doc/groups.dox
author Peter Kovacs <kpeter@inf.elte.hu>
Fri, 08 Mar 2013 01:12:05 +0100
changeset 1213 4e8787627db3
parent 1165 16f55008c863
parent 1204 dff32ce3db71
child 1217 7bf489cf624e
permissions -rw-r--r--
Improve images of the Connectivity section (#411)
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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) 2003-2010
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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 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 breadth-first \e search (BFS) and \e depth-first \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 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 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 Edmonds-Karp algorithm
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  \ref edmondskarp72theoretical.
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- \ref Preflow Goldberg-Tarjan's preflow push-relabel 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 push-relabel 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 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 \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".
kpeter@422
   397
kpeter@710
   398
LEMON contains several algorithms for this problem.
kpeter@656
   399
 - \ref NetworkSimplex Primal Network Simplex algorithm with various
kpeter@802
   400
   pivot strategies \ref dantzig63linearprog, \ref kellyoneill91netsimplex.
kpeter@879
   401
 - \ref CostScaling Cost Scaling algorithm based on push/augment and
kpeter@879
   402
   relabel operations \ref goldberg90approximation, \ref goldberg97efficient,
kpeter@802
   403
   \ref bunnagel98efficient.
kpeter@879
   404
 - \ref CapacityScaling Capacity Scaling algorithm based on the successive
kpeter@879
   405
   shortest path method \ref edmondskarp72theoretical.
kpeter@879
   406
 - \ref CycleCanceling Cycle-Canceling algorithms, two of which are
kpeter@879
   407
   strongly polynomial \ref klein67primal, \ref goldberg89cyclecanceling.
kpeter@656
   408
kpeter@1023
   409
In general, \ref NetworkSimplex and \ref CostScaling are the most efficient
kpeter@1165
   410
implementations.
kpeter@1165
   411
\ref NetworkSimplex is usually the fastest on relatively small graphs (up to
kpeter@1165
   412
several thousands of nodes) and on dense graphs, while \ref CostScaling is
kpeter@1165
   413
typically more efficient on large graphs (e.g. hundreds of thousands of
kpeter@1165
   414
nodes or above), especially if they are sparse.
kpeter@1165
   415
However, other algorithms could be faster in special cases.
kpeter@656
   416
For example, if the total supply and/or capacities are rather small,
kpeter@1023
   417
\ref CapacityScaling is usually the fastest algorithm (without effective scaling).
kpeter@1164
   418
kpeter@1164
   419
These classes are intended to be used with integer-valued input data
kpeter@1164
   420
(capacities, supply values, and costs), except for \ref CapacityScaling,
kpeter@1164
   421
which is capable of handling real-valued arc costs (other numerical
kpeter@1164
   422
data are required to be integer).
alpar@40
   423
*/
alpar@40
   424
alpar@40
   425
/**
kpeter@314
   426
@defgroup min_cut Minimum Cut Algorithms
alpar@209
   427
@ingroup algs
alpar@40
   428
kpeter@50
   429
\brief Algorithms for finding minimum cut in graphs.
alpar@40
   430
kpeter@606
   431
This group contains the algorithms for finding minimum cut in graphs.
alpar@40
   432
kpeter@422
   433
The \e minimum \e cut \e problem is to find a non-empty and non-complete
kpeter@422
   434
\f$X\f$ subset of the nodes with minimum overall capacity on
kpeter@422
   435
outgoing arcs. Formally, there is a \f$G=(V,A)\f$ digraph, a
kpeter@422
   436
\f$cap: A\rightarrow\mathbf{R}^+_0\f$ capacity function. The minimum
kpeter@50
   437
cut is the \f$X\f$ solution of the next optimization problem:
alpar@40
   438
alpar@210
   439
\f[ \min_{X \subset V, X\not\in \{\emptyset, V\}}
kpeter@760
   440
    \sum_{uv\in A: u\in X, v\not\in X}cap(uv) \f]
alpar@40
   441
kpeter@50
   442
LEMON contains several algorithms related to minimum cut problems:
alpar@40
   443
kpeter@422
   444
- \ref HaoOrlin "Hao-Orlin algorithm" for calculating minimum cut
kpeter@422
   445
  in directed graphs.
