Mercurial > octave
annotate libinterp/dldfcn/colamd.cc @ 21332:db574711df78
eliminate deprecated register storage class specifier
* colamd.cc, randmtzig.c: Don't declare variables with register
storage class specifier.
* libinterp/parse-tree/module.mk (libinterp/parse-tree/oct-gperf.h):
Filter out register storage class specifier from generated code.
* oct-tex-lexer.in.ll, lex.ll: Define away register storage class
specifier.
author | John W. Eaton <jwe@octave.org> |
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date | Wed, 24 Feb 2016 09:52:27 -0500 |
parents | 40de9f8f23a6 |
children | ad0599a0acc6 |
rev | line source |
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5164 | 1 /* |
2 | |
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3 Copyright (C) 2004-2015 David Bateman |
11523 | 4 Copyright (C) 1998-2004 Andy Adler |
7016 | 5 |
6 This file is part of Octave. | |
5164 | 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. | |
5164 | 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/>. | |
5164 | 21 |
22 */ | |
23 | |
24 // This is the octave interface to colamd, which bore the copyright given | |
25 // in the help of the functions. | |
26 | |
27 #ifdef HAVE_CONFIG_H | |
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28 # include "config.h" |
5164 | 29 #endif |
30 | |
31 #include <cstdlib> | |
32 | |
33 #include <string> | |
34 #include <vector> | |
35 | |
36 #include "ov.h" | |
37 #include "defun-dld.h" | |
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38 #include "errwarn.h" |
5164 | 39 #include "pager.h" |
40 #include "ov-re-mat.h" | |
41 | |
42 #include "ov-re-sparse.h" | |
43 #include "ov-cx-sparse.h" | |
44 | |
5451 | 45 #include "oct-sparse.h" |
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46 #include "oct-locbuf.h" |
5164 | 47 |
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48 #if defined (OCTAVE_ENABLE_64) |
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49 # define COLAMD_NAME(name) colamd_l ## name |
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50 # define SYMAMD_NAME(name) symamd_l ## name |
5440 | 51 #else |
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52 # define COLAMD_NAME(name) colamd ## name |
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53 # define SYMAMD_NAME(name) symamd ## name |
5440 | 54 #endif |
55 | |
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56 // The symmetric column elimination tree code take from the Davis LDL code. |
5164 | 57 // Copyright given elsewhere in this file. |
7924 | 58 static void |
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59 symetree (const octave_idx_type *ridx, const octave_idx_type *cidx, |
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60 octave_idx_type *Parent, octave_idx_type *P, octave_idx_type n) |
5164 | 61 { |
5440 | 62 OCTAVE_LOCAL_BUFFER (octave_idx_type, Flag, n); |
63 OCTAVE_LOCAL_BUFFER (octave_idx_type, Pinv, (P ? n : 0)); | |
5164 | 64 if (P) |
65 // If P is present then compute Pinv, the inverse of P | |
5440 | 66 for (octave_idx_type k = 0 ; k < n ; k++) |
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67 Pinv[P[k]] = k ; |
5164 | 68 |
5440 | 69 for (octave_idx_type k = 0 ; k < n ; k++) |
5164 | 70 { |
71 // L(k,:) pattern: all nodes reachable in etree from nz in A(0:k-1,k) | |
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72 Parent[k] = n ; // parent of k is not yet known |
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73 Flag[k] = k ; // mark node k as visited |
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74 octave_idx_type kk = (P) ? P[k] // kth original, or permuted, column |
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75 : (k) ; |
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76 octave_idx_type p2 = cidx[kk+1] ; |
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77 for (octave_idx_type p = cidx[kk] ; p < p2 ; p++) |
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78 { |
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79 // A (i,k) is nonzero (original or permuted A) |
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80 octave_idx_type i = (Pinv) ? (Pinv[ridx[p]]) : (ridx[p]) ; |
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81 if (i < k) |
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82 { |
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83 // follow path from i to root of etree, stop at flagged node |
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84 for ( ; Flag[i] != k ; i = Parent[i]) |
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85 { |
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86 // find parent of i if not yet determined |
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87 if (Parent[i] == n) |
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88 Parent[i] = k ; |
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89 Flag[i] = k ; // mark i as visited |
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90 } |
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91 } |
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92 } |
5164 | 93 } |
94 } | |
95 | |
96 // The elimination tree post-ordering code below is taken from SuperLU | |
7924 | 97 static inline octave_idx_type |
98 make_set (octave_idx_type i, octave_idx_type *pp) | |
5164 | 99 { |
100 pp[i] = i; | |
101 return i; | |
102 } | |
103 | |
7924 | 104 static inline octave_idx_type |
105 link (octave_idx_type s, octave_idx_type t, octave_idx_type *pp) | |
5164 | 106 { |
107 pp[s] = t; | |
108 return t; | |
109 } | |
110 | |
7924 | 111 static inline octave_idx_type |
112 find (octave_idx_type i, octave_idx_type *pp) | |
5164 | 113 { |
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114 octave_idx_type p = pp[i]; |
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115 octave_idx_type gp = pp[p]; |
7924 | 116 |
117 while (gp != p) | |
118 { | |
119 pp[i] = gp; | |
120 i = gp; | |
121 p = pp[i]; | |
122 gp = pp[p]; | |
123 } | |
124 | |
125 return p; | |
5164 | 126 } |
127 | |
7924 | 128 static octave_idx_type |
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129 etdfs (octave_idx_type v, octave_idx_type *first_kid, |
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130 octave_idx_type *next_kid, octave_idx_type *post, |
7924 | 131 octave_idx_type postnum) |
5164 | 132 { |
7924 | 133 for (octave_idx_type w = first_kid[v]; w != -1; w = next_kid[w]) |
5164 | 134 postnum = etdfs (w, first_kid, next_kid, post, postnum); |
7924 | 135 |
5164 | 136 post[postnum++] = v; |
137 | |
138 return postnum; | |
139 } | |
140 | |
7924 | 141 static void |
