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\title{Programs for Applying Symmetries of PDEs}
\author{Thomas Wolf \\                        
        Department of Mathematics \\
        Brock University \\
        St.Catharines \\
        Ontario, Canada L2S 3A1 \\
        twolf@brocku.ca}

\begin{document}
\maketitle
\begin{abstract}
In this paper the programs {\tt APPLYSYM}, {\tt QUASILINPDE} and
{\tt DETRAFO} are described which aim at the utilization
of infinitesimal symmetries of differential equations. The purpose
of {\tt QUASILINPDE} is the general solution of
quasilinear PDEs. This procedure is used by {\tt APPLYSYM}
for the application of point symmetries for either
\begin{itemize}
\item calculating similarity variables to perform a point transformation
which lowers the order of an ODE or effectively reduces the number of
explicitly occuring independent variables in a PDE(-system) or for
\item generalizing given special solutions of ODEs / PDEs with new constant
parameters.
\end{itemize}

The program {\tt DETRAFO} performs arbitrary point- and contact
transformations of ODEs / PDEs and is applied if similarity
and symmetry variables have been found.
The program {\tt APPLYSYM} is used in connection with the program
{\tt LIEPDE} for formulating and solving the conditions for point- and
contact symmetries which is described in \cite{LIEPDE}.
The actual problem solving is done in all these programs through a call
to the package {\tt CRACK} for solving overdetermined PDE-systems.
\end{abstract}

\tableofcontents
%-------------------------------------------------------------------------
\section{Introduction and overview of the symmetry \\ method}
The investigation of infinitesimal symmetries of differential equations
(DEs) with computer algebra programs attrackted considerable attention
over the last years. Corresponding programs are available in all
major computer algebra systems. In a review article by W.\ Hereman
\cite{WHer} about 200 references are given, many of them describing related
software.

One reason for the popularity of the symmetry method
is the fact that Sophus Lie's method
\cite{lie1},\cite{lie2} is the most widely
used method for computing exact solutions of non-linear DEs. Another reason is
that the first step in this
method, the formulation of the determining equation for the generators
of the symmetries, can already be very cumbersome, especially in the
case of PDEs of higher order and/or in case of many dependent and independent
variables. Also, the formulation of the conditions is a straight forward
task involving only differentiations and basic algebra - an ideal task for
computer algebra systems. Less straight forward is the automatic solution
of the symmetry conditions which is the strength of the program {\tt LIEPDE}
(for a comparison with another program see \cite{LIEPDE}).

The novelty described in this paper are programs aiming at
the final third step: Applying symmetries for
\begin{itemize}
\item calculating similarity variables to perform a point transformation
which lowers the order of an ODE or effectively reduces the number of
explicitly occuring independent variables of a PDE(-system) or for
\item generalizing given special solutions of ODEs/PDEs with new constant
parameters.
\end{itemize}
Programs which run on their own but also allow interactive user control
are indispensible for these calculations. On one hand the calculations can
become quite lengthy, like variable transformations of PDEs (of higher order,
with many variables). On the other hand the freedom of choosing the right
linear combination of symmetries and choosing the optimal new symmetry- and
similarity variables makes it necessary to `play' with the problem
interactively.

The focus in this paper is directed on questions of implementation and
efficiency, no principally new mathematics is presented.

In the following subsections a review of the first two steps of the symmetry
method is given as well as the third, i.e.\ the application step is outlined.
Each of the remaining sections is devoted to one procedure.
%---------------------------------------
\subsection{The first step: Formulating the symmetry conditions}

To obey classical Lie-symmetries, differential equations
\begin{equation}
H_A = 0              \label{PDEs}
\end{equation}
for unknown functions $y^\alpha,\;\;1\leq \alpha \leq p$
of independent variables $x^i,\;\;1\leq i \leq q$
must be forminvariant against infinitesimal transformations
\begin{equation}
\tilde{x}^i = x^i + \varepsilon \xi^i, \;\; \;\;\;
        \tilde{y}^\alpha = y^\alpha + \varepsilon \eta^\alpha  \label{tran}
\end{equation}
in first order of $\varepsilon.$ To transform the equations (\ref{PDEs})
by (\ref{tran}), derivatives of $y^\alpha$ must be transformed, i.e. the part
linear in $\varepsilon$ must be determined. The corresponding formulas are
(see e.g. \cite{Olv}, \cite{Step})
\begin{eqnarray}
\tilde{y}^\alpha_{j_1\ldots j_k} & = &
y^\alpha_{j_1\ldots j_k} + \varepsilon
\eta^\alpha_{j_1\ldots j_k} + O(\varepsilon^2)  \nonumber \\ \vspace{3mm}
\eta^\alpha_{j_1\ldots j_{k-1}j_k} & = &
  \frac{D \eta^\alpha_{j_1\ldots j_{k-1}}}{D x^k} -
  y^\alpha_{ij_1\ldots j_{k-1}}\frac{D \xi^i}{D x^k} \label{recur}
\end{eqnarray}
where $D/Dx^k$ means total differentiation w.r.t.\ $x^k$ and
from now on lower latin indices of functions $y^\alpha,$ 
(and later $u^\alpha$)
denote partial differentiation w.r.t.\ the independent variables $x^i,$
(and later $v^i$).
The complete symmetry condition then takes the form
\begin{eqnarray}
X H_A & = & 0 \;\; \; \; \mbox{mod} \; \; \; H_A = 0\  \label{sbed1} \\
X & = & \xi^i \frac{\partial}{\partial x^i} +
 \eta^\alpha \frac{\partial}{\partial y^\alpha} +
 \eta^\alpha_m \frac{\partial}{\partial y^\alpha_m} +
 \eta^\alpha_{mn} \frac{\partial}{\partial y^\alpha_{mn}} + \ldots +
 \eta^\alpha_{mn\ldots p} \frac{\partial}{\partial y^\alpha_{mn\ldots p}}.
\label{sbed2}
\end{eqnarray}
where mod $H_A = 0$ means that the original PDE-system is used to replace
some partial derivatives of $y^\alpha$ to reduce the number of independent
variables, because the symmetry condition (\ref{sbed1}) must be
fulfilled identically in $x^i, y^\alpha$ and all partial
derivatives of $y^\alpha.$

For point symmetries, $\xi^i, \eta^\alpha$ are functions of $x^j,
y^\beta$ and for contact symmetries they depend on $x^j, y^\beta$ and
$y^\beta_k.$ We restrict ourself to point symmetries as those are the only
ones that can be applied by the current version of the program {\tt APPLYSYM}
(see below). For literature about generalized symmetries see \cite{WHer}.

Though the formulation of the symmetry conditions (\ref{sbed1}),
(\ref{sbed2}), (\ref{recur})
is straightforward and handled in principle by all related
programs \cite{WHer}, the computational effort to formulate
the conditions (\ref{sbed1}) may cause problems if
the number of $x^i$ and $y^\alpha$ is high.  This can
partially be avoided if at first only a few conditions are formulated
and solved such that the remaining ones are much shorter and quicker to
formulate.

A first step in this direction is to investigate one PDE $H_A = 0$
after another, as done in \cite{Cham}.  Two methods to partition the
conditions for a single PDE are described by Bocharov/Bronstein
\cite{Alex} and Stephani \cite{Step}.

