LEMON/LEMON-main Changeset - r590:b61354458b59

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Changeset - r590:b61354458b59

« 589:630c48

593:7ac52d »

r590:b61354458b59

Wed, 15 Apr 2009 12:01:14

[Not Reviewed]

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kpeter (Peter Kovacs)
kpeter@inf.elte.hu

Imporvements for the matching algorithms (#264)

0 3 0

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3 files changed with 393 insertions and 188 deletions:

doc/groups.dox

lemon/max_matching.h

285

185

test/max_matching_test.cc

105

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doc/groups.dox

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@@ -246,387 +246,388 @@
 		/**
 		@defgroup paths Path Structures
 		@ingroup datas
 		\brief %Path structures implemented in LEMON.
 		This group contains the path structures implemented in LEMON.
 		LEMON provides flexible data structures to work with paths.
 		All of them have similar interfaces and they can be copied easily with
 		assignment operators and copy constructors. This makes it easy and
 		efficient to have e.g. the Dijkstra algorithm to store its result in
 		any kind of path structure.
 		\sa lemon::concepts::Path
 		*/
 		/**
 		@defgroup auxdat Auxiliary Data Structures
 		@ingroup datas
 		\brief Auxiliary data structures implemented in LEMON.
 		This group contains some data structures implemented in LEMON in
 		order to make it easier to implement combinatorial algorithms.
 		*/
 		/**
 		@defgroup algs Algorithms
 		\brief This group contains the several algorithms
 		implemented in LEMON.
 		This group contains the several algorithms
 		implemented in LEMON.
 		*/
 		/**
 		@defgroup search Graph Search
 		@ingroup algs
 		\brief Common graph search algorithms.
 		This group contains the common graph search algorithms, namely
 		\e breadth-first \e search (BFS) and \e depth-first \e search (DFS).
 		*/
 		/**
 		@defgroup shortest_path Shortest Path Algorithms
 		@ingroup algs
 		\brief Algorithms for finding shortest paths.
 		This group contains 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.
 		*/
 		/**
 		@defgroup max_flow Maximum Flow Algorithms
 		@ingroup algs
 		\brief Algorithms for finding maximum flows.
 		This group contains the algorithms for finding maximum flows and
 		feasible circulations.
 		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 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.
 		*/
 		/**
 		@defgroup min_cost_flow Minimum Cost Flow Algorithms
 		@ingroup algs
 		\brief Algorithms for finding minimum cost flows and circulations.
 		This group contains 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.
 		*/
 		/**
 		@defgroup min_cut Minimum Cut Algorithms
 		@ingroup algs
 		\brief Algorithms for finding minimum cut in graphs.
 		This group contains the algorithms for finding minimum cut in graphs.
 		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}cap(uv) \f]
 		LEMON contains several algorithms related to minimum cut problems:
 		- \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 GomoryHu "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,
 		see the \ref max_flow "maximum flow problem".
 		*/
 		/**
 		@defgroup graph_properties Connectivity and Other Graph Properties
 		@ingroup algs
 		\brief Algorithms for discovering the graph properties
 		This group contains the algorithms for discovering the graph properties
 		like connectivity, bipartiteness, euler property, simplicity etc.
 		\image html edge_biconnected_components.png
 		\image latex edge_biconnected_components.eps "bi-edge-connected components" width=\textwidth
 		*/
 		/**
 		@defgroup planar Planarity Embedding and Drawing
 		@ingroup algs
 		\brief Algorithms for planarity checking, embedding and drawing
 		This group contains the algorithms for planarity checking,
 		embedding and drawing.
 		\image html planar.png
 		\image latex planar.eps "Plane graph" width=\textwidth
 		*/
 		/**
 		@defgroup matching Matching Algorithms
 		@ingroup algs
 		\brief Algorithms for finding matchings in graphs and bipartite graphs.
-		This group contains algorithm objects and functions to calculate
+		This group contains the algorithms for calculating
 		matchings in graphs and bipartite graphs. The general matching problem is
-		finding a subset of the arcs which does not shares common endpoints.
+		finding a subset of the edges for which each node has at most one incident
 		edge.
 		There are several different algorithms for calculate matchings in
 		graphs.  The matching problems in bipartite graphs are generally
 		easier than in general graphs. The goal of the matching optimization
 		can be finding maximum cardinality, maximum weight or minimum cost
 		matching. The search can be constrained to find perfect or
 		maximum cardinality matching.
 		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
 		\image latex bipartite_matching.eps "Bipartite Matching" width=\textwidth
 		*/
 		/**
 		@defgroup spantree Minimum Spanning Tree Algorithms
 		@ingroup algs
 		\brief Algorithms for finding a minimum cost spanning tree in a graph.
 		This group contains the algorithms for finding a minimum cost spanning
 		tree in a graph.
 		*/
 		/**
 		@defgroup auxalg Auxiliary Algorithms
 		@ingroup algs
 		\brief Auxiliary algorithms implemented in LEMON.
 		This group contains some algorithms implemented in LEMON
 		in order to make it easier to implement complex algorithms.
 		*/
 		/**
 		@defgroup approx Approximation Algorithms
 		@ingroup algs
 		\brief Approximation algorithms.
 		This group contains the approximation and heuristic algorithms
 		implemented in LEMON.
 		*/
 		/**
 		@defgroup gen_opt_group General Optimization Tools
 		\brief This group contains some general optimization frameworks
 		implemented in LEMON.
 		This group contains some general optimization frameworks
 		implemented in LEMON.
 		*/
 		/**
 		@defgroup lp_group Lp and Mip Solvers
 		@ingroup gen_opt_group
 		\brief Lp and Mip solver interfaces for LEMON.
 		This group contains Lp and Mip solver interfaces for LEMON. The
 		various LP solvers could be used in the same manner with this
 		interface.
 		*/
 		/**
 		@defgroup lp_utils Tools for Lp and Mip Solvers
 		@ingroup lp_group
 		\brief Helper tools to the Lp and Mip solvers.
 		This group adds some helper tools to general optimization framework
 		implemented in LEMON.
 		*/
 		/**
 		@defgroup metah Metaheuristics
 		@ingroup gen_opt_group
 		\brief Metaheuristics for LEMON library.
 		This group contains some metaheuristic optimization tools.
 		*/
 		/**
 		@defgroup utils Tools and Utilities
 		\brief Tools and utilities for programming in LEMON
 		Tools and utilities for programming in LEMON.
 		*/
 		/**
 		@defgroup gutils Basic Graph Utilities
 		@ingroup utils
 		\brief Simple basic graph utilities.
 		This group contains some simple basic graph utilities.
 		*/
 		/**
 		@defgroup misc Miscellaneous Tools
 		@ingroup utils
 		\brief Tools for development, debugging and testing.
 		This group contains several useful tools for development,
 		debugging and testing.
 		*/
 		/**
 		@defgroup timecount Time Measuring and Counting
 		@ingroup misc
 		\brief Simple tools for measuring the performance of algorithms.
 		This group contains simple tools for measuring the performance
 		of algorithms.
 		*/
 		/**
 		@defgroup exceptions Exceptions
 		@ingroup utils
 		\brief Exceptions defined in LEMON.
 		This group contains the exceptions defined in LEMON.
 		*/
 		/**
 		@defgroup io_group Input-Output
 		\brief Graph Input-Output methods
 		This group contains the tools for importing and exporting graphs
 		and graph related data. Now it supports the \ref lgf-format
 		"LEMON Graph Format", the \c DIMACS format and the encapsulated
 		postscript (EPS) format.
 		*/
 		/**
 		@defgroup lemon_io LEMON Graph Format
 		@ingroup io_group
 		\brief Reading and writing LEMON Graph Format.
 		This group contains methods for reading and writing
 		\ref lgf-format "LEMON Graph Format".
 		*/
 		/**
 		@defgroup eps_io Postscript Exporting
 		@ingroup io_group
 		\brief General \c EPS drawer and graph exporter
 		This group contains general \c EPS drawing methods and special
 		graph exporting tools.
 		*/
 		/**
 		@defgroup dimacs_group DIMACS format
 		@ingroup io_group
 		\brief Read and write files in DIMACS format
 		Tools to read a digraph from or write it to a file in DIMACS format data.
 		*/
 		/**
 		@defgroup nauty_group NAUTY Format
 		@ingroup io_group
 		\brief Read \e Nauty format
 		Tool to read graphs from \e Nauty format data.
 		*/
 		/**
 		@defgroup concept Concepts
 		\brief Skeleton classes and concept checking classes
 		This group contains the data/algorithm skeletons and concept checking
 		classes implemented in LEMON.
 		The purpose of the classes in this group is fourfold.
 		- These classes contain the documentations of the %concepts. In order
 		  to avoid document multiplications, an implementation of a concept
 		  simply refers to the corresponding concept class.

