Index: doc/groups.dox
===================================================================
--- doc/groups.dox	(revision 318)
+++ doc/groups.dox	(revision 658)
@@ -3,5 +3,5 @@
  * This file is a part of LEMON, a generic C++ optimization library.
  *
- * Copyright (C) 2003-2008
+ * Copyright (C) 2003-2009
  * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
  * (Egervary Research Group on Combinatorial Optimization, EGRES).
@@ -17,7 +17,9 @@
  */
 
+namespace lemon {
+
 /**
 @defgroup datas Data Structures
-This group describes the several data structures implemented in LEMON.
+This group contains the several data structures implemented in LEMON.
 */
 
@@ -61,9 +63,85 @@
 
 /**
+@defgroup graph_adaptors Adaptor Classes for Graphs
+@ingroup graphs
+\brief Adaptor classes for digraphs and graphs
+
+This group contains several useful adaptor classes for digraphs and graphs.
+
+The main parts of LEMON are the different graph structures, generic
+graph algorithms, graph concepts, which couple them, 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 \ref concepts::Digraph "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 node or 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<ListDigraph> rg(g);
+int result = algorithm(rg);
+\endcode
+During running the algorithm, the original digraph \c g is untouched.
+This techniques give rise to an elegant code, and based on stable
+graph adaptors, complex algorithms can be implemented easily.
+
+In flow, circulation and matching problems, the residual
+graph is of particular importance. Combining an adaptor implementing
+this with shortest path algorithms or 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 meet depend
+on the graph adaptor, and the wrapped graph.
+For example, if an arc of a reversed digraph is deleted, this is carried
+out by deleting the corresponding arc of the original digraph, thus the
+adaptor modifies the original digraph.
+However in case of a residual digraph, this operation has no sense.
+
+Let us stand one more example here to simplify your work.
+ReverseDigraph 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) {
+  ReverseDigraph<const ListDigraph> rg(g);
+  return algorithm2(rg);
+}
+\endcode
+*/
+
+/**
 @defgroup semi_adaptors Semi-Adaptor Classes for Graphs
 @ingroup graphs
 \brief Graph types between real graphs and graph adaptors.
 
-This group describes some graph types between real graphs and graph adaptors.
+This group contains some graph types between real graphs and graph adaptors.
 These classes wrap graphs to give new functionality as the adaptors do it.
 On the other hand they are not light-weight structures as the adaptors.
@@ -75,5 +153,5 @@
 \brief Map structures implemented in LEMON.
 
-This group describes the map structures implemented in LEMON.
+This group contains the map structures implemented in LEMON.
 
 LEMON provides several special purpose maps and map adaptors that e.g. combine
@@ -88,6 +166,9 @@
 \brief Special graph-related maps.
 
-This group describes maps that are specifically designed to assign
-values to the nodes and arcs of graphs.
+This group contains maps that are specifically designed to assign
+values to the nodes and arcs/edges of graphs.
+
+If you are looking for the standard graph maps (\c NodeMap, \c ArcMap,
+\c EdgeMap), see the \ref graph_concepts "Graph Structure Concepts".
 */
 
@@ -97,8 +178,8 @@
 \brief Tools to create new maps from existing ones
 
-This group describes map adaptors that are used to create "implicit"
+This group contains map adaptors that are used to create "implicit"
 maps from other maps.
 
-Most of them are \ref lemon::concepts::ReadMap "read-only maps".
+Most of them are \ref concepts::ReadMap "read-only maps".
 They can make arithmetic and logical operations between one or two maps
 (negation, shifting, addition, multiplication, logical 'and', 'or',
@@ -160,5 +241,5 @@
 \brief Two dimensional data storages implemented in LEMON.
 
-This group describes two dimensional data storages implemented in LEMON.
+This group contains two dimensional data storages implemented in LEMON.
 */
 
@@ -168,5 +249,5 @@
 \brief %Path structures implemented in LEMON.
 
-This group describes the path structures implemented in LEMON.
+This group contains the path structures implemented in LEMON.
 
 LEMON provides flexible data structures to work with paths.
@@ -184,5 +265,5 @@
 \brief Auxiliary data structures implemented in LEMON.
 
