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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) 2003-2008
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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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 */
18 18

	
19 19
/**
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@defgroup datas Data Structures
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This group describes the several data structures implemented in LEMON.
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*/
23 23

	
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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.
28 28

	
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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 semi_adaptors Semi-Adaptor Classes for Graphs
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@ingroup graphs
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\brief Graph types between real graphs and graph adaptors.
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This group describes some graph types between real graphs and graph adaptors.
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These classes wrap graphs to give new functionality as the adaptors do it.
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On the other hand they are not light-weight structures as the adaptors.
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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 describes 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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*/
84 84

	
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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 describes maps that are specifically designed to assign
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values to the nodes and arcs of graphs.
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*/
93 93

	
94 94
/**
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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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99 99
This group describes 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 lemon::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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*/
156 156

	
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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 describes 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 describes 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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178 178
\sa lemon::concepts::Path
179 179
*/
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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 describes some data structures implemented in LEMON in
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order to make it easier to implement combinatorial algorithms.
188 188
*/
189 189

	
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/**
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@defgroup algs Algorithms
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\brief This group describes the several algorithms
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implemented in LEMON.
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This group describes the several algorithms
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implemented in LEMON.
197 197
*/
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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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204 204
This group describes the common graph search algorithms like
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Breadth-First Search (BFS) and Depth-First Search (DFS).
206 206
*/
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208 208
/**
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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.
212 212

	
213 213
This group describes the algorithms for finding shortest paths in graphs.
214 214
*/
215 215

	
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/**
217 217
@defgroup max_flow Maximum Flow Algorithms
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@ingroup algs
219 219
\brief Algorithms for finding maximum flows.
220 220

	
221 221
This group describes the algorithms for finding maximum flows and
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feasible circulations.
223 223

	
224 224
The maximum flow problem is to find a flow between a single source and
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a single target that is maximum. Formally, there is a \f$G=(V,A)\f$
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directed graph, an \f$c_a:A\rightarrow\mathbf{R}^+_0\f$ capacity
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function and given \f$s, t \in V\f$ source and target node. The
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maximum flow is the \f$f_a\f$ solution of the next optimization problem:
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230 230
\f[ 0 \le f_a \le c_a \f]
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\f[ \sum_{v\in\delta^{-}(u)}f_{vu}=\sum_{v\in\delta^{+}(u)}f_{uv}
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\qquad \forall u \in V \setminus \{s,t\}\f]
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\f[ \max \sum_{v\in\delta^{+}(s)}f_{uv} - \sum_{v\in\delta^{-}(s)}f_{vu}\f]
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235 235
LEMON contains several algorithms for solving maximum flow problems:
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- \ref lemon::EdmondsKarp "Edmonds-Karp"
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- \ref lemon::Preflow "Goldberg's Preflow algorithm"
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- \ref lemon::DinitzSleatorTarjan "Dinitz's blocking flow algorithm with dynamic trees"
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- \ref lemon::GoldbergTarjan "Preflow algorithm with dynamic trees"
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241 241
In most cases the \ref lemon::Preflow "Preflow" algorithm provides the
242 242
fastest method to compute the maximum flow. All impelementations
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provides functions to query the minimum cut, which is the dual linear
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programming problem of the maximum flow.
245 245
*/
246 246

	
247 247
/**
248 248
@defgroup min_cost_flow Minimum Cost Flow Algorithms
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@ingroup algs
250 250

	
251 251
\brief Algorithms for finding minimum cost flows and circulations.
252 252

	
253 253
This group describes the algorithms for finding minimum cost flows and
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circulations.
255 255
*/
256 256

	
257 257
/**
258 258
@defgroup min_cut Minimum Cut Algorithms
259 259
@ingroup algs
260 260

	
261 261
\brief Algorithms for finding minimum cut in graphs.
262 262

	
263 263
This group describes the algorithms for finding minimum cut in graphs.
264 264

	
265 265
The minimum cut problem is to find a non-empty and non-complete
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\f$X\f$ subset of the vertices with minimum overall capacity on
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outgoing arcs. Formally, there is \f$G=(V,A)\f$ directed graph, an
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\f$c_a:A\rightarrow\mathbf{R}^+_0\f$ capacity function. The minimum
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cut is the \f$X\f$ solution of the next optimization problem:
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271 271
\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}c_{uv}\f]
273 273