lemon/max_matching.h

Show inline comments

 		/* -*- mode: C++; indent-tabs-mode: nil; -*-
+		 *
 		 * This file is a part of LEMON, a generic C++ optimization library.
+		 *
 		 * Copyright (C) 2003-2009
 		 * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
 		 * (Egervary Research Group on Combinatorial Optimization, EGRES).
+		 *
 		 * Permission to use, modify and distribute this software is granted
 		 * provided that this copyright notice appears in all copies. For
 		 * precise terms see the accompanying LICENSE file.
+		 *
 		 * This software is provided "AS IS" with no warranty of any kind,
 		 * express or implied, and with no claim as to its suitability for any
 		 * purpose.
+		 *
 		 */
 		#ifndef LEMON_MAX_MATCHING_H
 		#define LEMON_MAX_MATCHING_H
 		#include <vector>
 		#include <queue>
 		#include <set>
 		#include <limits>
 		#include <lemon/core.h>
 		#include <lemon/unionfind.h>
 		#include <lemon/bin_heap.h>
 		#include <lemon/maps.h>
 		///\ingroup matching
 		///\file
 		///\brief Maximum matching algorithms in general graphs.
 		namespace lemon {
 		  /// \ingroup matching
 		  ///
-		  /// \brief Edmonds' alternating forest maximum matching algorithm.
+		  /// \brief Maximum cardinality matching in general graphs
 		  ///
 		  /// This class implements Edmonds' alternating forest matching
 		  /// algorithm. The algorithm can be started from an arbitrary initial
-		  /// matching (the default is the empty one)
+		  /// This class implements Edmonds' alternating forest matching algorithm
 		  /// for finding a maximum cardinality matching in a general graph.
 		  /// It can be started from an arbitrary initial matching
 		  /// (the default is the empty one).
 		  ///
 		  /// The dual solution of the problem is a map of the nodes to
 		  /// MaxMatching::Status, having values \c EVEN/D, \c ODD/A and \c
 		  /// MATCHED/C showing the Gallai-Edmonds decomposition of the
 		  /// graph. The nodes in \c EVEN/D induce a graph with
 		  /// factor-critical components, the nodes in \c ODD/A form the
 		  /// barrier, and the nodes in \c MATCHED/C induce a graph having a
 		  /// perfect matching. The number of the factor-critical components
 		  /// \ref MaxMatching::Status "Status", having values \c EVEN (or \c D),
 		  /// \c ODD (or \c A) and \c MATCHED (or \c C) defining the Gallai-Edmonds
 		  /// decomposition of the graph. The nodes in \c EVEN/D induce a subgraph
 		  /// with factor-critical components, the nodes in \c ODD/A form the
 		  /// canonical barrier, and the nodes in \c MATCHED/C induce a graph having
 		  /// a perfect matching. The number of the factor-critical components
 		  /// minus the number of barrier nodes is a lower bound on the
 		  /// unmatched nodes, and the matching is optimal if and only if this bound is
-		  /// tight. This decomposition can be attained by calling \c
+		  /// tight. This decomposition can be obtained by calling \c
 		  /// decomposition() after running the algorithm.
 		  ///
-		  /// \param GR The graph type the algorithm runs on.
+		  /// \tparam GR The graph type the algorithm runs on.
 		  template <typename GR>
 		  class MaxMatching {
 		  public:
 		    /// The graph type of the algorithm
 		    typedef GR Graph;
 		    typedef typename Graph::template NodeMap<typename Graph::Arc>
 		    MatchingMap;
-		    ///\brief Indicates the Gallai-Edmonds decomposition of the graph.
+		    ///\brief Status constants for Gallai-Edmonds decomposition.
 		    ///
 		    ///Indicates the Gallai-Edmonds decomposition of the graph. The
 		    ///nodes with Status \c EVEN/D induce a graph with factor-critical
 		    ///components, the nodes in \c ODD/A form the canonical barrier,
 		    ///and the nodes in \c MATCHED/C induce a graph having a perfect
-		    ///matching.
+		    ///These constants are used for indicating the Gallai-Edmonds
 		    ///decomposition of a graph. The nodes with status \c EVEN (or \c D)
 		    ///induce a subgraph with factor-critical components, the nodes with
 		    ///status \c ODD (or \c A) form the canonical barrier, and the nodes
 		    ///with status \c MATCHED (or \c C) induce a subgraph having a
 		    ///perfect matching.
 		    enum Status {
-		      EVEN = 1, D = 1, MATCHED = 0, C = 0, ODD = -1, A = -1, UNMATCHED = -2
+		      EVEN = 1,       ///< = 1. (\c D is an alias for \c EVEN.)
 		      D = 1,
 		      MATCHED = 0,    ///< = 0. (\c C is an alias for \c MATCHED.)
 		      C = 0,
 		      ODD = -1,       ///< = -1. (\c A is an alias for \c ODD.)
 		      A = -1,
 		      UNMATCHED = -2  ///< = -2.
 		    };
 		    typedef typename Graph::template NodeMap<Status> StatusMap;
 		  private:
 		    TEMPLATE_GRAPH_TYPEDEFS(Graph);
 		    typedef UnionFindEnum<IntNodeMap> BlossomSet;
 		    typedef ExtendFindEnum<IntNodeMap> TreeSet;
 		    typedef RangeMap<Node> NodeIntMap;
 		    typedef MatchingMap EarMap;
 		    typedef std::vector<Node> NodeQueue;
 		    const Graph& _graph;
 		    MatchingMap* _matching;
 		    StatusMap* _status;
 		    EarMap* _ear;
 		    IntNodeMap* _blossom_set_index;
 		    BlossomSet* _blossom_set;
 		    NodeIntMap* _blossom_rep;
 		    IntNodeMap* _tree_set_index;
 		    TreeSet* _tree_set;
 		    NodeQueue _node_queue;
 		    int _process, _postpone, _last;
 		    int _node_num;
 		  private:
 		    void createStructures() {
 		      _node_num = countNodes(_graph);
 		      if (!_matching) {
 		        _matching = new MatchingMap(_graph);
+		      }
 		      if (!_status) {
 		        _status = new StatusMap(_graph);
+		      }
 		      if (!_ear) {
 		        _ear = new EarMap(_graph);
+		      }
 		      if (!_blossom_set) {
 		        _blossom_set_index = new IntNodeMap(_graph);
 		        _blossom_set = new BlossomSet(*_blossom_set_index);
+		      }
 		      if (!_blossom_rep) {
 		        _blossom_rep = new NodeIntMap(_node_num);
+		      }
 		      if (!_tree_set) {
 		        _tree_set_index = new IntNodeMap(_graph);
 		        _tree_set = new TreeSet(*_tree_set_index);
+		      }
 		      _node_queue.resize(_node_num);
+		    }
 		    void destroyStructures() {
 		      if (_matching) {
 		        delete _matching;
+		      }
 		      if (_status) {
 		        delete _status;
+		      }
 		      if (_ear) {
 		        delete _ear;
+		      }
 		      if (_blossom_set) {
 		        delete _blossom_set;
 		        delete _blossom_set_index;
+		      }
 		      if (_blossom_rep) {
 		        delete _blossom_rep;
+		      }
 		      if (_tree_set) {
 		        delete _tree_set_index;
 		        delete _tree_set;
+		      }
+		    }
 		    void processDense(const Node& n) {
 		      _process = _postpone = _last = 0;
 		      _node_queue[_last++] = n;
 		      while (_process != _last) {
 		        Node u = _node_queue[_process++];
 		        for (OutArcIt a(_graph, u); a != INVALID; ++a) {
 		          Node v = _graph.target(a);
 		          if ((*_status)[v] == MATCHED) {
 		            extendOnArc(a);
 		          } else if ((*_status)[v] == UNMATCHED) {
 		            augmentOnArc(a);
 		            return;
+		          }
+		        }
+		      }
 		      while (_postpone != _last) {
 		        Node u = _node_queue[_postpone++];
 		        for (OutArcIt a(_graph, u); a != INVALID ; ++a) {
 		          Node v = _graph.target(a);
 		          if ((*_status)[v] == EVEN) {
 		            if (_blossom_set->find(u) != _blossom_set->find(v)) {
 		              shrinkOnEdge(a);
+		            }
+		          }
 		          while (_process != _last) {
 		            Node w = _node_queue[_process++];
 		            for (OutArcIt b(_graph, w); b != INVALID; ++b) {
 		              Node x = _graph.target(b);
 		              if ((*_status)[x] == MATCHED) {
 		                extendOnArc(b);
 		              } else if ((*_status)[x] == UNMATCHED) {
 		                augmentOnArc(b);
 		                return;
+		              }
+		            }
+		          }
+		        }
+		      }
+		    }
 		    void processSparse(const Node& n) {
 		      _process = _last = 0;
 		      _node_queue[_last++] = n;
 		      while (_process != _last) {
 		        Node u = _node_queue[_process++];
 		        for (OutArcIt a(_graph, u); a != INVALID; ++a) {
 		          Node v = _graph.target(a);
 		          if ((*_status)[v] == EVEN) {
 		            if (_blossom_set->find(u) != _blossom_set->find(v)) {
 		              shrinkOnEdge(a);
+		            }
 		          } else if ((*_status)[v] == MATCHED) {
 		            extendOnArc(a);
 		          } else if ((*_status)[v] == UNMATCHED) {
