diff --git a/Eigen/src/Sparse/BasicSparseCholesky.h b/Eigen/src/Sparse/BasicSparseCholesky.h
index 59c5b9913..92193a3bc 100644
--- a/Eigen/src/Sparse/BasicSparseCholesky.h
+++ b/Eigen/src/Sparse/BasicSparseCholesky.h
@@ -60,6 +60,7 @@ template<typename MatrixType> class BasicSparseCholesky
     /** \returns true if the matrix is positive definite */
     inline bool isPositiveDefinite(void) const { return m_isPositiveDefinite; }
 
+    // TODO impl the solver
 //     template<typename Derived>
 //     typename Derived::Eval solve(const MatrixBase<Derived> &b) const;
 
@@ -83,7 +84,6 @@ template<typename MatrixType> class BasicSparseCholesky
 
 /** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
   */
-#ifdef IGEN_BASICSPARSECHOLESKY_H
 template<typename MatrixType>
 void BasicSparseCholesky<MatrixType>::compute(const MatrixType& a)
 {
@@ -154,287 +154,4 @@ void BasicSparseCholesky<MatrixType>::compute(const MatrixType& a)
   m_matrix.endFill();
 }
 
-
-#else
-
-template<typename MatrixType>
-void BasicSparseCholesky<MatrixType>::compute(const MatrixType& a)
-{
-  assert(a.rows()==a.cols());
-  const int size = a.rows();
-  m_matrix.resize(size, size);
-  const RealScalar eps = ei_sqrt(precision<Scalar>());
-
-  // allocate a temporary buffer
-  Scalar* buffer = new Scalar[size*2];
-
-
-  m_matrix.startFill(a.nonZeros()*2);
-
-//   RealScalar x;
-//   x = ei_real(a.coeff(0,0));
-//   m_isPositiveDefinite = x > eps && ei_isMuchSmallerThan(ei_imag(a.coeff(0,0)), RealScalar(1));
-//   m_matrix.fill(0,0) = ei_sqrt(x);
-//   m_matrix.col(0).end(size-1) = a.row(0).end(size-1).adjoint() / ei_real(m_matrix.coeff(0,0));
-  for (int j = 0; j < size; ++j)
-  {
-//     std::cout << j << " " << std::flush;
-//     Scalar tmp = ei_real(a.coeff(j,j));
-//     if (j>0)
-//       tmp -= m_matrix.row(j).start(j).norm2();
-//     x = ei_real(tmp);
-//     std::cout << "x = " << x << "\n";
-//     if (x < eps || (!ei_isMuchSmallerThan(ei_imag(tmp), RealScalar(1))))
-//     {
-//       m_isPositiveDefinite = false;
-//       return;
-//     }
-//     m_matrix.fill(j,j) = x = ei_sqrt(x);
-
-    Scalar x = ei_real(a.coeff(j,j));
-//     if (j>0)
-//       x -= m_matrix.row(j).start(j).norm2();
-//     RealScalar rx = ei_sqrt(ei_real(x));
-//     m_matrix.fill(j,j) = rx;
-    int endSize = size-j-1;
-    /*if (endSize>0)*/ {
-      // Note that when all matrix columns have good alignment, then the following
-      // product is guaranteed to be optimal with respect to alignment.
-//       m_matrix.col(j).end(endSize) =
-//         (m_matrix.block(j+1, 0, endSize, j) * m_matrix.row(j).start(j).adjoint()).lazy();
-
-      // FIXME could use a.col instead of a.row
-//       m_matrix.col(j).end(endSize) = (a.row(j).end(endSize).adjoint()
-//         - m_matrix.col(j).end(endSize) ) / x;
-
-      // make sure to call innerSize/outerSize since we fake the storage order.
