1 /* -*- mode: C++; indent-tabs-mode: nil; -*-
3 * This file is a part of LEMON, a generic C++ optimization library.
5 * Copyright (C) 2003-2010
6 * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
7 * (Egervary Research Group on Combinatorial Optimization, EGRES).
9 * Permission to use, modify and distribute this software is granted
10 * provided that this copyright notice appears in all copies. For
11 * precise terms see the accompanying LICENSE file.
13 * This software is provided "AS IS" with no warranty of any kind,
14 * express or implied, and with no claim as to its suitability for any
22 @defgroup datas Data Structures
23 This group contains the several data structures implemented in LEMON.
27 @defgroup graphs Graph Structures
29 \brief Graph structures implemented in LEMON.
31 The implementation of combinatorial algorithms heavily relies on
32 efficient graph implementations. LEMON offers data structures which are
33 planned to be easily used in an experimental phase of implementation studies,
34 and thereafter the program code can be made efficient by small modifications.
36 The most efficient implementation of diverse applications require the
37 usage of different physical graph implementations. These differences
38 appear in the size of graph we require to handle, memory or time usage
39 limitations or in the set of operations through which the graph can be
40 accessed. LEMON provides several physical graph structures to meet
41 the diverging requirements of the possible users. In order to save on
42 running time or on memory usage, some structures may fail to provide
43 some graph features like arc/edge or node deletion.
45 Alteration of standard containers need a very limited number of
46 operations, these together satisfy the everyday requirements.
47 In the case of graph structures, different operations are needed which do
48 not alter the physical graph, but gives another view. If some nodes or
49 arcs have to be hidden or the reverse oriented graph have to be used, then
50 this is the case. It also may happen that in a flow implementation
51 the residual graph can be accessed by another algorithm, or a node-set
52 is to be shrunk for another algorithm.
53 LEMON also provides a variety of graphs for these requirements called
54 \ref graph_adaptors "graph adaptors". Adaptors cannot be used alone but only
55 in conjunction with other graph representations.
57 You are free to use the graph structure that fit your requirements
58 the best, most graph algorithms and auxiliary data structures can be used
59 with any graph structure.
61 <b>See also:</b> \ref graph_concepts "Graph Structure Concepts".
65 @defgroup graph_adaptors Adaptor Classes for Graphs
67 \brief Adaptor classes for digraphs and graphs
69 This group contains several useful adaptor classes for digraphs and graphs.
71 The main parts of LEMON are the different graph structures, generic
72 graph algorithms, graph concepts, which couple them, and graph
73 adaptors. While the previous notions are more or less clear, the
74 latter one needs further explanation. Graph adaptors are graph classes
75 which serve for considering graph structures in different ways.
77 A short example makes this much clearer. Suppose that we have an
78 instance \c g of a directed graph type, say ListDigraph and an algorithm
80 template <typename Digraph>
81 int algorithm(const Digraph&);
83 is needed to run on the reverse oriented graph. It may be expensive
84 (in time or in memory usage) to copy \c g with the reversed
85 arcs. In this case, an adaptor class is used, which (according
86 to LEMON \ref concepts::Digraph "digraph concepts") works as a digraph.
87 The adaptor uses the original digraph structure and digraph operations when
88 methods of the reversed oriented graph are called. This means that the adaptor
89 have minor memory usage, and do not perform sophisticated algorithmic
90 actions. The purpose of it is to give a tool for the cases when a
91 graph have to be used in a specific alteration. If this alteration is
92 obtained by a usual construction like filtering the node or the arc set or
93 considering a new orientation, then an adaptor is worthwhile to use.
94 To come back to the reverse oriented graph, in this situation
96 template<typename Digraph> class ReverseDigraph;
98 template class can be used. The code looks as follows
101 ReverseDigraph<ListDigraph> rg(g);
102 int result = algorithm(rg);
104 During running the algorithm, the original digraph \c g is untouched.
105 This techniques give rise to an elegant code, and based on stable
106 graph adaptors, complex algorithms can be implemented easily.