-This group describes some data structures implemented in LEMON in
+This group contains some data structures implemented in LEMON in
 order to make it easier to implement combinatorial algorithms.
 */
@@ -190,8 +271,8 @@
 /**
 @defgroup algs Algorithms
-\brief This group describes the several algorithms
+\brief This group contains the several algorithms
 implemented in LEMON.
 
-This group describes the several algorithms
+This group contains the several algorithms
 implemented in LEMON.
 */
@@ -202,6 +283,6 @@
 \brief Common graph search algorithms.
 
-This group describes the common graph search algorithms like
-Breadth-First Search (BFS) and Depth-First Search (DFS).
+This group contains the common graph search algorithms, namely
+\e breadth-first \e search (BFS) and \e depth-first \e search (DFS).
 */
 
@@ -211,5 +292,18 @@
 \brief Algorithms for finding shortest paths.
 
-This group describes the algorithms for finding shortest paths in graphs.
+This group contains the algorithms for finding shortest paths in digraphs.
+
+ - \ref Dijkstra algorithm for finding shortest paths from a source node
+   when all arc lengths are non-negative.
+ - \ref BellmanFord "Bellman-Ford" algorithm for finding shortest paths
+   from a source node when arc lenghts can be either positive or negative,
+   but the digraph should not contain directed cycles with negative total
+   length.
+ - \ref FloydWarshall "Floyd-Warshall" and \ref Johnson "Johnson" algorithms
+   for solving the \e all-pairs \e shortest \e paths \e problem when arc
+   lenghts can be either positive or negative, but the digraph should
+   not contain directed cycles with negative total length.
+ - \ref Suurballe A successive shortest path algorithm for finding
+   arc-disjoint paths between two nodes having minimum total length.
 */
 
@@ -219,28 +313,29 @@
 \brief Algorithms for finding maximum flows.
 
-This group describes the algorithms for finding maximum flows and
+This group contains the algorithms for finding maximum flows and
 feasible circulations.
 
-The maximum flow problem is to find a flow between a single source and
-a single target that is maximum. Formally, there is a \f$G=(V,A)\f$
-directed graph, an \f$c_a:A\rightarrow\mathbf{R}^+_0\f$ capacity
-function and given \f$s, t \in V\f$ source and target node. The
-maximum flow is the \f$f_a\f$ solution of the next optimization problem:
-
-\f[ 0 \le f_a \le c_a \f]
-\f[ \sum_{v\in\delta^{-}(u)}f_{vu}=\sum_{v\in\delta^{+}(u)}f_{uv}
-\qquad \forall u \in V \setminus \{s,t\}\f]
-\f[ \max \sum_{v\in\delta^{+}(s)}f_{uv} - \sum_{v\in\delta^{-}(s)}f_{vu}\f]
+The \e maximum \e flow \e problem is to find a flow of maximum value between
+a single source and a single target. Formally, there is a \f$G=(V,A)\f$
+digraph, a \f$cap: A\rightarrow\mathbf{R}^+_0\f$ capacity function and
+\f$s, t \in V\f$ source and target nodes.
+A maximum flow is an \f$f: A\rightarrow\mathbf{R}^+_0\f$ solution of the
+following optimization problem.
+
+\f[ \max\sum_{sv\in A} f(sv) - \sum_{vs\in A} f(vs) \f]
+\f[ \sum_{uv\in A} f(uv) = \sum_{vu\in A} f(vu)
+    \quad \forall u\in V\setminus\{s,t\} \f]
+\f[ 0 \leq f(uv) \leq cap(uv) \quad \forall uv\in A \f]
 
 LEMON contains several algorithms for solving maximum flow problems:
-- \ref lemon::EdmondsKarp "Edmonds-Karp"
-- \ref lemon::Preflow "Goldberg's Preflow algorithm"
-- \ref lemon::DinitzSleatorTarjan "Dinitz's blocking flow algorithm with dynamic trees"
-- \ref lemon::GoldbergTarjan "Preflow algorithm with dynamic trees"
-
-In most cases the \ref lemon::Preflow "Preflow" algorithm provides the
-fastest method to compute the maximum flow. All impelementations
-provides functions to query the minimum cut, which is the dual linear
-programming problem of the maximum flow.
+- \ref EdmondsKarp Edmonds-Karp algorithm.
+- \ref Preflow Goldberg-Tarjan's preflow push-relabel algorithm.
+- \ref DinitzSleatorTarjan Dinitz's blocking flow algorithm with dynamic trees.
+- \ref GoldbergTarjan Preflow push-relabel algorithm with dynamic trees.
+
+In most cases the \ref Preflow "Preflow" algorithm provides the
+fastest method for computing a maximum flow. All implementations
+provides functions to also query the minimum cut, which is the dual
+problem of the maximum flow.
 */
 
@@ -251,6 +346,101 @@
 \brief Algorithms for finding minimum cost flows and circulations.
 
