diff --git a/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
index 4c169aa..338e6f1 100644
--- a/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
+++ b/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
@@ -2,6 +2,7 @@
 // for linear algebra.
 //
 // Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 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
@@ -24,7 +25,7 @@
   * \param ind The array of index for the elements in @p row
   * \param ncut  The number of largest elements to keep
   **/ 
-template <typename VectorV, typename VectorI, typename Index>
+template <typename VectorV, typename VectorI>
 Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
 {
   typedef typename VectorV::RealScalar RealScalar;
@@ -66,6 +67,8 @@
   * \class IncompleteLUT
   * \brief Incomplete LU factorization with dual-threshold strategy
   *
+  * \implsparsesolverconcept
+  *
   * During the numerical factorization, two dropping rules are used :
   *  1) any element whose magnitude is less than some tolerance is dropped.
   *    This tolerance is obtained by multiplying the input tolerance @p droptol 
@@ -92,28 +95,36 @@
   * alternatively, on GMANE:
   *   http://comments.gmane.org/gmane.comp.lib.eigen/3302
   */
-template <typename _Scalar>
-class IncompleteLUT : internal::noncopyable
+template <typename _Scalar, typename _StorageIndex = int>
+class IncompleteLUT : public SparseSolverBase<IncompleteLUT<_Scalar, _StorageIndex> >
 {
+  protected:
+    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::Index Index;
+    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
+    typedef SparseMatrix<Scalar,RowMajor,StorageIndex> FactorType;
+
+    enum {
+      ColsAtCompileTime = Dynamic,
+      MaxColsAtCompileTime = Dynamic
+    };
 
   public:
-    typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
     
     IncompleteLUT()
       : m_droptol(NumTraits<Scalar>::dummy_precision()), m_fillfactor(10),
-        m_analysisIsOk(false), m_factorizationIsOk(false), m_isInitialized(false)
+        m_analysisIsOk(false), m_factorizationIsOk(false)
     {}
     
     template<typename MatrixType>
-    IncompleteLUT(const MatrixType& mat, const RealScalar& droptol=NumTraits<Scalar>::dummy_precision(), int fillfactor = 10)
+    explicit IncompleteLUT(const MatrixType& mat, const RealScalar& droptol=NumTraits<Scalar>::dummy_precision(), int fillfactor = 10)
       : m_droptol(droptol),m_fillfactor(fillfactor),
-        m_analysisIsOk(false),m_factorizationIsOk(false),m_isInitialized(false)
+        m_analysisIsOk(false),m_factorizationIsOk(false)
     {
       eigen_assert(fillfactor != 0);
       compute(mat); 
@@ -146,7 +157,7 @@
       * 
       **/
     template<typename MatrixType>
-    IncompleteLUT<Scalar>& compute(const MatrixType& amat)
+    IncompleteLUT& compute(const MatrixType& amat)
     {
       analyzePattern(amat); 
       factorize(amat);
@@ -157,23 +168,14 @@
     void setFillfactor(int fillfactor); 
     
     template<typename Rhs, typename Dest>
-    void _solve(const Rhs& b, Dest& x) const
+    void _solve_impl(const Rhs& b, Dest& x) const
     {
-      x = m_Pinv * b;  
+      x = m_Pinv * b;
       x = m_lu.template triangularView<UnitLower>().solve(x);
       x = m_lu.template triangularView<Upper>().solve(x);
       x = m_P * x; 
     }
 
-    template<typename Rhs> inline const internal::solve_retval<IncompleteLUT, Rhs>
-     solve(const MatrixBase<Rhs>& b) const
-    {
-      eigen_assert(m_isInitialized && "IncompleteLUT is not initialized.");
-      eigen_assert(cols()==b.rows()
-                && "IncompleteLUT::solve(): invalid number of rows of the right hand side matrix b");
-      return internal::solve_retval<IncompleteLUT, Rhs>(*this, b.derived());
-    }
-
 protected:
 
     /** keeps off-diagonal entries; drops diagonal entries */
@@ -191,18 +193,17 @@
     int m_fillfactor;
     bool m_analysisIsOk;
     bool m_factorizationIsOk;
-    bool m_isInitialized;
     ComputationInfo m_info;
-    PermutationMatrix<Dynamic,Dynamic,Index> m_P;     // Fill-reducing permutation
-    PermutationMatrix<Dynamic,Dynamic,Index> m_Pinv;  // Inverse permutation
+    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_P;     // Fill-reducing permutation
+    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_Pinv;  // Inverse permutation
 };
 
 /**
  * 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;   
 }
@@ -211,52 +212,62 @@
  * 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, Index> mat1 = amat;
-  SparseMatrix<Scalar,ColMajor, Index> mat2 = amat.transpose();
-  // Symmetrize the pattern
+  // Since ILUT does not perform any numerical pivoting,
+  // it is highly preferable to keep the diagonal through symmetric permutations.
+#ifndef EIGEN_MPL2_ONLY
+  // To this end, let's symmetrize the pattern and perform AMD on it.
+  SparseMatrix<Scalar,ColMajor, StorageIndex> mat1 = amat;
+  SparseMatrix<Scalar,ColMajor, StorageIndex> mat2 = amat.transpose();
   // FIXME for a matrix with nearly symmetric pattern, mat2+mat1 is the appropriate choice.
   //       on the other hand for a really non-symmetric pattern, mat2*mat1 should be prefered...
-  SparseMatrix<Scalar,ColMajor, Index> AtA = mat2 + mat1;
-  AtA.prune(keep_diag());
-  internal::minimum_degree_ordering<Scalar, Index>(AtA, m_P);  // Then compute the AMD ordering...
-
-  m_Pinv  = m_P.inverse(); // ... and the inverse permutation
+  SparseMatrix<Scalar,ColMajor, StorageIndex> AtA = mat2 + mat1;
+  AMDOrdering<StorageIndex> ordering;
+  ordering(AtA,m_P);
+  m_Pinv  = m_P.inverse(); // cache the inverse permutation
+#else
+  // If AMD is not available, (MPL2-only), then let's use the slower COLAMD routine.
+  SparseMatrix<Scalar,ColMajor, StorageIndex> mat1 = amat;
+  COLAMDOrdering<StorageIndex> ordering;
+  ordering(mat1,m_Pinv);
+  m_P = m_Pinv.inverse();
+#endif
 
