* rename QR to HouseholderQR because really that impacts the API, not just the impl.

* rename qr() to householderQr(), for same reason.
* clarify that it's non-pivoting, non-rank-revealing, so remove all the rank API, make solve() be void instead of bool, update the docs/test, etc.
* fix warning in SVD

9 files changed, 43 insertions, 204 deletions

diff --git a/Eigen/src/Core/MatrixBase.h b/Eigen/src/Core/MatrixBase.h
index 65ab02d62..941539214 100644
--- a/Eigen/src/Core/MatrixBase.h
+++ b/Eigen/src/Core/MatrixBase.h
@@ -653,7 +653,7 @@ template<typename Derived> class MatrixBase
 
 /////////// QR module ///////////
 
-    const QR<PlainMatrixType> qr() const;
+    const HouseholderQR<PlainMatrixType> householderQr() const;
 
     EigenvaluesReturnType eigenvalues() const;
     RealScalar operatorNorm() const;
diff --git a/Eigen/src/Core/util/ForwardDeclarations.h b/Eigen/src/Core/util/ForwardDeclarations.h
index b457268af..a2105604a 100644
--- a/Eigen/src/Core/util/ForwardDeclarations.h
+++ b/Eigen/src/Core/util/ForwardDeclarations.h
@@ -112,7 +112,7 @@ template<typename MatrixType, int Direction = BothDirections> class Reverse;
 
 template<typename MatrixType> class LU;
 template<typename MatrixType> class PartialLU;
-template<typename MatrixType> class QR;
+template<typename MatrixType> class HouseholderQR;
 template<typename MatrixType> class SVD;
 template<typename MatrixType> class LLT;
 template<typename MatrixType> class LDLT;
diff --git a/Eigen/src/QR/QR.h b/Eigen/src/QR/QR.h
index 90751dd42..d6d3d2081 100644
--- a/Eigen/src/QR/QR.h
+++ b/Eigen/src/QR/QR.h
@@ -22,24 +22,26 @@
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 
-#ifndef EIGEN_QR_H
-#define EIGEN_QR_H
+#ifndef EIGEN_HouseholderQR_H
+#define EIGEN_HouseholderQR_H
 
-/** \ingroup QR_Module
+/** \ingroup HouseholderQR_Module
   * \nonstableyet
   *
-  * \class QR
+  * \class HouseholderQR
   *
-  * \brief QR decomposition of a matrix
+  * \brief Householder QR decomposition of a matrix
   *
   * \param MatrixType the type of the matrix of which we are computing the QR decomposition
   *
   * This class performs a QR decomposition using Householder transformations. The result is
   * stored in a compact way compatible with LAPACK.
   *
+  * Note that no pivoting is performed. This is \b not a rank-revealing decomposition.
+  *
   * \sa MatrixBase::qr()
   */
-template<typename MatrixType> class QR
+template<typename MatrixType> class HouseholderQR
 {
   public:
 
@@ -53,88 +55,23 @@ template<typename MatrixType> class QR
     * \brief Default Constructor.
     *
     * The default constructor is useful in cases in which the user intends to
-    * perform decompositions via QR::compute(const MatrixType&).
+    * perform decompositions via HouseholderQR::compute(const MatrixType&).
     */
-    QR() : m_qr(), m_hCoeffs(), m_isInitialized(false) {}
+    HouseholderQR() : m_qr(), m_hCoeffs(), m_isInitialized(false) {}
 