In the first method only those terms of the symmetry condition
$X H_A = 0$ are calculated which contain 
at least a derivative of $y^\alpha$ of a minimal order $m.$ 
Setting coefficients
of these $u$-derivatives to zero provides symmetry conditions. Lowering the
minimal order $m$ successively then gradually provides all symmetry conditions.

The second method is even more selective. If $H_A$ is of order $n$
then only terms of the symmetry condition $X H_A = 0$ are generated which
contain $n'$th order derivatives of $y^\alpha.$ Furthermore these derivatives
must not occur in $H_A$ itself. They can therefore occur 
in the symmetry condition
(\ref{sbed1}) only in
$\eta^\alpha_{j_1\ldots j_n},$ i.e. in the terms
\[\eta^\alpha_{j_1\ldots j_n}
\frac{\partial H_A}{\partial y^\alpha_{j_1\ldots j_n}}. \]
If only coefficients of $n'$th order derivatives of $y^\alpha$ need to be
accurate to formulate preliminary conditions
then from the total derivatives to be taken in
(\ref{recur}) only that part is performed which differentiates w.r.t.\ the
highest $y^\alpha$-derivatives.
This means, for example, to form only
$y^\alpha_{mnk} \partial/\partial y^\alpha_{mn} $
if the expression, which is to be differentiated totally w.r.t.\ $x^k$,
contains at most second order derivatives of $y^\alpha.$

The second method is applied in {\tt LIEPDE}.
Already the formulation of the remaining conditions is speeded up
considerably through this iteration process. These methods can be applied if
systems of DEs or single PDEs of at least second order are investigated
concerning symmetries.
%---------------------------------------
\subsection{The second step: Solving the symmetry conditions}
The second step in applying the whole method consists in solving the
determining conditions (\ref{sbed1}), (\ref{sbed2}), (\ref{recur})
which are linear homogeneous PDEs for $\xi^i, \eta^\alpha$. The
complete solution of this system is not algorithmic any more because the
solution of a general linear PDE-system is as difficult as the solution of
its non-linear characteristic ODE-system which is not covered by algorithms
so far.

Still algorithms are used successfully to simplify the PDE-system by
calculating
its standard normal form and by integrating exact PDEs
if they turn up in this simplification process \cite{LIEPDE}.
One problem in this respect, for example,
concerns the optimization of the symbiosis of both algorithms. By that we
mean the ranking of priorities between integrating, adding integrability
conditions and doing simplifications by substitutions - all depending on
the length of expressions and the overall structure of the PDE-system.
Also the extension of the class of PDEs which can be integrated exactly is
a problem to be pursuit further.

The program {\tt LIEPDE} which formulates the symmetry conditions calls the
program {\tt CRACK} to solve them. This is done in a number of successive
calls in order to formulate and solve some first order PDEs of the
overdetermined system first and use their solution to formulate and solve the
next subset of conditions as described in the previous subsection. 
Also, {\tt LIEPDE} can work on DEs that contain parametric constants and
parametric functions. An ansatz for the symmetry generators can be
formulated. For more details see \cite{LIEPDE} or \cite{WoBra}.

The procedure {\tt LIEPDE} is called through \\
{\tt LIEPDE({\it problem,symtype,flist,inequ}); } \\
All parameters are lists. \vspace{6pt} \\
The first parameter specifies the DEs to be investigated: \\
{\it problem} has the form \{{\it equations, ulist, xlist}\} where
\begin{tabbing}
\hspace{0.5cm}
 {\it equations } \=  is a list of equations,
              each has the form {\tt df(ui,..)=...} where \\
       \> the LHS (left hand side) {\tt df(ui,..)} is selected such that \\
       \>  - The RHS (right h.s.) of an equations must not include     \\
       \>$\;\,$ the derivative on the LHS nor a derivative of it.  \\
       \>  - Neither the LHS nor any derivative of it of any equation \\
       \>$\;\,$ may occur in any other equation.\\
       \>  - Each of the unknown functions occurs on the LHS of \\
       \>$\;\,$ exactly one equation.  \\
\hspace{0.5cm}
 {\it ulist} \>  is a list of function names, which can be chosen freely \\
\hspace{0.5cm}
 {\it xlist}  \>  is a list of variable names, which can be chosen freely 
\end{tabbing}
Equations can be given as a list of single differential expressions and then
the program will try to bring them into the `solved form' {\tt df(ui,..)=...}
automatically. If equations are given in the solved form then the above
conditions are checked and execution is stopped it they are not satisfied.
An easy way to get the equations in the desired form is to use \\
\verb+    FIRST SOLVE({+{\it eq1,eq2,}...\verb+},{+{\it one highest
derivative for each function u}\verb+})+  \\
(see the example of the Karpman equations in {\tt LIEPDE.TST}). 
The example of the Burgers equation in {\tt LIEPDE.TST} demonstrates 
that the number of symmetries for a given maximal order of the infinitesimal
generators depends on the derivative chosen for the LHS.

