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comparison src/DLD-FUNCTIONS/colamd.cc @ 10840:89f4d7e294cc

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Grammarcheck .cc files
author Rik <octave@nomad.inbox5.com>
date Sat, 31 Jul 2010 11:18:11 -0700
parents b4d2080b6df7
children a4f482e66b65
comparison
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10839:65bc065bec95 10840:89f4d7e294cc
208 } 208 }
209 } 209 }
210 210
211 DEFUN_DLD (colamd, args, nargout, 211 DEFUN_DLD (colamd, args, nargout,
212 "-*- texinfo -*-\n\ 212 "-*- texinfo -*-\n\
213 @deftypefn {Loadable Function} {@var{p} =} colamd (@var{s})\n\ 213 @deftypefn {Loadable Function} {@var{p} =} colamd (@var{s})\n\
214 @deftypefnx {Loadable Function} {@var{p} =} colamd (@var{s}, @var{knobs})\n\ 214 @deftypefnx {Loadable Function} {@var{p} =} colamd (@var{s}, @var{knobs})\n\
215 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} colamd (@var{s})\n\ 215 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} colamd (@var{s})\n\
216 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} colamd (@var{s}, @var{knobs})\n\ 216 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} colamd (@var{s}, @var{knobs})\n\
217 \n\ 217 \n\
218 Column approximate minimum degree permutation. @code{@var{p} = colamd\n\ 218 Column approximate minimum degree permutation.\n\
219 (@var{s})} returns the column approximate minimum degree permutation\n\ 219 @code{@var{p} = colamd (@var{s})} returns the column approximate minimum degree\n\
220 vector for the sparse matrix @var{s}. For a non-symmetric matrix @var{s},\n\ 220 permutation vector for the sparse matrix @var{s}. For a non-symmetric matrix\n\
221 @var{s},\n\
221 @code{@var{s} (:,@var{p})} tends to have sparser LU factors than @var{s}.\n\ 222 @code{@var{s} (:,@var{p})} tends to have sparser LU factors than @var{s}.\n\
222 The Cholesky factorization of @code{@var{s} (:,@var{p})' * @var{s}\n\ 223 The Cholesky factorization of @code{@var{s} (:,@var{p})' * @var{s}\n\
223 (:,@var{p})} also tends to be sparser than that of @code{@var{s}' *\n\ 224 (:,@var{p})} also tends to be sparser than that of @code{@var{s}' *\n\
224 @var{s}}.\n\ 225 @var{s}}.\n\
225 \n\ 226 \n\
235 \n\ 236 \n\
236 @var{stats} is an optional 20-element output vector that provides data\n\ 237 @var{stats} is an optional 20-element output vector that provides data\n\
237 about the ordering and the validity of the input matrix @var{s}. Ordering\n\ 238 about the ordering and the validity of the input matrix @var{s}. Ordering\n\
238 statistics are in @code{@var{stats} (1:3)}. @code{@var{stats} (1)} and\n\ 239 statistics are in @code{@var{stats} (1:3)}. @code{@var{stats} (1)} and\n\
239 @code{@var{stats} (2)} are the number of dense or empty rows and columns\n\ 240 @code{@var{stats} (2)} are the number of dense or empty rows and columns\n\
240 ignored by COLAMD and @code{@var{stats} (3)} is the number of garbage\n\ 241 ignored by @sc{colamd} and @code{@var{stats} (3)} is the number of garbage\n\
241 collections performed on the internal data structure used by COLAMD\n\ 242 collections performed on the internal data structure used by @sc{colamd}\n\
242 (roughly of size @code{2.2 * nnz(@var{s}) + 4 * @var{m} + 7 * @var{n}}\n\ 243 (roughly of size @code{2.2 * nnz(@var{s}) + 4 * @var{m} + 7 * @var{n}}\n\
243 integers).\n\ 244 integers).\n\
244 \n\ 245 \n\
245 Octave built-in functions are intended to generate valid sparse matrices,\n\ 246 Octave built-in functions are intended to generate valid sparse matrices,\n\