-    QR(const MatrixType& matrix)
+    HouseholderQR(const MatrixType& matrix)
       : m_qr(matrix.rows(), matrix.cols()),
         m_hCoeffs(matrix.cols()),
         m_isInitialized(false)
     {
       compute(matrix);
     }
-    
-    /** \deprecated use isInjective()
-      * \returns whether or not the matrix is of full rank
-      *
-      * \note Since the rank is computed only once, i.e. the first time it is needed, this
-      *       method almost does not perform any further computation.
-      */
-    EIGEN_DEPRECATED bool isFullRank() const 
-    { 
-      ei_assert(m_isInitialized && "QR is not initialized.");
-      return rank() == m_qr.cols(); 
-    }
-    
-    /** \returns the rank of the matrix of which *this is the QR decomposition.
-      *
-      * \note Since the rank is computed only once, i.e. the first time it is needed, this
-      *       method almost does not perform any further computation.
-      */
-    int rank() const;
-    
-    /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition.
-      *
-      * \note Since the rank is computed only once, i.e. the first time it is needed, this
-      *       method almost does not perform any further computation.
-      */
-    inline int dimensionOfKernel() const
-    {
-      ei_assert(m_isInitialized && "QR is not initialized.");
-      return m_qr.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 only once, i.e. the first time it is needed, this
-      *       method almost does not perform any further computation.
-      */
-    inline bool isInjective() const
-    {
-      ei_assert(m_isInitialized && "QR is not initialized.");
-      return rank() == m_qr.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 only once, i.e. the first time it is needed, this
-      *       method almost does not perform any further computation.
-      */
-    inline bool isSurjective() const
-    {
-      ei_assert(m_isInitialized && "QR is not initialized.");
-      return rank() == m_qr.rows();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition is invertible.
-      *
-      * \note Since the rank is computed only once, i.e. the first time it is needed, this
-      *       method almost does not perform any further computation.
-      */
-    inline bool isInvertible() const
-    {
-      ei_assert(m_isInitialized && "QR is not initialized.");
-      return isInjective() && isSurjective();
-    }
-    
+        
     /** \returns a read-only expression of the matrix R of the actual the QR decomposition */
     const Part<NestByValue<MatrixRBlockType>, UpperTriangular>
     matrixR(void) const
     {
-      ei_assert(m_isInitialized && "QR is not initialized.");
+      ei_assert(m_isInitialized && "HouseholderQR is not initialized.");
       int cols = m_qr.cols();
       return MatrixRBlockType(m_qr, 0, 0, cols, cols).nestByValue().template part<UpperTriangular>();
     }
@@ -148,22 +85,14 @@ template<typename MatrixType> class QR
       *          Resized if necessary, so that result->rows()==A.cols() and result->cols()==b.cols().
       *          If no solution exists, *result is left with undefined coefficients.
       *
-      * \returns true if any solution exists, false if no solution exists.
-      *
-      * \note If there exist more than one solution, this method will arbitrarily choose one.
-      *       If you need a complete analysis of the space of solutions, take the one solution obtained
-      *       by this method and add to it elements of the kernel, as determined by kernel().
-      *
       * \note The case where b is a matrix is not yet implemented. Also, this
       *       code is space inefficient.
       *
-      * Example: \include QR_solve.cpp
-      * Output: \verbinclude QR_solve.out
-      *
-      * \sa MatrixBase::solveTriangular(), kernel(), computeKernel(), inverse(), computeInverse()
+      * Example: \include HouseholderQR_solve.cpp
+      * Output: \verbinclude HouseholderQR_solve.out
       */
     template<typename OtherDerived, typename ResultType>
-    bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const;
+    void solve(const MatrixBase<OtherDerived>& b, ResultType *result) const;
 
     MatrixType matrixQ(void) const;
 
@@ -172,34 +101,14 @@ template<typename MatrixType> class QR
   protected:
     MatrixType m_qr;
     VectorType m_hCoeffs;
-    mutable int m_rank;
-    mutable bool m_rankIsUptodate;
     bool m_isInitialized;
 };
 
-/** \returns the rank of the matrix of which *this is the QR decomposition. */
-template<typename MatrixType>
-int QR<MatrixType>::rank() const
-{
-  ei_assert(m_isInitialized && "QR is not initialized.");
-  if (!m_rankIsUptodate)
-  {
-    RealScalar maxCoeff = m_qr.diagonal().cwise().abs().maxCoeff();
-    int n = m_qr.cols();
-    m_rank = 0;
-    while(m_rank<n && !ei_isMuchSmallerThan(m_qr.diagonal().coeff(m_rank), maxCoeff))
-      ++m_rank;
-    m_rankIsUptodate = true;
-  }
-  return m_rank;
-}
-
 #ifndef EIGEN_HIDE_HEAVY_CODE
 
 template<typename MatrixType>
-void QR<MatrixType>::compute(const MatrixType& matrix)
+void HouseholderQR<MatrixType>::compute(const MatrixType& matrix)
 { 
-  m_rankIsUptodate = false;
   m_qr = matrix;
   m_hCoeffs.resize(matrix.cols());
 