kpeter@422
   446
- \ref NagamochiIbaraki "Nagamochi-Ibaraki algorithm" for
kpeter@422
   447
  calculating minimum cut in undirected graphs.
kpeter@606
   448
- \ref GomoryHu "Gomory-Hu tree computation" for calculating
kpeter@422
   449
  all-pairs minimum cut in undirected graphs.
alpar@40
   450
alpar@40
   451
If you want to find minimum cut just between two distinict nodes,
kpeter@422
   452
see the \ref max_flow "maximum flow problem".
alpar@40
   453
*/
alpar@40
   454
alpar@40
   455
/**
kpeter@815
   456
@defgroup min_mean_cycle Minimum Mean Cycle Algorithms
alpar@40
   457
@ingroup algs
kpeter@815
   458
\brief Algorithms for finding minimum mean cycles.
alpar@40
   459
kpeter@818
   460
This group contains the algorithms for finding minimum mean cycles
kpeter@1164
   461
\ref amo93networkflows, \ref karp78characterization.
alpar@40
   462
kpeter@815
   463
The \e minimum \e mean \e cycle \e problem is to find a directed cycle
kpeter@815
   464
of minimum mean length (cost) in a digraph.
kpeter@815
   465
The mean length of a cycle is the average length of its arcs, i.e. the
kpeter@815
   466
ratio between the total length of the cycle and the number of arcs on it.
alpar@40
   467
kpeter@815
   468
This problem has an important connection to \e conservative \e length
kpeter@815
   469
\e functions, too. A length function on the arcs of a digraph is called
kpeter@815
   470
conservative if and only if there is no directed cycle of negative total
kpeter@815
   471
length. For an arbitrary length function, the negative of the minimum
kpeter@815
   472
cycle mean is the smallest \f$\epsilon\f$ value so that increasing the
kpeter@815
   473
arc lengths uniformly by \f$\epsilon\f$ results in a conservative length
kpeter@815
   474
function.
alpar@40
   475
kpeter@815
   476
LEMON contains three algorithms for solving the minimum mean cycle problem:
kpeter@1164
   477
- \ref KarpMmc Karp's original algorithm \ref karp78characterization.
kpeter@959
   478
- \ref HartmannOrlinMmc Hartmann-Orlin's algorithm, which is an improved
kpeter@1164
   479
  version of Karp's algorithm \ref hartmann93finding.
kpeter@959
   480
- \ref HowardMmc Howard's policy iteration algorithm
kpeter@1164
   481
  \ref dasdan98minmeancycle, \ref dasdan04experimental.
alpar@40
   482
kpeter@1023
   483
In practice, the \ref HowardMmc "Howard" algorithm turned out to be by far the
kpeter@959
   484
most efficient one, though the best known theoretical bound on its running
kpeter@959
   485
time is exponential.
kpeter@959
   486
Both \ref KarpMmc "Karp" and \ref HartmannOrlinMmc "Hartmann-Orlin" algorithms
kpeter@959
   487
run in time O(ne) and use space O(n<sup>2</sup>+e), but the latter one is
kpeter@959
   488
typically faster due to the applied early termination scheme.
alpar@40
   489
*/
alpar@40
   490
alpar@40
   491
/**
kpeter@314
   492
@defgroup matching Matching Algorithms
alpar@40
   493
@ingroup algs
kpeter@50
   494
\brief Algorithms for finding matchings in graphs and bipartite graphs.
alpar@40
   495
kpeter@637
   496
This group contains the algorithms for calculating
alpar@40
   497
matchings in graphs and bipartite graphs. The general matching problem is
kpeter@637
   498
finding a subset of the edges for which each node has at most one incident
kpeter@637
   499
edge.
alpar@209
   500
alpar@40
   501
There are several different algorithms for calculate matchings in
alpar@40
   502
graphs.  The matching problems in bipartite graphs are generally
alpar@40
   503
easier than in general graphs. The goal of the matching optimization
kpeter@422
   504
can be finding maximum cardinality, maximum weight or minimum cost
alpar@40
   505
matching. The search can be constrained to find perfect or
alpar@40
   506
maximum cardinality matching.