142 tree_postorder (octave_idx_type n, octave_idx_type *parent, | |
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143 octave_idx_type *post) |
5164 | 144 { |
145 // Allocate storage for working arrays and results | |
5440 | 146 OCTAVE_LOCAL_BUFFER (octave_idx_type, first_kid, n+1); |
147 OCTAVE_LOCAL_BUFFER (octave_idx_type, next_kid, n+1); | |
5164 | 148 |
149 // Set up structure describing children | |
7924 | 150 for (octave_idx_type v = 0; v <= n; first_kid[v++] = -1) |
20946 | 151 ; // do nothing |
7924 | 152 |
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153 for (octave_idx_type v = n-1; v >= 0; v--) |
5164 | 154 { |
5440 | 155 octave_idx_type dad = parent[v]; |
5164 | 156 next_kid[v] = first_kid[dad]; |
157 first_kid[dad] = v; | |
158 } | |
159 | |
160 // Depth-first search from dummy root vertex #n | |
161 etdfs (n, first_kid, next_kid, post, 0); | |
162 } | |
163 | |
7924 | 164 static void |
165 coletree (const octave_idx_type *ridx, const octave_idx_type *colbeg, | |
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166 octave_idx_type *colend, octave_idx_type *parent, |
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167 octave_idx_type nr, octave_idx_type nc) |
5164 | 168 { |
5440 | 169 OCTAVE_LOCAL_BUFFER (octave_idx_type, root, nc); |
170 OCTAVE_LOCAL_BUFFER (octave_idx_type, pp, nc); | |
171 OCTAVE_LOCAL_BUFFER (octave_idx_type, firstcol, nr); | |
5164 | 172 |
173 // Compute firstcol[row] = first nonzero column in row | |
7924 | 174 for (octave_idx_type row = 0; row < nr; firstcol[row++] = nc) |
20946 | 175 ; // do nothing |
7924 | 176 |
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177 for (octave_idx_type col = 0; col < nc; col++) |
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178 for (octave_idx_type p = colbeg[col]; p < colend[col]; p++) |
5164 | 179 { |
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180 octave_idx_type row = ridx[p]; |
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181 if (firstcol[row] > col) |
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182 firstcol[row] = col; |
5164 | 183 } |
184 | |
185 // Compute etree by Liu's algorithm for symmetric matrices, | |
186 // except use (firstcol[r],c) in place of an edge (r,c) of A. | |
187 // Thus each row clique in A'*A is replaced by a star | |
188 // centered at its first vertex, which has the same fill. | |
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189 for (octave_idx_type col = 0; col < nc; col++) |
5164 | 190 { |
5440 | 191 octave_idx_type cset = make_set (col, pp); |
5164 | 192 root[cset] = col; |
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193 parent[col] = nc; |
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194 for (octave_idx_type p = colbeg[col]; p < colend[col]; p++) |
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195 { |
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196 octave_idx_type row = firstcol[ridx[p]]; |
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197 if (row >= col) |
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198 continue; |
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199 octave_idx_type rset = find (row, pp); |
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200 octave_idx_type rroot = root[rset]; |
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201 if (rroot != col) |
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202 { |
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203 parent[rroot] = col; |
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204 cset = link (cset, rset, pp); |
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205 root[cset] = col; |
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206 } |
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207 } |
5164 | 208 } |
209 } | |
210 | |
211 DEFUN_DLD (colamd, args, nargout, | |
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212 "-*- texinfo -*-\n\ |
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213 @deftypefn {} {@var{p} =} colamd (@var{S})\n\ |
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214 @deftypefnx {} {@var{p} =} colamd (@var{S}, @var{knobs})\n\ |
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215 @deftypefnx {} {[@var{p}, @var{stats}] =} colamd (@var{S})\n\ |
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216 @deftypefnx {} {[@var{p}, @var{stats}] =} colamd (@var{S}, @var{knobs})\n\ |
5164 | 217 \n\ |
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218 Compute the column approximate minimum degree permutation.\n\ |
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219 \n\ |
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220 @code{@var{p} = colamd (@var{S})} returns the column approximate minimum\n\ |
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221 degree permutation vector for the sparse matrix @var{S}. For a\n\ |
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222 non-symmetric matrix @var{S}, @code{@var{S}(:,@var{p})} tends to have\n\ |
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223 sparser LU@tie{}factors than @var{S}. The Cholesky@tie{}factorization of\n\ |
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224 @code{@var{S}(:,@var{p})' * @var{S}(:,@var{p})} also tends to be sparser\n\ |
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225 than that of @code{@var{S}' * @var{S}}.\n\ |
5164 | 226 \n\ |
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227 @var{knobs} is an optional one- to three-element input vector. If @var{S} is\n\ |
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228 m-by-n, then rows with more than @code{max(16,@var{knobs}(1)*sqrt(n))}\n\ |
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229 entries are ignored. Columns with more than\n\ |
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230 @code{max (16,@var{knobs}(2)*sqrt(min(m,n)))} entries are removed prior to\n\ |
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231 ordering, and ordered last in the output permutation @var{p}. Only\n\ |
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232 completely dense rows or columns are removed if @code{@var{knobs}(1)} and\n\ |
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233 @code{@var{knobs}(2)} are < 0, respectively. If @code{@var{knobs}(3)} is\n\ |
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234 nonzero, @var{stats} and @var{knobs} are printed. The default is\n\ |
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235 @code{@var{knobs} = [10 10 0]}. Note that @var{knobs} differs from earlier\n\ |