 		            augmentOnArc(a);
 		            return;
+		          }
+		        }
+		      }
+		    }
 		    void shrinkOnEdge(const Edge& e) {
 		      Node nca = INVALID;
+		      {
 		        std::set<Node> left_set, right_set;
 		        Node left = (*_blossom_rep)[_blossom_set->find(_graph.u(e))];
 		        left_set.insert(left);
 		        Node right = (*_blossom_rep)[_blossom_set->find(_graph.v(e))];
 		        right_set.insert(right);
 		        while (true) {
 		          if ((*_matching)[left] == INVALID) break;
 		          left = _graph.target((*_matching)[left]);
 		          left = (*_blossom_rep)[_blossom_set->
 		                                 find(_graph.target((*_ear)[left]))];
 		          if (right_set.find(left) != right_set.end()) {
 		            nca = left;
 		            break;
+		          }
 		          left_set.insert(left);
 		          if ((*_matching)[right] == INVALID) break;
 		          right = _graph.target((*_matching)[right]);
 		          right = (*_blossom_rep)[_blossom_set->
 		                                  find(_graph.target((*_ear)[right]))];
 		          if (left_set.find(right) != left_set.end()) {
 		            nca = right;
 		            break;
+		          }
 		          right_set.insert(right);
+		        }
 		        if (nca == INVALID) {
 		          if ((*_matching)[left] == INVALID) {
 		            nca = right;
 		            while (left_set.find(nca) == left_set.end()) {
 		              nca = _graph.target((*_matching)[nca]);
 		              nca =(*_blossom_rep)[_blossom_set->
 		                                   find(_graph.target((*_ear)[nca]))];
+		            }
 		          } else {
 		            nca = left;
 		            while (right_set.find(nca) == right_set.end()) {
 		              nca = _graph.target((*_matching)[nca]);
 		              nca = (*_blossom_rep)[_blossom_set->
 		                                   find(_graph.target((*_ear)[nca]))];
+		            }
+		          }
+		        }
+		      }
+		      {
 		        Node node = _graph.u(e);
 		        Arc arc = _graph.direct(e, true);
 		        Node base = (*_blossom_rep)[_blossom_set->find(node)];
 		        while (base != nca) {
 		          (*_ear)[node] = arc;
 		          Node n = node;
 		          while (n != base) {
 		            n = _graph.target((*_matching)[n]);
 		            Arc a = (*_ear)[n];
 		            n = _graph.target(a);
 		            (*_ear)[n] = _graph.oppositeArc(a);
+		          }
 		          node = _graph.target((*_matching)[base]);
 		          _tree_set->erase(base);
 		          _tree_set->erase(node);
 		          _blossom_set->insert(node, _blossom_set->find(base));
 		          (*_status)[node] = EVEN;
 		          _node_queue[_last++] = node;
 		          arc = _graph.oppositeArc((*_ear)[node]);
 		          node = _graph.target((*_ear)[node]);
 		          base = (*_blossom_rep)[_blossom_set->find(node)];
 		          _blossom_set->join(_graph.target(arc), base);
+		        }
+		      }
 		      (*_blossom_rep)[_blossom_set->find(nca)] = nca;
+		      {
 		        Node node = _graph.v(e);
 		        Arc arc = _graph.direct(e, false);
 		        Node base = (*_blossom_rep)[_blossom_set->find(node)];
 		        while (base != nca) {
 		          (*_ear)[node] = arc;
 		          Node n = node;
 		          while (n != base) {
 		            n = _graph.target((*_matching)[n]);
 		            Arc a = (*_ear)[n];
 		            n = _graph.target(a);
 		            (*_ear)[n] = _graph.oppositeArc(a);
+		          }
 		          node = _graph.target((*_matching)[base]);
 		          _tree_set->erase(base);
 		          _tree_set->erase(node);
 		          _blossom_set->insert(node, _blossom_set->find(base));
 		          (*_status)[node] = EVEN;
 		          _node_queue[_last++] = node;
 		          arc = _graph.oppositeArc((*_ear)[node]);
 		          node = _graph.target((*_ear)[node]);
 		          base = (*_blossom_rep)[_blossom_set->find(node)];
 		          _blossom_set->join(_graph.target(arc), base);
+		        }
+		      }
 		      (*_blossom_rep)[_blossom_set->find(nca)] = nca;
+		    }
 		    void extendOnArc(const Arc& a) {
 		      Node base = _graph.source(a);
 		      Node odd = _graph.target(a);
 		      (*_ear)[odd] = _graph.oppositeArc(a);
 		      Node even = _graph.target((*_matching)[odd]);
 		      (*_blossom_rep)[_blossom_set->insert(even)] = even;
 		      (*_status)[odd] = ODD;
 		      (*_status)[even] = EVEN;
 		      int tree = _tree_set->find((*_blossom_rep)[_blossom_set->find(base)]);
 		      _tree_set->insert(odd, tree);
 		      _tree_set->insert(even, tree);
 		      _node_queue[_last++] = even;
+		    }
 		    void augmentOnArc(const Arc& a) {
 		      Node even = _graph.source(a);
 		      Node odd = _graph.target(a);
 		      int tree = _tree_set->find((*_blossom_rep)[_blossom_set->find(even)]);
 		      (*_matching)[odd] = _graph.oppositeArc(a);
 		      (*_status)[odd] = MATCHED;
 		      Arc arc = (*_matching)[even];
 		      (*_matching)[even] = a;
 		      while (arc != INVALID) {
 		        odd = _graph.target(arc);
 		        arc = (*_ear)[odd];
 		        even = _graph.target(arc);
 		        (*_matching)[odd] = arc;
 		        arc = (*_matching)[even];
 		        (*_matching)[even] = _graph.oppositeArc((*_matching)[odd]);
+		      }
 		      for (typename TreeSet::ItemIt it(*_tree_set, tree);
 		           it != INVALID; ++it) {
 		        if ((*_status)[it] == ODD) {
 		          (*_status)[it] = MATCHED;
 		        } else {
 		          int blossom = _blossom_set->find(it);
 		          for (typename BlossomSet::ItemIt jt(*_blossom_set, blossom);
 		               jt != INVALID; ++jt) {
 		            (*_status)[jt] = MATCHED;
+		          }
 		          _blossom_set->eraseClass(blossom);
+		        }
+		      }
 		      _tree_set->eraseClass(tree);
+		    }
 		  public:
 		    /// \brief Constructor
 		    ///
 		    /// Constructor.
 		    MaxMatching(const Graph& graph)
 		      : _graph(graph), _matching(0), _status(0), _ear(0),
 		        _blossom_set_index(0), _blossom_set(0), _blossom_rep(0),
 		        _tree_set_index(0), _tree_set(0) {}
 		    ~MaxMatching() {
 		      destroyStructures();
+		    }
-		    /// \name Execution control
+		    /// \name Execution Control
 		    /// The simplest way to execute the algorithm is to use the
 		    /// \c run() member function.
 		    /// \n
 		    /// If you need better control on the execution, you must call
 		    /// \ref init(), \ref greedyInit() or \ref matchingInit()
 		    /// functions first, then you can start the algorithm with the \ref
-		    /// startSparse() or startDense() functions.
+		    /// \c run() member function.\n
 		    /// If you need better control on the execution, you have to call
 		    /// one of the functions \ref init(), \ref greedyInit() or
 		    /// \ref matchingInit() first, then you can start the algorithm with
 		    /// \ref startSparse() or \ref startDense().
 		    ///@{
-		    /// \brief Sets the actual matching to the empty matching.
+		    /// \brief Set the initial matching to the empty matching.
 		    ///
 		    /// Sets the actual matching to the empty matching.
 		    ///
 		    /// This function sets the initial matching to the empty matching.
 		    void init() {
 		      createStructures();
 		      for(NodeIt n(_graph); n != INVALID; ++n) {
 		        (*_matching)[n] = INVALID;
 		        (*_status)[n] = UNMATCHED;
+		      }
+		    }
-		    ///\brief Finds an initial matching in a greedy way
+		    /// \brief Find an initial matching in a greedy way.
 		    ///
-		    ///It finds an initial matching in a greedy way.
+		    /// This function finds an initial matching in a greedy way.
 		    void greedyInit() {
 		      createStructures();
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        (*_matching)[n] = INVALID;
 		        (*_status)[n] = UNMATCHED;
+		      }
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        if ((*_matching)[n] == INVALID) {
 		          for (OutArcIt a(_graph, n); a != INVALID ; ++a) {
 		            Node v = _graph.target(a);
 		            if ((*_matching)[v] == INVALID && v != n) {
 		              (*_matching)[n] = a;
 		              (*_status)[n] = MATCHED;
 		              (*_matching)[v] = _graph.oppositeArc(a);
 		              (*_status)[v] = MATCHED;
 		              break;
+		            }
+		          }
+		        }
+		      }
+		    }
-		    /// \brief Initialize the matching from a map containing.
