Changeset 418:ad483acf1654 in lemon-main for doc

Timestamp:

12/02/08 16:33:22 (16 years ago)

Author:

Alpar Juttner <alpar@…>

Branch:

default

Children:

419:9afe81e4c543, 420:6a2a33ad261b, 430:09e416d35896, 446:d369e885d196, 501:7f8560cb9d65

Parents:

413:d8b87e9b90c3 (diff), 417:6ff53afe98b5 (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.

Phase:

public

Message:

Merge

Files:

• doc/groups.dox (modified) (1 diff)
• doc/groups.dox (modified) (13 diffs)

doc/groups.dox

@@ -60 +60 @@
 
 <b>See also:</b> \ref graph_concepts "Graph Structure Concepts".
+*/
+
+/**
+@defgroup graph_adaptors Adaptor Classes for graphs
+@ingroup graphs
+\brief This group contains several adaptor classes for digraphs and graphs
+
+The main parts of LEMON are the different graph structures, generic
+graph algorithms, graph concepts which couple these, and graph
+adaptors. While the previous notions are more or less clear, the
+latter one needs further explanation. Graph adaptors are graph classes
+which serve for considering graph structures in different ways.
+
+A short example makes this much clearer.  Suppose that we have an
+instance \c g of a directed graph type say ListDigraph and an algorithm
+\code
+template <typename Digraph>
+int algorithm(const Digraph&);
+\endcode
+is needed to run on the reverse oriented graph.  It may be expensive
+(in time or in memory usage) to copy \c g with the reversed
+arcs.  In this case, an adaptor class is used, which (according
+to LEMON digraph concepts) works as a digraph.  The adaptor uses the
+original digraph structure and digraph operations when methods of the
+reversed oriented graph are called.  This means that the adaptor have
+minor memory usage, and do not perform sophisticated algorithmic
+actions.  The purpose of it is to give a tool for the cases when a
+graph have to be used in a specific alteration.  If this alteration is
+obtained by a usual construction like filtering the arc-set or
+considering a new orientation, then an adaptor is worthwhile to use.
+To come back to the reverse oriented graph, in this situation
+\code
+template<typename Digraph> class ReverseDigraph;
+\endcode
+template class can be used. The code looks as follows
+\code
+ListDigraph g;
+ReverseDigraph<ListGraph> rg(g);
+int result = algorithm(rg);
+\endcode
+After running the algorithm, the original graph \c g is untouched.
+This techniques gives rise to an elegant code, and based on stable
+graph adaptors, complex algorithms can be implemented easily.
+
+In flow, circulation and bipartite matching problems, the residual
+graph is of particular importance. Combining an adaptor implementing
+this, shortest path algorithms and minimum mean cycle algorithms,
+a range of weighted and cardinality optimization algorithms can be
+obtained. For other examples, the interested user is referred to the
+detailed documentation of particular adaptors.
+
+The behavior of graph adaptors can be very different. Some of them keep
+capabilities of the original graph while in other cases this would be
+meaningless. This means that the concepts that they are models of depend
+on the graph adaptor, and the wrapped graph(s).
+If an arc of \c rg is deleted, this is carried out by deleting the
+corresponding arc of \c g, thus the adaptor modifies the original graph.
+
+But for a residual graph, this operation has no sense.
+Let us stand one more example here to simplify your work.
+RevGraphAdaptor has constructor
+\code
+ReverseDigraph(Digraph& digraph);
+\endcode
+This means that in a situation, when a <tt>const ListDigraph&</tt>
+reference to a graph is given, then it have to be instantiated with
+<tt>Digraph=const ListDigraph</tt>.
+\code
+int algorithm1(const ListDigraph& g) {
+  RevGraphAdaptor<const ListDigraph> rg(g);
+  return algorithm2(rg);
+}
+\endcode
 */
 

doc/groups.dox

@@ -16 +16 @@
  *
  */
+
+namespace lemon {
 
 /**
@@ -162 +164 @@
 
 This group describes maps that are specifically designed to assign
-values to the nodes and arcs of graphs.
+values to the nodes and arcs/edges of graphs.
+
+If you are looking for the standard graph maps (\c NodeMap, \c ArcMap,
+\c EdgeMap), see the \ref graph_concepts "Graph Structure Concepts".
 */
 
@@ -173 +178 @@
 maps from other maps.
 
