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doc: Update more docstrings to have one sentence summary as first line. Reviewed build-aux, libinterp/dldfcn, libinterp/octave-value, libinterp/parse-tree directories. * build-aux/mk-opts.pl, libinterp/dldfcn/__magick_read__.cc, libinterp/dldfcn/amd.cc, libinterp/dldfcn/audiodevinfo.cc, libinterp/dldfcn/audioread.cc, libinterp/dldfcn/ccolamd.cc, libinterp/dldfcn/chol.cc, libinterp/dldfcn/colamd.cc, libinterp/dldfcn/convhulln.cc, libinterp/dldfcn/dmperm.cc, libinterp/dldfcn/fftw.cc, libinterp/dldfcn/qr.cc, libinterp/dldfcn/symbfact.cc, libinterp/dldfcn/symrcm.cc, libinterp/octave-value/ov-base.cc, libinterp/octave-value/ov-bool-mat.cc, libinterp/octave-value/ov-cell.cc, libinterp/octave-value/ov-class.cc, libinterp/octave-value/ov-fcn-handle.cc, libinterp/octave-value/ov-fcn-inline.cc, libinterp/octave-value/ov-java.cc, libinterp/octave-value/ov-null-mat.cc, libinterp/octave-value/ov-oncleanup.cc, libinterp/octave-value/ov-range.cc, libinterp/octave-value/ov-struct.cc, libinterp/octave-value/ov-typeinfo.cc, libinterp/octave-value/ov-usr-fcn.cc, libinterp/octave-value/ov.cc, libinterp/parse-tree/lex.ll, libinterp/parse-tree/oct-parse.in.yy, libinterp/parse-tree/pt-binop.cc, libinterp/parse-tree/pt-eval.cc, libinterp/parse-tree/pt-mat.cc: doc: Update more docstrings to have one sentence summary as first line.
author Rik <rik@octave.org>
date Sun, 03 May 2015 21:52:42 -0700
parents 4197fc428c7d
children
comparison
equal deleted inserted replaced
20162:2645f9ef8c88 20163:075a5e2e1ba5
57 @deftypefnx {Loadable Function} {@var{p} =} ccolamd (@var{S}, @var{knobs})\n\ 57 @deftypefnx {Loadable Function} {@var{p} =} ccolamd (@var{S}, @var{knobs})\n\
58 @deftypefnx {Loadable Function} {@var{p} =} ccolamd (@var{S}, @var{knobs}, @var{cmember})\n\ 58 @deftypefnx {Loadable Function} {@var{p} =} ccolamd (@var{S}, @var{knobs}, @var{cmember})\n\
59 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} ccolamd (@dots{})\n\ 59 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} ccolamd (@dots{})\n\
60 \n\ 60 \n\
61 Constrained column approximate minimum degree permutation.\n\ 61 Constrained column approximate minimum degree permutation.\n\
62 \n\
62 @code{@var{p} = ccolamd (@var{S})} returns the column approximate minimum\n\ 63 @code{@var{p} = ccolamd (@var{S})} returns the column approximate minimum\n\
63 degree permutation vector for the sparse matrix @var{S}. For a non-symmetric\n\ 64 degree permutation vector for the sparse matrix @var{S}. For a non-symmetric\n\
64 matrix\n\ 65 matrix @var{S}, @code{@var{S}(:, @var{p})} tends to have sparser\n\
65 @var{S},\n\ 66 LU@tie{}factors than @var{S}.\n\
66 @code{@var{S}(:, @var{p})} tends to have sparser LU@tie{}factors than\n\ 67 @code{chol (@var{S}(:, @var{p})' * @var{S}(:, @var{p}))} also tends to be\n\
67 @var{S}. @code{chol (@var{S}(:, @var{p})' * @var{S}(:, @var{p}))} also\n\ 68 sparser than @code{chol (@var{S}' * @var{S})}.\n\
68 tends to be sparser than @code{chol (@var{S}' * @var{S})}. @code{@var{p} =\n\ 69 @code{@var{p} = ccolamd (@var{S}, 1)} optimizes the ordering for\n\
