Mercurial > hg > octave-shane
changeset 11593:1577c6f80926
Use non-breaking spaces between certain adjectives and their nouns in docstrings.
author | Rik <octave@nomad.inbox5.com> |
---|---|
date | Thu, 20 Jan 2011 20:19:29 -0800 |
parents | ab61de78833e |
children | f2e868fd8500 |
files | doc/ChangeLog doc/interpreter/diagperm.txi doc/interpreter/linalg.txi doc/interpreter/sparse.txi scripts/ChangeLog scripts/linear-algebra/condest.m scripts/linear-algebra/onenormest.m scripts/linear-algebra/qzhess.m scripts/polynomial/polyfit.m scripts/special-matrix/pascal.m src/ChangeLog src/DLD-FUNCTIONS/chol.cc src/DLD-FUNCTIONS/luinc.cc src/DLD-FUNCTIONS/qz.cc |
diffstat | 14 files changed, 47 insertions(+), 27 deletions(-) [+] |
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--- a/doc/ChangeLog +++ b/doc/ChangeLog @@ -1,3 +1,9 @@ +2011-01-20 Rik <octave@nomad.inbox5.com> + + * doc/interpreter/diagperm.txi, doc/interpreter/linalg.txi, + doc/interpreter/sparse.txi: Use non-breaking spaces between certain + adjectives and their linked nouns in docstrings. + 2011-01-20 Rik <octave@nomad.inbox5.com> * doc/interpreter/doccheck/README: Update documentation for
--- a/doc/interpreter/diagperm.txi +++ b/doc/interpreter/diagperm.txi @@ -416,7 +416,7 @@ @section Some Examples of Usage The following can be used to solve a linear system @code{A*x = b} -using the pivoted LU factorization: +using the pivoted LU@tie{}factorization: @example @group
--- a/doc/interpreter/linalg.txi +++ b/doc/interpreter/linalg.txi @@ -56,9 +56,9 @@ @c backward substitution, and goto 5. @item If the matrix is square, Hermitian with a real positive diagonal, -attempt Cholesky factorization using the @sc{lapack} xPOTRF function. +attempt Cholesky@tie{}factorization using the @sc{lapack} xPOTRF function. -@item If the Cholesky factorization failed or the matrix is not +@item If the Cholesky@tie{}factorization failed or the matrix is not Hermitian with a real positive diagonal, and the matrix is square, factorize using the @sc{lapack} xGETRF function. @@ -74,7 +74,7 @@ used with care. It should be noted that the test for whether a matrix is a candidate for -Cholesky factorization, performed above and by the @code{matrix_type} +Cholesky@tie{}factorization, performed above and by the @code{matrix_type} function, does not give a certainty that the matrix is Hermitian. However, the attempt to factorize the matrix will quickly flag a non-Hermitian matrix.
--- a/doc/interpreter/sparse.txi +++ b/doc/interpreter/sparse.txi @@ -420,9 +420,9 @@ @end float @end ifset -The dependencies between the nodes of a Cholesky factorization can be +The dependencies between the nodes of a Cholesky@tie{}factorization can be calculated in linear time without explicitly needing to calculate the -Cholesky factorization by the @code{etree} command. This command +Cholesky@tie{}factorization by the @code{etree} command. This command returns the elimination tree of the matrix and can be displayed graphically by the command @code{treeplot(etree(A))} if @code{A} is symmetric or @code{treeplot(etree(A+A'))} otherwise. @@ -654,7 +654,7 @@ @caption{Structure of simple sparse matrix.} @end float -The standard Cholesky factorization of this matrix can be +The standard Cholesky@tie{}factorization of this matrix can be obtained by the same command that would be used for a full matrix. This can be visualized with the command @code{r = chol(A); spy(r);}. @@ -668,7 +668,7 @@ @ifset htmltex 598 @end ifset -non-zero terms, while this Cholesky factorization has +non-zero terms, while this Cholesky@tie{}factorization has @ifinfo @ifnothtml 71, @@ -696,19 +696,19 @@ @end ifset non-zero terms which is a significant improvement. -The Cholesky factorization itself can be used to determine the +The Cholesky@tie{}factorization itself can be used to determine the appropriate sparsity preserving reordering of the matrix during the factorization, In that case this might be obtained with three return arguments as r@code{[r, p, q] = chol(A); spy(r)}. @float Figure,fig:simplechol @center @image{spchol,4in} -@caption{Structure of the un-permuted Cholesky factorization of the above matrix.