@@ -262,12 +171,12 @@ void QR<MatrixType>::compute(const MatrixType& matrix)
 
 template<typename MatrixType>
 template<typename OtherDerived, typename ResultType>
-bool QR<MatrixType>::solve(
+void HouseholderQR<MatrixType>::solve(
   const MatrixBase<OtherDerived>& b,
   ResultType *result
 ) const
 {
-  ei_assert(m_isInitialized && "QR is not initialized.");
+  ei_assert(m_isInitialized && "HouseholderQR is not initialized.");
   const int rows = m_qr.rows();
   ei_assert(b.rows() == rows);
   result->resize(rows, b.cols());
@@ -276,27 +185,17 @@ bool QR<MatrixType>::solve(
   // Q^T without explicitly forming matrixQ(). Investigate.
   *result = matrixQ().transpose()*b;
   
-  if(!isSurjective())
-  {
-    // is result is in the image of R ?
-    RealScalar biggest_in_res = result->corner(TopLeft, m_rank, result->cols()).cwise().abs().maxCoeff();
-    for(int col = 0; col < result->cols(); ++col)
-      for(int row = m_rank; row < result->rows(); ++row)
-        if(!ei_isMuchSmallerThan(result->coeff(row,col), biggest_in_res))
-          return false;
-  }
-  m_qr.corner(TopLeft, m_rank, m_rank)
+  const int rank = std::min(result->rows(), result->cols());
+  m_qr.corner(TopLeft, rank, rank)
       .template marked<UpperTriangular>()
-      .solveTriangularInPlace(result->corner(TopLeft, m_rank, result->cols()));
-
-  return true;
+      .solveTriangularInPlace(result->corner(TopLeft, rank, result->cols()));
 }
 
 /** \returns the matrix Q */
 template<typename MatrixType>
-MatrixType QR<MatrixType>::matrixQ() const
+MatrixType HouseholderQR<MatrixType>::matrixQ() const
 {
-  ei_assert(m_isInitialized && "QR is not initialized.");
+  ei_assert(m_isInitialized && "HouseholderQR is not initialized.");
   // compute the product Q_0 Q_1 ... Q_n-1,
   // where Q_k is the k-th Householder transformation I - h_k v_k v_k'
   // and v_k is the k-th Householder vector [1,m_qr(k+1,k), m_qr(k+2,k), ...]
@@ -319,16 +218,16 @@ MatrixType QR<MatrixType>::matrixQ() const
 
 #endif // EIGEN_HIDE_HEAVY_CODE
 
-/** \return the QR decomposition of \c *this.
+/** \return the Householder QR decomposition of \c *this.
   *
-  * \sa class QR
+  * \sa class HouseholderQR
   */
 template<typename Derived>
-const QR<typename MatrixBase<Derived>::PlainMatrixType>
-MatrixBase<Derived>::qr() const
+const HouseholderQR<typename MatrixBase<Derived>::PlainMatrixType>
+MatrixBase<Derived>::householderQr() const
 {
-  return QR<PlainMatrixType>(eval());
+  return HouseholderQR<PlainMatrixType>(eval());
 }
 
 
-#endif // EIGEN_QR_H
+#endif // EIGEN_HouseholderQR_H
diff --git a/Eigen/src/SVD/SVD.h b/Eigen/src/SVD/SVD.h
index 71f6763a8..97f82c78f 100644
--- a/Eigen/src/SVD/SVD.h
+++ b/Eigen/src/SVD/SVD.h
@@ -145,7 +145,6 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
 {
   const int m = matrix.rows();
   const int n = matrix.cols();
-  const int nu = std::min(m,n);
 
