--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/libinterp/dldfcn/__ichol__.cc	Mon Aug 18 12:32:16 2014 +0100
@@ -0,0 +1,552 @@
+/**
+ * Copyright (C) 2013 Kai T. Ohlhus <k.ohlhus@gmail.com>
+ * Copyright (C) 2014 Eduardo Ramos Fernández <eduradical951@gmail.com>
+ *
+ * This file is part of Octave.
+ *
+ * Octave is free software; you can redistribute it and/or modify it under the
+ * terms of the GNU General Public License as published by the Free Software
+ * Foundation; either version 3 of the License, or (at your option) any later
+ * version.
+ *
+ * Octave is distributed in the hope that it will be useful, but WITHOUT ANY
+ * WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+ * FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
+ * details.
+ *
+ * You should have received a copy of the GNU General Public License along with
+ * Octave; see the file COPYING.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+#ifdef HAVE_CONFIG_H
+#include <config.h>
+#endif
+
+#include "defun-dld.h"
+#include "parse.h"
+
+// Secondary functions for complex and real case used
+// in ichol algorithms.
+Complex ichol_mult_complex (Complex a, Complex b)
+{
+  b.imag (-std::imag (b));
+  return a * b;
+}
+
+bool ichol_checkpivot_complex (Complex pivot)
+{
+  if (pivot.imag () != 0)
+    {
+      error ("ichol: Non-real pivot encountered. \
+              The matrix must be hermitian.");
+      return false;
+    }
+  else if (pivot.real () < 0)
+    {
+      error ("ichol: Non-positive pivot encountered.");
+      return false;
+    }
+  return true;
+
+}
+
+bool ichol_checkpivot_real (double pivot)
+{
+  if (pivot < 0)
+    {
+      error ("ichol: Non-positive pivot encountered.");
+      return false;
+    }
+  return true;
+}
+
+double ichol_mult_real (double a, double b)
+{
+  return a * b;
+}
+
+template <typename octave_matrix_t, typename T, T (*ichol_mult) (T, T), 
+          bool (*ichol_checkpivot) (T)>
+void ichol_0 (octave_matrix_t& sm, const std::string michol = "off") 
+{
+
+  const octave_idx_type n = sm.cols ();
+  octave_idx_type j1, jend, j2, jrow, jjrow, j, jw, i, k, jj, Llist_len, r;
+  T tl;
+  char opt;
+  enum {OFF, ON};
+  if (michol == "on")
+    opt = ON;
+  else
+    opt = OFF;
+
+  // Input matrix pointers
+  octave_idx_type* cidx = sm.cidx ();
+  octave_idx_type* ridx = sm.ridx ();
+  T* data = sm.data ();
+
+  // Working arrays
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, Lfirst, n);
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, Llist, n);
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, iw, n);
+  OCTAVE_LOCAL_BUFFER (T, dropsums, n);
+
+  // Initialize working arrays
+  for (i = 0; i < n; i++)
+    {
+      iw[i] = -1;
+      Llist[i] = -1;
+      Lfirst[i] = -1;
+      dropsums[i] = 0;
+    }
+
+  // Main loop 
+  for (k = 0; k < n; k++)
+    {
+      j1 = cidx[k];
+      j2 = cidx[k+1];
+      for (j = j1; j < j2; j++)
+        iw[ridx[j]] = j;
+
+      jrow = Llist [k];
+      // Iterate over each non-zero element in the actual row.
+      while (jrow != -1) 
+        {
+          jjrow = Lfirst[jrow];
+          jend = cidx[jrow+1];
+          for (jj = jjrow; jj < jend; jj++)
+            {
+              r = ridx[jj];
+              jw = iw[r];
+              tl = ichol_mult (data[jj], data[jjrow]);
+              if (jw != -1)
+                data[jw] -= tl;
+              else
+                // Because the simetry of the matrix we know the drops
+                // in the column r are also in the column k.
+                if (opt == ON)
+                  {
+                    dropsums[r] -= tl;
+                    dropsums[k] -= tl;
+                  }
+            }
+          // Update the linked list and the first entry of the
+          // actual column.
+          if ((jjrow + 1) < jend)
+            {
+              Lfirst[jrow]++;
+              j = jrow;
+              jrow = Llist[jrow];
+              Llist[j] = Llist[ridx[Lfirst[j]]];
+              Llist[ridx[Lfirst[j]]] = j;
+            }
+          else
+            jrow = Llist[jrow];
+        }
+
+      if (opt == ON)
+        data[j1] += dropsums[k];
+
+      if (ridx[j1] != k)
+        {
+          error ("ichol: There is a pivot equal to zero.");
+          break;
+        }
+
+      if (! ichol_checkpivot (data[j1]))
+        break;
+
+      data[cidx[k]] = std::sqrt (data[j1]);
+
+      // Update Llist and Lfirst with the k-column information.
+      // Also scale the column elements by the pivot and reset 
+      // the working array iw.
+      if (k < (n - 1)) 
+        {
+          iw[ridx[j1]] = -1;
+          for(i = j1 + 1; i < j2; i++)
+            {
+              iw[ridx[i]] = -1;
+              data[i] /= data[j1];
+            }
+          Lfirst[k] = j1;
+          if ((Lfirst[k] + 1) < j2)
+            {
+              Lfirst[k]++;
+              jjrow = ridx[Lfirst[k]];
+              Llist[k] = Llist[jjrow];
+              Llist[jjrow] = k;
+            }
+        }
+    }
+}
+
+DEFUN_DLD (__ichol0__, args, nargout, "-*- texinfo -*-\n\
+@deftypefn   {Loadable Function} {@var{L} =} __ichol0__ (@var{A})\n\
+@deftypefnx  {Loadable Function} {@var{L} =} __ichol0__ (@var{A}, @var{michol})\n\
+Undocumented internal function.\n\
+@end deftypefn")
+
+{
+  octave_value_list retval;
+
+  int nargin = args.length ();
+  std::string michol = "off";
+ 
+
+  if (nargout > 1 || nargin < 1 || nargin > 2)
+    {
+      print_usage ();
+      return retval;
+    }
+
+  if (args(0).is_scalar_type () || ! args(0).is_sparse_type ())
+    error ("__ichol0__: 1. parameter must be a sparse square matrix.");
+
+  if (args(0).is_empty ())
+    {
+      retval(0) = octave_value (SparseMatrix ());
+      return retval;
+    }
+
+
+  if (nargin == 2)
+    {
+      michol = args(1).string_value ();
+      if (error_state || ! (michol == "on" || michol == "off"))
+        error ("__ichol0__: 2. parameter must be 'on' or 'off' character string.");
+    }
+
+
+  if (!error_state)
+    {
+      // In ICHOL0 algorithm the zero-pattern of the input matrix is preserved so
+      // it's structure does not change during the algorithm. The same input
+      // matrix is used to build the output matrix due to that fact.
+      octave_value_list param_list;
+      if (!args(0).is_complex_type ())
+        {
+          SparseMatrix sm = args(0).sparse_matrix_value ();
+          param_list.append (sm);
+          sm = feval ("tril", param_list)(0).sparse_matrix_value (); 
+          ichol_0 <SparseMatrix, double, ichol_mult_real, ichol_checkpivot_real> (sm, michol);
+          if (! error_state)
+            retval(0) = octave_value (sm);
+        }
+      else
+        {
+          SparseComplexMatrix sm = args(0).sparse_complex_matrix_value ();
+          param_list.append (sm);
+          sm = feval ("tril", param_list)(0).sparse_complex_matrix_value (); 
+          ichol_0 <SparseComplexMatrix, Complex, ichol_mult_complex, ichol_checkpivot_complex> (sm, michol);
+          if (! error_state)
+            retval(0) = octave_value (sm);
+        }
+
+    }
+
+  return retval;
+}
+
+template <typename octave_matrix_t, typename T,  T (*ichol_mult) (T, T), 
+          bool (*ichol_checkpivot) (T)>
+void ichol_t (const octave_matrix_t& sm, octave_matrix_t& L, const T* cols_norm,
+              const T droptol, const std::string michol = "off")
+              
+{
+
+  const octave_idx_type n = sm.cols ();
+  octave_idx_type j, jrow, jend, jjrow, jw, i, k, jj, Llist_len, total_len, w_len,
+                  max_len, ind;
+
+  char opt;
+  enum {OFF, ON};
+  if (michol == "on")
+    opt = ON;
+  else
+    opt = OFF;
+
+  // Input matrix pointers
+  octave_idx_type* cidx = sm.cidx ();
+  octave_idx_type* ridx = sm.ridx ();
+  T* data = sm.data ();
+
+  // Output matrix data structures. Because it is not known the 
+  // final zero pattern of the output matrix due to fill-in elements,
+  // an heuristic approach has been adopted for memory allocation. The 
+  // size of ridx_out_l and data_out_l is incremented 10% of their actual
+  // size (nnz(A) in the beginning).  If that amount is less than n, their
+  // size is just incremented in n elements. This way the number of
+  // reallocations decrease throughout the process, obtaining a good performance.
+  max_len = sm.nnz ();
+  max_len += (0.1 * max_len) > n ? 0.1 * max_len : n;
+  Array <octave_idx_type> cidx_out_l (dim_vector (n + 1, 1));
+  octave_idx_type* cidx_l = cidx_out_l.fortran_vec ();
+  Array <octave_idx_type> ridx_out_l (dim_vector (max_len ,1));
+  octave_idx_type* ridx_l = ridx_out_l.fortran_vec ();
+  Array <T> data_out_l (dim_vector (max_len, 1));
+  T* data_l = data_out_l.fortran_vec ();
+
+  // Working arrays
+  OCTAVE_LOCAL_BUFFER (T, w_data, n);
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, Lfirst, n);
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, Llist, n);
+  OCTAVE_LOCAL_BUFFER (T, col_drops, n);
+  std::vector <octave_idx_type> vec;
+  vec.resize (n);
+
+
+  T zero = T (0);
+  cidx_l[0] = cidx[0];
+  for (i = 0; i < n; i++)
+    {
+      Llist[i] = -1;
+      Lfirst[i] = -1;
+      w_data[i] = 0;
+      col_drops[i] = zero;
+      vec[i] = 0;
+    }
+
+  total_len = 0;
+  for (k = 0; k < n; k++)
+    {
+      ind = 0;
+      for (j = cidx[k]; j < cidx[k+1]; j++)
+        {
+          w_data[ridx[j]] = data[j];
+          if (ridx[j] != k)
+            {
+              vec[ind] = ridx[j];
+              ind++;
+            }
+        }
+      jrow = Llist[k];
+      while (jrow != -1) 
+        {
+          jjrow = Lfirst[jrow];
+          jend = cidx_l[jrow+1];
+          for (jj = jjrow; jj < jend; jj++)
+            {
+              j = ridx_l[jj];
+              // If the element in the j position of the row is zero,
+              // then it will become non-zero, so we add it to the 
+              // vector that keeps track of non-zero elements in the working row.
+              if (w_data[j] == zero)
+                {
+                  vec[ind] = j; 
+                  ind++;
+                }
+              w_data[j] -=  ichol_mult (data_l[jj], data_l[jjrow]);
+
+            }
+          // Update the actual column first element and update the 
+          // linked list of the jrow row.
+          if ((jjrow + 1) < jend)
+            {
+              Lfirst[jrow]++;
+              j = jrow;
+              jrow = Llist[jrow];
+              Llist[j] = Llist[ridx_l[Lfirst[j]]];
+              Llist[ridx_l[Lfirst[j]]] = j;
+            }
+          else
+            jrow = Llist[jrow];
+        }
+
+      // Resizing output arrays
+      if ((max_len - total_len) < n)
+        {
+          max_len += (0.1 * max_len) > n ? 0.1 * max_len : n;
+          data_out_l.resize (dim_vector (max_len, 1));
+          data_l = data_out_l.fortran_vec ();
+          ridx_out_l.resize (dim_vector (max_len, 1));
+          ridx_l = ridx_out_l.fortran_vec ();
+        }
+      
+      // The sorting of the non-zero elements of the working column can be
+      // handled in a couple of ways. The most efficient two I found, are 
+      // keeping the elements in an ordered binary search tree dinamically 
+      // or keep them unsorted in a vector and at the end of the outer 
+      // iteration order them. The last approach exhibit lower execution 
+      // times.   
+      std::sort (vec.begin (), vec.begin () + ind);
+
+      data_l[total_len] = w_data[k];
+      ridx_l[total_len] = k;
+      w_len = 1;
+
+      // Extract then non-zero elements of working column and drop the
+      // elements that are lower than droptol * cols_norm[k].
+      for (i = 0; i < ind ; i++)
+        {
+          jrow = vec[i];
+          if (w_data[jrow] != zero)
+            {
+              if (std::abs (w_data[jrow]) < (droptol * cols_norm[k]))
+                {
+                  if (opt == ON)
+                    {
+                      col_drops[k] += w_data[jrow];
+                      col_drops[jrow] += w_data[jrow];
+                    }
+                }
+              else
+                {
+                  data_l[total_len + w_len] = w_data[jrow];
+                  ridx_l[total_len + w_len] = jrow;
+                  w_len++;
+                }
+              vec[i] = 0;
+            }
+          w_data[jrow] = zero;
+        }
+
+      // Compensate column sums --> michol option
+      if (opt == ON)
+        data_l[total_len] += col_drops[k];
+
+      if (data_l[total_len] == zero)
+        {
+          error ("ichol: There is a pivot equal to zero.");
+          break;
+        }
+      else if (! ichol_checkpivot (data_l[total_len]))
+        break;
+
+      // Once the elements are dropped and compensation of columns 
+      // sums are done, scale the elements by the pivot.
+      data_l[total_len] = std::sqrt (data_l[total_len]);
+      for (jj = total_len + 1; jj < (total_len + w_len); jj++)
+        data_l[jj] /=  data_l[total_len];
+      total_len += w_len;
+      // Check if there are too many elements to be indexed with octave_idx_type
+      // type due to fill-in during the process.
+      if (total_len < 0)
+        {
+          error ("ichol: Integer overflow. Too many fill-in elements in L");
+          break;
+        }
+      cidx_l[k+1] = cidx_l[k] - cidx_l[0] + w_len;
+
+      // Update Llist and Lfirst with the k-column information.
+      if (k < (n - 1)) 
+        {
+          Lfirst[k] = cidx_l[k];
+          if ((Lfirst[k] + 1) < cidx_l[k+1])
+            {
+              Lfirst[k]++;
+              jjrow = ridx_l[Lfirst[k]];
+              Llist[k] = Llist[jjrow];
+              Llist[jjrow] = k;
+            }
+        }
+        
+      }
+
+  if (! error_state)
+    {
+      // Build the output matrices
+      L = octave_matrix_t (n, n, total_len);
+      for (i = 0; i <= n; i++)
+        L.cidx (i) = cidx_l[i];
+      for (i = 0; i < total_len; i++)
+        {
+          L.ridx (i) = ridx_l[i];
+          L.data (i) = data_l[i];
+        }
+    }
+
+}
+
+DEFUN_DLD (__icholt__, args, nargout, "-*- texinfo -*-\n\
+@deftypefn   {Loadable Function} {@var{L} =} __icholt__ (@var{A})\n\
+@deftypefnx  {Loadable Function} {@var{L} =} __icholt__ (@var{A}, @var{droptol})\n\
+@deftypefnx  {Loadable Function} {@var{L} =} __icholt__ (@var{A}, @var{droptol}, @var{michol})\n\
+Undocumented internal function.\n\
+@end deftypefn")
+{
+  octave_value_list retval;
+  int nargin = args.length ();
+  // Default values of parameters
+  std::string michol = "off";
+  double droptol = 0;
+ 
+
+  if (nargout > 1 || nargin < 1 || nargin > 3)
+    {
+      print_usage ();
+      return retval;
+    }
+
+  if (args(0).is_scalar_type () || ! args(0).is_sparse_type ())
+    error ("__icholt__: 1. parameter must be a sparse square matrix.");
+
+  if (args(0).is_empty ())
+    {
+      retval(0) = octave_value (SparseMatrix ());
+      return retval;
+    }
+
+  if (! error_state && (nargin >= 2))
+    {
+      droptol = args(1).double_value ();
+      if (error_state || (droptol < 0) || ! args(1).is_real_scalar ())
+        error ("__icholt__: 2. parameter must be a positive real scalar.");
+    }
+
+  if (! error_state && (nargin == 3))
+    {
+      michol = args(2).string_value ();
+      if (error_state || !(michol == "on" || michol == "off"))
+        error ("__icholt__: 3. parameter must be 'on' or 'off' character string.");
+    }
+
+  if (!error_state)
+    {
+      octave_value_list param_list;
+      if (! args(0).is_complex_type ())
+        {
+          Array <double> cols_norm;
+          SparseMatrix L;
+          param_list.append (args(0).sparse_matrix_value ());
+          SparseMatrix sm_l = feval ("tril", 
+                                     param_list)(0).sparse_matrix_value (); 
+          param_list(0) = sm_l;
+          param_list(1) = 1;
+          param_list(2) = "cols";
+          cols_norm = feval ("norm", param_list)(0).vector_value ();
+          param_list.clear ();
+          ichol_t <SparseMatrix, 
+                   double, ichol_mult_real, ichol_checkpivot_real> 
+                   (sm_l, L, cols_norm.fortran_vec (), droptol, michol);
+          if (! error_state)
+            retval(0) = octave_value (L);
+        }
+      else
+        {
+          Array <Complex> cols_norm;
+          SparseComplexMatrix L;
+          param_list.append (args(0).sparse_complex_matrix_value ());
+          SparseComplexMatrix sm_l = feval ("tril", 
+                                            param_list)(0).sparse_complex_matrix_value (); 
+          param_list(0) = sm_l;
+          param_list(1) = 1;
+          param_list(2) = "cols";
+          cols_norm = feval ("norm", param_list)(0).complex_vector_value ();
+          param_list.clear ();
+          ichol_t <SparseComplexMatrix, 
+                   Complex, ichol_mult_complex, ichol_checkpivot_complex> 
+                   (sm_l, L, cols_norm.fortran_vec (), Complex (droptol), michol);
+          if (! error_state)
+            retval(0) = octave_value (L);
+        }
+
+    }
+
+  return retval;
+}
+
+/*
+## No test needed for internal helper function.
+%!assert (1)
+*/

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/libinterp/dldfcn/__ilu__.cc	Mon Aug 18 12:32:16 2014 +0100
@@ -0,0 +1,1164 @@
+/**
+ * Copyright (C) 2013 Kai T. Ohlhus <k.ohlhus@gmail.com>
+ * Copyright (C) 2014 Eduardo Ramos Fernández <eduradical951@gmail.com>
+ *
+ * This file is part of Octave.
+ *
+ * Octave is free software; you can redistribute it and/or modify it under the
+ * terms of the GNU General Public License as published by the Free Software
+ * Foundation; either version 3 of the License, or (at your option) any later
+ * version.
+ *
+ * Octave is distributed in the hope that it will be useful, but WITHOUT ANY
+ * WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+ * FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
+ * details.
