Mercurial > hg > octave-nkf
annotate src/DLD-FUNCTIONS/chol.cc @ 7554:40574114c514
implement Cholesky factorization updating
author | Jaroslav Hajek <highegg@gmail.com> |
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date | Tue, 04 Mar 2008 22:25:50 -0500 |
parents | f3c00dc0912b |
children | 07522d7dcdf8 |
rev | line source |
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2928 | 1 /* |
2 | |
7017 | 3 Copyright (C) 1996, 1997, 1999, 2000, 2002, 2005, 2006, 2007 |
4 John W. Eaton | |
2928 | 5 |
6 This file is part of Octave. | |
7 | |
8 Octave is free software; you can redistribute it and/or modify it | |
9 under the terms of the GNU General Public License as published by the | |
7016 | 10 Free Software Foundation; either version 3 of the License, or (at your |
11 option) any later version. | |
2928 | 12 |
13 Octave is distributed in the hope that it will be useful, but WITHOUT | |
14 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
15 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
16 for more details. | |
17 | |
18 You should have received a copy of the GNU General Public License | |
7016 | 19 along with Octave; see the file COPYING. If not, see |
20 <http://www.gnu.org/licenses/>. | |
2928 | 21 |
22 */ | |
23 | |
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24 // The cholupdate function was written by Jaroslav Hajek |
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25 // <highegg@gmail.com>, Copyright (C) 2008 VZLU Prague, a.s., Czech |
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26 // Republic. |
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27 |
2928 | 28 #ifdef HAVE_CONFIG_H |
29 #include <config.h> | |
30 #endif | |
31 | |
32 #include "CmplxCHOL.h" | |
33 #include "dbleCHOL.h" | |
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34 #include "SparseCmplxCHOL.h" |
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35 #include "SparsedbleCHOL.h" |
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36 #include "oct-spparms.h" |
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37 #include "sparse-util.h" |
2928 | 38 |
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39 #include "ov-re-sparse.h" |
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40 #include "ov-cx-sparse.h" |
2928 | 41 #include "defun-dld.h" |
42 #include "error.h" | |
43 #include "gripes.h" | |
44 #include "oct-obj.h" | |
45 #include "utils.h" | |
46 | |
47 DEFUN_DLD (chol, args, nargout, | |
3548 | 48 "-*- texinfo -*-\n\ |
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49 @deftypefn {Loadable Function} {@var{r}} = chol (@var{a})\n\ |
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50 @deftypefnx {Loadable Function} {[@var{r}, @var{p}]} = chol (@var{a})\n\ |
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51 @deftypefnx {Loadable Function} {[@var{r}, @var{p}, @var{q}]} = chol (@var{s})\n\ |
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52 @deftypefnx {Loadable Function} {[@var{r}, @var{p}, @var{q}]} = chol (@var{s}, 'vector')\n\ |
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53 @deftypefnx {Loadable Function} {[@var{l}, @dots{}]} = chol (@dots{}, 'lower')\n\ |
3372 | 54 @cindex Cholesky factorization\n\ |
55 Compute the Cholesky factor, @var{r}, of the symmetric positive definite\n\ | |
56 matrix @var{a}, where\n\ | |
57 @iftex\n\ | |
58 @tex\n\ | |
59 $ R^T R = A $.\n\ | |
60 @end tex\n\ | |
61 @end iftex\n\ | |
62 @ifinfo\n\ | |
63 \n\ | |
64 @example\n\ | |
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65 @var{r}' * @var{r} = @var{a}.\n\ |
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66 @end example\n\ |
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67 @end ifinfo\n\ |
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68 \n\ |
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69 Called with one output argument @code{chol} fails if @var{a} or @var{s} is\n\ |
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70 not positive definite. With two or more output arguments @var{p} flags\n\ |
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71 whether the matrix was positive definite and @code{chol} does not fail. A\n\ |
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72 zero value indicated that the matrix was positive definite and the @var{r}\n\ |
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73 gives the factorization, annd @var{p} will have a positive value otherwise.\n\ |
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74 \n\ |
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75 If called with 3 outputs then a sparsity preserving row/column permutation\n\ |
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76 is applied to @var{a} prior to the factorization. That is @var{r}\n\ |
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77 is the factorization of @code{@var{a}(@var{q},@var{q})} such that\n\ |
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78 @iftex\n\ |
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79 @tex\n\ |
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80 $ R^T R = Q^T A Q$.\n\ |
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81 @end tex\n\ |
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82 @end iftex\n\ |
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83 @ifinfo\n\ |
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84 \n\ |
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85 @example\n\ |
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86 @var{r}' * @var{r} = @var{q}' * @var{a} * @var{q}.\n\ |
3372 | 87 @end example\n\ |
88 @end ifinfo\n\ | |
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89 \n\ |
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90 The sparsity preserving permutation is generally returned as a matrix.\n\ |
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91 However, given the flag 'vector', @var{q} will be returned as a vector\n\ |
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92 such that\n\ |
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93 @iftex\n\ |
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94 @tex\n\ |
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95 $ R^T R = A (Q, Q)$.\n\ |
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96 @end tex\n\ |
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97 @end iftex\n\ |
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98 @ifinfo\n\ |
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99 \n\ |
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100 @example\n\ |
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101 @var{r}' * @var{r} = a (@var{q}, @var{q}).\n\ |
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102 @end example\n\ |