-This group describes the algorithms for finding minimum cost flows and
+This group contains the algorithms for finding minimum cost flows and
 circulations.
+
+The \e minimum \e cost \e flow \e problem is to find a feasible flow of
+minimum total cost from a set of supply nodes to a set of demand nodes
+in a network with capacity constraints (lower and upper bounds)
+and arc costs.
+Formally, let \f$G=(V,A)\f$ be a digraph,
+\f$lower, upper: A\rightarrow\mathbf{Z}^+_0\f$ denote the lower and
+upper bounds for the flow values on the arcs, for which
+\f$0 \leq lower(uv) \leq upper(uv)\f$ holds for all \f$uv\in A\f$.
+\f$cost: A\rightarrow\mathbf{Z}^+_0\f$ denotes the cost per unit flow
+on the arcs, and \f$sup: V\rightarrow\mathbf{Z}\f$ denotes the
+signed supply values of the nodes.
+If \f$sup(u)>0\f$, then \f$u\f$ is a supply node with \f$sup(u)\f$
+supply, if \f$sup(u)<0\f$, then \f$u\f$ is a demand node with
+\f$-sup(u)\f$ demand.
+A minimum cost flow is an \f$f: A\rightarrow\mathbf{Z}^+_0\f$ solution
+of the following optimization problem.
+
+\f[ \min\sum_{uv\in A} f(uv) \cdot cost(uv) \f]
+\f[ \sum_{uv\in A} f(uv) - \sum_{vu\in A} f(vu) \geq
+    sup(u) \quad \forall u\in V \f]
+\f[ lower(uv) \leq f(uv) \leq upper(uv) \quad \forall uv\in A \f]
+
+The sum of the supply values, i.e. \f$\sum_{u\in V} sup(u)\f$ must be
+zero or negative in order to have a feasible solution (since the sum
+of the expressions on the left-hand side of the inequalities is zero).
+It means that the total demand must be greater or equal to the total
+supply and all the supplies have to be carried out from the supply nodes,
+but there could be demands that are not satisfied.
+If \f$\sum_{u\in V} sup(u)\f$ is zero, then all the supply/demand
+constraints have to be satisfied with equality, i.e. all demands
+have to be satisfied and all supplies have to be used.
+
+If you need the opposite inequalities in the supply/demand constraints
+(i.e. the total demand is less than the total supply and all the demands
+have to be satisfied while there could be supplies that are not used),
+then you could easily transform the problem to the above form by reversing
+the direction of the arcs and taking the negative of the supply values
+(e.g. using \ref ReverseDigraph and \ref NegMap adaptors).
+However \ref NetworkSimplex algorithm also supports this form directly
+for the sake of convenience.
+
+A feasible solution for this problem can be found using \ref Circulation.
+
+Note that the above formulation is actually more general than the usual
+definition of the minimum cost flow problem, in which strict equalities
+are required in the supply/demand contraints, i.e.
+
+\f[ \sum_{uv\in A} f(uv) - \sum_{vu\in A} f(vu) =
+    sup(u) \quad \forall u\in V. \f]
+
+However if the sum of the supply values is zero, then these two problems
+are equivalent. So if you need the equality form, you have to ensure this
+additional contraint for the algorithms.
+
+The dual solution of the minimum cost flow problem is represented by node 
+potentials \f$\pi: V\rightarrow\mathbf{Z}\f$.
+An \f$f: A\rightarrow\mathbf{Z}^+_0\f$ feasible solution of the problem
+is optimal if and only if for some \f$\pi: V\rightarrow\mathbf{Z}\f$
+node potentials the following \e complementary \e slackness optimality
+conditions hold.
+
+ - For all \f$uv\in A\f$ arcs:
+   - if \f$cost^\pi(uv)>0\f$, then \f$f(uv)=lower(uv)\f$;
+   - if \f$lower(uv)<f(uv)<upper(uv)\f$, then \f$cost^\pi(uv)=0\f$;
+   - if \f$cost^\pi(uv)<0\f$, then \f$f(uv)=upper(uv)\f$.
+ - For all \f$u\in V\f$:
+   - if \f$\sum_{uv\in A} f(uv) - \sum_{vu\in A} f(vu) \neq sup(u)\f$,
+     then \f$\pi(u)=0\f$.
+ 
+Here \f$cost^\pi(uv)\f$ denotes the \e reduced \e cost of the arc
+\f$uv\in A\f$ with respect to the node potentials \f$\pi\f$, i.e.
+\f[ cost^\pi(uv) = cost(uv) + \pi(u) - \pi(v).\f]
+
+All algorithms provide dual solution (node potentials) as well
+if an optimal flow is found.
+
+LEMON contains several algorithms for solving minimum cost flow problems.
+ - \ref NetworkSimplex Primal Network Simplex algorithm with various
+   pivot strategies.
+ - \ref CostScaling Push-Relabel and Augment-Relabel algorithms based on
+   cost scaling.
+ - \ref CapacityScaling Successive Shortest %Path algorithm with optional
+   capacity scaling.
+ - \ref CancelAndTighten The Cancel and Tighten algorithm.
+ - \ref CycleCanceling Cycle-Canceling algorithms.
+
+Most of these implementations support the general inequality form of the
+minimum cost flow problem, but CancelAndTighten and CycleCanceling
+only support the equality form due to the primal method they use.
+
+In general NetworkSimplex is the most efficient implementation,
+but in special cases other algorithms could be faster.
+For example, if the total supply and/or capacities are rather small,
+CapacityScaling is usually the fastest algorithm (without effective scaling).
 */
 
