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1/* -*- mode: C++; indent-tabs-mode: nil; -*-
2 *
3 * This file is a part of LEMON, a generic C++ optimization library.
4 *
6 * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
7 * (Egervary Research Group on Combinatorial Optimization, EGRES).
8 *
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.
12 *
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
15 * purpose.
16 *
17 */
18
19namespace lemon {
20
21/**
22@defgroup datas Data Structures
23This group contains the several data structures implemented in LEMON.
24*/
25
26/**
27@defgroup graphs Graph Structures
28@ingroup datas
29\brief Graph structures implemented in LEMON.
30
31The implementation of combinatorial algorithms heavily relies on
32efficient graph implementations. LEMON offers data structures which are
33planned to be easily used in an experimental phase of implementation studies,
34and thereafter the program code can be made efficient by small modifications.
35
36The most efficient implementation of diverse applications require the
37usage of different physical graph implementations. These differences
38appear in the size of graph we require to handle, memory or time usage
39limitations or in the set of operations through which the graph can be
40accessed.  LEMON provides several physical graph structures to meet
41the diverging requirements of the possible users.  In order to save on
42running time or on memory usage, some structures may fail to provide
43some graph features like arc/edge or node deletion.
44
45Alteration of standard containers need a very limited number of
46operations, these together satisfy the everyday requirements.
47In the case of graph structures, different operations are needed which do
48not alter the physical graph, but gives another view. If some nodes or
49arcs have to be hidden or the reverse oriented graph have to be used, then
50this is the case. It also may happen that in a flow implementation
51the residual graph can be accessed by another algorithm, or a node-set
52is to be shrunk for another algorithm.
53LEMON also provides a variety of graphs for these requirements called
55in conjunction with other graph representations.
56
57You are free to use the graph structure that fit your requirements
58the best, most graph algorithms and auxiliary data structures can be used
59with any graph structure.
60
62*/
63
64/**
66@ingroup graphs
67\brief Adaptor classes for digraphs and graphs
68
69This group contains several useful adaptor classes for digraphs and graphs.
70
71The main parts of LEMON are the different graph structures, generic
72graph algorithms, graph concepts, which couple them, and graph
73adaptors. While the previous notions are more or less clear, the
74latter one needs further explanation. Graph adaptors are graph classes
75which serve for considering graph structures in different ways.
76
77A short example makes this much clearer.  Suppose that we have an
78instance \c g of a directed graph type, say ListDigraph and an algorithm
79\code
80template <typename Digraph>
81int algorithm(const Digraph&);
82\endcode
83is 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
85arcs.  In this case, an adaptor class is used, which (according
86to LEMON \ref concepts::Digraph "digraph concepts") works as a digraph.
87The adaptor uses the original digraph structure and digraph operations when
88methods of the reversed oriented graph are called.  This means that the adaptor
89have minor memory usage, and do not perform sophisticated algorithmic
90actions.  The purpose of it is to give a tool for the cases when a
91graph have to be used in a specific alteration.  If this alteration is
92obtained by a usual construction like filtering the node or the arc set or
93considering a new orientation, then an adaptor is worthwhile to use.
94To come back to the reverse oriented graph, in this situation
95\code
96template<typename Digraph> class ReverseDigraph;
97\endcode
98template class can be used. The code looks as follows
99\code
100ListDigraph g;
101ReverseDigraph<ListDigraph> rg(g);
102int result = algorithm(rg);
103\endcode
104During running the algorithm, the original digraph \c g is untouched.
105This techniques give rise to an elegant code, and based on stable
106graph adaptors, complex algorithms can be implemented easily.
107
108In flow, circulation and matching problems, the residual
109graph is of particular importance. Combining an adaptor implementing
110this with shortest path algorithms or minimum mean cycle algorithms,
111a range of weighted and cardinality optimization algorithms can be
112obtained. For other examples, the interested user is referred to the
114
115Since the adaptor classes conform to the \ref graph_concepts "graph concepts",
116an adaptor can even be applied to another one.
