doc/groups.dox
author Peter Kovacs <kpeter@inf.elte.hu>
Sun, 23 Aug 2009 11:09:22 +0200
changeset 782 853fcddcf282
parent 710 8b0df68370a4
child 789 8e68671af789
permissions -rw-r--r--
Doc improvements, fixes and unifications for graphs (#311)
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/* -*- mode: C++; indent-tabs-mode: nil; -*-
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 *
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 * This file is a part of LEMON, a generic C++ optimization library.
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 *
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 * Copyright (C) 2003-2009
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 * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
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 * (Egervary Research Group on Combinatorial Optimization, EGRES).
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 *
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 * Permission to use, modify and distribute this software is granted
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 * provided that this copyright notice appears in all copies. For
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 * precise terms see the accompanying LICENSE file.
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 *
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 * This software is provided "AS IS" with no warranty of any kind,
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 * express or implied, and with no claim as to its suitability for any
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 * purpose.
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 *
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 */
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namespace lemon {
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/**
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@defgroup datas Data Structures
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This group contains the several data structures implemented in LEMON.
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*/
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/**
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@defgroup graphs Graph Structures
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@ingroup datas
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\brief Graph structures implemented in LEMON.
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The implementation of combinatorial algorithms heavily relies on
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efficient graph implementations. LEMON offers data structures which are
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planned to be easily used in an experimental phase of implementation studies,
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and thereafter the program code can be made efficient by small modifications.
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The most efficient implementation of diverse applications require the
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usage of different physical graph implementations. These differences
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appear in the size of graph we require to handle, memory or time usage
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limitations or in the set of operations through which the graph can be
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accessed.  LEMON provides several physical graph structures to meet
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the diverging requirements of the possible users.  In order to save on
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running time or on memory usage, some structures may fail to provide
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some graph features like arc/edge or node deletion.
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Alteration of standard containers need a very limited number of
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operations, these together satisfy the everyday requirements.
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In the case of graph structures, different operations are needed which do
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not alter the physical graph, but gives another view. If some nodes or
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arcs have to be hidden or the reverse oriented graph have to be used, then
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this is the case. It also may happen that in a flow implementation
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the residual graph can be accessed by another algorithm, or a node-set
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is to be shrunk for another algorithm.
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LEMON also provides a variety of graphs for these requirements called
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\ref graph_adaptors "graph adaptors". Adaptors cannot be used alone but only
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in conjunction with other graph representations.
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You are free to use the graph structure that fit your requirements
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the best, most graph algorithms and auxiliary data structures can be used
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with any graph structure.
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<b>See also:</b> \ref graph_concepts "Graph Structure Concepts".
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*/
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/**
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@defgroup graph_adaptors Adaptor Classes for Graphs
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@ingroup graphs
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\brief Adaptor classes for digraphs and graphs
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This group contains several useful adaptor classes for digraphs and graphs.
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The main parts of LEMON are the different graph structures, generic
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graph algorithms, graph concepts, which couple them, and graph
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adaptors. While the previous notions are more or less clear, the
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latter one needs further explanation. Graph adaptors are graph classes
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which serve for considering graph structures in different ways.
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A short example makes this much clearer.  Suppose that we have an
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instance \c g of a directed graph type, say ListDigraph and an algorithm
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\code
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template <typename Digraph>
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int algorithm(const Digraph&);
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\endcode
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is needed to run on the reverse oriented graph.  It may be expensive
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(in time or in memory usage) to copy \c g with the reversed
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arcs.  In this case, an adaptor class is used, which (according
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to LEMON \ref concepts::Digraph "digraph concepts") works as a digraph.
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The adaptor uses the original digraph structure and digraph operations when
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methods of the reversed oriented graph are called.  This means that the adaptor
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have minor memory usage, and do not perform sophisticated algorithmic
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actions.  The purpose of it is to give a tool for the cases when a
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graph have to be used in a specific alteration.  If this alteration is
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obtained by a usual construction like filtering the node or the arc set or
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considering a new orientation, then an adaptor is worthwhile to use.