   m_analysisIsOk = true;
   m_factorizationIsOk = false;
-  m_isInitialized = false;
+  m_isInitialized = 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;
   using std::abs;
+  using internal::convert_index;
 
   eigen_assert((amat.rows() == amat.cols()) && "The factorization should be done on a square matrix");
   Index n = amat.cols();  // Size of the matrix
   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()");
-  SparseMatrix<Scalar,RowMajor, Index> mat;
+  SparseMatrix<Scalar,RowMajor, StorageIndex> mat;
   mat = amat.twistedBy(m_Pinv);
 
   // Initialization
@@ -265,7 +276,7 @@
   u.fill(0);
 
   // number of largest elements to keep in each row:
-  Index fill_in =   static_cast<Index> (amat.nonZeros()*m_fillfactor)/n+1;
+  Index fill_in = (amat.nonZeros()*m_fillfactor)/n + 1;
   if (fill_in > n) fill_in = n;
 
   // number of largest nonzero elements to keep in the L and the U part of the current row:
@@ -280,9 +291,9 @@
 
     Index sizeu = 1; // number of nonzero elements in the upper part of the current row
     Index sizel = 0; // number of nonzero elements in the lower part of the current row
-    ju(ii)    = ii;
+    ju(ii)    = convert_index<StorageIndex>(ii);
     u(ii)     = 0;
-    jr(ii)    = ii;
+    jr(ii)    = convert_index<StorageIndex>(ii);
     RealScalar rownorm = 0;
 
     typename FactorType::InnerIterator j_it(mat, ii); // Iterate through the current row ii
@@ -292,9 +303,9 @@
       if (k < ii)
       {
         // copy the lower part
-        ju(sizel) = k;
+        ju(sizel) = convert_index<StorageIndex>(k);
         u(sizel) = j_it.value();
-        jr(k) = sizel;
+        jr(k) = convert_index<StorageIndex>(sizel);
         ++sizel;
       }
       else if (k == ii)
@@ -305,9 +316,9 @@
       {
         // copy the upper part
         Index jpos = ii + sizeu;
-        ju(jpos) = k;
+        ju(jpos) = convert_index<StorageIndex>(k);
         u(jpos) = j_it.value();
-        jr(k) = jpos;
+        jr(k) = convert_index<StorageIndex>(jpos);
         ++sizeu;
       }
       rownorm += numext::abs2(j_it.value());
@@ -337,7 +348,8 @@
         // swap the two locations
         Index j = ju(jj);
         swap(ju(jj), ju(k));
-        jr(minrow) = jj;   jr(j) = k;
+        jr(minrow) = convert_index<StorageIndex>(jj);
+        jr(j) = convert_index<StorageIndex>(k);
         swap(u(jj), u(k));
       }
       // Reset this location
@@ -361,8 +373,8 @@
       for (; ki_it; ++ki_it)
       {
         Scalar prod = fact * ki_it.value();
-        Index j       = ki_it.index();
-        Index jpos    = jr(j);
+        Index j     = ki_it.index();
+        Index jpos  = jr(j);
         if (jpos == -1) // fill-in element
         {
           Index newpos;
@@ -378,16 +390,16 @@
             sizel++;
             eigen_internal_assert(sizel<=ii);
           }
-          ju(newpos) = j;
+          ju(newpos) = convert_index<StorageIndex>(j);
           u(newpos) = -prod;
-          jr(j) = newpos;
+          jr(j) = convert_index<StorageIndex>(newpos);
         }
         else
           u(jpos) -= prod;
       }
       // store the pivot element
-      u(len) = fact;
-      ju(len) = minrow;
+      u(len)  = fact;
+      ju(len) = convert_index<StorageIndex>(minrow);
       ++len;
 
       jj++;
@@ -402,7 +414,7 @@
     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
@@ -431,39 +443,20 @@
     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();
 
   m_factorizationIsOk = true;
-  m_isInitialized = m_factorizationIsOk;
   m_info = Success;
 }
 
-namespace internal {
-
-template<typename _MatrixType, typename Rhs>
-struct solve_retval<IncompleteLUT<_MatrixType>, Rhs>
-  : solve_retval_base<IncompleteLUT<_MatrixType>, Rhs>
-{
-  typedef IncompleteLUT<_MatrixType> Dec;
-  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
-
-  template<typename Dest> void evalTo(Dest& dst) const
-  {
-    dec()._solve(rhs(),dst);
-  }
-};
-
-} // end namespace internal
-
 } // end namespace Eigen
 
 #endif // EIGEN_INCOMPLETE_LUT_H