   m_matU.resize(m, m);
   m_matU.setZero();
diff --git a/doc/eigendoxy_header.html.in b/doc/eigendoxy_header.html.in
index 97cafd41d..572c47158 100644
--- a/doc/eigendoxy_header.html.in
+++ b/doc/eigendoxy_header.html.in
@@ -6,5 +6,5 @@
 </head><body>
 <a name="top"></a>
 <a class="logo" href="http://eigen.tuxfamily.org/">
-<img src="Eigen_Silly_Professor_64x64.png" width=64 height=64 alt="Eiegn's silly professor"
+<img src="Eigen_Silly_Professor_64x64.png" width=64 height=64 alt="Eigen's silly professor"
      style="position:absolute; border:none" /></a>
\ No newline at end of file
diff --git a/doc/snippets/QR_solve.cpp b/doc/snippets/HouseholderQR_solve.cpp
index 7e629f851..aa9404951 100644
--- a/doc/snippets/QR_solve.cpp
+++ b/doc/snippets/HouseholderQR_solve.cpp
@@ -4,11 +4,6 @@ Matrix3f y = Matrix3f::Random();
 cout << "Here is the matrix m:" << endl << m << endl;
 cout << "Here is the matrix y:" << endl << y << endl;
 Matrix3f x;
-if(m.qr().solve(y, &x))
-{
-  assert(y.isApprox(m*x));
-  cout << "Here is a solution x to the equation mx=y:" << endl << x << endl;
-}
-else
-  cout << "The equation mx=y does not have any solution." << endl;
-
+m.householderQr().solve(y, &x));
+assert(y.isApprox(m*x));
+cout << "Here is a solution x to the equation mx=y:" << endl << x << endl;
diff --git a/test/geo_hyperplane.cpp b/test/geo_hyperplane.cpp
index c5d2715db..f8281a16b 100644
--- a/test/geo_hyperplane.cpp
+++ b/test/geo_hyperplane.cpp
@@ -64,7 +64,7 @@ template<typename HyperplaneType> void hyperplane(const HyperplaneType& _plane)
   // transform
   if (!NumTraits<Scalar>::IsComplex)
   {
-    MatrixType rot = MatrixType::Random(dim,dim).qr().matrixQ();
+    MatrixType rot = MatrixType::Random(dim,dim).householderQr().matrixQ();
     DiagonalMatrix<Scalar,HyperplaneType::AmbientDimAtCompileTime> scaling(VectorType::Random());
     Translation<Scalar,HyperplaneType::AmbientDimAtCompileTime> translation(VectorType::Random());
 
diff --git a/test/main.h b/test/main.h
index a6a780603..53c8245c6 100644
--- a/test/main.h
+++ b/test/main.h
@@ -241,8 +241,8 @@ void createRandomMatrixOfRank(int desired_rank, int rows, int cols, Eigen::Matri
   const int diag_size = std::min(d.rows(),d.cols());
   d.diagonal().segment(desired_rank, diag_size-desired_rank) = VectorType::Zero(diag_size-desired_rank);
 
-  QR<MatrixType> qra(a);
-  QR<MatrixType> qrb(b);
+  HouseholderQR<MatrixType> qra(a);
+  HouseholderQR<MatrixType> qrb(b);
   m = (qra.matrixQ() * d * qrb.matrixQ()).lazy();
 }
 
diff --git a/test/qr.cpp b/test/qr.cpp
index 6e96f1e97..88a447c4b 100644
--- a/test/qr.cpp
+++ b/test/qr.cpp
@@ -36,7 +36,7 @@ template<typename MatrixType> void qr(const MatrixType& m)
   typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
 
   MatrixType a = MatrixType::Random(rows,cols);
-  QR<MatrixType> qrOfA(a);
+  HouseholderQR<MatrixType> qrOfA(a);
   VERIFY_IS_APPROX(a, qrOfA.matrixQ() * qrOfA.matrixR());
   VERIFY_IS_NOT_APPROX(a+MatrixType::Identity(rows, cols), qrOfA.matrixQ() * qrOfA.matrixR());
 
@@ -55,40 +55,6 @@ template<typename MatrixType> void qr(const MatrixType& m)
   VERIFY_IS_APPROX(b, hess.matrixQ() * hess.matrixH() * hess.matrixQ().adjoint());
 }
 