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To come back to the reverse oriented graph, in this situation
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\code
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template<typename Digraph> class ReverseDigraph;
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\endcode
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template class can be used. The code looks as follows
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\code
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ListDigraph g;
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ReverseDigraph<ListDigraph> rg(g);
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int result = algorithm(rg);
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\endcode
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During running the algorithm, the original digraph \c g is untouched.
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This techniques give rise to an elegant code, and based on stable
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graph adaptors, complex algorithms can be implemented easily.
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In flow, circulation and matching problems, the residual
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graph is of particular importance. Combining an adaptor implementing
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this with shortest path algorithms or minimum mean cycle algorithms,
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a range of weighted and cardinality optimization algorithms can be
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obtained. For other examples, the interested user is referred to the
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detailed documentation of particular adaptors.
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The behavior of graph adaptors can be very different. Some of them keep
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capabilities of the original graph while in other cases this would be
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meaningless. This means that the concepts that they meet depend
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on the graph adaptor, and the wrapped graph.
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For example, if an arc of a reversed digraph is deleted, this is carried
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out by deleting the corresponding arc of the original digraph, thus the
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adaptor modifies the original digraph.
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However in case of a residual digraph, this operation has no sense.
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Let us stand one more example here to simplify your work.
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ReverseDigraph has constructor
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\code
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ReverseDigraph(Digraph& digraph);
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\endcode
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This means that in a situation, when a <tt>const %ListDigraph&</tt>
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reference to a graph is given, then it have to be instantiated with
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<tt>Digraph=const %ListDigraph</tt>.
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\code
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int algorithm1(const ListDigraph& g) {
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  ReverseDigraph<const ListDigraph> rg(g);
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  return algorithm2(rg);
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}
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\endcode
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*/
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/**
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@defgroup maps Maps
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@ingroup datas
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\brief Map structures implemented in LEMON.
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This group contains the map structures implemented in LEMON.
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LEMON provides several special purpose maps and map adaptors that e.g. combine
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new maps from existing ones.
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<b>See also:</b> \ref map_concepts "Map Concepts".
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*/
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/**
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@defgroup graph_maps Graph Maps
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@ingroup maps
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\brief Special graph-related maps.
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This group contains maps that are specifically designed to assign
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values to the nodes and arcs/edges of graphs.
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If you are looking for the standard graph maps (\c NodeMap, \c ArcMap,
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\c EdgeMap), see the \ref graph_concepts "Graph Structure Concepts".
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*/
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/**
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\defgroup map_adaptors Map Adaptors
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\ingroup maps
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\brief Tools to create new maps from existing ones
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This group contains map adaptors that are used to create "implicit"
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maps from other maps.
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Most of them are \ref concepts::ReadMap "read-only maps".
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They can make arithmetic and logical operations between one or two maps
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(negation, shifting, addition, multiplication, logical 'and', 'or',
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'not' etc.) or e.g. convert a map to another one of different Value type.
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The typical usage of this classes is passing implicit maps to
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algorithms.  If a function type algorithm is called then the function
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type map adaptors can be used comfortable. For example let's see the
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usage of map adaptors with the \c graphToEps() function.
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\code
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  Color nodeColor(int deg) {
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    if (deg >= 2) {
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      return Color(0.5, 0.0, 0.5);
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    } else if (deg == 1) {
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      return Color(1.0, 0.5, 1.0);
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    } else {
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      return Color(0.0, 0.0, 0.0);
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    }
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  }
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  Digraph::NodeMap<int> degree_map(graph);
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  graphToEps(graph, "graph.eps")
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    .coords(coords).scaleToA4().undirected()
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    .nodeColors(composeMap(functorToMap(nodeColor), degree_map))
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    .run();
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\endcode
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The \c functorToMap() function makes an \c int to \c Color map from the
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\c nodeColor() function. The \c composeMap() compose the \c degree_map
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and the previously created map. The composed map is a proper function to
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get the color of each node.