 		    /// \brief Initialize the matching from a map.
 		    ///
 		    /// Initialize the matching from a \c bool valued \c Edge map. This
 		    /// map must have the property that there are no two incident edges
-		    /// with true value, ie. it contains a matching.
+		    /// This function initializes the matching from a \c bool valued edge
 		    /// map. This map should have the property that there are no two incident
 		    /// edges with \c true value, i.e. it really contains a matching.
 		    /// \return \c true if the map contains a matching.
 		    template <typename MatchingMap>
 		    bool matchingInit(const MatchingMap& matching) {
 		      createStructures();
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        (*_matching)[n] = INVALID;
 		        (*_status)[n] = UNMATCHED;
+		      }
 		      for(EdgeIt e(_graph); e!=INVALID; ++e) {
 		        if (matching[e]) {
 		          Node u = _graph.u(e);
 		          if ((*_matching)[u] != INVALID) return false;
 		          (*_matching)[u] = _graph.direct(e, true);
 		          (*_status)[u] = MATCHED;
 		          Node v = _graph.v(e);
 		          if ((*_matching)[v] != INVALID) return false;
 		          (*_matching)[v] = _graph.direct(e, false);
 		          (*_status)[v] = MATCHED;
+		        }
+		      }
 		      return true;
+		    }
-		    /// \brief Starts Edmonds' algorithm
+		    /// \brief Start Edmonds' algorithm
 		    ///
-		    /// If runs the original Edmonds' algorithm.
+		    /// This function runs the original Edmonds' algorithm.
 		    ///
 		    /// \pre \ref Init(), \ref greedyInit() or \ref matchingInit() must be
 		    /// called before using this function.
 		    void startSparse() {
 		      for(NodeIt n(_graph); n != INVALID; ++n) {
 		        if ((*_status)[n] == UNMATCHED) {
 		          (*_blossom_rep)[_blossom_set->insert(n)] = n;
 		          _tree_set->insert(n);
 		          (*_status)[n] = EVEN;
 		          processSparse(n);
+		        }
+		      }
+		    }
-		    /// \brief Starts Edmonds' algorithm.
+		    /// \brief Start Edmonds' algorithm with a heuristic improvement
 		    /// for dense graphs
 		    ///
-		    /// It runs Edmonds' algorithm with a heuristic of postponing
+		    /// This function runs Edmonds' algorithm with a heuristic of postponing
 		    /// shrinks, therefore resulting in a faster algorithm for dense graphs.
 		    ///
 		    /// \pre \ref Init(), \ref greedyInit() or \ref matchingInit() must be
 		    /// called before using this function.
 		    void startDense() {
 		      for(NodeIt n(_graph); n != INVALID; ++n) {
 		        if ((*_status)[n] == UNMATCHED) {
 		          (*_blossom_rep)[_blossom_set->insert(n)] = n;
 		          _tree_set->insert(n);
 		          (*_status)[n] = EVEN;
 		          processDense(n);
+		        }
+		      }
+		    }
-		    /// \brief Runs Edmonds' algorithm
+		    /// \brief Run Edmonds' algorithm
 		    ///
 		    /// Runs Edmonds' algorithm for sparse graphs (<tt>m<2*n</tt>)
 		    /// or Edmonds' algorithm with a heuristic of
-		    /// postponing shrinks for dense graphs.
+		    /// This function runs Edmonds' algorithm. An additional heuristic of
 		    /// postponing shrinks is used for relatively dense graphs
 		    /// (for which <tt>m>=2*n</tt> holds).
 		    void run() {
 		      if (countEdges(_graph) < 2 * countNodes(_graph)) {
 		        greedyInit();
 		        startSparse();
 		      } else {
 		        init();
 		        startDense();
+		      }
+		    }
 		    /// @}
 		    /// \name Primal solution
 		    /// Functions to get the primal solution, ie. the matching.
 		    /// \name Primal Solution
 		    /// Functions to get the primal solution, i.e. the maximum matching.
 		    /// @{
-		    ///\brief Returns the size of the current matching.
+		    /// \brief Return the size (cardinality) of the matching.
 		    ///
 		    ///Returns the size of the current matching. After \ref
 		    ///run() it returns the size of the maximum matching in the graph.
 		    /// This function returns the size (cardinality) of the current matching.
 		    /// After run() it returns the size of the maximum matching in the graph.
 		    int matchingSize() const {
 		      int size = 0;
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        if ((*_matching)[n] != INVALID) {
 		          ++size;
+		        }
+		      }
 		      return size / 2;
+		    }
-		    /// \brief Returns true when the edge is in the matching.
+		    /// \brief Return \c true if the given edge is in the matching.
 		    ///
-		    /// Returns true when the edge is in the matching.
+		    /// This function returns \c true if the given edge is in the current
 		    /// matching.
 		    bool matching(const Edge& edge) const {
 		      return edge == (*_matching)[_graph.u(edge)];
+		    }
-		    /// \brief Returns the matching edge incident to the given node.
+		    /// \brief Return the matching arc (or edge) incident to the given node.
 		    ///
 		    /// Returns the matching edge of a \c node in the actual matching or
 		    /// INVALID if the \c node is not covered by the actual matching.
 		    /// This function returns the matching arc (or edge) incident to the
 		    /// given node in the current matching or \c INVALID if the node is
 		    /// not covered by the matching.
 		    Arc matching(const Node& n) const {
 		      return (*_matching)[n];
+		    }
-		    ///\brief Returns the mate of a node in the actual matching.
+		    /// \brief Return the mate of the given node.
 		    ///
 		    ///Returns the mate of a \c node in the actual matching or
 		    ///INVALID if the \c node is not covered by the actual matching.
 		    /// This function returns the mate of the given node in the current
 		    /// matching or \c INVALID if the node is not covered by the matching.
 		    Node mate(const Node& n) const {
 		      return (*_matching)[n] != INVALID ?
 		        _graph.target((*_matching)[n]) : INVALID;
+		    }
 		    /// @}
 		    /// \name Dual solution
 		    /// Functions to get the dual solution, ie. the decomposition.
 		    /// \name Dual Solution
 		    /// Functions to get the dual solution, i.e. the Gallai-Edmonds
 		    /// decomposition.
 		    /// @{
-		    /// \brief Returns the class of the node in the Edmonds-Gallai
+		    /// \brief Return the status of the given node in the Edmonds-Gallai
 		    /// decomposition.
 		    ///
 		    /// Returns the class of the node in the Edmonds-Gallai
 		    /// decomposition.
 		    /// This function returns the \ref Status "status" of the given node
 		    /// in the Edmonds-Gallai decomposition.
 		    Status decomposition(const Node& n) const {
 		      return (*_status)[n];
+		    }
-		    /// \brief Returns true when the node is in the barrier.
+		    /// \brief Return \c true if the given node is in the barrier.
 		    ///
-		    /// Returns true when the node is in the barrier.
+		    /// This function returns \c true if the given node is in the barrier.
 		    bool barrier(const Node& n) const {
 		      return (*_status)[n] == ODD;
+		    }
 		    /// @}
 		  };
 		  /// \ingroup matching
 		  ///
 		  /// \brief Weighted matching in general graphs
 		  ///
 		  /// This class provides an efficient implementation of Edmond's
 		  /// maximum weighted matching algorithm. The implementation is based
 		  /// on extensive use of priority queues and provides
 		  /// \f$O(nm\log n)\f$ time complexity.
 		  ///
 		  /// The maximum weighted matching problem is to find undirected
 		  /// edges in the graph with maximum overall weight and no two of
 		  /// them shares their ends. The problem can be formulated with the
 		  /// following linear program.
 		  /// The maximum weighted matching problem is to find a subset of the
 		  /// edges in an undirected graph with maximum overall weight for which
 		  /// each node has at most one incident edge.
 		  /// It can be formulated with the following linear program.
 		  /// \f[ \sum_{e \in \delta(u)}x_e \le 1 \quad \forall u\in V\f]
 		  /** \f[ \sum_{e \in \gamma(B)}x_e \le \frac{\vert B \vert - 1}{2}
 		      \quad \forall B\in\mathcal{O}\f] */
 		  /// \f[x_e \ge 0\quad \forall e\in E\f]
 		  /// \f[\max \sum_{e\in E}x_ew_e\f]
 		  /// where \f$\delta(X)\f$ is the set of edges incident to a node in
 		  /// \f$X\f$, \f$\gamma(X)\f$ is the set of edges with both ends in
 		  /// \f$X\f$ and \f$\mathcal{O}\f$ is the set of odd cardinality
 		  /// subsets of the nodes.
 		  ///
 		  /// The algorithm calculates an optimal matching and a proof of the
