--- a/scripts/sparse/eigs.m	Tue Jan 20 09:55:41 2015 -0500
+++ b/scripts/sparse/eigs.m	Tue Jan 20 10:24:46 2015 -0500
@@ -40,7 +40,7 @@
 ## Calculate a limited number of eigenvalues and eigenvectors of @var{A},
 ## based on a selection criteria.  The number of eigenvalues and eigenvectors to
 ## calculate is given by @var{k} and defaults to 6.
-## 
+##
 ## By default, @code{eigs} solve the equation
 ## @tex
 ## $A \nu = \lambda \nu$,
@@ -63,113 +63,113 @@
 ## @ifinfo
 ## @code{A * v = lambda * B * v}.
 ## @end ifinfo
-## 
+##
 ## The argument @var{sigma} determines which eigenvalues are returned.
 ## @var{sigma} can be either a scalar or a string.  When @var{sigma} is a
 ## scalar, the @var{k} eigenvalues closest to @var{sigma} are returned.  If
 ## @var{sigma} is a string, it must have one of the following values.
-## 
+##
 ## @table @asis
 ## @item @qcode{"lm"}
 ## Largest Magnitude (default).
-## 
+##
 ## @item @qcode{"sm"}
 ## Smallest Magnitude.
-## 
+##
 ## @item @qcode{"la"}
 ## Largest Algebraic (valid only for real symmetric problems).
-## 
+##
 ## @item @qcode{"sa"}
 ## Smallest Algebraic (valid only for real symmetric problems).
-## 
+##
 ## @item @qcode{"be"}
 ## Both Ends, with one more from the high-end if @var{k} is odd (valid only for
 ## real symmetric problems).
-## 
+##
 ## @item @qcode{"lr"}
 ## Largest Real part (valid only for complex or unsymmetric problems).
-## 
+##
 ## @item @qcode{"sr"}
 ## Smallest Real part (valid only for complex or unsymmetric problems).
-## 
+##
 ## @item @qcode{"li"}
 ## Largest Imaginary part (valid only for complex or unsymmetric problems).
-## 
+##
 ## @item @qcode{"si"}
 ## Smallest Imaginary part (valid only for complex or unsymmetric problems).
 ## @end table
-## 
+##
 ## If @var{opts} is given, it is a structure defining possible options that
 ## @code{eigs} should use.  The fields of the @var{opts} structure are:
-## 
+##
 ## @table @code
 ## @item issym
 ## If @var{af} is given, then flags whether the function @var{af} defines a
 ## symmetric problem.  It is ignored if @var{A} is given.  The default is false.
-## 
+##
 ## @item isreal
 ## If @var{af} is given, then flags whether the function @var{af} defines a
 ## real problem.  It is ignored if @var{A} is given.  The default is true.
-## 
+##
 ## @item tol
 ## Defines the required convergence tolerance, calculated as
 ## @code{tol * norm (A)}.  The default is @code{eps}.
-## 
+##
 ## @item maxit
 ## The maximum number of iterations.  The default is 300.
-## 
+##
 ## @item p
 ## The number of Lanzcos basis vectors to use.  More vectors will result in
 ## faster convergence, but a greater use of memory.  The optimal value of
 ## @code{p} is problem dependent and should be in the range @var{k} to @var{n}.
 ## The default value is @code{2 * @var{k}}.
-## 
+##
 ## @item v0
 ## The starting vector for the algorithm.  An initial vector close to the
 ## final vector will speed up convergence.  The default is for @sc{arpack}
 ## to randomly generate a starting vector.  If specified, @code{v0} must be
 ## an @var{n}-by-1 vector where @code{@var{n} = rows (@var{A})}
-## 
+##
 ## @item disp
 ## The level of diagnostic printout (0|1|2).  If @code{disp} is 0 then
 ## diagnostics are disabled.  The default value is 0.
-## 
+##
 ## @item cholB
 ## Flag if @code{chol (@var{B})} is passed rather than @var{B}.  The default is
 ## false.
-## 
+##
 ## @item permB
 ## The permutation vector of the Cholesky@tie{}factorization of @var{B} if
 ## @code{cholB} is true.  That is @code{chol (@var{B}(permB, permB))}.  The
 ## default is @code{1:@var{n}}.
-## 
+##
 ## @end table
-## 
+##
 ## It is also possible to represent @var{A} by a function denoted @var{af}.
 ## @var{af} must be followed by a scalar argument @var{n} defining the length
 ## of the vector argument accepted by @var{af}.  @var{af} can be
 ## a function handle, an inline function, or a string.  When @var{af} is a
 ## string it holds the name of the function to use.
-## 
+##
 ## @var{af} is a function of the form @code{y = af (x)}
 ## where the required return value of @var{af} is determined by
 ## the value of @var{sigma}.  The four possible forms are
-## 
+##
 ## @table @code
 ## @item A * x
 ## if @var{sigma} is not given or is a string other than "sm".
-## 
+##
 ## @item A \ x
 ## if @var{sigma} is 0 or "sm".
-## 
+##
 ## @item (A - sigma * I) \ x
 ## for the standard eigenvalue problem, where @code{I} is the identity matrix of
 ## the same size as @var{A}.
-## 
+##
 ## @item (A - sigma * B) \ x
 ## for the general eigenvalue problem.
 ## @end table
-## 
+##
 ## The return arguments of @code{eigs} depend on the number of return arguments
 ## requested.  With a single return argument, a vector @var{d} of length @var{k}
 ## is returned containing the @var{k} eigenvalues that have been found.  With
@@ -178,7 +178,7 @@
 ## eigenvalues themselves are returned in @var{d} in the form of a
 ## @var{n}-by-@var{k} matrix, where the elements on the diagonal are the
 ## eigenvalues.
-## 
+##
 ## Given a third return argument @var{flag}, @code{eigs} returns the status
 ## of the convergence.  If @var{flag} is 0 then all eigenvalues have converged.
 ## Any other value indicates a failure to converge.
@@ -186,7 +186,7 @@
 ## This function is based on the @sc{arpack} package, written by
 ## @nospell{R. Lehoucq, K. Maschhoff, D. Sorensen, and C. Yang}.  For more
 ## information see @url{http://www.caam.rice.edu/software/ARPACK/}.
-## 
+##
 ## @seealso{eig, svds}
 ## @end deftypefn
 
@@ -215,7 +215,7 @@
     if (rows (a) < 9)
       call_eig = true;
     endif
-    
+
     if (nargin > 1 + offset)
       tmp = varargin{2+offset};
       if (isnumeric (tmp) && isscalar (tmp) && isreal (tmp)