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The usage with class type algorithms is little bit harder. In this
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case the function type map adaptors can not be used, because the
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function map adaptors give back temporary objects.
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\code
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  Digraph graph;
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  typedef Digraph::ArcMap<double> DoubleArcMap;
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  DoubleArcMap length(graph);
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  DoubleArcMap speed(graph);
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  typedef DivMap<DoubleArcMap, DoubleArcMap> TimeMap;
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  TimeMap time(length, speed);
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  Dijkstra<Digraph, TimeMap> dijkstra(graph, time);
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  dijkstra.run(source, target);
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\endcode
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We have a length map and a maximum speed map on the arcs of a digraph.
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The minimum time to pass the arc can be calculated as the division of
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the two maps which can be done implicitly with the \c DivMap template
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class. We use the implicit minimum time map as the length map of the
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\c Dijkstra algorithm.
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*/
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/**
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@defgroup matrices Matrices
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@ingroup datas
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\brief Two dimensional data storages implemented in LEMON.
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This group contains two dimensional data storages implemented in LEMON.
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*/
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/**
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@defgroup paths Path Structures
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@ingroup datas
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\brief %Path structures implemented in LEMON.
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This group contains the path structures implemented in LEMON.
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LEMON provides flexible data structures to work with paths.
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All of them have similar interfaces and they can be copied easily with
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assignment operators and copy constructors. This makes it easy and
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efficient to have e.g. the Dijkstra algorithm to store its result in
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any kind of path structure.
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\sa lemon::concepts::Path
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*/
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/**
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@defgroup auxdat Auxiliary Data Structures
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@ingroup datas
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\brief Auxiliary data structures implemented in LEMON.
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This group contains some data structures implemented in LEMON in
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order to make it easier to implement combinatorial algorithms.
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*/
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/**
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@defgroup algs Algorithms
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\brief This group contains the several algorithms
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implemented in LEMON.
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This group contains the several algorithms
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implemented in LEMON.
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*/
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/**
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@defgroup search Graph Search
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@ingroup algs
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\brief Common graph search algorithms.
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This group contains the common graph search algorithms, namely
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\e breadth-first \e search (BFS) and \e depth-first \e search (DFS).
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*/
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/**
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@defgroup shortest_path Shortest Path Algorithms
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@ingroup algs
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\brief Algorithms for finding shortest paths.
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This group contains the algorithms for finding shortest paths in digraphs.
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 - \ref Dijkstra algorithm for finding shortest paths from a source node
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   when all arc lengths are non-negative.
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 - \ref BellmanFord "Bellman-Ford" algorithm for finding shortest paths
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   from a source node when arc lenghts can be either positive or negative,
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   but the digraph should not contain directed cycles with negative total
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   length.
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 - \ref FloydWarshall "Floyd-Warshall" and \ref Johnson "Johnson" algorithms
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   for solving the \e all-pairs \e shortest \e paths \e problem when arc
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   lenghts can be either positive or negative, but the digraph should
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   not contain directed cycles with negative total length.
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 - \ref Suurballe A successive shortest path algorithm for finding
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   arc-disjoint paths between two nodes having minimum total length.
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*/
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/**
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@defgroup max_flow Maximum Flow Algorithms
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@ingroup algs
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\brief Algorithms for finding maximum flows.
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This group contains the algorithms for finding maximum flows and
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feasible circulations.
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The \e maximum \e flow \e problem is to find a flow of maximum value between
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a single source and a single target. Formally, there is a \f$G=(V,A)\f$
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digraph, a \f$cap: A\rightarrow\mathbf{R}^+_0\f$ capacity function and
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\f$s, t \in V\f$ source and target nodes.
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A maximum flow is an \f$f: A\rightarrow\mathbf{R}^+_0\f$ solution of the
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following optimization problem.