	
274 274
LEMON contains several algorithms related to minimum cut problems:
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- \ref lemon::HaoOrlin "Hao-Orlin algorithm" to calculate minimum cut
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  in directed graphs
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- \ref lemon::NagamochiIbaraki "Nagamochi-Ibaraki algorithm" to
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  calculate minimum cut in undirected graphs
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- \ref lemon::GomoryHuTree "Gomory-Hu tree computation" to calculate all
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  pairs minimum cut in undirected graphs
282 282

	
283 283
If you want to find minimum cut just between two distinict nodes,
284 284
please see the \ref max_flow "Maximum Flow page".
285 285
*/
286 286

	
287 287
/**
288 288
@defgroup graph_prop Connectivity and Other Graph Properties
289 289
@ingroup algs
290 290
\brief Algorithms for discovering the graph properties
291 291

	
292 292
This group describes the algorithms for discovering the graph properties
293 293
like connectivity, bipartiteness, euler property, simplicity etc.
294 294

	
295 295
\image html edge_biconnected_components.png
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\image latex edge_biconnected_components.eps "bi-edge-connected components" width=\textwidth
297 297
*/
298 298

	
299 299
/**
300 300
@defgroup planar Planarity Embedding and Drawing
301 301
@ingroup algs
302 302
\brief Algorithms for planarity checking, embedding and drawing
303 303

	
304 304
This group describes the algorithms for planarity checking,
305 305
embedding and drawing.
306 306

	
307 307
\image html planar.png
308 308
\image latex planar.eps "Plane graph" width=\textwidth
309 309
*/
310 310

	
311 311
/**
312 312
@defgroup matching Matching Algorithms
313 313
@ingroup algs
314 314
\brief Algorithms for finding matchings in graphs and bipartite graphs.
315 315

	
316 316
This group contains algorithm objects and functions to calculate
317 317
matchings in graphs and bipartite graphs. The general matching problem is
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finding a subset of the arcs which does not shares common endpoints.
319 319

	
320 320
There are several different algorithms for calculate matchings in
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graphs.  The matching problems in bipartite graphs are generally
322 322
easier than in general graphs. The goal of the matching optimization
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can be the finding maximum cardinality, maximum weight or minimum cost
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matching. The search can be constrained to find perfect or
325 325
maximum cardinality matching.
326 326

	
327 327
LEMON contains the next algorithms:
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- \ref lemon::MaxBipartiteMatching "MaxBipartiteMatching" Hopcroft-Karp
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  augmenting path algorithm for calculate maximum cardinality matching in
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  bipartite graphs
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- \ref lemon::PrBipartiteMatching "PrBipartiteMatching" Push-Relabel
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  algorithm for calculate maximum cardinality matching in bipartite graphs
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- \ref lemon::MaxWeightedBipartiteMatching "MaxWeightedBipartiteMatching"
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  Successive shortest path algorithm for calculate maximum weighted matching
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  and maximum weighted bipartite matching in bipartite graph
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- \ref lemon::MinCostMaxBipartiteMatching "MinCostMaxBipartiteMatching"
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  Successive shortest path algorithm for calculate minimum cost maximum
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  matching in bipartite graph
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- \ref lemon::MaxMatching "MaxMatching" Edmond's blossom shrinking algorithm
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  for calculate maximum cardinality matching in general graph
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- \ref lemon::MaxWeightedMatching "MaxWeightedMatching" Edmond's blossom
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  shrinking algorithm for calculate maximum weighted matching in general
343 343
  graph
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- \ref lemon::MaxWeightedPerfectMatching "MaxWeightedPerfectMatching"
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  Edmond's blossom shrinking algorithm for calculate maximum weighted
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  perfect matching in general graph
347 347

