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5451
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1 /* |
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2 |
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3 Copyright (C) 2005 David Bateman |
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4 |
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5 Octave is free software; you can redistribute it and/or modify it |
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6 under the terms of the GNU General Public License as published by the |
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7 Free Software Foundation; either version 2, or (at your option) any |
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8 later version. |
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9 |
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10 Octave is distributed in the hope that it will be useful, but WITHOUT |
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11 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or |
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12 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License |
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13 for more details. |
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14 |
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15 You should have received a copy of the GNU General Public License |
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16 along with this program; see the file COPYING. If not, write to the |
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17 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, |
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18 Boston, MA 02110-1301, USA. |
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19 |
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20 */ |
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21 |
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22 // This is the octave interface to ccolamd, which bore the copyright given |
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23 // in the help of the functions. |
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24 |
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25 #ifdef HAVE_CONFIG_H |
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26 #include <config.h> |
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27 #endif |
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28 |
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29 #include <cstdlib> |
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30 |
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31 #include <string> |
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32 #include <vector> |
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33 |
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34 #include "ov.h" |
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35 #include "defun-dld.h" |
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36 #include "pager.h" |
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37 #include "ov-re-mat.h" |
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38 |
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39 #include "ov-re-sparse.h" |
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40 #include "ov-cx-sparse.h" |
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41 |
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42 #include "oct-sparse.h" |
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43 |
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44 #ifdef IDX_TYPE_LONG |
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45 #define CCOLAMD_NAME(name) ccolamd_l ## name |
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46 #define CSYMAMD_NAME(name) csymamd_l ## name |
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47 #else |
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48 #define CCOLAMD_NAME(name) ccolamd ## name |
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49 #define CSYMAMD_NAME(name) csymamd ## name |
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50 #endif |
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51 |
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52 DEFUN_DLD (ccolamd, args, nargout, |
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53 "-*- texinfo -*-\n\ |
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54 @deftypefn {Loadable Function} {@var{p} =} ccolamd (@var{s})\n\ |
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55 @deftypefnx {Loadable Function} {@var{p} =} ccolamd (@var{s}, @var{knobs})\n\ |
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56 @deftypefnx {Loadable Function} {@var{p} =} ccolamd (@var{s}, @var{knobs}, @var{cmember})\n\ |
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57 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} ccolamd (@dots{})\n\ |
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58 \n\ |
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59 Constrained column approximate minimum degree permutation. @code{@var{p} =\n\ |
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60 ccolamd (@var{s})} returns the column approximate minimum degree permutation\n\ |
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61 vector for the sparse matrix @var{s}. For a non-symmetric matrix @var{s},\n\ |
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62 @code{@var{s}(:,@var{p})} tends to have sparser LU factors than @var{s}.\n\ |
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63 @code{chol (@var{s}(:,@var{p})'*@var{s}(:,@var{p}))} also tends to be\n\ |
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64 sparser than @code{chol (@var{s}'*@var{s})}. @code{@var{p} = ccolamd\n\ |
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65 (@var{s},1)} optimizes the ordering for @code{lu (@var{s}(:,@var{p}))}.\n\ |
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66 The ordering is followed by a column elimination tree post-ordering.\n\ |
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67 \n\ |
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68 @var{knobs} is an optional one- to five-element input vector, with a default\n\ |
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69 value of @code{[0 10 10 1 0]} if not present or empty. Entries not present\n\ |
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70 are set to their defaults.\n\ |
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71 \n\ |
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72 @table @code\n\ |
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73 @item @var{knobs}(1)\n\ |
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74 if nonzero, the ordering is optimized for @code{lu(S(:,p)). It will be a\n\ |
