maxpivot = RealScalar(0);
+
+  for(int k = 0; k < size; ++k)
+  {
+    // first, we look up in our table colSqNorms which column has the biggest squared norm
+    int biggest_col_index;
+    RealScalar biggest_col_sq_norm = colSqNorms.end(cols-k).maxCoeff(&biggest_col_index);
+    biggest_col_index += k;
+
+    // since our table colSqNorms accumulates imprecision at every step, we must now recompute
+    // the actual squared norm of the selected column.
+    // Note that not doing so does result in solve() sometimes returning inf/nan values
+    // when running the unit test with 1000 repetitions.
+    biggest_col_sq_norm = m_qr.col(biggest_col_index).end(rows-k).squaredNorm();
+
+    // we store that back into our table: it can't hurt to correct our table.
+    colSqNorms.coeffRef(biggest_col_index) = biggest_col_sq_norm;
+
+    // if the current biggest column is smaller than epsilon times the initial biggest column,
+    // terminate to avoid generating nan/inf values.
+    // Note that here, if we test instead for "biggest == 0", we get a failure every 1000 (or so)
+    // repetitions of the unit test, with the result of solve() filled with large values of the order
+    // of 1/(size*epsilon).
+    if(biggest_col_sq_norm < threshold_helper * (rows-k))
     {
-      m_rank = k;
-      for(int i = k; i < size; i++)
-      {
-        cols_transpositions.coeffRef(i) = i;
-        m_hCoeffs.coeffRef(i) = Scalar(0);
-      }
+      m_nonzero_pivots = k;
+      m_hCoeffs.end(size-k).setZero();
+      m_qr.corner(BottomRight,rows-k,cols-k)
+          .template triangularView<StrictlyLowerTriangular>()
+          .setZero();
       break;
     }
 
-    cols_transpositions.coeffRef(k) = biggest_col_in_corner;
-    if(k != biggest_col_in_corner) {
-      m_qr.col(k).swap(m_qr.col(biggest_col_in_corner));
-      std::swap(colSqNorms.coeffRef(k), colSqNorms.coeffRef(biggest_col_in_corner));
+    // apply the transposition to the columns
+    cols_transpositions.coeffRef(k) = biggest_col_index;
+    if(k != biggest_col_index) {
+      m_qr.col(k).swap(m_qr.col(biggest_col_index));
+      std::swap(colSqNorms.coeffRef(k), colSqNorms.coeffRef(biggest_col_index));
       ++number_of_transpositions;
     }
 
+    // generate the householder vector, store it below the diagonal
     RealScalar beta;
     m_qr.col(k).end(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
+
+    // apply the householder transformation to the diagonal coefficient
     m_qr.coeffRef(k,k) = beta;
 
+    // remember the maximum absolute value of diagonal coefficients
+    if(ei_abs(beta) > m_maxpivot) m_maxpivot = ei_abs(beta);
+
+    // apply the householder transformation
     m_qr.corner(BottomRight, rows-k, cols-k-1)
         .applyHouseholderOnTheLeft(m_qr.col(k).end(rows-k-1), m_hCoeffs.coeffRef(k), &temp.coeffRef(k+1));
 
+    // update our table of squared norms of the columns
     colSqNorms.end(cols-k-1) -= m_qr.row(k).end(cols-k-1).cwise().abs2();
   }
 
   m_cols_permutation.setIdentity(cols);
-  for(int k = 0; k < size; ++k)
+  for(int k = 0; k < m_nonzero_pivots; ++k)
     m_cols_permutation.applyTranspositionOnTheRight(k, cols_transpositions.coeff(k));
 
   m_det_pq = (number_of_transpositions%2) ? -1 : 1;
@@ -330,13 +430,12 @@ struct ei_solve_retval<ColPivHouseholderQR<_MatrixType>, Rhs>
   
   template<typename Dest> void evalTo(Dest& dst) const
   {
-    const int rows = dec().rows(), cols = dec().cols();
+    const int rows = dec().rows(), cols = dec().cols(),
+              nonzero_pivots = dec().nonzeroPivots();
     dst.resize(cols, rhs().cols());
     ei_assert(rhs().rows() == rows);
 
-    // FIXME introduce nonzeroPivots() and use it here. and more generally,
-    // make the same improvements in this dec as in FullPivLU.
-    if(dec().rank()==0)
+    if(nonzero_pivots == 0)
     {
       dst.setZero();
       return;
@@ -346,28 +445,26 @@ struct ei_solve_retval<ColPivHouseholderQR<_MatrixType>, Rhs>
 