+ *
+ * You should have received a copy of the GNU General Public License along with
+ * Octave; see the file COPYING.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+#ifdef HAVE_CONFIG_H
+#include <config.h>
+#endif
+
+#include "defun-dld.h"
+#include "parse.h"
+
+// That function implements the IKJ and JKI variants of gaussian elimination to
+// perform the ILUTP decomposition. The behaviour is controlled by milu
+// parameter. If milu = ['off'|'col'] the JKI version is performed taking
+// advantage of CCS format of the input matrix. If milu = 'row' the input matrix
+// has to be transposed to obtain the equivalent CRS structure so we can work
+// efficiently with rows. In this case IKJ version is used.
+template <typename octave_matrix_t, typename T>
+void ilu_0 (octave_matrix_t& sm, const std::string milu = "off") 
+{
+
+  const octave_idx_type n = sm.cols ();
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, iw, n);
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, uptr, n);
+  octave_idx_type j1, j2, jrow, jw, i, k, jj;
+  T tl, r;
+
+  enum {OFF, ROW, COL};
+  char opt;
+  if (milu == "row")
+    {
+      opt = ROW;
+      sm = sm.transpose ();
+    }
+  else if (milu == "col")
+    opt = COL;
+  else
+    opt = OFF;
+
+  octave_idx_type* cidx = sm.cidx ();
+  octave_idx_type* ridx = sm.ridx ();
+  T* data = sm.data ();
+  for (i = 0; i < n; i++)
+    iw[i] = -1;
+  for (k = 0; k < n; k++)
+    {
+      j1 = cidx[k];
+      j2 = cidx[k+1] - 1;
+      octave_idx_type j;
+      for (j = j1; j <= j2; j++)
+        {
+          iw[ridx[j]] = j;
+        }
+      r = 0;
+      j = j1;
+      jrow = ridx[j];
+      while ((jrow < k) && (j <= j2)) 
+        {
+          if (opt == ROW)
+            {
+              tl = data[j] / data[uptr[jrow]];
+              data[j] = tl;
+            }
+          for (jj = uptr[jrow] + 1; jj < cidx[jrow+1]; jj++)
+            {
+              jw = iw[ridx[jj]];
+              if (jw != -1)
+                if (opt == ROW)
+                  data[jw] -= tl * data[jj];
+                else
+                  data[jw] -= data[j] * data[jj];
+
+              else
+                // That is for the milu='row'
+                if (opt == ROW)
+                  r += tl * data[jj];
+                else if (opt == COL)
+                  r += data[j] * data[jj];
+            }
+          j++;
+          jrow = ridx[j];
+        }
+      uptr[k] = j;
+      if(opt != OFF)
+        data[uptr[k]] -= r;
+      if (opt != ROW)
+        for (jj = uptr[k] + 1; jj < cidx[k+1]; jj++)
+          data[jj] /=  data[uptr[k]];
+      if (k != jrow)
+        {
+          error ("ilu: Your input matrix has a zero in the diagonal.");
+          break;
+        }
+
+      if (data[j] == T(0))
+        {
+          error ("ilu: There is a pivot equal to zero.");
+          break;
+        }
+      for(i = j1; i <= j2; i++)
+        iw[ridx[i]] = -1;
+    }
+  if (opt == ROW)
+    sm = sm.transpose ();
+}
+
+DEFUN_DLD (__ilu0__, args, nargout, "-*- texinfo -*-\n\
+@deftypefn   {Loadable Function} {[@var{L}, @var{U}] =} __ilu0__ (@var{A})\n\
+@deftypefnx  {Loadable Function} {[@var{L}, @var{U}] =} __ilu0__ (@var{A}, @var{milu})\n\
+@deftypefnx  {Loadable Function} {[@var{L}, @var{U}, @var{P}] =} __ilu0__ (@var{A}, @dots{})\n\
+Undocumented internal function.\n\
+@end deftypefn")
+{
+  octave_value_list retval;
+
+  int nargin = args.length ();
+  std::string milu;
+ 
+
+  if (nargout > 2 || nargin < 1 || nargin > 2)
+    {
+      print_usage ();
+      return retval;
+    }
+
+  if (args (0).is_empty ())
+    {
+      retval(0) = octave_value (SparseMatrix());
+      retval(1) = octave_value (SparseMatrix());
+      return retval;
+    }
+
+  if (args(0).is_scalar_type () || ! args(0).is_sparse_type ())
+    error ("__ilu0__: 1. parameter must be a sparse square matrix.");
+
+  if (nargin == 2)
+    {
+      milu = args(1).string_value ();
+      if (error_state || !(milu == "row" || milu == "col" || milu == "off"))
+        error (
+          "__ilu0__: 2. parameter must be 'row', 'col' or 'off' character string.");
+    }
+
+
+  if (! error_state)
+    {
+      // In ILU0 algorithm the zero-pattern of the input matrix is preserved so
+      // it's structure does not change during the algorithm. The same input
+      // matrix is used to build the output matrix due to that fact.
+      octave_value_list param_list;
+      if (! args(0).is_complex_type ())
+        {
+          SparseMatrix sm = args(0).sparse_matrix_value ();
+          ilu_0 <SparseMatrix, double> (sm, milu);
+          if (!error_state)
+            {
+              param_list.append (sm);
+              retval(1) = octave_value (
+                feval ("triu", param_list)(0).sparse_matrix_value ()); 
+              SparseMatrix eye = feval ("speye",
+                octave_value_list (
+                  octave_value (sm.cols ())))(0).sparse_matrix_value ();
+              param_list.append (-1);
+              retval(0) = octave_value (
+                eye + feval ("tril", param_list)(0).sparse_matrix_value ()); 
+
+            }
+        }
+      else
+        {
+          SparseComplexMatrix sm = args(0).sparse_complex_matrix_value ();
+          ilu_0 <SparseComplexMatrix, Complex> (sm, milu);
+          if (! error_state)
+            {
+              param_list.append (sm);
+              retval(1) = octave_value (
+                feval ("triu", param_list)(0).sparse_complex_matrix_value ()); 
+              SparseComplexMatrix eye = feval ("speye",
+                octave_value_list (
+                  octave_value (sm.cols ())))(0).sparse_complex_matrix_value ();
+              param_list.append (-1);
+              retval(0) = octave_value (eye +
+                feval ("tril", param_list)(0).sparse_complex_matrix_value ()); 
+           }
+        }
+
+    }
+
+  return retval;
+}
+
+template <typename octave_matrix_t, typename T>
+void ilu_crout (octave_matrix_t& sm_l, octave_matrix_t& sm_u,
+                octave_matrix_t& L, octave_matrix_t& U, T* cols_norm,
+                T* rows_norm, const T droptol = 0,
+                const std::string milu = "off")
+{
+
+  // Map the strings into chars to faster comparation inside loops
+  char opt;
+  enum {OFF, ROW, COL};
+  if (milu == "row")
+    opt = ROW;
+  else if (milu == "col")
+    opt = COL;
+  else
+    opt = OFF;
+
+  octave_idx_type jrow, i, j, k, jj, total_len_l, total_len_u, max_len_u,
+                  max_len_l, w_len_u, w_len_l, cols_list_len, rows_list_len;
+
+  const octave_idx_type n = sm_u.cols ();
+  sm_u = sm_u.transpose ();
+
+  max_len_u = sm_u.nnz ();
+  max_len_u += (0.1 * max_len_u) > n ? 0.1 * max_len_u : n;
+  max_len_l = sm_l.nnz ();
+  max_len_l += (0.1 * max_len_l) > n ? 0.1 * max_len_l : n;
+  // Extract pointers to the arrays for faster access inside loops
+  octave_idx_type* cidx_in_u = sm_u.cidx ();
+  octave_idx_type* ridx_in_u = sm_u.ridx ();
+  T* data_in_u = sm_u.data ();
+  octave_idx_type* cidx_in_l = sm_l.cidx ();
+  octave_idx_type* ridx_in_l = sm_l.ridx ();
+  T* data_in_l = sm_l.data ();
+
+  // L output arrays
+  Array <octave_idx_type> ridx_out_l (dim_vector (max_len_l, 1));
+  octave_idx_type* ridx_l = ridx_out_l.fortran_vec ();
+  Array <T> data_out_l (dim_vector (max_len_l, 1));
+  T* data_l = data_out_l.fortran_vec ();
+
+  // U output arrays
+  Array <octave_idx_type> ridx_out_u (dim_vector (max_len_u, 1));
+  octave_idx_type* ridx_u = ridx_out_u.fortran_vec ();
+  Array <T> data_out_u (dim_vector (max_len_u, 1));
+  T* data_u = data_out_u.fortran_vec ();
+
+  // Working arrays
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, cidx_l, n + 1);
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, cidx_u, n + 1);
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, cols_list, n);
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, rows_list, n);
+  OCTAVE_LOCAL_BUFFER (T, w_data_l, n);
+  OCTAVE_LOCAL_BUFFER (T, w_data_u, n);
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, Ufirst, n);
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, Lfirst, n);
+  OCTAVE_LOCAL_BUFFER (T, cr_sum, n);
+
+  T zero = T (0);
+  
+  cidx_u[0] = cidx_in_u[0];
+  cidx_l[0] = cidx_in_l[0];
+  for (i = 0; i < n; i++)
+    {
+      w_data_u[i] = zero;
+      w_data_l[i] = zero;
+      cr_sum[i] = zero;
+    }
+
+  total_len_u = 0;
+  total_len_l = 0;
+  cols_list_len = 0;
+  rows_list_len = 0;
+
+  for (k = 0; k < n; k++)
+    {
+
+      // Load the working column and working row 
+      for (i = cidx_in_l[k]; i < cidx_in_l[k+1]; i++)
+        w_data_l[ridx_in_l[i]] = data_in_l[i];
+
+      for (i = cidx_in_u[k]; i < cidx_in_u[k+1]; i++)
+        w_data_u[ridx_in_u[i]] = data_in_u[i];
+
+      // Update U working row
+      for (j = 0; j < rows_list_len; j++)
+        {
+          if ((Ufirst[rows_list[j]] != -1))
+            for (jj = Ufirst[rows_list[j]]; jj < cidx_u[rows_list[j]+1]; jj++)
+              {
+                jrow = ridx_u[jj];
+                w_data_u[jrow] -= data_u[jj] * data_l[Lfirst[rows_list[j]]];
+              }
+        }
+      // Update L working column
+      for (j = 0; j < cols_list_len; j++)
+        {
+          if (Lfirst[cols_list[j]] != -1)
+            for (jj = Lfirst[cols_list[j]]; jj < cidx_l[cols_list[j]+1]; jj++)
+              {
+                jrow = ridx_l[jj];
+                w_data_l[jrow] -= data_l[jj] * data_u[Ufirst[cols_list[j]]];
+              }
+        }
+
+      if ((max_len_u - total_len_u) < n)
+        {
+          max_len_u += (0.1 * max_len_u) > n ? 0.1 * max_len_u : n;
+          data_out_u.resize (dim_vector (max_len_u, 1));
+          data_u = data_out_u.fortran_vec ();
+          ridx_out_u.resize (dim_vector (max_len_u, 1));
+          ridx_u = ridx_out_u.fortran_vec ();
+        }
+
+      if ((max_len_l - total_len_l) < n)
+        {
+          max_len_l += (0.1 * max_len_l) > n ? 0.1 * max_len_l : n;
+          data_out_l.resize (dim_vector (max_len_l, 1));
+          data_l = data_out_l.fortran_vec ();
+          ridx_out_l.resize (dim_vector (max_len_l, 1));
+          ridx_l = ridx_out_l.fortran_vec ();
+        }
+
+      // Expand the working row into the U output data structures
+      w_len_l = 0;
+      data_u[total_len_u] = w_data_u[k];
+      ridx_u[total_len_u] = k;
+      w_len_u = 1;
+      for (i = k + 1; i < n; i++)
+        {
+          if (w_data_u[i] != zero)
+            {
+              if (std::abs (w_data_u[i]) < (droptol * rows_norm[k]))
+                {
+                  if (opt == ROW)
+                    cr_sum[k] += w_data_u[i];
+                  else if (opt == COL)
+                    cr_sum[i] += w_data_u[i];
+                }
+              else
+                {
+                  data_u[total_len_u + w_len_u] = w_data_u[i];
+                  ridx_u[total_len_u + w_len_u] = i;
+                  w_len_u++;
+                }
+            }
+
+          // Expand the working column into the L output data structures
+          if (w_data_l[i] != zero)
+            {
+              if (std::abs (w_data_l[i]) < (droptol * cols_norm[k]))
+                {
+                  if (opt == COL)
+                    cr_sum[k] += w_data_l[i];
+                  else if (opt == ROW)
+                    cr_sum[i] += w_data_l[i];
+                }
+              else
+                {
+                  data_l[total_len_l + w_len_l] = w_data_l[i];
+                  ridx_l[total_len_l + w_len_l] = i;
+                  w_len_l++;
+                }
+            }
+          w_data_u[i] = zero;
+          w_data_l[i] = zero;
+        }
+
+      // Compensate row and column sums --> milu option
+      if (opt == COL || opt == ROW)
+        data_u[total_len_u] += cr_sum[k];
+
+      // Check if the pivot is zero
+      if (data_u[total_len_u] == zero)
+        {
+              error ("ilu: There is a pivot equal to zero.");
+              break;
+        }
+      
+      // Scale the elements in L by the pivot
+      for (i = total_len_l ; i < (total_len_l + w_len_l); i++)
+        data_l[i] /= data_u[total_len_u];
+
+
+      total_len_u += w_len_u;
+      total_len_l += w_len_l;
+      // Check if there are too many elements to be indexed with octave_idx_type
+      // type due to fill-in during the process.
+      if (total_len_l < 0 || total_len_u < 0)
+        {
+          error ("ilu: Integer overflow. Too many fill-in elements in L or U");
+          break;
+        }
+      cidx_u[k+1] = cidx_u[k] - cidx_u[0] + w_len_u;
+      cidx_l[k+1] = cidx_l[k] - cidx_l[0] + w_len_l;
+
+      // The tricky part of the algorithm. The arrays pointing to the first
+      // working element of each column in the next iteration (Lfirst) or
+      // the first working element of each row (Ufirst) are updated. Also the
+      // arrays working as lists cols_list and rows_list are filled with indexes
+      // pointing to Ufirst and Lfirst respectively.
+      // TODO: Maybe the -1 indicating in Ufirst and Lfirst, that no elements
+      // have to be considered in a certain column or row in next iteration, can
+      // be removed. It feels safer to me using such an indicator.
+      if (k < (n - 1))
+        {
+          if (w_len_u > 0)
+            Ufirst[k] = cidx_u[k];
+          else
+            Ufirst[k] = -1;
+          if (w_len_l > 0)
+            Lfirst[k] = cidx_l[k];
+          else
+            Lfirst[k] = -1;
+          cols_list_len = 0;
+          rows_list_len = 0;
+          for (i = 0; i <= k; i++)
+            {
+              if (Ufirst[i] != -1)
+                {
+                  jj = ridx_u[Ufirst[i]];
+                  if (jj < (k + 1))
+                    {
+                      if (Ufirst[i] < (cidx_u[i+1]))
+                        {
+                          Ufirst[i]++;
+                          if (Ufirst[i] == cidx_u[i+1])
+                            Ufirst[i] = -1;
+                          else
+                            jj = ridx_u[Ufirst[i]];
+                        }
+                    }
+                  if (jj == (k + 1)) 
+                    {
+                      cols_list[cols_list_len] = i;
+                      cols_list_len++;
+                    }
+                }
+
+              if (Lfirst[i] != -1)
+                {
+                  jj = ridx_l[Lfirst[i]];
+                  if (jj < (k + 1))
+                    if(Lfirst[i] < (cidx_l[i+1]))
+                      {
+                        Lfirst[i]++;
+                        if (Lfirst[i] == cidx_l[i+1])
+                          Lfirst[i] = -1;
+                        else
+                          jj = ridx_l[Lfirst[i]];
+                      }
+                  if (jj == (k + 1)) 
+                    {
+                      rows_list[rows_list_len] = i;
+                      rows_list_len++;
+                    }
+                }
+            }
+        }
+    }
+
+  if (! error_state)
+    {
+      // Build the output matrices
+      L = octave_matrix_t (n, n, total_len_l);
+      U = octave_matrix_t (n, n, total_len_u);
+      for (i = 0; i <= n; i++)
+        L.cidx (i) = cidx_l[i];
+      for (i = 0; i < total_len_l; i++)
+        {
+          L.ridx (i) = ridx_l[i];
+          L.data (i) = data_l[i];
+        }
+      for (i = 0; i <= n; i++)
+        U.cidx (i) = cidx_u[i];
+      for (i = 0; i < total_len_u; i++)
+        {
+          U.ridx (i) = ridx_u[i];
+          U.data (i) = data_u[i];
+        }
+      U = U.transpose ();
+    }
+}
+
+DEFUN_DLD (__iluc__, args, nargout, "-*- texinfo -*-\n\
+@deftypefn  {Loadable Function} {[@var{L}, @var{U}] =} __iluc__ (@var{A})\n\
+@deftypefnx {Loadable Function} {[@var{L}, @var{U}] =} __iluc__ (@var{A}, @var{droptol}) \n\
+@deftypefnx {Loadable Function} {[@var{L}, @var{U}] =} __iluc__ (@var{A}, @var{droptol}, @var{milu})\n\
+@deftypefnx {Loadable Function} {[@var{L}, @var{U}, @var{P}] =} __iluc__ (@var{A}, @dots{})\n\
+Undocumented internal function.\n\
+@end deftypefn")
+{
+
+  octave_value_list retval;
+  int nargin = args.length ();
+  std::string milu = "off";
+  double droptol = 0;
+
+  if (nargout != 2 || nargin < 1 || nargin > 3)
+    {
+      print_usage ();
+      return retval;
+    }
+
+  // To be matlab compatible 
+  if (args(0).is_empty ())
+    {
+      retval(0) = octave_value (SparseMatrix());
+      retval(1) = octave_value (SparseMatrix());
+      return retval;
+    }
+
+  if (args(0).is_scalar_type () || ! args(0).is_sparse_type ())
+    error ("__iluc__: 1. parameter must be a sparse square matrix.");
+
+  if (! error_state && (nargin >= 2))
+    {
+      droptol = args(1).double_value ();
+      if (error_state || (droptol < 0) || ! args(1).is_real_scalar ())
+        error ("__iluc__: 2. parameter must be a positive real scalar.");
+    }
+
+  if (! error_state && (nargin == 3))
+    {
+      milu = args(2).string_value ();
+      if (error_state || !(milu == "row" || milu == "col" || milu == "off"))
+        error ("__iluc__: 3. parameter must be 'row', 'col' or 'off' character string.");
+    }
+
+  if (! error_state)
+    {
+      octave_value_list param_list;
+      if (! args(0).is_complex_type ())
+        {
+          Array<double> cols_norm, rows_norm;
+          param_list.append (args(0).sparse_matrix_value ());
+          SparseMatrix sm_u =  feval ("triu", param_list)(0).sparse_matrix_value (); 
+          param_list.append (-1);
+          SparseMatrix sm_l =  feval ("tril", param_list)(0).sparse_matrix_value (); 
+          param_list(1) = "rows";
+          rows_norm = feval ("norm", param_list)(0).vector_value ();
+          param_list(1) = "cols";
+          cols_norm = feval ("norm", param_list)(0).vector_value ();
+          param_list.clear ();
+          SparseMatrix U;
+          SparseMatrix L;
+          ilu_crout <SparseMatrix, double> (sm_l, sm_u, L, U, cols_norm.fortran_vec (), 
+                                            rows_norm.fortran_vec (), droptol, milu);
+          if (! error_state)
+            {
+              param_list.append (octave_value (L.cols ()));
+              SparseMatrix eye = feval ("speye", param_list)(0).sparse_matrix_value ();
+              retval(0) = octave_value (L + eye);
+              retval(1) = octave_value (U);
+            }
+        }
+      else
+        {
+          Array<Complex> cols_norm, rows_norm;
+          param_list.append (args(0).sparse_complex_matrix_value ());
+          SparseComplexMatrix sm_u =  feval("triu", 
+                                            param_list)(0).sparse_complex_matrix_value (); 
+          param_list.append (-1);
+          SparseComplexMatrix sm_l =  feval("tril", 
+                                            param_list)(0).sparse_complex_matrix_value (); 
+          param_list(1) = "rows";
+          rows_norm = feval ("norm", param_list)(0).complex_vector_value ();
+          param_list(1) = "cols";
+          cols_norm = feval ("norm", param_list)(0).complex_vector_value ();
+          param_list.clear ();
+          SparseComplexMatrix U;
+          SparseComplexMatrix L;
+          ilu_crout < SparseComplexMatrix, Complex > 
+                    (sm_l, sm_u, L, U, cols_norm.fortran_vec () , 
+                     rows_norm.fortran_vec (), Complex (droptol), milu);
+          if (! error_state)
+            {
+              param_list.append (octave_value (L.cols ()));
+              SparseComplexMatrix eye = feval ("speye", 
+                                                param_list)(0).sparse_complex_matrix_value ();
+              retval(0) = octave_value (L + eye);
+              retval(1) = octave_value (U);
+            }
+        }
+    }
+  return retval;
+}
+
+// That function implements the IKJ and JKI variants of gaussian elimination 
+// to perform the ILUTP decomposition. The behaviour is controlled by milu 
+// parameter. If milu = ['off'|'col'] the JKI version is performed taking 
+// advantage of CCS format of the input matrix. Row pivoting is performed. 
