source: lemon-main/doc/groups.dox @ 1144:760a5f690163

/* -*- mode: C++; indent-tabs-mode: nil; -*-
 *
 * This file is a part of LEMON, a generic C++ optimization library.
 *
 * Copyright (C) 2003-2013
 * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
 * (Egervary Research Group on Combinatorial Optimization, EGRES).
 *
 * Permission to use, modify and distribute this software is granted
 * provided that this copyright notice appears in all copies. For
 * precise terms see the accompanying LICENSE file.
 *
 * This software is provided "AS IS" with no warranty of any kind,
 * express or implied, and with no claim as to its suitability for any
 * purpose.
 *
 */

namespace lemon {

/**
@defgroup datas Data Structures
This group contains the several data structures implemented in LEMON.
*/

/**
@defgroup graphs Graph Structures
@ingroup datas
\brief Graph structures implemented in LEMON.

The implementation of combinatorial algorithms heavily relies on
efficient graph implementations. LEMON offers data structures which are
planned to be easily used in an experimental phase of implementation studies,
and thereafter the program code can be made efficient by small modifications.

The most efficient implementation of diverse applications require the
usage of different physical graph implementations. These differences
appear in the size of graph we require to handle, memory or time usage
limitations or in the set of operations through which the graph can be
accessed.  LEMON provides several physical graph structures to meet
the diverging requirements of the possible users.  In order to save on
running time or on memory usage, some structures may fail to provide
some graph features like arc/edge or node deletion.

Alteration of standard containers need a very limited number of
operations, these together satisfy the everyday requirements.
In the case of graph structures, different operations are needed which do
not alter the physical graph, but gives another view. If some nodes or
arcs have to be hidden or the reverse oriented graph have to be used, then
this is the case. It also may happen that in a flow implementation
the residual graph can be accessed by another algorithm, or a node-set
is to be shrunk for another algorithm.
LEMON also provides a variety of graphs for these requirements called
\ref graph_adaptors "graph adaptors". Adaptors cannot be used alone but only
in conjunction with other graph representations.

You are free to use the graph structure that fit your requirements
the best, most graph algorithms and auxiliary data structures can be used
with any graph structure.

<b>See also:</b> \ref graph_concepts "Graph Structure Concepts".
*/

/**
@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.

Since the adaptor classes conform to the \ref graph_concepts "graph concepts",
an adaptor can even be applied to another one.
The following image illustrates a situation when a \ref SubDigraph adaptor
is applied on a digraph and \ref Undirector is applied on the subgraph.

\image html adaptors2.png
\image latex adaptors2.eps "Using graph adaptors" width=\textwidth

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 maps Maps
@ingroup datas
\brief 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
new maps from existing ones.

<b>See also:</b> \ref map_concepts "Map Concepts".
*/

/**
@defgroup graph_maps Graph Maps
@ingroup maps
\brief Special graph-related maps.

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".
*/

/**
\defgroup map_adaptors Map Adaptors
\ingroup maps
\brief Tools to create new maps from existing ones

This group contains map adaptors that are used to create "implicit"
maps from other 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',
'not' etc.) or e.g. convert a map to another one of different Value type.

The typical usage of this classes is passing implicit maps to
algorithms.  If a function type algorithm is called then the function
type map adaptors can be used comfortable. For example let's see the
usage of map adaptors with the \c graphToEps() function.
\code
  Color nodeColor(int deg) {
    if (deg >= 2) {
      return Color(0.5, 0.0, 0.5);
    } else if (deg == 1) {
      return Color(1.0, 0.5, 1.0);
    } else {
      return Color(0.0, 0.0, 0.0);
    }
  }

  Digraph::NodeMap<int> degree_map(graph);

  graphToEps(graph, "graph.eps")
    .coords(coords).scaleToA4().undirected()
    .nodeColors(composeMap(functorToMap(nodeColor), degree_map))
    .run();
\endcode
The \c functorToMap() function makes an \c int to \c Color map from the
\c nodeColor() function. The \c composeMap() compose the \c degree_map
and the previously created map. The composed map is a proper function to
get the color of each node.