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103 @end ifinfo\n\ |
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104 \n\ |
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105 Called with either a sparse or full matrix and uing the 'lower' flag,\n\ |
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106 @code{chol} returns the lower triangular factorization such that\n\ |
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107 @iftex\n\ |
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108 @tex\n\ |
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109 $ L L^T = A $.\n\ |
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110 @end tex\n\ |
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111 @end iftex\n\ |
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112 @ifinfo\n\ |
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113 \n\ |
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114 @example\n\ |
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115 @var{l} * @var{l}' = @var{a}.\n\ |
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116 @end example\n\ |
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117 @end ifinfo\n\ |
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118 \n\ |
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119 In general the lower trinagular factorization is significantly faster for\n\ |
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120 sparse matrices.\n\ |
5340 | 121 @seealso{cholinv, chol2inv}\n\ |
3372 | 122 @end deftypefn") |
2928 | 123 { |
124 octave_value_list retval; | |
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125 int nargin = args.length (); |
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126 bool LLt = false; |
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127 bool vecout = false; |
2928 | 128 |
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129 if (nargin < 1 || nargin > 3 || nargout > 3 |
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130 || (! args(0).is_sparse_type () && nargout > 2)) |
2928 | 131 { |
5823 | 132 print_usage (); |
2928 | 133 return retval; |
134 } | |
135 | |
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136 int n = 1; |
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137 while (n < nargin && ! error_state) |
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138 { |
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139 std::string tmp = args(n++).string_value (); |
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140 |
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141 if (! error_state ) |
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142 { |
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143 if (tmp.compare ("vector") == 0) |
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144 vecout = true; |
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145 else if (tmp.compare ("lower") == 0) |
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146 LLt = true; |
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147 else if (tmp.compare ("upper") == 0) |
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148 LLt = false; |
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149 else |
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150 error ("chol: unexpected second or third input"); |
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151 } |
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152 else |
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153 error ("chol: expecting trailing string arguments"); |
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154 } |
2928 | 155 |
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156 if (! error_state) |
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157 { |
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158 octave_value arg = args(0); |
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159 |
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160 octave_idx_type nr = arg.rows (); |
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161 octave_idx_type nc = arg.columns (); |
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162 bool natural = (nargout != 3); |
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163 |
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164 int arg_is_empty = empty_arg ("chol", nr, nc); |
2928 | 165 |
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166 if (arg_is_empty < 0) |
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167 return retval; |
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168 if (arg_is_empty > 0) |
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169 return octave_value (Matrix ()); |
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170 |
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171 if (arg.is_sparse_type ()) |
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172 { |
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173 if (arg.is_real_type ()) |
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174 { |
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175 SparseMatrix m = arg.sparse_matrix_value (); |
2928 | 176 |
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177 if (! error_state) |
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178 { |
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179 octave_idx_type info; |
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180 SparseCHOL fact (m, info, natural); |
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181 if (nargout == 3) |
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182 if (vecout) |
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183 retval(2) = fact.perm (); |
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184 else |
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185 retval(2) = fact.Q(); |
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187 if (nargout > 1 || info == 0) |
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188 { |
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189 retval(1) = fact.P(); |
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190 if (LLt) |
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191 retval(0) = fact.L(); |
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192 else |
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193 retval(0) = fact.R(); |
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194 } |
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195 else |
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196 error ("chol: matrix not positive definite"); |
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197 } |
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198 } |
