Add support for Sparse QR factorization

Eigen/src/CholmodSupport/CholmodSupport.h

@@ -77,9 +77,13 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
   {
     res.itype = CHOLMOD_INT;
   }
+  else if (internal::is_same<_Index,UF_long>::value)
+  {
+    res.itype = CHOLMOD_LONG;
+  }
   else
   {
-    eigen_assert(false && "Index type different than int is not supported yet");
+    eigen_assert(false && "Index type not supported yet");
   }
 
   // setup res.xtype

Eigen/src/SPQRSupport/CMakeLists.txt

@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_SPQRSupport_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_SPQRSupport_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SPQRSupport/ COMPONENT Devel
+  )

Eigen/src/SPQRSupport/SuiteSparseQRSupport.h

@@ -0,0 +1,230 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@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
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SUITESPARSEQRSUPPORT_H
+#define EIGEN_SUITESPARSEQRSUPPORT_H
+
+namespace Eigen {
+  
+  template<typename MatrixType> class SPQR; 
+  template<typename SPQRType> struct SPQRMatrixQReturnType; 
+  template<typename SPQRType> struct SPQRMatrixQTransposeReturnType; 
+  template <typename SPQRType, typename Derived> struct SPQR_QProduct;
+  namespace internal {
+    template <typename SPQRType> struct traits<SPQRMatrixQReturnType<SPQRType> >
+    {
+      typedef typename SPQRType::MatrixType ReturnType;
+    };
+    template <typename SPQRType> struct traits<SPQRMatrixQTransposeReturnType<SPQRType> >
+    {
+      typedef typename SPQRType::MatrixType ReturnType;
+    };
+    template <typename SPQRType, typename Derived> struct traits<SPQR_QProduct<SPQRType, Derived> >
+    {
+      typedef typename Derived::PlainObject ReturnType;
+    };
+  } // End namespace internal
+  
+/**
+ * \ingroup SPQRSupport_Module
+ * \class SPQR
+ * \brief Sparse QR factorization based on SuiteSparseQR library
+ * 
+ * This class is used to perform a multithreaded and multifrontal QR decomposition 
+ * of sparse matrices. The result is then used to solve linear leasts_square systems.
+ * Clearly, a QR factorization is returned such that A*P = Q*R where :
+ * 
+ * P is the column permutation. Use colsPermutation() to get it.
+ * 
+ * Q is the orthogonal matrix represented as Householder reflectors. 
+ * Use matrixQ() to get an expression and matrixQ().transpose() to get the transpose.
+ * You can then apply it to a vector.
+ * 
+ * R is the sparse triangular factor. Use matrixQR() to get it as SparseMatrix.
+ * NOTE : The Index type of R is always UF_long. You can get it with SPQR::Index
+ * 
+ * \tparam _MatrixType The type of the sparse matrix A, must be a SparseMatrix<>, either row-major or column-major. 
+ * NOTE 
+ * 
+ */
+template<typename _MatrixType>
+class SPQR
+{
+  public:
+    typedef typename _MatrixType::Scalar Scalar;
+    typedef typename _MatrixType::RealScalar RealScalar;
+    typedef UF_long Index ; 
+    typedef SparseMatrix<Scalar, _MatrixType::Flags, Index> MatrixType;
+  public:
+    SPQR() 
+    : m_ordering(SPQR_ORDERING_DEFAULT),
+      m_allow_tol(SPQR_DEFAULT_TOL),
+      m_tolerance (NumTraits<Scalar>::epsilon())
+    { 
+      cholmod_l_start(&m_cc);
+    }
+    
+    SPQR(const _MatrixType& matrix) : SPQR()
+    {
+      compute(matrix);
+    }
+    
+    ~SPQR()
+    {
+      // Calls SuiteSparseQR_free()
+      cholmod_free_sparse(&m_H, &m_cc); 
+      cholmod_free_dense(&m_HTau, &m_cc);
+      delete[] m_E;
+      delete[] m_HPinv; 
+    }
+    void compute(const MatrixType& matrix)
+    {
+      MatrixType mat(matrix);
+      cholmod_sparse A; 
+      A = viewAsCholmod(mat);
+      Index col = matrix.cols();
+      m_rank = SuiteSparseQR<Scalar>(m_ordering, m_tolerance, col, &A, 
+                             &m_cR, &m_E, &m_H, &m_HPinv, &m_HTau, &m_cc);
+
+      if (!m_cR)
+      {
+        m_info = NumericalIssue; 
+        m_isInitialized = false;
+        return;
+      }
+      m_info = Success;
+      m_isInitialized = true;
+    }
+    template<typename Rhs, typename Dest>
+    void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
+    {
+      eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()");
+      eigen_assert(b.cols()==1 && "This method is for vectors only");
+      
+      //Compute Q^T * b
+      // NOTE : We may have called directly the corresponding routines in SPQR codes.