+// If milu = 'row' the input matrix has to be transposed to obtain the 
+// equivalent CRS structure so we can work efficiently with rows. In that
+// case IKJ version is used and column pivoting is performed.
+
+template <typename octave_matrix_t, typename T>
+void ilu_tp (octave_matrix_t& sm, octave_matrix_t& L, octave_matrix_t& U, 
+             octave_idx_type nnz_u, octave_idx_type nnz_l, T* cols_norm,  
+             Array <octave_idx_type>& perm_vec, const T droptol = T(0),
+             const T thresh = T(0), const  std::string milu = "off", 
+             const double udiag = 0)
+{
+  char opt;
+  enum {OFF, ROW, COL};
+  if (milu == "row")
+    opt = ROW;
+  else if (milu == "col")
+    opt = COL;
+  else
+    opt = OFF;
+  
+  const octave_idx_type n = sm.cols ();
+
+  // That is necessary for the JKI (milu = "row") variant.
+  if (opt == ROW)
+    sm = sm.transpose();
+
+  // Extract pointers to the arrays for faster access inside loops
+  octave_idx_type* cidx_in = sm.cidx ();
+  octave_idx_type* ridx_in = sm.ridx ();
+  T* data_in = sm.data ();
+  octave_idx_type jrow, i, j, k, jj, c, total_len_l, total_len_u, p_perm, 
+                  max_ind, max_len_l, max_len_u;
+  T tl, aux, maximum;
+
+  max_len_u = nnz_u;
+  max_len_u += (0.1 * max_len_u) > n ? 0.1 * max_len_u : n;
+  max_len_l = nnz_l;
+  max_len_l += (0.1 * max_len_l) > n ? 0.1 * max_len_l : n;
+
+  Array <octave_idx_type> cidx_out_l (dim_vector (n + 1, 1));
+  octave_idx_type* cidx_l = cidx_out_l.fortran_vec ();
+  Array <octave_idx_type> ridx_out_l (dim_vector (max_len_l, 1));
+  octave_idx_type* ridx_l = ridx_out_l.fortran_vec ();
+  Array <T> data_out_l (dim_vector (max_len_l ,1));
+  T* data_l = data_out_l.fortran_vec ();
+  // Data for U
+  Array <octave_idx_type> cidx_out_u (dim_vector (n + 1, 1));
+  octave_idx_type* cidx_u = cidx_out_u.fortran_vec ();
+  Array <octave_idx_type> ridx_out_u (dim_vector (max_len_u, 1));
+  octave_idx_type* ridx_u = ridx_out_u.fortran_vec ();
+  Array <T> data_out_u (dim_vector (max_len_u, 1));
+  T* data_u = data_out_u.fortran_vec();
+
+  // Working arrays and permutation arrays
+  octave_idx_type w_len_u, w_len_l;
+  T total_sum, partial_col_sum, partial_row_sum;
+  std::set <octave_idx_type> iw_l;
+  std::set <octave_idx_type> iw_u;
+  std::set <octave_idx_type>::iterator it, it2;
+  OCTAVE_LOCAL_BUFFER (T, w_data, n);
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, iperm, n);
+  octave_idx_type* perm = perm_vec.fortran_vec ();
+  OCTAVE_LOCAL_BUFFER (octave_idx_type, uptr, n);
+
+
+  T zero = T(0);
+  cidx_l[0] = cidx_in[0];
+  cidx_u[0] = cidx_in[0];
+  for (i = 0; i < n; i++)
+    {
+      w_data[i] = 0;
+      perm[i] = i;
+      iperm[i] = i;
+    }
+  total_len_u = 0;
+  total_len_l = 0;
+
+  for (k = 0; k < n; k++)
+    {
+
+      for (j = cidx_in[k]; j < cidx_in[k+1]; j++)
+        {
+          p_perm = iperm[ridx_in[j]];
+          w_data[iperm[ridx_in[j]]] = data_in[j];
+          if (p_perm > k)
+            iw_l.insert (ridx_in[j]);
+          else
+            iw_u.insert (p_perm);
+        }
+
+      it = iw_u.begin ();
+      jrow = *it;
+      total_sum = zero;
+      while ((jrow < k) && (it != iw_u.end ())) 
+        {
+          if (opt == COL)
+            partial_col_sum = w_data[jrow];
+          if (w_data[jrow] != zero)
+            {
+              if (opt == ROW)
+                {
+                  partial_row_sum = w_data[jrow];
+                  tl = w_data[jrow] / data_u[uptr[jrow]];
+                }
+              for (jj = cidx_l[jrow]; jj < cidx_l[jrow+1]; jj++)
+                {
+                  p_perm = iperm[ridx_l[jj]];
+                  aux = w_data[p_perm];
+                  if (opt == ROW)
+                    {
+                      w_data[p_perm] -= tl * data_l[jj];
+                      partial_row_sum += tl * data_l[jj];
+                    }
+                  else
+                    {
+                      tl = data_l[jj] * w_data[jrow]; 
+                      w_data[p_perm] -= tl;
+                      if (opt == COL)
+                        partial_col_sum += tl;
+                    }
+
+                  if ((aux == zero) && (w_data[p_perm] != zero))
+                    {
+                      if (p_perm > k)
+                        iw_l.insert (ridx_l[jj]);
+                      else
+                        iw_u.insert (p_perm);
+                    }
+                }
+
+                // Drop element from the U part in IKJ and L part in JKI 
+                // variant (milu = [col|off])
+                if ((std::abs (w_data[jrow]) < (droptol * cols_norm[k])) 
+                    && (w_data[jrow] != zero))
+                  {
+                    if (opt == COL)
+                      total_sum += partial_col_sum;
+                    else if (opt == ROW)
+                      total_sum += partial_row_sum;
+                    w_data[jrow] = zero;
+                    it2 = it;
+                    it++;
+                    iw_u.erase (it2);
+                    jrow = *it;
+                    continue;
+                  }
+                else 
+                  // This is the element scaled by the pivot in the actual iteration
+                  if (opt == ROW)
+                    w_data[jrow] = tl;
+            }
+          jrow = *(++it);
+        }
+
+      // Search for the pivot and update iw_l and iw_u if the pivot is not the
+      // diagonal element
+      if ((thresh > zero) && (k < (n - 1)))
+        {
+          maximum = std::abs (w_data[k]) / thresh;
+          max_ind = perm[k];
+          for (it = iw_l.begin (); it != iw_l.end (); ++it) 
+            {
+              p_perm = iperm[*it];
+              if (std::abs (w_data[p_perm]) > maximum)
+                {
+                  maximum = std::abs (w_data[p_perm]);
+                  max_ind = *it;
+                  it2 = it; 
+                }
+            }
+          // If the pivot is not the diagonal element update all.
+          p_perm = iperm[max_ind];
+          if (max_ind != perm[k])
+            {
+              iw_l.erase (it2);
+              if (w_data[k] != zero)
+                iw_l.insert (perm[k]);
+              else
+                  iw_u.insert (k);
+              // Swap data and update permutation vectors
+              aux = w_data[k];
+              iperm[perm[p_perm]] = k;
+              iperm[perm[k]] = p_perm;
+              c = perm[k];
+              perm[k] = perm[p_perm];
+              perm[p_perm] = c;
+              w_data[k] = w_data[p_perm];
+              w_data[p_perm] = aux;
+            }
+          
+      }              
+
+      // Drop elements in the L part in the IKJ and from the U part in the JKI
+      // version.
+      it = iw_l.begin ();
+      while (it != iw_l.end ()) 
+        {
+          p_perm = iperm[*it];
+          if (droptol > zero)
+            if (std::abs (w_data[p_perm]) < (droptol * cols_norm[k]))
+              {
+                if (opt != OFF)
+                  total_sum += w_data[p_perm];
+                w_data[p_perm] = zero;
+                it2 = it;
+                it++;
+                iw_l.erase (it2);
+                continue;
+              }
+
+          it++;
+        }
+
+      // If milu =[row|col] sumation is preserved --> Compensate diagonal element.
+      if (opt != OFF)
+        {
+          if ((total_sum > zero) && (w_data[k] == zero))
+            iw_u.insert (k);
+          w_data[k] += total_sum;
+        }
+          
+
+
+      // Check if the pivot is zero and if udiag is active.
+      // NOTE: If the pivot == 0 and udiag is active, then if milu = [col|row]
+      //       will not preserve the row sum for that column/row.
+      if (w_data[k] == zero)
+        {
+          if (udiag == 1)
+            {
+              w_data[k] = droptol;
+              iw_u.insert (k);
+            }
+          else
+            {
+              error ("ilu: There is a pivot equal to zero.");
+              break;
+            }
+        }
+
+      // Scale the elements on the L part for IKJ version (milu = [col|off])  
+      if (opt != ROW)
+        for (it = iw_l.begin (); it != iw_l.end (); ++it) 
+          {
+              p_perm = iperm[*it];
+              w_data[p_perm] = w_data[p_perm] / w_data[k];
+          }
+      
+
+      if ((max_len_u - total_len_u) < n)
+        {
+          max_len_u += (0.1 * max_len_u) > n ? 0.1 * max_len_u : n;
+          data_out_u.resize (dim_vector (max_len_u, 1));
+          data_u = data_out_u.fortran_vec ();
+          ridx_out_u.resize (dim_vector (max_len_u, 1));
+          ridx_u = ridx_out_u.fortran_vec ();
+        }
+
+      if ((max_len_l - total_len_l) < n)
+        {
+          max_len_l += (0.1 * max_len_l) > n ? 0.1 * max_len_l : n;
+          data_out_l.resize (dim_vector (max_len_l, 1));
+          data_l = data_out_l.fortran_vec ();
+          ridx_out_l.resize (dim_vector (max_len_l, 1));
+          ridx_l = ridx_out_l.fortran_vec ();
+        }
+
+      // Expand working vector into U.
+      w_len_u = 0;
+      for (it = iw_u.begin (); it != iw_u.end (); ++it)
+        {
+          if (w_data[*it] != zero)
+            {
+              data_u[total_len_u + w_len_u] = w_data[*it];
+              ridx_u[total_len_u + w_len_u] = *it;
+              w_len_u++;
+            }
+          w_data[*it] = 0;
+        }
+      // Expand working vector into L.
+      w_len_l = 0;
+      for (it = iw_l.begin (); it != iw_l.end (); ++it)
+        {
+          p_perm = iperm[*it];
+          if (w_data[p_perm] != zero)
+            {
+              data_l[total_len_l + w_len_l] = w_data[p_perm];
+              ridx_l[total_len_l + w_len_l] = *it;
+              w_len_l++;
+            }
+          w_data[p_perm] = 0;
+        }
+      total_len_u += w_len_u;
+      total_len_l += w_len_l;
+      // Check if there are too many elements to be indexed with octave_idx_type
+      // type due to fill-in during the process.
+      if (total_len_l < 0 || total_len_u < 0)
+        {
+          error ("ilu: Integer overflow. Too many fill-in elements in L or U");
+          break;
+        }
+      if (opt == ROW)
+        uptr[k] = total_len_u - 1;
+      cidx_u[k+1] = cidx_u[k] - cidx_u[0] + w_len_u;
+      cidx_l[k+1] = cidx_l[k] - cidx_l[0] + w_len_l;
+
+      iw_l.clear ();
+      iw_u.clear ();
+    }
+
+  if (! error_state)
+    {
+      octave_matrix_t *L_ptr; 
+      octave_matrix_t *U_ptr;
+      octave_matrix_t diag (n, n, n);
+      
+      // L and U are interchanged if milu = 'row'. It is a matter
+      // of nomenclature to re-use code with both IKJ and JKI
+      // versions of the algorithm.
+      if (opt == ROW)
+        {
+          L_ptr = &U;
+          U_ptr = &L;
+          L = octave_matrix_t (n, n, total_len_u - n);
+          U = octave_matrix_t (n, n, total_len_l);
+        }
+      else
+        {
+          L_ptr = &L;
+          U_ptr = &U;
+          L = octave_matrix_t (n, n, total_len_l);
+          U = octave_matrix_t (n, n, total_len_u);
+        }
+
+      for (i = 0; i <= n; i++)
+        {
+          L_ptr->cidx (i) = cidx_l[i];
+          U_ptr->cidx (i) = cidx_u[i];
+          if (opt == ROW)
+            U_ptr->cidx (i) -= i;
+        }
+
+      for (i = 0; i < n; i++) 
+        {
+          if (opt == ROW)
+            diag.elem (i,i) = data_u[uptr[i]];
+          j = cidx_l[i];
+
+          while (j < cidx_l[i+1])
+            {
+              L_ptr->ridx (j) = ridx_l[j];
+              L_ptr->data (j) = data_l[j];
+              j++;
+            }
+          j = cidx_u[i];
+
+          while (j < cidx_u[i+1])
+            {
+              c = j;
+              if (opt == ROW)
+                {
+                  // The diagonal is removed from L if milu = 'row'.
+                  // That is because is convenient to have it inside 
+                  // the L part to carry out the process.
+                  if (ridx_u[j] == i)
+                    {
+                      j++;
+                      continue;
+                    }
+                  else
+                    c -= i;
+                }
+              U_ptr->data (c) = data_u[j];
+              U_ptr->ridx (c) = ridx_u[j];
+              j++;
+            }
+        }
+
+      if (opt == ROW) 
+        {
+          U = U.transpose ();
+          // The diagonal, conveniently permuted is added to U
+          U += diag.index (idx_vector::colon, perm_vec);
+          L = L.transpose ();
+        }
+    }
+}
+
+DEFUN_DLD (__ilutp__, args, nargout, "-*- texinfo -*-\n\
+@deftypefn  {Loadable Function} {[@var{L}, @var{U}] =} __ilutp__ (@var{A})\n\
+@deftypefnx {Loadable Function} {[@var{L}, @var{U}] =} __ilutp__ (@var{A}, @var{droptol})\n\
+@deftypefnx {Loadable Function} {[@var{L}, @var{U}] =} __ilutp__ (@var{A}, @var{droptol}, @var{thresh})\n\
+@deftypefnx {Loadable Function} {[@var{L}, @var{U}] =} __ilutp__ (@var{A}, @var{droptol}, @var{thresh}, @var{milu})\n\
+@deftypefnx {Loadable Function} {[@var{L}, @var{U}] =} __ilutp__ (@var{A}, @var{droptol}, @var{thresh}, @var{milu}, @var{udiag})\n\
+@deftypefnx {Loadable Function} {[@var{L}, @var{U}, @var{P}] =} __ilutp__ (@var{A}, @dots{})\n\
+Undocumented internal function.\n\
+@end deftypefn")
+{
+  octave_value_list retval;
+
+  int nargin = args.length ();
+  std::string milu = "";
+  double droptol, thresh;
+  double udiag;
+
+
+  if (nargout < 2 || nargout > 3 || nargin < 1 || nargin > 5)
+    {
+      print_usage ();
+      return retval;
+    }
+
+  // To be matlab compatible 
+  if (args(0).is_empty ())
+    {
+      retval(0) = octave_value (SparseMatrix ());
+      retval(1) = octave_value (SparseMatrix ());
+      if (nargout == 3)
+        retval(2) = octave_value (SparseMatrix ()); 
+      return retval;
+    }
+
+  if (args(0).is_scalar_type () || ! args(0).is_sparse_type () )
+    error ("__ilutp__: 1. parameter must be a sparse square matrix.");
+
+  if (! error_state && (nargin >= 2))
+    {
+      droptol = args(1).double_value ();
+      if (error_state || (droptol < 0) || ! args(1).is_real_scalar ())
+        error ("__ilutp__: 2. parameter must be a positive scalar.");
+    }
+
+  if (! error_state && (nargin >= 3))
+    {
+      thresh = args(2).double_value ();
+      if (error_state || ! args(2).is_real_scalar () || (thresh < 0) || thresh > 1)
+        error ("__ilutp__: 3. parameter must be a scalar 0 <= thresh <= 1.");
+    }
+
+  if (! error_state && (nargin >= 4))
+    {
+      milu = args(3).string_value ();
+      if (error_state || !(milu == "row" || milu == "col" || milu == "off"))
+        error ("__ilutp__: 4. parameter must be 'row', 'col' or 'off' character string.");
+    }
+
+  if (! error_state && (nargin == 5))
+    {
+      udiag = args(4).double_value ();
+      if (error_state || ! args(4).is_real_scalar () || ((udiag != 0) 
+          && (udiag != 1)))
+        error ("__ilutp__: 5. parameter must be a scalar with value 1 or 0.");
+    }
+
+  if (! error_state)
+    {
+      octave_value_list param_list;
+      octave_idx_type nnz_u, nnz_l;
+      if (! args(0).is_complex_type ())
+        {
+          Array <double> rc_norm;
+          SparseMatrix sm = args(0).sparse_matrix_value ();
+          param_list.append (sm);
+          nnz_u =  (feval ("triu", param_list)(0).sparse_matrix_value ()).nnz (); 
+          param_list.append (-1);
+          nnz_l =  (feval ("tril", param_list)(0).sparse_matrix_value ()).nnz (); 
+          if (milu == "row")
+            param_list (1) = "rows";
+          else
+            param_list (1) = "cols";
+          rc_norm = feval ("norm", param_list)(0).vector_value ();
+          param_list.clear ();
+          Array <octave_idx_type> perm (dim_vector (sm.cols (), 1)); 
+          SparseMatrix U;
+          SparseMatrix L;
+          ilu_tp <SparseMatrix, double> (sm, L, U, nnz_u, nnz_l, rc_norm.fortran_vec (),
+                                         perm, droptol, thresh, milu, udiag);
+          if (! error_state)
+            {
+              param_list.append (octave_value (L.cols ()));
+              SparseMatrix eye = feval ("speye", param_list)(0).sparse_matrix_value ();
+              if (milu == "row")
+                {
+                  retval(0) = octave_value (L + eye);
+                  if (nargout == 2) 
+                    retval(1) = octave_value (U);
+                  else if (nargout == 3)
+                    {
+                     retval(1) = octave_value (U.index (idx_vector::colon, perm));
+                     retval(2) = octave_value (eye.index (idx_vector::colon, perm));
+                    }
+                }
+              else
+                {
+                  retval(1) = octave_value (U);
+                  if (nargout == 2) 
+                    retval(0) = octave_value (L + eye.index (perm, idx_vector::colon));
+                  else if (nargout == 3)
+                    {
+                      retval(0) = octave_value (L.index (perm, idx_vector::colon)  + eye);
+                      retval(2) = octave_value (eye.index (perm, idx_vector::colon));
+                    }
+                }
+            }
+        }
+      else
+        {
+          Array <Complex> rc_norm;
+          SparseComplexMatrix sm = args(0).sparse_complex_matrix_value ();
+          param_list.append (sm);
+          nnz_u =  feval ("triu", param_list)(0).sparse_complex_matrix_value ().nnz (); 
+          param_list.append (-1);
+          nnz_l =  feval ("tril", param_list)(0).sparse_complex_matrix_value ().nnz (); 
+          if (milu == "row")
+            param_list (1) = "rows";
+          else
+            param_list (1) = "cols";
+          rc_norm = feval ("norm", param_list)(0).complex_vector_value ();
+          Array <octave_idx_type> perm (dim_vector (sm.cols (), 1)); 
+          param_list.clear ();
+          SparseComplexMatrix U;
+          SparseComplexMatrix L;
+          ilu_tp < SparseComplexMatrix, Complex> 
+                  (sm, L, U, nnz_u, nnz_l, rc_norm.fortran_vec (), perm, 
+                   Complex (droptol), Complex (thresh), milu, udiag);
+
+          if (! error_state)
+            {
+              param_list.append (octave_value (L.cols ()));
+              SparseComplexMatrix eye = feval ("speye",
+                                               param_list)(0).sparse_complex_matrix_value ();
+              if (milu == "row")
+                {
+                  retval(0) = octave_value (L + eye);
+                  if (nargout == 2) 
+                    retval(1) = octave_value (U);
+                  else if (nargout == 3)
+                    {
+                     retval(1) = octave_value (U.index (idx_vector::colon, perm));
+                     retval(2) = octave_value (eye.index (idx_vector::colon, perm));
+                    }
+                }
+              else
+                {
+                  retval(1) = octave_value (U);
+                  if (nargout == 2) 
+                    retval(0) = octave_value (L + eye.index (perm, idx_vector::colon)) ;
+                  else if (nargout == 3)
+                    {
+                      retval(0) = octave_value (L.index (perm, idx_vector::colon)  + eye);
+                      retval(2) = octave_value (eye.index (perm, idx_vector::colon));
+                    }
+                }
+            }
+        }
+
+    }
+
+  return retval;
+}
+
+/*
+## No test needed for internal helper function.