246 with no duplicate entries, with ascending row indices of the nonzeros\n\ 247 with no duplicate entries, with ascending row indices of the nonzeros\n\
247 in each column, with a non-negative number of entries in each column (!)\n\ 248 in each column, with a non-negative number of entries in each column (!)\n\
248 and so on. If a matrix is invalid, then COLAMD may or may not be able\n\ 249 and so on. If a matrix is invalid, then @sc{colamd} may or may not be able\n\
249 to continue. If there are duplicate entries (a row index appears two or\n\ 250 to continue. If there are duplicate entries (a row index appears two or\n\
250 more times in the same column) or if the row indices in a column are out\n\ 251 more times in the same column) or if the row indices in a column are out\n\
251 of order, then COLAMD can correct these errors by ignoring the duplicate\n\ 252 of order, then @sc{colamd} can correct these errors by ignoring the duplicate\n\
252 entries and sorting each column of its internal copy of the matrix\n\ 253 entries and sorting each column of its internal copy of the matrix\n\
253 @var{s} (the input matrix @var{s} is not repaired, however). If a matrix\n\ 254 @var{s} (the input matrix @var{s} is not repaired, however). If a matrix\n\
254 is invalid in other ways then COLAMD cannot continue, an error message is\n\ 255 is invalid in other ways then @sc{colamd} cannot continue, an error message is\n\
255 printed, and no output arguments (@var{p} or @var{stats}) are returned.\n\ 256 printed, and no output arguments (@var{p} or @var{stats}) are returned.\n\
256 COLAMD is thus a simple way to check a sparse matrix to see if it's\n\ 257 @sc{colamd} is thus a simple way to check a sparse matrix to see if it's\n\
257 valid.\n\ 258 valid.\n\
258 \n\ 259 \n\
259 @code{@var{stats} (4:7)} provide information if COLAMD was able to\n\ 260 @code{@var{stats} (4:7)} provide information if COLAMD was able to\n\
260 continue. The matrix is OK if @code{@var{stats} (4)} is zero, or 1 if\n\ 261 continue. The matrix is OK if @code{@var{stats} (4)} is zero, or 1 if\n\
261 invalid. @code{@var{stats} (5)} is the rightmost column index that is\n\ 262 invalid. @code{@var{stats} (5)} is the rightmost column index that is\n\
262 unsorted or contains duplicate entries, or zero if no such column exists.\n\ 263 unsorted or contains duplicate entries, or zero if no such column exists.\n\
263 @code{@var{stats} (6)} is the last seen duplicate or out-of-order row\n\ 264 @code{@var{stats} (6)} is the last seen duplicate or out-of-order row\n\
264 index in the column index given by @code{@var{stats} (5)}, or zero if no\n\ 265 index in the column index given by @code{@var{stats} (5)}, or zero if no\n\
265 such row index exists. @code{@var{stats} (7)} is the number of duplicate\n\ 266 such row index exists. @code{@var{stats} (7)} is the number of duplicate\n\
266 or out-of-order row indices. @code{@var{stats} (8:20)} is always zero in\n\ 267 or out-of-order row indices. @code{@var{stats} (8:20)} is always zero in\n\
267 the current version of COLAMD (reserved for future use).\n\ 268 the current version of @sc{colamd} (reserved for future use).\n\
268 \n\ 269 \n\
269 The ordering is followed by a column elimination tree post-ordering.\n\ 270 The ordering is followed by a column elimination tree post-ordering.\n\
270 \n\ 271 \n\
271 The authors of the code itself are Stefan I. Larimore and Timothy A.\n\ 272 The authors of the code itself are Stefan I. Larimore and Timothy A.\n\