-
-
-
-
-      // estimate the number of non zero entries
-//       float ratioLhs = float(lhs.nonZeros())/float(lhs.rows()*lhs.cols());
-//       float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
-//       float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
-
-
-//       for (int j1=0; j1<cols; ++j1)
-      {
-        // let's do a more accurate determination of the nnz ratio for the current column j of res
-        //float ratioColRes = std::min(ratioLhs * rhs.innerNonZeros(j), 1.f);
-        // FIXME find a nice way to get the number of nonzeros of a sub matrix (here an inner vector)
-//         float ratioColRes = ratioRes;
-//         if (ratioColRes>0.1)
-        if (true)
-        {
-          // dense path, the scalar * columns products are accumulated into a dense column
-          Scalar* __restrict__ tmp = buffer;
-          // set to zero
-          for (int k=j+1; k<size; ++k)
-            tmp[k] = 0;
-          // init with current matrix a
-          {
-            typename MatrixType::InnerIterator it(a,j);
-            ++it;
-            for (; it; ++it)
-              tmp[it.index()] = it.value();
-          }
-          for (int k=0; k<j+1; ++k)
-          {
-//             Scalar y = m_matrix.coeff(j,k);
-//             if (!ei_isMuchSmallerThan(ei_abs(y),Scalar(1)))
-//             {
-            typename MatrixType::InnerIterator it(m_matrix, k);
-            while (it && it.index()<j)
-              ++it;
-            if (it && it.index()==j)
-            {
-              Scalar y = it.value();
-              x -= ei_abs2(y);
-//               if (!ei_isMuchSmallerThan(ei_abs(y),Scalar(0.1)))
-              {
-                ++it;
-                for (; it; ++it)
-                {
-                  tmp[it.index()] -= it.value() * y;
-                }
-              }
-            }
-          }
-          // copy the temporary to the respective m_matrix.col()
-          RealScalar rx = ei_sqrt(ei_real(x));
-          m_matrix.fill(j,j) = rx;
-          Scalar y = Scalar(1)/rx;
-          for (int k=j+1; k<size; ++k)
-            //if (tmp[k]!=0)
-            if (!ei_isMuchSmallerThan(ei_abs(tmp[k]),Scalar(0.01)))
-              m_matrix.fill(k, j) = tmp[k]*y;
-        }
-        else
-        {
-          ListEl* __restrict__ tmp = reinterpret_cast<ListEl*>(buffer);
-          // sparse path, the scalar * columns products are accumulated into a linked list
-          int tmp_size = 0;
-          int tmp_start = -1;
-
-          {
-            int tmp_el = tmp_start;
-            typename MatrixType::InnerIterator it(a,j);
-            if (it)
-            {
-              ++it;
-              for (; it; ++it)
-              {
-                Scalar v = it.value();
-                int id = it.index();
-                if (tmp_size==0)
-                {
-                  tmp_start = 0;
-                  tmp_el = 0;
-                  tmp_size++;
-                  tmp[0].value = v;
-                  tmp[0].index = id;
-                  tmp[0].next = -1;
-                }
-                else if (id<tmp[tmp_start].index)
-                {
-                  tmp[tmp_size].value = v;
-                  tmp[tmp_size].index = id;
-                  tmp[tmp_size].next = tmp_start;
-                  tmp_start = tmp_size;
-                  tmp_el = tmp_start;
-                  tmp_size++;
-                }
-                else
-                {
-                  int nextel = tmp[tmp_el].next;
-                  while (nextel >= 0 && tmp[nextel].index<=id)
-                  {
-                    tmp_el = nextel;
-                    nextel = tmp[nextel].next;
-                  }
-
-                  if (tmp[tmp_el].index==id)
-                  {
-                    tmp[tmp_el].value = v;
-                  }
-                  else
-                  {
-                    tmp[tmp_size].value = v;
-                    tmp[tmp_size].index = id;