69 ccolamd (@var{S}, 1)} optimizes the ordering for @code{lu (@var{S}(:,\n\ 70 @code{lu (@var{S}(:, @var{p}))}. The ordering is followed by a column\n\
70 @var{p}))}. The ordering is followed by a column elimination tree\n\ 71 elimination tree post-ordering.\n\
71 post-ordering.\n\
72 \n\ 72 \n\
73 @var{knobs} is an optional 1-element to 5-element input vector, with a\n\ 73 @var{knobs} is an optional 1-element to 5-element input vector, with a\n\
74 default value of @code{[0 10 10 1 0]} if not present or empty. Entries not\n\ 74 default value of @code{[0 10 10 1 0]} if not present or empty. Entries not\n\
75 present are set to their defaults.\n\ 75 present are set to their defaults.\n\
76 \n\ 76 \n\
77 @table @code\n\ 77 @table @code\n\
78 @item @var{knobs}(1)\n\ 78 @item @var{knobs}(1)\n\
79 if nonzero, the ordering is optimized for @code{lu (S(:, p))}. It will be a\n\ 79 if nonzero, the ordering is optimized for @code{lu (S(:, p))}. It will be a\n\
80 poor ordering for @code{chol (@var{S}(:, @var{p})' * @var{S}(:,\n\ 80 poor ordering for @code{chol (@var{S}(:, @var{p})' * @var{S}(:, @var{p}))}.\n\
81 @var{p}))}. This is the most important knob for ccolamd.\n\ 81 This is the most important knob for ccolamd.\n\
82 \n\ 82 \n\
83 @item @var{knobs}(2)\n\ 83 @item @var{knobs}(2)\n\
84 if @var{S} is m-by-n, rows with more than @code{max (16, @var{knobs}(2) *\n\ 84 if @var{S} is m-by-n, rows with more than\n\
85 sqrt (n))} entries are ignored.\n\ 85 @code{max (16, @var{knobs}(2) * sqrt (n))} entries are ignored.\n\
86 \n\ 86 \n\
87 @item @var{knobs}(3)\n\ 87 @item @var{knobs}(3)\n\
88 columns with more than @code{max (16, @var{knobs}(3) * sqrt (min (@var{m},\n\ 88 columns with more than\n\
89 @var{n})))} entries are ignored and ordered last in the output permutation\n\ 89 @code{max (16, @var{knobs}(3) * sqrt (min (@var{m}, @var{n})))} entries are\n\
90 ignored and ordered last in the output permutation\n\
90 (subject to the cmember constraints).\n\ 91 (subject to the cmember constraints).\n\
91 \n\ 92 \n\
92 @item @var{knobs}(4)\n\ 93 @item @var{knobs}(4)\n\
93 if nonzero, aggressive absorption is performed.\n\ 94 if nonzero, aggressive absorption is performed.\n\
94 \n\ 95 \n\
342 @deftypefn {Loadable Function} {@var{p} =} csymamd (@var{S})\n\ 343 @deftypefn {Loadable Function} {@var{p} =} csymamd (@var{S})\n\
343 @deftypefnx {Loadable Function} {@var{p} =} csymamd (@var{S}, @var{knobs})\n\ 344 @deftypefnx {Loadable Function} {@var{p} =} csymamd (@var{S}, @var{knobs})\n\
344 @deftypefnx {Loadable Function} {@var{p} =} csymamd (@var{S}, @var{knobs}, @var{cmember})\n\ 345 @deftypefnx {Loadable Function} {@var{p} =} csymamd (@var{S}, @var{knobs}, @var{cmember})\n\
345 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} csymamd (@dots{})\n\ 346 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} csymamd (@dots{})\n\
346 \n\ 347 \n\
347 For a symmetric positive definite matrix @var{S}, returns the permutation\n\ 348 For a symmetric positive definite matrix @var{S}, return the permutation\n\
348 vector @var{p} such that @code{@var{S}(@var{p},@var{p})} tends to have a\n\ 349 vector @var{p} such that @code{@var{S}(@var{p},@var{p})} tends to have a\n\