The usage with class type algorithms is little bit harder. In this
case the function type map adaptors can not be used, because the
function map adaptors give back temporary objects.
\code
  Digraph graph;

  typedef Digraph::ArcMap<double> DoubleArcMap;
  DoubleArcMap length(graph);
  DoubleArcMap speed(graph);

  typedef DivMap<DoubleArcMap, DoubleArcMap> TimeMap;
  TimeMap time(length, speed);

  Dijkstra<Digraph, TimeMap> dijkstra(graph, time);
  dijkstra.run(source, target);
\endcode
We have a length map and a maximum speed map on the arcs of a digraph.
The minimum time to pass the arc can be calculated as the division of
the two maps which can be done implicitly with the \c DivMap template
class. We use the implicit minimum time map as the length map of the
\c Dijkstra algorithm.
*/

/**
@defgroup paths Path Structures
@ingroup datas
\brief %Path structures implemented in LEMON.

This group contains the path structures implemented in LEMON.

LEMON provides flexible data structures to work with paths.
All of them have similar interfaces and they can be copied easily with
assignment operators and copy constructors. This makes it easy and
efficient to have e.g. the Dijkstra algorithm to store its result in
any kind of path structure.

\sa \ref concepts::Path "Path concept"
*/

/**
@defgroup heaps Heap Structures
@ingroup datas
\brief %Heap structures implemented in LEMON.

This group contains the heap structures implemented in LEMON.

LEMON provides several heap classes. They are efficient implementations
of the abstract data type \e priority \e queue. They store items with
specified values called \e priorities in such a way that finding and
removing the item with minimum priority are efficient.
The basic operations are adding and erasing items, changing the priority
of an item, etc.

Heaps are crucial in several algorithms, such as Dijkstra and Prim.
The heap implementations have the same interface, thus any of them can be
used easily in such algorithms.

\sa \ref concepts::Heap "Heap concept"
*/

/**
@defgroup auxdat Auxiliary Data Structures
@ingroup datas
\brief Auxiliary data structures implemented in LEMON.

This group contains some data structures implemented in LEMON in
order to make it easier to implement combinatorial algorithms.
*/

/**
@defgroup geomdat Geometric Data Structures
@ingroup auxdat
\brief Geometric data structures implemented in LEMON.

This group contains geometric data structures implemented in LEMON.

 - \ref lemon::dim2::Point "dim2::Point" implements a two dimensional
   vector with the usual operations.
 - \ref lemon::dim2::Box "dim2::Box" can be used to determine the
   rectangular bounding box of a set of \ref lemon::dim2::Point
   "dim2::Point"'s.
*/

/**
@defgroup matrices Matrices
@ingroup auxdat
\brief Two dimensional data storages implemented in LEMON.

This group contains two dimensional data storages implemented in LEMON.
*/

/**
@defgroup algs Algorithms
\brief This group contains the several algorithms
implemented in LEMON.

This group contains the several algorithms
implemented in LEMON.
*/

/**
@defgroup search Graph Search
@ingroup algs
\brief Common graph search algorithms.

This group contains the common graph search algorithms, namely
\e breadth-first \e search (BFS) and \e depth-first \e search (DFS)
\cite clrs01algorithms.
*/

/**
@defgroup shortest_path Shortest Path Algorithms
@ingroup algs
\brief Algorithms for finding shortest paths.

This group contains the algorithms for finding shortest paths in digraphs
\cite clrs01algorithms.

 - \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.
*/

/**
@defgroup spantree Minimum Spanning Tree Algorithms
@ingroup algs
\brief Algorithms for finding minimum cost spanning trees and arborescences.

This group contains the algorithms for finding minimum cost spanning
trees and arborescences \cite clrs01algorithms.
*/

/**
@defgroup max_flow Maximum Flow Algorithms
@ingroup algs
\brief Algorithms for finding maximum flows.

This group contains the algorithms for finding maximum flows and
feasible circulations \cite clrs01algorithms, \cite amo93networkflows.

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 EdmondsKarp Edmonds-Karp algorithm
  \cite edmondskarp72theoretical.
- \ref Preflow Goldberg-Tarjan's preflow push-relabel algorithm
  \cite goldberg88newapproach.
- \ref DinitzSleatorTarjan Dinitz's blocking flow algorithm with dynamic trees
  \cite dinic70algorithm, \cite sleator83dynamic.
- \ref GoldbergTarjan !Preflow push-relabel algorithm with dynamic trees
  \cite goldberg88newapproach, \cite sleator83dynamic.

In most cases the \ref Preflow algorithm provides the
fastest method for computing a maximum flow. All implementations
also provide functions to query the minimum cut, which is the dual
problem of maximum flow.