 		  /// optimality. The solution of the dual problem can be used to check
 		  /// the result of the algorithm. The dual linear problem is the
 		  /// following.
 		  /** \f[ y_u + y_v + \sum_{B \in \mathcal{O}, uv \in \gamma(B)}
 		      z_B \ge w_{uv} \quad \forall uv\in E\f] */
 		  /// \f[y_u \ge 0 \quad \forall u \in V\f]
 		  /// \f[z_B \ge 0 \quad \forall B \in \mathcal{O}\f]
 		  /** \f[\min \sum_{u \in V}y_u + \sum_{B \in \mathcal{O}}
 		      \frac{\vert B \vert - 1}{2}z_B\f] */
 		  ///
 		  /// The algorithm can be executed with \c run() or the \c init() and
 		  /// then the \c start() member functions. After it the matching can
 		  /// be asked with \c matching() or mate() functions. The dual
 		  /// solution can be get with \c nodeValue(), \c blossomNum() and \c
 		  /// blossomValue() members and \ref MaxWeightedMatching::BlossomIt
 		  /// "BlossomIt" nested class, which is able to iterate on the nodes
 		  /// of a blossom. If the value type is integral then the dual
 		  /// solution is multiplied by \ref MaxWeightedMatching::dualScale "4".
 		  /// The algorithm can be executed with the run() function.
 		  /// After it the matching (the primal solution) and the dual solution
 		  /// can be obtained using the query functions and the
 		  /// \ref MaxWeightedMatching::BlossomIt "BlossomIt" nested class,
 		  /// which is able to iterate on the nodes of a blossom.
 		  /// If the value type is integer, then the dual solution is multiplied
 		  /// by \ref MaxWeightedMatching::dualScale "4".
 		  ///
 		  /// \tparam GR The graph type the algorithm runs on.
 		  /// \tparam WM The type edge weight map. The default type is
 		  /// \ref concepts::Graph::EdgeMap "GR::EdgeMap<int>".
 		#ifdef DOXYGEN
 		  template <typename GR, typename WM>
 		#else
 		  template <typename GR,
 		            typename WM = typename GR::template EdgeMap<int> >
 		#endif
 		  class MaxWeightedMatching {
 		  public:
-		    ///\e
+		    /// The graph type of the algorithm
 		    typedef GR Graph;
-		    ///\e
+		    /// The type of the edge weight map
 		    typedef WM WeightMap;
-		    ///\e
+		    /// The value type of the edge weights
 		    typedef typename WeightMap::Value Value;
 		    typedef typename Graph::template NodeMap<typename Graph::Arc>
 		    MatchingMap;
 		    /// \brief Scaling factor for dual solution
 		    ///
-		    /// Scaling factor for dual solution, it is equal to 4 or 1
+		    /// Scaling factor for dual solution. It is equal to 4 or 1
 		    /// according to the value type.
 		    static const int dualScale =
 		      std::numeric_limits<Value>::is_integer ? 4 : 1;
 		    typedef typename Graph::template NodeMap<typename Graph::Arc>
 		    MatchingMap;
 		  private:
 		    TEMPLATE_GRAPH_TYPEDEFS(Graph);
 		    typedef typename Graph::template NodeMap<Value> NodePotential;
 		    typedef std::vector<Node> BlossomNodeList;
 		    struct BlossomVariable {
 		      int begin, end;
 		      Value value;
 		      BlossomVariable(int _begin, int _end, Value _value)
 		        : begin(_begin), end(_end), value(_value) {}
 		    };
 		    typedef std::vector<BlossomVariable> BlossomPotential;
 		    const Graph& _graph;
 		    const WeightMap& _weight;
 		    MatchingMap* _matching;
 		    NodePotential* _node_potential;
 		    BlossomPotential _blossom_potential;
 		    BlossomNodeList _blossom_node_list;
 		    int _node_num;
 		    int _blossom_num;
 		    typedef RangeMap<int> IntIntMap;
 		    enum Status {
 		      EVEN = -1, MATCHED = 0, ODD = 1, UNMATCHED = -2
 		    };
 		    typedef HeapUnionFind<Value, IntNodeMap> BlossomSet;
 		    struct BlossomData {
 		      int tree;
 		      Status status;
 		      Arc pred, next;
 		      Value pot, offset;
 		      Node base;
 		    };
 		    IntNodeMap *_blossom_index;
 		    BlossomSet *_blossom_set;
 		    RangeMap<BlossomData>* _blossom_data;
 		    IntNodeMap *_node_index;
 		    IntArcMap *_node_heap_index;
 		    struct NodeData {
 		      NodeData(IntArcMap& node_heap_index)
 		        : heap(node_heap_index) {}
 		      int blossom;
 		      Value pot;
 		      BinHeap<Value, IntArcMap> heap;
 		      std::map<int, Arc> heap_index;
 		      int tree;
 		    };
 		    RangeMap<NodeData>* _node_data;
 		    typedef ExtendFindEnum<IntIntMap> TreeSet;
 		    IntIntMap *_tree_set_index;
 		    TreeSet *_tree_set;
 		    IntNodeMap *_delta1_index;
 		    BinHeap<Value, IntNodeMap> *_delta1;
 		    IntIntMap *_delta2_index;
 		    BinHeap<Value, IntIntMap> *_delta2;
 		    IntEdgeMap *_delta3_index;
 		    BinHeap<Value, IntEdgeMap> *_delta3;
 		    IntIntMap *_delta4_index;
 		    BinHeap<Value, IntIntMap> *_delta4;
 		    Value _delta_sum;
 		    void createStructures() {
 		      _node_num = countNodes(_graph);
 		      _blossom_num = _node_num * 3 / 2;
 		      if (!_matching) {
 		        _matching = new MatchingMap(_graph);
+		      }
 		      if (!_node_potential) {
 		        _node_potential = new NodePotential(_graph);
+		      }
 		      if (!_blossom_set) {
 		        _blossom_index = new IntNodeMap(_graph);
 		        _blossom_set = new BlossomSet(*_blossom_index);
 		        _blossom_data = new RangeMap<BlossomData>(_blossom_num);
+		      }
 		      if (!_node_index) {
 		        _node_index = new IntNodeMap(_graph);
 		        _node_heap_index = new IntArcMap(_graph);
 		        _node_data = new RangeMap<NodeData>(_node_num,
 		                                              NodeData(*_node_heap_index));
+		      }
 		      if (!_tree_set) {
 		        _tree_set_index = new IntIntMap(_blossom_num);
 		        _tree_set = new TreeSet(*_tree_set_index);
+		      }
 		      if (!_delta1) {
 		        _delta1_index = new IntNodeMap(_graph);
 		        _delta1 = new BinHeap<Value, IntNodeMap>(*_delta1_index);
+		      }
 		      if (!_delta2) {
 		        _delta2_index = new IntIntMap(_blossom_num);
 		        _delta2 = new BinHeap<Value, IntIntMap>(*_delta2_index);
+		      }
 		      if (!_delta3) {
 		        _delta3_index = new IntEdgeMap(_graph);
 		        _delta3 = new BinHeap<Value, IntEdgeMap>(*_delta3_index);
+		      }
 		      if (!_delta4) {
 		        _delta4_index = new IntIntMap(_blossom_num);
 		        _delta4 = new BinHeap<Value, IntIntMap>(*_delta4_index);
+		      }
+		    }
 		    void destroyStructures() {
 		      _node_num = countNodes(_graph);
 		      _blossom_num = _node_num * 3 / 2;
 		      if (_matching) {
 		        delete _matching;
+		      }
 		      if (_node_potential) {
 		        delete _node_potential;
+		      }
 		      if (_blossom_set) {
 		        delete _blossom_index;
 		        delete _blossom_set;
 		        delete _blossom_data;
+		      }
 		      if (_node_index) {
 		        delete _node_index;
 		        delete _node_heap_index;
 		        delete _node_data;
+		      }
 		      if (_tree_set) {
 		        delete _tree_set_index;
 		        delete _tree_set;
+		      }
 		      if (_delta1) {
 		        delete _delta1_index;
 		        delete _delta1;
+		      }
 		      if (_delta2) {
 		        delete _delta2_index;
 		        delete _delta2;
+		      }
 		      if (_delta3) {
 		        delete _delta3_index;
 		        delete _delta3;
+		      }
 		      if (_delta4) {
 		        delete _delta4_index;
 		        delete _delta4;
+		      }
+		    }
 		    void matchedToEven(int blossom, int tree) {
 		      if (_delta2->state(blossom) == _delta2->IN_HEAP) {
 		        _delta2->erase(blossom);
+		      }
 		      if (!_blossom_set->trivial(blossom)) {
 		        (*_blossom_data)[blossom].pot -=
 * (_delta_sum - (*_blossom_data)[blossom].offset);
+		      }
 		      for (typename BlossomSet::ItemIt n(*_blossom_set, blossom);
 		           n != INVALID; ++n) {
 		        _blossom_set->increase(n, std::numeric_limits<Value>::max());
 		        int ni = (*_node_index)[n];
@@ -1442,753 +1464,798 @@
 		      oddToMatched(blossom);
 		      if (_delta2->state(blossom) == _delta2->IN_HEAP) {
 		        _delta2->erase(blossom);
+		      }
 		      std::vector<int> subblossoms;
 		      _blossom_set->split(blossom, std::back_inserter(subblossoms));
 		      Value offset = (*_blossom_data)[blossom].offset;
 		      int b = _blossom_set->find(_graph.source(pred));
 		      int d = _blossom_set->find(_graph.source(next));
 		      int ib = -1, id = -1;
 		      for (int i = 0; i < int(subblossoms.size()); ++i) {
 		        if (subblossoms[i] == b) ib = i;
 		        if (subblossoms[i] == d) id = i;
 		        (*_blossom_data)[subblossoms[i]].offset = offset;