117The following image illustrates a situation when a \ref SubDigraph adaptor
118is applied on a digraph and \ref Undirector is applied on the subgraph.
119
122
123The behavior of graph adaptors can be very different. Some of them keep
124capabilities of the original graph while in other cases this would be
125meaningless. This means that the concepts that they meet depend
126on the graph adaptor, and the wrapped graph.
127For example, if an arc of a reversed digraph is deleted, this is carried
128out by deleting the corresponding arc of the original digraph, thus the
130However in case of a residual digraph, this operation has no sense.
131
132Let us stand one more example here to simplify your work.
133ReverseDigraph has constructor
134\code
135ReverseDigraph(Digraph& digraph);
136\endcode
137This means that in a situation, when a <tt>const %ListDigraph&</tt>
138reference to a graph is given, then it have to be instantiated with
139<tt>Digraph=const %ListDigraph</tt>.
140\code
141int algorithm1(const ListDigraph& g) {
142  ReverseDigraph<const ListDigraph> rg(g);
143  return algorithm2(rg);
144}
145\endcode
146*/
147
148/**
149@defgroup maps Maps
150@ingroup datas
151\brief Map structures implemented in LEMON.
152
153This group contains the map structures implemented in LEMON.
154
155LEMON provides several special purpose maps and map adaptors that e.g. combine
156new maps from existing ones.
157
159*/
160
161/**
162@defgroup graph_maps Graph Maps
163@ingroup maps
164\brief Special graph-related maps.
165
166This group contains maps that are specifically designed to assign
167values to the nodes and arcs/edges of graphs.
168
169If you are looking for the standard graph maps (\c NodeMap, \c ArcMap,
170\c EdgeMap), see the \ref graph_concepts "Graph Structure Concepts".
171*/
172
173/**
175\ingroup maps
176\brief Tools to create new maps from existing ones
177
178This group contains map adaptors that are used to create "implicit"
179maps from other maps.
180
182They 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.
185
186The typical usage of this classes is passing implicit maps to
187algorithms.  If a function type algorithm is called then the function
188type map adaptors can be used comfortable. For example let's see the
189usage of map adaptors with the \c graphToEps() function.
190\code
191  Color nodeColor(int deg) {
192    if (deg >= 2) {
193      return Color(0.5, 0.0, 0.5);
194    } else if (deg == 1) {
195      return Color(1.0, 0.5, 1.0);
196    } else {
197      return Color(0.0, 0.0, 0.0);
198    }
199  }
200
201  Digraph::NodeMap<int> degree_map(graph);
202
203  graphToEps(graph, "graph.eps")
204    .coords(coords).scaleToA4().undirected()
205    .nodeColors(composeMap(functorToMap(nodeColor), degree_map))
206    .run();
207\endcode
208The \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
210and the previously created map. The composed map is a proper function to
211get the color of each node.
212
213The usage with class type algorithms is little bit harder. In this
214case the function type map adaptors can not be used, because the
215function map adaptors give back temporary objects.
216\code
217  Digraph graph;
218
219  typedef Digraph::ArcMap<double> DoubleArcMap;
220  DoubleArcMap length(graph);
221  DoubleArcMap speed(graph);
222
223  typedef DivMap<DoubleArcMap, DoubleArcMap> TimeMap;
224  TimeMap time(length, speed);
225
226  Dijkstra<Digraph, TimeMap> dijkstra(graph, time);
227  dijkstra.run(source, target);
228\endcode
229We have a length map and a maximum speed map on the arcs of a digraph.
230The minimum time to pass the arc can be calculated as the division of
231the two maps which can be done implicitly with the \c DivMap template
232class. We use the implicit minimum time map as the length map of the
233\c Dijkstra algorithm.
234*/
235
236/**
237@defgroup paths Path Structures
238@ingroup datas
239\brief %Path structures implemented in LEMON.
240
241This group contains the path structures implemented in LEMON.
242
243LEMON provides flexible data structures to work with paths.