	
348 348
\image html bipartite_matching.png
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\image latex bipartite_matching.eps "Bipartite Matching" width=\textwidth
350 350
*/
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352 352
/**
353 353
@defgroup spantree Minimum Spanning Tree Algorithms
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@ingroup algs
355 355
\brief Algorithms for finding a minimum cost spanning tree in a graph.
356 356

	
357 357
This group describes the algorithms for finding a minimum cost spanning
358 358
tree in a graph
359 359
*/
360 360

	
361 361
/**
362 362
@defgroup auxalg Auxiliary Algorithms
363 363
@ingroup algs
364 364
\brief Auxiliary algorithms implemented in LEMON.
365 365

	
366 366
This group describes some algorithms implemented in LEMON
367 367
in order to make it easier to implement complex algorithms.
368 368
*/
369 369

	
370 370
/**
371 371
@defgroup approx Approximation Algorithms
372 372
@ingroup algs
373 373
\brief Approximation algorithms.
374 374

	
375 375
This group describes the approximation and heuristic algorithms
376 376
implemented in LEMON.
377 377
*/
378 378

	
379 379
/**
380 380
@defgroup gen_opt_group General Optimization Tools
381 381
\brief This group describes some general optimization frameworks
382 382
implemented in LEMON.
383 383

	
384 384
This group describes some general optimization frameworks
385 385
implemented in LEMON.
386 386
*/
387 387

	
388 388
/**
389 389
@defgroup lp_group Lp and Mip Solvers
390 390
@ingroup gen_opt_group
391 391
\brief Lp and Mip solver interfaces for LEMON.
392 392

	
393 393
This group describes Lp and Mip solver interfaces for LEMON. The
394 394
various LP solvers could be used in the same manner with this
395 395
interface.
396 396
*/
397 397

	
398 398
/**
399 399
@defgroup lp_utils Tools for Lp and Mip Solvers
400 400
@ingroup lp_group
401 401
\brief Helper tools to the Lp and Mip solvers.
402 402

	
403 403
This group adds some helper tools to general optimization framework
404 404
implemented in LEMON.
405 405
*/
406 406

	
407 407
/**
408 408
@defgroup metah Metaheuristics
409 409
@ingroup gen_opt_group
410 410
\brief Metaheuristics for LEMON library.
411 411

	
412 412
This group describes some metaheuristic optimization tools.
413 413
*/
414 414

	
415 415
/**
416 416
@defgroup utils Tools and Utilities
417 417
\brief Tools and utilities for programming in LEMON
418 418

	
419 419
Tools and utilities for programming in LEMON.
420 420
*/
421 421

	
422 422
/**
423 423
@defgroup gutils Basic Graph Utilities
424 424
@ingroup utils
425 425
\brief Simple basic graph utilities.
426 426

	
427 427
This group describes some simple basic graph utilities.
428 428
*/
429 429

	
430 430
/**
431 431
@defgroup misc Miscellaneous Tools
432 432
@ingroup utils
433 433
\brief Tools for development, debugging and testing.
434 434

	
435 435
This group describes several useful tools for development,
436 436
debugging and testing.
437 437
*/
438 438

	
439 439
/**
440 440
@defgroup timecount Time Measuring and Counting
441 441
@ingroup misc
442 442
\brief Simple tools for measuring the performance of algorithms.
443 443

	
444 444
This group describes simple tools for measuring the performance
445 445
of algorithms.
446 446
*/
447 447

	
448 448
/**
449 449
@defgroup exceptions Exceptions
450 450
@ingroup utils
451 451
\brief Exceptions defined in LEMON.
452 452

	
453 453
This group describes the exceptions defined in LEMON.
454 454
*/
455 455

	
456 456
/**
457 457
@defgroup io_group Input-Output
458 458
\brief Graph Input-Output methods
459 459

	
460 460
This group describes the tools for importing and exporting graphs
461 461
and graph related data. Now it supports the \ref lgf-format
462 462
"LEMON Graph Format", the \c DIMACS format and the encapsulated
463 463
postscript (EPS) format.
464 464
*/
465 465

	
466 466
/**
467
@defgroup lemon_io LEMON Input-Output
467
@defgroup lemon_io LEMON Graph Format
468 468
@ingroup io_group
469 469
\brief Reading and writing LEMON Graph Format.
470 470