} +@caption{Structure of the un-permuted Cholesky@tie{}factorization of the above matrix.} @end float @float Figure,fig:simplecholperm @center @image{spcholperm,4in} -@caption{Structure of the permuted Cholesky factorization of the above matrix.} +@caption{Structure of the permuted Cholesky@tie{}factorization of the above matrix.} @end float In the case of an asymmetric matrix, the appropriate sparsity @@ -763,7 +763,7 @@ @enumerate @item If the matrix is Hermitian, with a positive real diagonal, attempt - Cholesky factorization using @sc{lapack} xPTSV. + Cholesky@tie{}factorization using @sc{lapack} xPTSV. @item If the above failed or the matrix is not Hermitian with a positive real diagonal use Gaussian elimination with pivoting using @@ -771,7 +771,7 @@ @end enumerate @item If the matrix is Hermitian with a positive real diagonal, attempt - Cholesky factorization using @sc{lapack} xPBTRF. + Cholesky@tie{}factorization using @sc{lapack} xPBTRF. @item if the above failed or the matrix is not Hermitian with a positive real diagonal use Gaussian elimination with pivoting using @@ -786,9 +786,9 @@ or backward substitution, and goto 8 @item If the matrix is square, Hermitian with a real positive diagonal, attempt -sparse Cholesky factorization using @sc{cholmod}. +sparse Cholesky@tie{}factorization using @sc{cholmod}. -@item If the sparse Cholesky factorization failed or the matrix is not +@item If the sparse Cholesky@tie{}factorization failed or the matrix is not Hermitian with a real positive diagonal, and the matrix is square, factorize using @sc{umfpack}. @@ -804,11 +804,11 @@ solvers can be entirely disabled by using @dfn{spparms} to set @code{bandden} to 1 (i.e., @code{spparms ("bandden", 1)}). -The QR solver factorizes the problem with a Dulmage-Mendelsohn, to +The QR@tie{}solver factorizes the problem with a Dulmage-Mendelsohn, to separate the problem into blocks that can be treated as over-determined, multiple well determined blocks, and a final over-determined block. For matrices with blocks of strongly connected nodes this is a big win as -LU decomposition can be used for many blocks. It also significantly +LU@tie{}decomposition can be used for many blocks. It also significantly improves the chance of finding a solution to over-determined problems rather than just returning a vector of @dfn{NaN}'s.
--- a/scripts/ChangeLog +++ b/scripts/ChangeLog @@ -1,3 +1,11 @@ +2011-01-20 Rik <octave@nomad.inbox5.com> + + * scripts/linear-algebra/condest.m, + scripts/linear-algebra/onenormest.m, scripts/linear-algebra/qzhess.m, + scripts/polynomial/polyfit.m, scripts/special-matrix/pascal.m: Use + non-breaking spaces between certain adjectives and their linked nouns + in docstrings + 2011-01-20 Rik <octave@nomad.inbox5.com> * image/imread.m, image/imwrite.m, signal/periodogram.m,
--- a/scripts/linear-algebra/condest.m +++ b/scripts/linear-algebra/condest.m @@ -28,7 +28,7 @@ ## If @var{t} exceeds 5, then only 5 test vectors are used. ## ## If the matrix is not explicit, e.g., when estimating the condition -## number of @var{A} given an LU factorization, @code{condest} uses the +## number of @var{A} given an LU@tie{}factorization, @code{condest} uses the ## following functions: ## ## @table @var
--- a/scripts/linear-algebra/onenormest.m +++ b/scripts/linear-algebra/onenormest.m @@ -25,7 +25,7 @@ ## only 5 test vectors are used. ## ## If the matrix is not explicit, e.g., when estimating the norm of -## @code{inv (@var{A})} given an LU factorization, @code{onenormest} applies +## @code{inv (@var{A})} given an LU@tie{}factorization, @code{onenormest} applies ## @var{A} and its conjugate transpose through a pair of functions ## @var{apply} and @var{apply_t}, respectively, to a dense matrix of size ## @var{n} by @var{t}. The implicit version requires an explicit dimension
--- a/scripts/linear-algebra/qzhess.m +++ b/scripts/linear-algebra/qzhess.m @@ -35,7 +35,7 @@ ## @end example ## ## The Hessenberg-triangular decomposition is the first step in -## Moler and Stewart's QZ decomposition algorithm. +## Moler and Stewart's QZ@tie{}decomposition algorithm. ## ## Algorithm taken from Golub and Van Loan, @cite{Matrix Computations, 2nd ## edition}.