@@ -261,34 +451,34 @@
 \brief Algorithms for finding minimum cut in graphs.
 
-This group describes the algorithms for finding minimum cut in graphs.
-
-The minimum cut problem is to find a non-empty and non-complete
-\f$X\f$ subset of the vertices with minimum overall capacity on
-outgoing arcs. Formally, there is \f$G=(V,A)\f$ directed graph, an
-\f$c_a:A\rightarrow\mathbf{R}^+_0\f$ capacity function. The minimum
+This group contains the algorithms for finding minimum cut in graphs.
+
+The \e minimum \e cut \e problem is to find a non-empty and non-complete
+\f$X\f$ subset of the nodes with minimum overall capacity on
+outgoing arcs. Formally, there is a \f$G=(V,A)\f$ digraph, a
+\f$cap: A\rightarrow\mathbf{R}^+_0\f$ capacity function. The minimum
 cut is the \f$X\f$ solution of the next optimization problem:
 
 \f[ \min_{X \subset V, X\not\in \{\emptyset, V\}}
-\sum_{uv\in A, u\in X, v\not\in X}c_{uv}\f]
+    \sum_{uv\in A, u\in X, v\not\in X}cap(uv) \f]
 
 LEMON contains several algorithms related to minimum cut problems:
 
-- \ref lemon::HaoOrlin "Hao-Orlin algorithm" to calculate minimum cut
-  in directed graphs
-- \ref lemon::NagamochiIbaraki "Nagamochi-Ibaraki algorithm" to
-  calculate minimum cut in undirected graphs
-- \ref lemon::GomoryHuTree "Gomory-Hu tree computation" to calculate all
-  pairs minimum cut in undirected graphs
+- \ref HaoOrlin "Hao-Orlin algorithm" for calculating minimum cut
+  in directed graphs.
+- \ref NagamochiIbaraki "Nagamochi-Ibaraki algorithm" for
+  calculating minimum cut in undirected graphs.
+- \ref GomoryHu "Gomory-Hu tree computation" for calculating
+  all-pairs minimum cut in undirected graphs.
 
 If you want to find minimum cut just between two distinict nodes,
-please see the \ref max_flow "Maximum Flow page".
-*/
-
-/**
-@defgroup graph_prop Connectivity and Other Graph Properties
+see the \ref max_flow "maximum flow problem".
+*/
+
+/**
+@defgroup graph_properties Connectivity and Other Graph Properties
 @ingroup algs
 \brief Algorithms for discovering the graph properties
 
-This group describes the algorithms for discovering the graph properties
+This group contains the algorithms for discovering the graph properties
 like connectivity, bipartiteness, euler property, simplicity etc.
 