The second parameter {\it symtype} of {\tt LIEPDE} is a list $\{\;\}$ that
specifies the symmetry to be calculated. {\it symtype} can have the following
values and meanings:
\begin{tabbing}
\verb+{"point"}         + \= Point symmetries with $\xi^i=\xi^i(x^j,u^{\beta}),\;
                           \eta^{\alpha}=\eta^{\alpha}(x^j,u^{\beta})$ are \\
                          \> determined.\\
\verb+{"contact"}+ \> Contact symmetries with $\xi^i=0, \;
                            \eta=\eta(x^j,u,u_k)$ are \\
\> determined $(u_k = \partial u/\partial x^k)$, which is only applicable if a \\
                      \> single equation (\ref{PDEs}) with an order $>1$ for a 
                             single function \\
                      \> $u$ is to be investigated. (The {\it symtype}
                             \verb+{"contact"}+ \\
\> is equivalent to \verb+{"general",1}+ (see below) apart from \\
\> the additional checks done for \verb+{"contact"}+.)\\
\verb+{"general"+,{\it order}\verb+}+ \> where {\it order} is an integer $>0$. 
      Generalized symmetries $\xi^i=0,$ \\
\> $\eta^{\alpha}=\eta^{\alpha}(x^j,u^{\beta},\ldots,u^{\beta}_K)$ 
      of a specified order are determined \\
\> (where $_K$ is a multiple index representing {\it order} many indices.) \\
\> NOTE: Characteristic functions of generalized symmetries \\
\> ($= \eta^{\alpha}$ if $\xi^i=0$) are equivalent if they are equal on\\
\> the solution manifold. Therefore, all dependences of\\
\> characteristic functions on the substituted derivatives \\
\> and their derivatives are dropped. For example, if the heat \\
\> equation is given as $u_t=u_{xx}$ (i.e.\ $u_t$ is substituted by $u_{xx}$) \\
\> then \verb+{"general",2}+ would not include characteristic \\
\> functions depending on $u_{tx}$ or $u_{xxx}$. \\
\> THEREFORE: \\
\> If you want to find {\it all} symmetries up to a given order then either \\
\> - avoid using $H_A=0$ to substitute lower order \\
\> $\;\,$derivatives by expressions involving higher derivatives, or \\
\> - increase the order specified in {\it symtype}. \\
\> For an illustration of this effect see the two symmetry \\
\> determinations of the Burgers equation in the file \\
\> {\tt LIEPDE.TST}. \\
\verb+{xi!_+{\it x1}\verb+ =...,..., +  \>  \\ 
\verb+ eta!_+{\it u1}\verb+=...,...}+  \> It is possible to specify an
ansatz for the symmetry. Such \\
\> an ansatz must specify all $\xi^i$ for all independent variables and \\
\> all $\eta^{\alpha}$ for all dependent variables in terms of differential \\
\> expressions which may involve unknown functions/constants. \\
\> The dependences of the unknown functions have to be declared \\
\> in advance by using the {\tt DEPEND} command. For example, \\
\> \verb+    DEPEND f, t, x, u$  + \\
\> specifies $f$ to be a function of $t,x,u$. If one wants to have $f$ as \\
\> a function of derivatives of $u(t,x)$, say $f$ depending on $u_{txx}$, \\
\> then one \underline{{\it cannot}} write \\
\> \verb+    DEPEND f, df(u,t,x,2)$ + \\ 
\> but instead must write \\
\> \verb+    DEPEND f, u!`1!`2!`2$ + \\
\> assuming {\it xlist} has been specified as \verb+ {t,x}+. 
   Because $t$ is the \\
\> first variable and $x$ is the second variable in {\it xlist} and $u$ is \\
\> differentiated oncs wrt.\ $t$ and twice wrt.\ $x$ we therefore \\
\> use \verb+ u!`1!`2!`2+. The character {\tt !} is the escape character \\
\> to allow special characters like ` to occur in an identifier. \\
\> \hspace{4mm} For generalized symmetries one usually sets all $\xi^i=0$.\\
\> Then the $\eta^{\alpha}$ are equal to the characteristic functions.
\end{tabbing}
\noindent The third parameter {\it flist} of {\tt LIEPDE} is a list $\{\;\}$ 
that includes
\begin{itemize}
\item all parameters and functions in the equations which are to
      be determined such that symmetries exist (if any such 
      parameters/functions are 
      specified in {\it flist} then the symmetry conditions 
      formulated in {\tt LIEPDE}
      become non-linear conditions which may be much harder for
      {\tt CRACK} to solve with many cases and subcases to be considered.)
\item all unknown functions and constants in the ansatz 
      \verb+xi!_..+ and \verb+eta!_..+
      if that has been specified in {\it symtype}.
\end{itemize}
\noindent The fourth parameter {\it inequ} of {\tt LIEPDE} is a list $\{\;\}$ 
that includes all non-vanishing expressions which represent
inequalities for the functions in flist.

The result of {\tt LIEPDE} is a list with 3 elements, each of which
is a list:
\[ \{\{{\it con}_1,{\it con}_2,\ldots\},
     \{{\tt xi}\__{\ldots}=\ldots, \ldots,
       {\tt eta}\__{\ldots}=\ldots, \ldots\},
     \{{\it flist}\}\}. \]
The first list contains remaining unsolved symmetry conditions {\it con}$_i$. It
is the empty list \{\} if all conditions have been solved. The second list
gives the symmetry generators, i.e.\ expressions for $\xi_i$ and $\eta_j$. The 
last list contains all free constants and functions occuring in the first
and second list.

%That the automatic calculation of symmetries run in most practical cases
%is shown with the following example. It is insofar difficult, as many
%symmetries exist and the solution consequently more difficult is to deriv.
%
%---------------------------------------
%\subsection{Example}
%For the following PDE-system, which takes its simplest form in the
%formalism of exterior forms:
%
%\begin{eqnarray*}
%0 & = & 3k_t,_{tt}-2k_t,_{xx}-2k_t,_{yy}-2k_t,_{zz}-k_x,_{tx}-2k_zk_x,_y \\
%  &   & +2k_yk_x,_z-k_y,_{ty}+2k_zk_y,_x-2k_xk_y,_z-k_z,_{tz}-2k_yk_z,_x+2k_xk_z,_y \\
%0 & = & k_t,_{tx}-2k_zk_t,_y+2k_yk_t,_z+2k_x,_{tt}-3k_x,_{xx}-2k_x,_{yy} \\
%  &   & -2k_x,_{zz}+2k_zk_y,_t-k_y,_{xy}-2k_tk_y,_z-2k_yk_z,_t-k_z,_{xz}+2k_tk_z,_y \\
%0 & = & k_t,_{ty}+2k_zk_t,_x-2k_xk_t,_z-2k_zk_x,_t-k_x,_{xy}+2k_tk_x,_z \\
%  &   & +2k_y,_{tt}-2k_y,_{xx}-3k_y,_{yy}-2k_y,_{zz}+2k_xk_z,_t-2k_tk_z,_x-k_z,_{yz} \\
%0 & = & k_t,_{tz}-2k_yk_t,_x+2k_xk_t,_y+2k_yk_x,_t-k_x,_{xz}-2k_tk_x,_y \\
%  &   & -2k_xk_y,_t+2k_tk_y,_x-k_y,_{yz}+2k_z,_{tt}-2k_z,_{xx}-2k_z,_{yy}-3k_z,_{zz}
%\end{eqnarray*}
%---------------------------------------
\subsection{The third step: Application of infinitesimal symmetries}
If infinitesimal symmetries have been found then
the program {\tt APPLYSYM} can use them for the following purposes:
\begin{enumerate}
\item Calculation of one symmetry variable and further similarity variables.
After transforming
the DE(-system) to these variables, the symmetry variable will not occur
explicitly any more. For ODEs this has the consequence that their order has
effectively been reduced.
\item Generalization of a special solution by one or more constants of
integration.
\end{enumerate}
Both methods are described in the following section.
%-------------------------------------------------------------------------
\section{Applying symmetries with {\tt APPLYSYM}}
%---------------------------------------
\subsection{The first mode: Calculation of similarity and symmetry variables}
In the following we assume that a symmetry generator $X$, given
in (\ref{sbed2}), is known such that ODE(s)/PDE(s) $H_A=0$
satisfy the symmetry condition (\ref{sbed1}). The aim is to
find new dependent functions $u^\alpha = u^\alpha(x^j,y^\beta)$ and
new independent variables $v^i = v^i(x^j,y^\beta),\;\;
1\leq\alpha,\beta\leq p,\;1\leq i,j \leq q$
such that the symmetry generator
$X = \xi^i(x^j,y^\beta)\partial_{x^i} +
     \eta^\alpha(x^j,y^\beta)\partial_{y^\alpha}$
transforms to
\begin{equation}
X = \partial_{v^1}.    \label{sbed3}
\end{equation}

Inverting the above transformation to $x^i=x^i(v^j,u^\beta),
y^\alpha=y^\alpha(v^j,u^\beta)$ and setting \\
$H_A(x^i(v^j,u^\beta), y^\alpha(v^j,u^\beta),\ldots) =
h_A(v^j, u^\beta,\ldots)$
this means that
\begin{eqnarray*}
 0 & = & X H_A(x^i,y^\alpha,y^\beta_j,\ldots)\;\;\; \mbox{mod} \;\;\; H_A=0 \\
   & = & X h_A(v^i,u^\alpha,u^\beta_j,\ldots)\;\;\; \mbox{mod} \;\;\; h_A=0 \\
   & = & \partial_{v^1}h_A(v^i,u^\alpha,u^\beta_j,\ldots)\;\;\; \mbox{mod}
         \;\;\; h_A=0.
\end{eqnarray*}
Consequently, the variable $v^1$ does not occur explicitly in $h_A$.
In the case of an ODE(-system) $(v^1=v)$
the new equations $0=h_A(v,u^\alpha,du^\beta/dv,\ldots)$
are then of lower total order
after the transformation $z = z(u^1) = du^1/dv$ with now $z, u^2,\ldots u^p$
as unknown functions and $u^1$ as independent variable.

