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comparison src/DLD-FUNCTIONS/colamd.cc @ 10846:a4f482e66b65

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Grammarcheck more of the documentation. Use @noindent macro appropriately. Limit line length to 80 characters.
author Rik <octave@nomad.inbox5.com>
date Sun, 01 Aug 2010 20:22:17 -0700
parents 89f4d7e294cc
children fd0a3ac60b0e
comparison
equal deleted inserted replaced
10845:c0ffe159ba1a 10846:a4f482e66b65
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.\n\ 218 Column approximate minimum degree permutation.\n\
219 @code{@var{p} = colamd (@var{s})} returns the column approximate minimum degree\n\ 219 @code{@var{p} = colamd (@var{s})} returns the column approximate minimum\n\
220 permutation vector for the sparse matrix @var{s}. For a non-symmetric matrix\n\ 220 degree permutation vector for the sparse matrix @var{s}. For a\n\
221 @var{s},\n\ 221 non-symmetric matrix @var{s}, @code{@var{s} (:,@var{p})} tends to have\n\
222 @code{@var{s} (:,@var{p})} tends to have sparser LU factors than @var{s}.\n\ 222 sparser LU factors than @var{s}. The Cholesky factorization of\n\
223 The Cholesky factorization of @code{@var{s} (:,@var{p})' * @var{s}\n\ 223 @code{@var{s}(:,@var{p})' * @var{s} (:,@var{p})} also tends to be sparser\n\
224 (:,@var{p})} also tends to be sparser than that of @code{@var{s}' *\n\ 224 than that of @code{@var{s}' * @var{s}}.\n\
225 @var{s}}.\n\
226 \n\ 225 \n\
227 @var{knobs} is an optional one- to three-element input vector. If @var{s} is\n\ 226 @var{knobs} is an optional one- to three-element input vector. If @var{s} is\n\
228 m-by-n, then rows with more than @code{max(16,@var{knobs}(1)*sqrt(n))} entries\n\ 227 m-by-n, then rows with more than @code{max(16,@var{knobs}(1)*sqrt(n))}\n\
229 are ignored. Columns with more than @code{max(16,knobs(2)*sqrt(min(m,n)))}\n\ 228 entries are ignored. Columns with more than\n\
230 entries are removed prior to ordering, and ordered last in the output\n\ 229 @code{max(16,knobs(2)*sqrt(min(m,n)))} entries are removed prior to\n\
231 permutation @var{p}. Only completely dense rows or columns are removed\n\ 230 ordering, and ordered last in the output permutation @var{p}. Only\n\
232 if @code{@var{knobs} (1)} and @code{@var{knobs} (2)} are < 0, respectively.\n\ 231 completely dense rows or columns are removed if @code{@var{knobs} (1)} and\n\
233 If @code{@var{knobs} (3)} is nonzero, @var{stats} and @var{knobs} are\n\ 232 @code{@var{knobs} (2)} are < 0, respectively. If @code{@var{knobs} (3)} is\n\
234 printed. The default is @code{@var{knobs} = [10 10 0]}. Note that\n\ 233 nonzero, @var{stats} and @var{knobs} are printed. The default is\n\
235 @var{knobs} differs from earlier versions of colamd\n\ 234 @code{@var{knobs} = [10 10 0]}. Note that @var{knobs} differs from earlier\n\
235 versions of colamd\n\
236 \n\ 236 \n\
237 @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\
238 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\
239 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\
240 @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\
250 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\
251 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\
252 of order, then @sc{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\
253 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\
254 @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\
255 is invalid in other ways then @sc{colamd} cannot continue, an error message is\n\ 255 is invalid in other ways then @sc{colamd} cannot continue, an error message\n\
256 printed, and no output arguments (@var{p} or @var{stats}) are returned.\n\ 256 is printed, and no output arguments (@var{p} or @var{stats}) are returned.\n\
257 @sc{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\
258 valid.\n\ 258 valid.\n\
259 \n\ 259 \n\
260 @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\
261 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\