108 In flow, circulation and matching problems, the residual
109 graph is of particular importance. Combining an adaptor implementing
110 this with shortest path algorithms or minimum mean cycle algorithms,
111 a range of weighted and cardinality optimization algorithms can be
112 obtained. For other examples, the interested user is referred to the
113 detailed documentation of particular adaptors.
115 Since the adaptor classes conform to the \ref graph_concepts "graph concepts",
116 an adaptor can even be applied to another one.
117 The following image illustrates a situation when a \ref SubDigraph adaptor
118 is applied on a digraph and \ref Undirector is applied on the subgraph.
120 \image html adaptors2.png
121 \image latex adaptors2.eps "Using graph adaptors" width=\textwidth
123 The behavior of graph adaptors can be very different. Some of them keep
124 capabilities of the original graph while in other cases this would be
125 meaningless. This means that the concepts that they meet depend
126 on the graph adaptor, and the wrapped graph.
127 For example, if an arc of a reversed digraph is deleted, this is carried
128 out by deleting the corresponding arc of the original digraph, thus the
129 adaptor modifies the original digraph.
130 However in case of a residual digraph, this operation has no sense.
132 Let us stand one more example here to simplify your work.
133 ReverseDigraph has constructor
135 ReverseDigraph(Digraph& digraph);
137 This means that in a situation, when a <tt>const %ListDigraph&</tt>
138 reference to a graph is given, then it have to be instantiated with
139 <tt>Digraph=const %ListDigraph</tt>.
141 int algorithm1(const ListDigraph& g) {
142 ReverseDigraph<const ListDigraph> rg(g);
143 return algorithm2(rg);
151 \brief Map structures implemented in LEMON.
153 This group contains the map structures implemented in LEMON.
155 LEMON provides several special purpose maps and map adaptors that e.g. combine
156 new maps from existing ones.
158 <b>See also:</b> \ref map_concepts "Map Concepts".
162 @defgroup graph_maps Graph Maps
164 \brief Special graph-related maps.
166 This group contains maps that are specifically designed to assign
167 values to the nodes and arcs/edges of graphs.
169 If you are looking for the standard graph maps (\c NodeMap, \c ArcMap,
170 \c EdgeMap), see the \ref graph_concepts "Graph Structure Concepts".
174 \defgroup map_adaptors Map Adaptors
176 \brief Tools to create new maps from existing ones
178 This group contains map adaptors that are used to create "implicit"
179 maps from other maps.
181 Most of them are \ref concepts::ReadMap "read-only maps".
182 They can make arithmetic and logical operations between one or two maps
183 (negation, shifting, addition, multiplication, logical 'and', 'or',
184 'not' etc.) or e.g. convert a map to another one of different Value type.
186 The typical usage of this classes is passing implicit maps to
187 algorithms. If a function type algorithm is called then the function
188 type map adaptors can be used comfortable. For example let's see the
189 usage of map adaptors with the \c graphToEps() function.
191 Color nodeColor(int deg) {
193 return Color(0.5, 0.0, 0.5);
194 } else if (deg == 1) {
195 return Color(1.0, 0.5, 1.0);
197 return Color(0.0, 0.0, 0.0);
201 Digraph::NodeMap<int> degree_map(graph);
203 graphToEps(graph, "graph.eps")
204 .coords(coords).scaleToA4().undirected()
205 .nodeColors(composeMap(functorToMap(nodeColor), degree_map))
208 The \c functorToMap() function makes an \c int to \c Color map from the
209 \c nodeColor() function. The \c composeMap() compose the \c degree_map
210 and the previously created map. The composed map is a proper function to
211 get the color of each node.
213 The usage with class type algorithms is little bit harder. In this
214 case the function type map adaptors can not be used, because the
215 function map adaptors give back temporary objects.
219 typedef Digraph::ArcMap<double> DoubleArcMap;
220 DoubleArcMap length(graph);
221 DoubleArcMap speed(graph);
223 typedef DivMap<DoubleArcMap, DoubleArcMap> TimeMap;
224 TimeMap time(length, speed);
226 Dijkstra<Digraph, TimeMap> dijkstra(graph, time);
227 dijkstra.run(source, target);
229 We have a length map and a maximum speed map on the arcs of a digraph.
230 The minimum time to pass the arc can be calculated as the division of
231 the two maps which can be done implicitly with the \c DivMap template
232 class. We use the implicit minimum time map as the length map of the
233 \c Dijkstra algorithm.