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236 versions of colamd.\n\ |
5164 | 237 \n\ |
238 @var{stats} is an optional 20-element output vector that provides data\n\ | |
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239 about the ordering and the validity of the input matrix @var{S}. Ordering\n\ |
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240 statistics are in @code{@var{stats}(1:3)}. @code{@var{stats}(1)} and\n\ |
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241 @code{@var{stats}(2)} are the number of dense or empty rows and columns\n\ |
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242 ignored by @sc{colamd} and @code{@var{stats}(3)} is the number of garbage\n\ |
10840 | 243 collections performed on the internal data structure used by @sc{colamd}\n\ |
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244 (roughly of size @code{2.2 * nnz(@var{S}) + 4 * @var{m} + 7 * @var{n}}\n\ |
5164 | 245 integers).\n\ |
246 \n\ | |
247 Octave built-in functions are intended to generate valid sparse matrices,\n\ | |
248 with no duplicate entries, with ascending row indices of the nonzeros\n\ | |
249 in each column, with a non-negative number of entries in each column (!)\n\ | |
10840 | 250 and so on. If a matrix is invalid, then @sc{colamd} may or may not be able\n\ |
5164 | 251 to continue. If there are duplicate entries (a row index appears two or\n\ |
252 more times in the same column) or if the row indices in a column are out\n\ | |
10840 | 253 of order, then @sc{colamd} can correct these errors by ignoring the duplicate\n\ |
5164 | 254 entries and sorting each column of its internal copy of the matrix\n\ |
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255 @var{S} (the input matrix @var{S} is not repaired, however). If a matrix\n\ |
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256 is invalid in other ways then @sc{colamd} cannot continue, an error message\n\ |
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257 is printed, and no output arguments (@var{p} or @var{stats}) are returned.\n\ |
10840 | 258 @sc{colamd} is thus a simple way to check a sparse matrix to see if it's\n\ |
5164 | 259 valid.\n\ |
260 \n\ | |
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261 @code{@var{stats}(4:7)} provide information if @sc{colamd} was able to\n\ |
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262 continue. The matrix is OK if @code{@var{stats}(4)} is zero, or 1 if\n\ |
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263 invalid. @code{@var{stats}(5)} is the rightmost column index that is\n\ |
5164 | 264 unsorted or contains duplicate entries, or zero if no such column exists.\n\ |
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265 @code{@var{stats}(6)} is the last seen duplicate or out-of-order row\n\ |
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266 index in the column index given by @code{@var{stats}(5)}, or zero if no\n\ |
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267 such row index exists. @code{@var{stats}(7)} is the number of duplicate\n\ |
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268 or out-of-order row indices. @code{@var{stats}(8:20)} is always zero in\n\ |
10840 | 269 the current version of @sc{colamd} (reserved for future use).\n\ |
5164 | 270 \n\ |
271 The ordering is followed by a column elimination tree post-ordering.\n\ | |
272 \n\ | |
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273 The authors of the code itself are @nospell{Stefan I. Larimore} and\n\ |
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274 @nospell{Timothy A. Davis @email{davis@@cise.ufl.edu}}, University of Florida. The algorithm was developed in collaboration with @nospell{John Gilbert},\n\ |
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275 Xerox PARC, and @nospell{Esmond Ng}, Oak Ridge National Laboratory. (see\n\ |
5164 | 276 @url{http://www.cise.ufl.edu/research/sparse/colamd})\n\ |
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277 @seealso{colperm, symamd, ccolamd}\n\ |
5642 | 278 @end deftypefn") |
5164 | 279 { |
5451 | 280 #ifdef HAVE_COLAMD |
5164 | 281 int nargin = args.length (); |
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282 |
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283 if (nargin < 1 || nargin > 2) |
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285 |
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286 octave_value_list retval (nargout == 2 ? 2 : 1); |
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287 int spumoni = 0; |
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288 |
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289 // Get knobs |
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290 OCTAVE_LOCAL_BUFFER (double, knobs, COLAMD_KNOBS); |
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291 COLAMD_NAME (_set_defaults) (knobs); |
5164 | 292 |
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293 // Check for user-passed knobs |
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294 if (nargin == 2) |
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295 { |
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296 NDArray User_knobs = args(1).array_value (); |
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297 int nel_User_knobs = User_knobs.numel (); |
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298 |
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299 if (nel_User_knobs > 0) |
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300 knobs[COLAMD_DENSE_ROW] = User_knobs(0); |
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301 if (nel_User_knobs > 1) |
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302 knobs[COLAMD_DENSE_COL] = User_knobs(1) ; |
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303 if (nel_User_knobs > 2) |
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304 spumoni = static_cast<int> (User_knobs(2)); |
5440 | 305 |
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306 // print knob settings if spumoni is set |
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307 if (spumoni) |
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308 { |
5164 | 309 |
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310 octave_stdout << "\ncolamd version " << COLAMD_MAIN_VERSION |
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311 << "." << COLAMD_SUB_VERSION |
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312 << ", " << COLAMD_DATE << ":\n"; |
5440 | 313 |
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314 if (knobs[COLAMD_DENSE_ROW] >= 0) |