\ref Circulation is a preflow push-relabel algorithm implemented directly
for finding feasible circulations, which is a somewhat different problem,
but it is strongly related to maximum flow.
For more information, see \ref Circulation.
*/

/**
@defgroup min_cost_flow_algs Minimum Cost Flow Algorithms
@ingroup algs

\brief Algorithms for finding minimum cost flows and circulations.

This group contains the algorithms for finding minimum cost flows and
circulations \cite amo93networkflows. For more information about this
problem and its dual solution, see: \ref min_cost_flow
"Minimum Cost Flow Problem".

LEMON contains several algorithms for this problem.
 - \ref NetworkSimplex Primal Network Simplex algorithm with various
   pivot strategies \cite dantzig63linearprog, \cite kellyoneill91netsimplex.
 - \ref CostScaling Cost Scaling algorithm based on push/augment and
   relabel operations \cite goldberg90approximation, \cite goldberg97efficient,
   \cite bunnagel98efficient.
 - \ref CapacityScaling Capacity Scaling algorithm based on the successive
   shortest path method \cite edmondskarp72theoretical.
 - \ref CycleCanceling Cycle-Canceling algorithms, two of which are
   strongly polynomial \cite klein67primal, \cite goldberg89cyclecanceling.

In general, \ref NetworkSimplex and \ref CostScaling are the most efficient
implementations.
\ref NetworkSimplex is usually the fastest on relatively small graphs (up to
several thousands of nodes) and on dense graphs, while \ref CostScaling is
typically more efficient on large graphs (e.g. hundreds of thousands of
nodes or above), especially if they are sparse.
However, other algorithms could be faster in special cases.
For example, if the total supply and/or capacities are rather small,
\ref CapacityScaling is usually the fastest algorithm
(without effective scaling).

These classes are intended to be used with integer-valued input data
(capacities, supply values, and costs), except for \ref CapacityScaling,
which is capable of handling real-valued arc costs (other numerical
data are required to be integer).

For more details about these implementations and for a comprehensive
experimental study, see the paper \cite KiralyKovacs12MCF.
It also compares these codes to other publicly available
minimum cost flow solvers.
*/

/**
@defgroup min_cut Minimum Cut Algorithms
@ingroup algs

\brief Algorithms for finding minimum cut in graphs.

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}cap(uv) \f]

LEMON contains several algorithms related to minimum cut problems:

- \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,
see the \ref max_flow "maximum flow problem".
*/

/**
@defgroup min_mean_cycle Minimum Mean Cycle Algorithms
@ingroup algs
\brief Algorithms for finding minimum mean cycles.

This group contains the algorithms for finding minimum mean cycles
\cite amo93networkflows, \cite karp78characterization.

The \e minimum \e mean \e cycle \e problem is to find a directed cycle
of minimum mean length (cost) in a digraph.
The mean length of a cycle is the average length of its arcs, i.e. the
ratio between the total length of the cycle and the number of arcs on it.

This problem has an important connection to \e conservative \e length
\e functions, too. A length function on the arcs of a digraph is called
conservative if and only if there is no directed cycle of negative total
length. For an arbitrary length function, the negative of the minimum
cycle mean is the smallest \f$\epsilon\f$ value so that increasing the
arc lengths uniformly by \f$\epsilon\f$ results in a conservative length
function.

LEMON contains three algorithms for solving the minimum mean cycle problem:
- \ref KarpMmc Karp's original algorithm \cite karp78characterization.
- \ref HartmannOrlinMmc Hartmann-Orlin's algorithm, which is an improved
  version of Karp's algorithm \cite hartmann93finding.
- \ref HowardMmc Howard's policy iteration algorithm
  \cite dasdan98minmeancycle, \cite dasdan04experimental.

In practice, the \ref HowardMmc "Howard" algorithm turned out to be by far the
most efficient one, though the best known theoretical bound on its running
time is exponential.
Both \ref KarpMmc "Karp" and \ref HartmannOrlinMmc "Hartmann-Orlin" algorithms
run in time O(nm) and use space O(n<sup>2</sup>+m).
*/

/**
@defgroup matching Matching Algorithms
@ingroup algs
\brief Algorithms for finding matchings in graphs and bipartite graphs.

This group contains the algorithms for calculating
matchings in graphs and bipartite graphs. The general matching problem is
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 finding maximum cardinality, maximum weight or minimum cost
matching. The search can be constrained to find perfect or
maximum cardinality matching.