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199 else if (arg.is_complex_type ()) |
3243 | 200 { |
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201 SparseComplexMatrix m = arg.sparse_complex_matrix_value (); |
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202 |
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203 if (! error_state) |
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204 { |
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205 octave_idx_type info; |
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206 SparseComplexCHOL fact (m, info, natural); |
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207 |
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208 if (nargout == 3) |
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209 if (vecout) |
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210 retval(2) = fact.perm (); |
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211 else |
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212 retval(2) = fact.Q(); |
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213 |
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214 if (nargout > 1 || info == 0) |
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215 { |
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216 retval(1) = fact.P(); |
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217 if (LLt) |
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218 retval(0) = fact.L(); |
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219 else |
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220 retval(0) = fact.R(); |
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221 } |
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222 else |
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223 error ("chol: matrix not positive definite"); |
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224 } |
3243 | 225 } |
3244 | 226 else |
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227 gripe_wrong_type_arg ("chol", arg); |
2928 | 228 } |
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229 else |
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230 { |
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231 if (arg.is_real_type ()) |
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232 { |
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233 Matrix m = arg.matrix_value (); |
2928 | 234 |
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235 if (! error_state) |
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236 { |
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237 octave_idx_type info; |
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238 CHOL fact (m, info); |
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239 if (nargout == 2 || info == 0) |
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240 { |
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241 retval(1) = static_cast<double> (info); |
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242 if (LLt) |
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243 retval(0) = fact.chol_matrix ().transpose (); |
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244 else |
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245 retval(0) = fact.chol_matrix (); |
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246 } |
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247 else |
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248 error ("chol: matrix not positive definite"); |
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249 } |
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250 } |
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251 else if (arg.is_complex_type ()) |
3243 | 252 { |
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253 ComplexMatrix m = arg.complex_matrix_value (); |
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254 |
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255 if (! error_state) |
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256 { |
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257 octave_idx_type info; |
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258 ComplexCHOL fact (m, info); |
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259 if (nargout == 2 || info == 0) |
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260 { |
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261 retval(1) = static_cast<double> (info); |
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262 if (LLt) |
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263 retval(0) = fact.chol_matrix ().hermitian (); |
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264 else |
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265 retval(0) = fact.chol_matrix (); |
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266 } |
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267 else |
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268 error ("chol: matrix not positive definite"); |
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269 } |
3243 | 270 } |
3244 | 271 else |
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272 gripe_wrong_type_arg ("chol", arg); |
2928 | 273 } |
274 } | |
275 | |
276 return retval; | |
277 } | |
278 | |
5760 | 279 DEFUN_DLD (cholinv, args, , |
5340 | 280 "-*- texinfo -*-\n\ |
281 @deftypefn {Loadable Function} {} cholinv (@var{a})\n\ | |
5448 | 282 Use the Cholesky factorization to compute the inverse of the\n\ |
5340 | 283 symmetric positive definite matrix @var{a}.\n\ |
284 @seealso{chol, chol2inv}\n\ | |
285 @end deftypefn") | |
286 { | |
287 octave_value retval; | |
288 | |
289 int nargin = args.length (); | |
290 | |
291 if (nargin == 1) | |
292 { | |
293 octave_value arg = args(0); | |
294 | |
295 octave_idx_type nr = arg.rows (); | |
296 octave_idx_type nc = arg.columns (); | |
297 | |
298 if (nr == 0 || nc == 0) | |
299 retval = Matrix (); | |
300 else | |
301 { | |
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302 if (arg.is_sparse_type ()) |
5340 | 303 { |
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304 if (arg.is_real_type ()) |
5340 | 305 { |
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306 SparseMatrix m = arg.sparse_matrix_value (); |
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307 |
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308 if (! error_state) |
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309 { |
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310 octave_idx_type info; |
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311 SparseCHOL chol (m, info); |
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312 if (info == 0) |