	
471 471
This group describes methods for reading and writing
472 472
\ref lgf-format "LEMON Graph Format".
473 473
*/
474 474

	
475 475
/**
476 476
@defgroup eps_io Postscript Exporting
477 477
@ingroup io_group
478 478
\brief General \c EPS drawer and graph exporter
479 479

	
480 480
This group describes general \c EPS drawing methods and special
481 481
graph exporting tools.
482 482
*/
483 483

	
484 484
/**
485
@defgroup nauty_group NAUTY Format
486
@ingroup io_group
487
\brief Read \e Nauty format
488
Tool to read graphs from \e Nauty format data.
489
*/
490

	
491
/**
485 492
@defgroup concept Concepts
486 493
\brief Skeleton classes and concept checking classes
487 494

	
488 495
This group describes the data/algorithm skeletons and concept checking
489 496
classes implemented in LEMON.
490 497

	
491 498
The purpose of the classes in this group is fourfold.
492 499

	
493 500
- These classes contain the documentations of the %concepts. In order
494 501
  to avoid document multiplications, an implementation of a concept
495 502
  simply refers to the corresponding concept class.
496 503

	
497 504
- These classes declare every functions, <tt>typedef</tt>s etc. an
498 505
  implementation of the %concepts should provide, however completely
499 506
  without implementations and real data structures behind the
500 507
  interface. On the other hand they should provide nothing else. All
501 508
  the algorithms working on a data structure meeting a certain concept
502 509
  should compile with these classes. (Though it will not run properly,
503 510
  of course.) In this way it is easily to check if an algorithm
504 511
  doesn't use any extra feature of a certain implementation.
505 512

	
506 513
- The concept descriptor classes also provide a <em>checker class</em>
507 514
  that makes it possible to check whether a certain implementation of a
508 515
  concept indeed provides all the required features.
509 516

	
510 517
- Finally, They can serve as a skeleton of a new implementation of a concept.
511 518
*/
512 519

	
513 520
/**
514 521
@defgroup graph_concepts Graph Structure Concepts
515 522
@ingroup concept
516 523
\brief Skeleton and concept checking classes for graph structures
517 524

	
518 525
This group describes the skeletons and concept checking classes of LEMON's
519 526
graph structures and helper classes used to implement these.
520 527
*/
521 528

	
522 529
/**
523 530
@defgroup map_concepts Map Concepts
524 531
@ingroup concept
525 532
\brief Skeleton and concept checking classes for maps
526 533

	
527 534
This group describes the skeletons and concept checking classes of maps.
528 535
*/
529 536

	
530 537
/**
531 538
\anchor demoprograms
532 539

	
533 540
@defgroup demos Demo programs
534 541

	
535 542
Some demo programs are listed here. Their full source codes can be found in
536 543
the \c demo subdirectory of the source tree.
537 544

	
538 545
It order to compile them, use <tt>--enable-demo</tt> configure option when
539 546
build the library.
540 547
*/
541 548

	
542 549
/**
543 550
@defgroup tools Standalone utility applications
544 551

	
545 552
Some utility applications are listed here.
546 553

	
547 554
The standard compilation procedure (<tt>./configure;make</tt>) will compile
548 555
them, as well.
549 556
*/
550 557

	
Ignore white space 1536 line context
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/* -*- mode: C++; indent-tabs-mode: nil; -*-
2 2
 *
3 3
 * This file is a part of LEMON, a generic C++ optimization library.
4 4
 *
5 5
 * Copyright (C) 2003-2008
6 6
 * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
7 7
 * (Egervary Research Group on Combinatorial Optimization, EGRES).
8 8
 *
9 9
 * Permission to use, modify and distribute this software is granted
10 10
 * provided that this copyright notice appears in all copies. For
11 11
 * precise terms see the accompanying LICENSE file.
12 12
 *
13 13
 * This software is provided "AS IS" with no warranty of any kind,
14 14
 * express or implied, and with no claim as to its suitability for any
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 * purpose.
16 16
 *
17 17
 */
18 18