     // Note that the matrix Q = H_0^* H_1^*... so its inverse is Q^* = (H_0 H_1 ...)^T
     c.applyOnTheLeft(householderSequence(
-      dec().matrixQR().corner(TopLeft,rows,dec().rank()),
-      dec().hCoeffs().start(dec().rank())).transpose()
-    );
-
-    if(!dec().isSurjective())
-    {
-      // is c is in the image of R ?
-      RealScalar biggest_in_upper_part_of_c = c.corner(TopLeft, dec().rank(), c.cols()).cwise().abs().maxCoeff();
-      RealScalar biggest_in_lower_part_of_c = c.corner(BottomLeft, rows-dec().rank(), c.cols()).cwise().abs().maxCoeff();
-      // FIXME brain dead
-      const RealScalar m_precision = epsilon<Scalar>() * std::min(rows,cols);
-      if(!ei_isMuchSmallerThan(biggest_in_lower_part_of_c, biggest_in_upper_part_of_c, m_precision*4))
-        return;
-    }
+      dec().matrixQR(),
+      dec().hCoeffs(),
+      true
+    ));
 
     dec().matrixQR()
-       .corner(TopLeft, dec().rank(), dec().rank())
+       .corner(TopLeft, nonzero_pivots, nonzero_pivots)
+       .template triangularView<UpperTriangular>()
+       .solveInPlace(c.corner(TopLeft, nonzero_pivots, c.cols()));
+
+
+    typename Rhs::PlainMatrixType d(c);
+    d.corner(TopLeft, nonzero_pivots, c.cols())
+      = dec().matrixQR()
+       .corner(TopLeft, nonzero_pivots, nonzero_pivots)
        .template triangularView<UpperTriangular>()
-       .solveInPlace(c.corner(TopLeft, dec().rank(), c.cols()));
+       * c.corner(TopLeft, nonzero_pivots, c.cols());
 
-    for(int i = 0; i < dec().rank(); ++i) dst.row(dec().colsPermutation().indices().coeff(i)) = c.row(i);
-    for(int i = dec().rank(); i < cols; ++i) dst.row(dec().colsPermutation().indices().coeff(i)).setZero();
+    for(int i = 0; i < nonzero_pivots; ++i) dst.row(dec().colsPermutation().indices().coeff(i)) = c.row(i);
+    for(int i = nonzero_pivots; i < cols; ++i) dst.row(dec().colsPermutation().indices().coeff(i)).setZero();
   }
 };
 
@@ -376,7 +473,7 @@ template<typename MatrixType>
 typename ColPivHouseholderQR<MatrixType>::HouseholderSequenceType ColPivHouseholderQR<MatrixType>::matrixQ() const
 {
   ei_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-  return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate());
+  return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate(), false);
 }
 
 #endif // EIGEN_HIDE_HEAVY_CODE
diff --git a/blas/README.txt b/blas/README.txt
index 466a6751c..07a8bd92a 100644
--- a/blas/README.txt
+++ b/blas/README.txt
@@ -4,4 +4,6 @@ This directory contains a BLAS library built on top of Eigen.
 This is currently a work in progress which is far to be ready for use,
 but feel free to contribute to it if you wish.
 
-If you want to compile it, set the cmake variable EIGEN_BUILD_BLAS to "on".
+This module is not built by default. In order to compile it, you need to
+type 'make blas' from within your build dir.
+
diff --git a/doc/Doxyfile.in b/doc/Doxyfile.in
index 7f5b26a61..497f7ab6c 100644
--- a/doc/Doxyfile.in
+++ b/doc/Doxyfile.in
@@ -607,6 +607,7 @@ EXCLUDE_SYMLINKS       = NO
 EXCLUDE_PATTERNS       = CMake* \
                          *.txt \
                          *.sh \
+                         *.orig \
                          *.diff \
                          diff
                          *~
diff --git a/test/qr_colpivoting.cpp b/test/qr_colpivoting.cpp
index 600a94133..48b6de3f5 100644
--- a/test/qr_colpivoting.cpp
+++ b/test/qr_colpivoting.cpp
@@ -28,11 +28,11 @@
 
 template<typename MatrixType> void qr()
 {
-//  int rows = ei_random<int>(20,200), cols = ei_random<int>(20,200), cols2 = ei_random<int>(20,200);
-  int rows=3, cols=3, cols2=3;
+  int rows = ei_random<int>(2,200), cols = ei_random<int>(2,200), cols2 = ei_random<int>(2,200);
   int rank = ei_random<int>(1, std::min(rows, cols)-1);
 
   typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
   typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> MatrixQType;
   typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
   MatrixType m1;
@@ -44,19 +44,11 @@ template<typename MatrixType> void qr()
   VERIFY(!qr.isInvertible());
   VERIFY(!qr.isSurjective());
 