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315 octave_stdout << "knobs(1): " << User_knobs (0) |
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316 << ", rows with > max (16," |
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317 << knobs[COLAMD_DENSE_ROW] << "*sqrt (size(A,2)))" |
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318 << " entries removed\n"; |
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319 else |
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320 octave_stdout << "knobs(1): " << User_knobs (0) |
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321 << ", only completely dense rows removed\n"; |
5440 | 322 |
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323 if (knobs[COLAMD_DENSE_COL] >= 0) |
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324 octave_stdout << "knobs(2): " << User_knobs (1) |
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325 << ", cols with > max (16," |
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326 << knobs[COLAMD_DENSE_COL] << "*sqrt (size(A)))" |
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327 << " entries removed\n"; |
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328 else |
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329 octave_stdout << "knobs(2): " << User_knobs (1) |
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330 << ", only completely dense columns removed\n"; |
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332 octave_stdout << "knobs(3): " << User_knobs (2) |
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333 << ", statistics and knobs printed\n"; |
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334 |
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335 } |
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336 } |
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337 |
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338 octave_idx_type n_row, n_col, nnz; |
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339 octave_idx_type *ridx, *cidx; |
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340 SparseComplexMatrix scm; |
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341 SparseMatrix sm; |
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342 |
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343 if (args(0).is_sparse_type ()) |
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344 { |
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345 if (args(0).is_complex_type ()) |
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346 { |
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347 scm = args(0). sparse_complex_matrix_value (); |
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348 n_row = scm.rows (); |
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349 n_col = scm.cols (); |
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350 nnz = scm.nnz (); |
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351 ridx = scm.xridx (); |
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352 cidx = scm.xcidx (); |
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353 } |
5164 | 354 else |
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355 { |
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356 sm = args(0).sparse_matrix_value (); |
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358 n_row = sm.rows (); |
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359 n_col = sm.cols (); |
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360 nnz = sm.nnz (); |
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361 ridx = sm.xridx (); |
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362 cidx = sm.xcidx (); |
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363 } |
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364 } |
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365 else |
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366 { |
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367 if (args(0).is_complex_type ()) |
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368 sm = SparseMatrix (real (args(0).complex_matrix_value ())); |
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369 else |
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370 sm = SparseMatrix (args(0).matrix_value ()); |
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372 n_row = sm.rows (); |
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373 n_col = sm.cols (); |
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374 nnz = sm.nnz (); |
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375 ridx = sm.xridx (); |
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376 cidx = sm.xcidx (); |
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377 } |
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379 // Allocate workspace for colamd |
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380 OCTAVE_LOCAL_BUFFER (octave_idx_type, p, n_col+1); |
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381 for (octave_idx_type i = 0; i < n_col+1; i++) |
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382 p[i] = cidx[i]; |
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384 octave_idx_type Alen = COLAMD_NAME (_recommended) (nnz, n_row, n_col); |
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385 OCTAVE_LOCAL_BUFFER (octave_idx_type, A, Alen); |
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386 for (octave_idx_type i = 0; i < nnz; i++) |
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387 A[i] = ridx[i]; |
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389 // Order the columns (destroys A) |
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390 OCTAVE_LOCAL_BUFFER (octave_idx_type, stats, COLAMD_STATS); |
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391 if (! COLAMD_NAME () (n_row, n_col, Alen, A, p, knobs, stats)) |
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392 { |
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393 COLAMD_NAME (_report) (stats) ; |
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394 |
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395 error ("colamd: internal error!"); |
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396 } |
5164 | 397 |
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398 // column elimination tree post-ordering (reuse variables) |
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399 OCTAVE_LOCAL_BUFFER (octave_idx_type, colbeg, n_col + 1); |
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400 OCTAVE_LOCAL_BUFFER (octave_idx_type, colend, n_col + 1); |