-Most of them are \ref lemon::concepts::ReadMap "read-only maps".
+Most of them are \ref concepts::ReadMap "read-only maps".
 They can make arithmetic and logical operations between one or two maps
 (negation, shifting, addition, multiplication, logical 'and', 'or',
@@ -275 +280 @@
 \brief Common graph search algorithms.
 
-This group describes the common graph search algorithms like
-Breadth-First Search (BFS) and Depth-First Search (DFS).
+This group describes the common graph search algorithms, namely
+\e breadth-first \e search (BFS) and \e depth-first \e search (DFS).
 */
 
@@ -284 +289 @@
 \brief Algorithms for finding shortest paths.
 
-This group describes the algorithms for finding shortest paths in graphs.
+This group describes the algorithms for finding shortest paths in digraphs.
+
+ - \ref Dijkstra algorithm for finding shortest paths from a source node
+   when all arc lengths are non-negative.
+ - \ref BellmanFord "Bellman-Ford" algorithm for finding shortest paths
+   from a source node when arc lenghts can be either positive or negative,
+   but the digraph should not contain directed cycles with negative total
+   length.
+ - \ref FloydWarshall "Floyd-Warshall" and \ref Johnson "Johnson" algorithms
+   for solving the \e all-pairs \e shortest \e paths \e problem when arc
+   lenghts can be either positive or negative, but the digraph should
+   not contain directed cycles with negative total length.
+ - \ref Suurballe A successive shortest path algorithm for finding
+   arc-disjoint paths between two nodes having minimum total length.
 */
 
@@ -295 +313 @@
 feasible circulations.
 
-The maximum flow problem is to find a flow between a single source and
-a single target that is maximum. Formally, there is a \f$G=(V,A)\f$
-directed graph, an \f$c_a:A\rightarrow\mathbf{R}^+_0\f$ capacity
-function and given \f$s, t \in V\f$ source and target node. The
-maximum flow is the \f$f_a\f$ solution of the next optimization problem:
-
-\f[ 0 \le f_a \le c_a \f]
-\f[ \sum_{v\in\delta^{-}(u)}f_{vu}=\sum_{v\in\delta^{+}(u)}f_{uv}
-\qquad \forall u \in V \setminus \{s,t\}\f]
-\f[ \max \sum_{v\in\delta^{+}(s)}f_{uv} - \sum_{v\in\delta^{-}(s)}f_{vu}\f]
+The \e maximum \e flow \e problem is to find a flow of maximum value between
+a single source and a single target. Formally, there is a \f$G=(V,A)\f$
+digraph, a \f$cap:A\rightarrow\mathbf{R}^+_0\f$ capacity function and
+\f$s, t \in V\f$ source and target nodes.
+A maximum flow is an \f$f:A\rightarrow\mathbf{R}^+_0\f$ solution of the
+following optimization problem.
+
+\f[ \max\sum_{a\in\delta_{out}(s)}f(a) - \sum_{a\in\delta_{in}(s)}f(a) \f]
+\f[ \sum_{a\in\delta_{out}(v)} f(a) = \sum_{a\in\delta_{in}(v)} f(a)
+    \qquad \forall v\in V\setminus\{s,t\} \f]
+\f[ 0 \leq f(a) \leq cap(a) \qquad \forall a\in A \f]
 