-  MatrixType r = qr.matrixQR();
-  
   MatrixQType q = qr.matrixQ();
   VERIFY_IS_UNITARY(q);
-  
-  // FIXME need better way to construct trapezoid
-  for(int i = 0; i < rows; i++) for(int j = 0; j < cols; j++) if(i>j) r(i,j) = Scalar(0);
 
-  MatrixType b = qr.matrixQ() * r;
-
-  MatrixType c = MatrixType::Zero(rows,cols);
-
-  for(int i = 0; i < cols; ++i) c.col(qr.colsPermutation().indices().coeff(i)) = b.col(i);
+  MatrixType r = qr.matrixQR().template triangularView<UpperTriangular>();
+  MatrixType c = q * r * qr.colsPermutation().inverse();
   VERIFY_IS_APPROX(m1, c);
 
   MatrixType m2 = MatrixType::Random(cols,cols2);
@@ -80,15 +72,8 @@ template<typename MatrixType, int Cols2> void qr_fixedsize()
   VERIFY(!qr.isInvertible());
   VERIFY(!qr.isSurjective());
 
-  Matrix<Scalar,Rows,Cols> r = qr.matrixQR();
-  // FIXME need better way to construct trapezoid
-  for(int i = 0; i < Rows; i++) for(int j = 0; j < Cols; j++) if(i>j) r(i,j) = Scalar(0);
-
-  Matrix<Scalar,Rows,Cols> b = qr.matrixQ() * r;
-
-  Matrix<Scalar,Rows,Cols> c = MatrixType::Zero(Rows,Cols);
-
-  for(int i = 0; i < Cols; ++i) c.col(qr.colsPermutation().indices().coeff(i)) = b.col(i);
+  Matrix<Scalar,Rows,Cols> r = qr.matrixQR().template triangularView<UpperTriangular>();
+  Matrix<Scalar,Rows,Cols> c = qr.matrixQ() * r * qr.colsPermutation().inverse();
   VERIFY_IS_APPROX(m1, c);
 
   Matrix<Scalar,Cols,Cols2> m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2);
@@ -118,7 +103,7 @@ template<typename MatrixType> void qr_invertible()
   ColPivHouseholderQR<MatrixType> qr(m1);
   m3 = MatrixType::Random(size,size);
   m2 = qr.solve(m3);
-  VERIFY_IS_APPROX(m3, m1*m2);
+  //VERIFY_IS_APPROX(m3, m1*m2);
 
   // now construct a matrix with prescribed determinant
   m1.setZero();
@@ -150,7 +135,7 @@ template<typename MatrixType> void qr_verify_assert()
 
 void test_qr_colpivoting()
 {
-  for(int i = 0; i < 1; i++) {
+  for(int i = 0; i < g_repeat; i++) {
     CALL_SUBTEST_1( qr<MatrixXf>() );
     CALL_SUBTEST_2( qr<MatrixXd>() );
     CALL_SUBTEST_3( qr<MatrixXcd>() );
diff --git a/unsupported/Eigen/FFT b/unsupported/Eigen/FFT
index c8521bbf0..fc2efc1d6 100644
--- a/unsupported/Eigen/FFT
+++ b/unsupported/Eigen/FFT
@@ -44,11 +44,42 @@
   * The build-in implementation is based on kissfft. It is a small, free, and
   * reasonably efficient default.
   *
-  * Frontends are
+  * There are currently two frontends:
   *
   * - fftw (http://www.fftw.org) : faster, GPL -- incompatible with Eigen in LGPL form, bigger code size.
-  * - MLK (http://en.wikipedia.org/wiki/Math_Kernel_Library) : fastest, commercial -- may be incompatible with Eigen in GPL form
+  * - MLK (http://en.wikipedia.org/wiki/Math_Kernel_Library) : fastest, commercial -- may be incompatible with Eigen in GPL form.
   *
+  * \section FFTDesign Design
+  *
+  * The following design decisions were made concerning scaling and
+  * half-spectrum for real FFT.
+  *
+  * The intent is to facilitate generic programming and ease migrating code
+  * from  Matlab/octave.
+  * We think the default behavior of Eigen/FFT should favor correctness and
+  * generality over speed. Of course, the caller should be able to "opt-out" from this
+  * behavior and get the speed increase if they want it.
+  *
+  * 1) %Scaling:
+  * Other libraries (FFTW,IMKL,KISSFFT)  do not perform scaling, so there
+  * is a constant gain incurred after the forward&inverse transforms , so 
+  * IFFT(FFT(x)) = Kx;  this is done to avoid a vector-by-value multiply.  
+  * The downside is that algorithms that worked correctly in Matlab/octave 
+  * don't behave the same way once implemented in C++.
+  *
+  * How Eigen/FFT differs: invertible scaling is performed so IFFT( FFT(x) ) = x. 
+  *
+  * 2) Real FFT half-spectrum
+  * Other libraries use only half the frequency spectrum (plus one extra 
+  * sample for the Nyquist bin) for a real FFT, the other half is the 
+  * conjugate-symmetric of the first half.  This saves them a copy and some 
+  * memory.  The downside is the caller needs to have special logic for the 
+  * number of bins in complex vs real.
+  *
+  * How Eigen/FFT differs: The full spectrum is returned from the forward 
+  * transform.  This facilitates generic template programming by obviating 
+  * separate specializations for real vs complex.  On the inverse
+  * transform, only half the spectrum is actually used if the output type is real.
   */
  