+      // This version is used to test directly the corresponding part of the code
+      dest = matrixQ().transpose() * b;
+      
+        // Solves with the triangular matrix R
+      Dest y; 
+      y = this->matrixQR().template triangularView<Upper>().solve(dest.derived());
+      // Apply the column permutation //TODO Check the terminology behind the permutation
+      for (int j = 0; j < y.size(); j++)  dest(m_E[j]) = y(j); 
+      
+      m_info = Success;
+    }
+    /// Get the sparse triangular matrix R. It is a sparse matrix
+    MatrixType matrixQR() const
+    {
+      MatrixType R; 
+      R = viewAsEigen<Scalar, MatrixType::Flags, Index>(*m_cR);
+      return R; 
+    }
+    /// Get an expression of the matrix Q
+    SPQRMatrixQReturnType<SPQR> matrixQ() const
+    {
+      return SPQRMatrixQReturnType<SPQR>(*this);
+    }
+    /// Get the permutation that was applied to columns of A
+    Index *colsPermutation() { return m_E; }
+    
+    /// Set the fill-reducing ordering method to be used
+    void setOrdering(int ord) { m_ordering = ord;}
+    /// Set the tolerance tol to treat columns with 2-norm < =tol as zero
+    void setTolerance(RealScalar tol) { m_tolerance = tol; }
+      
+    /// Return a pointer to SPQR workspace 
+    cholmod_common *cc() const { return &m_cc; }
+    cholmod_sparse * H() const { return m_H; }
+    Index  *HPinv() const { return m_HPinv; }
+    cholmod_dense* HTau() const { return m_HTau; }
+    
+    
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was succesful,
+      *          \c NumericalIssue if the sparse QR can not be computed
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+  protected:
+    bool m_isInitialized;
+    bool m_analysisIsOk;
+    bool m_factorizationIsOk;
+    mutable ComputationInfo m_info;
+    int m_ordering; // Ordering method to use, see SPQR's manual
+    int m_allow_tol; // Allow to use some tolerance during numerical factorization.
+    RealScalar m_tolerance; // treat columns with 2-norm below this tolerance as zero
+    mutable cholmod_sparse *m_cR; // The sparse R factor in cholmod format
+    mutable Index *m_E; // The permutation applied to columns
+    mutable cholmod_sparse *m_H;  //The householder vectors
+    mutable Index *m_HPinv; // The row permutation of H
+    mutable cholmod_dense *m_HTau; // The Householder coefficients
+    mutable Index m_rank; // The rank of the matrix
+    mutable cholmod_common m_cc; // Workspace and parameters
+};
+
+template <typename SPQRType, typename Derived>
+struct SPQR_QProduct : ReturnByValue<SPQR_QProduct<SPQRType,Derived> >
+{
+  typedef typename SPQRType::Scalar Scalar;
+  //Define the constructor to get reference to argument types
+  SPQR_QProduct(const SPQRType& spqr, const Derived& other, bool transpose) : m_spqr(spqr),m_other(other),m_transpose(transpose) {}
+  
+  // Assign to a vector
+  template<typename ResType>
+  void evalTo(ResType& res) const
+  {
+    cholmod_dense y_cd;
+    cholmod_dense *x_cd; 
+    int method = m_transpose ? SPQR_QTX : SPQR_QX; 
+    cholmod_common *cc = m_spqr.cc();
+    y_cd = viewAsCholmod(m_other.const_cast_derived());
+    x_cd = SuiteSparseQR_qmult<Scalar>(method, m_spqr.H(), m_spqr.HTau(), m_spqr.HPinv(), &y_cd, cc);
+    res = Matrix<Scalar,ResType::RowsAtCompileTime,ResType::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x), x_cd->nrow, x_cd->ncol);
+    cholmod_free_dense(&x_cd, cc); 
+  }
+  const SPQRType& m_spqr; 
+  const Derived& m_other; 
+  bool m_transpose; 
+  
+};
+template<typename SPQRType>
+struct SPQRMatrixQReturnType{
+  
+  SPQRMatrixQReturnType(const SPQRType& spqr) : m_spqr(spqr) {}
+  template<typename Derived>
+  SPQR_QProduct<SPQRType, Derived> operator*(const MatrixBase<Derived>& other)
+  {
+    return SPQR_QProduct<SPQRType,Derived>(m_spqr,other.derived(),false);
+  }
+  // To use for operations with the transpose of Q
+  SPQRMatrixQTransposeReturnType<SPQRType> transpose() const
+  {
+    return SPQRMatrixQTransposeReturnType<SPQRType>(m_spqr);
+  }
+  const SPQRType& m_spqr;
+};
+
+template<typename SPQRType>
+struct SPQRMatrixQTransposeReturnType{
+  SPQRMatrixQTransposeReturnType(const SPQRType& spqr) : m_spqr(spqr) {}
+  template<typename Derived>
+  SPQR_QProduct<SPQRType,Derived> operator*(const MatrixBase<Derived>& other)
+  {
+    return SPQR_QProduct<SPQRType,Derived>(m_spqr,other.derived(), true);
+  }
+  const SPQRType& m_spqr;
+};
+}// End namespace Eigen
+#endif