alpar@40
   507
kpeter@422
   508
The matching algorithms implemented in LEMON:
kpeter@422
   509
- \ref MaxBipartiteMatching Hopcroft-Karp augmenting path algorithm
kpeter@422
   510
  for calculating maximum cardinality matching in bipartite graphs.
kpeter@422
   511
- \ref PrBipartiteMatching Push-relabel algorithm
kpeter@422
   512
  for calculating maximum cardinality matching in bipartite graphs.
kpeter@422
   513
- \ref MaxWeightedBipartiteMatching
kpeter@422
   514
  Successive shortest path algorithm for calculating maximum weighted
kpeter@422
   515
  matching and maximum weighted bipartite matching in bipartite graphs.
kpeter@422
   516
- \ref MinCostMaxBipartiteMatching
kpeter@422
   517
  Successive shortest path algorithm for calculating minimum cost maximum
kpeter@422
   518
  matching in bipartite graphs.
kpeter@422
   519
- \ref MaxMatching Edmond's blossom shrinking algorithm for calculating
kpeter@422
   520
  maximum cardinality matching in general graphs.
kpeter@422
   521
- \ref MaxWeightedMatching Edmond's blossom shrinking algorithm for calculating
kpeter@422
   522
  maximum weighted matching in general graphs.
kpeter@422
   523
- \ref MaxWeightedPerfectMatching
kpeter@422
   524
  Edmond's blossom shrinking algorithm for calculating maximum weighted
kpeter@422
   525
  perfect matching in general graphs.
deba@948
   526
- \ref MaxFractionalMatching Push-relabel algorithm for calculating
deba@948
   527
  maximum cardinality fractional matching in general graphs.
deba@948
   528
- \ref MaxWeightedFractionalMatching Augmenting path algorithm for calculating
deba@948
   529
  maximum weighted fractional matching in general graphs.
deba@948
   530
- \ref MaxWeightedPerfectFractionalMatching
deba@948
   531
  Augmenting path algorithm for calculating maximum weighted
deba@948
   532
  perfect fractional matching in general graphs.
alpar@40
   533
alpar@943
   534
\image html matching.png
alpar@952
   535
\image latex matching.eps "Min Cost Perfect Matching" width=\textwidth
alpar@40
   536
*/
alpar@40
   537
alpar@40
   538
/**
kpeter@761
   539
@defgroup graph_properties Connectivity and Other Graph Properties
alpar@40
   540
@ingroup algs
kpeter@761
   541
\brief Algorithms for discovering the graph properties
alpar@40
   542
kpeter@761
   543
This group contains the algorithms for discovering the graph properties
kpeter@761
   544
like connectivity, bipartiteness, euler property, simplicity etc.
kpeter@761
   545
kpeter@761
   546
\image html connected_components.png
kpeter@761
   547
\image latex connected_components.eps "Connected components" width=\textwidth
kpeter@761
   548
*/
kpeter@761
   549
kpeter@761
   550
/**
kpeter@1023
   551
@defgroup planar Planar Embedding and Drawing
kpeter@761
   552
@ingroup algs
kpeter@761
   553
\brief Algorithms for planarity checking, embedding and drawing
kpeter@761
   554
kpeter@761
   555
This group contains the algorithms for planarity checking,
kpeter@761
   556
embedding and drawing.
kpeter@761
   557
kpeter@761
   558
\image html planar.png
kpeter@761
   559
\image latex planar.eps "Plane graph" width=\textwidth
kpeter@761
   560
*/
kpeter@1200
   561
 
kpeter@1200
   562
/**
kpeter@1200
   563
@defgroup tsp Traveling Salesman Problem
kpeter@1200
   564
@ingroup algs
kpeter@1200
   565
\brief Algorithms for the symmetric traveling salesman problem
kpeter@1200
   566
kpeter@1200
   567
This group contains basic heuristic algorithms for the the symmetric
kpeter@1200
   568
\e traveling \e salesman \e problem (TSP).