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\f[ \max\sum_{sv\in A} f(sv) - \sum_{vs\in A} f(vs) \f]
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\f[ \sum_{uv\in A} f(uv) = \sum_{vu\in A} f(vu)
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    \quad \forall u\in V\setminus\{s,t\} \f]
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\f[ 0 \leq f(uv) \leq cap(uv) \quad \forall uv\in A \f]
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LEMON contains several algorithms for solving maximum flow problems:
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- \ref EdmondsKarp Edmonds-Karp algorithm.
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- \ref Preflow Goldberg-Tarjan's preflow push-relabel algorithm.
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- \ref DinitzSleatorTarjan Dinitz's blocking flow algorithm with dynamic trees.
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- \ref GoldbergTarjan Preflow push-relabel algorithm with dynamic trees.
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In most cases the \ref Preflow "Preflow" algorithm provides the
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fastest method for computing a maximum flow. All implementations
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also provide functions to query the minimum cut, which is the dual
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problem of maximum flow.
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\ref Circulation is a preflow push-relabel algorithm implemented directly 
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for finding feasible circulations, which is a somewhat different problem,
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but it is strongly related to maximum flow.
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For more information, see \ref Circulation.
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*/
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/**
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@defgroup min_cost_flow_algs Minimum Cost Flow Algorithms
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@ingroup algs
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\brief Algorithms for finding minimum cost flows and circulations.
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This group contains the algorithms for finding minimum cost flows and
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circulations. For more information about this problem and its dual
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solution see \ref min_cost_flow "Minimum Cost Flow Problem".
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LEMON contains several algorithms for this problem.
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 - \ref NetworkSimplex Primal Network Simplex algorithm with various
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   pivot strategies.
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 - \ref CostScaling Push-Relabel and Augment-Relabel algorithms based on
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   cost scaling.
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 - \ref CapacityScaling Successive Shortest %Path algorithm with optional
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   capacity scaling.
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 - \ref CancelAndTighten The Cancel and Tighten algorithm.
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 - \ref CycleCanceling Cycle-Canceling algorithms.
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In general NetworkSimplex is the most efficient implementation,
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but in special cases other algorithms could be faster.
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For example, if the total supply and/or capacities are rather small,
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CapacityScaling is usually the fastest algorithm (without effective scaling).
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*/
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/**
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@defgroup min_cut Minimum Cut Algorithms
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@ingroup algs
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\brief Algorithms for finding minimum cut in graphs.
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This group contains the algorithms for finding minimum cut in graphs.
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The \e minimum \e cut \e problem is to find a non-empty and non-complete
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\f$X\f$ subset of the nodes with minimum overall capacity on
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outgoing arcs. Formally, there is a \f$G=(V,A)\f$ digraph, a
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\f$cap: A\rightarrow\mathbf{R}^+_0\f$ capacity function. The minimum
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cut is the \f$X\f$ solution of the next optimization problem:
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\f[ \min_{X \subset V, X\not\in \{\emptyset, V\}}
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    \sum_{uv\in A, u\in X, v\not\in X}cap(uv) \f]
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LEMON contains several algorithms related to minimum cut problems:
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- \ref HaoOrlin "Hao-Orlin algorithm" for calculating minimum cut
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  in directed graphs.
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- \ref NagamochiIbaraki "Nagamochi-Ibaraki algorithm" for
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  calculating minimum cut in undirected graphs.
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- \ref GomoryHu "Gomory-Hu tree computation" for calculating
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  all-pairs minimum cut in undirected graphs.
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If you want to find minimum cut just between two distinict nodes,
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see the \ref max_flow "maximum flow problem".
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*/
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/**
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@defgroup graph_properties Connectivity and Other Graph Properties
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@ingroup algs
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   396
\brief Algorithms for discovering the graph properties
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   397
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   398
This group contains the algorithms for discovering the graph properties
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   399
like connectivity, bipartiteness, euler property, simplicity etc.