+%!assert (1)
+*/

--- a/libinterp/dldfcn/ichol0.cc	Tue Aug 12 15:58:18 2014 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,363 +0,0 @@
-/**
- * Copyright (C) 2014 Eduardo Ramos Fernández <eduradical951@gmail.com>
- *
- * This file is part of Octave.
- *
- * Octave is free software; you can redistribute it and/or modify it under the
- * terms of the GNU General Public License as published by the Free Software
- * Foundation; either version 3 of the License, or (at your option) any later
- * version.
- *
- * Octave is distributed in the hope that it will be useful, but WITHOUT ANY
- * WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
- * FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
- * details.
- *
- * You should have received a copy of the GNU General Public License along with
- * Octave; see the file COPYING.  If not, see <http://www.gnu.org/licenses/>.
- */
-
-#ifdef HAVE_CONFIG_H
-#include <config.h>
-#endif
-
-#include "defun-dld.h"
-#include "parse.h"
-
-// Secondary functions specialiced for complex or real case used
-// in icholt algorithms.
-template < typename T > inline T
-ichol_mult_complex (T a, T b)
-{
-  b.imag (-std::imag (b));
-  return a * b;
-}
-
-template < typename T > inline bool
-ichol_checkpivot_complex (T pivot)
-{
-  if (pivot.imag () != 0)
-    {
-      error ("ichol0: Non-real pivot encountered. \
-              The matrix must be hermitian.");
-      return false;
-    }
-  else if (pivot.real () < 0)
-    {
-      error ("ichol0: Non-positive pivot encountered.");
-      return false;
-    }
-  return true;
-
-}
-
-template < typename T > inline bool
-ichol_checkpivot_real (T pivot)
-{
-  if (pivot < T(0))
-    {
-      error ("ichol0: Non-positive pivot encountered.");
-      return false;
-    }
-  return true;
-}
-
-template < typename T> inline T 
-ichol_mult_real (T a, T b)
-{
-  return a * b;
-}
-
-
-template <typename octave_matrix_t, typename T, T (*ichol_mult) (T, T), 
-          bool (*ichol_checkpivot) (T)>
-void ichol_0 (octave_matrix_t& sm, const std::string michol = "off") 
-{
-
-  const octave_idx_type n = sm.cols ();
-  octave_idx_type j1, jend, j2, jrow, jjrow, j, jw, i, k, jj, Llist_len, r;
-
-  T tl;
-  char opt;
-  enum {OFF, ON};
-  if (michol == "on")
-    opt = ON;
-  else
-    opt = OFF;
-
-  // Input matrix pointers
-  octave_idx_type* cidx = sm.cidx ();
-  octave_idx_type* ridx = sm.ridx ();
-  T* data = sm.data ();
-
-  // Working arrays
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, Lfirst, n);
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, Llist, n);
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, iw, n);
-  OCTAVE_LOCAL_BUFFER (T, dropsums, n);
-
-  // Initialise working arrays
-  for (i = 0; i < n; i++)
-    {
-      iw[i] = -1;
-      Llist[i] = -1;
-      Lfirst[i] = -1;
-      dropsums[i] = 0;
-    }
-
-  // Main loop 
-  for (k = 0; k < n; k++)
-    {
-      j1 = cidx[k];
-      j2 = cidx[k+1];
-      for (j = j1; j < j2; j++)
-        iw[ridx[j]] = j;
-
-      jrow = Llist [k];
-      // Iterate over each non-zero element in the actual row.
-      while (jrow != -1) 
-        {
-          jjrow = Lfirst[jrow];
-          jend = cidx[jrow+1];
-          for (jj = jjrow; jj < jend; jj++)
-            {
-              r = ridx[jj];
-              jw = iw[r];
-              tl = ichol_mult (data[jj], data[jjrow]);
-              if (jw != -1)
-                data[jw] -= tl;
-              else
-                // Because of simetry of the matrix we know the drops
-                // in the column r are also in the column k.
-                if (opt == ON)
-                  {
-                    dropsums[r] -= tl;
-                    dropsums[k] -= tl;
-                  }
-            }
-          // Update the linked list and the first entry of the
-          // actual column.
-          if ((jjrow + 1) < jend)
-            {
-              Lfirst[jrow]++;
-              j = jrow;
-              jrow = Llist[jrow];
-              Llist[j] = Llist[ridx[Lfirst[j]]];
-              Llist[ridx[Lfirst[j]]] = j;
-            }
-          else
-            jrow = Llist[jrow];
-        }
-
-      if (opt == ON)
-        data[j1] += dropsums[k];
-
-      if (ridx[j1] != k)
-        {
-          error ("ichol0: There is a pivot equal to zero.");
-          break;
-        }
-
-      if (!ichol_checkpivot (data[j1]))
-        break;
-
-      data[cidx[k]] = std::sqrt (data[j1]);
-
-      // Update Llist and Lfirst with the k-column information.
-      // Also scale the column elements by the pivot and reset 
-      // the working array iw.
-      if (k < (n - 1)) 
-        {
-          iw[ridx[j1]] = -1;
-          for(i = j1 + 1; i < j2; i++)
-            {
-              iw[ridx[i]] = -1;
-              data[i] /=  data[j1];
-            }
-          Lfirst[k] = j1;
-          if ((Lfirst[k] + 1) < j2)
-            {
-              Lfirst[k]++;
-              jjrow = ridx[Lfirst[k]];
-              Llist[k] = Llist[jjrow];
-              Llist[jjrow] = k;
-            }
-        }
-    }
-}
-
-DEFUN_DLD (ichol0, args, nargout, "-*- texinfo -*-\n\
-@deftypefn  {Loadable Function} {@var{L} =} ichol0 (@var{A}, @var{michol})\n\
-\n\
-Computes the no fill Incomplete Cholesky factorization [IC(0)] of A \
-which must be an square hermitian matrix in the complex case and a symmetric \
-positive definite matrix in the real one. \
-\n\
-\n\
-@code{@var{L} = ichol0 (@var{A}, @var{michol})} \
-computes the IC(0) of @var{A}, such that @code{@var{L} * @var{L}'} which \
-is an approximation of the square sparse hermitian matrix @var{A}. \
-The parameter @var{michol} decides whether the Modified IC(0) should \
-be performed. This compensates the main diagonal of \
-@var{L}, such that @code{@var{A} * @var{e} = @var{L} * @var{L}' * @var{e}} \
-with @code{@var{e} = ones (size (@var{A}, 2), 1))} holds. \n\
-\n\
-For more information about the algorithms themselves see:\n\
-\n\
-[1] Saad, Yousef. \"Preconditioning Techniques.\" Iterative Methods for Sparse Linear \
-Systems. PWS Publishing Company, 1996. \
-\n\
-@seealso{ichol, icholt, chol, ilu}\n\
-@end deftypefn")
-
-{
-  octave_value_list retval;
-
-  int nargin = args.length ();
-  std::string michol = "off";
- 
-
-  if (nargout > 1 || nargin < 1 || nargin > 2)
-    {
-      print_usage ();
-      return retval;
-    }
-
-  if (args (0).is_scalar_type () || !args (0).is_sparse_type ())
-    error ("ichol0: 1. parameter must be a sparse square matrix.");
-
-  if (args (0).is_empty ())
-    {
-      retval (0) = octave_value (SparseMatrix ());
-      return retval;
-    }
-
-
-  if (nargin == 2)
-    {
-      michol = args (1).string_value ();
-      if (error_state || ! (michol == "on" || michol == "off"))
-        error ("ichol0: 2. parameter must be 'on' or 'off' character string.");
-    }
-
-
-  if (!error_state)
-    {
-      // In ICHOL0 algorithm the zero-pattern of the input matrix is preserved so
-      // it's structure does not change during the algorithm. The same input
-      // matrix is used to build the output matrix due to that fact.
-      octave_value_list param_list;
-      if (!args (0).is_complex_type ())
-        {
-          SparseMatrix sm = args (0).sparse_matrix_value ();
-          param_list.append (sm);
-          sm = feval ("tril", param_list)(0).sparse_matrix_value (); 
-          ichol_0 <SparseMatrix, double, ichol_mult_real, ichol_checkpivot_real> (sm, michol);
-          if (! error_state)
-            retval (0) = octave_value (sm);
-        }
-      else
-        {
-          SparseComplexMatrix sm = args (0).sparse_complex_matrix_value ();
-          param_list.append (sm);
-          sm = feval ("tril", param_list) (0).sparse_complex_matrix_value (); 
-          ichol_0 <SparseComplexMatrix, Complex, ichol_mult_complex, ichol_checkpivot_complex> (sm, michol);
-          if (! error_state)
-            retval (0) = octave_value (sm);
-        }
-
-    }
-
-  return retval;
-}
-
-/*
-%% Real matrices
-%!shared A_1, A_2, A_3, A_4, A_5
-%! A_1 = [ 0.37, -0.05,  -0.05,  -0.07;
-%!        -0.05,  0.116,  0.0,   -0.05;
-%!        -0.05,  0.0,    0.116, -0.05;
-%!        -0.07, -0.05,  -0.05,   0.202];
-%! A_1 = sparse(A_1);
-%!
-%! A_2 = gallery ('poisson', 30);
-%!
-%! A_3 = gallery ('tridiag', 50);
-%!
-%! nx = 400; ny = 200;
-%! hx = 1 / (nx + 1); hy = 1 / (ny + 1);
-%! Dxx = spdiags ([ones(nx, 1), -2 * ones(nx, 1), ones(nx, 1)], [-1 0 1 ], nx, nx) / (hx ^ 2);
-%! Dyy = spdiags ([ones(ny, 1), -2 * ones(ny, 1), ones(ny, 1)], [-1 0 1 ], ny, ny) / (hy ^ 2);
-%! A_4 = -kron (Dxx, speye (ny)) - kron (speye (nx), Dyy);
-%! A_4 = sparse (A_4);
-%!
-%! A_5 = [ 0.37, -0.05,          -0.05,  -0.07;
-%!        -0.05,  0.116,          0.0,   -0.05 + 0.05i;
-%!        -0.05,  0.0,            0.116, -0.05;
-%!        -0.07, -0.05 - 0.05i,  -0.05,   0.202];
-%! A_5 = sparse(A_5);
-%! A_6 = [ 0.37,    -0.05 - i, -0.05,  -0.07;
-%!        -0.05 + i, 0.116,     0.0,   -0.05;
-%!        -0.05,     0.0,       0.116, -0.05;
-%!        -0.07,    -0.05,     -0.05,   0.202];
-%! A_6 = sparse(A_6);
-%! A_7 = A_5;
-%! A_7(1) = 2i;
-%!
-%% Test input
-%!test
-%!error ichol0 ([]);
-%!error ichol0 ([],[]);
-%!error [~,~] = ichol0 ([],[],[]);
-%!error [L] = ichol0 ([], 'foo');
-%!error [L] = ichol0 (A_1, [], 'off');
-%!error [L, E] = ichol0 (A_1, 'off');
-%!error ichol0 (sparse (0), 'off');
-%!error ichol0 ([], 'foo');
-%!
-%!test
-%! L = ichol0 (sparse (1), 'off');
-%! assert (L, sparse (1));
-%! L = ichol0 (sparse (2), 'off');
-%! assert (L, sparse (sqrt (2)));
-%! L = ichol0 (sparse ([]), 'off');
-%! assert (L, sparse ([]));
-%!
-%!test
-%! L = ichol0 (A_1, 'off');
-%! assert (norm (A_1 - L*L', 'fro') / norm (A_1, 'fro'), 1e-2, 1e-2);
-%! L = ichol0 (A_1, 'on');
-%! assert (norm (A_1 - L*L', 'fro') / norm (A_1, 'fro'), 2e-2, 1e-2);
-%!
-%!test
-%! L = ichol0 (A_2, 'off');
-%! assert (norm (A_2 - L*L', 'fro') / norm (A_2, 'fro'), 1e-1, 1e-1)
-%! L = ichol0 (A_2, 'on');
-%! assert (norm (A_2 - L*L', 'fro') / norm (A_2, 'fro'), 2e-1, 1e-1)
-%!
-%!test
-%! L = ichol0 (A_3, 'off');
-%! assert (norm (A_3 - L*L', 'fro') / norm (A_3, 'fro'), eps, eps);
-%! L = ichol0 (A_3, 'on');
-%! assert (norm (A_3 - L*L', 'fro') / norm (A_3, 'fro'), eps, eps);
-%!
-%!test
-%! L = ichol0 (A_4, 'off');
-%! assert (norm (A_4 - L*L', 'fro') / norm (A_4, 'fro'), 1e-1, 1e-1);
-%! L = ichol0 (A_4, 'on');
-%! assert (norm (A_4 - L*L', 'fro') / norm (A_4, 'fro'), 1e-1, 1e-1);
-%!
-%% Complex matrices
-%!test
-%! L = ichol0 (A_5, 'off');
-%! assert (norm (A_5 - L*L', 'fro') / norm (A_5, 'fro'), 1e-2, 1e-2);
-%! L = ichol0 (A_5, 'on');
-%! assert (norm (A_5 - L*L', 'fro') / norm (A_5, 'fro'), 2e-2, 1e-2);
-%% Negative pivot 
-%!error ichol0 (A_6, 'off');
-%% Complex entry in the diagonal
-%!error ichol0 (A_7, 'off');
-
-*/
-
-

--- a/libinterp/dldfcn/icholt.cc	Tue Aug 12 15:58:18 2014 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,480 +0,0 @@
-/**
- * Copyright (C) 2014 Eduardo Ramos Fernández <eduradical951@gmail.com>
- *
- * This file is part of Octave.
- *
- * Octave is free software; you can redistribute it and/or modify it under the
- * terms of the GNU General Public License as published by the Free Software
- * Foundation; either version 3 of the License, or (at your option) any later
- * version.
- *
- * Octave is distributed in the hope that it will be useful, but WITHOUT ANY
- * WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
- * FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
- * details.
- *
- * You should have received a copy of the GNU General Public License along with
- * Octave; see the file COPYING.  If not, see <http://www.gnu.org/licenses/>.
- */
-
-#ifdef HAVE_CONFIG_H
-#include <config.h>
-#endif
-
-#include "defun-dld.h"
-#include "parse.h"
-
-// Secondary functions specialiced for complex or real case used
-// in icholt algorithms.
-template < typename T > inline T
-ichol_mult_complex (T a, T b)
-{
-  b.imag (-std::imag (b));
-  return a * b;
-}
-
-template < typename T > inline bool
-ichol_checkpivot_complex (T pivot)
-{
-  if (pivot.imag () != 0)
-    {
-      error ("icholt: Non-real pivot encountered. \
-              The matrix must be hermitian");
-      return false;
-    }
-  else if (pivot.real () < 0)
-    {
-      error ("icholt: Non-positive pivot encountered.");
-      return false;
-    }
-  return true;
-
-}
-
-template < typename T > inline bool
-ichol_checkpivot_real (T pivot)
-{
-  if (pivot < T (0))
-    {
-      error ("icholt: Non-positive pivot encountered.");
-      return false;
-    }
-  return true;
-}
-
-template < typename T> inline T 
-ichol_mult_real (T a, T b)
-{
-  return a * b;
-}
-
-
-template <typename octave_matrix_t, typename T,  T (*ichol_mult) (T, T), 
-          bool (*ichol_checkpivot) (T)>
-void ichol_t (const octave_matrix_t& sm, octave_matrix_t& L, const T* cols_norm,
-              const T droptol, const std::string michol = "off")
-              
-{
-
-  const octave_idx_type n = sm.cols ();
-  octave_idx_type j, jrow, jend, jjrow, jw, i, k, jj, Llist_len, total_len, w_len,
-                  max_len, ind;
-
-  char opt;
-  enum {OFF, ON};
-  if (michol == "on")
-    opt = ON;
-  else
-    opt = OFF;
-
-  // Input matrix pointers
-  octave_idx_type* cidx = sm.cidx ();
-  octave_idx_type* ridx = sm.ridx ();
-  T* data = sm.data ();
-
-  // Output matrix data structures. Because it is not known the 
-  // final zero pattern of the output matrix due to fill-in elements,
-  // an heuristic approach has been adopted for memory allocation. The 
-  // size of ridx_out_l and data_out_l is incremented 10% of their actual
-  // size (nnz(A) in the beginning).  If that amount is less than n, their
-  // size is just incremented in n elements. This way the number of
-  // reallocations decrease throughout the process, obtaining a good performance.