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313 retval = chol.inverse (); |
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314 else |
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315 error ("cholinv: matrix not positive definite"); |
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316 } |
5340 | 317 } |
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318 else if (arg.is_complex_type ()) |
5340 | 319 { |
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320 SparseComplexMatrix m = arg.sparse_complex_matrix_value (); |
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321 |
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322 if (! error_state) |
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323 { |
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324 octave_idx_type info; |
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325 SparseComplexCHOL chol (m, info); |
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326 if (info == 0) |
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327 retval = chol.inverse (); |
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328 else |
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329 error ("cholinv: matrix not positive definite"); |
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330 } |
5340 | 331 } |
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332 else |
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333 gripe_wrong_type_arg ("cholinv", arg); |
5340 | 334 } |
335 else | |
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336 { |
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337 if (arg.is_real_type ()) |
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338 { |
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339 Matrix m = arg.matrix_value (); |
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340 |
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341 if (! error_state) |
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342 { |
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343 octave_idx_type info; |
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344 CHOL chol (m, info); |
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345 if (info == 0) |
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346 retval = chol.inverse (); |
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347 else |
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348 error ("cholinv: matrix not positive definite"); |
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349 } |
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350 } |
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351 else if (arg.is_complex_type ()) |
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352 { |
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353 ComplexMatrix m = arg.complex_matrix_value (); |
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354 |
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355 if (! error_state) |
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356 { |
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357 octave_idx_type info; |
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358 ComplexCHOL chol (m, info); |
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359 if (info == 0) |
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360 retval = chol.inverse (); |
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361 else |
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362 error ("cholinv: matrix not positive definite"); |
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363 } |
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364 } |
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365 else |
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366 gripe_wrong_type_arg ("chol", arg); |
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367 } |
5340 | 368 } |
369 } | |
370 else | |
5823 | 371 print_usage (); |
5340 | 372 |
373 return retval; | |
374 } | |
375 | |
5760 | 376 DEFUN_DLD (chol2inv, args, , |
5340 | 377 "-*- texinfo -*-\n\ |
5343 | 378 @deftypefn {Loadable Function} {} chol2inv (@var{u})\n\ |
5340 | 379 Invert a symmetric, positive definite square matrix from its Cholesky\n\ |
5343 | 380 decomposition, @var{u}. Note that @var{u} should be an upper-triangular\n\ |
381 matrix with positive diagonal elements. @code{chol2inv (@var{u})}\n\ | |
382 provides @code{inv (@var{u}'*@var{u})} but it is much faster than\n\ | |
383 using @code{inv}.\n\ | |
5340 | 384 @seealso{chol, cholinv}\n\ |
385 @end deftypefn") | |
386 { | |
387 octave_value retval; | |
388 | |
389 int nargin = args.length (); | |
390 | |
391 if (nargin == 1) | |
392 { | |
393 octave_value arg = args(0); | |
394 | |
395 octave_idx_type nr = arg.rows (); | |
396 octave_idx_type nc = arg.columns (); | |
397 | |
398 if (nr == 0 || nc == 0) | |
399 retval = Matrix (); | |
400 else | |
401 { | |
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402 if (arg.is_sparse_type ()) |
5340 | 403 { |
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404 if (arg.is_real_type ()) |
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405 { |
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406 SparseMatrix r = arg.sparse_matrix_value (); |
5340 | 407 |
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408 if (! error_state) |
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409 retval = chol2inv (r); |
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410 } |
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411 else if (arg.is_complex_type ()) |
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412 { |
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413 SparseComplexMatrix r = arg.sparse_complex_matrix_value (); |
5340 | 414 |
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415 if (! error_state) |
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416 retval = chol2inv (r); |
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417 } |
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418 else |
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419 gripe_wrong_type_arg ("chol2inv", arg); |
5340 | 420 } |
421 else | |
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422 { |
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423 if (arg.is_real_type ()) |
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424 { |
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425 Matrix r = arg.matrix_value (); |
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426 |
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427 if (! error_state) |
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428 retval = chol2inv (r); |