237 @defgroup paths Path Structures
239 \brief %Path structures implemented in LEMON.
241 This group contains the path structures implemented in LEMON.
243 LEMON provides flexible data structures to work with paths.
244 All of them have similar interfaces and they can be copied easily with
245 assignment operators and copy constructors. This makes it easy and
246 efficient to have e.g. the Dijkstra algorithm to store its result in
247 any kind of path structure.
249 \sa \ref concepts::Path "Path concept"
253 @defgroup heaps Heap Structures
255 \brief %Heap structures implemented in LEMON.
257 This group contains the heap structures implemented in LEMON.
259 LEMON provides several heap classes. They are efficient implementations
260 of the abstract data type \e priority \e queue. They store items with
261 specified values called \e priorities in such a way that finding and
262 removing the item with minimum priority are efficient.
263 The basic operations are adding and erasing items, changing the priority
266 Heaps are crucial in several algorithms, such as Dijkstra and Prim.
267 The heap implementations have the same interface, thus any of them can be
268 used easily in such algorithms.
270 \sa \ref concepts::Heap "Heap concept"
274 @defgroup auxdat Auxiliary Data Structures
276 \brief Auxiliary data structures implemented in LEMON.
278 This group contains some data structures implemented in LEMON in
279 order to make it easier to implement combinatorial algorithms.
283 @defgroup geomdat Geometric Data Structures
285 \brief Geometric data structures implemented in LEMON.
287 This group contains geometric data structures implemented in LEMON.
289 - \ref lemon::dim2::Point "dim2::Point" implements a two dimensional
290 vector with the usual operations.
291 - \ref lemon::dim2::Box "dim2::Box" can be used to determine the
292 rectangular bounding box of a set of \ref lemon::dim2::Point
297 @defgroup matrices Matrices
299 \brief Two dimensional data storages implemented in LEMON.
301 This group contains two dimensional data storages implemented in LEMON.
305 @defgroup algs Algorithms
306 \brief This group contains the several algorithms
307 implemented in LEMON.
309 This group contains the several algorithms
310 implemented in LEMON.
314 @defgroup search Graph Search
316 \brief Common graph search algorithms.
318 This group contains the common graph search algorithms, namely
319 \e breadth-first \e search (BFS) and \e depth-first \e search (DFS)
320 \ref clrs01algorithms.
324 @defgroup shortest_path Shortest Path Algorithms
326 \brief Algorithms for finding shortest paths.
328 This group contains the algorithms for finding shortest paths in digraphs
329 \ref clrs01algorithms.
331 - \ref Dijkstra algorithm for finding shortest paths from a source node
332 when all arc lengths are non-negative.
333 - \ref BellmanFord "Bellman-Ford" algorithm for finding shortest paths
334 from a source node when arc lenghts can be either positive or negative,
335 but the digraph should not contain directed cycles with negative total
337 - \ref FloydWarshall "Floyd-Warshall" and \ref Johnson "Johnson" algorithms
338 for solving the \e all-pairs \e shortest \e paths \e problem when arc
339 lenghts can be either positive or negative, but the digraph should
340 not contain directed cycles with negative total length.
341 - \ref Suurballe A successive shortest path algorithm for finding
342 arc-disjoint paths between two nodes having minimum total length.
346 @defgroup spantree Minimum Spanning Tree Algorithms
348 \brief Algorithms for finding minimum cost spanning trees and arborescences.
350 This group contains the algorithms for finding minimum cost spanning
351 trees and arborescences \ref clrs01algorithms.
355 @defgroup max_flow Maximum Flow Algorithms
357 \brief Algorithms for finding maximum flows.
359 This group contains the algorithms for finding maximum flows and
360 feasible circulations \ref clrs01algorithms, \ref amo93networkflows.