-  max_len = sm.nnz ();
-  max_len += (0.1 * max_len) > n ? 0.1 * max_len : n;
-  Array <octave_idx_type> cidx_out_l (dim_vector (n + 1,1));
-  octave_idx_type* cidx_l = cidx_out_l.fortran_vec ();
-  Array <octave_idx_type> ridx_out_l (dim_vector (max_len ,1));
-  octave_idx_type* ridx_l = ridx_out_l.fortran_vec ();
-  Array <T> data_out_l (dim_vector (max_len, 1));
-  T* data_l = data_out_l.fortran_vec ();
-
-  // Working arrays
-  OCTAVE_LOCAL_BUFFER (T, w_data, n);
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, Lfirst, n);
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, Llist, n);
-  OCTAVE_LOCAL_BUFFER (T, col_drops, n);
-  std::vector <octave_idx_type> vec;
-  vec.resize (n);
-
-
-  T zero = T (0);
-  cidx_l[0] = cidx[0];
-  for (i = 0; i < n; i++)
-    {
-      Llist[i] = -1;
-      Lfirst[i] = -1;
-      w_data[i] = 0;
-      col_drops[i] = zero;
-      vec[i] = 0;
-    }
-
-  total_len = 0;
-  for (k = 0; k < n; k++)
-    {
-      ind = 0;
-      for (j = cidx[k]; j < cidx[k+1]; j++)
-        {
-          w_data[ridx[j]] = data[j];
-          if (ridx[j] != k)
-            {
-              vec[ind] = ridx[j];
-              ind++;
-            }
-        }
-      jrow = Llist[k];
-      while (jrow != -1) 
-        {
-          jjrow = Lfirst[jrow];
-          jend = cidx_l[jrow+1];
-          for (jj = jjrow; jj < jend; jj++)
-            {
-              j = ridx_l[jj];
-              // If the element in the j position of the row is zero,
-              // then it will become non-zero, so we add it to the 
-              // vector that keeps track of non-zero elements in the working row.
-              if (w_data[j] == zero)
-                {
-                  vec[ind] = j; 
-                  ind++;
-                }
-              w_data[j] -=  ichol_mult (data_l[jj], data_l[jjrow]);
-
-            }
-          // Update the actual column first element and update the 
-          // linked list of the jrow row.
-          if ((jjrow + 1) < jend)
-            {
-              Lfirst[jrow]++;
-              j = jrow;
-              jrow = Llist[jrow];
-              Llist[j] = Llist[ridx_l[Lfirst[j]]];
-              Llist[ridx_l[Lfirst[j]]] = j;
-            }
-          else
-            jrow = Llist[jrow];
-        }
-
-      // Resizing output arrays
-      if ((max_len - total_len) < n)
-        {
-          max_len += (0.1 * max_len) > n ? 0.1 * max_len : n;
-          data_out_l.resize (dim_vector (max_len, 1));
-          data_l = data_out_l.fortran_vec ();
-          ridx_out_l.resize (dim_vector (max_len, 1));
-          ridx_l = ridx_out_l.fortran_vec ();
-        }
-      
-      // The sorting of the non-zero elements of the working column can be
-      // handled in a couple of ways. The most efficient two I found, are 
-      // keeping the elements in an ordered binary search tree dinamically 
-      // or keep them unsorted in a vector and at the end of the outer 
-      // iteration order them. The last approach exhibit lower execution 
-      // times.   
-      std::sort (vec.begin (), vec.begin () + ind);
-
-      data_l[total_len] = w_data[k];
-      ridx_l[total_len] = k;
-      w_len = 1;
-
-      // Extract then non-zero elements of working column and drop the
-      // elements that are lower than droptol * cols_norm[k].
-      for (i = 0; i < ind ; i++)
-        {
-          jrow = vec[i];
-          if (w_data[jrow] != zero)
-            {
-              if (std::abs (w_data[jrow]) < (droptol * cols_norm[k]))
-                {
-                  if (opt == ON)
-                    {
-                      col_drops[k] += w_data[jrow];
-                      col_drops[jrow] += w_data[jrow];
-                    }
-                }
-              else
-                {
-                  data_l[total_len + w_len] = w_data[jrow];
-                  ridx_l[total_len + w_len] = jrow;
-                  w_len++;
-                }
-              vec[i] = 0;
-            }
-          w_data[jrow] = zero;
-        }
-
-      // Compensate column sums --> michol option
-      if (opt == ON)
-        data_l[total_len] += col_drops[k];
-
-      if (data_l[total_len] == zero)
-        {
-          error ("icholt: There is a pivot equal to zero.");
-          break;
-        }
-      else if (!ichol_checkpivot (data_l[total_len]))
-        break;
-
-      // Once the elements are dropped and compensation of columns 
-      // sums are done, scale the elements by the pivot.
-      data_l[total_len] = std::sqrt (data_l[total_len]);
-      for (jj = total_len + 1; jj < (total_len + w_len); jj++)
-        data_l[jj] /=  data_l[total_len];
-      total_len += w_len;
-      cidx_l[k+1] = cidx_l[k] - cidx_l[0] + w_len;
-
-      // Update Llist and Lfirst with the k-column information.
-      if (k < (n - 1)) 
-        {
-          Lfirst[k] = cidx_l[k];
-          if ((Lfirst[k] + 1) < cidx_l[k+1])
-            {
-              Lfirst[k]++;
-              jjrow = ridx_l[Lfirst[k]];
-              Llist[k] = Llist[jjrow];
-              Llist[jjrow] = k;
-            }
-        }
-        
-      }
-
-  if (! error_state)
-    {
-      // Build the output matrices
-      L = octave_matrix_t (n, n, total_len);
-      for (i = 0; i <= n; i++)
-        L.cidx (i) = cidx_l[i];
-      for (i = 0; i < total_len; i++)
-        {
-          L.ridx (i) = ridx_l[i];
-          L.data (i) = data_l[i];
-        }
-    }
-
-}
-
-DEFUN_DLD (icholt, args, nargout, "-*- texinfo -*-\n\
-@deftypefn  {Loadable Function} {@var{L} =} icholt (@var{A}, @var{droptol}, @var{michol})\n\
-\n\
-Computes the thresholded Incomplete Cholesky factorization [ICT] of A \
-which must be an square hermitian matrix in the complex case and a symmetric \
-positive definite matrix in the real one. \
-\n\
-@code{[@var{L}] = icholt (@var{A}, @var{droptol}, @var{michol})} \
-computes the ICT of @var{A}, such that @code{@var{L} * @var{L}'} is an \
-approximation of the square sparse hermitian matrix @var{A}. @var{droptol} is \
-a non-negative scalar used as a drop tolerance when performing ICT. Elements \
-which are smaller in magnitude than @code{@var{droptol} * norm(@var{A}(j:end, j), 1)} \
-, are dropped from the resulting factor @var{L}. The parameter @var{michol} \
-decides whether the Modified IC(0) should be performed. This compensates the \
-main diagonal of @var{L}, such that @code{@var{A} * @var{e} = @var{L} * @var{L}' \
- * @var{e}} with @code{@var{e} = ones (size (@var{A}, 2), 1))} holds. \n\
-\n\
-For more information about the algorithms themselves see:\n\
-\n\
-[1] Saad, Yousef. \"Preconditioning Techniques.\" Iterative Methods for Sparse Linear \
-Systems. PWS Publishing Company, 1996. \
-\n\
-\n\
-[2] Jones, Mark T. and Plassmann, Paul E.: An Improved Incomplete Cholesky \
-Factorization (1992). \
-\n\
-@seealso{ichol, ichol0, chol, ilu}\n\
-@end deftypefn")
-{
-  octave_value_list retval;
-
-  int nargin = args.length ();
-  // Default values of parameters
-  std::string michol = "off";
-  double droptol = 0;
- 
-
-  if (nargout > 1 || nargin < 1 || nargin > 3)
-    {
-      print_usage ();
-      return retval;
-    }
-
-  if (args (0).is_scalar_type () || !args (0).is_sparse_type ())
-    error ("icholt: 1. parameter must be a sparse square matrix.");
-
-  if (args (0).is_empty ())
-    {
-      retval (0) = octave_value (SparseMatrix ());
-      return retval;
-    }
-
-  if (! error_state && (nargin >= 2))
-    {
-      droptol = args (1).double_value ();
-      if (error_state || (droptol < 0) || ! args (1).is_real_scalar ())
-        error ("icholt: 2. parameter must be a positive real scalar.");
-    }
-
-  if (! error_state && (nargin == 3))
-    {
-      michol = args (2).string_value ();
-      if (error_state || !(michol == "on" || michol == "off"))
-        error ("icholt: 3. parameter must be 'on' or 'off' character string.");
-    }
-
-  if (!error_state)
-    {
-      octave_value_list param_list;
-      if (! args (0).is_complex_type ())
-        {
-          Array <double> cols_norm;
-          SparseMatrix L;
-          param_list.append (args (0).sparse_matrix_value ());
-          SparseMatrix sm_l = feval ("tril", 
-                                     param_list) (0).sparse_matrix_value (); 
-          param_list (0) = sm_l;
-          param_list (1) = 1;
-          param_list (2) = "cols";
-          cols_norm = feval ("norm", param_list) (0).vector_value ();
-          param_list.clear ();
-          ichol_t <SparseMatrix, 
-                   double, ichol_mult_real, ichol_checkpivot_real> 
-                   (sm_l, L, cols_norm.fortran_vec (), droptol, michol);
-          if (! error_state)
-            retval (0) = octave_value (L);
-        }
-      else
-        {
-          Array <Complex> cols_norm;
-          SparseComplexMatrix L;
-          param_list.append (args (0).sparse_complex_matrix_value ());
-          SparseComplexMatrix sm_l = feval ("tril", 
-                                            param_list) (0).sparse_complex_matrix_value (); 
-          param_list (0) = sm_l;
-          param_list (1) = 1;
-          param_list (2) = "cols";
-          cols_norm = feval ("norm", param_list) (0).complex_vector_value ();
-          param_list.clear ();
-          ichol_t <SparseComplexMatrix, 
-                   Complex, ichol_mult_complex, ichol_checkpivot_complex> 
-                   (sm_l, L, cols_norm.fortran_vec (), Complex (droptol), michol);
-          if (! error_state)
-            retval (0) = octave_value (L);
-        }
-
-    }
-
-  return retval;
-}
-
-/*
-%% Real matrices
-%!shared A_1, A_2, A_3, A_4, A_5
-%! A_1 = [ 0.37, -0.05,  -0.05,  -0.07;
-%!        -0.05,  0.116,  0.0,   -0.05;
-%!        -0.05,  0.0,    0.116, -0.05;
-%!        -0.07, -0.05,  -0.05,   0.202];
-%! A_1 = sparse(A_1);
-%!
-%! A_2 = gallery ('poisson', 30);
-%!
-%! A_3 = gallery ('tridiag', 50);
-%!
-%! nx = 400; ny = 200;
-%! hx = 1 / (nx + 1); hy = 1 / (ny + 1);
-%! Dxx = spdiags ([ones(nx, 1), -2 * ones(nx, 1), ones(nx, 1)], [-1 0 1 ], nx, nx) / (hx ^ 2);
-%! Dyy = spdiags ([ones(ny, 1), -2 * ones(ny, 1), ones(ny, 1)], [-1 0 1 ], ny, ny) / (hy ^ 2);
-%! A_4 = -kron (Dxx, speye (ny)) - kron (speye (nx), Dyy);
-%! A_4 = sparse (A_4);
-%!
-%! A_5 = [ 0.37, -0.05,          -0.05,  -0.07;
-%!        -0.05,  0.116,          0.0,   -0.05 + 0.05i;
-%!        -0.05,  0.0,            0.116, -0.05;
-%!        -0.07, -0.05 - 0.05i,  -0.05,   0.202];
-%! A_5 = sparse(A_5);
-%! A_6 = [ 0.37,    -0.05 - i, -0.05,  -0.07;
-%!        -0.05 + i, 0.116,     0.0,   -0.05;
-%!        -0.05,     0.0,       0.116, -0.05;
-%!        -0.07,    -0.05,     -0.05,   0.202];
-%! A_6 = sparse(A_6);
-%! A_7 = A_5;
-%! A_7(1) = 2i;
-%!
-%!test
-%!error icholt ([]);
-%!error icholt ([],[]);
-%!error icholt ([],[],[]);
-%!error [~] = icholt ([],[],[]);
-%!error [L] = icholt ([],[],[]);
-%!error [L] = icholt ([], 1e-4, 1);
-%!error [L] = icholt (A_1, [], 'off');
-%!error [L] = icholt (A_1, 1e-4, []);
-%!error [L, E] = icholt (A_1, 1e-4, 'off');
-%!error [L] = icholt (A_1, 1e-4, 'off', A_1);
-%!error icholt (sparse (0), 1e-4, 'off');
-%!error icholt (sparse (-0), 1e-4, 'off');
-%!error icholt (sparse (-1), 1e-4, 'off');
-%!error icholt (sparse (i), 1e-4, 'off');
-%!error icholt (sparse (-i), 1e-4, 'off');
-%!error icholt (sparse (1 + 1i), 1e-4, 'off');
-%!error icholt (sparse (1 - 1i), 1e-4, 'off');
-%!
-%!test
-%! L = icholt (sparse (1), 1e-4, 'off');
-%! assert (L, sparse (1));
-%! L = icholt (sparse (4), 1e-4, 'off');
-%! assert (L, sparse (2));
-%!
-%!test
-%! L = icholt (A_1, 1e-4, 'off');
-%! assert (norm (A_1 - L*L', 'fro') / norm (A_1, 'fro'), eps, eps);
-%! L = icholt (A_1, 1e-4, 'on');
-%! assert (norm (A_1 - L*L', 'fro') / norm (A_1, 'fro'), eps, eps);
-%!
-%!test
-%! L = icholt (A_2, 1e-4, 'off');
-%! assert (norm (A_2 - L*L', 'fro') / norm (A_2, 'fro'), 1e-4, 1e-4);
-%! L = icholt (A_2, 1e-4, 'on');
-%! assert (norm (A_2 - L*L', 'fro') / norm (A_2, 'fro'), 3e-4, 1e-4);
-%!
-%!test
-%! L = icholt (A_3, 1e-4, 'off');
-%! assert (norm (A_3 - L*L', 'fro') / norm (A_3, 'fro'), eps, eps);
-%! L = icholt (A_3, 1e-4, 'on');
-%! assert (norm (A_3 - L*L', 'fro') / norm (A_3, 'fro'), eps, eps);
-%!
-%!test
-%! L = icholt (A_4, 1e-4, 'off');
-%! assert (norm (A_4 - L*L', 'fro') / norm (A_4, 'fro'), 2e-4, 1e-4);
-%! L = icholt (A_4, 1e-4, 'on');
-%! assert (norm (A_4 - L*L', 'fro') / norm (A_4, 'fro'), 7e-4, 1e-4);
-%!
-%% Complex matrices
-%!test
-%! L = ichol0 (A_5, 'off');
-%! assert (norm (A_5 - L*L', 'fro') / norm (A_5, 'fro'), 1e-2, 1e-2);
-%! L = ichol0 (A_5, 'on');
-%! assert (norm (A_5 - L*L', 'fro') / norm (A_5, 'fro'), 2e-2, 1e-2);
-%% Negative pivot 
-%!error ichol0 (A_6, 'off');
-%% Complex entry in the diagonal
-%!error ichol0 (A_7, 'off');
-*/
-
-

--- a/libinterp/dldfcn/ilu0.cc	Tue Aug 12 15:58:18 2014 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,311 +0,0 @@
-/**
- * Copyright (C) 2014 Eduardo Ramos Fernández <eduradical951@gmail.com>
- *
- * This file is part of Octave.
- *
- * Octave is free software; you can redistribute it and/or modify it under the
- * terms of the GNU General Public License as published by the Free Software
- * Foundation; either version 3 of the License, or (at your option) any later
- * version.
- *
- * Octave is distributed in the hope that it will be useful, but WITHOUT ANY
- * WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
- * FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
- * details.
- *
- * You should have received a copy of the GNU General Public License along with
- * Octave; see the file COPYING.  If not, see <http://www.gnu.org/licenses/>.
- */
-
-#ifdef HAVE_CONFIG_H
-#include <config.h>
-#endif
-
-#include "defun-dld.h"
-#include "parse.h"
-
-/* 
- * That function implements the IKJ and JKI variants of gaussian elimination to
- * perform the ILUTP decomposition. The behaviour is controlled by milu
- * parameter. If milu = ['off'|'col'] the JKI version is performed taking
- * advantage of CCS format of the input matrix. If milu = 'row' the input matrix
- * has to be transposed to obtain the equivalent CRS structure so we can work
- * efficiently with rows. In this case IKJ version is used.
