--- a/doc/ChangeLog	Thu Jan 20 19:34:54 2011 -0800
+++ b/doc/ChangeLog	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/doc/interpreter/diagperm.txi	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/doc/interpreter/linalg.txi	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/doc/interpreter/sparse.txi	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/scripts/ChangeLog	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/scripts/linear-algebra/condest.m	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/scripts/linear-algebra/onenormest.m	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/scripts/linear-algebra/qzhess.m	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/scripts/polynomial/polyfit.m	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/scripts/special-matrix/pascal.m	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/src/ChangeLog	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/src/DLD-FUNCTIONS/chol.cc	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/src/DLD-FUNCTIONS/luinc.cc	Thu Jan 20 20:19:29 2011 -0800
@@ -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	Thu Jan 20 19:34:54 2011 -0800
+++ b/src/DLD-FUNCTIONS/qz.cc	Thu Jan 20 20:19:29 2011 -0800
@@ -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\