The new form (\ref{sbed3}) of $X$ leads directly to conditions for the
symmetry variable $v^1$ and the similarity variables
$v^i|_{i\neq 1}, u^\alpha$ (all functions of $x^k,y^\gamma$):
\begin{eqnarray}
 X v^1 = 1 & = & \xi^i(x^k,y^\gamma)\partial_{x^i}v^1 +
                \eta^\alpha(x^k,y^\gamma)\partial_{y^\alpha}v^1 \label{ql1} \\
 X v^j|_{j\neq 1} = X u^\beta = 0 & = &
                 \xi^i(x^k,y^\gamma)\partial_{x^i}u^\beta +
                 \eta^\alpha(x^k,y^\gamma)\partial_{y^\alpha}u^\beta \label{ql2}
\end{eqnarray}
The general solutions of (\ref{ql1}), (\ref{ql2}) involve free functions
of $p+q-1$ arguments. From the general solution of equation (\ref{ql2}),
$p+q-1$ functionally independent special solutions have to be selected
($v^2,\ldots,v^p$ and $u^1,\ldots,u^q$), 
whereas from (\ref{ql1}) only one solution $v^1$ is needed.
Together, the expressions for the symmetry and similarity variables must
define a non-singular transformation $x,y \rightarrow u,v$.

Different special solutions selected at this stage
will result in different
resulting DEs which are equivalent under point transformations but may
look quite differently. A transformation that is more difficult than another
one will in general
only complicate the new DE(s) compared with the simpler transformation.
We therefore seek the simplest possible special
solutions of (\ref{ql1}), (\ref{ql2}). They also
have to be simple because the transformation has to be inverted to solve for
the old variables in order to do the transformations.

The following steps are performed in the corresponding mode of the
program {\tt APPLYSYM}:
\begin{itemize}
\item The user is asked to specify a symmetry by selecting one symmetry
from all the known symmetries or by specifying a linear combination of them.
\item Through a call of the procedure {\tt QUASILINPDE} (described in a later
section) the two linear first order PDEs (\ref{ql1}), (\ref{ql2}) are
investigated and, if possible, solved.
\item From the general solution of (\ref{ql1}) 1 special solution
is selected and from (\ref{ql2}) $p+q-1$ special
solutions are selected which should be as simple as possible.
\item The user is asked whether the symmetry variable should be one of the
independent variables (as it has been assumed so far) or one of the new
functions (then only derivatives of this function and not the function itself
turn up in the new DE(s)).
\item Through a call of the procedure {\tt DETRAFO} the transformation
$x^i,y^\alpha \rightarrow v^j,u^\beta$ of the DE(s) $H_A=0$ is finally done.
\item The program returns to the starting menu.
\end{itemize}
%---------------------------------------
\subsection{The second mode: Generalization of special solutions}
A second application of infinitesimal symmetries is the generalization
of a known special solution given in implicit form through
$0 = F(x^i,y^\alpha)$. If one knows a symmetry variable $v^1$ and
similarity variables $v^r, u^\alpha,\;\;2\leq r\leq p$ then
$v^1$ can be shifted by a constant $c$ because of
$\partial_{v^1}H_A = 0$ and
therefore the DEs $0 = H_A(v^r,u^\alpha,u^\beta_j,\ldots)$
are unaffected by the shift. Hence from
\[0 = F(x^i, y^\alpha) = F(x^i(v^j,u^\beta), y^\alpha(v^j,u^\beta)) =
\bar{F}(v^j,u^\beta)\] follows that
\[ 0 = \bar{F}(v^1+c,v^r,u^\beta) =
\bar{F}(v^1(x^i,y^\alpha)+c, v^r(x^i,y^\alpha), u^\beta(x^i,y^\alpha))\]
defines implicitly a generalized solution $y^\alpha=y^\alpha(x^i,c)$.

This generalization works only if $\partial_{v^1}\bar{F} \neq 0$ and
if $\bar{F}$ does not already have
a constant additive to $v^1$.

The method above needs to know $x^i=x^i(u^\beta,v^j),\;
y^\alpha=y^\alpha(u^\beta,v^j)$ \underline{and}
$u^\alpha = u^\alpha(x^j,y^\beta), v^\alpha = v^\alpha(x^j,y^\beta)$
which may be practically impossible.
Better is, to integrate $x^i,y^\alpha$ along $X$:
\begin{equation}
\frac{d\bar{x}^i}{d\varepsilon} = \xi^i(\bar{x}^j(\varepsilon),
                                  \bar{y}^\beta(\varepsilon)), \;\;\;\;\;
\frac{d\bar{y}^\alpha}{d\varepsilon} = \eta^\alpha(\bar{x}^j(\varepsilon),
                                       \bar{y}^\beta(\varepsilon))
\label{ODEsys}
\end{equation}
with initial values $\bar{x}^i = x^i, \bar{y}^\alpha = y^\alpha$
for $\varepsilon = 0.$
(This ODE-system is the characteristic system of (\ref{ql2}).)

Knowing only the finite transformations
\begin{equation}
\bar{x}^i = \bar{x}^i(x^j,y^\beta,\varepsilon),\;\;
\bar{y}^\alpha = \bar{y}^\alpha(x^j,y^\beta,\varepsilon)  \label{ODEsol}
\end{equation}
gives immediately the inverse transformation
$\bar{x}^i = \bar{x}^i(x^j,y^\beta,\varepsilon),\;\;
\bar{y}^\alpha = \bar{y}^\alpha(x^j,y^\beta,\varepsilon)$
just by $\varepsilon \rightarrow -\varepsilon$ and renaming
$x^i,y^\alpha \leftrightarrow \bar{x}^i,\bar{y}^\alpha.$

The special solution $0 = F(x^i,y^\alpha)$
is generalized by the new constant
$\varepsilon$ through
\[ 0 = F(x^i,y^\alpha) = F(x^i(\bar{x}^j,\bar{y}^\beta,\varepsilon),
                  y^\alpha(\bar{x}^j,\bar{y}^\beta,\varepsilon)) \]
after dropping the $\bar{~}$.