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   400
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   401
\image html edge_biconnected_components.png
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\image latex edge_biconnected_components.eps "bi-edge-connected components" width=\textwidth
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   403
*/
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   404
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/**
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   406
@defgroup planar Planarity Embedding and Drawing
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   407
@ingroup algs
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   408
\brief Algorithms for planarity checking, embedding and drawing
alpar@40
   409
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   410
This group contains the algorithms for planarity checking,
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   411
embedding and drawing.
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   412
alpar@40
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\image html planar.png
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   414
\image latex planar.eps "Plane graph" width=\textwidth
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   415
*/
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   416
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/**
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   418
@defgroup matching Matching Algorithms
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   419
@ingroup algs
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   420
\brief Algorithms for finding matchings in graphs and bipartite graphs.
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   421
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   422
This group contains the algorithms for calculating
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   423
matchings in graphs and bipartite graphs. The general matching problem is
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finding a subset of the edges for which each node has at most one incident
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edge.
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   426
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   427
There are several different algorithms for calculate matchings in
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   428
graphs.  The matching problems in bipartite graphs are generally
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   429
easier than in general graphs. The goal of the matching optimization
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can be finding maximum cardinality, maximum weight or minimum cost
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   431
matching. The search can be constrained to find perfect or
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   432
maximum cardinality matching.
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   433
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The matching algorithms implemented in LEMON:
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- \ref MaxBipartiteMatching Hopcroft-Karp augmenting path algorithm
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   436
  for calculating maximum cardinality matching in bipartite graphs.
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   437
- \ref PrBipartiteMatching Push-relabel algorithm
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   438
  for calculating maximum cardinality matching in bipartite graphs.
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   439
- \ref MaxWeightedBipartiteMatching
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   440
  Successive shortest path algorithm for calculating maximum weighted
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   441
  matching and maximum weighted bipartite matching in bipartite graphs.
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   442
- \ref MinCostMaxBipartiteMatching
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  Successive shortest path algorithm for calculating minimum cost maximum
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   444
  matching in bipartite graphs.
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   445
- \ref MaxMatching Edmond's blossom shrinking algorithm for calculating
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   446
  maximum cardinality matching in general graphs.
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   447
- \ref MaxWeightedMatching Edmond's blossom shrinking algorithm for calculating
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   448
  maximum weighted matching in general graphs.
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   449
- \ref MaxWeightedPerfectMatching
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   450
  Edmond's blossom shrinking algorithm for calculating maximum weighted
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   451
  perfect matching in general graphs.
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   452
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   453
\image html bipartite_matching.png
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\image latex bipartite_matching.eps "Bipartite Matching" width=\textwidth
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   455
*/
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   456
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   457
/**
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@defgroup spantree Minimum Spanning Tree Algorithms
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   459
@ingroup algs
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   460
\brief Algorithms for finding minimum cost spanning trees and arborescences.
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   461
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   462
This group contains the algorithms for finding minimum cost spanning
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   463
trees and arborescences.
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   464
*/
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   465
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   466
/**
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   467
@defgroup auxalg Auxiliary Algorithms
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   468
@ingroup algs
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   469
\brief Auxiliary algorithms implemented in LEMON.
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   470
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   471
This group contains some algorithms implemented in LEMON
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   472
in order to make it easier to implement complex algorithms.
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   473
*/
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   474
alpar@40
   475
/**
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   476
@defgroup approx Approximation Algorithms
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   477
@ingroup algs
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   478
\brief Approximation algorithms.
alpar@40
   479
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   480
This group contains the approximation and heuristic algorithms
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   481
implemented in LEMON.
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   482
*/
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   483
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   484
/**
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   485
@defgroup gen_opt_group General Optimization Tools
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   486
\brief This group contains some general optimization frameworks
alpar@40
   487
implemented in LEMON.
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   488
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   489
This group contains some general optimization frameworks
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   490
implemented in LEMON.
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   491
*/
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   492
alpar@40
   493
/**
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   494
@defgroup lp_group Lp and Mip Solvers
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   495
@ingroup gen_opt_group
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   496
\brief Lp and Mip solver interfaces for LEMON.