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75 poor ordering for @code{chol(@var{s}(:,@var{p})'*@var{s}(:,@var{p}))}. This\n\ |
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76 is the most important knob for ccolamd.\n\ |
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77 \n\ |
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78 @item @var{knob}(2)\n\ |
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79 if @var{s} is m-by-n, rows with more than @code{max(16,@var{knobs}(2)*\n\ |
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80 sqrt(n))} entries are ignored.\n\ |
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81 \n\ |
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82 @item @var{knob}(3)\n\ |
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83 columns with more than @code{max(16,@var{knobs}(3)*sqrt(min(m,n)))}\n\ |
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84 entries are ignored and ordered last in the output permutation (subject\n\ |
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85 to the cmember constraints).\n\ |
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86 \n\ |
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87 @item @var{knob}(4)\n\ |
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88 if nonzero, aggressive absorption is performed.\n\ |
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89 \n\ |
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90 @item @var{knob}(5)\n\ |
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91 if nonzero, statistics and knobs are printed.\n\ |
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92 \n\ |
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93 @end table\n\ |
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94 \n\ |
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95 @var{cmember} is an optional vector of length n. It defines the constraints\n\ |
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96 on the column ordering. If @code{@var{cmember}(j) = @var{c}}, then column j\n\ |
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97 is in constraint set @var{c} (@var{c} must be in the range 1 to n). In\n\ |
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98 the output permutation @var{p}, all columns in set 1 appear first, followed\n\ |
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99 by all columns in set 2, and so on. @code{@var{cmember} = ones(1,n)} if\n\ |
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100 not present or empty. @code{ccolamd (@var{s},[],1:n)} returns @code{1:n}\n\ |
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101 \n\ |
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102 @code{@var{p} = ccolamd(@var{s})} is about the same as @code{@var{p} =\n\ |
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103 colamd(@var{s}). @var{knobs} and its default values differ. @code{colamd}\n\ |
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104 always does aggressive absorption, and it finds an ordering suitable for\n\ |
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105 both @code{lu(@var{s}(:,@var{p}))} and @code{chol(@var{S}(:,@var{p})'*\n\ |
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106 @var{s}(:,@var{p}))}; it cannot optimize its ordering for @code{lu(@var{s}\n\ |
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107 (:,@var{p}))} to the extent that @code{ccolamd(@var{s},1)} can.\n\ |
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108 \n\ |
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109 @var{stats} is an optional 20-element output vector that provides data\n\ |
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110 about the ordering and the validity of the input matrix @var{s}. Ordering\n\ |
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111 statistics are in @code{@var{stats} (1:3)}. @code{@var{stats} (1)} and\n\ |
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112 @code{@var{stats} (2)} are the number of dense or empty rows and columns\n\ |
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113 ignored by CCOLAMD and @code{@var{stats} (3)} is the number of garbage\n\ |
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114 collections performed on the internal data structure used by CCOLAMD\n\ |
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115 (roughly of size @code{2.2 * nnz(@var{s}) + 4 * @var{m} + 7 * @var{n}}\n\ |
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116 integers).\n\ |
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117 \n\ |
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118 @code{@var{stats} (4:7)} provide information if CCOLAMD was able to\n\ |
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119 continue. The matrix is OK if @code{@var{stats} (4)} is zero, or 1 if\n\ |
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120 invalid. @code{@var{stats} (5)} is the rightmost column index that is\n\ |
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121 unsorted or contains duplicate entries, or zero if no such column exists.\n\ |
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122 @code{@var{stats} (6)} is the last seen duplicate or out-of-order row\n\ |
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123 index in the column index given by @code{@var{stats} (5)}, or zero if no\n\ |
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124 such row index exists. @code{@var{stats} (7)} is the number of duplicate\n\ |
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125 or out-of-order row indices. @code{@var{stats} (8:20)} is always zero in\n\ |
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126 the current version of CCOLAMD (reserved for future use).\n\ |
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127 \n\ |
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128 The authors of the code itself are S. Larimore, T. Davis (Uni of Florida)\n\ |
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129 and S. Rajamanickam in collaboration with J. Bilbert and E. Ng. Supported\n\ |
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130 by the National Science Foundation (DMS-9504974, DMS-9803599, CCR-0203270),\n\ |
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131 and a grant from Sandia National Lab. See\n\ |
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132 @url{http://www.cise.ufl.edu/research/sparse} for ccolamd, csymamd, amd,\n\ |
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133 colamd, symamd, and other related orderings.\n\ |
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134 \n\ |
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135 @end deftypefn\n\ |
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136 @seealso{colamd, csymamd}") |
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137 { |
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138 #ifdef HAVE_CCOLAMD |