kpeter@1200
   569
Given an \ref FullGraph "undirected full graph" with a cost map on its edges,
kpeter@1200
   570
the problem is to find a shortest possible tour that visits each node exactly
kpeter@1200
   571
once (i.e. the minimum cost Hamiltonian cycle).
kpeter@1200
   572
kpeter@1202
   573
These TSP algorithms are intended to be used with a \e metric \e cost
kpeter@1202
   574
\e function, i.e. the edge costs should satisfy the triangle inequality.
kpeter@1202
   575
Otherwise the algorithms could yield worse results.
kpeter@1200
   576
kpeter@1200
   577
LEMON provides five well-known heuristics for solving symmetric TSP:
kpeter@1200
   578
 - \ref NearestNeighborTsp Neareast neighbor algorithm
kpeter@1200
   579
 - \ref GreedyTsp Greedy algorithm
kpeter@1200
   580
 - \ref InsertionTsp Insertion heuristic (with four selection methods)
kpeter@1200
   581
 - \ref ChristofidesTsp Christofides algorithm
kpeter@1200
   582
 - \ref Opt2Tsp 2-opt algorithm
kpeter@1200
   583
kpeter@1204
   584
\ref NearestNeighborTsp, \ref GreedyTsp, and \ref InsertionTsp are the fastest
kpeter@1204
   585
solution methods. Furthermore, \ref InsertionTsp is usually quite effective.
kpeter@1204
   586
kpeter@1204
   587
\ref ChristofidesTsp is somewhat slower, but it has the best guaranteed
kpeter@1204
   588
approximation factor: 3/2.
kpeter@1204
   589
kpeter@1204
   590
\ref Opt2Tsp usually provides the best results in practice, but
kpeter@1204
   591
it is the slowest method. It can also be used to improve given tours,
kpeter@1204
   592
for example, the results of other algorithms.
kpeter@1204
   593
kpeter@1200
   594
\image html tsp.png
kpeter@1200
   595
\image latex tsp.eps "Traveling salesman problem" width=\textwidth
kpeter@1200
   596
*/
kpeter@761
   597
kpeter@761
   598
/**
kpeter@999
   599
@defgroup approx_algs Approximation Algorithms
kpeter@761
   600
@ingroup algs
kpeter@761
   601
\brief Approximation algorithms.
kpeter@761
   602
kpeter@761
   603
This group contains the approximation and heuristic algorithms
kpeter@761
   604
implemented in LEMON.
kpeter@999
   605
kpeter@999
   606
<b>Maximum Clique Problem</b>
kpeter@999
   607
  - \ref GrossoLocatelliPullanMc An efficient heuristic algorithm of
kpeter@999
   608
    Grosso, Locatelli, and Pullan.
alpar@40
   609
*/
alpar@40
   610
alpar@40
   611
/**
kpeter@314
   612
@defgroup auxalg Auxiliary Algorithms
alpar@40
   613
@ingroup algs
kpeter@50
   614
\brief Auxiliary algorithms implemented in LEMON.
alpar@40
   615
kpeter@606
   616
This group contains some algorithms implemented in LEMON
kpeter@50
   617
in order to make it easier to implement complex algorithms.
alpar@40
   618
*/
alpar@40
   619
alpar@40
   620
/**
alpar@40
   621
@defgroup gen_opt_group General Optimization Tools
kpeter@606
   622
\brief This group contains some general optimization frameworks
alpar@40
   623
implemented in LEMON.
alpar@40
   624
kpeter@606
   625
This group contains some general optimization frameworks
alpar@40
   626
implemented in LEMON.
alpar@40
   627
*/
alpar@40
   628
alpar@40
   629
/**
kpeter@802
   630
@defgroup lp_group LP and MIP Solvers
alpar@40
   631
@ingroup gen_opt_group
kpeter@802
   632
\brief LP and MIP solver interfaces for LEMON.
alpar@40
   633
kpeter@802
   634
This group contains LP and MIP solver interfaces for LEMON.
kpeter@802
   635
Various LP solvers could be used in the same manner with this
kpeter@802
   636
high-level interface.
kpeter@802
   637
kpeter@802
   638
The currently supported solvers are \ref glpk, \ref clp, \ref cbc,
kpeter@802
   639
\ref cplex, \ref soplex.