244All of them have similar interfaces and they can be copied easily with
245assignment operators and copy constructors. This makes it easy and
246efficient to have e.g. the Dijkstra algorithm to store its result in
247any kind of path structure.
248
249\sa \ref concepts::Path "Path concept"
250*/
251
252/**
253@defgroup heaps Heap Structures
254@ingroup datas
255\brief %Heap structures implemented in LEMON.
256
257This group contains the heap structures implemented in LEMON.
258
259LEMON provides several heap classes. They are efficient implementations
260of the abstract data type \e priority \e queue. They store items with
261specified values called \e priorities in such a way that finding and
262removing the item with minimum priority are efficient.
263The basic operations are adding and erasing items, changing the priority
264of an item, etc.
265
266Heaps are crucial in several algorithms, such as Dijkstra and Prim.
267The heap implementations have the same interface, thus any of them can be
268used easily in such algorithms.
269
270\sa \ref concepts::Heap "Heap concept"
271*/
272
273/**
274@defgroup auxdat Auxiliary Data Structures
275@ingroup datas
276\brief Auxiliary data structures implemented in LEMON.
277
278This group contains some data structures implemented in LEMON in
279order to make it easier to implement combinatorial algorithms.
280*/
281
282/**
283@defgroup geomdat Geometric Data Structures
284@ingroup auxdat
285\brief Geometric data structures implemented in LEMON.
286
287This group contains geometric data structures implemented in LEMON.
288
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
293   "dim2::Point"'s.
294*/
295
296/**
297@defgroup matrices Matrices
298@ingroup auxdat
299\brief Two dimensional data storages implemented in LEMON.
300
301This group contains two dimensional data storages implemented in LEMON.
302*/
303
304/**
305@defgroup algs Algorithms
306\brief This group contains the several algorithms
307implemented in LEMON.
308
309This group contains the several algorithms
310implemented in LEMON.
311*/
312
313/**
314@defgroup search Graph Search
315@ingroup algs
316\brief Common graph search algorithms.
317
318This group contains the common graph search algorithms, namely
319\e breadth-first \e search (BFS) and \e depth-first \e search (DFS)
320\cite clrs01algorithms.
321*/
322
323/**
324@defgroup shortest_path Shortest Path Algorithms
325@ingroup algs
326\brief Algorithms for finding shortest paths.
327
328This group contains the algorithms for finding shortest paths in digraphs
329\cite clrs01algorithms.
330
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
336   length.
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.
343*/
344
345/**
346@defgroup spantree Minimum Spanning Tree Algorithms
347@ingroup algs
348\brief Algorithms for finding minimum cost spanning trees and arborescences.
349
350This group contains the algorithms for finding minimum cost spanning
351trees and arborescences \cite clrs01algorithms.
352*/
353
354/**
355@defgroup max_flow Maximum Flow Algorithms
356@ingroup algs
357\brief Algorithms for finding maximum flows.
358
359This group contains the algorithms for finding maximum flows and
360feasible circulations \cite clrs01algorithms, \cite amo93networkflows.
361
362The \e maximum \e flow \e problem is to find a flow of maximum value between
363a single source and a single target. Formally, there is a \f$G=(V,A)\f$
364digraph, a \f$cap: A\rightarrow\mathbf{R}^+_0\f$ capacity function and
365\f$s, t \in V\f$ source and target nodes.
366A maximum flow is an \f$f: A\rightarrow\mathbf{R}^+_0\f$ solution of the
367following optimization problem.
368
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]
373
374LEMON contains several algorithms for solving maximum flow problems:
375- \ref EdmondsKarp Edmonds-Karp algorithm
376  \cite edmondskarp72theoretical.
377- \ref Preflow Goldberg-Tarjan's preflow push-relabel algorithm
378  \cite goldberg88newapproach.
379- \ref DinitzSleatorTarjan Dinitz's blocking flow algorithm with dynamic trees
380  \cite dinic70algorithm, \cite sleator83dynamic.
381- \ref GoldbergTarjan !Preflow push-relabel algorithm with dynamic trees
382  \cite goldberg88newapproach, \cite sleator83dynamic.