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139 octave_value_list retval; |
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140 int nargin = args.length (); |
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141 int spumoni = 0; |
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142 |
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143 if (nargout < 0 || nargout > 2 || nargin < 0 || nargin > 3) |
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144 usage ("ccolamd: incorrect number of input and/or output arguments"); |
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145 else |
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146 { |
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147 // Get knobs |
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148 OCTAVE_LOCAL_BUFFER (double, knobs, CCOLAMD_KNOBS); |
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149 CCOLAMD_NAME (_set_defaults) (knobs); |
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150 |
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151 // Check for user-passed knobs |
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152 if (nargin > 1) |
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153 { |
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154 NDArray User_knobs = args(1).array_value (); |
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155 int nel_User_knobs = User_knobs.length (); |
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156 |
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157 if (nel_User_knobs > 0) |
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158 knobs [CCOLAMD_LU] = (User_knobs (0) != 0); |
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159 if (nel_User_knobs > 1) |
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160 knobs [CCOLAMD_DENSE_ROW] = User_knobs (1); |
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161 if (nel_User_knobs > 2) |
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162 knobs [CCOLAMD_DENSE_COL] = User_knobs (2); |
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163 if (nel_User_knobs > 3) |
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164 knobs [CCOLAMD_AGGRESSIVE] = (User_knobs (3) != 0); |
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165 if (nel_User_knobs > 4) |
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166 spumoni = (User_knobs (4) != 0); |
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167 |
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168 // print knob settings if spumoni is set |
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169 if (spumoni) |
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170 { |
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171 octave_stdout << "\nccolamd version " << CCOLAMD_MAIN_VERSION << "." |
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172 << CCOLAMD_SUB_VERSION << ", " << CCOLAMD_DATE |
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173 << ":\nknobs(1): " << User_knobs (0) << ", order for "; |
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174 if ( knobs [CCOLAMD_LU] != 0) |
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175 octave_stdout << "lu(A)\n"; |
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176 else |
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177 octave_stdout << "chol(A'*A)\n"; |
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178 |
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179 if (knobs [CCOLAMD_DENSE_ROW] >= 0) |
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180 octave_stdout << "knobs(2): " << User_knobs (1) |
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181 << ", rows with > max(16," |
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182 << knobs [CCOLAMD_DENSE_ROW] << "*sqrt(size(A,2)))" |
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183 << " entries removed\n"; |
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184 else |
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185 octave_stdout << "knobs(2): " << User_knobs (1) |
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186 << ", no dense rows removed\n"; |
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187 |
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188 if (knobs [CCOLAMD_DENSE_COL] >= 0) |
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189 octave_stdout << "knobs(3): " << User_knobs (2) |
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190 << ", cols with > max(16," |
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191 << knobs [CCOLAMD_DENSE_COL] << "*sqrt(size(A)))" |
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192 << " entries removed\n"; |
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193 else |
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194 octave_stdout << "knobs(3): " << User_knobs (2) |
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195 << ", no dense columns removed\n"; |
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196 |
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197 if (knobs [CCOLAMD_AGGRESSIVE] != 0) |
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198 octave_stdout << "knobs(4): " << User_knobs(3) |
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199 << ", aggressive absorption: yes"; |
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200 else |
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201 octave_stdout << "knobs(4): " << User_knobs(3) |
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202 << ", aggressive absorption: no"; |
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203 |
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204 octave_stdout << "knobs(5): " << User_knobs (4) |
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205 << ", statistics and knobs printed\n"; |
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206 } |
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207 } |
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208 |
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209 octave_idx_type n_row, n_col, nnz; |
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210 octave_idx_type *ridx, *cidx; |
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211 SparseComplexMatrix scm; |
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212 SparseMatrix sm; |
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213 |
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214 if (args(0).class_name () == "sparse") |
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215 { |
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216 if (args(0).is_complex_type ()) |
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217 { |
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218 scm = args(0). sparse_complex_matrix_value (); |