- */
-
-template <typename octave_matrix_t, typename T>
-void ilu_0 (octave_matrix_t& sm, const std::string milu = "off") {
-
-  const octave_idx_type n = sm.cols ();
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, iw, n);
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, uptr, n);
-  octave_idx_type j1, j2, jrow, jw, i, k, jj;
-  T tl, r;
-
-  char opt;
-  enum {OFF, ROW, COL};
-  if (milu == "row")
-    {
-      opt = ROW;
-      sm = sm.transpose ();
-    }
-  else if (milu == "col")
-    opt = COL;
-  else
-    opt = OFF;
-
-  octave_idx_type* cidx = sm.cidx ();
-  octave_idx_type* ridx = sm.ridx ();
-  T* data = sm.data ();
-  for (i = 0; i < n; i++)
-    iw[i] = -1;
-  for (k = 0; k < n; k++)
-    {
-      j1 = cidx[k];
-      j2 = cidx[k+1] - 1;
-      octave_idx_type j;
-      for (j = j1; j <= j2; j++)
-        {
-          iw[ridx[j]] = j;
-        }
-      r = 0;
-      j = j1;
-      jrow = ridx[j];
-      while ((jrow < k) && (j <= j2)) 
-        {
-          if (opt == ROW)
-            {
-              tl = data[j] / data[uptr[jrow]];
-              data[j] = tl;
-            }
-          for (jj = uptr[jrow] + 1; jj < cidx[jrow+1]; jj++)
-            {
-              jw = iw[ridx[jj]];
-              if (jw != -1)
-                if (opt == ROW)
-                  data[jw] -= tl * data[jj];
-                else
-                  data[jw] -= data[j] * data[jj];
-
-              else
-                // That is for the milu='row'
-                if (opt == ROW)
-                  r += tl * data[jj];
-                else if (opt == COL)
-                  r += data[j] * data[jj];
-            }
-          j++;
-          jrow = ridx[j];
-        }
-      uptr[k] = j;
-      if(opt != OFF)
-        data[uptr[k]] -= r;
-      if (opt != ROW)
-        for (jj = uptr[k] + 1; jj < cidx[k+1]; jj++)
-          data[jj] /=  data[uptr[k]];
-      if (k != jrow)
-        {
-          error ("ilu0: Your input matrix has a zero in the diagonal.");
-          break;
-        }
-
-      if (data[j] == T(0))
-        {
-          error ("ilu0: There is a pivot equal to zero.");
-          break;
-        }
-      for(i = j1; i <= j2; i++)
-        iw[ridx[i]] = -1;
-    }
-  if (opt == ROW)
-    sm = sm.transpose ();
-}
-
-DEFUN_DLD (ilu0, args, nargout, "-*- texinfo -*-\n\
-@deftypefn  {Loadable Function} {[@var{L}, @var{U}] =} ilu0 (@var{A})\n\
-@deftypefnx  {Loadable Function} {[@var{L}, @var{U}] =} ilu0 (@var{A}, @var{milu})\n\
-\n\
-NOTE: No pivoting is performed.\n\
-\n\
-Computes the incomplete LU-factorization (ILU) with 0-order level of fill of \
-@var{A}.\n\
-\n\
-@code{[@var{L}, @var{U}] = ilu0 (@var{A})} computes the zero fill-in ILU-\
-factorization ILU(0) of @var{A}, such that @code{@var{L} * @var{U}} is an \
-approximation of the square sparse matrix @var{A}. Parameter @var{milu} = \
-['off'|'row'|'col'] set if no row nor column sums are preserved, row sums \
-are preserved or column sums are preserved respectively.\n\
-\n\
-For a full description of ILU0 and its options see ilu documentation.\n\
-\n\
-For more information about the algorithms themselves see:\n\
-\n\
-[1] Saad, Yousef: Iterative Methods for Sparse Linear Systems. Second Edition. \
-Minneapolis, Minnesota: Siam 2003.\n\
-\n\
-    @seealso{ilu, ilutp, iluc, ichol}\n\
-    @end deftypefn")
-{
-  octave_value_list retval;
-
-  int nargin = args.length ();
-  std::string milu;
- 
-
-  if (nargout > 2 || nargin < 1 || nargin > 2)
-    {
-      print_usage ();
-      return retval;
-    }
-
-  if (args (0).is_empty ())
-    {
-      retval (0) = octave_value (SparseMatrix());
-      retval (1) = octave_value (SparseMatrix());
-      return retval;
-    }
-
-  if (args (0).is_scalar_type () || !args (0).is_sparse_type ())
-    error ("ilu0: 1. parameter must be a sparse square matrix.");
-
-  if (nargin == 2)
-    {
-      milu = args (1).string_value ();
-      if (error_state || !(milu == "row" || milu == "col" || milu == "off"))
-        error (
-          "ilu0: 2. parameter must be 'row', 'col' or 'off' character string.");
-      // maybe resolve milu to a numerical value / enum type already here!
-    }
-
-
-  if (!error_state)
-    {
-      // In ILU0 algorithm the zero-pattern of the input matrix is preserved so
-      // it's structure does not change during the algorithm. The same input
-      // matrix is used to build the output matrix due to that fact.
-      octave_value_list param_list;
-      if (!args (0).is_complex_type ())
-        {
-          SparseMatrix sm = args (0).sparse_matrix_value ();
-          ilu_0 <SparseMatrix, double> (sm, milu);
-          if (!error_state)
-            {
-              param_list.append (sm);
-              retval (1) = octave_value (
-                feval ("triu", param_list)(0).sparse_matrix_value ()); 
-              SparseMatrix eye = feval ("speye",
-                octave_value_list (
-                  octave_value (sm.cols ())))(0).sparse_matrix_value ();
-              param_list.append (-1);
-              retval (0) = octave_value (
-                eye + feval ("tril", param_list)(0).sparse_matrix_value ()); 
-
-            }
-        }
-      else
-        {
-          SparseComplexMatrix sm = args (0).sparse_complex_matrix_value ();
-          ilu_0 <SparseComplexMatrix, Complex> (sm, milu);
-          if (!error_state)
-            {
-              param_list.append (sm);
-              retval (1) = octave_value (
-                feval ("triu", param_list)(0).sparse_complex_matrix_value ()); 
-              SparseComplexMatrix eye = feval ("speye",
-                octave_value_list (
-                  octave_value (sm.cols ())))(0).sparse_complex_matrix_value ();
-              param_list.append (-1);
-              retval (0) = octave_value (eye +
-                feval ("tril", param_list)(0).sparse_complex_matrix_value ()); 
-           }
-        }
-
-    }
-
-  return retval;
-}
-
-/* Test cases for real numbers.
-%!shared n_tiny, n_small, n_medium, n_large, A_tiny, A_small, A_medium, A_large
-%! n_tiny = 5;
-%! n_small = 40;
-%! n_medium = 600;
-%! n_large = 10000;
-%! A_tiny = spconvert ([1 4 2 3 3 4 2 5; 1 1 2 3 4 4 5 5; 1 2 3 4 5 6 7 8]');
-%! A_small = sprand (n_small, n_small, 1/n_small) + speye (n_small);
-%! A_medium = sprand (n_medium, n_medium, 1/n_medium) + speye (n_medium);
-%! A_large = sprand (n_large, n_large, 1/n_large/10) + speye (n_large);
-%!# Input validation tests
-%!test 
-%!error [L,U] = ilu0(A_tiny, 1);
-%!error [L,U] = ilu0(A_tiny, [1, 2]);
-%!error [L,U] = ilu0(A_tiny, '');
-%!error [L,U] = ilu0(A_tiny, 'foo');
-%! [L,U] = ilu0 ([]);
-%! assert (isempty (L), logical (1));
-%! assert (isempty (U), logical (1));
-%!error [L,U] = ilu0 (0);
-%!error [L,U] = ilu0 (sparse (0));
-%! [L,U] = ilu0 (sparse (2));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2));
-%!test 
-%! [L,U] = ilu0 (A_tiny);
-%! assert (norm (A_tiny - L * U, "fro") / norm (A_tiny, "fro"), 0, n_tiny*eps);
-%!test 
-%! [L,U] = ilu0 (A_small);
-%! assert (norm (A_small - L * U, "fro") / norm (A_small, "fro"), 0, 1);
-%!test 
-%! [L,U] = ilu0 (A_medium);
-%! assert (norm (A_medium - L * U, "fro") / norm (A_medium, "fro"), 0, 1);
-%!test 
-%! [L,U] = ilu0 (A_large);
-%! assert (norm (A_large - L * U, "fro") / norm (A_large, "fro"), 0, 1);
-*/
-
-/* Test cases for complex numbers
-%!shared n_tiny, n_small, n_medium, n_large, A_tiny, A_small, A_medium, A_large
-%! n_tiny = 5;
-%! n_small = 40;
-%! n_medium = 600;
-%! n_large = 10000;
-%! A_tiny = spconvert([1 4 2 3 3 4 2 5; 1 1 2 3 4 4 5 5; 1 2 3 4 5 6 7 8]');
-%! A_tiny(1,1) += 1i;
-%! A_small = sprand(n_small, n_small, 1/n_small) + ...
-%!   i * sprand(n_small, n_small, 1/n_small) + speye (n_small);
-%! A_medium = sprand(n_medium, n_medium, 1/n_medium) + ...
-%!   i * sprand(n_medium, n_medium, 1/n_medium) + speye (n_medium);
-%! A_large = sprand(n_large, n_large, 1/n_large/10) + ...
-%!   i * sprand(n_large, n_large, 1/n_large/10) + speye (n_large);
-%!test 
-%! [L,U] = ilu0 ([]);
-%! assert (isempty (L), logical (1));
-%! assert (isempty (U), logical (1));
-%!error [L,U] = ilu0 (0+0i);
-%!error [L,U] = ilu0 (0i);
-%!error [L,U] = ilu0 (sparse (0+0i));
-%!error [L,U] = ilu0 (sparse (0i));
-%!test 
-%! [L,U] = ilu0 (sparse (2+0i));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2));
-%! [L,U] = ilu0 (sparse (2+2i));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2+2i));
-%! [L,U] = ilu0 (sparse (2i));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2i));
-%!test 
-%! [L,U] = ilu0 (A_tiny);
-%! assert (norm (A_tiny - L * U, "fro") / norm (A_tiny, "fro"), 0, n_tiny*eps);
-%!test 
-%! [L,U] = ilu0 (A_small);
-%! assert (norm (A_small - L * U, "fro") / norm (A_small, "fro"), 0, 1);
-%!test 
-%! [L,U] = ilu0 (A_medium);
-%! assert (norm (A_medium - L * U, "fro") / norm (A_medium, "fro"), 0, 1);
-%!test 
-%! [L,U] = ilu0 (A_large);
-%! assert (norm (A_large - L * U, "fro") / norm (A_large, "fro"), 0, 1);
-*/
-

--- a/libinterp/dldfcn/iluc.cc	Tue Aug 12 15:58:18 2014 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,518 +0,0 @@
-/**
- * Copyright (C) 2014 Eduardo Ramos Fernández <eduradical951@gmail.com>
- *
- * This file is part of Octave.
- *
- * Octave is free software; you can redistribute it and/or modify it under the
- * terms of the GNU General Public License as published by the Free Software
- * Foundation; either version 3 of the License, or (at your option) any later
- * version.
- *
- * Octave is distributed in the hope that it will be useful, but WITHOUT ANY
- * WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
- * FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
- * details.
- *
- * You should have received a copy of the GNU General Public License along with
- * Octave; see the file COPYING.  If not, see <http://www.gnu.org/licenses/>.
- */
-
-#ifdef HAVE_CONFIG_H
-#include <config.h>
-#endif
-
-#include "defun-dld.h"
-#include "parse.h"
-
-template <typename octave_matrix_t, typename T>
-void ilu_crout (octave_matrix_t& sm_l, octave_matrix_t& sm_u,
-                octave_matrix_t& L, octave_matrix_t& U, T* cols_norm,
-                T* rows_norm, const T droptol = 0,
-                const std::string milu = "off")
-{
-
-  // Map the strings into chars to faster comparation inside loops
-  #define ROW  1
-  #define COL  2
-  #define OFF  0
-  char opt;
-  if (milu == "row")
-    opt = ROW;
-  else if (milu == "col")
-    opt = COL;
-  else
-    opt = OFF;
-
-  octave_idx_type jrow, i, j, k, jj, total_len_l, total_len_u, max_len_u,
-                  max_len_l, w_len_u, w_len_l, cols_list_len, rows_list_len;
-
-  const octave_idx_type n = sm_u.cols ();
-  sm_u = sm_u.transpose ();
-
-  max_len_u = sm_u.nnz ();
-  max_len_u += (0.1 * max_len_u) > n ? 0.1 * max_len_u : n;
-  max_len_l = sm_l.nnz ();
-  max_len_l += (0.1 * max_len_l) > n ? 0.1 * max_len_l : n;
-  // Extract pointers to the arrays for faster access inside loops
-  octave_idx_type* cidx_in_u = sm_u.cidx ();
-  octave_idx_type* ridx_in_u = sm_u.ridx ();
-  T* data_in_u = sm_u.data ();
-  octave_idx_type* cidx_in_l = sm_l.cidx ();
-  octave_idx_type* ridx_in_l = sm_l.ridx ();
-  T* data_in_l = sm_l.data ();
-  T tl, pivot;
-
-  // L output arrays
-  Array <octave_idx_type> ridx_out_l (dim_vector (max_len_l, 1));
-  octave_idx_type* ridx_l = ridx_out_l.fortran_vec ();
-  Array <T> data_out_l (dim_vector (max_len_l, 1));
-  T* data_l = data_out_l.fortran_vec ();
-
-  // U output arrays
-  Array <octave_idx_type> ridx_out_u (dim_vector (max_len_u, 1));
-  octave_idx_type* ridx_u = ridx_out_u.fortran_vec ();
-  Array <T> data_out_u (dim_vector (max_len_u, 1));
-  T* data_u = data_out_u.fortran_vec ();
-
-  // Working arrays
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, cidx_l, n + 1);
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, cidx_u, n + 1);
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, cols_list, n);
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, rows_list, n);
-  OCTAVE_LOCAL_BUFFER (T, w_data_l, n);
-  OCTAVE_LOCAL_BUFFER (T, w_data_u, n);
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, Ufirst, n);
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, Lfirst, n);
-  OCTAVE_LOCAL_BUFFER (T, cr_sum, n);
-
-  T zero = T (0);
-  
-  cidx_u[0] = cidx_in_u[0];
-  cidx_l[0] = cidx_in_l[0];
-  for (i = 0; i < n; i++)
-    {
-      w_data_u[i] = zero;
-      w_data_l[i] = zero;
-      cr_sum[i] = zero;
-    }
-
-  total_len_u = 0;
-  total_len_l = 0;
-  cols_list_len = 0;
-  rows_list_len = 0;
-
-  for (k = 0; k < n; k++)
-    {
-      // Load the working column and working row 
-      for (i = cidx_in_l[k]; i < cidx_in_l[k+1]; i++)
-        w_data_l[ridx_in_l[i]] = data_in_l[i];
-
-      for (i = cidx_in_u[k]; i < cidx_in_u[k+1]; i++)
-        w_data_u[ridx_in_u[i]] = data_in_u[i];
-
-      // Update U working row
-      for (j = 0; j < rows_list_len; j++)
-        {
-          if ((Ufirst[rows_list[j]] != -1))
-            for (jj = Ufirst[rows_list[j]]; jj < cidx_u[rows_list[j]+1]; jj++)
-              {
-                jrow = ridx_u[jj];
-                w_data_u[jrow] -= data_u[jj] * data_l[Lfirst[rows_list[j]]];
-              }
-        }
-      // Update L working column
-      for (j = 0; j < cols_list_len; j++)
-        {
-          if (Lfirst[cols_list[j]] != -1)
-            for (jj = Lfirst[cols_list[j]]; jj < cidx_l[cols_list[j]+1]; jj++)
-              {
-                jrow = ridx_l[jj];
-                w_data_l[jrow] -= data_l[jj] * data_u[Ufirst[cols_list[j]]];
-              }
-        }
-
-      if ((max_len_u - total_len_u) < n)
-        {
-          max_len_u += (0.1 * max_len_u) > n ? 0.1 * max_len_u : n;
-          data_out_u.resize (dim_vector (max_len_u, 1));
-          data_u = data_out_u.fortran_vec ();
-          ridx_out_u.resize (dim_vector (max_len_u, 1));
-          ridx_u = ridx_out_u.fortran_vec ();
-        }
-
-      if ((max_len_l - total_len_l) < n)
-        {
-          max_len_l += (0.1 * max_len_l) > n ? 0.1 * max_len_l : n;
-          data_out_l.resize (dim_vector (max_len_l, 1));
-          data_l = data_out_l.fortran_vec ();
-          ridx_out_l.resize (dim_vector (max_len_l, 1));
-          ridx_l = ridx_out_l.fortran_vec ();
-        }
-
-      // Expand the working row into the U output data structures
-      w_len_l = 0;
-      data_u[total_len_u] = w_data_u[k];
-      ridx_u[total_len_u] = k;
-      w_len_u = 1;
-      for (i = k + 1; i < n; i++)
-        {
-          if (w_data_u[i] != zero)
-            {
-              if (std::abs (w_data_u[i]) < (droptol * rows_norm[k]))
-                {
-                  if (opt == ROW)
-                    cr_sum[k] += w_data_u[i];
-                  else if (opt == COL)
-                    cr_sum[i] += w_data_u[i];
-                }
-              else
-                {
-                  data_u[total_len_u + w_len_u] = w_data_u[i];
-                  ridx_u[total_len_u + w_len_u] = i;
-                  w_len_u++;
-                }
-            }
-
-          // Expand the working column into the L output data structures
-          if (w_data_l[i] != zero)
-            {
-              if (std::abs (w_data_l[i]) < (droptol * cols_norm[k]))
-                {
-                  if (opt == COL)
-                    cr_sum[k] += w_data_l[i];
-                  else if (opt == ROW)
-                    cr_sum[i] += w_data_l[i];
-                }
-              else
-                {
-                  data_l[total_len_l + w_len_l] = w_data_l[i];
-                  ridx_l[total_len_l + w_len_l] = i;
-                  w_len_l++;
-                }
-            }
-          w_data_u[i] = zero;
-          w_data_l[i] = zero;
-        }
-
-      // Compensate row and column sums --> milu option
-      if (opt == COL || opt == ROW)
-        data_u[total_len_u] += cr_sum[k];
-
-      // Check if the pivot is zero
-      if (data_u[total_len_u] == zero)
-        {
-              error ("iluc: There is a pivot equal to zero.");
-              break;
-        }
-      
-      // Scale the elements in L by the pivot
-      for (i = total_len_l ; i < (total_len_l + w_len_l); i++)
-        data_l[i] /= data_u[total_len_u];
-
-
-      total_len_u += w_len_u;
-      cidx_u[k+1] = cidx_u[k] - cidx_u[0] + w_len_u;
-      total_len_l += w_len_l;
-      cidx_l[k+1] = cidx_l[k] - cidx_l[0] + w_len_l;
-
-      // The tricky part of the algorithm. The arrays pointing to the first
-      // working element of each column in the next iteration (Lfirst) or
-      // the first working element of each row (Ufirst) are updated. Also the
-      // arrays working as lists cols_list and rows_list are filled with indexes
-      // pointing to Ufirst and Lfirst respectively.
-      // TODO: Maybe the -1 indicating in Ufirst and Lfirst, that no elements
-      // have to be considered in a certain column or row in next iteration, can
-      // be removed. It feels safer to me using such an indicator.
