From 224a1fe4c6991c863986d8c8bd3d41af5aa4ff80 Mon Sep 17 00:00:00 2001
From: Gael Guennebaud <g.gael@free.fr>
Date: Mon, 9 Mar 2015 13:55:20 +0100
Subject: bug #963: make IncompleteLUT compatible with non-default storage
 index types.

---
 Eigen/src/IterativeLinearSolvers/IncompleteLUT.h | 41 ++++++++++++------------
 1 file changed, 21 insertions(+), 20 deletions(-)

(limited to 'Eigen/src/IterativeLinearSolvers')

diff --git a/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
index 6d63d45e4..b7f8debb3 100644
--- a/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
+++ b/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
@@ -93,21 +93,23 @@ Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
   * alternatively, on GMANE:
   *   http://comments.gmane.org/gmane.comp.lib.eigen/3302
   */
-template <typename _Scalar>
-class IncompleteLUT : public SparseSolverBase<IncompleteLUT<_Scalar> >
+template <typename _Scalar, typename _StorageIndex = int>
+class IncompleteLUT : public SparseSolverBase<IncompleteLUT<_Scalar, _StorageIndex> >
 {
   protected:
-    typedef SparseSolverBase<IncompleteLUT<_Scalar> > Base;
+    typedef SparseSolverBase<IncompleteLUT> Base;
     using Base::m_isInitialized;
   public:
     typedef _Scalar Scalar;
+    typedef _StorageIndex StorageIndex;
     typedef typename NumTraits<Scalar>::Real RealScalar;
     typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef SparseMatrix<Scalar,RowMajor> FactorType;
-    typedef SparseMatrix<Scalar,ColMajor> PermutType;
-    typedef typename FactorType::StorageIndex StorageIndex;
+    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
+    typedef SparseMatrix<Scalar,RowMajor,StorageIndex> FactorType;
 
   public:
+    
+    // this typedef is only to export the scalar type and compile-time dimensions to solve_retval
     typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
     
     IncompleteLUT()
@@ -151,7 +153,7 @@ class IncompleteLUT : public SparseSolverBase<IncompleteLUT<_Scalar> >
       * 
       **/
     template<typename MatrixType>
-    IncompleteLUT<Scalar>& compute(const MatrixType& amat)
+    IncompleteLUT& compute(const MatrixType& amat)
     {
       analyzePattern(amat); 
       factorize(amat);
@@ -197,8 +199,8 @@ protected:
  * Set control parameter droptol
  *  \param droptol   Drop any element whose magnitude is less than this tolerance 
  **/ 
-template<typename Scalar>
-void IncompleteLUT<Scalar>::setDroptol(const RealScalar& droptol)
+template<typename Scalar, typename StorageIndex>
+void IncompleteLUT<Scalar,StorageIndex>::setDroptol(const RealScalar& droptol)
 {
   this->m_droptol = droptol;   
 }
@@ -207,15 +209,15 @@ void IncompleteLUT<Scalar>::setDroptol(const RealScalar& droptol)
  * Set control parameter fillfactor
  * \param fillfactor  This is used to compute the  number @p fill_in of largest elements to keep on each row. 
  **/ 
-template<typename Scalar>
-void IncompleteLUT<Scalar>::setFillfactor(int fillfactor)
+template<typename Scalar, typename StorageIndex>
+void IncompleteLUT<Scalar,StorageIndex>::setFillfactor(int fillfactor)
 {
   this->m_fillfactor = fillfactor;   
 }
 
-template <typename Scalar>
+template <typename Scalar, typename StorageIndex>
 template<typename _MatrixType>
-void IncompleteLUT<Scalar>::analyzePattern(const _MatrixType& amat)
+void IncompleteLUT<Scalar,StorageIndex>::analyzePattern(const _MatrixType& amat)
 {
   // Compute the Fill-reducing permutation
   SparseMatrix<Scalar,ColMajor, StorageIndex> mat1 = amat;
@@ -232,9 +234,9 @@ void IncompleteLUT<Scalar>::analyzePattern(const _MatrixType& amat)
   m_analysisIsOk = true;
 }
 
-template <typename Scalar>
+template <typename Scalar, typename StorageIndex>
 template<typename _MatrixType>
-void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
+void IncompleteLUT<Scalar,StorageIndex>::factorize(const _MatrixType& amat)
 {
   using std::sqrt;
   using std::swap;
@@ -246,8 +248,8 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
   m_lu.resize(n,n);
   // Declare Working vectors and variables
   Vector u(n) ;     // real values of the row -- maximum size is n --
-  VectorXi ju(n);   // column position of the values in u -- maximum size  is n
-  VectorXi jr(n);   // Indicate the position of the nonzero elements in the vector u -- A zero location is indicated by -1
+  VectorI ju(n);   // column position of the values in u -- maximum size  is n
+  VectorI jr(n);   // Indicate the position of the nonzero elements in the vector u -- A zero location is indicated by -1
 
   // Apply the fill-reducing permutation
   eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
@@ -398,7 +400,7 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
     sizel = len;
     len = (std::min)(sizel, nnzL);
     typename Vector::SegmentReturnType ul(u.segment(0, sizel));
-    typename VectorXi::SegmentReturnType jul(ju.segment(0, sizel));
+    typename VectorI::SegmentReturnType jul(ju.segment(0, sizel));
     internal::QuickSplit(ul, jul, len);
 
     // store the largest m_fill elements of the L part
@@ -427,14 +429,13 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
     sizeu = len + 1; // +1 to take into account the diagonal element
     len = (std::min)(sizeu, nnzU);
     typename Vector::SegmentReturnType uu(u.segment(ii+1, sizeu-1));
-    typename VectorXi::SegmentReturnType juu(ju.segment(ii+1, sizeu-1));
+    typename VectorI::SegmentReturnType juu(ju.segment(ii+1, sizeu-1));
     internal::QuickSplit(uu, juu, len);
 
     // store the largest elements of the U part
     for(Index k = ii + 1; k < ii + len; k++)
       m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
   }
-
   m_lu.finalize();
   m_lu.makeCompressed();
 
-- 
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