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219 n_row = scm.rows (); |
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220 n_col = scm.cols (); |
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221 nnz = scm.nnz (); |
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222 ridx = scm.xridx (); |
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223 cidx = scm.xcidx (); |
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224 } |
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225 else |
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226 { |
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227 sm = args(0).sparse_matrix_value (); |
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228 |
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229 n_row = sm.rows (); |
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230 n_col = sm.cols (); |
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231 nnz = sm.nnz (); |
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232 ridx = sm.xridx (); |
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233 cidx = sm.xcidx (); |
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234 } |
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235 } |
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236 else |
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237 { |
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238 if (args(0).is_complex_type ()) |
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239 sm = SparseMatrix (real (args(0).complex_matrix_value ())); |
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240 else |
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241 sm = SparseMatrix (args(0).matrix_value ()); |
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242 |
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243 n_row = sm.rows (); |
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244 n_col = sm.cols (); |
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245 nnz = sm.nnz (); |
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246 ridx = sm.xridx (); |
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247 cidx = sm.xcidx (); |
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248 } |
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249 |
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250 // Allocate workspace for ccolamd |
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251 OCTAVE_LOCAL_BUFFER (octave_idx_type, p, n_col+1); |
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252 for (octave_idx_type i = 0; i < n_col+1; i++) |
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253 p[i] = cidx [i]; |
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254 |
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255 octave_idx_type Alen = CCOLAMD_NAME (_recommended) (nnz, n_row, n_col); |
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256 OCTAVE_LOCAL_BUFFER (octave_idx_type, A, Alen); |
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257 for (octave_idx_type i = 0; i < nnz; i++) |
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258 A[i] = ridx [i]; |
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259 |
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260 OCTAVE_LOCAL_BUFFER (octave_idx_type, stats, CCOLAMD_STATS); |
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261 |
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262 if (nargin > 2) |
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263 { |
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264 NDArray in_cmember = args(2).array_value(); |
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265 octave_idx_type cslen = in_cmember.length(); |
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266 OCTAVE_LOCAL_BUFFER (octave_idx_type, cmember, cslen); |
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267 for (octave_idx_type i = 0; i < cslen; i++) |
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268 // convert cmember from 1-based to 0-based |
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269 cmember[i] = static_cast<octave_idx_type>(in_cmember(i) - 1); |
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270 |
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271 if (cslen != n_col) |
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272 error ("ccolamd: cmember must be of length equal to #cols of A"); |
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273 else |
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274 // Order the columns (destroys A) |
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275 if (! CCOLAMD_NAME () (n_row, n_col, Alen, A, p, knobs, stats, cmember)) |
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276 { |
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277 CCOLAMD_NAME (_report) (stats) ; |
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278 error ("ccolamd: internal error!"); |
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279 return retval; |
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280 } |
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281 } |
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282 else |
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283 { |
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284 // Order the columns (destroys A) |
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285 if (! CCOLAMD_NAME () (n_row, n_col, Alen, A, p, knobs, stats, NULL)) |
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286 { |
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287 CCOLAMD_NAME (_report) (stats) ; |
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288 error ("ccolamd: internal error!"); |
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289 return retval; |
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290 } |
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291 } |
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292 |
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293 // return the permutation vector |
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294 NDArray out_perm (dim_vector (1, n_col)); |
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295 for (octave_idx_type i = 0; i < n_col; i++) |
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296 out_perm(i) = p [i] + 1; |
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297 |
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298 retval (0) = out_perm; |
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299 |
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300 // print stats if spumoni > 0 |
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301 if (spumoni > 0) |
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302 CCOLAMD_NAME (_report) (stats) ; |
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303 |