-      if (k < (n - 1))
-        {
-          if (w_len_u > 0)
-            Ufirst[k] = cidx_u[k];
-          else
-            Ufirst[k] = -1;
-          if (w_len_l > 0)
-            Lfirst[k] = cidx_l[k];
-          else
-            Lfirst[k] = -1;
-          cols_list_len = 0;
-          rows_list_len = 0;
-          for (i = 0; i <= k; i++)
-            {
-              if (Ufirst[i] != -1)
-                {
-                  jj = ridx_u[Ufirst[i]];
-                  if (jj < (k + 1))
-                    {
-                      if (Ufirst[i] < (cidx_u[i+1]))
-                        {
-                          Ufirst[i]++;
-                          if (Ufirst[i] == cidx_u[i+1])
-                            Ufirst[i] = -1;
-                          else
-                            jj = ridx_u[Ufirst[i]];
-                        }
-                    }
-                  if (jj == (k + 1)) 
-                    {
-                      cols_list[cols_list_len] = i;
-                      cols_list_len++;
-                    }
-                }
-
-              if (Lfirst[i] != -1)
-                {
-                  jj = ridx_l[Lfirst[i]];
-                  if (jj < (k + 1))
-                    if(Lfirst[i] < (cidx_l[i+1]))
-                      {
-                        Lfirst[i]++;
-                        if (Lfirst[i] == cidx_l[i+1])
-                          Lfirst[i] = -1;
-                        else
-                          jj = ridx_l[Lfirst[i]];
-                      }
-                  if (jj == (k + 1)) 
-                    {
-                      rows_list[rows_list_len] = i;
-                      rows_list_len++;
-                    }
-                }
-            }
-        }
-    }
-
-  if (!error_state)
-    {
-      // Build the output matrices
-      L = octave_matrix_t (n, n, total_len_l);
-      U = octave_matrix_t (n, n, total_len_u);
-      for (i = 0; i <= n; i++)
-        L.cidx (i) = cidx_l[i];
-      for (i = 0; i < total_len_l; i++)
-        {
-          L.ridx (i) = ridx_l[i];
-          L.data (i) = data_l[i];
-        }
-      for (i = 0; i <= n; i++)
-        U.cidx (i) = cidx_u[i];
-      for (i = 0; i < total_len_u; i++)
-        {
-          U.ridx (i) = ridx_u[i];
-          U.data (i) = data_u[i];
-        }
-      U = U.transpose ();
-    }
-}
-
-DEFUN_DLD (iluc, args, nargout, "-*- texinfo -*-\n\
-@deftypefn  {Loadable Function} {[@var{L}, @var{U}] =} iluc (@var{A})\n\
-@deftypefnx {Loadable Function} {[@var{L}, @var{U}] =} iluc (@var{A}, @var{droptol}, \
-@var{milu})\n\
-\n\
-Computes the crout version incomplete LU-factorization (ILU) with threshold of @var{A}.\n\
-\n\
-NOTE: No pivoting is performed.\n\
-\n\
-@code{[@var{L}, @var{U}] = iluc (@var{A})} computes the default crout version\n\
-ILU-factorization with threshold ILUT of @var{A}, such that \
-@code{@var{L} * @var{U}} is an approximation of the square sparse matrix \
-@var{A}. This version of ILU algorithms is significantly faster than ILUT or ILU(0). \
-Parameter @code{@var{droptol}>=0} is the scalar double threshold. All elements \
-@code{x<=@var{droptol}} will be dropped in the factorization. Parameter @var{milu} \
-= ['off'|'row'|'col'] set if no row nor column sums are preserved, row sums are \
-preserved or column sums are preserved respectively.\n\
-\n\
-For a full description of ILUC behaviour and its options see ilu documentation.\n\
-\n\
-For more information about the algorithms themselves see:\n\
-\n\
-[1] Saad, Yousef: Iterative Methods for Sparse Linear Systems. Second Edition. \
-Minneapolis, Minnesota: Siam 2003.\n\
-\n\
-@seealso{ilu, ilu0, ilutp, ichol}\n\
-@end deftypefn")
-{
-
-  octave_value_list retval;
-  int nargin = args.length ();
-  std::string milu = "off";
-  double droptol = 0;
-  double thresh = 0;
-
-  if (nargout != 2 || nargin < 1 || nargin > 3)
-    {
-      print_usage ();
-      return retval;
-    }
-
-  // To be matlab compatible 
-  if (args (0).is_empty ())
-    {
-      retval (0) = octave_value (SparseMatrix());
-      retval (1) = octave_value (SparseMatrix());
-      return retval;
-    }
-
-  if (args (0).is_scalar_type () || !args (0).is_sparse_type ())
-    error ("iluc: 1. parameter must be a sparse square matrix.");
-
-  if (! error_state && (nargin >= 2))
-    {
-      droptol = args (1).double_value ();
-      if (error_state || (droptol < 0) || ! args (1).is_real_scalar ())
-        error ("iluc: 2. parameter must be a positive real scalar.");
-    }
-
-  if (! error_state && (nargin == 3))
-    {
-      milu = args (2).string_value ();
-      if (error_state || !(milu == "row" || milu == "col" || milu == "off"))
-        error ("iluc: 3. parameter must be 'row', 'col' or 'off' character string.");
-    }
-
-  if (! error_state)
-    {
-      octave_value_list param_list;
-      if (!args (0).is_complex_type ())
-        {
-          Array<double> cols_norm, rows_norm;
-          param_list.append (args (0).sparse_matrix_value ());
-          SparseMatrix sm_u =  feval ("triu", param_list)(0).sparse_matrix_value (); 
-          param_list.append (-1);
-          SparseMatrix sm_l =  feval ("tril", param_list)(0).sparse_matrix_value (); 
-          param_list (1) = "rows";
-          rows_norm = feval ("norm", param_list)(0).vector_value ();
-          param_list (1) = "cols";
-          cols_norm = feval ("norm", param_list)(0).vector_value ();
-          param_list.clear ();
-          SparseMatrix U;
-          SparseMatrix L;
-          ilu_crout <SparseMatrix, double> (sm_l, sm_u, L, U, cols_norm.fortran_vec (), 
-                                            rows_norm.fortran_vec (), droptol, milu);
-          if (! error_state)
-            {
-              param_list.append (octave_value (L.cols ()));
-              SparseMatrix eye = feval ("speye", param_list)(0).sparse_matrix_value ();
-              retval (0) = octave_value (L + eye);
-              retval (1) = octave_value (U);
-            }
-        }
-      else
-        {
-          Array<Complex> cols_norm, rows_norm;
-          param_list.append (args (0).sparse_complex_matrix_value ());
-          SparseComplexMatrix sm_u =  feval("triu", 
-                                            param_list)(0).sparse_complex_matrix_value (); 
-          param_list.append (-1);
-          SparseComplexMatrix sm_l =  feval("tril", 
-                                            param_list)(0).sparse_complex_matrix_value (); 
-          param_list (1) = "rows";
-          rows_norm = feval ("norm", param_list)(0).complex_vector_value ();
-          param_list (1) = "cols";
-          cols_norm = feval ("norm", param_list)(0).complex_vector_value ();
-          param_list.clear ();
-          SparseComplexMatrix U;
-          SparseComplexMatrix L;
-          ilu_crout < SparseComplexMatrix, Complex > 
-                    (sm_l, sm_u, L, U, cols_norm.fortran_vec () , 
-                     rows_norm.fortran_vec (), Complex (droptol), milu);
-          if (! error_state)
-            {
-              param_list.append (octave_value (L.cols ()));
-              SparseComplexMatrix eye = feval ("speye", 
-                                                param_list)(0).sparse_complex_matrix_value ();
-              retval (0) = octave_value (L + eye);
-              retval (1) = octave_value (U);
-            }
-        }
-
-
-    }
-
-  return retval;
-}
-
-
-/* Test cases for complex numbers
-%!shared n_tiny, n_small, n_medium, n_large, A_tiny, A_small, A_medium, A_large
-%! n_tiny = 5;
-%! n_small = 40;
-%! n_medium = 600;
-%! n_large = 10000;
-%! A_tiny = spconvert([1 4 2 3 3 4 2 5; 1 1 2 3 4 4 5 5; 1 2 3 4 5 6 7 8]');
-%! A_tiny(1,1) += 1i;
-%! A_small = sprand(n_small, n_small, 1/n_small) + i * sprand(n_small, n_small, 1/n_small) + speye (n_small);
-%! A_medium = sprand(n_medium, n_medium, 1/n_medium) + i * sprand(n_medium, n_medium, 1/n_medium) + speye (n_medium);
-%! A_large = sprand(n_large, n_large, 1/n_large/10) + i * sprand(n_large, n_large, 1/n_large/10) + speye (n_large);
-%!# Input validation tests
-%!test 
-%!error [L,U] = iluc(A_tiny, -1);
-%!error [L,U] = iluc(A_tiny, [1,2]);
-%!error [L,U] = iluc(A_tiny, 2i);
-%!error [L,U] = iluc(A_tiny, 1, 'foo');
-%!error [L,U] = iluc(A_tiny, 1, '');
-%!error [L,U] = iluc(A_tiny, 1, 1);
-%!error [L,U] = iluc(A_tiny, 1, [1,2]);
-%! [L,U] = iluc ([]);
-%! assert (isempty (L), logical (1));
-%! assert (isempty (U), logical (1));
-%!error [L,U] = iluc (0+0i);
-%!error [L,U] = iluc (0i);
-%!error [L,U] = iluc (sparse (0+0i));
-%!error [L,U] = iluc (sparse (0i));
-%! [L,U] = iluc (sparse (2+0i));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2));
-%! [L,U] = iluc (sparse (2+2i));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2+2i));
-%! [L,U] = iluc (sparse (2i));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2i));
-%!# Output tests
-%!test 
-%! [L,U] = iluc (A_tiny);
-%! assert (norm (A_tiny - L * U, "fro") / norm (A_tiny, "fro"), 0, n_tiny*eps);
-%!test 
-%! [L,U] = iluc (A_small);
-%! assert (norm (A_small - L * U, "fro") / norm (A_small, "fro"), 0, 1);
-%!test 
-%! [L,U] = iluc (A_medium);
-%! assert (norm (A_medium - L * U, "fro") / norm (A_medium, "fro"), 0, 1);
-%!test 
-%! [L,U] = iluc (A_large);
-%! assert (norm (A_large - L * U, "fro") / norm (A_large, "fro"), 0, 1);
-*/
-
-/* Test cases for real numbers.
-%!shared n_tiny, n_small, n_medium, n_large, A_tiny, A_small, A_medium, A_large
-%! n_tiny = 5;
-%! n_small = 40;
-%! n_medium = 600;
-%! n_large = 10000;
-%! A_tiny = spconvert ([1 4 2 3 3 4 2 5; 1 1 2 3 4 4 5 5; 1 2 3 4 5 6 7 8]');
-%! A_small = sprand (n_small, n_small, 1/n_small) + speye (n_small);
-%! A_medium = sprand (n_medium, n_medium, 1/n_medium) + speye (n_medium);
-%! A_large = sprand (n_large, n_large, 1/n_large/10) + speye (n_large);
-%!test 
-%! [L,U] = iluc ([]);
-%! assert (isempty (L), logical (1));
-%! assert (isempty (U), logical (1));
-%!error [L,U] = iluc (0);
-%!error [L,U] = iluc (sparse (0));
-%!test 
-%! [L,U] = iluc (sparse (2));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2));
-%!test 
-%! [L,U] = iluc (A_tiny);
-%! assert (norm (A_tiny - L * U, "fro") / norm (A_tiny, "fro"), 0, n_tiny*eps);
-%!test 
-%! [L,U] = iluc (A_small);
-%! assert (norm (A_small - L * U, "fro") / norm (A_small, "fro"), 0, 1);
-%!test 
-%! [L,U] = iluc (A_medium);
-%! assert (norm (A_medium - L * U, "fro") / norm (A_medium, "fro"), 0, 1);
-%!test 
-%! [L,U] = iluc (A_large);
-%! assert (norm (A_large - L * U, "fro") / norm (A_large, "fro"), 0, 1);
-*/

--- a/libinterp/dldfcn/ilutp.cc	Tue Aug 12 15:58:18 2014 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,707 +0,0 @@
-/**
- * Copyright (C) 2014 Eduardo Ramos Fernández <eduradical951@gmail.com>
- *
- * This file is part of Octave.
- *
- * Octave is free software; you can redistribute it and/or modify it under the
- * terms of the GNU General Public License as published by the Free Software
- * Foundation; either version 3 of the License, or (at your option) any later
- * version.
- *
- * Octave is distributed in the hope that it will be useful, but WITHOUT ANY
- * WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
- * FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
- * details.
- *
- * You should have received a copy of the GNU General Public License along with
- * Octave; see the file COPYING.  If not, see <http://www.gnu.org/licenses/>.
- */
-
-#ifdef HAVE_CONFIG_H
-#include <config.h>
-#endif
-
-#include "defun-dld.h"
-#include "parse.h"
-
-
-// That function implements the IKJ and JKI variants of gaussian elimination 
-// to perform the ILUTP decomposition. The behaviour is controlled by milu 
-// parameter. If milu = ['off'|'col'] the JKI version is performed taking 
-// advantage of CCS format of the input matrix. Row pivoting is performed. 
-// If milu = 'row' the input matrix has to be transposed to obtain the 
-// equivalent CRS structure so we can work efficiently with rows. In that
-// case IKJ version is used and column pivoting is performed.
-
-template <typename octave_matrix_t, typename T>
-void ilu_tp (octave_matrix_t& sm, octave_matrix_t& L, octave_matrix_t& U, 
-             octave_idx_type nnz_u, octave_idx_type nnz_l, T* cols_norm,  
-             Array <octave_idx_type>& perm_vec, const T droptol = T(0),
-             const T thresh = T(0), const  std::string milu = "off", 
-             const double udiag = 0)
-  {
-  
-  // Map the strings into chars to faster comparation inside loops
-  enum {OFF, ROW, COL};
-  char opt;
-  if (milu == "row")
-    opt = ROW;
-  else if (milu == "col")
-    opt = COL;
-  else
-    opt = OFF;
-  
-  const octave_idx_type n = sm.cols ();
-
-  // That is necessary for the JKI (milu = "row") variant.
-  if (opt == ROW)
-    sm = sm.transpose();
-
-  // Extract pointers to the arrays for faster access inside loops
-  octave_idx_type* cidx_in = sm.cidx ();
-  octave_idx_type* ridx_in = sm.ridx ();
-  T* data_in = sm.data ();
-  octave_idx_type jrow, i, j, k, jj, c, total_len_l, total_len_u, p_perm, res, 
-                  max_ind, max_len_l, max_len_u;
-  T tl, aux, maximum;
-
-  max_len_u = nnz_u;
-  max_len_u += (0.1 * max_len_u) > n ? 0.1 * max_len_u : n;
-  max_len_l = nnz_l;
-  max_len_l += (0.1 * max_len_l) > n ? 0.1 * max_len_l : n;
-
-  Array <octave_idx_type> cidx_out_l (dim_vector (n + 1, 1));
-  octave_idx_type* cidx_l = cidx_out_l.fortran_vec ();
-  Array <octave_idx_type> ridx_out_l (dim_vector (max_len_l, 1));
-  octave_idx_type* ridx_l = ridx_out_l.fortran_vec ();
-  Array <T> data_out_l (dim_vector (max_len_l ,1));
-  T* data_l = data_out_l.fortran_vec ();
-  // Data for U
-  Array <octave_idx_type> cidx_out_u (dim_vector (n + 1, 1));
-  octave_idx_type* cidx_u = cidx_out_u.fortran_vec ();
-  Array <octave_idx_type> ridx_out_u (dim_vector (max_len_u, 1));
-  octave_idx_type* ridx_u = ridx_out_u.fortran_vec ();
-  Array <T> data_out_u (dim_vector (max_len_u, 1));
-  T* data_u = data_out_u.fortran_vec();
-
-  // Working arrays and permutation arrays
-  octave_idx_type w_len_u, w_len_l;
-  T total_sum, partial_col_sum, partial_row_sum;
-  std::set <octave_idx_type> iw_l;
-  std::set <octave_idx_type> iw_u;
-  std::set <octave_idx_type>::iterator it, it2;
-  OCTAVE_LOCAL_BUFFER (T, w_data, n);
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, iperm, n);
-  octave_idx_type* perm = perm_vec.fortran_vec ();
-  OCTAVE_LOCAL_BUFFER (octave_idx_type, uptr, n);
-
-
-  T zero = T(0);
-  cidx_l[0] = cidx_in[0];
-  cidx_u[0] = cidx_in[0];
-  /**
-  for (i = 0; i < ; i++)
-    {
-      ridx_u[i] = 0;
-      data_u[i] = 0;
-      ridx_l[i] = 0;
-      data_l[i] = 0;
-    }
-**/
-  for (i = 0; i < n; i++)
-    {
-      w_data[i] = 0;
-      perm[i] = i;
-      iperm[i] = i;
-    }
-  total_len_u = 0;
-  total_len_l = 0;
-
-  for (k = 0; k < n; k++)
-    {
-
-      for (j = cidx_in[k]; j < cidx_in[k+1]; j++)
-        {
-          p_perm = iperm[ridx_in[j]];
-          w_data[iperm[ridx_in[j]]] = data_in[j];
-          if (p_perm > k)
-            iw_l.insert (ridx_in[j]);
-          else
-            iw_u.insert (p_perm);
-        }
-
-      it = iw_u.begin ();
-      jrow = *it;
-      total_sum = zero;
-      while ((jrow < k) && (it != iw_u.end ())) 
-        {
-          if (opt == COL)
-            partial_col_sum = w_data[jrow];
-          if (w_data[jrow] != zero)
-            {
-              if (opt == ROW)
-                {
-                  partial_row_sum = w_data[jrow];
-                  tl = w_data[jrow] / data_u[uptr[jrow]];
-                }
-              for (jj = cidx_l[jrow]; jj < cidx_l[jrow+1]; jj++)
-                {
-                  p_perm = iperm[ridx_l[jj]];
-                  aux = w_data[p_perm];
-                  if (opt == ROW)
-                    {
-                      w_data[p_perm] -= tl * data_l[jj];
-                      partial_row_sum += tl * data_l[jj];
-                    }
-                  else
-                    {
-                      tl = data_l[jj] * w_data[jrow]; 
-                      w_data[p_perm] -= tl;
-                      if (opt == COL)
-                        partial_col_sum += tl;
-                    }
-
-                  if ((aux == zero) && (w_data[p_perm] != zero))
-                    {
-                      if (p_perm > k)
-                        iw_l.insert (ridx_l[jj]);
-                      else
-                        iw_u.insert (p_perm);
-                    }
-                }
-
-                // Drop element from the U part in IKJ and L part in JKI 
-                // variant (milu = [col|off])
-                if ((std::abs (w_data[jrow]) < (droptol * cols_norm[k])) 
-                    && (w_data[jrow] != zero))
-                  {
-                    if (opt == COL)
-                      total_sum += partial_col_sum;
-                    else if (opt == ROW)
-                      total_sum += partial_row_sum;
-                    w_data[jrow] = zero;
-                    it2 = it;
-                    it++;
-                    iw_u.erase (it2);
-                    jrow = *it;
-                    continue;
-                  }
-                else 
-                  // This is the element scaled by the pivot in the actual iteration
-                  if (opt == ROW)
-                    w_data[jrow] = tl;
-            }
-          jrow = *(++it);
-        }
-
-      // Search for the pivot and update iw_l and iw_u if the pivot is not the
-      // diagonal element
-      if ((thresh > zero) && (k < (n-1)))
-        {
-          maximum = std::abs (w_data[k]) / thresh;
-          max_ind = perm[k];
-          for (it = iw_l.begin (); it != iw_l.end (); ++it) 
-            {
-              p_perm = iperm[*it];
-              if (std::abs (w_data[p_perm]) > maximum)
-                {
-                  maximum = std::abs (w_data[p_perm]);
-                  max_ind = *it;
-                  it2 = it; 
-                }
-            }
-          // If the pivot is not the diagonal element update all.
-          p_perm = iperm[max_ind];
-          if (max_ind != perm[k])
-            {
-              iw_l.erase (it2);
-              if (w_data[k] != zero)
-                iw_l.insert (perm[k]);
-              else
-                  iw_u.insert (k);
-              // Swap data and update permutation vectors
-              aux = w_data[k];
-              iperm[perm[p_perm]] = k;
-              iperm[perm[k]] = p_perm;
-              c = perm[k];
-              perm[k] = perm[p_perm];
-              perm[p_perm] = c;
-              w_data[k] = w_data[p_perm];
-              w_data[p_perm] = aux;
-            }
-          
-      }              
-
-      // Drop elements in the L part in the IKJ and from the U part in the JKI
-      // version.