362 The \e maximum \e flow \e problem is to find a flow of maximum value between
363 a single source and a single target. Formally, there is a \f$G=(V,A)\f$
364 digraph, a \f$cap: A\rightarrow\mathbf{R}^+_0\f$ capacity function and
365 \f$s, t \in V\f$ source and target nodes.
366 A maximum flow is an \f$f: A\rightarrow\mathbf{R}^+_0\f$ solution of the
367 following optimization problem.
369 \f[ \max\sum_{sv\in A} f(sv) - \sum_{vs\in A} f(vs) \f]
370 \f[ \sum_{uv\in A} f(uv) = \sum_{vu\in A} f(vu)
371 \quad \forall u\in V\setminus\{s,t\} \f]
372 \f[ 0 \leq f(uv) \leq cap(uv) \quad \forall uv\in A \f]
374 LEMON contains several algorithms for solving maximum flow problems:
375 - \ref EdmondsKarp Edmonds-Karp algorithm
376 \ref edmondskarp72theoretical.
377 - \ref Preflow Goldberg-Tarjan's preflow push-relabel algorithm
378 \ref goldberg88newapproach.
379 - \ref DinitzSleatorTarjan Dinitz's blocking flow algorithm with dynamic trees
380 \ref dinic70algorithm, \ref sleator83dynamic.
381 - \ref GoldbergTarjan !Preflow push-relabel algorithm with dynamic trees
382 \ref goldberg88newapproach, \ref sleator83dynamic.
384 In most cases the \ref Preflow algorithm provides the
385 fastest method for computing a maximum flow. All implementations
386 also provide functions to query the minimum cut, which is the dual
387 problem of maximum flow.
389 \ref Circulation is a preflow push-relabel algorithm implemented directly
390 for finding feasible circulations, which is a somewhat different problem,
391 but it is strongly related to maximum flow.
392 For more information, see \ref Circulation.
396 @defgroup min_cost_flow_algs Minimum Cost Flow Algorithms
399 \brief Algorithms for finding minimum cost flows and circulations.
401 This group contains the algorithms for finding minimum cost flows and
402 circulations \ref amo93networkflows. For more information about this
403 problem and its dual solution, see: \ref min_cost_flow
404 "Minimum Cost Flow Problem".
406 LEMON contains several algorithms for this problem.
407 - \ref NetworkSimplex Primal Network Simplex algorithm with various
408 pivot strategies \ref dantzig63linearprog, \ref kellyoneill91netsimplex.
409 - \ref CostScaling Cost Scaling algorithm based on push/augment and
410 relabel operations \ref goldberg90approximation, \ref goldberg97efficient,
411 \ref bunnagel98efficient.
412 - \ref CapacityScaling Capacity Scaling algorithm based on the successive
413 shortest path method \ref edmondskarp72theoretical.
414 - \ref CycleCanceling Cycle-Canceling algorithms, two of which are
415 strongly polynomial \ref klein67primal, \ref goldberg89cyclecanceling.
417 In general, \ref NetworkSimplex and \ref CostScaling are the most efficient
419 \ref NetworkSimplex is usually the fastest on relatively small graphs (up to
420 several thousands of nodes) and on dense graphs, while \ref CostScaling is
421 typically more efficient on large graphs (e.g. hundreds of thousands of
422 nodes or above), especially if they are sparse.
423 However, other algorithms could be faster in special cases.
424 For example, if the total supply and/or capacities are rather small,
425 \ref CapacityScaling is usually the fastest algorithm (without effective scaling).
427 These classes are intended to be used with integer-valued input data
428 (capacities, supply values, and costs), except for \ref CapacityScaling,
429 which is capable of handling real-valued arc costs (other numerical
430 data are required to be integer).
434 @defgroup min_cut Minimum Cut Algorithms
437 \brief Algorithms for finding minimum cut in graphs.
