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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 aa36fb998a4d
comparison
equal deleted inserted replaced
20197:2645f9ef8c88 20198:075a5e2e1ba5
214 @deftypefn {Loadable Function} {@var{p} =} colamd (@var{S})\n\ 214 @deftypefn {Loadable Function} {@var{p} =} colamd (@var{S})\n\
215 @deftypefnx {Loadable Function} {@var{p} =} colamd (@var{S}, @var{knobs})\n\ 215 @deftypefnx {Loadable Function} {@var{p} =} colamd (@var{S}, @var{knobs})\n\
216 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} colamd (@var{S})\n\ 216 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} colamd (@var{S})\n\
217 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} colamd (@var{S}, @var{knobs})\n\ 217 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} colamd (@var{S}, @var{knobs})\n\
218 \n\ 218 \n\
219 Column approximate minimum degree permutation.\n\ 219 Compute the column approximate minimum degree permutation.\n\
220 \n\
220 @code{@var{p} = colamd (@var{S})} returns the column approximate minimum\n\ 221 @code{@var{p} = colamd (@var{S})} returns the column approximate minimum\n\
221 degree permutation vector for the sparse matrix @var{S}. For a\n\ 222 degree permutation vector for the sparse matrix @var{S}. For a\n\
222 non-symmetric matrix @var{S}, @code{@var{S}(:,@var{p})} tends to have\n\ 223 non-symmetric matrix @var{S}, @code{@var{S}(:,@var{p})} tends to have\n\
223 sparser LU@tie{}factors than @var{S}. The Cholesky@tie{}factorization of\n\ 224 sparser LU@tie{}factors than @var{S}. The Cholesky@tie{}factorization of\n\
224 @code{@var{S}(:,@var{p})' * @var{S}(:,@var{p})} also tends to be sparser\n\ 225 @code{@var{S}(:,@var{p})' * @var{S}(:,@var{p})} also tends to be sparser\n\
256 is invalid in other ways then @sc{colamd} cannot continue, an error message\n\ 257 is invalid in other ways then @sc{colamd} cannot continue, an error message\n\
257 is printed, and no output arguments (@var{p} or @var{stats}) are returned.\n\ 258 is printed, and no output arguments (@var{p} or @var{stats}) are returned.\n\
258 @sc{colamd} is thus a simple way to check a sparse matrix to see if it's\n\ 259 @sc{colamd} is thus a simple way to check a sparse matrix to see if it's\n\
259 valid.\n\ 260 valid.\n\
260 \n\ 261 \n\
261 @code{@var{stats}(4:7)} provide information if COLAMD was able to\n\ 262 @code{@var{stats}(4:7)} provide information if @sc{colamd} was able to\n\
262 continue. The matrix is OK if @code{@var{stats}(4)} is zero, or 1 if\n\ 263 continue. The matrix is OK if @code{@var{stats}(4)} is zero, or 1 if\n\
263 invalid. @code{@var{stats}(5)} is the rightmost column index that is\n\ 264 invalid. @code{@var{stats}(5)} is the rightmost column index that is\n\
264 unsorted or contains duplicate entries, or zero if no such column exists.\n\ 265 unsorted or contains duplicate entries, or zero if no such column exists.\n\
265 @code{@var{stats}(6)} is the last seen duplicate or out-of-order row\n\ 266 @code{@var{stats}(6)} is the last seen duplicate or out-of-order row\n\
266 index in the column index given by @code{@var{stats}(5)}, or zero if no\n\ 267 index in the column index given by @code{@var{stats}(5)}, or zero if no\n\
456 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} symamd (@var{S})\n\ 457 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} symamd (@var{S})\n\
457 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} symamd (@var{S}, @var{knobs})\n\ 458 @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} symamd (@var{S}, @var{knobs})\n\
458 \n\ 459 \n\
459 For a symmetric positive definite matrix @var{S}, returns the permutation\n\ 460 For a symmetric positive definite matrix @var{S}, returns the permutation\n\
460 vector p such that @code{@var{S}(@var{p}, @var{p})} tends to have a\n\ 461 vector p such that @code{@var{S}(@var{p}, @var{p})} tends to have a\n\
461 sparser Cholesky@tie{}factor than @var{S}. Sometimes @code{symamd} works\n\ 462 sparser Cholesky@tie{}factor than @var{S}.\n\
462 well for symmetric indefinite matrices too. The matrix @var{S} is assumed\n\ 463 \n\
463 to be symmetric; only the strictly lower triangular part is referenced.\n\ 464 Sometimes @code{symamd} works well for symmetric indefinite matrices too. \n\