-                    tmp[tmp_size].next = tmp[tmp_el].next;
-                    tmp[tmp_el].next = tmp_size;
-                    tmp_size++;
-                  }
-                }
-              }
-            }
-          }
-//           for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
-          for (int k=0; k<j+1; ++k)
-          {
-//             Scalar y = m_matrix.coeff(j,k);
-//             if (!ei_isMuchSmallerThan(ei_abs(y),Scalar(1)))
-//             {
-            int tmp_el = tmp_start;
-            typename MatrixType::InnerIterator it(m_matrix, k);
-            while (it && it.index()<j)
-              ++it;
-            if (it && it.index()==j)
-            {
-              Scalar y = it.value();
-              x -= ei_abs2(y);
-              for (; it; ++it)
-              {
-                Scalar v = -it.value() * y;
-                int id = it.index();
-                if (tmp_size==0)
-                {
-//                   std::cout << "insert because size==0\n";
-                  tmp_start = 0;
-                  tmp_el = 0;
-                  tmp_size++;
-                  tmp[0].value = v;
-                  tmp[0].index = id;
-                  tmp[0].next = -1;
-                }
-                else if (id<tmp[tmp_start].index)
-                {
-//                   std::cout << "insert because not in (0) " << id << " " << tmp[tmp_start].index << " " << tmp_start << "\n";
-                  tmp[tmp_size].value = v;
-                  tmp[tmp_size].index = id;
-                  tmp[tmp_size].next = tmp_start;
-                  tmp_start = tmp_size;
-                  tmp_el = tmp_start;
-                  tmp_size++;
-                }
-                else
-                {
-                  int nextel = tmp[tmp_el].next;
-                  while (nextel >= 0 && tmp[nextel].index<=id)
-                  {
-                    tmp_el = nextel;
-                    nextel = tmp[nextel].next;
-                  }
-
-                  if (tmp[tmp_el].index==id)
-                  {
-                    tmp[tmp_el].value -= v;
-                  }
-                  else
-                  {
-//                     std::cout << "insert because not in (1)\n";
-                    tmp[tmp_size].value = v;
-                    tmp[tmp_size].index = id;
-                    tmp[tmp_size].next = tmp[tmp_el].next;
-                    tmp[tmp_el].next = tmp_size;
-                    tmp_size++;
-                  }
-                }
-              }
-            }
-          }
-          RealScalar rx = ei_sqrt(ei_real(x));
-          m_matrix.fill(j,j) = rx;
-          Scalar y = Scalar(1)/rx;
-          int k = tmp_start;
-          while (k>=0)
-          {
-            if (!ei_isMuchSmallerThan(ei_abs(tmp[k].value),Scalar(0.01)))
-            {
-//               std::cout << "fill " << tmp[k].index << "," << j << "\n";
-              m_matrix.fill(tmp[k].index, j) = tmp[k].value * y;
-            }
-            k = tmp[k].next;
-          }
-        }
-      }
-
-    }
-  }
-  m_matrix.endFill();
-}
-
-#endif
-
-/** \returns the solution of \f$ A x = b \f$ using the current decomposition of A.
-  * In other words, it returns \f$ A^{-1} b \f$ computing
-  * \f$ {L^{*}}^{-1} L^{-1} b \f$ from right to left.
-  * \param b the column vector \f$ b \f$, which can also be a matrix.
-  *
-  * Example: \include Cholesky_solve.cpp
-  * Output: \verbinclude Cholesky_solve.out
-  *
-  * \sa MatrixBase::cholesky(), CholeskyWithoutSquareRoot::solve()
-  */
-// template<typename MatrixType>
-// template<typename Derived>
-// typename Derived::Eval Cholesky<MatrixType>::solve(const MatrixBase<Derived> &b) const
-// {
-//   const int size = m_matrix.rows();
-//   ei_assert(size==b.rows());
-//
-//   return m_matrix.adjoint().template part<Upper>().solveTriangular(matrixL().solveTriangular(b));
-// }
-
 #endif // EIGEN_BASICSPARSECHOLESKY_H