 LEMON contains several algorithms for solving maximum flow problems:
-- \ref lemon::EdmondsKarp "Edmonds-Karp"
-- \ref lemon::Preflow "Goldberg's Preflow algorithm"
-- \ref lemon::DinitzSleatorTarjan "Dinitz's blocking flow algorithm with dynamic trees"
-- \ref lemon::GoldbergTarjan "Preflow algorithm with dynamic trees"
-
-In most cases the \ref lemon::Preflow "Preflow" algorithm provides the
-fastest method to compute the maximum flow. All impelementations
-provides functions to query the minimum cut, which is the dual linear
-programming problem of the maximum flow.
+- \ref EdmondsKarp Edmonds-Karp algorithm.
+- \ref Preflow Goldberg-Tarjan's preflow push-relabel algorithm.
+- \ref DinitzSleatorTarjan Dinitz's blocking flow algorithm with dynamic trees.
+- \ref GoldbergTarjan Preflow push-relabel algorithm with dynamic trees.
+
+In most cases the \ref Preflow "Preflow" algorithm provides the
+fastest method for computing a maximum flow. All implementations
+provides functions to also query the minimum cut, which is the dual
+problem of the maximum flow.
 */
 
@@ -326 +345 @@
 This group describes the algorithms for finding minimum cost flows and
 circulations.
+
+The \e minimum \e cost \e flow \e problem is to find a feasible flow of
+minimum total cost from a set of supply nodes to a set of demand nodes
+in a network with capacity constraints and arc costs.
+Formally, let \f$G=(V,A)\f$ be a digraph,
+\f$lower, upper: A\rightarrow\mathbf{Z}^+_0\f$ denote the lower and
+upper bounds for the flow values on the arcs,
+\f$cost: A\rightarrow\mathbf{Z}^+_0\f$ denotes the cost per unit flow
+on the arcs, and
+\f$supply: V\rightarrow\mathbf{Z}\f$ denotes the supply/demand values
+of the nodes.
+A minimum cost flow is an \f$f:A\rightarrow\mathbf{R}^+_0\f$ solution of
+the following optimization problem.
+
+\f[ \min\sum_{a\in A} f(a) cost(a) \f]
+\f[ \sum_{a\in\delta_{out}(v)} f(a) - \sum_{a\in\delta_{in}(v)} f(a) =
+    supply(v) \qquad \forall v\in V \f]
+\f[ lower(a) \leq f(a) \leq upper(a) \qquad \forall a\in A \f]
+
+LEMON contains several algorithms for solving minimum cost flow problems:
+ - \ref CycleCanceling Cycle-canceling algorithms.
+ - \ref CapacityScaling Successive shortest path algorithm with optional
+   capacity scaling.
+ - \ref CostScaling Push-relabel and augment-relabel algorithms based on
+   cost scaling.
+ - \ref NetworkSimplex Primal network simplex algorithm with various
+   pivot strategies.
 */
 
@@ -336 +382 @@
 This group describes the algorithms for finding minimum cut in graphs.
 
-The minimum cut problem is to find a non-empty and non-complete
-\f$X\f$ subset of the vertices with minimum overall capacity on
-outgoing arcs. Formally, there is \f$G=(V,A)\f$ directed graph, an
-\f$c_a:A\rightarrow\mathbf{R}^+_0\f$ capacity function. The minimum
+The \e minimum \e cut \e problem is to find a non-empty and non-complete
+\f$X\f$ subset of the nodes with minimum overall capacity on
+outgoing arcs. Formally, there is a \f$G=(V,A)\f$ digraph, a
+\f$cap: A\rightarrow\mathbf{R}^+_0\f$ capacity function. The minimum
 cut is the \f$X\f$ solution of the next optimization problem:
 
 \f[ \min_{X \subset V, X\not\in \{\emptyset, V\}}
-\sum_{uv\in A, u\in X, v\not\in X}c_{uv}\f]
+    \sum_{uv\in A, u\in X, v\not\in X}cap(uv) \f]
 
 LEMON contains several algorithms related to minimum cut problems:
 
-- \ref lemon::HaoOrlin "Hao-Orlin algorithm" to calculate minimum cut
-  in directed graphs
-- \ref lemon::NagamochiIbaraki "Nagamochi-Ibaraki algorithm" to
-  calculate minimum cut in undirected graphs
-- \ref lemon::GomoryHuTree "Gomory-Hu tree computation" to calculate all
-  pairs minimum cut in undirected graphs
+- \ref HaoOrlin "Hao-Orlin algorithm" for calculating minimum cut
+  in directed graphs.
+- \ref NagamochiIbaraki "Nagamochi-Ibaraki algorithm" for
+  calculating minimum cut in undirected graphs.
+- \ref GomoryHuTree "Gomory-Hu tree computation" for calculating
+  all-pairs minimum cut in undirected graphs.
 