464 @var{S} must be square.\n\ 465 The matrix @var{S} is assumed to be symmetric; only the strictly lower\n\
466 triangular part is referenced. @var{S} must be square.\n\
465 \n\ 467 \n\
466 @var{knobs} is an optional one- to two-element input vector. If @var{S} is\n\ 468 @var{knobs} is an optional one- to two-element input vector. If @var{S} is\n\
467 n-by-n, then rows and columns with more than\n\ 469 n-by-n, then rows and columns with more than\n\
468 @code{max (16,@var{knobs}(1)*sqrt(n))} entries are removed prior to ordering,\n\ 470 @code{max (16,@var{knobs}(1)*sqrt(n))} entries are removed prior to ordering,\n\
469 and ordered last in the output permutation @var{p}. No rows/columns are\n\ 471 and ordered last in the output permutation @var{p}. No rows/columns are\n\
470 removed if @code{@var{knobs}(1) < 0}. If @code{@var{knobs} (2)} is nonzero,\n\ 472 removed if @code{@var{knobs}(1) < 0}. If @code{@var{knobs} (2)} is nonzero,\n\
471 @code{stats} and @var{knobs} are printed. The default is @code{@var{knobs}\n\ 473 @code{stats} and @var{knobs} are printed. The default is\n\
472 = [10 0]}. Note that @var{knobs} differs from earlier versions of symamd.\n\ 474 @code{@var{knobs} = [10 0]}. Note that @var{knobs} differs from earlier\n\
475 versions of @code{symamd}.\n\
473 \n\ 476 \n\
474 @var{stats} is an optional 20-element output vector that provides data\n\ 477 @var{stats} is an optional 20-element output vector that provides data\n\
475 about the ordering and the validity of the input matrix @var{S}. Ordering\n\ 478 about the ordering and the validity of the input matrix @var{S}. Ordering\n\
476 statistics are in @code{@var{stats}(1:3)}. @code{@var{stats}(1) =\n\ 479 statistics are in @code{@var{stats}(1:3)}.\n\
477 @var{stats}(2)} is the number of dense or empty rows and columns\n\ 480 @code{@var{stats}(1) = @var{stats}(2)} is the number of dense or empty rows\n\
478 ignored by SYMAMD and @code{@var{stats}(3)} is the number of garbage\n\ 481 and columns ignored by SYMAMD and @code{@var{stats}(3)} is the number of\n\
479 collections performed on the internal data structure used by SYMAMD\n\ 482 garbage collections performed on the internal data structure used by SYMAMD\n\
480 (roughly of size @code{8.4 * nnz (tril (@var{S}, -1)) + 9 * @var{n}}\n\ 483 (roughly of size @code{8.4 * nnz (tril (@var{S}, -1)) + 9 * @var{n}}\n\
481 integers).\n\ 484 integers).\n\
482 \n\ 485 \n\
483 Octave built-in functions are intended to generate valid sparse matrices,\n\ 486 Octave built-in functions are intended to generate valid sparse matrices,\n\
484 with no duplicate entries, with ascending row indices of the nonzeros\n\ 487 with no duplicate entries, with ascending row indices of the nonzeros\n\
646 "-*- texinfo -*-\n\ 649 "-*- texinfo -*-\n\
647 @deftypefn {Loadable Function} {@var{p} =} etree (@var{S})\n\ 650 @deftypefn {Loadable Function} {@var{p} =} etree (@var{S})\n\
648 @deftypefnx {Loadable Function} {@var{p} =} etree (@var{S}, @var{typ})\n\ 651 @deftypefnx {Loadable Function} {@var{p} =} etree (@var{S}, @var{typ})\n\
649 @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\
650 \n\ 653 \n\
651 Return the elimination tree for the matrix @var{S}. By default @var{S}\n\ 654 Return the elimination tree for the matrix @var{S}.\n\
652 is assumed to be symmetric and the symmetric elimination tree is\n\ 655 \n\
653 returned. The argument @var{typ} controls whether a symmetric or\n\ 656 By default @var{S} is assumed to be symmetric and the symmetric elimination\n\
657 tree is returned. The argument @var{typ} controls whether a symmetric or\n\
654 column elimination tree is returned. Valid values of @var{typ} are\n\ 658 column elimination tree is returned. Valid values of @var{typ} are\n\
655 @qcode{\"sym\"} or @qcode{\"col\"}, for symmetric or column elimination tree\n\ 659 @qcode{\"sym\"} or @qcode{\"col\"}, for symmetric or column elimination tree\n\
656 respectively.\n\ 660 respectively.\n\
657 \n\ 661 \n\
658 Called with a second argument, @code{etree} also returns the postorder\n\ 662 Called with a second argument, @code{etree} also returns the postorder\n\