383
384In most cases the \ref Preflow algorithm provides the
385fastest method for computing a maximum flow. All implementations
386also provide functions to query the minimum cut, which is the dual
387problem of maximum flow.
388
389\ref Circulation is a preflow push-relabel algorithm implemented directly
390for finding feasible circulations, which is a somewhat different problem,
391but it is strongly related to maximum flow.
393*/
394
395/**
396@defgroup min_cost_flow_algs Minimum Cost Flow Algorithms
397@ingroup algs
398
399\brief Algorithms for finding minimum cost flows and circulations.
400
401This group contains the algorithms for finding minimum cost flows and
403problem and its dual solution, see: \ref min_cost_flow
404"Minimum Cost Flow Problem".
405
406LEMON contains several algorithms for this problem.
407 - \ref NetworkSimplex Primal Network Simplex algorithm with various
408   pivot strategies \cite dantzig63linearprog, \cite kellyoneill91netsimplex.
409 - \ref CostScaling Cost Scaling algorithm based on push/augment and
410   relabel operations \cite goldberg90approximation, \cite goldberg97efficient,
411   \cite bunnagel98efficient.
412 - \ref CapacityScaling Capacity Scaling algorithm based on the successive
413   shortest path method \cite edmondskarp72theoretical.
414 - \ref CycleCanceling Cycle-Canceling algorithms, two of which are
415   strongly polynomial \cite klein67primal, \cite goldberg89cyclecanceling.
416
417In general, \ref NetworkSimplex and \ref CostScaling are the most efficient
418implementations.
419\ref NetworkSimplex is usually the fastest on relatively small graphs (up to
420several thousands of nodes) and on dense graphs, while \ref CostScaling is
421typically more efficient on large graphs (e.g. hundreds of thousands of
422nodes or above), especially if they are sparse.
423However, other algorithms could be faster in special cases.
424For example, if the total supply and/or capacities are rather small,
425\ref CapacityScaling is usually the fastest algorithm
426(without effective scaling).
427
428These classes are intended to be used with integer-valued input data
429(capacities, supply values, and costs), except for \ref CapacityScaling,
430which is capable of handling real-valued arc costs (other numerical
431data are required to be integer).
432
433For more details about these implementations and for a comprehensive
434experimental study, see the paper \cite KiralyKovacs12MCF.
435It also compares these codes to other publicly available
436minimum cost flow solvers.
437*/
438
439/**
440@defgroup min_cut Minimum Cut Algorithms
441@ingroup algs
442
443\brief Algorithms for finding minimum cut in graphs.
444
445This group contains the algorithms for finding minimum cut in graphs.
446
447The \e minimum \e cut \e problem is to find a non-empty and non-complete
448\f$X\f$ subset of the nodes with minimum overall capacity on
449outgoing arcs. Formally, there is a \f$G=(V,A)\f$ digraph, a
450\f$cap: A\rightarrow\mathbf{R}^+_0\f$ capacity function. The minimum
451cut is the \f$X\f$ solution of the next optimization problem:
452
453\f[ \min_{X \subset V, X\not\in \{\emptyset, V\}}
454    \sum_{uv\in A: u\in X, v\not\in X}cap(uv) \f]
455
456LEMON contains several algorithms related to minimum cut problems:
457
458- \ref HaoOrlin "Hao-Orlin algorithm" for calculating minimum cut
459  in directed graphs.
460- \ref NagamochiIbaraki "Nagamochi-Ibaraki algorithm" for
461  calculating minimum cut in undirected graphs.
462- \ref GomoryHu "Gomory-Hu tree computation" for calculating
463  all-pairs minimum cut in undirected graphs.
464
465If you want to find minimum cut just between two distinict nodes,
466see the \ref max_flow "maximum flow problem".
467*/
468
469/**
470@defgroup min_mean_cycle Minimum Mean Cycle Algorithms
471@ingroup algs
472\brief Algorithms for finding minimum mean cycles.
473
474This group contains the algorithms for finding minimum mean cycles
475\cite amo93networkflows, \cite karp78characterization.