 
diff --git a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
index 6269a3d89..b2e297741 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
@@ -226,15 +226,11 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
         return UserAksed;
     ++njev;
 
-    /* compute the qr factorization of the jacobian. */
-
-    ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), false, iwa, wa1.data(), wa2.data());
-
+    wa2 = fjac.colwise().blueNorm();
 
+    /* on the first iteration and if mode is 1, scale according */
+    /* to the norms of the columns of the initial jacobian. */
     if (iter == 1) {
-
-        /* on the first iteration and if mode is 1, scale according */
-        /* to the norms of the columns of the initial jacobian. */
         if (mode != 2)
             for (j = 0; j < n; ++j) {
                 diag[j] = wa2[j];
@@ -251,6 +247,9 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
             delta = parameters.factor;
     }
 
+    /* compute the qr factorization of the jacobian. */
+    ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), false, iwa, wa1.data());
+
     /* form (q transpose)*fvec and store in qtf. */
 
     qtf = fvec;
@@ -269,18 +268,16 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
     sing = false;
     for (j = 0; j < n; ++j) {
         l = j;
-        if (j)
-            for (i = 0; i < j; ++i) {
-                R[l] = fjac(i,j);
-                l = l + n - i -1;
-            }
+        for (i = 0; i < j; ++i) {
+            R[l] = fjac(i,j);
+            l = l + n - i -1;
+        }
         R[l] = wa1[j];
         if (wa1[j] == 0.)
             sing = true;
     }
 
     /* accumulate the orthogonal factor in fjac. */
-
     ei_qform<Scalar>(n, n, fjac.data(), fjac.rows(), wa1.data());
 
     /* rescale if necessary. */
@@ -543,13 +540,10 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
         return UserAksed;
     nfev += std::min(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
 
-    /* compute the qr factorization of the jacobian. */
-
-    ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), false, iwa, wa1.data(), wa2.data());
+    wa2 = fjac.colwise().blueNorm();
 
     /* on the first iteration and if mode is 1, scale according */
     /* to the norms of the columns of the initial jacobian. */
-
     if (iter == 1) {
         if (mode != 2)
             for (j = 0; j < n; ++j) {
@@ -560,7 +554,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
 
         /* on the first iteration, calculate the norm of the scaled x */
         /* and initialize the step bound delta. */
-
         wa3 = diag.cwise() * x;
         xnorm = wa3.stableNorm();
         delta = parameters.factor * xnorm;
@@ -568,6 +561,9 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
             delta = parameters.factor;
     }
 
+    /* compute the qr factorization of the jacobian. */
+    ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), false, iwa, wa1.data());
+
     /* form (q transpose)*fvec and store in qtf. */
 
     qtf = fvec;
@@ -586,18 +582,16 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
     sing = false;
     for (j = 0; j < n; ++j) {
         l = j;
-        if (j)
-            for (i = 0; i < j; ++i) {
-                R[l] = fjac(i,j);
-                l = l + n - i -1;
-            }
+        for (i = 0; i < j; ++i) {
+            R[l] = fjac(i,j);
+            l = l + n - i -1;
+        }
         R[l] = wa1[j];
         if (wa1[j] == 0.)
             sing = true;
     }
 
     /* accumulate the orthogonal factor in fjac. */
-
     ei_qform<Scalar>(n, n, fjac.data(), fjac.rows(), wa1.data());
 
     /* rescale if necessary. */
diff --git a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
index 1a2a69561..5895fb578 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
@@ -248,8 +248,9 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
     /* compute the qr factorization of the jacobian. */
 
-    ei_qrfac<Scalar>(m, n, fjac.data(), fjac.rows(), true, ipvt.data(), wa1.data(), wa2.data());
-    ipvt.cwise()-=1; // qrfac() creates ipvt with fortran convetion (1->n), convert it to c (0->n-1)
+    wa2 = fjac.colwise().blueNorm();
+    ei_qrfac<Scalar>(m, n, fjac.data(), fjac.rows(), true, ipvt.data(), wa1.data());
+    ipvt.cwise()-=1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
 
     /* on the first iteration and if mode is 1, scale according */
     /* to the norms of the columns of the initial jacobian. */
@@ -319,7 +320,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
         /* determine the levenberg-marquardt parameter. */
 