--- a/scripts/polynomial/polyfit.m +++ b/scripts/polynomial/polyfit.m @@ -29,7 +29,7 @@ ## ## @table @samp ## @item R -## Triangular factor R from the QR decomposition. +## Triangular factor R from the QR@tie{}decomposition. ## ## @item X ## The Vandermonde matrix used to compute the polynomial coefficients.
--- a/scripts/special-matrix/pascal.m +++ b/scripts/special-matrix/pascal.m @@ -21,7 +21,7 @@ ## @deftypefn {Function File} {} pascal (@var{n}) ## @deftypefnx {Function File} {} pascal (@var{n}, @var{t}) ## Return the Pascal matrix of order @var{n} if @code{@var{t} = 0}. -## @var{t} defaults to 0. Return lower triangular Cholesky factor of +## @var{t} defaults to 0. Return lower triangular Cholesky@tie{}factor of ## the Pascal matrix if @code{@var{t} = 1}. This matrix is its own ## inverse, that is @code{pascal (@var{n}, 1) ^ 2 == eye (@var{n})}. ## If @code{@var{t} = -1}, return its absolute value. This is the
--- a/src/ChangeLog +++ b/src/ChangeLog @@ -1,3 +1,9 @@ +2011-01-20 Rik <octave@nomad.inbox5.com> + + * src/DLD-FUNCTIONS/chol.cc, src/DLD-FUNCTIONS/luinc.cc, + src/DLD-FUNCTIONS/qz.cc: Use non-breaking spaces between certain + adjectives and their linked nouns in docstrings + 2011-01-20 Rik <octave@nomad.inbox5.com> * src/DLD-FUNCTIONS/str2double.cc, src/data.cc, src/mappers.cc,
--- a/src/DLD-FUNCTIONS/chol.cc +++ b/src/DLD-FUNCTIONS/chol.cc @@ -68,7 +68,7 @@ @deftypefnx {Loadable Function} {[@var{R}, @var{p}, @var{Q}] =} chol (@var{S}, 'vector')\n\ @deftypefnx {Loadable Function} {[@var{L}, @dots{}] =} chol (@dots{}, 'lower')\n\ @cindex Cholesky factorization\n\ -Compute the Cholesky factor, @var{R}, of the symmetric positive definite\n\ +Compute the Cholesky@tie{}factor, @var{R}, of the symmetric positive definite\n\ matrix @var{A}, where\n\ @tex\n\ $ R^T R = A $.\n\
--- a/src/DLD-FUNCTIONS/luinc.cc +++ b/src/DLD-FUNCTIONS/luinc.cc @@ -43,7 +43,7 @@ @deftypefnx {Loadable Function} {[@var{L}, @var{U}, @var{P}, @var{Q}] =} luinc (@var{A}, @var{droptol})\n\ @deftypefnx {Loadable Function} {[@var{L}, @var{U}, @var{P}, @var{Q}] =} luinc (@var{A}, @var{opts})\n\ @cindex LU decomposition\n\ -Produce the incomplete LU factorization of the sparse matrix @var{A}.\n\ +Produce the incomplete LU@tie{}factorization of the sparse matrix @var{A}.\n\ Two types of incomplete factorization are possible, and the type\n\ is determined by the second argument to @code{luinc}.\n\ \n\
--- a/src/DLD-FUNCTIONS/qz.cc +++ b/src/DLD-FUNCTIONS/qz.cc @@ -291,8 +291,8 @@ "-*- texinfo -*-\n\ @deftypefn {Loadable Function} {@var{lambda} =} qz (@var{A}, @var{B})\n\ @deftypefnx {Loadable Function} {@var{lambda} =} qz (@var{A}, @var{B}, @var{opt})\n\ -QZ decomposition of the generalized eigenvalue problem (@math{A x = s B x}).\n\ -There are three ways to call this function:\n\ +QZ@tie{}decomposition of the generalized eigenvalue problem\n\ +(@math{A x = s B x}). There are three ways to call this function:\n\ @enumerate\n\ @item @code{@var{lambda} = qz (@var{A}, @var{B})}\n\ \n\ @@ -307,7 +307,7 @@ \n\ @item @code{[AA, BB, Q, Z, V, W, @var{lambda}] = qz (@var{A}, @var{B})}\n\ \n\ -Computes QZ decomposition, generalized eigenvectors, and \n\ +Computes QZ@tie{}decomposition, generalized eigenvectors, and \n\ generalized eigenvalues of @math{(A - s B)}\n\ @tex\n\ $$ AV = BV{ \\rm diag }(\\lambda) $$\n\