 If you want to find minimum cut just between two distinict nodes,
-please see the \ref max_flow "Maximum Flow page".
+see the \ref max_flow "maximum flow problem".
 */
 
@@ -394 +440 @@
 graphs.  The matching problems in bipartite graphs are generally
 easier than in general graphs. The goal of the matching optimization
-can be the finding maximum cardinality, maximum weight or minimum cost
+can be finding maximum cardinality, maximum weight or minimum cost
 matching. The search can be constrained to find perfect or
 maximum cardinality matching.
 
-LEMON contains the next algorithms:
-- \ref lemon::MaxBipartiteMatching "MaxBipartiteMatching" Hopcroft-Karp
-  augmenting path algorithm for calculate maximum cardinality matching in
-  bipartite graphs
-- \ref lemon::PrBipartiteMatching "PrBipartiteMatching" Push-Relabel
-  algorithm for calculate maximum cardinality matching in bipartite graphs
-- \ref lemon::MaxWeightedBipartiteMatching "MaxWeightedBipartiteMatching"
-  Successive shortest path algorithm for calculate maximum weighted matching
-  and maximum weighted bipartite matching in bipartite graph
-- \ref lemon::MinCostMaxBipartiteMatching "MinCostMaxBipartiteMatching"
-  Successive shortest path algorithm for calculate minimum cost maximum
-  matching in bipartite graph
-- \ref lemon::MaxMatching "MaxMatching" Edmond's blossom shrinking algorithm
-  for calculate maximum cardinality matching in general graph
-- \ref lemon::MaxWeightedMatching "MaxWeightedMatching" Edmond's blossom
-  shrinking algorithm for calculate maximum weighted matching in general
-  graph
-- \ref lemon::MaxWeightedPerfectMatching "MaxWeightedPerfectMatching"
-  Edmond's blossom shrinking algorithm for calculate maximum weighted
-  perfect matching in general graph
+The matching algorithms implemented in LEMON:
+- \ref MaxBipartiteMatching Hopcroft-Karp augmenting path algorithm
+  for calculating maximum cardinality matching in bipartite graphs.
+- \ref PrBipartiteMatching Push-relabel algorithm
+  for calculating maximum cardinality matching in bipartite graphs.
+- \ref MaxWeightedBipartiteMatching
+  Successive shortest path algorithm for calculating maximum weighted
+  matching and maximum weighted bipartite matching in bipartite graphs.
+- \ref MinCostMaxBipartiteMatching
+  Successive shortest path algorithm for calculating minimum cost maximum
+  matching in bipartite graphs.
+- \ref MaxMatching Edmond's blossom shrinking algorithm for calculating
+  maximum cardinality matching in general graphs.
+- \ref MaxWeightedMatching Edmond's blossom shrinking algorithm for calculating
+  maximum weighted matching in general graphs.
+- \ref MaxWeightedPerfectMatching
+  Edmond's blossom shrinking algorithm for calculating maximum weighted
+  perfect matching in general graphs.
 
 \image html bipartite_matching.png
@@ -429 +473 @@
 
 This group describes the algorithms for finding a minimum cost spanning
-tree in a graph
+tree in a graph.
 */
 
@@ -620 +664 @@
 \anchor demoprograms
 
-@defgroup demos Demo programs
+@defgroup demos Demo Programs
 
 Some demo programs are listed here. Their full source codes can be found in
@@ -630 +674 @@
 
 /**
-@defgroup tools Standalone utility applications
+@defgroup tools Standalone Utility Applications
 
 Some utility applications are listed here.
@@ -638 +682 @@
 */
 
+}