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/* * mode: C++; indenttabsmode: 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) 20032010

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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 nodeset

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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 graphrelated 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 "readonly 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 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 \ref concepts::Path "Path concept"

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*/

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/**

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@defgroup heaps Heap Structures

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@ingroup datas

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\brief %Heap structures implemented in LEMON.

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This group contains the heap structures implemented in LEMON.

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LEMON provides several heap classes. They are efficient implementations

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of the abstract data type \e priority \e queue. They store items with

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specified values called \e priorities in such a way that finding and

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removing the item with minimum priority are efficient.

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The basic operations are adding and erasing items, changing the priority

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of an item, etc.

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Heaps are crucial in several algorithms, such as Dijkstra and Prim.

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The heap implementations have the same interface, thus any of them can be

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used easily in such algorithms.

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

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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 geomdat Geometric Data Structures

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@ingroup auxdat

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\brief Geometric data structures implemented in LEMON.

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This group contains geometric data structures implemented in LEMON.

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 \ref lemon::dim2::Point "dim2::Point" implements a two dimensional

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vector with the usual operations.

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 \ref lemon::dim2::Box "dim2::Box" can be used to determine the

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rectangular bounding box of a set of \ref lemon::dim2::Point

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"dim2::Point"'s.

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*/

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/**

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@defgroup matrices Matrices

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@ingroup auxdat

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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 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 breadthfirst \e search (BFS) and \e depthfirst \e search (DFS)

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\ref clrs01algorithms.

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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 clrs01algorithms.

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 \ref Dijkstra algorithm for finding shortest paths from a source node

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when all arc lengths are nonnegative.

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 \ref BellmanFord "BellmanFord" 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 "FloydWarshall" and \ref Johnson "Johnson" algorithms

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for solving the \e allpairs \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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arcdisjoint paths between two nodes having minimum total length.

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*/

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/**

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@defgroup spantree Minimum Spanning Tree Algorithms

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@ingroup algs

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\brief Algorithms for finding minimum cost spanning trees and arborescences.

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This group contains the algorithms for finding minimum cost spanning

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trees and arborescences \ref clrs01algorithms.

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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 \ref clrs01algorithms, \ref amo93networkflows.

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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 EdmondsKarp algorithm

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\ref edmondskarp72theoretical.

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 \ref Preflow GoldbergTarjan's preflow pushrelabel algorithm

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\ref goldberg88newapproach.

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 \ref DinitzSleatorTarjan Dinitz's blocking flow algorithm with dynamic trees

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\ref dinic70algorithm, \ref sleator83dynamic.

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 \ref GoldbergTarjan !Preflow pushrelabel algorithm with dynamic trees

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\ref goldberg88newapproach, \ref sleator83dynamic.

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In most cases the \ref 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 pushrelabel 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 \ref amo93networkflows. For more information about this

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problem and its dual solution, see \ref min_cost_flow

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"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 \ref dantzig63linearprog, \ref kellyoneill91netsimplex.

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 \ref CostScaling Cost Scaling algorithm based on push/augment and

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relabel operations \ref goldberg90approximation, \ref goldberg97efficient,

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\ref bunnagel98efficient.

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 \ref CapacityScaling Capacity Scaling algorithm based on the successive

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shortest path method \ref edmondskarp72theoretical.

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 \ref CycleCanceling CycleCanceling algorithms, two of which are

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strongly polynomial \ref klein67primal, \ref goldberg89cyclecanceling.

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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 nonempty and noncomplete

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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

kpeter@422

426 
\f$cap: A\rightarrow\mathbf{R}^+_0\f$ capacity function. The minimum

kpeter@50

427 
cut is the \f$X\f$ solution of the next optimization problem:

alpar@40

428 

alpar@210

429 
\f[ \min_{X \subset V, X\not\in \{\emptyset, V\}}

kpeter@760

430 
\sum_{uv\in A: u\in X, v\not\in X}cap(uv) \f]

alpar@40

431 

kpeter@50

432 
LEMON contains several algorithms related to minimum cut problems:

alpar@40

433 

kpeter@422

434 
 \ref HaoOrlin "HaoOrlin algorithm" for calculating minimum cut

kpeter@422

435 
in directed graphs.

kpeter@422

436 
 \ref NagamochiIbaraki "NagamochiIbaraki algorithm" for

kpeter@422

437 
calculating minimum cut in undirected graphs.

