/**
@defgroup graph_adaptors Adaptor Classes for Graphs
@ingroup graphs
\brief This group contains several adaptor classes for 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 ListGraph and an algorithm
\code template int algorithm(const Graph&); \endcode
is needed to run on the reversed oriented graph.
It may be expensive (in time or in memory usage) to copy
\c g with the reversed orientation.
In this case, an adaptor class is used, which
(according to LEMON graph concepts) works as a graph.
The adaptor uses
the original graph structure and graph 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 edge-set or considering a new orientation, then
an adaptor is worthwhile to use.
To come back to the reversed oriented graph, in this situation
\code template class RevGraphAdaptor; \endcode
template class can be used.
The code looks as follows
\code
ListGraph g;
RevGraphAdaptor rga(g);
int result=algorithm(rga);
\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 edge of \c rga is deleted, this is carried out by
deleting the corresponding edge 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
RevGraphAdaptor(Graph& _g);
\endcode
This means that in a situation,
when a ` const ListGraph& ` reference to a graph is given,
then it have to be instantiated with `Graph=const ListGraph`.
\code
int algorithm1(const ListGraph& g) {
RevGraphAdaptor rga(g);
return algorithm2(rga);
}
\endcode
*/