The steps performed in the corresponding mode of the
program {\tt APPLYSYM} show features of both techniques:
\begin{itemize}
\item The user is asked to specify a symmetry by selecting one symmetry
from all the known symmetries or by specifying a linear combination of them.
\item The special solution to be generalized and the name of the new
constant have to be put in.
\item Through a call of the procedure {\tt QUASILINPDE}, the PDE (\ref{ql1})
is solved which amounts to a solution of its characteristic ODE system
(\ref{ODEsys}) where $v^1=\varepsilon$.
\item {\tt QUASILINPDE} returns a list of constant expressions
\begin{equation}
c_i = c_i(x^k, y^\beta, \varepsilon),\;\;1\leq i\leq p+q
\end{equation}
which are solved for
$x^j=x^j(c_i,\varepsilon),\;\; y^\alpha=y^\alpha(c_i,\varepsilon)$
to obtain the generalized solution through
\[ 0 = F(x^j, y^\alpha)
     = F(     x^j(c_i(x^k, y^\beta, 0), \varepsilon),
         y^\alpha(c_i(x^k, y^\beta, 0), \varepsilon)). \]
\item The new solution is availabe for further generalizations w.r.t.\ other
symmetries.
\end{itemize}
If one would like to generalize a given special solution with $m$ new
constants because $m$ symmetries are known, then one could run the whole
program $m$ times, each time with a different symmetry or one could run the
program once with a linear combination of $m$ symmetry generators which
again is a symmetry generator. Running the program once adds one constant
but we have in addition $m-1$ arbitrary constants in the linear combination
of the symmetries, so $m$ new constants are added.
Usually one will generalize the solution gradually to make solving
(\ref{ODEsys}) gradually more difficult.
%---------------------------------------
\subsection{Syntax}
The call of {\tt APPLYSYM} is
{\tt APPLYSYM}(\{{\it de}, {\it fun}, {\it var}\}, \{{\it sym}, {\it cons}\});
\begin{itemize}
\item {\it de} is a single DE or a list of DEs in the form of a vanishing
      expression or in the form $\ldots=\ldots\;\;$.
\item {\it fun} is the single function or the list of functions occuring
      in {\it de}.
\item {\it var} is the single variable or the list of variables in {\it de}.
\item {\it sym} is a linear combination of all symmetries, each with a
      different constant coefficient, in form of a list of the $\xi^i$ and
      $\eta^\alpha$: \{xi\_\ldots=\ldots,\ldots,eta\_\ldots=\ldots,\ldots\},
      where the indices after `xi\_' are the variable names and after `eta\_'
      the function names.
\item {\it cons} is the list of constants in {\it sym}, one constant for each
      symmetry.
\end{itemize}
The list that is the first argument of {\tt APPLYSYM} is the same as the
first argument of {\tt LIEPDE} and the
second argument is the list that {\tt LIEPDE} returns without its first
element (the unsolved conditions). An example is given below.

What {\tt APPLYSYM} returns depends on the last performed modus.
After modus 1 the return is \\
\{\{{\it newde}, {\it newfun}, {\it newvar}\}, {\it trafo}\} \\
where
\begin{itemize}
\item {\it newde} lists the transformed equation(s)
\item {\it newfun} lists the new function name(s)
\item {\it newvar} lists the new variable name(s)
\item {\it trafo} lists the transformations $x^i=x^i(v^j,u^\beta),
      y^\alpha=y^\alpha(v^j,u^\beta)$
\end{itemize}
After modus 2, {\tt APPLYSYM} returns the generalized special solution.
%---------------------------------------
\subsection{Example: A second order ODE}
Weyl's class of solutions of Einsteins field equations consists of
axialsymmetric time independent metrics of the form
\begin{equation}
{\rm{d}} s^2 = e^{-2 U} \left[ e^{2 k}  \left( \rm{d} \rho^2 + \rm{d}
z^2 \right)+\rho^2 \rm{d} \varphi^2 \right] - e^{2 U} \rm{d} t^2,
\end{equation}
where $U$ and $k$ are functions of $\rho$ and $z$. If one is interested in
generalizing these solutions to have a time dependence then the resulting
DEs can be transformed such that one longer third order ODE for $U$ results
which contains only $\rho$ derivatives \cite{Markus}. Because $U$ appears
not alone but only as derivative, a substitution
\begin{equation}
g = dU/d\rho      \label{g1dgl}
\end{equation}
lowers the order and the introduction of a function
\begin{equation}
h = \rho g - 1    \label{g2dgl}
\end{equation}
simplifies the ODE to
\begin{equation}
0 = 3\rho^2h\,h''
-5\rho^2\,h'^2+5\rho\,h\,h'-20\rho\,h^3h'-20\,h^4+16\,h^6+4\,h^2. \label{hdgl}
\end{equation}
where $'= d/d\rho$.
Calling {\tt LIEPDE} through
\small \begin{verbatim}
depend h,r;
prob:={{-20*h**4+16*h**6+3*r**2*h*df(h,r,2)+5*r*h*df(h,r)
        -20*h**3*r*df(h,r)+4*h**2-5*r**2*df(h,r)**2},
       {h}, {r}};
sym:=liepde(prob, {"point"},{},{});
end; \end{verbatim} \normalsize
gives \small \begin{verbatim}
                          3                       2
sym := {{}, {xi_r= - c10*r  - c11*r, eta_h=c10*h*r }, {c10,c11}}.
\end{verbatim} \normalsize
All conditions have been solved because the first element of {\tt sym}
is $\{\}$. The two existing symmetries are therefore
\begin{equation}
  - \rho^3 \partial_{\rho} +  h \rho^2 \,\partial_{h} \;\;\;\;\;\;\mbox{and}
  \;\;\;\;\;\;\rho \partial_{\rho}.
\end{equation}
Corresponding finite
transformations can be calculated with {\tt APPLYSYM} through
\small \begin{verbatim}
newde:=applysym(prob,rest sym);
\end{verbatim} \normalsize
The interactive session is given below with the user input following
the prompt `{\tt Input:3:}' or following `?'. (Empty lines have been deleted.)
\small \begin{verbatim}
Do you want to find similarity and symmetry variables (enter `1;')
or generalize a special solution with new parameters  (enter `2;')
or exit the program                                   (enter  `;')
Input:3: 1;
\end{verbatim} \normalsize
We enter `1;' because we want to reduce dependencies by finding similarity
variables and one symmetry variable and then doing the transformation such
that the symmetry variable does not explicitly occur in the DE.
\small \begin{verbatim}
----------------------   The 1.  symmetry is:
         3
xi_r= - r
         2
eta_h=h*r
----------------------   The 2.  symmetry is:
xi_r= - r
----------------------
Which single symmetry or linear combination of symmetries
do you want to apply? 
Enter an expression with `sy_(i)' for the i'th symmetry.
sy_(1);
\end{verbatim} \normalsize
We could have entered `sy\_(2);' or a combination of both
as well with the calculation running then
differently.
\small \begin{verbatim}
The symmetry to be applied in the following is
          3          2
{xi_r= - r ,eta_h=h*r }
Enter the name of the new dependent variables:
Input:3: u;
Enter the name of the new independent variables:
Input:3: v;
\end{verbatim} \normalsize
This was the input part, now the real calculation starts.
\small \begin{verbatim}
The ODE/PDE (-system) under investigation is :
                   2            2  2               3
0 = 3*df(h,r,2)*h*r  - 5*df(h,r) *r  - 20*df(h,r)*h *r
                           6       4      2
     + 5*df(h,r)*h*r + 16*h  - 20*h  + 4*h
for the function(s) : h.
It will be looked for a new dependent variable u
and an independent variable v such that the transformed
de(-system) does not depend on u or v.
1. Determination of the similarity variable
                           2
The quasilinear PDE:  0 = r *(df(u_,h)*h - df(u_,r)*r).
The equivalent characteristic system:
               3
0= - df(u_,r)*r
      2
0= - r *(df(h,r)*r + h)
for the functions: h(r)  u_(r).
\end{verbatim} \normalsize
The PDE is equation (\ref{ql2}).
\small \begin{verbatim}
The general solution of the PDE is given through
0 = ff(u_,h*r)
with arbitrary function ff(..).
A suggestion for this function ff provides:
0 =  - h*r + u_
Do you like this choice? (Y or N)
?y
\end{verbatim} \normalsize
For the following calculation only a single special solution of the PDE is
necessary
and this has to be specified from the general solution by choosing a special
function {\tt ff}. (This function is called {\tt ff} to prevent a clash with
names of user variables/functions.) In principle any choice of {\tt ff} would
work, if it defines a non-singular coordinate transformation, i.e.\ here $r$
must be a function of $u\_$. If we have $q$ independent variables and
$p$ functions of them then {\tt ff} has $p+q$ arguments. Because of the
condition $0 = ${\tt ff} one has essentially the freedom of choosing a function
of $p+q-1$ arguments freely. This freedom is also necessary to select $p+q-1$
different functions {\tt ff} and to find as many functionally independent
solutions $u\_$ which all become the new similarity variables. $q$ of them
become the new functions $u^\alpha$ and $p-1$ of them the new variables
$v^2,\ldots,v^p$. Here we have $p=q=1$ (one single ODE).