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304 // Return the stats vector |
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305 if (nargout == 2) |
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306 { |
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307 NDArray out_stats (dim_vector (1, CCOLAMD_STATS)); |
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308 for (octave_idx_type i = 0 ; i < CCOLAMD_STATS ; i++) |
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309 out_stats (i) = stats [i] ; |
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310 retval(1) = out_stats; |
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311 |
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312 // fix stats (5) and (6), for 1-based information on |
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313 // jumbled matrix. note that this correction doesn't |
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314 // occur if symamd returns FALSE |
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315 out_stats (CCOLAMD_INFO1) ++ ; |
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316 out_stats (CCOLAMD_INFO2) ++ ; |
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317 } |
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318 } |
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319 |
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320 return retval; |
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321 #else |
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322 |
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323 error ("ccolamd: not available in this version of Octave"); |
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324 |
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325 #endif |
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326 } |
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327 |
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328 DEFUN_DLD (csymamd, args, nargout, |
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329 "-*- texinfo -*-\n\ |
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330 @deftypefn {Loadable Function} {@var{p} =} csymamd (@var{s})\n\ |
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331 @deftypefnx {Loadable Function} {@var{p} =} csymamd (@var{s}, @var{knobs})\n\ |
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332 @deftypefnx {Loadable Function} {@var{p} =} csymamd (@var{s}, @var{knobs}, @var{cmember})\n\ |
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333 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} csymamd (@dots{})\n\ |
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334 \n\ |
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335 For a symmetric positive definite matrix @var{s}, returns the permutation\n\ |
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336 vector @var{p} such that @code{@var{s}(@var{p},@var{p})} tends to have a\n\ |
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337 sparser Cholesky factor than @var{s}. Sometimes @code{csymamd} works well\n\ |
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338 for symmetric indefinite matrices too. The matrix @var{s} is assumed to\n\ |
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339 be symmetric; only the strictly lower triangular part is referenced.\n\ |
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340 @var{s} must be square. The ordering is followed by an elimination tree\n\ |
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341 post-ordering.\n\ |
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342 \n\ |
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343 @var{knobs} is an optional one- to three-element input vector, with a\n\ |
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344 default value of @code{[10 1 0]} if present or empty. Entries not\n\ |
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345 present are set to their defaults.\n\ |
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346 \n\ |
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347 @table @code\n\ |
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348 @item @var{knobs}(1)\n\ |
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349 If @var{s} is n-by-n, then rows and columns with more than\n\ |
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350 @code{max(16,@var{knobs}(1)*sqrt(n))} entries are ignored, and ordered\n\ |
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351 last in the output permutation (subject to the cmember constraints).\n\ |
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352 \n\ |
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353 @item @var{knobs}(2)\n\ |
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354 If nonzero, aggressive absorption is performed.\n\ |
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355 \n\ |
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356 @item @var{knobs}(3)\n\ |
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357 If nonzero, statistics and knobs are printed.\n\ |
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358 \n\ |
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359 @end table\n\ |
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360 \n\ |
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361 @var{cmember} is an optional vector of length n. It defines the constraints\n\ |
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362 on the ordering. If @code{@var{cmember}(j) = @var{s}}, then row/column j is\n\ |
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363 in constraint set @var{c} (@var{c} must be in the range 1 to n). In the\n\ |
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364 output permutation @var{p}, rows/columns in set 1 appear first, followed\n\ |
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365 by all rows/columns in set 2, and so on. @code{@var{cmember} = ones(1,n)}\n\ |
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366 if not present or empty. @code{csymamd(@var{s},[],1:n)} returns @code{1:n}.\n\ |
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367 \n\ |
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368 @code{@var{p} = csymamd(@var{s})} is about the same as @code{@var{p} =\n\ |
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369 symamd(@var{s})}. @var{knobs} and its default values differ.\n\ |
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370 \n\ |
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371 @code{@var{stats} (4:7)} provide information if CCOLAMD was able to\n\ |
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372 continue. The matrix is OK if @code{@var{stats} (4)} is zero, or 1 if\n\ |
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373 invalid. @code{@var{stats} (5)} is the rightmost column index that is\n\ |