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   497
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   498
This group contains Lp and Mip solver interfaces for LEMON. The
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   499
various LP solvers could be used in the same manner with this
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   500
interface.
alpar@40
   501
*/
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   502
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   503
/**
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   504
@defgroup lp_utils Tools for Lp and Mip Solvers
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   505
@ingroup lp_group
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   506
\brief Helper tools to the Lp and Mip solvers.
alpar@40
   507
alpar@40
   508
This group adds some helper tools to general optimization framework
alpar@40
   509
implemented in LEMON.
alpar@40
   510
*/
alpar@40
   511
alpar@40
   512
/**
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   513
@defgroup metah Metaheuristics
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   514
@ingroup gen_opt_group
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   515
\brief Metaheuristics for LEMON library.
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   516
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   517
This group contains some metaheuristic optimization tools.
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   518
*/
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   519
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   520
/**
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   521
@defgroup utils Tools and Utilities
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   522
\brief Tools and utilities for programming in LEMON
alpar@40
   523
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   524
Tools and utilities for programming in LEMON.
alpar@40
   525
*/
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   526
alpar@40
   527
/**
alpar@40
   528
@defgroup gutils Basic Graph Utilities
alpar@40
   529
@ingroup utils
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   530
\brief Simple basic graph utilities.
alpar@40
   531
kpeter@606
   532
This group contains some simple basic graph utilities.
alpar@40
   533
*/
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   534
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   535
/**
alpar@40
   536
@defgroup misc Miscellaneous Tools
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   537
@ingroup utils
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   538
\brief Tools for development, debugging and testing.
kpeter@50
   539
kpeter@606
   540
This group contains several useful tools for development,
alpar@40
   541
debugging and testing.
alpar@40
   542
*/
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   543
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   544
/**
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   545
@defgroup timecount Time Measuring and Counting
alpar@40
   546
@ingroup misc
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   547
\brief Simple tools for measuring the performance of algorithms.
kpeter@50
   548
kpeter@606
   549
This group contains simple tools for measuring the performance
alpar@40
   550
of algorithms.
alpar@40
   551
*/
alpar@40
   552
alpar@40
   553
/**
alpar@40
   554
@defgroup exceptions Exceptions
alpar@40
   555
@ingroup utils
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   556
\brief Exceptions defined in LEMON.
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   557
kpeter@606
   558
This group contains the exceptions defined in LEMON.
alpar@40
   559
*/
alpar@40
   560
alpar@40
   561
/**
alpar@40
   562
@defgroup io_group Input-Output
kpeter@50
   563
\brief Graph Input-Output methods
alpar@40
   564
kpeter@606
   565
This group contains the tools for importing and exporting graphs
kpeter@314
   566
and graph related data. Now it supports the \ref lgf-format
kpeter@314
   567
"LEMON Graph Format", the \c DIMACS format and the encapsulated
kpeter@314
   568
postscript (EPS) format.
alpar@40
   569
*/
alpar@40
   570
alpar@40
   571
/**
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   572
@defgroup lemon_io LEMON Graph Format
alpar@40
   573
@ingroup io_group
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   574
\brief Reading and writing LEMON Graph Format.
alpar@40
   575
kpeter@606
   576
This group contains methods for reading and writing
ladanyi@236
   577
\ref lgf-format "LEMON Graph Format".
alpar@40
   578
*/
alpar@40
   579
alpar@40
   580
/**
kpeter@314
   581
@defgroup eps_io Postscript Exporting
alpar@40
   582
@ingroup io_group
alpar@40
   583
\brief General \c EPS drawer and graph exporter
alpar@40
   584
kpeter@606
   585
This group contains general \c EPS drawing methods and special
alpar@209
   586
graph exporting tools.
alpar@40
   587
*/
alpar@40
   588
alpar@40
   589
/**
kpeter@403
   590
@defgroup dimacs_group DIMACS format
kpeter@403
   591
@ingroup io_group
kpeter@403
   592
\brief Read and write files in DIMACS format
kpeter@403
   593
kpeter@403
   594
Tools to read a digraph from or write it to a file in DIMACS format data.