kpeter@606

438 
 \ref GomoryHu "GomoryHu tree computation" for calculating

kpeter@422

439 
allpairs minimum cut in undirected graphs.

alpar@40

440 

alpar@40

441 
If you want to find minimum cut just between two distinict nodes,

kpeter@422

442 
see the \ref max_flow "maximum flow problem".

alpar@40

443 
*/

alpar@40

444 

alpar@40

445 
/**

kpeter@815

446 
@defgroup min_mean_cycle Minimum Mean Cycle Algorithms

alpar@40

447 
@ingroup algs

kpeter@815

448 
\brief Algorithms for finding minimum mean cycles.

alpar@40

449 

kpeter@818

450 
This group contains the algorithms for finding minimum mean cycles

kpeter@818

451 
\ref clrs01algorithms, \ref amo93networkflows.

alpar@40

452 

kpeter@815

453 
The \e minimum \e mean \e cycle \e problem is to find a directed cycle

kpeter@815

454 
of minimum mean length (cost) in a digraph.

kpeter@815

455 
The mean length of a cycle is the average length of its arcs, i.e. the

kpeter@815

456 
ratio between the total length of the cycle and the number of arcs on it.

alpar@40

457 

kpeter@815

458 
This problem has an important connection to \e conservative \e length

kpeter@815

459 
\e functions, too. A length function on the arcs of a digraph is called

kpeter@815

460 
conservative if and only if there is no directed cycle of negative total

kpeter@815

461 
length. For an arbitrary length function, the negative of the minimum

kpeter@815

462 
cycle mean is the smallest \f$\epsilon\f$ value so that increasing the

kpeter@815

463 
arc lengths uniformly by \f$\epsilon\f$ results in a conservative length

kpeter@815

464 
function.

alpar@40

465 

kpeter@815

466 
LEMON contains three algorithms for solving the minimum mean cycle problem:

kpeter@959

467 
 \ref KarpMmc Karp's original algorithm \ref amo93networkflows,

kpeter@818

468 
\ref dasdan98minmeancycle.

kpeter@959

469 
 \ref HartmannOrlinMmc HartmannOrlin's algorithm, which is an improved

kpeter@818

470 
version of Karp's algorithm \ref dasdan98minmeancycle.

kpeter@959

471 
 \ref HowardMmc Howard's policy iteration algorithm

kpeter@818

472 
\ref dasdan98minmeancycle.

alpar@40

473 

kpeter@959

474 
In practice, the \ref HowardMmc "Howard" algorithm proved to be by far the

kpeter@959

475 
most efficient one, though the best known theoretical bound on its running

kpeter@959

476 
time is exponential.

kpeter@959

477 
Both \ref KarpMmc "Karp" and \ref HartmannOrlinMmc "HartmannOrlin" algorithms

kpeter@959

478 
run in time O(ne) and use space O(n<sup>2</sup>+e), but the latter one is

kpeter@959

479 
typically faster due to the applied early termination scheme.

alpar@40

480 
*/

alpar@40

481 

alpar@40

482 
/**

kpeter@314

483 
@defgroup matching Matching Algorithms

alpar@40

484 
@ingroup algs

kpeter@50

485 
\brief Algorithms for finding matchings in graphs and bipartite graphs.

alpar@40

486 

kpeter@637

487 
This group contains the algorithms for calculating

alpar@40

488 
matchings in graphs and bipartite graphs. The general matching problem is

kpeter@637

489 
finding a subset of the edges for which each node has at most one incident

kpeter@637

490 
edge.

alpar@209

491 

alpar@40

492 
There are several different algorithms for calculate matchings in

alpar@40

493 
graphs. The matching problems in bipartite graphs are generally

alpar@40

494 
easier than in general graphs. The goal of the matching optimization

kpeter@422

495 
can be finding maximum cardinality, maximum weight or minimum cost

alpar@40

496 
matching. The search can be constrained to find perfect or

alpar@40

497 
maximum cardinality matching.

alpar@40

498 

kpeter@422

499 
The matching algorithms implemented in LEMON:

kpeter@422

500 
 \ref MaxBipartiteMatching HopcroftKarp augmenting path algorithm

kpeter@422

501 
for calculating maximum cardinality matching in bipartite graphs.

kpeter@422

502 
 \ref PrBipartiteMatching Pushrelabel algorithm

kpeter@422

503 
for calculating maximum cardinality matching in bipartite graphs.