-template<typename MatrixType> void qr_non_invertible()
-{
-  /* this test covers the following files: QR.h */
-  int rows = ei_random<int>(20,200), cols = ei_random<int>(20,rows), cols2 = ei_random<int>(20,rows);
-  int rank = ei_random<int>(1, std::min(rows, cols)-1);
-
-  MatrixType m1(rows, cols), m2(cols, cols2), m3(rows, cols2), k(1,1);
-  createRandomMatrixOfRank(rank, rows, cols, m1);
-
-  QR<MatrixType> lu(m1);
-//   typename LU<MatrixType>::KernelResultType m1kernel = lu.kernel();
-//   typename LU<MatrixType>::ImageResultType m1image = lu.image();
-  std::cerr << rows << "x" << cols << "   " << rank << " " << lu.rank() << "\n";
-  if (rank != lu.rank())
-    std::cerr << lu.matrixR().diagonal().transpose() << "\n";
-  VERIFY(rank == lu.rank());
-  VERIFY(cols - lu.rank() == lu.dimensionOfKernel());
-  VERIFY(!lu.isInjective());
-  VERIFY(!lu.isInvertible());
-  VERIFY(lu.isSurjective() == (lu.rank() == rows));
-//   VERIFY((m1 * m1kernel).isMuchSmallerThan(m1));
-//   VERIFY(m1image.lu().rank() == rank);
-//   MatrixType sidebyside(m1.rows(), m1.cols() + m1image.cols());
-//   sidebyside << m1, m1image;
-//   VERIFY(sidebyside.lu().rank() == rank);
-  m2 = MatrixType::Random(cols,cols2);
-  m3 = m1*m2;
-  m2 = MatrixType::Random(cols,cols2);
-  lu.solve(m3, &m2);
-  VERIFY_IS_APPROX(m3, m1*m2);
-  m3 = MatrixType::Random(rows,cols2);
-  VERIFY(!lu.solve(m3, &m2));
-}
-
 template<typename MatrixType> void qr_invertible()
 {
   /* this test covers the following files: QR.h */
@@ -105,33 +71,18 @@ template<typename MatrixType> void qr_invertible()
     m1 += a * a.adjoint();
   }
 
-  QR<MatrixType> lu(m1);
-  VERIFY(0 == lu.dimensionOfKernel());
-  VERIFY(size == lu.rank());
-  VERIFY(lu.isInjective());
-  VERIFY(lu.isSurjective());
-  VERIFY(lu.isInvertible());
-//   VERIFY(lu.image().lu().isInvertible());
+  HouseholderQR<MatrixType> qr(m1);
   m3 = MatrixType::Random(size,size);
-  lu.solve(m3, &m2);
+  qr.solve(m3, &m2);
   //std::cerr << m3 - m1*m2 << "\n\n";
   VERIFY_IS_APPROX(m3, m1*m2);
-//   VERIFY_IS_APPROX(m2, lu.inverse()*m3);
-  m3 = MatrixType::Random(size,size);
-  VERIFY(lu.solve(m3, &m2));
 }
 
 template<typename MatrixType> void qr_verify_assert()
 {
   MatrixType tmp;
 
-  QR<MatrixType> qr;
-  VERIFY_RAISES_ASSERT(qr.isFullRank())
-  VERIFY_RAISES_ASSERT(qr.rank())
-  VERIFY_RAISES_ASSERT(qr.dimensionOfKernel())
-  VERIFY_RAISES_ASSERT(qr.isInjective())
-  VERIFY_RAISES_ASSERT(qr.isSurjective())
-  VERIFY_RAISES_ASSERT(qr.isInvertible())
+  HouseholderQR<MatrixType> qr;
   VERIFY_RAISES_ASSERT(qr.matrixR())
   VERIFY_RAISES_ASSERT(qr.solve(tmp,&tmp))
   VERIFY_RAISES_ASSERT(qr.matrixQ())
@@ -149,11 +100,6 @@ void test_qr()
   }
 
   for(int i = 0; i < g_repeat; i++) {
-    CALL_SUBTEST( qr_non_invertible<MatrixXf>() );
-    CALL_SUBTEST( qr_non_invertible<MatrixXd>() );
-    // TODO fix issue with complex
-//     CALL_SUBTEST( qr_non_invertible<MatrixXcf>() );
-//     CALL_SUBTEST( qr_non_invertible<MatrixXcd>() );
     CALL_SUBTEST( qr_invertible<MatrixXf>() );
     CALL_SUBTEST( qr_invertible<MatrixXd>() );
     // TODO fix issue with complex