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diff src/DLD-FUNCTIONS/colamd.cc @ 9064:7c02ec148a3c
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author | Rik <rdrider0-list@yahoo.com> |
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date | Sat, 28 Mar 2009 13:57:22 -0700 |
parents | eb63fbe60fab |
children | 40dfc0c99116 |
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--- a/src/DLD-FUNCTIONS/colamd.cc Mon Mar 30 19:48:56 2009 -0400 +++ b/src/DLD-FUNCTIONS/colamd.cc Sat Mar 28 13:57:22 2009 -0700 @@ -215,9 +215,9 @@ @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} colamd (@var{s})\n\ @deftypefnx {Loadable Function} {[@var{p}, @var{stats}] =} colamd (@var{s}, @var{knobs})\n\ \n\ -Column approximate minimum degree permutation. @code{@var{p} = colamd\n\ +Column approximate minimum degree permutation. @code{@var{p} = colamd\n\ (@var{s})} returns the column approximate minimum degree permutation\n\ -vector for the sparse matrix @var{s}. For a non-symmetric matrix @var{s},\n\ +vector for the sparse matrix @var{s}. For a non-symmetric matrix @var{s},\n\ @code{@var{s} (:,@var{p})} tends to have sparser LU factors than @var{s}.\n\ The Cholesky factorization of @code{@var{s} (:,@var{p})' * @var{s}\n\ (:,@var{p})} also tends to be sparser than that of @code{@var{s}' *\n\ @@ -225,17 +225,17 @@ \n\ @var{knobs} is an optional one- to three-element input vector. If @var{s} is\n\ m-by-n, then rows with more than @code{max(16,@var{knobs}(1)*sqrt(n))} entries\n\ -are ignored. Columns with more than @code{max(16,knobs(2)*sqrt(min(m,n)))}\n\ +are ignored. Columns with more than @code{max(16,knobs(2)*sqrt(min(m,n)))}\n\ entries are removed prior to ordering, and ordered last in the output\n\ -permutation @var{p}. Only completely dense rows or columns are removed\n\ +permutation @var{p}. Only completely dense rows or columns are removed\n\ if @code{@var{knobs} (1)} and @code{@var{knobs} (2)} are < 0, respectively.\n\ If @code{@var{knobs} (3)} is nonzero, @var{stats} and @var{knobs} are\n\ printed. The default is @code{@var{knobs} = [10 10 0]}. Note that\n\ @var{knobs} differs from earlier versions of colamd\n\ \n\ @var{stats} is an optional 20-element output vector that provides data\n\ -about the ordering and the validity of the input matrix @var{s}. Ordering\n\ -statistics are in @code{@var{stats} (1:3)}. @code{@var{stats} (1)} and\n\ +about the ordering and the validity of the input matrix @var{s}. Ordering\n\ +statistics are in @code{@var{stats} (1:3)}. @code{@var{stats} (1)} and\n\ @code{@var{stats} (2)} are the number of dense or empty rows and columns\n\ ignored by COLAMD and @code{@var{stats} (3)} is the number of garbage\n\ collections performed on the internal data structure used by COLAMD\n\ @@ -250,20 +250,20 @@ more times in the same column) or if the row indices in a column are out\n\ of order, then COLAMD can correct these errors by ignoring the duplicate\n\ entries and sorting each column of its internal copy of the matrix\n\ -@var{s} (the input matrix @var{s} is not repaired, however). If a matrix\n\ +@var{s} (the input matrix @var{s} is not repaired, however). If a matrix\n\ is invalid in other ways then COLAMD cannot continue, an error message is\n\ printed, and no output arguments (@var{p} or @var{stats}) are returned.\n\ COLAMD is thus a simple way to check a sparse matrix to see if it's\n\ valid.\n\ \n\ @code{@var{stats} (4:7)} provide information if COLAMD was able to\n\ -continue. The matrix is OK if @code{@var{stats} (4)} is zero, or 1 if\n\ -invalid. @code{@var{stats} (5)} is the rightmost column index that is\n\ +continue. The matrix is OK if @code{@var{stats} (4)} is zero, or 1 if\n\ +invalid. @code{@var{stats} (5)} is the rightmost column index that is\n\ unsorted or contains duplicate entries, or zero if no such column exists.\n\ @code{@var{stats} (6)} is the last seen duplicate or out-of-order row\n\ index in the column index given by @code{@var{stats} (5)}, or zero if no\n\ -such row index exists. @code{@var{stats} (7)} is the number of duplicate\n\ -or out-of-order row indices. @code{@var{stats} (8:20)} is always zero in\n\ +such row index exists. @code{@var{stats} (7)} is the number of duplicate\n\ +or out-of-order row indices. @code{@var{stats} (8:20)} is always zero in\n\ the current version of COLAMD (reserved for future use).\n\ \n\ The ordering is followed by a column elimination tree post-ordering.\n\ @@ -271,7 +271,7 @@ The authors of the code itself are Stefan I. Larimore and Timothy A.