kpeter@422

504 
 \ref MaxWeightedBipartiteMatching

kpeter@422

505 
Successive shortest path algorithm for calculating maximum weighted

kpeter@422

506 
matching and maximum weighted bipartite matching in bipartite graphs.

kpeter@422

507 
 \ref MinCostMaxBipartiteMatching

kpeter@422

508 
Successive shortest path algorithm for calculating minimum cost maximum

kpeter@422

509 
matching in bipartite graphs.

kpeter@422

510 
 \ref MaxMatching Edmond's blossom shrinking algorithm for calculating

kpeter@422

511 
maximum cardinality matching in general graphs.

kpeter@422

512 
 \ref MaxWeightedMatching Edmond's blossom shrinking algorithm for calculating

kpeter@422

513 
maximum weighted matching in general graphs.

kpeter@422

514 
 \ref MaxWeightedPerfectMatching

kpeter@422

515 
Edmond's blossom shrinking algorithm for calculating maximum weighted

kpeter@422

516 
perfect matching in general graphs.

deba@948

517 
 \ref MaxFractionalMatching Pushrelabel algorithm for calculating

deba@948

518 
maximum cardinality fractional matching in general graphs.

deba@948

519 
 \ref MaxWeightedFractionalMatching Augmenting path algorithm for calculating

deba@948

520 
maximum weighted fractional matching in general graphs.

deba@948

521 
 \ref MaxWeightedPerfectFractionalMatching

deba@948

522 
Augmenting path algorithm for calculating maximum weighted

deba@948

523 
perfect fractional matching in general graphs.

alpar@40

524 

alpar@943

525 
\image html matching.png

alpar@952

526 
\image latex matching.eps "Min Cost Perfect Matching" width=\textwidth

alpar@40

527 
*/

alpar@40

528 

alpar@40

529 
/**

kpeter@761

530 
@defgroup graph_properties Connectivity and Other Graph Properties

alpar@40

531 
@ingroup algs

kpeter@761

532 
\brief Algorithms for discovering the graph properties

alpar@40

533 

kpeter@761

534 
This group contains the algorithms for discovering the graph properties

kpeter@761

535 
like connectivity, bipartiteness, euler property, simplicity etc.

kpeter@761

536 

kpeter@761

537 
\image html connected_components.png

kpeter@761

538 
\image latex connected_components.eps "Connected components" width=\textwidth

kpeter@761

539 
*/

kpeter@761

540 

kpeter@761

541 
/**

kpeter@761

542 
@defgroup planar Planarity Embedding and Drawing

kpeter@761

543 
@ingroup algs

kpeter@761

544 
\brief Algorithms for planarity checking, embedding and drawing

kpeter@761

545 

kpeter@761

546 
This group contains the algorithms for planarity checking,

kpeter@761

547 
embedding and drawing.

kpeter@761

548 

kpeter@761

549 
\image html planar.png

kpeter@761

550 
\image latex planar.eps "Plane graph" width=\textwidth

kpeter@761

551 
*/

kpeter@761

552 

kpeter@761

553 
/**

kpeter@761

554 
@defgroup approx Approximation Algorithms

kpeter@761

555 
@ingroup algs

kpeter@761

556 
\brief Approximation algorithms.

kpeter@761

557 

kpeter@761

558 
This group contains the approximation and heuristic algorithms

kpeter@761

559 
implemented in LEMON.

alpar@40

560 
*/

alpar@40

561 

alpar@40

562 
/**

kpeter@314

563 
@defgroup auxalg Auxiliary Algorithms

alpar@40

564 
@ingroup algs

kpeter@50

565 
\brief Auxiliary algorithms implemented in LEMON.

alpar@40

566 

kpeter@606

567 
This group contains some algorithms implemented in LEMON

kpeter@50

568 
in order to make it easier to implement complex algorithms.

alpar@40

569 
*/

alpar@40

570 

alpar@40

571 
/**

alpar@40

572 
@defgroup gen_opt_group General Optimization Tools

kpeter@606

573 
\brief This group contains some general optimization frameworks

alpar@40

574 
implemented in LEMON.

alpar@40

575 

kpeter@606

576 
This group contains some general optimization frameworks

alpar@40

577 
implemented in LEMON.