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401 OCTAVE_LOCAL_BUFFER (octave_idx_type, etree, n_col + 1); |
5164 | 402 |
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403 for (octave_idx_type i = 0; i < n_col; i++) |
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404 { |
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405 colbeg[i] = cidx[p[i]]; |
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406 colend[i] = cidx[p[i]+1]; |
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407 } |
5164 | 408 |
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409 coletree (ridx, colbeg, colend, etree, n_row, n_col); |
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410 |
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411 // Calculate the tree post-ordering |
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412 tree_postorder (n_col, etree, colbeg); |
5164 | 413 |
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414 // return the permutation vector |
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415 NDArray out_perm (dim_vector (1, n_col)); |
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416 for (octave_idx_type i = 0; i < n_col; i++) |
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417 out_perm(i) = p[colbeg[i]] + 1; |
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418 |
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419 retval(0) = out_perm; |
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420 |
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421 // print stats if spumoni > 0 |
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422 if (spumoni > 0) |
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423 COLAMD_NAME (_report) (stats) ; |
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425 // Return the stats vector |
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426 if (nargout == 2) |
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427 { |
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428 NDArray out_stats (dim_vector (1, COLAMD_STATS)); |
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429 for (octave_idx_type i = 0 ; i < COLAMD_STATS ; i++) |
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430 out_stats(i) = stats[i] ; |
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431 retval(1) = out_stats; |
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433 // fix stats (5) and (6), for 1-based information on |
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434 // jumbled matrix. note that this correction doesn't |
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435 // occur if symamd returns FALSE |
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436 out_stats (COLAMD_INFO1) ++ ; |
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437 out_stats (COLAMD_INFO2) ++ ; |
5164 | 438 } |
439 | |
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440 return retval; |
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441 |
5451 | 442 #else |
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443 err_disabled_feature ("colamd", "COLAMD"); |
5451 | 444 #endif |
5164 | 445 } |
446 | |
447 DEFUN_DLD (symamd, args, nargout, | |
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448 "-*- texinfo -*-\n\ |
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449 @deftypefn {} {@var{p} =} symamd (@var{S})\n\ |
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450 @deftypefnx {} {@var{p} =} symamd (@var{S}, @var{knobs})\n\ |
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451 @deftypefnx {} {[@var{p}, @var{stats}] =} symamd (@var{S})\n\ |
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452 @deftypefnx {} {[@var{p}, @var{stats}] =} symamd (@var{S}, @var{knobs})\n\ |
5164 | 453 \n\ |
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454 For a symmetric positive definite matrix @var{S}, returns the permutation\n\ |
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455 vector p such that @code{@var{S}(@var{p}, @var{p})} tends to have a\n\ |
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456 sparser Cholesky@tie{}factor than @var{S}.\n\ |
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457 \n\ |
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458 Sometimes @code{symamd} works well for symmetric indefinite matrices too. \n\ |
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459 The matrix @var{S} is assumed to be symmetric; only the strictly lower\n\ |
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460 triangular part is referenced. @var{S} must be square.\n\ |
5164 | 461 \n\ |
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462 @var{knobs} is an optional one- to two-element input vector. If @var{S} is\n\ |
5440 | 463 n-by-n, then rows and columns with more than\n\ |
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464 @code{max (16,@var{knobs}(1)*sqrt(n))} entries are removed prior to ordering,\n\ |
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465 and ordered last in the output permutation @var{p}. No rows/columns are\n\ |
5440 | 466 removed if @code{@var{knobs}(1) < 0}. If @code{@var{knobs} (2)} is nonzero,\n\ |
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467 @code{stats} and @var{knobs} are printed. The default is\n\ |
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468 @code{@var{knobs} = [10 0]}. Note that @var{knobs} differs from earlier\n\ |
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469 versions of @code{symamd}.\n\ |
5164 | 470 \n\ |
471 @var{stats} is an optional 20-element output vector that provides data\n\ | |
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472 about the ordering and the validity of the input matrix @var{S}. Ordering\n\ |
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473 statistics are in @code{@var{stats}(1:3)}.\n\ |
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474 @code{@var{stats}(1) = @var{stats}(2)} is the number of dense or empty rows\n\ |
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475 and columns ignored by SYMAMD and @code{@var{stats}(3)} is the number of\n\ |
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476 garbage collections performed on the internal data structure used by SYMAMD\n\ |
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477 (roughly of size @code{8.4 * nnz (tril (@var{S}, -1)) + 9 * @var{n}}\n\ |
5164 | 478 integers).\n\ |
479 \n\ | |
480 Octave built-in functions are intended to generate valid sparse matrices,\n\ | |
481 with no duplicate entries, with ascending row indices of the nonzeros\n\ | |