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374 unsorted or contains duplicate entries, or zero if no such column exists.\n\ |
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375 @code{@var{stats} (6)} is the last seen duplicate or out-of-order row\n\ |
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376 index in the column index given by @code{@var{stats} (5)}, or zero if no\n\ |
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377 such row index exists. @code{@var{stats} (7)} is the number of duplicate\n\ |
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378 or out-of-order row indices. @code{@var{stats} (8:20)} is always zero in\n\ |
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379 the current version of CCOLAMD (reserved for future use).\n\ |
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380 \n\ |
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381 The authors of the code itself are S. Larimore, T. Davis (Uni of Florida)\n\ |
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382 and S. Rajamanickam in collaboration with J. Bilbert and E. Ng. Supported\n\ |
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383 by the National Science Foundation (DMS-9504974, DMS-9803599, CCR-0203270),\n\ |
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384 and a grant from Sandia National Lab. See\n\ |
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385 @url{http://www.cise.ufl.edu/research/sparse} for ccolamd, csymamd, amd,\n\ |
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386 colamd, symamd, and other related orderings.\n\ |
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387 \n\ |
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388 @end deftypefn\n\ |
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389 @seealso{symamd, ccolamd}") |
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390 { |
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391 #if HAVE_CCOLAMD |
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392 octave_value_list retval; |
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393 int nargin = args.length (); |
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394 int spumoni = 0; |
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395 |
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396 if (nargout < 0 || nargout > 2 || nargin < 0 || nargin > 3) |
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397 usage ("ccolamd: incorrect number of input and/or output arguments"); |
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398 else |
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399 { |
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400 // Get knobs |
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401 OCTAVE_LOCAL_BUFFER (double, knobs, CCOLAMD_KNOBS); |
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402 CCOLAMD_NAME (_set_defaults) (knobs); |
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403 |
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404 // Check for user-passed knobs |
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405 if (nargin > 1) |
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406 { |
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407 NDArray User_knobs = args(1).array_value (); |
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408 int nel_User_knobs = User_knobs.length (); |
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409 |
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410 if (nel_User_knobs > 0) |
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411 knobs [CCOLAMD_DENSE_ROW] = User_knobs (0); |
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412 if (nel_User_knobs > 0) |
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413 knobs [CCOLAMD_AGGRESSIVE] = User_knobs (1); |
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414 if (nel_User_knobs > 1) |
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415 spumoni = (int) User_knobs (2); |
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416 |
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417 // print knob settings if spumoni is set |
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418 if (spumoni) |
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419 { |
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420 octave_stdout << "\ncsymamd version " << CCOLAMD_MAIN_VERSION << "." |
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421 << CCOLAMD_SUB_VERSION << ", " << CCOLAMD_DATE << "\n"; |
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422 |
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423 if (knobs [CCOLAMD_DENSE_ROW] >= 0) |
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424 octave_stdout << "knobs(1): " << User_knobs (0) |
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425 << ", rows/cols with > max(16," |
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426 << knobs [CCOLAMD_DENSE_ROW] << "*sqrt(size(A,2)))" |
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427 << " entries removed\n"; |
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428 else |
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429 octave_stdout << "knobs(1): " << User_knobs (0) |
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430 << ", no dense rows/cols removed\n"; |
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431 |
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432 if (knobs [CCOLAMD_AGGRESSIVE] != 0) |
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433 octave_stdout << "knobs(2): " << User_knobs(1) |
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434 << ", aggressive absorption: yes"; |
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435 else |
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436 octave_stdout << "knobs(2): " << User_knobs(1) |
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437 << ", aggressive absorption: no"; |
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438 |
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439 |
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440 octave_stdout << "knobs(3): " << User_knobs (2) |
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441 << ", statistics and knobs printed\n"; |
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442 } |
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443 } |
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444 |
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445 octave_idx_type n_row, n_col, nnz; |
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446 octave_idx_type *ridx, *cidx; |
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447 SparseMatrix sm; |
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448 SparseComplexMatrix scm; |
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449 |
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450 if (args(0).class_name () == "sparse") |