439 This group contains the algorithms for finding minimum cut in graphs.
441 The \e minimum \e cut \e problem is to find a non-empty and non-complete
442 \f$X\f$ subset of the nodes with minimum overall capacity on
443 outgoing arcs. Formally, there is a \f$G=(V,A)\f$ digraph, a
444 \f$cap: A\rightarrow\mathbf{R}^+_0\f$ capacity function. The minimum
445 cut is the \f$X\f$ solution of the next optimization problem:
447 \f[ \min_{X \subset V, X\not\in \{\emptyset, V\}}
448 \sum_{uv\in A: u\in X, v\not\in X}cap(uv) \f]
450 LEMON contains several algorithms related to minimum cut problems:
452 - \ref HaoOrlin "Hao-Orlin algorithm" for calculating minimum cut
454 - \ref NagamochiIbaraki "Nagamochi-Ibaraki algorithm" for
455 calculating minimum cut in undirected graphs.
456 - \ref GomoryHu "Gomory-Hu tree computation" for calculating
457 all-pairs minimum cut in undirected graphs.
459 If you want to find minimum cut just between two distinict nodes,
460 see the \ref max_flow "maximum flow problem".
464 @defgroup min_mean_cycle Minimum Mean Cycle Algorithms
466 \brief Algorithms for finding minimum mean cycles.
468 This group contains the algorithms for finding minimum mean cycles
469 \ref amo93networkflows, \ref karp78characterization.
471 The \e minimum \e mean \e cycle \e problem is to find a directed cycle
472 of minimum mean length (cost) in a digraph.
473 The mean length of a cycle is the average length of its arcs, i.e. the
474 ratio between the total length of the cycle and the number of arcs on it.
476 This problem has an important connection to \e conservative \e length
477 \e functions, too. A length function on the arcs of a digraph is called
478 conservative if and only if there is no directed cycle of negative total
479 length. For an arbitrary length function, the negative of the minimum
480 cycle mean is the smallest \f$\epsilon\f$ value so that increasing the
481 arc lengths uniformly by \f$\epsilon\f$ results in a conservative length
484 LEMON contains three algorithms for solving the minimum mean cycle problem:
485 - \ref KarpMmc Karp's original algorithm \ref karp78characterization.
486 - \ref HartmannOrlinMmc Hartmann-Orlin's algorithm, which is an improved
487 version of Karp's algorithm \ref hartmann93finding.
488 - \ref HowardMmc Howard's policy iteration algorithm
489 \ref dasdan98minmeancycle, \ref dasdan04experimental.
491 In practice, the \ref HowardMmc "Howard" algorithm turned out to be by far the
492 most efficient one, though the best known theoretical bound on its running
494 Both \ref KarpMmc "Karp" and \ref HartmannOrlinMmc "Hartmann-Orlin" algorithms
495 run in time O(ne) and use space O(n<sup>2</sup>+e).
499 @defgroup matching Matching Algorithms
501 \brief Algorithms for finding matchings in graphs and bipartite graphs.
503 This group contains the algorithms for calculating
504 matchings in graphs and bipartite graphs. The general matching problem is
505 finding a subset of the edges for which each node has at most one incident
508 There are several different algorithms for calculate matchings in
509 graphs. The matching problems in bipartite graphs are generally
510 easier than in general graphs. The goal of the matching optimization
511 can be finding maximum cardinality, maximum weight or minimum cost
512 matching. The search can be constrained to find perfect or
513 maximum cardinality matching.
515 The matching algorithms implemented in LEMON:
516 - \ref MaxBipartiteMatching Hopcroft-Karp augmenting path algorithm
517 for calculating maximum cardinality matching in bipartite graphs.
518 - \ref PrBipartiteMatching Push-relabel algorithm
519 for calculating maximum cardinality matching in bipartite graphs.
520 - \ref MaxWeightedBipartiteMatching
521 Successive shortest path algorithm for calculating maximum weighted
522 matching and maximum weighted bipartite matching in bipartite graphs.
523 - \ref MinCostMaxBipartiteMatching
524 Successive shortest path algorithm for calculating minimum cost maximum
525 matching in bipartite graphs.
526 - \ref MaxMatching Edmond's blossom shrinking algorithm for calculating
527 maximum cardinality matching in general graphs.