482 in each column, with a non-negative number of entries in each column (!)\n\ | |
483 and so on. If a matrix is invalid, then SYMAMD may or may not be able\n\ | |
484 to continue. If there are duplicate entries (a row index appears two or\n\ | |
485 more times in the same column) or if the row indices in a column are out\n\ | |
486 of order, then SYMAMD can correct these errors by ignoring the duplicate\n\ | |
487 entries and sorting each column of its internal copy of the matrix S (the\n\ | |
488 input matrix S is not repaired, however). If a matrix is invalid in\n\ | |
489 other ways then SYMAMD cannot continue, an error message is printed, and\n\ | |
490 no output arguments (@var{p} or @var{stats}) are returned. SYMAMD is\n\ | |
491 thus a simple way to check a sparse matrix to see if it's valid.\n\ | |
492 \n\ | |
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493 @code{@var{stats}(4:7)} provide information if SYMAMD was able to\n\ |
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494 continue. The matrix is OK if @code{@var{stats} (4)} is zero, or 1\n\ |
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495 if invalid. @code{@var{stats}(5)} is the rightmost column index that\n\ |
5164 | 496 is unsorted or contains duplicate entries, or zero if no such column\n\ |
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497 exists. @code{@var{stats}(6)} is the last seen duplicate or out-of-order\n\ |
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498 row index in the column index given by @code{@var{stats}(5)}, or zero\n\ |
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499 if no such row index exists. @code{@var{stats}(7)} is the number of\n\ |
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500 duplicate or out-of-order row indices. @code{@var{stats}(8:20)} is\n\ |
5164 | 501 always zero in the current version of SYMAMD (reserved for future use).\n\ |
502 \n\ | |
503 The ordering is followed by a column elimination tree post-ordering.\n\ | |
504 \n\ | |
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505 The authors of the code itself are @nospell{Stefan I. Larimore} and\n\ |
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506 @nospell{Timothy A. Davis @email{davis@@cise.ufl.edu}}, University of Florida. The algorithm was developed in collaboration with @nospell{John Gilbert},\n\ |
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507 Xerox PARC, and @nospell{Esmond Ng}, Oak Ridge National Laboratory. (see\n\ |
5164 | 508 @url{http://www.cise.ufl.edu/research/sparse/colamd})\n\ |
5642 | 509 @seealso{colperm, colamd}\n\ |
510 @end deftypefn") | |
5164 | 511 { |
5451 | 512 #ifdef HAVE_COLAMD |
513 | |
5164 | 514 int nargin = args.length (); |
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515 |
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516 if (nargin < 1 || nargin > 2) |
5823 | 517 print_usage (); |
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518 |
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519 octave_value_list retval (nargin == 2 ? 2 : 1); |
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520 int spumoni = 0; |
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521 |
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522 // Get knobs |
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523 OCTAVE_LOCAL_BUFFER (double, knobs, COLAMD_KNOBS); |
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524 COLAMD_NAME (_set_defaults) (knobs); |
5164 | 525 |
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526 // Check for user-passed knobs |
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527 if (nargin == 2) |
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528 { |
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529 NDArray User_knobs = args(1).array_value (); |
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530 int nel_User_knobs = User_knobs.numel (); |
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531 |
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532 if (nel_User_knobs > 0) |
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533 knobs[COLAMD_DENSE_ROW] = User_knobs(COLAMD_DENSE_ROW); |
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534 if (nel_User_knobs > 1) |
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535 spumoni = static_cast<int> (User_knobs (1)); |
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536 } |
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537 |
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538 // print knob settings if spumoni is set |
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539 if (spumoni > 0) |
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540 octave_stdout << "symamd: dense row/col fraction: " |
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541 << knobs[COLAMD_DENSE_ROW] << std::endl; |
5164 | 542 |
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543 octave_idx_type n_row, n_col; |
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544 octave_idx_type *ridx, *cidx; |
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545 SparseMatrix sm; |
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546 SparseComplexMatrix scm; |
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547 |
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548 if (args(0).is_sparse_type ()) |
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549 { |
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550 if (args(0).is_complex_type ()) |
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551 { |
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552 scm = args(0).sparse_complex_matrix_value (); |
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553 n_row = scm.rows (); |
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554 n_col = scm.cols (); |
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555 ridx = scm.xridx (); |
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556 cidx = scm.xcidx (); |
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557 } |
5164 | 558 else |
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559 { |
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560 sm = args(0).sparse_matrix_value (); |
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561 n_row = sm.rows (); |
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562 n_col = sm.cols (); |
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563 ridx = sm.xridx (); |
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564 cidx = sm.xcidx (); |
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565 } |