 		        if (!_blossom_set->trivial(subblossoms[i])) {
 		          (*_blossom_data)[subblossoms[i]].pot -= 2 * offset;
+		        }
 		        if (_blossom_set->classPrio(subblossoms[i]) !=
 		            std::numeric_limits<Value>::max()) {
 		          _delta2->push(subblossoms[i],
 		                        _blossom_set->classPrio(subblossoms[i]) -
 		                        (*_blossom_data)[subblossoms[i]].offset);
+		        }
+		      }
 		      if (id > ib ? ((id - ib) % 2 == 0) : ((ib - id) % 2 == 1)) {
 		        for (int i = (id + 1) % subblossoms.size();
 		             i != ib; i = (i + 2) % subblossoms.size()) {
 		          int sb = subblossoms[i];
 		          int tb = subblossoms[(i + 1) % subblossoms.size()];
 		          (*_blossom_data)[sb].next =
 		            _graph.oppositeArc((*_blossom_data)[tb].next);
+		        }
 		        for (int i = ib; i != id; i = (i + 2) % subblossoms.size()) {
 		          int sb = subblossoms[i];
 		          int tb = subblossoms[(i + 1) % subblossoms.size()];
 		          int ub = subblossoms[(i + 2) % subblossoms.size()];
 		          (*_blossom_data)[sb].status = ODD;
 		          matchedToOdd(sb);
 		          _tree_set->insert(sb, tree);
 		          (*_blossom_data)[sb].pred = pred;
 		          (*_blossom_data)[sb].next =
 		                           _graph.oppositeArc((*_blossom_data)[tb].next);
 		          pred = (*_blossom_data)[ub].next;
 		          (*_blossom_data)[tb].status = EVEN;
 		          matchedToEven(tb, tree);
 		          _tree_set->insert(tb, tree);
 		          (*_blossom_data)[tb].pred = (*_blossom_data)[tb].next;
+		        }
 		        (*_blossom_data)[subblossoms[id]].status = ODD;
 		        matchedToOdd(subblossoms[id]);
 		        _tree_set->insert(subblossoms[id], tree);
 		        (*_blossom_data)[subblossoms[id]].next = next;
 		        (*_blossom_data)[subblossoms[id]].pred = pred;
 		      } else {
 		        for (int i = (ib + 1) % subblossoms.size();
 		             i != id; i = (i + 2) % subblossoms.size()) {
 		          int sb = subblossoms[i];
 		          int tb = subblossoms[(i + 1) % subblossoms.size()];
 		          (*_blossom_data)[sb].next =
 		            _graph.oppositeArc((*_blossom_data)[tb].next);
+		        }
 		        for (int i = id; i != ib; i = (i + 2) % subblossoms.size()) {
 		          int sb = subblossoms[i];
 		          int tb = subblossoms[(i + 1) % subblossoms.size()];
 		          int ub = subblossoms[(i + 2) % subblossoms.size()];
 		          (*_blossom_data)[sb].status = ODD;
 		          matchedToOdd(sb);
 		          _tree_set->insert(sb, tree);
 		          (*_blossom_data)[sb].next = next;
 		          (*_blossom_data)[sb].pred =
 		            _graph.oppositeArc((*_blossom_data)[tb].next);
 		          (*_blossom_data)[tb].status = EVEN;
 		          matchedToEven(tb, tree);
 		          _tree_set->insert(tb, tree);
 		          (*_blossom_data)[tb].pred =
 		            (*_blossom_data)[tb].next =
 		            _graph.oppositeArc((*_blossom_data)[ub].next);
 		          next = (*_blossom_data)[ub].next;
+		        }
 		        (*_blossom_data)[subblossoms[ib]].status = ODD;
 		        matchedToOdd(subblossoms[ib]);
 		        _tree_set->insert(subblossoms[ib], tree);
 		        (*_blossom_data)[subblossoms[ib]].next = next;
 		        (*_blossom_data)[subblossoms[ib]].pred = pred;
+		      }
 		      _tree_set->erase(blossom);
+		    }
 		    void extractBlossom(int blossom, const Node& base, const Arc& matching) {
 		      if (_blossom_set->trivial(blossom)) {
 		        int bi = (*_node_index)[base];
 		        Value pot = (*_node_data)[bi].pot;
 		        (*_matching)[base] = matching;
 		        _blossom_node_list.push_back(base);
 		        (*_node_potential)[base] = pot;
 		      } else {
 		        Value pot = (*_blossom_data)[blossom].pot;
 		        int bn = _blossom_node_list.size();
 		        std::vector<int> subblossoms;
 		        _blossom_set->split(blossom, std::back_inserter(subblossoms));
 		        int b = _blossom_set->find(base);
 		        int ib = -1;
 		        for (int i = 0; i < int(subblossoms.size()); ++i) {
 		          if (subblossoms[i] == b) { ib = i; break; }
+		        }
 		        for (int i = 1; i < int(subblossoms.size()); i += 2) {
 		          int sb = subblossoms[(ib + i) % subblossoms.size()];
 		          int tb = subblossoms[(ib + i + 1) % subblossoms.size()];
 		          Arc m = (*_blossom_data)[tb].next;
 		          extractBlossom(sb, _graph.target(m), _graph.oppositeArc(m));
 		          extractBlossom(tb, _graph.source(m), m);
+		        }
 		        extractBlossom(subblossoms[ib], base, matching);
 		        int en = _blossom_node_list.size();
 		        _blossom_potential.push_back(BlossomVariable(bn, en, pot));
+		      }
+		    }
 		    void extractMatching() {
 		      std::vector<int> blossoms;
 		      for (typename BlossomSet::ClassIt c(*_blossom_set); c != INVALID; ++c) {
 		        blossoms.push_back(c);
+		      }
 		      for (int i = 0; i < int(blossoms.size()); ++i) {
 		        if ((*_blossom_data)[blossoms[i]].status == MATCHED) {
 		          Value offset = (*_blossom_data)[blossoms[i]].offset;
 		          (*_blossom_data)[blossoms[i]].pot += 2 * offset;
 		          for (typename BlossomSet::ItemIt n(*_blossom_set, blossoms[i]);
 		               n != INVALID; ++n) {
 		            (*_node_data)[(*_node_index)[n]].pot -= offset;
+		          }
 		          Arc matching = (*_blossom_data)[blossoms[i]].next;
 		          Node base = _graph.source(matching);
 		          extractBlossom(blossoms[i], base, matching);
 		        } else {
 		          Node base = (*_blossom_data)[blossoms[i]].base;
 		          extractBlossom(blossoms[i], base, INVALID);
+		        }
+		      }
+		    }
 		  public:
 		    /// \brief Constructor
 		    ///
 		    /// Constructor.
 		    MaxWeightedMatching(const Graph& graph, const WeightMap& weight)
 		      : _graph(graph), _weight(weight), _matching(0),
 		        _node_potential(0), _blossom_potential(), _blossom_node_list(),
 		        _node_num(0), _blossom_num(0),
 		        _blossom_index(0), _blossom_set(0), _blossom_data(0),
 		        _node_index(0), _node_heap_index(0), _node_data(0),
 		        _tree_set_index(0), _tree_set(0),
 		        _delta1_index(0), _delta1(0),
 		        _delta2_index(0), _delta2(0),
 		        _delta3_index(0), _delta3(0),
 		        _delta4_index(0), _delta4(0),
 		        _delta_sum() {}
 		    ~MaxWeightedMatching() {
 		      destroyStructures();
+		    }
-		    /// \name Execution control
+		    /// \name Execution Control
 		    /// The simplest way to execute the algorithm is to use the
-		    /// \c run() member function.
+		    /// \ref run() member function.
 		    ///@{
 		    /// \brief Initialize the algorithm
 		    ///
-		    /// Initialize the algorithm
+		    /// This function initializes the algorithm.
 		    void init() {
 		      createStructures();
 		      for (ArcIt e(_graph); e != INVALID; ++e) {
 		        (*_node_heap_index)[e] = BinHeap<Value, IntArcMap>::PRE_HEAP;
+		      }
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        (*_delta1_index)[n] = _delta1->PRE_HEAP;
+		      }
 		      for (EdgeIt e(_graph); e != INVALID; ++e) {
 		        (*_delta3_index)[e] = _delta3->PRE_HEAP;
+		      }
 		      for (int i = 0; i < _blossom_num; ++i) {
 		        (*_delta2_index)[i] = _delta2->PRE_HEAP;
 		        (*_delta4_index)[i] = _delta4->PRE_HEAP;
+		      }
 		      int index = 0;
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        Value max = 0;
 		        for (OutArcIt e(_graph, n); e != INVALID; ++e) {
 		          if (_graph.target(e) == n) continue;
 		          if ((dualScale * _weight[e]) / 2 > max) {
 		            max = (dualScale * _weight[e]) / 2;
+		          }
+		        }
 		        (*_node_index)[n] = index;
 		        (*_node_data)[index].pot = max;
 		        _delta1->push(n, max);
 		        int blossom =
 		          _blossom_set->insert(n, std::numeric_limits<Value>::max());
 		        _tree_set->insert(blossom);
 		        (*_blossom_data)[blossom].status = EVEN;
 		        (*_blossom_data)[blossom].pred = INVALID;
 		        (*_blossom_data)[blossom].next = INVALID;
 		        (*_blossom_data)[blossom].pot = 0;
 		        (*_blossom_data)[blossom].offset = 0;
 		        ++index;
+		      }
 		      for (EdgeIt e(_graph); e != INVALID; ++e) {
 		        int si = (*_node_index)[_graph.u(e)];
 		        int ti = (*_node_index)[_graph.v(e)];
 		        if (_graph.u(e) != _graph.v(e)) {