-      it = iw_l.begin ();
-      while (it != iw_l.end ()) 
-        {
-          p_perm = iperm[*it];
-          if (droptol > zero)
-            if (std::abs (w_data[p_perm]) < (droptol * cols_norm[k]))
-              {
-                if (opt != OFF)
-                  total_sum += w_data[p_perm];
-                w_data[p_perm] = zero;
-                it2 = it;
-                it++;
-                iw_l.erase (it2);
-                continue;
-              }
-
-          it++;
-        }
-
-      // If milu =[row|col] sumation is preserved --> Compensate diagonal element.
-      if (opt != OFF)
-        {
-          if ((total_sum > zero) && (w_data[k] == zero))
-            iw_u.insert (k);
-          w_data[k] += total_sum;
-        }
-          
-
-
-      // Check if the pivot is zero and if udiag is active.
-      // NOTE: If the pivot == 0 and udiag is active, then if milu = [col|row]
-      //       will not preserve the row sum for that column/row.
-      if (w_data[k] == zero)
-        {
-          if (udiag == 1)
-            {
-              w_data[k] = droptol;
-              iw_u.insert (k);
-            }
-          else
-            {
-              error ("ilutp: There is a pivot equal to zero.");
-              break;
-            }
-        }
-
-      // Scale the elements on the L part for IKJ version (milu = [col|off])  
-      if (opt != ROW)
-        for (it = iw_l.begin (); it != iw_l.end (); ++it) 
-          {
-              p_perm = iperm[*it];
-              w_data[p_perm] = w_data[p_perm] / w_data[k];
-          }
-      
-
-      if ((max_len_u - total_len_u) < n)
-        {
-          max_len_u += (0.1 * max_len_u) > n ? 0.1 * max_len_u : n;
-          data_out_u.resize (dim_vector (max_len_u, 1));
-          data_u = data_out_u.fortran_vec ();
-          ridx_out_u.resize (dim_vector (max_len_u, 1));
-          ridx_u = ridx_out_u.fortran_vec ();
-        }
-
-      if ((max_len_l - total_len_l) < n)
-        {
-          max_len_l += (0.1 * max_len_l) > n ? 0.1 * max_len_l : n;
-          data_out_l.resize (dim_vector (max_len_l, 1));
-          data_l = data_out_l.fortran_vec ();
-          ridx_out_l.resize (dim_vector (max_len_l, 1));
-          ridx_l = ridx_out_l.fortran_vec ();
-        }
-
-      // Expand working vector into U.
-      w_len_u = 0;
-      for (it = iw_u.begin (); it != iw_u.end (); ++it)
-        {
-          if (w_data[*it] != zero)
-            {
-              data_u[total_len_u + w_len_u] = w_data[*it];
-              ridx_u[total_len_u + w_len_u] = *it;
-              w_len_u++;
-            }
-          w_data[*it] = 0;
-        }
-      total_len_u += w_len_u;
-      if (opt == ROW)
-        uptr[k] = total_len_u -1;
-      cidx_u[k+1] = cidx_u[k] - cidx_u[0] + w_len_u;
-
-      // Expand working vector into L.
-      w_len_l = 0;
-      for (it = iw_l.begin (); it != iw_l.end (); ++it)
-        {
-          p_perm = iperm[*it];
-          if (w_data[p_perm] != zero)
-            {
-              data_l[total_len_l + w_len_l] = w_data[p_perm];
-              ridx_l[total_len_l + w_len_l] = *it;
-              w_len_l++;
-            }
-          w_data[*it] = 0;
-        }
-      total_len_l += w_len_l;
-      cidx_l[k+1] = cidx_l[k] - cidx_l[0] + w_len_l;
-
-      iw_l.clear ();
-      iw_u.clear ();
-    }
-
-  if (!error_state)
-    {
-      octave_matrix_t *L_ptr; 
-      octave_matrix_t *U_ptr;
-      octave_matrix_t diag (n, n, n);
-      
-      // L and U are interchanged if milu = 'row'. It is a matter
-      // of nomenclature to re-use code with both IKJ and JKI
-      // versions of the algorithm.
-      if (opt == ROW)
-        {
-          L_ptr = &U;
-          U_ptr = &L;
-          L = octave_matrix_t (n, n, total_len_u - n);
-          U = octave_matrix_t (n, n, total_len_l);
-        }
-      else
-        {
-          L_ptr = &L;
-          U_ptr = &U;
-          L = octave_matrix_t (n, n, total_len_l);
-          U = octave_matrix_t (n, n, total_len_u);
-        }
-
-      for (i = 0; i <= n; i++)
-        {
-          L_ptr->cidx (i) = cidx_l[i];
-          U_ptr->cidx (i) = cidx_u[i];
-          if (opt == ROW)
-            U_ptr->cidx (i) -= i;
-        }
-
-      for (octave_idx_type i = 0; i < n; i++) 
-        {
-          if (opt == ROW)
-            diag.elem (i,i) = data_u[uptr[i]];
-          j = cidx_l[i];
-
-          while (j < cidx_l[i+1])
-            {
-              L_ptr->ridx (j) = ridx_l[j];
-              L_ptr->data (j) = data_l[j];
-              j++;
-            }
-          j = cidx_u[i];
-
-          while (j < cidx_u[i+1])
-            {
-              c = j;
-              if (opt == ROW)
-                {
-                  // The diagonal is removed from from L if milu = 'row'
-                  // That is because is convenient to have it inside 
-                  // the L part to carry out the process.
-                  if (ridx_u[j] == i)
-                    {
-                      j++;
-                      continue;
-                    }
-                  else
-                    c -= i;
-                }
-              U_ptr->data (c) = data_u[j];
-              U_ptr->ridx (c) = ridx_u[j];
-              j++;
-            }
-        }
-
-      if (opt == ROW) 
-        {
-          U = U.transpose ();
-          // The diagonal, conveniently permuted is added to U
-          U += diag.index (idx_vector::colon, perm_vec);
-          L = L.transpose ();
-        }
-    }
-}
-
-DEFUN_DLD (ilutp, args, nargout, "-*- texinfo -*-\n\
-@deftypefn  {Loadable Function} {[@var{L}, @var{U}] =} ilutp (@var{A})\n\
-@deftypefnx {Loadable Function} {[@var{L}, @var{U}] =} ilutp (@var{A}, \
-@var{droptol}, @var{thresh}, @var{milu}, @var{udiag})\n\
-@deftypefnx {Loadable Function} {[@var{L}, @var{U}, @var{P}] =} ilutp (@var{A})\n\
-@deftypefnx {Loadable Function} {[@var{L}, @var{U}, @var{P}] =} ilutp \
-(@var{A}, @var{droptol}, @var{thresh}, @var{milu}, @var{udiag})\n\
-\n\
-Computes the incomplete LU-factorization (ILU) with threshold and pivoting.\n\
-@code{[@var{L}, @var{U}] = ilutp (@var{A})} computes the default version of\n\
-ILU-factorization with threshold ILUT of @var{A}, such that \
-@code{@var{L} * @var{U}} is an approximation of the square sparse matrix \
-@var{A}. Pivoting is performed. Parameter @var{droptol} controls the fill-in of \
-output matrices. Default @var{droptol} = 0. Parameter @var{milu} = ['off'|'row'|'col'] \
-set if no row nor column sums are preserved, row sums are preserved or column sums are \
-preserved respectively. There are also additional diferences in the output matrices \
-depending on @var{milu} parameter. Default milu = 'off'. @var{thresh} controls the \
-selection of the pivot. Default @var{thresh} = 0. Parameter @var{udiag} indicates if \
-there will be replacement of 0s in the upper triangular factor with the value of \
-@var{droptol}. Default @var{udiag} = 0.\n\
-\n\
-For a full description of ILUTP behaviour and its options see ilu documentation.\n\
-\n\
-For more information about the algorithms themselves see:\n\n\
-[1] Saad, Yousef: Iterative Methods for Sparse Linear Systems. Second Edition. \
-Minneapolis, Minnesota: Siam 2003.\n\
-\n\
-@seealso{ilu, ilu0, iluc, ichol}\n\
-@end deftypefn")
-{
-  octave_value_list retval;
-
-  int nargin = args.length ();
-  std::string milu = "";
-  double droptol, thresh;
-  double udiag;
-
-
-  if (nargout < 2 || nargout > 3 || nargin < 1 || nargin > 5)
-    {
-      print_usage ();
-      return retval;
-    }
-
-  // To be matlab compatible 
-  if (args (0).is_empty ())
-    {
-      retval (0) = octave_value (SparseMatrix ());
-      retval (1) = octave_value (SparseMatrix ());
-      if (nargout == 3)
-        retval (2) = octave_value (SparseMatrix ()); 
-      return retval;
-    }
-
-  if (args (0).is_scalar_type () || !args (0).is_sparse_type () )
-    error ("ilutp: 1. parameter must be a sparse square matrix.");
-
-  if (! error_state && (nargin >= 2))
-    {
-      droptol = args (1).double_value ();
-      if (error_state || (droptol < 0) || ! args (1).is_real_scalar ())
-        error ("ilutp: 2. parameter must be a positive scalar.");
-    }
-
-  if (! error_state && (nargin >= 3))
-    {
-      thresh = args (2).double_value ();
-      if (error_state || !args (2).is_real_scalar () || (thresh < 0) || thresh > 1)
-        error ("ilutp: 3. parameter must be a scalar 0 <= thresh <= 1.");
-    }
-
-  if (! error_state && (nargin >= 4))
-    {
-      milu = args (3).string_value ();
-      if (error_state || !(milu == "row" || milu == "col" || milu == "off"))
-        error ("ilutp: 3. parameter must be 'row', 'col' or 'off' character string.");
-    }
-
-  if (! error_state && (nargin == 5))
-    {
-      udiag = args (4).double_value ();
-      if (error_state || ! args (4).is_real_scalar () || ((udiag != 0) 
-          && (udiag != 1)))
-        error ("ilutp: 5. parameter must be a scalar with value 1 or 0.");
-    }
-
-  if (! error_state)
-    {
-      octave_value_list param_list;
-      octave_idx_type nnz_u, nnz_l;
-      if (!args (0).is_complex_type ())
-        {
-          Array <double> rc_norm;
-          SparseMatrix sm = args (0).sparse_matrix_value ();
-          param_list.append (sm);
-          nnz_u =  (feval ("triu", param_list)(0).sparse_matrix_value ()).nnz (); 
-          param_list.append (-1);
-          nnz_l =  (feval ("tril", param_list)(0).sparse_matrix_value ()).nnz (); 
-          if (milu == "row")
-            param_list (1) = "rows";
-          else
-            param_list (1) = "cols";
-          rc_norm = feval ("norm", param_list)(0).vector_value ();
-          param_list.clear ();
-          Array <octave_idx_type> perm (dim_vector (sm.cols (), 1)); 
-          SparseMatrix U;
-          SparseMatrix L;
-          ilu_tp <SparseMatrix, double> (sm, L, U, nnz_u, nnz_l, rc_norm.fortran_vec (),
-                                         perm, droptol, thresh, milu, udiag);
-          if (! error_state)
-            {
-              param_list.append (octave_value (L.cols ()));
-              SparseMatrix eye = feval ("speye", param_list)(0).sparse_matrix_value ();
-              if (milu == "row")
-                {
-                  retval (0) = octave_value (L + eye);
-                  if (nargout == 2) 
-                    retval (1) = octave_value (U);
-                  else if (nargout == 3)
-                    {
-                     retval (1) = octave_value (U.index (idx_vector::colon, perm));
-                     retval (2) = octave_value (eye.index (idx_vector::colon, perm));
-                    }
-                }
-              else
-                {
-                  retval (1) = octave_value (U);
-                  if (nargout == 2) 
-                    retval (0) = octave_value (L + eye.index (perm, idx_vector::colon));
-                  else if (nargout == 3)
-                    {
-                      retval (0) = octave_value (L.index (perm, idx_vector::colon)  + eye);
-                      retval (2) = octave_value (eye.index (perm, idx_vector::colon));
-                    }
-                }
-            }
-        }
-      else
-        {
-          Array <Complex> rc_norm;
-          SparseComplexMatrix sm = args (0).sparse_complex_matrix_value ();
-          param_list.append (sm);
-          nnz_u =  feval ("triu", param_list)(0).sparse_complex_matrix_value ().nnz (); 
-          param_list.append (-1);
-          nnz_l =  feval ("tril", param_list)(0).sparse_complex_matrix_value ().nnz (); 
-          if (milu == "row")
-            param_list (1) = "rows";
-          else
-            param_list (1) = "cols";
-          rc_norm = feval ("norm", param_list)(0).complex_vector_value ();
-          Array <octave_idx_type> perm (dim_vector (sm.cols (), 1)); 
-          param_list.clear ();
-          SparseComplexMatrix U;
-          SparseComplexMatrix L;
-          ilu_tp < SparseComplexMatrix, Complex> 
-                  (sm, L, U, nnz_u, nnz_l, rc_norm.fortran_vec (), perm, 
-                   Complex (droptol), Complex (thresh), milu, udiag);
-
-          if (! error_state)
-            {
-              param_list.append (octave_value (L.cols ()));
-              SparseComplexMatrix eye = feval ("speye",
-                                               param_list)(0).sparse_complex_matrix_value ();
-              if (milu == "row")
-                {
-                  retval (0) = octave_value (L + eye);
-                  if (nargout == 2) 
-                    retval (1) = octave_value (U);
-                  else if (nargout == 3)
-                    {
-                     retval (1) = octave_value (U.index (idx_vector::colon, perm));
-                     retval (2) = octave_value (eye.index (idx_vector::colon, perm));
-                    }
-                }
-              else
-                {
-                  retval (1) = octave_value (U);
-                  if (nargout == 2) 
-                    retval (0) = octave_value (L + eye.index (perm, idx_vector::colon)) ;
-                  else if (nargout == 3)
-                    {
-                      retval (0) = octave_value (L.index (perm, idx_vector::colon)  + eye);
-                      retval (2) = octave_value (eye.index (perm, idx_vector::colon));
-                    }
-                }
-            }
-        }
-
-    }
-
-  return retval;
-}
-
-/* Test cases
-%!shared n_tiny, n_small, n_medium, n_large, A_tiny, A_small, A_medium, A_large
-%! n_tiny = 5;
-%! n_small = 40;
-%! n_medium = 600;
-%! n_large = 10000;
-%! A_tiny = spconvert([1 4 2 3 3 4 2 5; 1 1 2 3 4 4 5 5; 1 2 3 4 5 6 7 8]');
-%! A_tiny(1,1) += 1i;
-%! A_small = sprand(n_small, n_small, 1/n_small) + i * sprand(n_small, n_small, 1/n_small) + speye (n_small);
-%! A_medium = sprand(n_medium, n_medium, 1/n_medium) + i * sprand(n_medium, n_medium, 1/n_medium) + speye (n_medium);
-%! A_large = sprand(n_large, n_large, 1/n_large/10) + i * sprand(n_large, n_large, 1/n_large/10) + speye (n_large);
-%!# Input validation tests
-%!test 
-%!error [L,U] = ilutp(A_tiny, -1);
-%!error [L,U] = ilutp(A_tiny, [1,2]);
-%!error [L,U] = ilutp(A_tiny, 2i);
-%!error [L,U] = ilutp(A_tiny, 1, -1);
-%!error [L,U] = ilutp(A_tiny, 1, 2);
-%!error [L,U] = ilutp(A_tiny, 1, [1, 0]);
-%!error [L,U] = ilutp(A_tiny, 1, 1, 'foo');
-%!error [L,U] = ilutp(A_tiny, 1, 1, '');
-%!error [L,U] = ilutp(A_tiny, 1, 1, 1);
-%!error [L,U] = ilutp(A_tiny, 1, 1, [1,2]);
-%!error [L,U] = ilutp(A_tiny, 1, 1, 'off', 0.5);
-%!error [L,U] = ilutp(A_tiny, 1, 1, 'off', -1);
-%!error [L,U] = ilutp(A_tiny, 1, 1, 'off', 2);
-%!error [L,U] = ilutp(A_tiny, 1, 1, 'off', [1 ,0]);
-%! [L,U] = iluc ([]);
-%! assert (isempty (L), logical (1));
-%! assert (isempty (U), logical (1));
-%!error [L,U] = iluc (0+0i);
-%!error [L,U] = iluc (0i);
-%!error [L,U] = iluc (sparse (0+0i));
-%!error [L,U] = iluc (sparse (0i));
-%! [L,U] = iluc (sparse (2+0i));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2));
-%! [L,U] = iluc (sparse (2+2i));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2+2i));
-%! [L,U] = iluc (sparse (2i));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2i));
-%!test 
-%! [L,U] = iluc (A_tiny);
-%! assert (norm (A_tiny - L * U, "fro") / norm (A_tiny, "fro"), 0, n_tiny*eps);
-%!test 
-%! [L,U] = iluc (A_small);
-%! assert (norm (A_small - L * U, "fro") / norm (A_small, "fro"), 0, 1);
-%!test 
-%! [L,U] = iluc (A_medium);
-%! assert (norm (A_medium - L * U, "fro") / norm (A_medium, "fro"), 0, 1);
-%!test 
-%! [L,U] = iluc (A_large);
-%! assert (norm (A_large - L * U, "fro") / norm (A_large, "fro"), 0, 1);
-*/
-
-/* Test cases for real numbers.
-%!shared n_tiny, n_small, n_medium, n_large, A_tiny, A_small, A_medium, A_large
-%! n_tiny = 5;
-%! n_small = 40;
-%! n_medium = 600;
-%! n_large = 10000;
-%! A_tiny = spconvert ([1 4 2 3 3 4 2 5; 1 1 2 3 4 4 5 5; 1 2 3 4 5 6 7 8]');
-%! A_small = sprand (n_small, n_small, 1/n_small) + speye (n_small);
-%! A_medium = sprand (n_medium, n_medium, 1/n_medium) + speye (n_medium);
-%! A_large = sprand (n_large, n_large, 1/n_large/10) + speye (n_large);
-%!test 
-%! [L,U] = iluc ([]);
-%! assert (isempty (L), logical (1));
-%! assert (isempty (U), logical (1));
-%!error [L,U] = iluc (0);
-%!error [L,U] = iluc (sparse (0));
-%!test 
-%! [L,U] = iluc (sparse (2));
-%! assert (L, sparse (1));
-%! assert (U, sparse (2));
-%!test 
-%! [L,U] = iluc (A_tiny);
-%! assert (norm (A_tiny - L * U, "fro") / norm (A_tiny, "fro"), 0, n_tiny*eps);
-%!test 
-%! [L,U] = iluc (A_small);
-%! assert (norm (A_small - L * U, "fro") / norm (A_small, "fro"), 0, 1);
-%!test 
-%! [L,U] = iluc (A_medium);
-%! assert (norm (A_medium - L * U, "fro") / norm (A_medium, "fro"), 0, 1);
-%!test 
-%! [L,U] = iluc (A_large);
-%! assert (norm (A_large - L * U, "fro") / norm (A_large, "fro"), 0, 1);
-*/