*/

 /**

 @defgroup graph_adaptors Adaptor Classes for graphs

 @ingroup graphs

 \brief This group contains several adaptor classes for digraphs and graphs

 The main parts of LEMON are the different graph structures, generic

 graph algorithms, graph concepts which couple these, and graph

 adaptors. While the previous notions are more or less clear, the

 latter one needs further explanation. Graph adaptors are graph classes

 which serve for considering graph structures in different ways.

 A short example makes this much clearer.  Suppose that we have an

 instance \c g of a directed graph type say ListDigraph and an algorithm

 \code

 template <typename Digraph>

 int algorithm(const Digraph&);

 \endcode

 is needed to run on the reverse oriented graph.  It may be expensive

 (in time or in memory usage) to copy \c g with the reversed

 arcs.  In this case, an adaptor class is used, which (according

 to LEMON digraph concepts) works as a digraph.  The adaptor uses the

 original digraph structure and digraph operations when methods of the

 reversed oriented graph are called.  This means that the adaptor have

 minor memory usage, and do not perform sophisticated algorithmic

 actions.  The purpose of it is to give a tool for the cases when a

 graph have to be used in a specific alteration.  If this alteration is

 obtained by a usual construction like filtering the arc-set or

 considering a new orientation, then an adaptor is worthwhile to use.

 To come back to the reverse oriented graph, in this situation

 \code

 template<typename Digraph> class ReverseDigraph;

 \endcode

 template class can be used. The code looks as follows

 \code

 ListDigraph g;

 ReverseDigraph<ListGraph> rg(g);

 int result = algorithm(rg);

 \endcode

 After running the algorithm, the original graph \c g is untouched.

 This techniques gives rise to an elegant code, and based on stable

 graph adaptors, complex algorithms can be implemented easily.

 In flow, circulation and bipartite matching problems, the residual

 graph is of particular importance. Combining an adaptor implementing

 this, shortest path algorithms and minimum mean cycle algorithms,

 a range of weighted and cardinality optimization algorithms can be

 obtained. For other examples, the interested user is referred to the

 detailed documentation of particular adaptors.

 The behavior of graph adaptors can be very different. Some of them keep

 capabilities of the original graph while in other cases this would be

 meaningless. This means that the concepts that they are models of depend

 on the graph adaptor, and the wrapped graph(s).

 If an arc of \c rg is deleted, this is carried out by deleting the

 corresponding arc of \c g, thus the adaptor modifies the original graph.

 But for a residual graph, this operation has no sense.

 Let us stand one more example here to simplify your work.

 RevGraphAdaptor has constructor

 \code

 ReverseDigraph(Digraph& digraph);

 \endcode

 This means that in a situation, when a <tt>const ListDigraph&</tt>

 reference to a graph is given, then it have to be instantiated with

 <tt>Digraph=const ListDigraph</tt>.

 \code

 int algorithm1(const ListDigraph& g) {

   RevGraphAdaptor<const ListDigraph> rg(g);

   return algorithm2(rg);

 }

 \endcode