kpeter@403
   595
*/
kpeter@403
   596
kpeter@403
   597
/**
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   598
@defgroup nauty_group NAUTY Format
kpeter@363
   599
@ingroup io_group
kpeter@363
   600
\brief Read \e Nauty format
kpeter@403
   601
kpeter@363
   602
Tool to read graphs from \e Nauty format data.
kpeter@363
   603
*/
kpeter@363
   604
kpeter@363
   605
/**
alpar@40
   606
@defgroup concept Concepts
alpar@40
   607
\brief Skeleton classes and concept checking classes
alpar@40
   608
kpeter@606
   609
This group contains the data/algorithm skeletons and concept checking
alpar@40
   610
classes implemented in LEMON.
alpar@40
   611
alpar@40
   612
The purpose of the classes in this group is fourfold.
alpar@209
   613
kpeter@318
   614
- These classes contain the documentations of the %concepts. In order
alpar@40
   615
  to avoid document multiplications, an implementation of a concept
alpar@40
   616
  simply refers to the corresponding concept class.
alpar@40
   617
alpar@40
   618
- These classes declare every functions, <tt>typedef</tt>s etc. an
kpeter@318
   619
  implementation of the %concepts should provide, however completely
alpar@40
   620
  without implementations and real data structures behind the
alpar@40
   621
  interface. On the other hand they should provide nothing else. All
alpar@40
   622
  the algorithms working on a data structure meeting a certain concept
alpar@40
   623
  should compile with these classes. (Though it will not run properly,
alpar@40
   624
  of course.) In this way it is easily to check if an algorithm
alpar@40
   625
  doesn't use any extra feature of a certain implementation.
alpar@40
   626
alpar@40
   627
- The concept descriptor classes also provide a <em>checker class</em>
kpeter@50
   628
  that makes it possible to check whether a certain implementation of a
alpar@40
   629
  concept indeed provides all the required features.
alpar@40
   630
alpar@40
   631
- Finally, They can serve as a skeleton of a new implementation of a concept.
alpar@40
   632
*/
alpar@40
   633
alpar@40
   634
/**
alpar@40
   635
@defgroup graph_concepts Graph Structure Concepts
alpar@40
   636
@ingroup concept
alpar@40
   637
\brief Skeleton and concept checking classes for graph structures
alpar@40
   638
kpeter@782
   639
This group contains the skeletons and concept checking classes of
kpeter@782
   640
graph structures.
alpar@40
   641
*/
alpar@40
   642
kpeter@314
   643
/**
kpeter@314
   644
@defgroup map_concepts Map Concepts
kpeter@314
   645
@ingroup concept
kpeter@314
   646
\brief Skeleton and concept checking classes for maps
kpeter@314
   647
kpeter@606
   648
This group contains the skeletons and concept checking classes of maps.
alpar@40
   649
*/
alpar@40
   650
alpar@40
   651
/**
alpar@40
   652
\anchor demoprograms
alpar@40
   653
kpeter@422
   654
@defgroup demos Demo Programs
alpar@40
   655
alpar@40
   656
Some demo programs are listed here. Their full source codes can be found in
alpar@40
   657
the \c demo subdirectory of the source tree.
alpar@40
   658
ladanyi@611
   659
In order to compile them, use the <tt>make demo</tt> or the
ladanyi@611
   660
<tt>make check</tt> commands.
alpar@40
   661
*/
alpar@40
   662
alpar@40
   663
/**
kpeter@422
   664
@defgroup tools Standalone Utility Applications
alpar@40
   665
alpar@209
   666
Some utility applications are listed here.
alpar@40
   667
alpar@40
   668
The standard compilation procedure (<tt>./configure;make</tt>) will compile
alpar@209
   669
them, as well.
alpar@40
   670
*/
alpar@40
   671
kpeter@422
   672
}