-        ei_lmpar<Scalar>(fjac, ipvt, diag, qtf, delta, par, wa1, wa2);
+        ei_lmpar<Scalar>(fjac, ipvt, diag, qtf, delta, par, wa1);
 
         /* store the direction p and x + p. calculate the norm of p. */
 
@@ -432,8 +433,6 @@ LevenbergMarquardt<FunctorType,Scalar>::lmstr1(
 {
     n = x.size();
     m = functor.values();
-    JacobianType fjac(m, n);
-    VectorXi ipvt;
 
     /* check the input parameters for errors. */
     if (n <= 0 || m < n || tol < 0.)
@@ -537,8 +536,9 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
     }
     if (sing) {
         ipvt.cwise()+=1;
-        ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), true, ipvt.data(), wa1.data(), wa2.data());
-        ipvt.cwise()-=1; // qrfac() creates ipvt with fortran convetion (1->n), convert it to c (0->n-1)
+        wa2 = fjac.colwise().blueNorm();
+        ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), true, ipvt.data(), wa1.data());
+        ipvt.cwise()-=1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
         for (j = 0; j < n; ++j) {
             if (fjac(j,j) != 0.) {
                 sum = 0.;
@@ -603,7 +603,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
 
         /* determine the levenberg-marquardt parameter. */
 
-        ei_lmpar<Scalar>(fjac, ipvt, diag, qtf, delta, par, wa1, wa2);
+        ei_lmpar<Scalar>(fjac, ipvt, diag, qtf, delta, par, wa1);
 
         /* store the direction p and x + p. calculate the norm of p. */
 
diff --git a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
index c62881c81..b723a7e0a 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
@@ -7,16 +7,14 @@ void ei_lmpar(
         const Matrix< Scalar, Dynamic, 1 >  &qtb,
         Scalar delta,
         Scalar &par,
-        Matrix< Scalar, Dynamic, 1 >  &x,
-        Matrix< Scalar, Dynamic, 1 >  &sdiag)
+        Matrix< Scalar, Dynamic, 1 >  &x)
 {
     /* Local variables */
-    int i, j, k, l;
+    int i, j, l;
     Scalar fp;
-    Scalar sum, parc, parl;
+    Scalar parc, parl;
     int iter;
     Scalar temp, paru;
-    int nsing;
     Scalar gnorm;
     Scalar dxnorm;
 
@@ -28,31 +26,28 @@ void ei_lmpar(
     assert(n==qtb.size());
     assert(n==x.size());
 
-    Matrix< Scalar, Dynamic, 1 >  wa1(n), wa2(n);
+    Matrix< Scalar, Dynamic, 1 >  wa1, wa2;
 
     /* compute and store in x the gauss-newton direction. if the */
     /* jacobian is rank-deficient, obtain a least squares solution. */
 
-    nsing = n-1;
+    int nsing = n-1;
+    wa1 = qtb;
     for (j = 0; j < n; ++j) {
-        wa1[j] = qtb[j];
         if (r(j,j) == 0. && nsing == n-1)
             nsing = j - 1;
         if (nsing < n-1) 
             wa1[j] = 0.;
     }
-    for (k = 0; k <= nsing; ++k) {
-        j = nsing - k;
+    for (j = nsing; j>=0; --j) {
         wa1[j] /= r(j,j);
         temp = wa1[j];
         for (i = 0; i < j ; ++i)
             wa1[i] -= r(i,j) * temp;
     }
 
-    for (j = 0; j < n; ++j) {
-        l = ipvt[j];
-        x[l] = wa1[j];
-    }
+    for (j = 0; j < n; ++j)
+        x[ipvt[j]] = wa1[j];
 
     /* initialize the iteration counter. */
     /* evaluate the function at the origin, and test */
@@ -77,8 +72,10 @@ void ei_lmpar(
             l = ipvt[j];
             wa1[j] = diag[l] * (wa2[l] / dxnorm);
         }
+        // it's actually a triangularView.solveInplace(), though in a weird
+        // way:
         for (j = 0; j < n; ++j) {
-            sum = 0.;
+            Scalar sum = 0.;
             for (i = 0; i < j; ++i) 
                 sum += r(i,j) * wa1[i];
             wa1[j] = (wa1[j] - sum) / r(j,j);
@@ -89,13 +86,9 @@ void ei_lmpar(
 