@@ -302,5 +492,5 @@
 \brief Algorithms for planarity checking, embedding and drawing
 
-This group describes the algorithms for planarity checking,
+This group contains the algorithms for planarity checking,
 embedding and drawing.
 
@@ -314,35 +504,34 @@
 \brief Algorithms for finding matchings in graphs and bipartite graphs.
 
-This group contains algorithm objects and functions to calculate
+This group contains the algorithms for calculating
 matchings in graphs and bipartite graphs. The general matching problem is
-finding a subset of the arcs which does not shares common endpoints.
+finding a subset of the edges for which each node has at most one incident
+edge.
 
 There are several different algorithms for calculate matchings in
 graphs.  The matching problems in bipartite graphs are generally
 easier than in general graphs. The goal of the matching optimization
-can be the finding maximum cardinality, maximum weight or minimum cost
+can be finding maximum cardinality, maximum weight or minimum cost
 matching. The search can be constrained to find perfect or
 maximum cardinality matching.
 
-LEMON contains the next algorithms:
-- \ref lemon::MaxBipartiteMatching "MaxBipartiteMatching" Hopcroft-Karp
-  augmenting path algorithm for calculate maximum cardinality matching in
-  bipartite graphs
-- \ref lemon::PrBipartiteMatching "PrBipartiteMatching" Push-Relabel
-  algorithm for calculate maximum cardinality matching in bipartite graphs
-- \ref lemon::MaxWeightedBipartiteMatching "MaxWeightedBipartiteMatching"
-  Successive shortest path algorithm for calculate maximum weighted matching
-  and maximum weighted bipartite matching in bipartite graph
-- \ref lemon::MinCostMaxBipartiteMatching "MinCostMaxBipartiteMatching"
-  Successive shortest path algorithm for calculate minimum cost maximum
-  matching in bipartite graph
-- \ref lemon::MaxMatching "MaxMatching" Edmond's blossom shrinking algorithm
-  for calculate maximum cardinality matching in general graph
-- \ref lemon::MaxWeightedMatching "MaxWeightedMatching" Edmond's blossom
-  shrinking algorithm for calculate maximum weighted matching in general
-  graph
-- \ref lemon::MaxWeightedPerfectMatching "MaxWeightedPerfectMatching"
-  Edmond's blossom shrinking algorithm for calculate maximum weighted
-  perfect matching in general graph
+The matching algorithms implemented in LEMON:
+- \ref MaxBipartiteMatching Hopcroft-Karp augmenting path algorithm
+  for calculating maximum cardinality matching in bipartite graphs.
+- \ref PrBipartiteMatching Push-relabel algorithm
+  for calculating maximum cardinality matching in bipartite graphs.
+- \ref MaxWeightedBipartiteMatching
+  Successive shortest path algorithm for calculating maximum weighted
+  matching and maximum weighted bipartite matching in bipartite graphs.
+- \ref MinCostMaxBipartiteMatching
+  Successive shortest path algorithm for calculating minimum cost maximum
+  matching in bipartite graphs.
+- \ref MaxMatching Edmond's blossom shrinking algorithm for calculating
+  maximum cardinality matching in general graphs.
+- \ref MaxWeightedMatching Edmond's blossom shrinking algorithm for calculating
+  maximum weighted matching in general graphs.
+- \ref MaxWeightedPerfectMatching
+  Edmond's blossom shrinking algorithm for calculating maximum weighted
+  perfect matching in general graphs.
 
 \image html bipartite_matching.png
@@ -355,6 +544,6 @@
 \brief Algorithms for finding a minimum cost spanning tree in a graph.
 
-This group describes the algorithms for finding a minimum cost spanning
-tree in a graph
+This group contains the algorithms for finding a minimum cost spanning
+tree in a graph.
 */
 
@@ -364,5 +553,5 @@
 \brief Auxiliary algorithms implemented in LEMON.
 
-This group describes some algorithms implemented in LEMON
+This group contains some algorithms implemented in LEMON
 in order to make it easier to implement complex algorithms.
 */
@@ -373,5 +562,5 @@
 \brief Approximation algorithms.
 