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566 } |
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567 else |
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568 { |
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569 if (args(0).is_complex_type ()) |
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570 sm = SparseMatrix (real (args(0).complex_matrix_value ())); |
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571 else |
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572 sm = SparseMatrix (args(0).matrix_value ()); |
5164 | 573 |
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574 n_row = sm.rows (); |
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575 n_col = sm.cols (); |
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576 ridx = sm.xridx (); |
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577 cidx = sm.xcidx (); |
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578 } |
5164 | 579 |
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580 if (n_row != n_col) |
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581 err_square_matrix_required ("symamd", "S"); |
5164 | 582 |
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583 // Allocate workspace for symamd |
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584 OCTAVE_LOCAL_BUFFER (octave_idx_type, perm, n_col+1); |
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585 OCTAVE_LOCAL_BUFFER (octave_idx_type, stats, COLAMD_STATS); |
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586 if (! SYMAMD_NAME () (n_col, ridx, cidx, perm, |
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587 knobs, stats, &calloc, &free)) |
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588 { |
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589 SYMAMD_NAME (_report) (stats) ; |
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590 |
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591 error ("symamd: internal error!") ; |
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592 } |
5164 | 593 |
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594 // column elimination tree post-ordering |
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595 OCTAVE_LOCAL_BUFFER (octave_idx_type, etree, n_col + 1); |
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596 symetree (ridx, cidx, etree, perm, n_col); |
5164 | 597 |
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598 // Calculate the tree post-ordering |
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599 OCTAVE_LOCAL_BUFFER (octave_idx_type, post, n_col + 1); |
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600 tree_postorder (n_col, etree, post); |
5164 | 601 |
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602 // return the permutation vector |
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603 NDArray out_perm (dim_vector (1, n_col)); |
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604 for (octave_idx_type i = 0; i < n_col; i++) |
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605 out_perm(i) = perm[post[i]] + 1; |
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606 |
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607 retval(0) = out_perm; |
5164 | 608 |
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609 // print stats if spumoni > 0 |
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610 if (spumoni > 0) |
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611 SYMAMD_NAME (_report) (stats) ; |
5164 | 612 |
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613 // Return the stats vector |
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614 if (nargout == 2) |
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615 { |
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616 NDArray out_stats (dim_vector (1, COLAMD_STATS)); |
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617 for (octave_idx_type i = 0 ; i < COLAMD_STATS ; i++) |
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618 out_stats(i) = stats[i] ; |
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619 retval(1) = out_stats; |
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620 |
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621 // fix stats (5) and (6), for 1-based information on |
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622 // jumbled matrix. note that this correction doesn't |
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623 // occur if symamd returns FALSE |
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624 out_stats (COLAMD_INFO1) ++ ; |
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625 out_stats (COLAMD_INFO2) ++ ; |
5164 | 626 } |
627 | |
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628 return retval; |
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629 |
5451 | 630 #else |
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631 err_disabled_feature ("symamd", "COLAMD"); |
5451 | 632 #endif |
5164 | 633 } |
634 | |
635 DEFUN_DLD (etree, args, nargout, | |
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636 "-*- texinfo -*-\n\ |
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637 @deftypefn {} {@var{p} =} etree (@var{S})\n\ |
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638 @deftypefnx {} {@var{p} =} etree (@var{S}, @var{typ})\n\ |
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639 @deftypefnx {} {[@var{p}, @var{q}] =} etree (@var{S}, @var{typ})\n\ |
5164 | 640 \n\ |
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641 Return the elimination tree for the matrix @var{S}.\n\ |
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642 \n\ |
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643 By default @var{S} is assumed to be symmetric and the symmetric elimination\n\ |
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644 tree is returned. The argument @var{typ} controls whether a symmetric or\n\ |
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645 column elimination tree is returned. Valid values of @var{typ} are\n\ |
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646 @qcode{\"sym\"} or @qcode{\"col\"}, for symmetric or column elimination tree\n\ |
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647 respectively.\n\ |
5164 | 648 \n\ |
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649 Called with a second argument, @code{etree} also returns the postorder\n\ |
5164 | 650 permutations on the tree.\n\ |
651 @end deftypefn") | |
652 { | |
653 int nargin = args.length (); | |
654 | |