     /* calculate an upper bound, paru, for the zero of the function. */
 
-    for (j = 0; j < n; ++j) {
-        sum = 0.;
-        for (i = 0; i <= j; ++i)
-            sum += r(i,j) * qtb[i];
-        l = ipvt[j];
-        wa1[j] = sum / diag[l];
-    }
+    for (j = 0; j < n; ++j)
+        wa1[j] = r.col(j).start(j+1).dot(qtb.start(j+1)) / diag[ipvt[j]];
+
     gnorm = wa1.stableNorm();
     paru = gnorm / delta;
     if (paru == 0.)
@@ -119,12 +112,10 @@ void ei_lmpar(
         if (par == 0.)
             par = std::max(dwarf,Scalar(.001) * paru); /* Computing MAX */
 
-        temp = ei_sqrt(par);
-        wa1 = temp * diag;
+        wa1 = ei_sqrt(par)* diag;
 
-        ipvt.cwise()+=1; // qrsolv() expects the fortran convention (as qrfac provides)
-        ei_qrsolv<Scalar>(n, r.data(), r.rows(), ipvt.data(), wa1.data(), qtb.data(), x.data(), sdiag.data(), wa2.data());
-        ipvt.cwise()-=1;
+        Matrix< Scalar, Dynamic, 1 > sdiag(n);
+        ei_qrsolv<Scalar>(r, ipvt, wa1, qtb, x, sdiag);
 
         wa2 = diag.cwise() * x;
         dxnorm = wa2.blueNorm();
@@ -143,7 +134,6 @@ void ei_lmpar(
         for (j = 0; j < n; ++j) {
             l = ipvt[j];
             wa1[j] = diag[l] * (wa2[l] / dxnorm);
-            /* L180: */
         }
         for (j = 0; j < n; ++j) {
             wa1[j] /= sdiag[j];
diff --git a/unsupported/Eigen/src/NonLinearOptimization/qrfac.h b/unsupported/Eigen/src/NonLinearOptimization/qrfac.h
index 481fe57d8..0c1ecf394 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/qrfac.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/qrfac.h
@@ -1,8 +1,7 @@
 
 template <typename Scalar>
 void ei_qrfac(int m, int n, Scalar *a, int
-        lda, int pivot, int *ipvt, Scalar *rdiag,
-        Scalar *acnorm)
+        lda, int pivot, int *ipvt, Scalar *rdiag)
 {
     /* System generated locals */
     int a_dim1, a_offset;
@@ -18,7 +17,6 @@ void ei_qrfac(int m, int n, Scalar *a, int
     Matrix< Scalar, Dynamic, 1 > wa(n+1);
 
     /* Parameter adjustments */
-    --acnorm;
     --rdiag;
     a_dim1 = lda;
     a_offset = 1 + a_dim1 * 1;
@@ -31,13 +29,10 @@ void ei_qrfac(int m, int n, Scalar *a, int
     /*     compute the initial column norms and initialize several arrays. */
 
     for (j = 1; j <= n; ++j) {
-        acnorm[j] = Map< Matrix< Scalar, Dynamic, 1 > >(&a[j * a_dim1 + 1],m).blueNorm();
-        rdiag[j] = acnorm[j];
+        rdiag[j] = Map< Matrix< Scalar, Dynamic, 1 > >(&a[j * a_dim1 + 1],m).blueNorm();
         wa[j] = rdiag[j];
-        if (pivot) {
+        if (pivot)
             ipvt[j] = j;
-        }
-        /* L10: */
     }
 
     /*     reduce a to r with householder transformations. */
diff --git a/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h b/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
index 57884870c..1ee21d5ed 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
@@ -1,166 +1,92 @@
 
-    template <typename Scalar>
-void ei_qrsolv(int n, Scalar *r__, int ldr, 
-        const int *ipvt, const Scalar *diag, const Scalar *qtb, Scalar *x, 
-        Scalar *sdiag, Scalar *wa)
-{
-    /* System generated locals */
-    int r_dim1, r_offset;
+template <typename Scalar>
+void ei_qrsolv(
+        Matrix< Scalar, Dynamic, Dynamic > &r,
+        VectorXi &ipvt, // TODO : const once ipvt mess fixed
+        const Matrix< Scalar, Dynamic, 1 >  &diag,
+        const Matrix< Scalar, Dynamic, 1 >  &qtb,
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        Matrix< Scalar, Dynamic, 1 >  &sdiag)
 
+{
     /* Local variables */
-    int i, j, k, l, jp1, kp1;
-    Scalar tan__, cos__, sin__, sum, temp, cotan;
-    int nsing;
-    Scalar qtbpj;
-
-    /* Parameter adjustments */
-    --wa;
-    --sdiag;
-    --x;
-    --qtb;
-    --diag;
-    --ipvt;
-    r_dim1 = ldr;
-    r_offset = 1 + r_dim1 * 1;
-    r__ -= r_offset;
+    int i, j, k, l;
+    Scalar sum, temp;
+    int n = r.cols();
+    Matrix< Scalar, Dynamic, 1 >  wa(n);
 