alpar@40
   640
*/
alpar@40
   641
alpar@209
   642
/**
kpeter@314
   643
@defgroup lp_utils Tools for Lp and Mip Solvers
alpar@40
   644
@ingroup lp_group
kpeter@50
   645
\brief Helper tools to the Lp and Mip solvers.
alpar@40
   646
alpar@40
   647
This group adds some helper tools to general optimization framework
alpar@40
   648
implemented in LEMON.
alpar@40
   649
*/
alpar@40
   650
alpar@40
   651
/**
alpar@40
   652
@defgroup metah Metaheuristics
alpar@40
   653
@ingroup gen_opt_group
alpar@40
   654
\brief Metaheuristics for LEMON library.
alpar@40
   655
kpeter@606
   656
This group contains some metaheuristic optimization tools.
alpar@40
   657
*/
alpar@40
   658
alpar@40
   659
/**
alpar@209
   660
@defgroup utils Tools and Utilities
kpeter@50
   661
\brief Tools and utilities for programming in LEMON
alpar@40
   662
kpeter@50
   663
Tools and utilities for programming in LEMON.
alpar@40
   664
*/
alpar@40
   665
alpar@40
   666
/**
alpar@40
   667
@defgroup gutils Basic Graph Utilities
alpar@40
   668
@ingroup utils
kpeter@50
   669
\brief Simple basic graph utilities.
alpar@40
   670
kpeter@606
   671
This group contains some simple basic graph utilities.
alpar@40
   672
*/
alpar@40
   673
alpar@40
   674
/**
alpar@40
   675
@defgroup misc Miscellaneous Tools
alpar@40
   676
@ingroup utils
kpeter@50
   677
\brief Tools for development, debugging and testing.
kpeter@50
   678
kpeter@606
   679
This group contains several useful tools for development,
alpar@40
   680
debugging and testing.
alpar@40
   681
*/
alpar@40
   682
alpar@40
   683
/**
kpeter@314
   684
@defgroup timecount Time Measuring and Counting
alpar@40
   685
@ingroup misc
kpeter@50
   686
\brief Simple tools for measuring the performance of algorithms.
kpeter@50
   687
kpeter@606
   688
This group contains simple tools for measuring the performance
alpar@40
   689
of algorithms.
alpar@40
   690
*/
alpar@40
   691
alpar@40
   692
/**
alpar@40
   693
@defgroup exceptions Exceptions
alpar@40
   694
@ingroup utils
kpeter@50
   695
\brief Exceptions defined in LEMON.
kpeter@50
   696
kpeter@606
   697
This group contains the exceptions defined in LEMON.
alpar@40
   698
*/
alpar@40
   699
alpar@40
   700
/**
alpar@40
   701
@defgroup io_group Input-Output
kpeter@50
   702
\brief Graph Input-Output methods
alpar@40
   703
kpeter@606
   704
This group contains the tools for importing and exporting graphs
kpeter@314
   705
and graph related data. Now it supports the \ref lgf-format
kpeter@314
   706
"LEMON Graph Format", the \c DIMACS format and the encapsulated
kpeter@314
   707
postscript (EPS) format.
alpar@40
   708
*/
alpar@40
   709
alpar@40
   710
/**
kpeter@363
   711
@defgroup lemon_io LEMON Graph Format
alpar@40
   712
@ingroup io_group
kpeter@314
   713
\brief Reading and writing LEMON Graph Format.
alpar@40
   714
kpeter@606
   715
This group contains methods for reading and writing
ladanyi@236
   716
\ref lgf-format "LEMON Graph Format".
alpar@40
   717
*/
alpar@40
   718
alpar@40
   719
/**
kpeter@314
   720
@defgroup eps_io Postscript Exporting
alpar@40
   721
@ingroup io_group
alpar@40
   722
\brief General \c EPS drawer and graph exporter
alpar@40
   723
kpeter@606
   724
This group contains general \c EPS drawing methods and special
alpar@209
   725
graph exporting tools.