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.
- \ref MaxFractionalMatching Push-relabel algorithm for calculating
  maximum cardinality fractional matching in general graphs.
- \ref MaxWeightedFractionalMatching Augmenting path algorithm for calculating
  maximum weighted fractional matching in general graphs.
- \ref MaxWeightedPerfectFractionalMatching
  Augmenting path algorithm for calculating maximum weighted
  perfect fractional matching in general graphs.

\image html matching.png
\image latex matching.eps "Min Cost Perfect Matching" width=\textwidth
*/

/**
@defgroup graph_properties Connectivity and Other Graph Properties
@ingroup algs
\brief Algorithms for discovering the graph properties

This group contains the algorithms for discovering the graph properties
like connectivity, bipartiteness, euler property, simplicity etc.

\image html connected_components.png
\image latex connected_components.eps "Connected components" width=\textwidth
*/

/**
@defgroup graph_isomorphism Graph Isomorphism
@ingroup algs
\brief Algorithms for testing (sub)graph isomorphism

This group contains algorithms for finding isomorph copies of a
given graph in another one, or simply check whether two graphs are isomorphic.

The formal definition of subgraph isomorphism is as follows.

We are given two graphs, \f$G_1=(V_1,E_1)\f$ and \f$G_2=(V_2,E_2)\f$. A
function \f$f:V_1\longrightarrow V_2\f$ is called \e mapping or \e
embedding if \f$f(u)\neq f(v)\f$ whenever \f$u\neq v\f$.

The standard <em>Subgraph Isomorphism Problem (SIP)</em> looks for a
mapping with the property that whenever \f$(u,v)\in E_1\f$, then
\f$(f(u),f(v))\in E_2\f$.

In case of <em>Induced Subgraph Isomorphism Problem (ISIP)</em> one
also requires that if \f$(u,v)\not\in E_1\f$, then \f$(f(u),f(v))\not\in
E_2\f$

In addition, the graph nodes may be \e labeled, i.e. we are given two
node labelings \f$l_1:V_1\longrightarrow L\f$ and \f$l_2:V_2\longrightarrow
L\f$ and we require that \f$l_1(u)=l_2(f(u))\f$ holds for all nodes \f$u \in
G\f$.

*/

/**
@defgroup planar Planar Embedding and Drawing
@ingroup algs
\brief Algorithms for planarity checking, embedding and drawing

This group contains the algorithms for planarity checking,
embedding and drawing.

\image html planar.png
\image latex planar.eps "Plane graph" width=\textwidth
*/

/**
@defgroup tsp Traveling Salesman Problem
@ingroup algs
\brief Algorithms for the symmetric traveling salesman problem

This group contains basic heuristic algorithms for the the symmetric
\e traveling \e salesman \e problem (TSP).
Given an \ref FullGraph "undirected full graph" with a cost map on its edges,
the problem is to find a shortest possible tour that visits each node exactly
once (i.e. the minimum cost Hamiltonian cycle).

These TSP algorithms are intended to be used with a \e metric \e cost
\e function, i.e. the edge costs should satisfy the triangle inequality.
Otherwise the algorithms could yield worse results.

LEMON provides five well-known heuristics for solving symmetric TSP:
 - \ref NearestNeighborTsp Neareast neighbor algorithm
 - \ref GreedyTsp Greedy algorithm
 - \ref InsertionTsp Insertion heuristic (with four selection methods)
 - \ref ChristofidesTsp Christofides algorithm
 - \ref Opt2Tsp 2-opt algorithm

\ref NearestNeighborTsp, \ref GreedyTsp, and \ref InsertionTsp are the fastest
solution methods. Furthermore, \ref InsertionTsp is usually quite effective.

\ref ChristofidesTsp is somewhat slower, but it has the best guaranteed
approximation factor: 3/2.

\ref Opt2Tsp usually provides the best results in practice, but
it is the slowest method. It can also be used to improve given tours,
for example, the results of other algorithms.

\image html tsp.png
\image latex tsp.eps "Traveling salesman problem" width=\textwidth
*/

/**
@defgroup approx_algs Approximation Algorithms
@ingroup algs
\brief Approximation algorithms.

This group contains the approximation and heuristic algorithms
implemented in LEMON.