Though the program could have done that alone, once the general solution
{\tt ff(..)} is known, the user can interfere here to enter a simpler solution,
if possible.
\small \begin{verbatim}
2. Determination of the symmetry variable
                                      2             3
The quasilinear PDE:  0 = df(u_,h)*h*r  - df(u_,r)*r  - 1.
The equivalent characteristic system:
              3
0=df(r,u_) + r
                2
0=df(h,u_) - h*r
for the functions: r(u_)  h(u_)  .
New attempt with a different independent variable
The equivalent characteristic system:
              2
0=df(u_,h)*h*r  - 1
   2
0=r *(df(r,h)*h + r)
for the functions: r(h)  u_(h)  .
The general solution of the PDE is given through
                  2  2       2
             - 2*h *r *u_ + h
0 = ff(h*r,--------------------)
                    2
with arbitrary function ff(..).
A suggestion for this function ff(..) yields:
      2        2
     h *( - 2*r *u_ + 1)
0 = ---------------------
              2
Do you like this choice? (Y or N)
?y
\end{verbatim} \normalsize
Similar to above.
\small \begin{verbatim}
The suggested solution of the algebraic system which will
do the transformation is:
                        sqrt(v)*sqrt(2)
{h=sqrt(v)*sqrt(2)*u,r=-----------------}
                              2*v
Is the solution ok? (Y or N)
?y
In the intended transformation shown above the dependent
variable is u and the independent variable is v.
The symmetry variable is v, i.e. the transformed expression
will be free of v.
Is this selection of dependent and independent variables ok? (Y or N)
?n
\end{verbatim} \normalsize
We so far assumed that the symmetry variable is one of the new variables, but,
of course we also could choose it to be one of the new functions.
If it is one of the functions then only derivatives of this function occur
in the new DE, not the function itself. If it is one of the variables then 
this variable will not occur explicitly.

In our case we prefer (without strong reason) to have the function as
symmetry variable. We therefore answered with `no'. As a consequence, $u$ and
$v$ will exchange names such that still all new functions have the name $u$
and the new variables have name $v$:
\small \begin{verbatim}
Please enter a list of substitutions. For example, to
make the variable, which is so far call u1, to an
independent variable v2 and the variable, which is
so far called v2, to an dependent variable u1,
enter: `{u1=v2, v2=u1};'
Input:3: {u=v,v=u};

The transformed equation which should be free of u:
                            3  6             2  3
0=3*df(u,v,2)*v - 16*df(u,v) *v  - 20*df(u,v) *v  + 5*df(u,v)
Do you want to find similarity and symmetry variables (enter `1;')
or generalize a special solution with new parameters  (enter `2;')
or exit the program                                   (enter  `;')
Input:3: ;
\end{verbatim}
We stop here. The following is returned from our {\tt APPLYSYM} call:
\small \begin{verbatim}
                             3  6             2  3
{{{3*df(u,v,2)*v - 16*df(u,v) *v  - 20*df(u,v) *v + 5*df(u,v)},
  {u},
  {v}},
     sqrt(u)*sqrt(2)
 {r=-----------------, h=sqrt(u)*sqrt(2)*v }}
           2*u
\end{verbatim} \normalsize
The use of {\tt APPLYSYM} effectively provided us the finite
transformation
\begin{equation}
  \rho=(2\,u)^{-1/2},\;\;\;\;\;h=(2\,u)^{1/2}\,v \label{trafo1}.
\end{equation}
and the new ODE
\begin{equation}
0 = 3u''v - 16u'^3v^6 - 20u'^2v^3 + 5u'        \label{udgl}
\end{equation}
where $u=u(v)$ and $'=d/dv.$
Using one symmetry we reduced the 2.\,order ODE (\ref{hdgl})
to a first order ODE (\ref{udgl}) for $u'$ plus one
integration. The second symmetry can be used to reduce the remaining ODE
to an integration too by introducing a variable $w$ through $v^3d/dv = d/dw$,
i.e. $w = -1/(2v^2)$. With
\begin{equation}
p=du/dw                                       \label{udot}
\end{equation}
the remaining ODE is
\[0 = 3\,w\,\frac{dp}{dw} + 2\,p\,(p+1)(4\,p+1)   \]
with solution
\[ \tilde{c}w^{-2}/4 = \tilde{c}v^4 = \frac{p^3(p+1)}{(4\,p+1)^4},\;\;\;
 \tilde{c}=const. \]
Writing (\ref{udot}) as $p = v^3(du/dp)/(dv/dp)$ we get $u$ by integration
and with (\ref{trafo1}) further a parametric solution for $\rho,h$:
\begin{eqnarray}
\rho & = & \left(\frac{3c_1^2(2p-1)}{p^{1/2}(p+1)^{1/2}}+c_2\right)^{-1/2} \\
h & = & \frac{(c_2p^{1/2}(p+1)^{1/2}+6c_1^2p-3c_1^2)^{1/2}p^{1/2}}{c_1(4p+1)}
\end{eqnarray}
where $c_1, c_2 = const.$ and $c_1=\tilde{c}^{1/4}.$ Finally, the metric
function $U(p)$ is obtained as an integral from (\ref{g1dgl}),(\ref{g2dgl}).
%---------------------------------------
\subsection{Limitations of {\tt APPLYSYM}}
Restrictions of the applicability of the program {\tt APPLYSYM} result
from limitations of the program {\tt QUASILINPDE} described in a section below.
Essentially this means that symmetry generators may only be polynomially
non-linear in $x^i, y^\alpha$.
Though even then the solvability can not be guaranteed, the
generators of Lie-symmetries are mostly very simple such that the
resulting PDE (\ref{PDE}) and the corresponding characteristic 
ODE-system have good chances to be solvable.

Apart from these limitations implied through the solution of differential
equations with {\tt CRACK} and algebraic equations with {\tt SOLVE}
the program {\tt APPLYSYM} itself is free of restrictions,
i.e.\ if once new versions of {\tt CRACK, SOLVE}
would be available then {\tt APPLYSYM} would not have to be changed.