alpar@40
   726
*/
alpar@40
   727
alpar@40
   728
/**
kpeter@761
   729
@defgroup dimacs_group DIMACS Format
kpeter@403
   730
@ingroup io_group
kpeter@403
   731
\brief Read and write files in DIMACS format
kpeter@403
   732
kpeter@403
   733
Tools to read a digraph from or write it to a file in DIMACS format data.
kpeter@403
   734
*/
kpeter@403
   735
kpeter@403
   736
/**
kpeter@363
   737
@defgroup nauty_group NAUTY Format
kpeter@363
   738
@ingroup io_group
kpeter@363
   739
\brief Read \e Nauty format
kpeter@403
   740
kpeter@363
   741
Tool to read graphs from \e Nauty format data.
kpeter@363
   742
*/
kpeter@363
   743
kpeter@363
   744
/**
alpar@40
   745
@defgroup concept Concepts
alpar@40
   746
\brief Skeleton classes and concept checking classes
alpar@40
   747
kpeter@606
   748
This group contains the data/algorithm skeletons and concept checking
alpar@40
   749
classes implemented in LEMON.
alpar@40
   750
alpar@40
   751
The purpose of the classes in this group is fourfold.
alpar@209
   752
kpeter@318
   753
- These classes contain the documentations of the %concepts. In order
alpar@40
   754
  to avoid document multiplications, an implementation of a concept
alpar@40
   755
  simply refers to the corresponding concept class.
alpar@40
   756
alpar@40
   757
- These classes declare every functions, <tt>typedef</tt>s etc. an
kpeter@318
   758
  implementation of the %concepts should provide, however completely
alpar@40
   759
  without implementations and real data structures behind the
alpar@40
   760
  interface. On the other hand they should provide nothing else. All
alpar@40
   761
  the algorithms working on a data structure meeting a certain concept
alpar@40
   762
  should compile with these classes. (Though it will not run properly,
alpar@40
   763
  of course.) In this way it is easily to check if an algorithm
alpar@40
   764
  doesn't use any extra feature of a certain implementation.
alpar@40
   765
alpar@40
   766
- The concept descriptor classes also provide a <em>checker class</em>
kpeter@50
   767
  that makes it possible to check whether a certain implementation of a
alpar@40
   768
  concept indeed provides all the required features.
alpar@40
   769
alpar@40
   770
- Finally, They can serve as a skeleton of a new implementation of a concept.
alpar@40
   771
*/
alpar@40
   772
alpar@40
   773
/**
alpar@40
   774
@defgroup graph_concepts Graph Structure Concepts
alpar@40
   775
@ingroup concept
alpar@40
   776
\brief Skeleton and concept checking classes for graph structures
alpar@40
   777
kpeter@782
   778
This group contains the skeletons and concept checking classes of
kpeter@782
   779
graph structures.
alpar@40
   780
*/
alpar@40
   781
kpeter@314
   782
/**
kpeter@314
   783
@defgroup map_concepts Map Concepts
kpeter@314
   784
@ingroup concept
kpeter@314
   785
\brief Skeleton and concept checking classes for maps
kpeter@314
   786
kpeter@606
   787
This group contains the skeletons and concept checking classes of maps.
alpar@40
   788
*/
alpar@40
   789
alpar@40
   790
/**
kpeter@761
   791
@defgroup tools Standalone Utility Applications
kpeter@761
   792
kpeter@761
   793
Some utility applications are listed here.
kpeter@761
   794
kpeter@761
   795
The standard compilation procedure (<tt>./configure;make</tt>) will compile
kpeter@761
   796
them, as well.
kpeter@761
   797
*/
kpeter@761
   798
kpeter@761
   799
/**
alpar@40
   800
\anchor demoprograms
alpar@40
   801
kpeter@422
   802
@defgroup demos Demo Programs
alpar@40
   803
alpar@40
   804
Some demo programs are listed here. Their full source codes can be found in
alpar@40
   805
the \c demo subdirectory of the source tree.
alpar@40
   806
ladanyi@611
   807
In order to compile them, use the <tt>make demo</tt> or the
ladanyi@611
   808
<tt>make check</tt> commands.
alpar@40
   809
*/
alpar@40
   810
kpeter@422
   811
}