476
477The \e minimum \e mean \e cycle \e problem is to find a directed cycle
478of minimum mean length (cost) in a digraph.
479The mean length of a cycle is the average length of its arcs, i.e. the
480ratio between the total length of the cycle and the number of arcs on it.
481
482This problem has an important connection to \e conservative \e length
483\e functions, too. A length function on the arcs of a digraph is called
484conservative if and only if there is no directed cycle of negative total
485length. For an arbitrary length function, the negative of the minimum
486cycle mean is the smallest \f$\epsilon\f$ value so that increasing the
487arc lengths uniformly by \f$\epsilon\f$ results in a conservative length
488function.
489
490LEMON contains three algorithms for solving the minimum mean cycle problem:
491- \ref KarpMmc Karp's original algorithm \cite karp78characterization.
492- \ref HartmannOrlinMmc Hartmann-Orlin's algorithm, which is an improved
493  version of Karp's algorithm \cite hartmann93finding.
494- \ref HowardMmc Howard's policy iteration algorithm
495  \cite dasdan98minmeancycle, \cite dasdan04experimental.
496
497In practice, the \ref HowardMmc "Howard" algorithm turned out to be by far the
498most efficient one, though the best known theoretical bound on its running
499time is exponential.
500Both \ref KarpMmc "Karp" and \ref HartmannOrlinMmc "Hartmann-Orlin" algorithms
501run in time O(nm) and use space O(n<sup>2</sup>+m).
502*/
503
504/**
505@defgroup matching Matching Algorithms
506@ingroup algs
507\brief Algorithms for finding matchings in graphs and bipartite graphs.
508
509This group contains the algorithms for calculating
510matchings in graphs and bipartite graphs. The general matching problem is
511finding a subset of the edges for which each node has at most one incident
512edge.
513
514There are several different algorithms for calculate matchings in
515graphs.  The matching problems in bipartite graphs are generally
516easier than in general graphs. The goal of the matching optimization
517can be finding maximum cardinality, maximum weight or minimum cost
518matching. The search can be constrained to find perfect or
519maximum cardinality matching.
520
521The matching algorithms implemented in LEMON:
522- \ref MaxBipartiteMatching Hopcroft-Karp augmenting path algorithm
523  for calculating maximum cardinality matching in bipartite graphs.
524- \ref PrBipartiteMatching Push-relabel algorithm
525  for calculating maximum cardinality matching in bipartite graphs.
526- \ref MaxWeightedBipartiteMatching
527  Successive shortest path algorithm for calculating maximum weighted
528  matching and maximum weighted bipartite matching in bipartite graphs.
529- \ref MinCostMaxBipartiteMatching
530  Successive shortest path algorithm for calculating minimum cost maximum
531  matching in bipartite graphs.
532- \ref MaxMatching Edmond's blossom shrinking algorithm for calculating
533  maximum cardinality matching in general graphs.
534- \ref MaxWeightedMatching Edmond's blossom shrinking algorithm for calculating
535  maximum weighted matching in general graphs.
536- \ref MaxWeightedPerfectMatching
537  Edmond's blossom shrinking algorithm for calculating maximum weighted
538  perfect matching in general graphs.
539- \ref MaxFractionalMatching Push-relabel algorithm for calculating
540  maximum cardinality fractional matching in general graphs.
541- \ref MaxWeightedFractionalMatching Augmenting path algorithm for calculating
542  maximum weighted fractional matching in general graphs.
543- \ref MaxWeightedPerfectFractionalMatching
544  Augmenting path algorithm for calculating maximum weighted
545  perfect fractional matching in general graphs.
546
547\image html matching.png
548\image latex matching.eps "Min Cost Perfect Matching" width=\textwidth
549*/
550
551/**
552@defgroup graph_properties Connectivity and Other Graph Properties
553@ingroup algs
554\brief Algorithms for discovering the graph properties
555
556This group contains the algorithms for discovering the graph properties
557like connectivity, bipartiteness, euler property, simplicity etc.