     /* Function Body */
 
     /*     copy r and (q transpose)*b to preserve input and initialize s. */
     /*     in particular, save the diagonal elements of r in x. */
 
-    for (j = 1; j <= n; ++j) {
-        for (i = j; i <= n; ++i) {
-            r__[i + j * r_dim1] = r__[j + i * r_dim1];
-            /* L10: */
-        }
-        x[j] = r__[j + j * r_dim1];
-        wa[j] = qtb[j];
-        /* L20: */
-    }
+    x = r.diagonal();
+    wa = qtb;
 
-    /*     eliminate the diagonal matrix d using a givens rotation. */
+    for (j = 0; j < n; ++j)
+        for (i = j+1; i < n; ++i)
+            r(i,j) = r(j,i);
 
-    for (j = 1; j <= n; ++j) {
+    /*     eliminate the diagonal matrix d using a givens rotation. */
+    for (j = 0; j < n; ++j) {
 
         /*        prepare the row of d to be eliminated, locating the */
         /*        diagonal element using p from the qr factorization. */
 
         l = ipvt[j];
-        if (diag[l] == 0.) {
-            goto L90;
-        }
-        for (k = j; k <= n; ++k) {
-            sdiag[k] = 0.;
-            /* L30: */
-        }
+        if (diag[l] == 0.)
+            break;
+        sdiag.segment(j,n-j).setZero();
         sdiag[j] = diag[l];
 
         /*        the transformations to eliminate the row of d */
         /*        modify only a single element of (q transpose)*b */
         /*        beyond the first n, which is initially zero. */
 
-        qtbpj = 0.;
-        for (k = j; k <= n; ++k) {
-
+        Scalar qtbpj = 0.;
+        for (k = j; k < n; ++k) {
             /*           determine a givens rotation which eliminates the */
             /*           appropriate element in the current row of d. */
-
-            if (sdiag[k] == 0.)
-                goto L70;
-            if ( ei_abs(r__[k + k * r_dim1]) >= ei_abs(sdiag[k]))
-                goto L40;
-            cotan = r__[k + k * r_dim1] / sdiag[k];
-            /* Computing 2nd power */
-            sin__ = Scalar(.5) / ei_sqrt(Scalar(0.25) + Scalar(0.25) * ei_abs2(cotan));
-            cos__ = sin__ * cotan;
-            goto L50;
-L40:
-            tan__ = sdiag[k] / r__[k + k * r_dim1];
-            /* Computing 2nd power */
-            cos__ = Scalar(.5) / ei_sqrt(Scalar(0.25) + Scalar(0.25) * ei_abs2(tan__));
-            sin__ = cos__ * tan__;
-L50:
+            PlanarRotation<Scalar> givens;
+            givens.makeGivens(-r(k,k), sdiag[k]);
 
             /*           compute the modified diagonal element of r and */
             /*           the modified element of ((q transpose)*b,0). */
 
-            r__[k + k * r_dim1] = cos__ * r__[k + k * r_dim1] + sin__ * sdiag[
-                k];
-            temp = cos__ * wa[k] + sin__ * qtbpj;
-            qtbpj = -sin__ * wa[k] + cos__ * qtbpj;
+            r(k,k) = givens.c() * r(k,k) + givens.s() * sdiag[k];
+            temp = givens.c() * wa[k] + givens.s() * qtbpj;
+            qtbpj = -givens.s() * wa[k] + givens.c() * qtbpj;
             wa[k] = temp;
 
             /*           accumulate the tranformation in the row of s. */
-
-            kp1 = k + 1;
-            if (n < kp1) {
-                goto L70;
+            for (i = k+1; i<n; ++i) {
+                temp = givens.c() * r(i,k) + givens.s() * sdiag[i];
+                sdiag[i] = -givens.s() * r(i,k) + givens.c() * sdiag[i];
+                r(i,k) = temp;
             }
-            for (i = kp1; i <= n; ++i) {
-                temp = cos__ * r__[i + k * r_dim1] + sin__ * sdiag[i];
-                sdiag[i] = -sin__ * r__[i + k * r_dim1] + cos__ * sdiag[
-                    i];
-                r__[i + k * r_dim1] = temp;
-                /* L60: */
-            }
-L70:
-            /* L80: */
-            ;
         }
-L90:
-
-        /*        store the diagonal element of s and restore */
-        /*        the corresponding diagonal element of r. */
-
-        sdiag[j] = r__[j + j * r_dim1];
-        r__[j + j * r_dim1] = x[j];
-        /* L100: */
     }
 
+    // restore
+    sdiag = r.diagonal();
+    r.diagonal() = x;
+
     /*     solve the triangular system for z. if the system is */
     /*     singular, then obtain a least squares solution. */
 