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429 } |
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430 else if (arg.is_complex_type ()) |
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431 { |
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432 ComplexMatrix r = arg.complex_matrix_value (); |
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433 |
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434 if (! error_state) |
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435 retval = chol2inv (r); |
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436 } |
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437 else |
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438 gripe_wrong_type_arg ("chol2inv", arg); |
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439 } |
5340 | 440 } |
441 } | |
442 else | |
5823 | 443 print_usage (); |
5340 | 444 |
445 return retval; | |
446 } | |
447 | |
7554
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448 DEFUN_DLD (cholupdate, args, nargout, |
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449 "-*- texinfo -*-\n\ |
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450 @deftypefn {Loadable Function} {[@var{R1}, @var{info}]} = cholupdate (@var{R}, @var{u}, @var{op})\n\ |
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451 Update or downdate a Cholesky factorization. Given an upper triangular\n\ |
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452 matrix @var{R} and a column vector @var{u}, attempt to determine another\n\ |
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453 upper triangular matrix @var{R1} such that\n\ |
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454 @itemize @bullet\n\ |
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455 @item\n\ |
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456 @var{R1}'*@var{R1} = @var{R}'*@var{R} + @var{u}*@var{u}'\n\ |
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457 if @var{op} is \"+\"\n\ |
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458 @item\n\ |
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459 @var{R1}'*@var{R1} = @var{R}'*@var{R} - @var{u}*@var{u}'\n\ |
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460 if @var{op} is \"-\"\n\ |
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461 @end itemize\n\ |
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462 \n\ |
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463 If @var{op} is \"-\", @var{info} is set to\n\ |
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464 @itemize\n\ |
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465 @item 0 if the downdate was successful,\n\ |
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466 @item 1 if @var{R}'*@var{R} - @var{u}*@var{u}' is not positive definite,\n\ |
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467 @item 2 if @var{R} is singular.\n\ |
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468 @end itemize\n\ |
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469 \n\ |
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470 If @var{info} is not present, an error message is printed in cases 1 and 2.\n\ |
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471 @seealso{chol, qrupdate}\n\ |
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472 @end deftypefn") |
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473 { |
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474 int nargin = args.length (); |
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475 |
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476 octave_value_list retval; |
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477 |
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478 octave_value argR,argu,argop; |
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479 |
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480 if ((nargin == 3 || nargin == 2) |
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481 && (argR = args(0), argR.is_matrix_type ()) |
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482 && (argu = args(1), argu.is_matrix_type ()) |
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483 && (nargin < 3 || (argop = args(2), argop.is_string ()))) |
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484 { |
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485 octave_idx_type n = argR.rows (); |
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486 |
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487 std::string op = (nargin < 3) ? "+" : argop.string_value(); |
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488 |
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489 bool down = false; |
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490 |
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491 if (nargin < 3 || (op == "+") || (down = op == "-")) |
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492 if (argR.columns () == n |
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493 && argu.rows () == n && argu.columns () == 1) |
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494 { |
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495 if (argR.is_real_matrix () && argu.is_real_matrix ()) |
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496 { |
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497 // real case |
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498 Matrix R = argR.matrix_value (); |
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499 Matrix u = argu.matrix_value (); |
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500 |
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501 CHOL fact; |
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502 fact.set (R); |
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503 int err = 0; |
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504 |
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505 if (down) |
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506 err = fact.downdate (u); |
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507 else |
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508 fact.update (u); |
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509 |
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510 if (nargout > 1) |
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511 retval(1) = err; |
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512 else if (err) |
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513 error ("cholupdate: downdate violates positiveness"); |
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514 |
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515 retval(0) = fact.chol_matrix (); |