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655 if (nargin < 1 || nargin > 2) |
5823 | 656 print_usage (); |
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657 |
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658 octave_value_list retval (nargout == 2 ? 2 : 1); |
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659 |
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660 octave_idx_type n_row = 0; |
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661 octave_idx_type n_col = 0; |
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662 octave_idx_type *ridx = 0; |
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663 octave_idx_type *cidx = 0; |
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664 |
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665 if (args(0).is_sparse_type ()) |
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666 error ("etree: S must be a sparse matrix"); |
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667 |
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668 if (args(0).is_complex_type ()) |
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669 { |
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670 SparseComplexMatrix scm = args(0).sparse_complex_matrix_value (); |
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671 |
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672 n_row = scm.rows (); |
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673 n_col = scm.cols (); |
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674 ridx = scm.xridx (); |
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675 cidx = scm.xcidx (); |
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676 } |
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677 else |
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678 { |
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679 SparseMatrix sm = args(0).sparse_matrix_value (); |
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680 |
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681 n_row = sm.rows (); |
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682 n_col = sm.cols (); |
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683 ridx = sm.xridx (); |
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684 cidx = sm.xcidx (); |
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685 } |
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686 |
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687 bool is_sym = true; |
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688 |
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689 if (nargin == 2) |
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690 { |
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691 std::string str = args(1).xstring_value ("etree: TYP must be a string"); |
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692 if (str.find ("C") == 0 || str.find ("c") == 0) |
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693 is_sym = false; |
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694 } |
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695 |
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696 // column elimination tree post-ordering (reuse variables) |
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697 OCTAVE_LOCAL_BUFFER (octave_idx_type, etree, n_col + 1); |
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698 |
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699 if (is_sym) |
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700 { |
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701 if (n_row != n_col) |
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702 error ("etree: S is marked as symmetric, but is not square"); |
5164 | 703 |
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704 symetree (ridx, cidx, etree, 0, n_col); |
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705 } |
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706 else |
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707 { |
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708 OCTAVE_LOCAL_BUFFER (octave_idx_type, colbeg, n_col); |
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709 OCTAVE_LOCAL_BUFFER (octave_idx_type, colend, n_col); |
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710 |
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711 for (octave_idx_type i = 0; i < n_col; i++) |
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712 { |
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713 colbeg[i] = cidx[i]; |
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714 colend[i] = cidx[i+1]; |
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715 } |
7924 | 716 |
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717 coletree (ridx, colbeg, colend, etree, n_row, n_col); |
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718 } |
7924 | 719 |
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720 NDArray tree (dim_vector (1, n_col)); |
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721 for (octave_idx_type i = 0; i < n_col; i++) |
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722 // We flag a root with n_col while Matlab does it with zero |
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723 // Convert for matlab compatiable output |
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724 if (etree[i] == n_col) |
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725 tree(i) = 0; |
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726 else |
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727 tree(i) = etree[i] + 1; |
5164 | 728 |
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729 retval(0) = tree; |
5164 | 730 |
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731 if (nargout == 2) |
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732 { |
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733 // Calculate the tree post-ordering |
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734 OCTAVE_LOCAL_BUFFER (octave_idx_type, post, n_col + 1); |
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735 tree_postorder (n_col, etree, post); |
5164 | 736 |
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737 NDArray postorder (dim_vector (1, n_col)); |
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738 for (octave_idx_type i = 0; i < n_col; i++) |
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739 postorder(i) = post[i] + 1; |
5164 | 740 |
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741 retval(1) = postorder; |
5164 | 742 } |
743 | |
744 return retval; | |
745 } |