Currently, whenever a computational step could not be performed
the user is informed and has the possibility of entering interactively
the solution of the unsolved algebraic system or the unsolved linear PDE.
%-------------------------------------------------------------------------
\section{Solving quasilinear PDEs}
%---------------------------------------
\subsection{The content of {\tt QUASILINPDE}}
The generalization of special solutions of DEs as well as the computation of
similarity and symmetry variables involve the general solution of single
first order linear PDEs.
The procedure {\tt QUASILINPDE} is a general procedure
aiming at the general solution of
PDEs
\begin{equation}
   a_1(w_i,\phi)\phi_{w_1} + a_2(w_i,\phi)\phi_{w_2} + \ldots +
   a_n(w_i,\phi)\phi_{w_n} = b(w_i,\phi) \label{PDE}
\end{equation}
in $n$ independent variables $w_i, i=1\ldots n$ for one unknown function
$\phi=\phi(w_i)$.
\begin{enumerate}
\item
The first step in solving a quasilinear PDE (\ref{PDE})
is the formulation of the corresponding characteristic ODE-system
\begin{eqnarray}
\frac{dw_i}{d\varepsilon} & = & a_i(w_j,\phi) \label{char1}   \\
\frac{d\phi}{d\varepsilon} & = & b(w_j,\phi)  \label{char2}
\end{eqnarray}
for $\phi, w_i$ regarded now as functions of one variable $\varepsilon$.

Because the $a_i$ and $b$ do not depend explicitly on $\varepsilon$, one of the
equations (\ref{char1}),(\ref{char2}) with non-vanishing right hand side
can be used to divide all others through it and by that having a system
with one less ODE to solve. 
If the equation to divide through is one of
(\ref{char1}) then the remaining system would be
\begin{eqnarray}
\frac{dw_i}{dw_k} & = & \frac{a_i}{a_k} , \;\;\;i=1,2,\ldots k-1,k+1,\ldots n
  \label{char3} \\
\frac{d\phi}{dw_k} & = & \frac{b}{a_k}  \label{char4}
\end{eqnarray}
with the independent variable $w_k$ instead of $\varepsilon$.
If instead we divide through equation
(\ref{char2}) then the remaining system would be
\begin{eqnarray}
\frac{dw_i}{d\phi} & = & \frac{a_i}{b} , \;\;\;i=1,2,\ldots n
  \label{char3a}
\end{eqnarray}
with the independent variable $\phi$ instead of $\varepsilon$.

The equation to divide through is chosen by a
subroutine with a heuristic to find the ``simplest'' non-zero
right hand side ($a_k$ or $b$), i.e.\ one which
\begin{itemize}
\item is constant or
\item depends only on one variable or
\item is a product of factors, each of which depends only on
one variable.
\end{itemize}

One purpose of this division is to reduce the number of ODEs by one.
Secondly, the general solution of (\ref{char1}), (\ref{char2}) involves
an additive constant to $\varepsilon$ which is not relevant and would
have to be set to zero. By dividing through one ODE we eliminate
$\varepsilon$ and lose the problem of identifying this constant in the
general solution before we would have to set it to zero.

\item % from enumerate
To solve the system (\ref{char3}), (\ref{char4}) or (\ref{char3a}),
the procedure {\tt CRACK} is called.
Although being designed primarily for the solution of overdetermined
PDE-systems, {\tt CRACK} can also be used to solve simple not
overdetermined ODE-systems. This solution
process is not completely algorithmic. Improved versions of {\tt CRACK}
could be used, without making any changes of {\tt QUASILINPDE} 
necessary.

If the characteristic ODE-system can not be solved in the form
(\ref{char3}), (\ref{char4}) or (\ref{char3a})
then successively all other ODEs of (\ref{char1}), (\ref{char2})
with non-vanishing right hand side are used for division until 
one is found
such that the resulting ODE-system can be solved completely.
Otherwise the PDE can not be solved by {\tt QUASILINPDE}.

\item % from enumerate
If the characteristic ODE-system (\ref{char1}), (\ref{char2}) has been
integrated completely and in full generality to the implicit solution
\begin{equation}
0 = G_i(\phi, w_j, c_k, \varepsilon),\;\;
i,k=1,\ldots,n+1,\;\;j=1,\ldots,n              \label{charsol1}
\end{equation}
then according to the general theory for solving first order PDEs,
$\varepsilon$ has
to be eliminated from one of the equations and to be substituted in the
others to have left $n$ equations.
Also the constant that turns up additively to $\varepsilon$
is to be set to zero. Both tasks are automatically
fulfilled, if, as described above, $\varepsilon$ is already eliminated
from the beginning by dividing all equations of (\ref{char1}),
(\ref{char2})
through one of them.

On either way one ends up with $n$ equations 
\begin{equation}
0=g_i(\phi,w_j,c_k),\;\;i,j,k=1\ldots n             \label{charsol2}
\end{equation}
involving $n$ constants $c_k$.

The final step is to solve (\ref{charsol2}) for the $c_i$ to obtain
\begin{equation}
c_i = c_i(\phi, w_1,\ldots ,w_n) \;\;\;\;\;i=1,\ldots n  . \label{cons}
\end{equation}
The final solution $\phi = \phi(w_i)$ of the PDE (\ref{PDE}) is then
given implicitly through
\[ 0 = F(c_1(\phi,w_i),c_2(\phi,w_i),\ldots,c_n(\phi,w_i)) \]
where $F$ is an arbitrary function with $n$ arguments.
\end{enumerate}
%---------------------------------------
\subsection{Syntax}
The call of {\tt QUASILINPDE} is \\
{\tt QUASILINPDE}({\it de}, {\it fun}, {\it varlist});
\begin{itemize}
\item
{\it de} is the differential expression which vanishes due to the PDE
{\it de}$\; = 0$ or, {\it de} may be the differential equation itself in the
form $\;\;\ldots = \ldots\;\;$.
\item
{\it fun} is the unknown function.
\item
{\it varlist} is the list of variables of {\it fun}.
\end{itemize}
The result of {\tt QUASILINPDE} is a list of general solutions
\[      \{{\it sol}_1, {\it sol}_2, \ldots \}.  \]
If {\tt QUASILINPDE} can not solve the PDE then it returns $\{\}$.
Each solution ${\it sol}_i$ is a list of expressions
\[      \{{\it ex}_1, {\it ex}_2, \ldots \}  \]
such that the dependent function ($\phi$ in (\ref{PDE})) is determined
implicitly through an arbitrary function $F$ and the algebraic
equation \[ 0 = F({\it ex}_1, {\it ex}_2, \ldots). \]
%---------------------------------------
\subsection{Examples}
{\em Example 1:}\\
To solve the quasilinear first order PDE \[1 = xu,_x + uu,_y - zu,_z\]
for the function $u = u(x,y,z),$ the input would be
\small \begin{verbatim}
depend u,x,y,z;
de:=x*df(u,x)+u*df(u,y)-z*df(u,z) - 1;
varlist:={x,y,z};
QUASILINPDE(de,u,varlist);
\end{verbatim} \normalsize
In this example the procedure returns
\[\{ \{ x/e^u, ze^u, u^2 - 2y \} \},\] 
i.e. there is one general solution (because the outer list has only one
element which itself is a list) and $u$ is given implicitly through 
the algebraic equation
\[ 0 = F(x/e^u, ze^u, u^2 - 2y)\] 
with arbitrary function $F.$ \\
{\em Example 2:}\\
For the linear inhomogeneous PDE
\[ 0 = y z,_x + x z,_y - 1, \;\;\;\;\mbox{for}\;\;\;\;z=z(x,y)\]
{\tt QUASILINPDE} returns the result that for an arbitrary function $F,$ the
equation
\[ 0 = F\left(\frac{x+y}{e^z},e^z(x-y)\right) \]
defines the general solution for $z$. \\
{\em Example 3:}\\
For the linear inhomogeneous PDE (3.8) from \cite{KamkePDE}
\[ 0 = x w,_x + (y+z)(w,_y - w,_z), \;\;\;\;\mbox{for}\;\;\;\;w=w(x,y,z)\]
{\tt QUASILINPDE} returns the result 
that for an arbitrary function $F,$ the equation
\[ 0 = F\left(w, \;y+z, \;\ln(x)(y+z)-y\right) \]
defines the general solution for $w$, i.e.\ for any function $f$
\[ w = f\left(y+z, \;\ln(x)(y+z)-y\right) \]
solves the PDE.
%---------------------------------------
\subsection{Limitations of {\tt QUASILINPDE}}
One restriction on the applicability of {\tt QUASILINPDE} results from
the program {\tt CRACK} which tries to solve the
characteristic ODE-system of the PDE. So far {\tt CRACK} can be
applied only to polynomially non-linear DE's, i.e.\ the characteristic 
ODE-system (\ref{char3}),(\ref{char4}) or (\ref{char3a}) may
only be polynomially non-linear, i.e.\ in the PDE (\ref{PDE})
the expressions $a_i$ and $b$ may only be rational in $w_j,\phi$.