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516 } |
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517 else |
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518 { |
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519 // complex case |
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520 ComplexMatrix R = argR.complex_matrix_value (); |
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521 ComplexMatrix u = argu.complex_matrix_value (); |
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522 |
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523 ComplexCHOL fact; |
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524 fact.set (R); |
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525 int err = 0; |
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526 |
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527 if (down) |
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528 err = fact.downdate (u); |
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529 else |
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530 fact.update (u); |
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531 |
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532 if (nargout > 1) |
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533 retval(1) = err; |
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534 else if (err) |
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535 error ("cholupdate: downdate violates positiveness"); |
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536 |
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537 retval(0) = fact.chol_matrix (); |
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538 } |
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539 } |
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540 else |
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541 error ("cholupdate: dimension mismatch"); |
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542 else |
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543 error ("cholupdate: op must be \"+\" or \"-\""); |
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544 } |
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545 else |
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546 print_usage (); |
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547 |
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548 return retval; |
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549 } |
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550 |
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551 /* |
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552 %!test |
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553 %! A = [ 0.436997 -0.131721 0.124120 -0.061673 ; |
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554 %! -0.131721 0.738529 0.019851 -0.140295 ; |
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555 %! 0.124120 0.019851 0.354879 -0.059472 ; |
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556 %! -0.061673 -0.140295 -0.059472 0.600939 ]; |
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557 %! |
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558 %! u = [ 0.98950 ; |
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559 %! 0.39844 ; |
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560 %! 0.63484 ; |
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561 %! 0.13351 ]; |
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562 %! |
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563 %! R = chol(A); |
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564 %! |
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565 %! R1 = cholupdate(R,u); |
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566 %! |
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567 %! assert(norm(triu(R1)-R1,Inf) == 0) |
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568 %! assert(norm(R1'*R1 - R'*R - u*u',Inf) < 1e1*eps) |
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569 %! |
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570 %! R1 = cholupdate(R1,u,"-"); |
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571 %! |
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572 %! assert(norm(triu(R1)-R1,Inf) == 0) |
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573 %! assert(norm(R1 - R,Inf) < 1e1*eps) |
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574 %! |
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575 %!test |
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576 %! A = [ 0.5585528 + 0.0000000i -0.1662088 - 0.0315341i 0.0107873 + 0.0236411i -0.0276775 - 0.0186073i ; |
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577 %! -0.1662088 + 0.0315341i 0.6760061 + 0.0000000i 0.0011452 - 0.0475528i 0.0145967 + 0.0247641i ; |
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578 %! 0.0107873 - 0.0236411i 0.0011452 + 0.0475528i 0.6263149 - 0.0000000i -0.1585837 - 0.0719763i ; |
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579 %! -0.0276775 + 0.0186073i 0.0145967 - 0.0247641i -0.1585837 + 0.0719763i 0.6034234 - 0.0000000i ]; |
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580 %! |
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581 %! u = [ 0.54267 + 0.91519i ; |
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582 %! 0.99647 + 0.43141i ; |
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583 %! 0.83760 + 0.68977i ; |
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584 %! 0.39160 + 0.90378i ]; |
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585 %! |
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586 %! R = chol(A); |
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587 %! |
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588 %! R1 = cholupdate(R,u); |
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589 %! |
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590 %! assert(norm(triu(R1)-R1,Inf) == 0) |
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591 %! assert(norm(R1'*R1 - R'*R - u*u',Inf) < 1e1*eps) |
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592 %! |
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593 %! R1 = cholupdate(R1,u,"-"); |
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594 %! |
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595 %! assert(norm(triu(R1)-R1,Inf) == 0) |
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596 %! assert(norm(R1 - R,Inf) < 1e1*eps) |
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597 */ |
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598 |
2928 | 599 /* |
600 ;;; Local Variables: *** | |
601 ;;; mode: C++ *** | |
602 ;;; End: *** | |
603 */ | |
604 |