558
559\image html connected_components.png
560\image latex connected_components.eps "Connected components" width=\textwidth
561*/
562
563/**
564@defgroup planar Planar Embedding and Drawing
565@ingroup algs
566\brief Algorithms for planarity checking, embedding and drawing
567
568This group contains the algorithms for planarity checking,
569embedding and drawing.
570
571\image html planar.png
572\image latex planar.eps "Plane graph" width=\textwidth
573*/
574
575/**
576@defgroup tsp Traveling Salesman Problem
577@ingroup algs
578\brief Algorithms for the symmetric traveling salesman problem
579
580This group contains basic heuristic algorithms for the the symmetric
581\e traveling \e salesman \e problem (TSP).
582Given an \ref FullGraph "undirected full graph" with a cost map on its edges,
583the problem is to find a shortest possible tour that visits each node exactly
584once (i.e. the minimum cost Hamiltonian cycle).
585
586These TSP algorithms are intended to be used with a \e metric \e cost
587\e function, i.e. the edge costs should satisfy the triangle inequality.
588Otherwise the algorithms could yield worse results.
589
590LEMON provides five well-known heuristics for solving symmetric TSP:
591 - \ref NearestNeighborTsp Neareast neighbor algorithm
592 - \ref GreedyTsp Greedy algorithm
593 - \ref InsertionTsp Insertion heuristic (with four selection methods)
594 - \ref ChristofidesTsp Christofides algorithm
595 - \ref Opt2Tsp 2-opt algorithm
596
597\ref NearestNeighborTsp, \ref GreedyTsp, and \ref InsertionTsp are the fastest
598solution methods. Furthermore, \ref InsertionTsp is usually quite effective.
599
600\ref ChristofidesTsp is somewhat slower, but it has the best guaranteed
601approximation factor: 3/2.
602
603\ref Opt2Tsp usually provides the best results in practice, but
604it is the slowest method. It can also be used to improve given tours,
605for example, the results of other algorithms.
606
607\image html tsp.png
608\image latex tsp.eps "Traveling salesman problem" width=\textwidth
609*/
610
611/**
612@defgroup approx_algs Approximation Algorithms
613@ingroup algs
614\brief Approximation algorithms.
615
616This group contains the approximation and heuristic algorithms
617implemented in LEMON.
618
619<b>Maximum Clique Problem</b>
620  - \ref GrossoLocatelliPullanMc An efficient heuristic algorithm of
621    Grosso, Locatelli, and Pullan.
622*/
623
624/**
625@defgroup auxalg Auxiliary Algorithms
626@ingroup algs
627\brief Auxiliary algorithms implemented in LEMON.
628
629This group contains some algorithms implemented in LEMON
630in order to make it easier to implement complex algorithms.
631*/
632
633/**
634@defgroup gen_opt_group General Optimization Tools
635\brief This group contains some general optimization frameworks
636implemented in LEMON.
637
638This group contains some general optimization frameworks
639implemented in LEMON.
640*/
641
642/**
643@defgroup lp_group LP and MIP Solvers
644@ingroup gen_opt_group
645\brief LP and MIP solver interfaces for LEMON.
646
647This group contains LP and MIP solver interfaces for LEMON.
648Various LP solvers could be used in the same manner with this
649high-level interface.
650
651The currently supported solvers are \cite glpk, \cite clp, \cite cbc,
652\cite cplex, \cite soplex.
653*/
654
655/**
656@defgroup lp_utils Tools for Lp and Mip Solvers
657@ingroup lp_group
658\brief Helper tools to the Lp and Mip solvers.
659
660This group adds some helper tools to general optimization framework
661implemented in LEMON.
662*/
663
664/**
665@defgroup metah Metaheuristics
666@ingroup gen_opt_group
667\brief Metaheuristics for LEMON library.
668
669This group contains some metaheuristic optimization tools.
670*/
671
672/**
673@defgroup utils Tools and Utilities
674\brief Tools and utilities for programming in LEMON
675
676Tools and utilities for programming in LEMON.