\n\ Davis (davis@@cise.ufl.edu), University of Florida. The algorithm was\n\ developed in collaboration with John Gilbert, Xerox PARC, and Esmond\n\ -Ng, Oak Ridge National Laboratory. (see\n\ +Ng, Oak Ridge National Laboratory. (see\n\ @url{http://www.cise.ufl.edu/research/sparse/colamd})\n\ @seealso{colperm, symamd}\n\ @end deftypefn") @@ -456,22 +456,22 @@ \n\ For a symmetric positive definite matrix @var{s}, returns the permutation\n\ vector p such that @code{@var{s} (@var{p}, @var{p})} tends to have a\n\ -sparser Cholesky factor than @var{s}. Sometimes SYMAMD works well for\n\ -symmetric indefinite matrices too. The matrix @var{s} is assumed to be\n\ -symmetric; only the strictly lower triangular part is referenced. @var{s}\n\ +sparser Cholesky factor than @var{s}. Sometimes SYMAMD works well for\n\ +symmetric indefinite matrices too. The matrix @var{s} is assumed to be\n\ +symmetric; only the strictly lower triangular part is referenced. @var{s}\n\ must be square.\n\ \n\ @var{knobs} is an optional one- to two-element input vector. If @var{s} is\n\ n-by-n, then rows and columns with more than\n\ @code{max(16,@var{knobs}(1)*sqrt(n))} entries are removed prior to ordering,\n\ -and ordered last in the output permutation @var{p}. No rows/columns are\n\ +and ordered last in the output permutation @var{p}. No rows/columns are\n\ removed if @code{@var{knobs}(1) < 0}. If @code{@var{knobs} (2)} is nonzero,\n\ @code{stats} and @var{knobs} are printed. The default is @code{@var{knobs} \n\ = [10 0]}. Note that @var{knobs} differs from earlier versions of symamd.\n\ \n\ @var{stats} is an optional 20-element output vector that provides data\n\ -about the ordering and the validity of the input matrix @var{s}. Ordering\n\ -statistics are in @code{@var{stats} (1:3)}. @code{@var{stats} (1) =\n\ +about the ordering and the validity of the input matrix @var{s}. Ordering\n\ +statistics are in @code{@var{stats} (1:3)}. @code{@var{stats} (1) =\n\ @var{stats} (2)} is the number of dense or empty rows and columns\n\ ignored by SYMAMD and @code{@var{stats} (3)} is the number of garbage\n\ collections performed on the internal data structure used by SYMAMD\n\ @@ -492,13 +492,13 @@ thus a simple way to check a sparse matrix to see if it's valid.\n\ \n\ @code{@var{stats} (4:7)} provide information if SYMAMD was able to\n\ -continue. The matrix is OK if @code{@var{stats} (4)} is zero, or 1\n\ -if invalid. @code{@var{stats} (5)} is the rightmost column index that\n\ +continue. The matrix is OK if @code{@var{stats} (4)} is zero, or 1\n\ +if invalid. @code{@var{stats} (5)} is the rightmost column index that\n\ is unsorted or contains duplicate entries, or zero if no such column\n\ -exists. @code{@var{stats} (6)} is the last seen duplicate or out-of-order\n\ +exists. @code{@var{stats} (6)} is the last seen duplicate or out-of-order\n\ row index in the column index given by @code{@var{stats} (5)}, or zero\n\ -if no such row index exists. @code{@var{stats} (7)} is the number of\n\ -duplicate or out-of-order row indices. @code{@var{stats} (8:20)} is\n\ +if no such row index exists. @code{@var{stats} (7)} is the number of\n\ +duplicate or out-of-order row indices. @code{@var{stats} (8:20)} is\n\ always zero in the current version of SYMAMD (reserved for future use).\n\ \n\ The ordering is followed by a column elimination tree post-ordering.\n\ @@ -507,7 +507,7 @@ The authors of the code itself are Stefan I. Larimore and Timothy A.\n\ Davis (davis@@cise.ufl.edu), University of Florida. The algorithm was\n\ developed in collaboration with John Gilbert, Xerox PARC, and Esmond\n\ -Ng, Oak Ridge National Laboratory. (see\n\ +Ng, Oak Ridge National Laboratory. (see\n\ @url{http://www.cise.ufl.edu/research/sparse/colamd})\n\ @seealso{colperm, colamd}\n\ @end deftypefn") @@ -650,10 +650,10 @@ @deftypefnx {Loadable Function} {@var{p} =} etree (@var{s}, @var{typ})\n\ @deftypefnx {Loadable Function} {[@var{p}, @var{q}] =} etree (@var{s}, @var{typ})\n\ \n\ -Returns the elimination tree for the matrix @var{s}. By default @var{s}\n\ +Returns the elimination tree for the matrix @var{s}. By default @var{s}\n\ is assumed to be symmetric and the symmetric elimination tree is\n\ -returned. The argument @var{typ} controls whether a symmetric or\n\ -column elimination tree is returned. Valid values of @var{typ} are\n\ +returned. The argument @var{typ} controls whether a symmetric or\n\ +column elimination tree is returned. Valid values of @var{typ} are\n\ 'sym' or 'col', for symmetric or column elimination tree respectively\n\ \n\ Called with a second argument, @dfn{etree} also returns the postorder\n\