349 sparser Cholesky@tie{}factor than @var{S}. Sometimes @code{csymamd} works\n\ 350 sparser Cholesky@tie{}factor than @var{S}.\n\
350 well for symmetric indefinite matrices too. The matrix @var{S} is assumed\n\ 351 \n\
351 to be symmetric; only the strictly lower triangular part is referenced.\n\ 352 Sometimes @code{csymamd} works well for symmetric indefinite matrices too. \n\
352 @var{S} must be square. The ordering is followed by an elimination tree\n\ 353 The matrix @var{S} is assumed to be symmetric; only the strictly lower\n\
353 post-ordering.\n\ 354 triangular part is referenced. @var{S} must be square. The ordering is\n\
355 followed by an elimination tree post-ordering.\n\
354 \n\ 356 \n\
355 @var{knobs} is an optional 1-element to 3-element input vector, with a\n\ 357 @var{knobs} is an optional 1-element to 3-element input vector, with a\n\
356 default value of @code{[10 1 0]} if present or empty. Entries not\n\ 358 default value of @code{[10 1 0]}. Entries not present are set to their\n\
357 present are set to their defaults.\n\ 359 defaults.\n\
358 \n\ 360 \n\
359 @table @code\n\ 361 @table @code\n\
360 @item @var{knobs}(1)\n\ 362 @item @var{knobs}(1)\n\
361 If @var{S} is n-by-n, then rows and columns with more than\n\ 363 If @var{S} is n-by-n, then rows and columns with more than\n\
362 @code{max(16,@var{knobs}(1)*sqrt(n))} entries are ignored, and ordered\n\ 364 @code{max(16,@var{knobs}(1)*sqrt(n))} entries are ignored, and ordered\n\
375 in constraint set @var{c} (@var{c} must be in the range 1 to n). In the\n\ 377 in constraint set @var{c} (@var{c} must be in the range 1 to n). In the\n\
376 output permutation @var{p}, rows/columns in set 1 appear first, followed\n\ 378 output permutation @var{p}, rows/columns in set 1 appear first, followed\n\
377 by all rows/columns in set 2, and so on. @code{@var{cmember} = ones (1,n)}\n\ 379 by all rows/columns in set 2, and so on. @code{@var{cmember} = ones (1,n)}\n\
378 if not present or empty. @code{csymamd (@var{S},[],1:n)} returns @code{1:n}.\n\ 380 if not present or empty. @code{csymamd (@var{S},[],1:n)} returns @code{1:n}.\n\
379 \n\ 381 \n\
380 @code{@var{p} = csymamd (@var{S})} is about the same as @code{@var{p} =\n\ 382 @code{@var{p} = csymamd (@var{S})} is about the same as\n\
381 symamd (@var{S})}. @var{knobs} and its default values differ.\n\ 383 @code{@var{p} = symamd (@var{S})}. @var{knobs} and its default values\n\
384 differ.\n\
382 \n\ 385 \n\
383 @code{@var{stats}(4:7)} provide information if CCOLAMD was able to\n\ 386 @code{@var{stats}(4:7)} provide information if CCOLAMD was able to\n\
384 continue. The matrix is OK if @code{@var{stats}(4)} is zero, or 1 if\n\ 387 continue. The matrix is OK if @code{@var{stats}(4)} is zero, or 1 if\n\
385 invalid. @code{@var{stats}(5)} is the rightmost column index that is\n\ 388 invalid. @code{@var{stats}(5)} is the rightmost column index that is\n\
386 unsorted or contains duplicate entries, or zero if no such column exists.\n\ 389 unsorted or contains duplicate entries, or zero if no such column exists.\n\