alpar@40

578 
*/

alpar@40

579 

alpar@40

580 
/**

kpeter@802

581 
@defgroup lp_group LP and MIP Solvers

alpar@40

582 
@ingroup gen_opt_group

kpeter@802

583 
\brief LP and MIP solver interfaces for LEMON.

alpar@40

584 

kpeter@802

585 
This group contains LP and MIP solver interfaces for LEMON.

kpeter@802

586 
Various LP solvers could be used in the same manner with this

kpeter@802

587 
highlevel interface.

kpeter@802

588 

kpeter@802

589 
The currently supported solvers are \ref glpk, \ref clp, \ref cbc,

kpeter@802

590 
\ref cplex, \ref soplex.

alpar@40

591 
*/

alpar@40

592 

alpar@209

593 
/**

kpeter@314

594 
@defgroup lp_utils Tools for Lp and Mip Solvers

alpar@40

595 
@ingroup lp_group

kpeter@50

596 
\brief Helper tools to the Lp and Mip solvers.

alpar@40

597 

alpar@40

598 
This group adds some helper tools to general optimization framework

alpar@40

599 
implemented in LEMON.

alpar@40

600 
*/

alpar@40

601 

alpar@40

602 
/**

alpar@40

603 
@defgroup metah Metaheuristics

alpar@40

604 
@ingroup gen_opt_group

alpar@40

605 
\brief Metaheuristics for LEMON library.

alpar@40

606 

kpeter@606

607 
This group contains some metaheuristic optimization tools.

alpar@40

608 
*/

alpar@40

609 

alpar@40

610 
/**

alpar@209

611 
@defgroup utils Tools and Utilities

kpeter@50

612 
\brief Tools and utilities for programming in LEMON

alpar@40

613 

kpeter@50

614 
Tools and utilities for programming in LEMON.

alpar@40

615 
*/

alpar@40

616 

alpar@40

617 
/**

alpar@40

618 
@defgroup gutils Basic Graph Utilities

alpar@40

619 
@ingroup utils

kpeter@50

620 
\brief Simple basic graph utilities.

alpar@40

621 

kpeter@606

622 
This group contains some simple basic graph utilities.

alpar@40

623 
*/

alpar@40

624 

alpar@40

625 
/**

alpar@40

626 
@defgroup misc Miscellaneous Tools

alpar@40

627 
@ingroup utils

kpeter@50

628 
\brief Tools for development, debugging and testing.

kpeter@50

629 

kpeter@606

630 
This group contains several useful tools for development,

alpar@40

631 
debugging and testing.

alpar@40

632 
*/

alpar@40

633 

alpar@40

634 
/**

kpeter@314

635 
@defgroup timecount Time Measuring and Counting

alpar@40

636 
@ingroup misc

kpeter@50

637 
\brief Simple tools for measuring the performance of algorithms.

kpeter@50

638 

kpeter@606

639 
This group contains simple tools for measuring the performance

alpar@40

640 
of algorithms.

alpar@40

641 
*/

alpar@40

642 

alpar@40

643 
/**

alpar@40

644 
@defgroup exceptions Exceptions

alpar@40

645 
@ingroup utils

kpeter@50

646 
\brief Exceptions defined in LEMON.

kpeter@50

647 

kpeter@606

648 
This group contains the exceptions defined in LEMON.

alpar@40

649 
*/

alpar@40

650 

alpar@40

651 
/**

alpar@40

652 
@defgroup io_group InputOutput

kpeter@50

653 
\brief Graph InputOutput methods

alpar@40

654 

kpeter@606

655 
This group contains the tools for importing and exporting graphs

kpeter@314

656 
and graph related data. Now it supports the \ref lgfformat

kpeter@314

657 
"LEMON Graph Format", the \c DIMACS format and the encapsulated

kpeter@314

658 
postscript (EPS) format.

alpar@40

659 
*/

alpar@40

660 

alpar@40

661 
/**

kpeter@363

662 
@defgroup lemon_io LEMON Graph Format

alpar@40

663 
@ingroup io_group

kpeter@314

664 
\brief Reading and writing LEMON Graph Format.

alpar@40

665 

kpeter@606

666 
This group contains methods for reading and writing

ladanyi@236

667 
\ref lgfformat "LEMON Graph Format".

alpar@40

668 
*/

alpar@40

669 

alpar@40

670 
/**

kpeter@314

671 
@defgroup eps_io Postscript Exporting

alpar@40

672 
@ingroup io_group

alpar@40

673 
\brief General \c EPS drawer and graph exporter

alpar@40

674 

kpeter@606

675 
This group contains general \c EPS drawing methods and special

alpar@209

676 
graph exporting tools.