272 Davis (davis@@cise.ufl.edu), University of Florida. The algorithm was\n\ 273 Davis @email{davis@@cise.ufl.edu}, University of Florida. The algorithm was\n\
273 developed in collaboration with John Gilbert, Xerox PARC, and Esmond\n\ 274 developed in collaboration with John Gilbert, Xerox PARC, and Esmond\n\
274 Ng, Oak Ridge National Laboratory. (see\n\ 275 Ng, Oak Ridge National Laboratory. (see\n\
275 @url{http://www.cise.ufl.edu/research/sparse/colamd})\n\ 276 @url{http://www.cise.ufl.edu/research/sparse/colamd})\n\
276 @seealso{colperm, symamd}\n\ 277 @seealso{colperm, symamd}\n\
277 @end deftypefn") 278 @end deftypefn")
447 return retval; 448 return retval;
448 } 449 }
449 450
450 DEFUN_DLD (symamd, args, nargout, 451 DEFUN_DLD (symamd, args, nargout,
451 "-*- texinfo -*-\n\ 452 "-*- texinfo -*-\n\
452 @deftypefn {Loadable Function} {@var{p} =} symamd (@var{s})\n\ 453 @deftypefn {Loadable Function} {@var{p} =} symamd (@var{s})\n\
453 @deftypefnx {Loadable Function} {@var{p} =} symamd (@var{s}, @var{knobs})\n\ 454 @deftypefnx {Loadable Function} {@var{p} =} symamd (@var{s}, @var{knobs})\n\
454 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} symamd (@var{s})\n\ 455 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} symamd (@var{s})\n\
455 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} symamd (@var{s}, @var{knobs})\n\ 456 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} symamd (@var{s}, @var{knobs})\n\
456 \n\ 457 \n\
457 For a symmetric positive definite matrix @var{s}, returns the permutation\n\ 458 For a symmetric positive definite matrix @var{s}, returns the permutation\n\
503 \n\ 504 \n\
504 The ordering is followed by a column elimination tree post-ordering.\n\ 505 The ordering is followed by a column elimination tree post-ordering.\n\
505 \n\ 506 \n\
506 \n\ 507 \n\
507 The authors of the code itself are Stefan I. Larimore and Timothy A.\n\ 508 The authors of the code itself are Stefan I. Larimore and Timothy A.\n\
508 Davis (davis@@cise.ufl.edu), University of Florida. The algorithm was\n\ 509 Davis @email{davis@@cise.ufl.edu}, University of Florida. The algorithm was\n\
509 developed in collaboration with John Gilbert, Xerox PARC, and Esmond\n\ 510 developed in collaboration with John Gilbert, Xerox PARC, and Esmond\n\
510 Ng, Oak Ridge National Laboratory. (see\n\ 511 Ng, Oak Ridge National Laboratory. (see\n\
511 @url{http://www.cise.ufl.edu/research/sparse/colamd})\n\ 512 @url{http://www.cise.ufl.edu/research/sparse/colamd})\n\
512 @seealso{colperm, colamd}\n\ 513 @seealso{colperm, colamd}\n\
513 @end deftypefn") 514 @end deftypefn")
644 return retval; 645 return retval;
645 } 646 }
646 647
647 DEFUN_DLD (etree, args, nargout, 648 DEFUN_DLD (etree, args, nargout,
648 "-*- texinfo -*-\n\ 649 "-*- texinfo -*-\n\
649 @deftypefn {Loadable Function} {@var{p} =} etree (@var{s})\n\ 650 @deftypefn {Loadable Function} {@var{p} =} etree (@var{s})\n\
650 @deftypefnx {Loadable Function} {@var{p} =} etree (@var{s}, @var{typ})\n\ 651 @deftypefnx {Loadable Function} {@var{p} =} etree (@var{s}, @var{typ})\n\
651 @deftypefnx {Loadable Function} {[@var{p}, @var{q}] =} etree (@var{s}, @var{typ})\n\ 652 @deftypefnx {Loadable Function} {[@var{p}, @var{q}] =} etree (@var{s}, @var{typ})\n\
652 \n\ 653 \n\
653 Returns the elimination tree for the matrix @var{s}. By default @var{s}\n\ 654 Returns the elimination tree for the matrix @var{s}. By default @var{s}\n\
654 is assumed to be symmetric and the symmetric elimination tree is\n\ 655 is assumed to be symmetric and the symmetric elimination tree is\n\