	
19 19
#ifndef LEMON_NAUTY_READER_H
20 20
#define LEMON_NAUTY_READER_H
21 21

	
22 22
#include <vector>
23 23
#include <iostream>
24 24
#include <string>
25 25

	
26
/// \ingroup io_group
27
///
28
/// @defgroup nauty_group NAUTY format
29
///
30
/// \brief Read \e Nauty format
31
///
32
/// Tool to read graphs from \e Nauty format data
33

	
34 26
/// \ingroup nauty_group
35 27
/// \file
36 28
/// \brief Nauty file reader.
29

	
37 30
namespace lemon {
38 31

	
39 32
  /// \ingroup nauty_group
40 33
  ///
41 34
  /// \brief Nauty file reader
42 35
  ///
43 36
  /// The \e geng program is in the \e gtools suite of the nauty
44 37
  /// package. This tool can generate all non-isomorphic undirected
45
  /// graphs with given node number from several classes (for example,
38
  /// graphs of several classes with given node number (e.g.
46 39
  /// general, connected, biconnected, triangle-free, 4-cycle-free,
47 40
  /// bipartite and graphs with given edge number and degree
48
  /// constraints). This function reads a \e nauty \e graph6 \e format
41
  /// constraints). This function reads a \e nauty \e graph \e format
49 42
  /// line from the given stream and builds it in the given graph.
50 43
  ///
51 44
  /// The site of nauty package: http://cs.anu.edu.au/~bdm/nauty/
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  ///
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  /// For example, the number of all non-isomorphic connected graphs
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  /// can be computed with following code.
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  /// For example, the number of all non-isomorphic planar graphs
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  /// can be computed with the following code.
55 48
  ///\code
56 49
  /// int num = 0;
57 50
  /// SmartGraph graph;
58 51
  /// while (readNauty(graph, std::cin)) {
59 52
  ///   PlanarityChecking<SmartGraph> pc(graph);
60 53
  ///   if (pc.run()) ++num;
61 54
  /// }
62 55
  /// std::cout << "Number of planar graphs: " << num << std::endl;
63 56
  ///\endcode
64 57
  ///
65 58
  /// The nauty files are quite huge, therefore instead of the direct
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  /// file generation the pipelining is recommended.
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  /// file generation pipelining is recommended. For example,
67 60
  ///\code
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  /// ./geng -c 10 | ./num_of_pg
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  /// ./geng -c 10 | ./num_of_planar_graphs
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  ///\endcode
70 63
  template <typename Graph>
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  std::istream& readNauty(Graph& graph, std::istream& is) {
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  std::istream& readNauty(Graph& graph, std::istream& is = std::cin) {
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    graph.clear();
73 66

	
74 67
    std::string line;
75 68
    if (getline(is, line)) {
76 69
      int index = 0;
77 70

	
78 71
      int n;
79 72

	
80 73
      if (line[index] == '>') {
81 74
        index += 10;
82 75
      }
83 76

	
84 77
      char c = line[index++]; c -= 63;
85 78
      if (c != 63) {
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        n = int(c);
87 80
      } else {
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        c = line[index++]; c -= 63;
89 82
        n = (int(c) << 12);
90 83
        c = line[index++]; c -= 63;
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        n |= (int(c) << 6);
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        c = line[index++]; c -= 63;
93 86
        n |= int(c);
94 87
      }
95 88

	
96 89
      std::vector<typename Graph::Node> nodes;
97 90
      for (int i = 0; i < n; ++i) {
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        nodes.push_back(graph.addNode());
99 92
      }
100 93

	
101 94
      int bit = -1;
102 95
      for (int j = 0; j < n; ++j) {
103 96
        for (int i = 0; i < j; ++i) {
104 97
          if (bit == -1) {
105 98
            c = line[index++]; c -= 63;
106 99
            bit = 5;
107 100
          }
108 101
          bool b = (c & (1 << (bit--))) != 0;
109 102

	
110 103
          if (b) {
111 104
            graph.addEdge(nodes[i], nodes[j]);
112 105
          }
113 106
        }
114 107
      }
115 108
    }
116 109
    return is;
117 110
  }
118 111
}
119 112

	
120 113
#endif
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