alpar@40

677 
*/

alpar@40

678 

alpar@40

679 
/**

kpeter@761

680 
@defgroup dimacs_group DIMACS Format

kpeter@403

681 
@ingroup io_group

kpeter@403

682 
\brief Read and write files in DIMACS format

kpeter@403

683 

kpeter@403

684 
Tools to read a digraph from or write it to a file in DIMACS format data.

kpeter@403

685 
*/

kpeter@403

686 

kpeter@403

687 
/**

kpeter@363

688 
@defgroup nauty_group NAUTY Format

kpeter@363

689 
@ingroup io_group

kpeter@363

690 
\brief Read \e Nauty format

kpeter@403

691 

kpeter@363

692 
Tool to read graphs from \e Nauty format data.

kpeter@363

693 
*/

kpeter@363

694 

kpeter@363

695 
/**

alpar@40

696 
@defgroup concept Concepts

alpar@40

697 
\brief Skeleton classes and concept checking classes

alpar@40

698 

kpeter@606

699 
This group contains the data/algorithm skeletons and concept checking

alpar@40

700 
classes implemented in LEMON.

alpar@40

701 

alpar@40

702 
The purpose of the classes in this group is fourfold.

alpar@209

703 

kpeter@318

704 
 These classes contain the documentations of the %concepts. In order

alpar@40

705 
to avoid document multiplications, an implementation of a concept

alpar@40

706 
simply refers to the corresponding concept class.

alpar@40

707 

alpar@40

708 
 These classes declare every functions, <tt>typedef</tt>s etc. an

kpeter@318

709 
implementation of the %concepts should provide, however completely

alpar@40

710 
without implementations and real data structures behind the

alpar@40

711 
interface. On the other hand they should provide nothing else. All

alpar@40

712 
the algorithms working on a data structure meeting a certain concept

alpar@40

713 
should compile with these classes. (Though it will not run properly,

alpar@40

714 
of course.) In this way it is easily to check if an algorithm

alpar@40

715 
doesn't use any extra feature of a certain implementation.

alpar@40

716 

alpar@40

717 
 The concept descriptor classes also provide a <em>checker class</em>

kpeter@50

718 
that makes it possible to check whether a certain implementation of a

alpar@40

719 
concept indeed provides all the required features.

alpar@40

720 

alpar@40

721 
 Finally, They can serve as a skeleton of a new implementation of a concept.

alpar@40

722 
*/

alpar@40

723 

alpar@40

724 
/**

alpar@40

725 
@defgroup graph_concepts Graph Structure Concepts

alpar@40

726 
@ingroup concept

alpar@40

727 
\brief Skeleton and concept checking classes for graph structures

alpar@40

728 

kpeter@782

729 
This group contains the skeletons and concept checking classes of

kpeter@782

730 
graph structures.

alpar@40

731 
*/

alpar@40

732 

kpeter@314

733 
/**

kpeter@314

734 
@defgroup map_concepts Map Concepts

kpeter@314

735 
@ingroup concept

kpeter@314

736 
\brief Skeleton and concept checking classes for maps

kpeter@314

737 

kpeter@606

738 
This group contains the skeletons and concept checking classes of maps.

alpar@40

739 
*/

alpar@40

740 

alpar@40

741 
/**

kpeter@761

742 
@defgroup tools Standalone Utility Applications

kpeter@761

743 

kpeter@761

744 
Some utility applications are listed here.

kpeter@761

745 

kpeter@761

746 
The standard compilation procedure (<tt>./configure;make</tt>) will compile

kpeter@761

747 
them, as well.

kpeter@761

748 
*/

kpeter@761

749 

kpeter@761

750 
/**

alpar@40

751 
\anchor demoprograms

alpar@40

752 

kpeter@422

753 
@defgroup demos Demo Programs

alpar@40

754 

alpar@40

755 
Some demo programs are listed here. Their full source codes can be found in

alpar@40

756 
the \c demo subdirectory of the source tree.

alpar@40

757 

ladanyi@611

758 
In order to compile them, use the <tt>make demo</tt> or the

ladanyi@611

759 
<tt>make check</tt> commands.

alpar@40

760 
*/

alpar@40

761 

kpeter@422

762 
}