 		          _delta3->push(e, ((*_node_data)[si].pot + (*_node_data)[ti].pot -
 		                            dualScale * _weight[e]) / 2);
+		        }
+		      }
+		    }
-		    /// \brief Starts the algorithm
+		    /// \brief Start the algorithm
 		    ///
-		    /// Starts the algorithm
+		    /// This function starts the algorithm.
 		    ///
 		    /// \pre \ref init() must be called before using this function.
 		    void start() {
 		      enum OpType {
 		        D1, D2, D3, D4
 		      };
 		      int unmatched = _node_num;
 		      while (unmatched > 0) {
 		        Value d1 = !_delta1->empty() ?
 		          _delta1->prio() : std::numeric_limits<Value>::max();
 		        Value d2 = !_delta2->empty() ?
 		          _delta2->prio() : std::numeric_limits<Value>::max();
 		        Value d3 = !_delta3->empty() ?
 		          _delta3->prio() : std::numeric_limits<Value>::max();
 		        Value d4 = !_delta4->empty() ?
 		          _delta4->prio() : std::numeric_limits<Value>::max();
 		        _delta_sum = d1; OpType ot = D1;
 		        if (d2 < _delta_sum) { _delta_sum = d2; ot = D2; }
 		        if (d3 < _delta_sum) { _delta_sum = d3; ot = D3; }
 		        if (d4 < _delta_sum) { _delta_sum = d4; ot = D4; }
 		        switch (ot) {
 		        case D1:
+		          {
 		            Node n = _delta1->top();
 		            unmatchNode(n);
 		            --unmatched;
+		          }
 		          break;
 		        case D2:
+		          {
 		            int blossom = _delta2->top();
 		            Node n = _blossom_set->classTop(blossom);
 		            Arc e = (*_node_data)[(*_node_index)[n]].heap.top();
 		            extendOnArc(e);
+		          }
 		          break;
 		        case D3:
+		          {
 		            Edge e = _delta3->top();
 		            int left_blossom = _blossom_set->find(_graph.u(e));
 		            int right_blossom = _blossom_set->find(_graph.v(e));
 		            if (left_blossom == right_blossom) {
 		              _delta3->pop();
 		            } else {
 		              int left_tree;
 		              if ((*_blossom_data)[left_blossom].status == EVEN) {
 		                left_tree = _tree_set->find(left_blossom);
 		              } else {
 		                left_tree = -1;
 		                ++unmatched;
+		              }
 		              int right_tree;
 		              if ((*_blossom_data)[right_blossom].status == EVEN) {
 		                right_tree = _tree_set->find(right_blossom);
 		              } else {
 		                right_tree = -1;
 		                ++unmatched;
+		              }
 		              if (left_tree == right_tree) {
 		                shrinkOnEdge(e, left_tree);
 		              } else {
 		                augmentOnEdge(e);
 		                unmatched -= 2;
+		              }
+		            }
 		          } break;
 		        case D4:
 		          splitBlossom(_delta4->top());
 		          break;
+		        }
+		      }
 		      extractMatching();
+		    }
-		    /// \brief Runs %MaxWeightedMatching algorithm.
+		    /// \brief Run the algorithm.
 		    ///
 		    /// This method runs the %MaxWeightedMatching algorithm.
+		    /// This method runs the \c %MaxWeightedMatching algorithm.
 		    ///
 		    /// \note mwm.run() is just a shortcut of the following code.
 		    /// \code
 		    ///   mwm.init();
 		    ///   mwm.start();
 		    /// \endcode
 		    void run() {
 		      init();
 		      start();
+		    }
 		    /// @}
 		    /// \name Primal solution
 		    /// Functions to get the primal solution, ie. the matching.
 		    /// \name Primal Solution
 		    /// Functions to get the primal solution, i.e. the maximum weighted
 		    /// matching.\n
 		    /// Either \ref run() or \ref start() function should be called before
 		    /// using them.
 		    /// @{
-		    /// \brief Returns the weight of the matching.
+		    /// \brief Return the weight of the matching.
 		    ///
-		    /// Returns the weight of the matching.
+		    /// This function returns the weight of the found matching.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Value matchingValue() const {
 		      Value sum = 0;
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        if ((*_matching)[n] != INVALID) {
 		          sum += _weight[(*_matching)[n]];
+		        }
+		      }
 		      return sum /= 2;
+		    }
-		    /// \brief Returns the cardinality of the matching.
+		    /// \brief Return the size (cardinality) of the matching.
 		    ///
-		    /// Returns the cardinality of the matching.
+		    /// This function returns the size (cardinality) of the found matching.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    int matchingSize() const {
 		      int num = 0;
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        if ((*_matching)[n] != INVALID) {
 		          ++num;
+		        }
+		      }
 		      return num /= 2;
+		    }
-		    /// \brief Returns true when the edge is in the matching.
+		    /// \brief Return \c true if the given edge is in the matching.
 		    ///
-		    /// Returns true when the edge is in the matching.
+		    /// This function returns \c true if the given edge is in the found
 		    /// matching.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    bool matching(const Edge& edge) const {
 		      return edge == (*_matching)[_graph.u(edge)];
+		    }
-		    /// \brief Returns the incident matching arc.
+		    /// \brief Return the matching arc (or edge) incident to the given node.
 		    ///
 		    /// Returns the incident matching arc from given node. If the
 		    /// node is not matched then it gives back \c INVALID.
 		    /// This function returns the matching arc (or edge) incident to the
 		    /// given node in the found matching or \c INVALID if the node is
 		    /// not covered by the matching.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Arc matching(const Node& node) const {
 		      return (*_matching)[node];
+		    }
-		    /// \brief Returns the mate of the node.
+		    /// \brief Return the mate of the given node.
 		    ///
 		    /// Returns the adjancent node in a mathcing arc. If the node is
 		    /// not matched then it gives back \c INVALID.
 		    /// This function returns the mate of the given node in the found
 		    /// matching or \c INVALID if the node is not covered by the matching.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Node mate(const Node& node) const {
 		      return (*_matching)[node] != INVALID ?
 		        _graph.target((*_matching)[node]) : INVALID;
+		    }
 		    /// @}
 		    /// \name Dual solution
 		    /// Functions to get the dual solution.
 		    /// \name Dual Solution
 		    /// Functions to get the dual solution.\n
 		    /// Either \ref run() or \ref start() function should be called before
 		    /// using them.
 		    /// @{
-		    /// \brief Returns the value of the dual solution.
+		    /// \brief Return the value of the dual solution.
 		    ///
 		    /// Returns the value of the dual solution. It should be equal to
 		    /// the primal value scaled by \ref dualScale "dual scale".
 		    /// This function returns the value of the dual solution.
 		    /// It should be equal to the primal value scaled by \ref dualScale
 		    /// "dual scale".
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Value dualValue() const {
 		      Value sum = 0;
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        sum += nodeValue(n);
+		      }
 		      for (int i = 0; i < blossomNum(); ++i) {
 		        sum += blossomValue(i) * (blossomSize(i) / 2);
+		      }
 		      return sum;
+		    }
-		    /// \brief Returns the value of the node.
+		    /// \brief Return the dual value (potential) of the given node.
 		    ///
-		    /// Returns the the value of the node.
+		    /// This function returns the dual value (potential) of the given node.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Value nodeValue(const Node& n) const {
 		      return (*_node_potential)[n];
+		    }
-		    /// \brief Returns the number of the blossoms in the basis.
+		    /// \brief Return the number of the blossoms in the basis.
 		    ///
-		    /// Returns the number of the blossoms in the basis.
+		    /// This function returns the number of the blossoms in the basis.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    /// \see BlossomIt
 		    int blossomNum() const {
 		      return _blossom_potential.size();
+		    }
 		    /// \brief Returns the number of the nodes in the blossom.
 		    /// \brief Return the number of the nodes in the given blossom.