528 - \ref MaxWeightedMatching Edmond's blossom shrinking algorithm for calculating
529 maximum weighted matching in general graphs.
530 - \ref MaxWeightedPerfectMatching
531 Edmond's blossom shrinking algorithm for calculating maximum weighted
532 perfect matching in general graphs.
533 - \ref MaxFractionalMatching Push-relabel algorithm for calculating
534 maximum cardinality fractional matching in general graphs.
535 - \ref MaxWeightedFractionalMatching Augmenting path algorithm for calculating
536 maximum weighted fractional matching in general graphs.
537 - \ref MaxWeightedPerfectFractionalMatching
538 Augmenting path algorithm for calculating maximum weighted
539 perfect fractional matching in general graphs.
541 \image html matching.png
542 \image latex matching.eps "Min Cost Perfect Matching" width=\textwidth
546 @defgroup graph_properties Connectivity and Other Graph Properties
548 \brief Algorithms for discovering the graph properties
550 This group contains the algorithms for discovering the graph properties
551 like connectivity, bipartiteness, euler property, simplicity etc.
553 \image html connected_components.png
554 \image latex connected_components.eps "Connected components" width=\textwidth
558 @defgroup planar Planar Embedding and Drawing
560 \brief Algorithms for planarity checking, embedding and drawing
562 This group contains the algorithms for planarity checking,
563 embedding and drawing.
565 \image html planar.png
566 \image latex planar.eps "Plane graph" width=\textwidth
570 @defgroup tsp Traveling Salesman Problem
572 \brief Algorithms for the symmetric traveling salesman problem
574 This group contains basic heuristic algorithms for the the symmetric
575 \e traveling \e salesman \e problem (TSP).
576 Given an \ref FullGraph "undirected full graph" with a cost map on its edges,
577 the problem is to find a shortest possible tour that visits each node exactly
578 once (i.e. the minimum cost Hamiltonian cycle).
580 These TSP algorithms are intended to be used with a \e metric \e cost
581 \e function, i.e. the edge costs should satisfy the triangle inequality.
582 Otherwise the algorithms could yield worse results.
584 LEMON provides five well-known heuristics for solving symmetric TSP:
585 - \ref NearestNeighborTsp Neareast neighbor algorithm
586 - \ref GreedyTsp Greedy algorithm
587 - \ref InsertionTsp Insertion heuristic (with four selection methods)
588 - \ref ChristofidesTsp Christofides algorithm
589 - \ref Opt2Tsp 2-opt algorithm
591 \ref NearestNeighborTsp, \ref GreedyTsp, and \ref InsertionTsp are the fastest
592 solution methods. Furthermore, \ref InsertionTsp is usually quite effective.
594 \ref ChristofidesTsp is somewhat slower, but it has the best guaranteed
595 approximation factor: 3/2.
597 \ref Opt2Tsp usually provides the best results in practice, but
598 it is the slowest method. It can also be used to improve given tours,
599 for example, the results of other algorithms.
602 \image latex tsp.eps "Traveling salesman problem" width=\textwidth
606 @defgroup approx_algs Approximation Algorithms
608 \brief Approximation algorithms.
610 This group contains the approximation and heuristic algorithms
611 implemented in LEMON.
613 <b>Maximum Clique Problem</b>
614 - \ref GrossoLocatelliPullanMc An efficient heuristic algorithm of
615 Grosso, Locatelli, and Pullan.
619 @defgroup auxalg Auxiliary Algorithms
621 \brief Auxiliary algorithms implemented in LEMON.
623 This group contains some algorithms implemented in LEMON
624 in order to make it easier to implement complex algorithms.
628 @defgroup gen_opt_group General Optimization Tools
629 \brief This group contains some general optimization frameworks
630 implemented in LEMON.
632 This group contains some general optimization frameworks
633 implemented in LEMON.
637 @defgroup lp_group LP and MIP Solvers
638 @ingroup gen_opt_group
639 \brief LP and MIP solver interfaces for LEMON.
641 This group contains LP and MIP solver interfaces for LEMON.
642 Various LP solvers could be used in the same manner with this
643 high-level interface.