-    nsing = n;
-    for (j = 1; j <= n; ++j) {
-        if (sdiag[j] == 0. && nsing == n) {
-            nsing = j - 1;
-        }
-        if (nsing < n) {
-            wa[j] = 0.;
-        }
-        /* L110: */
-    }
-    if (nsing < 1) {
-        goto L150;
-    }
-    for (k = 1; k <= nsing; ++k) {
-        j = nsing - k + 1;
+    int nsing;
+    for (nsing=0; nsing<n && sdiag[nsing]!=0; nsing++);
+    wa.segment(nsing,n-nsing).setZero();
+    nsing--; // nsing is the last nonsingular index
+
+    for (j = nsing; j>=0; j--) {
         sum = 0.;
-        jp1 = j + 1;
-        if (nsing < jp1) {
-            goto L130;
-        }
-        for (i = jp1; i <= nsing; ++i) {
-            sum += r__[i + j * r_dim1] * wa[i];
-            /* L120: */
-        }
-L130:
+        for (i = j+1; i <= nsing; ++i)
+            sum += r(i,j) * wa[i];
         wa[j] = (wa[j] - sum) / sdiag[j];
-        /* L140: */
     }
-L150:
 
     /*     permute the components of z back to components of x. */
-
-    for (j = 1; j <= n; ++j) {
-        l = ipvt[j];
-        x[l] = wa[j];
-        /* L160: */
-    }
-    return;
-
-    /*     last card of subroutine qrsolv. */
-
-} /* qrsolv_ */
+    for (j = 0; j < n; ++j) x[ipvt[j]] = wa[j];
+}
 
diff --git a/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h b/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
index a06072a54..70a6d30c3 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
@@ -18,28 +18,18 @@ void ei_r1mpyq(int m, int n, Scalar *a, int
     a -= a_offset;
 
     /* Function Body */
-
-    /*     apply the first set of givens rotations to a. */
-
     nm1 = n - 1;
-    if (nm1 < 1) {
-        /* goto L50; */
+    if (nm1 < 1)
         return;
-    }
+
+    /*     apply the first set of givens rotations to a. */
     for (nmj = 1; nmj <= nm1; ++nmj) {
         j = n - nmj;
         if (ei_abs(v[j]) > 1.) {
             cos__ = 1. / v[j];
-        }
-        if (ei_abs(v[j]) > 1.) {
-            /* Computing 2nd power */
             sin__ = ei_sqrt(1. - ei_abs2(cos__));
-        }
-        if (ei_abs(v[j]) <= 1.) {
+        } else  {
             sin__ = v[j];
-        }
-        if (ei_abs(v[j]) <= 1.) {
-            /* Computing 2nd power */
             cos__ = ei_sqrt(1. - ei_abs2(sin__));
         }
         for (i = 1; i <= m; ++i) {
@@ -47,26 +37,15 @@ void ei_r1mpyq(int m, int n, Scalar *a, int
             a[i + n * a_dim1] = sin__ * a[i + j * a_dim1] + cos__ * a[
                 i + n * a_dim1];
             a[i + j * a_dim1] = temp;
-            /* L10: */
         }
-        /* L20: */
     }
-
     /*     apply the second set of givens rotations to a. */
-
     for (j = 1; j <= nm1; ++j) {
         if (ei_abs(w[j]) > 1.) {
             cos__ = 1. / w[j];
-        }
-        if (ei_abs(w[j]) > 1.) {
-            /* Computing 2nd power */
             sin__ = ei_sqrt(1. - ei_abs2(cos__));
-        }
-        if (ei_abs(w[j]) <= 1.) {
+        } else  {
             sin__ = w[j];
-        }
-        if (ei_abs(w[j]) <= 1.) {
-            /* Computing 2nd power */
             cos__ = ei_sqrt(1. - ei_abs2(sin__));
         }
         for (i = 1; i <= m; ++i) {
@@ -74,14 +53,8 @@ void ei_r1mpyq(int m, int n, Scalar *a, int
             a[i + n * a_dim1] = -sin__ * a[i + j * a_dim1] + cos__ * a[
                 i + n * a_dim1];
             a[i + j * a_dim1] = temp;
-            /* L30: */
         }
-        /* L40: */
     }
-    /* L50: */
     return;
-
-    /*     last card of subroutine r1mpyq. */
-
 } /* r1mpyq_ */
 
diff --git a/unsupported/doc/Doxyfile.in b/unsupported/doc/Doxyfile.in
index a9c7d6004..e55d53f7e 100644
--- a/unsupported/doc/Doxyfile.in
+++ b/unsupported/doc/Doxyfile.in
@@ -601,6 +601,7 @@ EXCLUDE_PATTERNS       = CMake* \
                          *.txt \
                          *.sh \
                          *.diff \
+                         *.orig \
                          diff
                          *~