677*/
678
679/**
680@defgroup gutils Basic Graph Utilities
681@ingroup utils
682\brief Simple basic graph utilities.
683
684This group contains some simple basic graph utilities.
685*/
686
687/**
688@defgroup misc Miscellaneous Tools
689@ingroup utils
690\brief Tools for development, debugging and testing.
691
692This group contains several useful tools for development,
693debugging and testing.
694*/
695
696/**
697@defgroup timecount Time Measuring and Counting
698@ingroup misc
699\brief Simple tools for measuring the performance of algorithms.
700
701This group contains simple tools for measuring the performance
702of algorithms.
703*/
704
705/**
706@defgroup exceptions Exceptions
707@ingroup utils
708\brief Exceptions defined in LEMON.
709
710This group contains the exceptions defined in LEMON.
711*/
712
713/**
714@defgroup io_group Input-Output
715\brief Graph Input-Output methods
716
717This group contains the tools for importing and exporting graphs
718and graph related data. Now it supports the \ref lgf-format
719"LEMON Graph Format", the \c DIMACS format and the encapsulated
720postscript (EPS) format.
721*/
722
723/**
724@defgroup lemon_io LEMON Graph Format
725@ingroup io_group
726\brief Reading and writing LEMON Graph Format.
727
728This group contains methods for reading and writing
729\ref lgf-format "LEMON Graph Format".
730*/
731
732/**
733@defgroup eps_io Postscript Exporting
734@ingroup io_group
735\brief General \c EPS drawer and graph exporter
736
737This group contains general \c EPS drawing methods and special
738graph exporting tools.
739
740\image html graph_to_eps.png
741*/
742
743/**
744@defgroup dimacs_group DIMACS Format
745@ingroup io_group
746\brief Read and write files in DIMACS format
747
748Tools to read a digraph from or write it to a file in DIMACS format data.
749*/
750
751/**
752@defgroup nauty_group NAUTY Format
753@ingroup io_group
755
756Tool to read graphs from \e Nauty format data.
757*/
758
759/**
760@defgroup concept Concepts
761\brief Skeleton classes and concept checking classes
762
763This group contains the data/algorithm skeletons and concept checking
764classes implemented in LEMON.
765
766The purpose of the classes in this group is fourfold.
767
768- These classes contain the documentations of the %concepts. In order
769  to avoid document multiplications, an implementation of a concept
770  simply refers to the corresponding concept class.
771
772- These classes declare every functions, <tt>typedef</tt>s etc. an
773  implementation of the %concepts should provide, however completely
774  without implementations and real data structures behind the
775  interface. On the other hand they should provide nothing else. All
776  the algorithms working on a data structure meeting a certain concept
777  should compile with these classes. (Though it will not run properly,
778  of course.) In this way it is easily to check if an algorithm
779  doesn't use any extra feature of a certain implementation.
780
781- The concept descriptor classes also provide a <em>checker class</em>
782  that makes it possible to check whether a certain implementation of a
783  concept indeed provides all the required features.
784
785- Finally, They can serve as a skeleton of a new implementation of a concept.
786*/
787
788/**
789@defgroup graph_concepts Graph Structure Concepts
790@ingroup concept
791\brief Skeleton and concept checking classes for graph structures
792
793This group contains the skeletons and concept checking classes of
794graph structures.
795*/
796
797/**
798@defgroup map_concepts Map Concepts
799@ingroup concept
800\brief Skeleton and concept checking classes for maps
801
802This group contains the skeletons and concept checking classes of maps.
803*/
804
805/**
806@defgroup tools Standalone Utility Applications
807
808Some utility applications are listed here.
809
810The standard compilation procedure (<tt>./configure;make</tt>) will compile
811them, as well.
812*/
813
814/**
815\anchor demoprograms
816
817@defgroup demos Demo Programs
818
819Some demo programs are listed here. Their full source codes can be found in
820the \c demo subdirectory of the source tree.
821
822In order to compile them, use the <tt>make demo</tt> or the
823<tt>make check</tt> commands.
824*/
825
826}
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