% -*- texinfo -*-
% @deftypefn {Function File} {[@var{y}, @var{J}] =} D (@var{F}, @var{x}, @var{varargin})
% Evaluate @var{F} for a given input @var{x} and compute the jacobian, such that
% @example
%
% d
% @var{J}(i,j) = ----- @var{y}(i) where @var{y} = @var{F}(@var{x}, @var{varargin}@{:@})
% d@var{x}(j)
%
% @end example
%
% If @var{x} is complex, the above holds for the directional derivatives
% along the real axis
%
% Derivatives are computed analytically via Automatic Differentiation
% @end deftypefn
% @seealso{use_sparse_jacobians}