-This group describes the approximation and heuristic algorithms
+This group contains the approximation and heuristic algorithms
 implemented in LEMON.
 */
@@ -379,8 +568,8 @@
 /**
 @defgroup gen_opt_group General Optimization Tools
-\brief This group describes some general optimization frameworks
+\brief This group contains some general optimization frameworks
 implemented in LEMON.
 
-This group describes some general optimization frameworks
+This group contains some general optimization frameworks
 implemented in LEMON.
 */
@@ -391,5 +580,5 @@
 \brief Lp and Mip solver interfaces for LEMON.
 
-This group describes Lp and Mip solver interfaces for LEMON. The
+This group contains Lp and Mip solver interfaces for LEMON. The
 various LP solvers could be used in the same manner with this
 interface.
@@ -410,5 +599,5 @@
 \brief Metaheuristics for LEMON library.
 
-This group describes some metaheuristic optimization tools.
+This group contains some metaheuristic optimization tools.
 */
 
@@ -425,5 +614,5 @@
 \brief Simple basic graph utilities.
 
-This group describes some simple basic graph utilities.
+This group contains some simple basic graph utilities.
 */
 
@@ -433,5 +622,5 @@
 \brief Tools for development, debugging and testing.
 
-This group describes several useful tools for development,
+This group contains several useful tools for development,
 debugging and testing.
 */
@@ -442,5 +631,5 @@
 \brief Simple tools for measuring the performance of algorithms.
 
-This group describes simple tools for measuring the performance
+This group contains simple tools for measuring the performance
 of algorithms.
 */
@@ -451,5 +640,5 @@
 \brief Exceptions defined in LEMON.
 
-This group describes the exceptions defined in LEMON.
+This group contains the exceptions defined in LEMON.
 */
 
@@ -458,5 +647,5 @@
 \brief Graph Input-Output methods
 
-This group describes the tools for importing and exporting graphs
+This group contains the tools for importing and exporting graphs
 and graph related data. Now it supports the \ref lgf-format
 "LEMON Graph Format", the \c DIMACS format and the encapsulated
@@ -465,9 +654,9 @@
 
 /**
-@defgroup lemon_io LEMON Input-Output
+@defgroup lemon_io LEMON Graph Format
 @ingroup io_group
 \brief Reading and writing LEMON Graph Format.
 
-This group describes methods for reading and writing
+This group contains methods for reading and writing
 \ref lgf-format "LEMON Graph Format".
 */
@@ -478,6 +667,22 @@
 \brief General \c EPS drawer and graph exporter
 
-This group describes general \c EPS drawing methods and special
+This group contains general \c EPS drawing methods and special
 graph exporting tools.
+*/
+
+/**
+@defgroup dimacs_group DIMACS format
+@ingroup io_group
+\brief Read and write files in DIMACS format
+
+Tools to read a digraph from or write it to a file in DIMACS format data.
+*/
+
+/**
+@defgroup nauty_group NAUTY Format
+@ingroup io_group
+\brief Read \e Nauty format
+
+Tool to read graphs from \e Nauty format data.
 */
 
@@ -486,5 +691,5 @@
 \brief Skeleton classes and concept checking classes
 
-This group describes the data/algorithm skeletons and concept checking
+This group contains the data/algorithm skeletons and concept checking
 classes implemented in LEMON.
 
@@ -516,5 +721,5 @@
 \brief Skeleton and concept checking classes for graph structures
 
-This group describes the skeletons and concept checking classes of LEMON's
+This group contains the skeletons and concept checking classes of LEMON's
 graph structures and helper classes used to implement these.
 */
@@ -525,5 +730,5 @@
 \brief Skeleton and concept checking classes for maps
 
-This group describes the skeletons and concept checking classes of maps.
+This group contains the skeletons and concept checking classes of maps.
 */
 
@@ -531,15 +736,15 @@
 \anchor demoprograms
 
-@defgroup demos Demo programs
+@defgroup demos Demo Programs
 
 Some demo programs are listed here. Their full source codes can be found in
 the \c demo subdirectory of the source tree.
 
-It order to compile them, use <tt>--enable-demo</tt> configure option when
-build the library.
-*/
-
-/**
-@defgroup tools Standalone utility applications
+In order to compile them, use the <tt>make demo</tt> or the
+<tt>make check</tt> commands.
+*/
+
+/**
+@defgroup tools Standalone Utility Applications
 
 Some utility applications are listed here.
@@ -549,2 +754,3 @@
 */
 
+}