The task of {\tt CRACK} is simplified as (\ref{charsol1}) does not have to
be solved for $w_j, \phi$. On the other hand (\ref{charsol1}) has to be
solved for the $c_i$. This gives a 
second restriction coming from the REDUCE function {\tt SOLVE}.
Though {\tt SOLVE} can be applied
to polynomial and transzendential equations, again no guarantee for
solvability can be given.
%-------------------------------------------------------------------------
\section{Transformation of DEs}
%---------------------------------------
\subsection{The content of {\tt DETRAFO}}
Finally, after having found the finite transformations,
the program {\tt APPLYSYM} calls the procedure
{\tt DETRAFO} to perform the transformations. {\tt DETRAFO}
can also be used alone to do point- or higher order transformations
which involve a considerable computational effort if the
differential order of the expression to be transformed is high and
if many dependent and independent variables are involved.
This might be especially useful if one wants to experiment
and try out different coordinate transformations interactively,
using {\tt DETRAFO} as standalone procedure.

To run {\tt DETRAFO}, the old functions $y^{\alpha}$ and old 
variables $x^i$ must be 
known explicitly in terms of algebraic or
differential expressions of the new functions $u^{\beta}$ 
and new variables $v^j$. Then for point transformations the identity
\begin{eqnarray}
dy^{\alpha} & = & \left(y^{\alpha},_{v^i} +
                  y^{\alpha},_{u^{\beta}}u^{\beta},_{v^i}\right) dv^i \\
            & = & y^{\alpha},_{x^j}dx^j  \\
            & = & y^{\alpha},_{x^j}\left(x^j,_{v^i} +
                  x^j,_{u^{\beta}}u^{\beta},_{v^i}\right) dv^i
\end{eqnarray}
provides the transformation
\begin{equation}
y^{\alpha},_{x^j} = \frac{dy^\alpha}{dv^i}\cdot
                    \left(\frac{dx^j}{dv^i}\right)^{-1}   \label{trafo}
\end{equation}
with {\it det}$\left(dx^j/dv^i\right) \neq 0$ because of the regularity
of the transformation which is checked by {\tt DETRAFO}. Non-regular
transformations are not performed.

{\tt DETRAFO} is not restricted to point transformations.
In the case of
contact- or higher order transformations, the total
derivatives $dy^{\alpha}/dv^i$ and $dx^j/dv^i$ then only include all
$v^i-$ derivatives of $u^{\beta}$ which occur in
\begin{eqnarray*}
y^{\alpha} & = & y^{\alpha}(v^i,u^{\beta},u^{\beta},_{v^j},\ldots) \\
x^k        & = & x^k(v^i,u^{\beta},u^{\beta},_{v^j},\ldots).
\end{eqnarray*}
%---------------------------------------
\subsection{Syntax}
The call of {\tt DETRAFO} is
\begin{tabbing}
{\tt DETRAFO}(\=\{{\it ex}$_1$, {\it ex}$_2$, \ldots , {\it ex}$_m$\}, \\
              \>\{{\it ofun}$_1=${\it fex}$_1$, {\it ofun}$_2=${\it fex}$_2$,
               \ldots ,{\it ofun}$_p=${\it fex}$_p$\},  \\
              \>\{{\it ovar}$_1=${\it vex}$_1$, {\it ovar}$_2=${\it vex}$_2$, \ldots ,
                  {\it ovar}$_q=${\it vex}$_q$\},  \\
              \>\{{\it nfun}$_1$, {\it nfun}$_2$, \ldots , {\it nfun}$_p$\},\\
              \>\{{\it nvar}$_1$, {\it nvar}$_2$, \ldots , {\it nvar}$_q$\});
\end{tabbing}     
where $m,p,q$ are arbitrary.
\begin{itemize}
\item
The {\it ex}$_i$ are differential expressions to be transformed.
\item
The second list is the list of old functions {\it ofun} expressed
as expressions {\it fex} in terms
of new functions {\it nfun} and new independent variables {\it nvar}.
\item
Similarly the third list expresses the old independent variables {\it ovar}
as expressions {\it vex} in terms of new functions
{\it nfun} and new independent variables {\it nvar}.
\item
The last two lists include the new functions {\it nfun}
and new independent variables {\it nvar}.
\end{itemize}
Names for {\it ofun, ovar, nfun} and {\it nvar} can be arbitrarily
chosen.

As the result {\tt DETRAFO} returns the first argument of its input,
i.e.\ the list
\[\{{\it ex}_1, {\it ex}_2, \ldots , {\it ex}_m\}\]
where all ${\it ex}_i$ are transformed.
%---------------------------------------
\subsection{Limitations of {\tt DETRAFO}}
The only requirement is that
the old independent variables $x^i$ and old functions $y^\alpha$ must be
given explicitly in terms of new variables $v^j$ and new functions $u^\beta$
as indicated in the syntax.
Then all calculations involve only differentiations and basic algebra.
%-------------------------------------------------------------------------
\section{Availability}
The programs run under {\tt REDUCE 3.6} and are available
by anonymous ftp from \\ {\tt ftp.maths.qmw.ac.uk}, directory 
{\tt pub/tw}.

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DIFFGROB2 (Liz Mansfield) Maple \\
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HSYM (Vladimir Gerdt) Reduce \\
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Liesymm (John Carminati, John Devitt and Greg Fee) Maple \\
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SPDE (Fritz Schwarz) Reduce and Axiom \\
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\end{thebibliography}

\end{document}
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