<b>Maximum Clique Problem</b>
  - \ref GrossoLocatelliPullanMc An efficient heuristic algorithm of
    Grosso, Locatelli, and Pullan.
*/

/**
@defgroup auxalg Auxiliary Algorithms
@ingroup algs
\brief Auxiliary algorithms implemented in LEMON.

This group contains some algorithms implemented in LEMON
in order to make it easier to implement complex algorithms.
*/

/**
@defgroup gen_opt_group General Optimization Tools
\brief This group contains some general optimization frameworks
implemented in LEMON.

This group contains some general optimization frameworks
implemented in LEMON.
*/

/**
@defgroup lp_group LP and MIP Solvers
@ingroup gen_opt_group
\brief LP and MIP solver interfaces for LEMON.

This group contains LP and MIP solver interfaces for LEMON.
Various LP solvers could be used in the same manner with this
high-level interface.

The currently supported solvers are \cite glpk, \cite clp, \cite cbc,
\cite cplex, \cite soplex.
*/

/**
@defgroup lp_utils Tools for Lp and Mip Solvers
@ingroup lp_group
\brief Helper tools to the Lp and Mip solvers.

This group adds some helper tools to general optimization framework
implemented in LEMON.
*/

/**
@defgroup metah Metaheuristics
@ingroup gen_opt_group
\brief Metaheuristics for LEMON library.

This group contains some metaheuristic optimization tools.
*/

/**
@defgroup utils Tools and Utilities
\brief Tools and utilities for programming in LEMON

Tools and utilities for programming in LEMON.
*/

/**
@defgroup gutils Basic Graph Utilities
@ingroup utils
\brief Simple basic graph utilities.

This group contains some simple basic graph utilities.
*/

/**
@defgroup misc Miscellaneous Tools
@ingroup utils
\brief Tools for development, debugging and testing.

This group contains several useful tools for development,
debugging and testing.
*/

/**
@defgroup timecount Time Measuring and Counting
@ingroup misc
\brief Simple tools for measuring the performance of algorithms.

This group contains simple tools for measuring the performance
of algorithms.
*/

/**
@defgroup exceptions Exceptions
@ingroup utils
\brief Exceptions defined in LEMON.

This group contains the exceptions defined in LEMON.
*/

/**
@defgroup io_group Input-Output
\brief Graph Input-Output methods

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
postscript (EPS) format.
*/

/**
@defgroup lemon_io LEMON Graph Format
@ingroup io_group
\brief Reading and writing LEMON Graph Format.

This group contains methods for reading and writing
\ref lgf-format "LEMON Graph Format".
*/

/**
@defgroup eps_io Postscript Exporting
@ingroup io_group
\brief General \c EPS drawer and graph exporter

This group contains general \c EPS drawing methods and special
graph exporting tools.

\image html graph_to_eps.png
*/

/**
@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.
*/

/**
@defgroup concept Concepts
\brief Skeleton classes and concept checking classes

This group contains the data/algorithm skeletons and concept checking
classes implemented in LEMON.

The purpose of the classes in this group is fourfold.

- These classes contain the documentations of the %concepts. In order
  to avoid document multiplications, an implementation of a concept
  simply refers to the corresponding concept class.

- These classes declare every functions, <tt>typedef</tt>s etc. an
  implementation of the %concepts should provide, however completely
  without implementations and real data structures behind the
  interface. On the other hand they should provide nothing else. All
  the algorithms working on a data structure meeting a certain concept
  should compile with these classes. (Though it will not run properly,
  of course.) In this way it is easily to check if an algorithm
  doesn't use any extra feature of a certain implementation.

- The concept descriptor classes also provide a <em>checker class</em>
  that makes it possible to check whether a certain implementation of a
  concept indeed provides all the required features.

- Finally, They can serve as a skeleton of a new implementation of a concept.
*/

/**
@defgroup graph_concepts Graph Structure Concepts
@ingroup concept
\brief Skeleton and concept checking classes for graph structures

This group contains the skeletons and concept checking classes of
graph structures.
*/

/**
@defgroup map_concepts Map Concepts
@ingroup concept
\brief Skeleton and concept checking classes for maps

This group contains the skeletons and concept checking classes of maps.
*/

/**
@defgroup tools Standalone Utility Applications

Some utility applications are listed here.

The standard compilation procedure (<tt>./configure;make</tt>) will compile
them, as well.
*/

/**
\anchor demoprograms

@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.

In order to compile them, use the <tt>make demo</tt> or the
<tt>make check</tt> commands.
*/

}