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451 { |
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452 if (args(0).is_complex_type ()) |
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453 { |
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454 scm = args(0).sparse_complex_matrix_value (); |
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455 n_row = scm.rows (); |
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456 n_col = scm.cols (); |
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457 nnz = scm.nnz (); |
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458 ridx = scm.xridx (); |
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459 cidx = scm.xcidx (); |
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460 } |
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461 else |
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462 { |
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463 sm = args(0).sparse_matrix_value (); |
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464 n_row = sm.rows (); |
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465 n_col = sm.cols (); |
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466 nnz = sm.nnz (); |
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467 ridx = sm.xridx (); |
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468 cidx = sm.xcidx (); |
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469 } |
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470 } |
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471 else |
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472 { |
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473 if (args(0).is_complex_type ()) |
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474 sm = SparseMatrix (real (args(0).complex_matrix_value ())); |
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475 else |
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476 sm = SparseMatrix (args(0).matrix_value ()); |
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477 |
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478 n_row = sm.rows (); |
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479 n_col = sm.cols (); |
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480 nnz = sm.nnz (); |
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481 ridx = sm.xridx (); |
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482 cidx = sm.xcidx (); |
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483 } |
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484 |
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485 if (n_row != n_col) |
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486 { |
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487 error ("symamd: matrix must be square"); |
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488 return retval; |
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489 } |
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490 |
|
|
491 // Allocate workspace for symamd |
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492 OCTAVE_LOCAL_BUFFER (octave_idx_type, perm, n_col+1); |
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493 OCTAVE_LOCAL_BUFFER (octave_idx_type, stats, CCOLAMD_STATS); |
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494 |
|
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495 if (nargin > 2) |
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496 { |
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497 NDArray in_cmember = args(2).array_value(); |
|
|
498 octave_idx_type cslen = in_cmember.length(); |
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499 OCTAVE_LOCAL_BUFFER (octave_idx_type, cmember, cslen); |
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500 for (octave_idx_type i = 0; i < cslen; i++) |
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501 // convert cmember from 1-based to 0-based |
|
|
502 cmember[i] = static_cast<octave_idx_type>(in_cmember(i) - 1); |
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503 |
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504 if (cslen != n_col) |
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505 error ("ccolamd: cmember must be of length equal to #cols of A"); |
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506 else |
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|
507 if (!CSYMAMD_NAME () (n_col, ridx, cidx, perm, knobs, stats, |
|
|
508 &calloc, &free, cmember, -1)) |
|
|
509 { |
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|
510 CSYMAMD_NAME (_report) (stats) ; |
|
|
511 error ("symamd: internal error!") ; |
|
|
512 return retval; |
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|
513 } |
|
|
514 } |
|
|
515 else |
|
|
516 { |
|
|
517 if (!CSYMAMD_NAME () (n_col, ridx, cidx, perm, knobs, stats, |
|
|
518 &calloc, &free, NULL, -1)) |
|
|
519 { |
|
|
520 CSYMAMD_NAME (_report) (stats) ; |
|
|
521 error ("symamd: internal error!") ; |
|
|
522 return retval; |
|
|
523 } |
|
|
524 } |
|
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525 |
|
|
526 // return the permutation vector |
|
|
527 NDArray out_perm (dim_vector (1, n_col)); |
|
|
528 for (octave_idx_type i = 0; i < n_col; i++) |
|
|
529 out_perm(i) = perm [i] + 1; |
|
|
530 |
|
|
531 retval (0) = out_perm; |
|
|
532 |
|
|
533 // Return the stats vector |
|
|
534 if (nargout == 2) |
|
|
535 { |
|
|
536 NDArray out_stats (dim_vector (1, CCOLAMD_STATS)); |
|
|
537 for (octave_idx_type i = 0 ; i < CCOLAMD_STATS ; i++) |
|
|
538 out_stats (i) = stats [i] ; |
|
|
539 retval(1) = out_stats; |
|
|
540 |
|
|
541 // fix stats (5) and (6), for 1-based information on |
|
|
542 // jumbled matrix. note that this correction doesn't |
|
|
543 // occur if symamd returns FALSE |
|
|
544 out_stats (CCOLAMD_INFO1) ++ ; |
|
|
545 out_stats (CCOLAMD_INFO2) ++ ; |
|
|
546 } |
|
|
547 |
|
|
548 // print stats if spumoni > 0 |
|
|
549 if (spumoni > 0) |
|
|
550 CSYMAMD_NAME (_report) (stats) ; |
|
|
551 |
|
|
552 // Return the stats vector |
|
|
553 if (nargout == 2) |
|
|
554 { |
|
|
555 NDArray out_stats (dim_vector (1, CCOLAMD_STATS)); |
|
|
556 for (octave_idx_type i = 0 ; i < CCOLAMD_STATS ; i++) |
|
|
557 out_stats (i) = stats [i] ; |
|
|
558 retval(1) = out_stats; |
|
|
559 |
|
|
560 // fix stats (5) and (6), for 1-based information on |
|
|
561 // jumbled matrix. note that this correction doesn't |
|
|
562 // occur if symamd returns FALSE |
|
|
563 out_stats (CCOLAMD_INFO1) ++ ; |
|
|
564 out_stats (CCOLAMD_INFO2) ++ ; |
|
|
565 } |
|
|
566 } |
|
|
567 |
|
|
568 return retval; |
|
|
569 #else |
|
|
570 |
|
|
571 error ("csymamd: not available in this version of Octave"); |
|
|
572 |
|
|
573 #endif |
|
|
574 } |
|
|
575 |
|
|
576 /* |
|
|
577 ;;; Local Variables: *** |
|
|
578 ;;; mode: C++ *** |
|
|
579 ;;; End: *** |
|
|
580 */ |