645 The currently supported solvers are \ref glpk, \ref clp, \ref cbc,
646 \ref cplex, \ref soplex.
650 @defgroup lp_utils Tools for Lp and Mip Solvers
652 \brief Helper tools to the Lp and Mip solvers.
654 This group adds some helper tools to general optimization framework
655 implemented in LEMON.
659 @defgroup metah Metaheuristics
660 @ingroup gen_opt_group
661 \brief Metaheuristics for LEMON library.
663 This group contains some metaheuristic optimization tools.
667 @defgroup utils Tools and Utilities
668 \brief Tools and utilities for programming in LEMON
670 Tools and utilities for programming in LEMON.
674 @defgroup gutils Basic Graph Utilities
676 \brief Simple basic graph utilities.
678 This group contains some simple basic graph utilities.
682 @defgroup misc Miscellaneous Tools
684 \brief Tools for development, debugging and testing.
686 This group contains several useful tools for development,
687 debugging and testing.
691 @defgroup timecount Time Measuring and Counting
693 \brief Simple tools for measuring the performance of algorithms.
695 This group contains simple tools for measuring the performance
700 @defgroup exceptions Exceptions
702 \brief Exceptions defined in LEMON.
704 This group contains the exceptions defined in LEMON.
708 @defgroup io_group Input-Output
709 \brief Graph Input-Output methods
711 This group contains the tools for importing and exporting graphs
712 and graph related data. Now it supports the \ref lgf-format
713 "LEMON Graph Format", the \c DIMACS format and the encapsulated
714 postscript (EPS) format.
718 @defgroup lemon_io LEMON Graph Format
720 \brief Reading and writing LEMON Graph Format.
722 This group contains methods for reading and writing
723 \ref lgf-format "LEMON Graph Format".
727 @defgroup eps_io Postscript Exporting
729 \brief General \c EPS drawer and graph exporter
731 This group contains general \c EPS drawing methods and special
732 graph exporting tools.
734 \image html graph_to_eps.png
738 @defgroup dimacs_group DIMACS Format
740 \brief Read and write files in DIMACS format
742 Tools to read a digraph from or write it to a file in DIMACS format data.
746 @defgroup nauty_group NAUTY Format
748 \brief Read \e Nauty format
750 Tool to read graphs from \e Nauty format data.
754 @defgroup concept Concepts
755 \brief Skeleton classes and concept checking classes
757 This group contains the data/algorithm skeletons and concept checking
758 classes implemented in LEMON.
760 The purpose of the classes in this group is fourfold.
762 - These classes contain the documentations of the %concepts. In order
763 to avoid document multiplications, an implementation of a concept
764 simply refers to the corresponding concept class.
766 - These classes declare every functions, <tt>typedef</tt>s etc. an
767 implementation of the %concepts should provide, however completely
768 without implementations and real data structures behind the
769 interface. On the other hand they should provide nothing else. All
770 the algorithms working on a data structure meeting a certain concept
771 should compile with these classes. (Though it will not run properly,
772 of course.) In this way it is easily to check if an algorithm
773 doesn't use any extra feature of a certain implementation.
775 - The concept descriptor classes also provide a <em>checker class</em>
776 that makes it possible to check whether a certain implementation of a
777 concept indeed provides all the required features.
779 - Finally, They can serve as a skeleton of a new implementation of a concept.
783 @defgroup graph_concepts Graph Structure Concepts
785 \brief Skeleton and concept checking classes for graph structures
787 This group contains the skeletons and concept checking classes of
792 @defgroup map_concepts Map Concepts
794 \brief Skeleton and concept checking classes for maps
796 This group contains the skeletons and concept checking classes of maps.
800 @defgroup tools Standalone Utility Applications
802 Some utility applications are listed here.
804 The standard compilation procedure (<tt>./configure;make</tt>) will compile
811 @defgroup demos Demo Programs
813 Some demo programs are listed here. Their full source codes can be found in
814 the \c demo subdirectory of the source tree.
816 In order to compile them, use the <tt>make demo</tt> or the
817 <tt>make check</tt> commands.