 		    ///
-		    /// Returns the number of the nodes in the blossom.
+		    /// This function returns the number of the nodes in the given blossom.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    /// \see BlossomIt
 		    int blossomSize(int k) const {
 		      return _blossom_potential[k].end - _blossom_potential[k].begin;
+		    }
-		    /// \brief Returns the value of the blossom.
+		    /// \brief Return the dual value (ptential) of the given blossom.
 		    ///
 		    /// Returns the the value of the blossom.
 		    /// \see BlossomIt
 		    /// This function returns the dual value (ptential) of the given blossom.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Value blossomValue(int k) const {
 		      return _blossom_potential[k].value;
+		    }
-		    /// \brief Iterator for obtaining the nodes of the blossom.
+		    /// \brief Iterator for obtaining the nodes of a blossom.
 		    ///
 		    /// Iterator for obtaining the nodes of the blossom. This class
 		    /// provides a common lemon style iterator for listing a
-		    /// subset of the nodes.
+		    /// This class provides an iterator for obtaining the nodes of the
 		    /// given blossom. It lists a subset of the nodes.
 		    /// Before using this iterator, you must allocate a
 		    /// MaxWeightedMatching class and execute it.
 		    class BlossomIt {
 		    public:
 		      /// \brief Constructor.
 		      ///
 		      /// Constructor to get the nodes of the variable.
+		      /// Constructor to get the nodes of the given variable.
 		      ///
 		      /// \pre Either \ref MaxWeightedMatching::run() "algorithm.run()" or
 		      /// \ref MaxWeightedMatching::start() "algorithm.start()" must be
 		      /// called before initializing this iterator.
 		      BlossomIt(const MaxWeightedMatching& algorithm, int variable)
 		        : _algorithm(&algorithm)
+		      {
 		        _index = _algorithm->_blossom_potential[variable].begin;
 		        _last = _algorithm->_blossom_potential[variable].end;
+		      }
-		      /// \brief Conversion to node.
+		      /// \brief Conversion to \c Node.
 		      ///
-		      /// Conversion to node.
+		      /// Conversion to \c Node.
 		      operator Node() const {
 		        return _algorithm->_blossom_node_list[_index];
+		      }
 		      /// \brief Increment operator.
 		      ///
 		      /// Increment operator.
 		      BlossomIt& operator++() {
 		        ++_index;
 		        return *this;
+		      }
 		      /// \brief Validity checking
 		      ///
 		      /// Checks whether the iterator is invalid.
 		      bool operator==(Invalid) const { return _index == _last; }
 		      /// \brief Validity checking
 		      ///
 		      /// Checks whether the iterator is valid.
 		      bool operator!=(Invalid) const { return _index != _last; }
 		    private:
 		      const MaxWeightedMatching* _algorithm;
 		      int _last;
 		      int _index;
 		    };
 		    /// @}
 		  };
 		  /// \ingroup matching
 		  ///
 		  /// \brief Weighted perfect matching in general graphs
 		  ///
 		  /// This class provides an efficient implementation of Edmond's
 		  /// maximum weighted perfect matching algorithm. The implementation
 		  /// is based on extensive use of priority queues and provides
 		  /// \f$O(nm\log n)\f$ time complexity.
 		  ///
 		  /// The maximum weighted matching problem is to find undirected
 		  /// edges in the graph with maximum overall weight and no two of
 		  /// them shares their ends and covers all nodes. The problem can be
 		  /// formulated with the following linear program.
 		  /// The maximum weighted perfect matching problem is to find a subset of
 		  /// the edges in an undirected graph with maximum overall weight for which
 		  /// each node has exactly one incident edge.
 		  /// It can be formulated with the following linear program.
 		  /// \f[ \sum_{e \in \delta(u)}x_e = 1 \quad \forall u\in V\f]
 		  /** \f[ \sum_{e \in \gamma(B)}x_e \le \frac{\vert B \vert - 1}{2}
 		      \quad \forall B\in\mathcal{O}\f] */
 		  /// \f[x_e \ge 0\quad \forall e\in E\f]
 		  /// \f[\max \sum_{e\in E}x_ew_e\f]
 		  /// where \f$\delta(X)\f$ is the set of edges incident to a node in
 		  /// \f$X\f$, \f$\gamma(X)\f$ is the set of edges with both ends in
 		  /// \f$X\f$ and \f$\mathcal{O}\f$ is the set of odd cardinality
 		  /// subsets of the nodes.
 		  ///
 		  /// The algorithm calculates an optimal matching and a proof of the
 		  /// optimality. The solution of the dual problem can be used to check
 		  /// the result of the algorithm. The dual linear problem is the
 		  /// following.
 		  /** \f[ y_u + y_v + \sum_{B \in \mathcal{O}, uv \in \gamma(B)}z_B \ge
 		      w_{uv} \quad \forall uv\in E\f] */
 		  /// \f[z_B \ge 0 \quad \forall B \in \mathcal{O}\f]
 		  /** \f[\min \sum_{u \in V}y_u + \sum_{B \in \mathcal{O}}
 		      \frac{\vert B \vert - 1}{2}z_B\f] */
 		  ///
 		  /// The algorithm can be executed with \c run() or the \c init() and
 		  /// then the \c start() member functions. After it the matching can
 		  /// be asked with \c matching() or mate() functions. The dual
 		  /// solution can be get with \c nodeValue(), \c blossomNum() and \c
 		  /// blossomValue() members and \ref MaxWeightedMatching::BlossomIt
 		  /// "BlossomIt" nested class which is able to iterate on the nodes
 		  /// of a blossom. If the value type is integral then the dual
 		  /// solution is multiplied by \ref MaxWeightedMatching::dualScale "4".
 		  /// The algorithm can be executed with the run() function.
 		  /// After it the matching (the primal solution) and the dual solution
 		  /// can be obtained using the query functions and the
 		  /// \ref MaxWeightedPerfectMatching::BlossomIt "BlossomIt" nested class,
 		  /// which is able to iterate on the nodes of a blossom.
 		  /// If the value type is integer, then the dual solution is multiplied
 		  /// by \ref MaxWeightedMatching::dualScale "4".
 		  ///
 		  /// \tparam GR The graph type the algorithm runs on.
 		  /// \tparam WM The type edge weight map. The default type is
 		  /// \ref concepts::Graph::EdgeMap "GR::EdgeMap<int>".
 		#ifdef DOXYGEN
 		  template <typename GR, typename WM>
 		#else
 		  template <typename GR,
 		            typename WM = typename GR::template EdgeMap<int> >
 		#endif
 		  class MaxWeightedPerfectMatching {
 		  public:
 		    /// The graph type of the algorithm
 		    typedef GR Graph;
 		    /// The type of the edge weight map
 		    typedef WM WeightMap;
 		    /// The value type of the edge weights
 		    typedef typename WeightMap::Value Value;
 		    /// \brief Scaling factor for dual solution
 		    ///
 		    /// Scaling factor for dual solution, it is equal to 4 or 1
 		    /// according to the value type.
 		    static const int dualScale =
 		      std::numeric_limits<Value>::is_integer ? 4 : 1;
 		    typedef typename Graph::template NodeMap<typename Graph::Arc>
 		    MatchingMap;
 		  private:
 		    TEMPLATE_GRAPH_TYPEDEFS(Graph);
 		    typedef typename Graph::template NodeMap<Value> NodePotential;
 		    typedef std::vector<Node> BlossomNodeList;
 		    struct BlossomVariable {
 		      int begin, end;
 		      Value value;
 		      BlossomVariable(int _begin, int _end, Value _value)
 		        : begin(_begin), end(_end), value(_value) {}
 		    };
 		    typedef std::vector<BlossomVariable> BlossomPotential;
 		    const Graph& _graph;
 		    const WeightMap& _weight;
 		    MatchingMap* _matching;
 		    NodePotential* _node_potential;
 		    BlossomPotential _blossom_potential;
 		    BlossomNodeList _blossom_node_list;
 		    int _node_num;
 		    int _blossom_num;
 		    typedef RangeMap<int> IntIntMap;
 		    enum Status {
 		      EVEN = -1, MATCHED = 0, ODD = 1
 		    };
 		    typedef HeapUnionFind<Value, IntNodeMap> BlossomSet;
 		    struct BlossomData {
 		      int tree;
 		      Status status;
 		      Arc pred, next;
 		      Value pot, offset;
 		    };
 		    IntNodeMap *_blossom_index;
 		    BlossomSet *_blossom_set;
 		    RangeMap<BlossomData>* _blossom_data;
 		    IntNodeMap *_node_index;
 		    IntArcMap *_node_heap_index;
 		    struct NodeData {
 		      NodeData(IntArcMap& node_heap_index)
 		        : heap(node_heap_index) {}
 		      int blossom;
 		      Value pot;
 		      BinHeap<Value, IntArcMap> heap;
 		      std::map<int, Arc> heap_index;
 		      int tree;
 		    };
 		    RangeMap<NodeData>* _node_data;
 		    typedef ExtendFindEnum<IntIntMap> TreeSet;
 		    IntIntMap *_tree_set_index;
 		    TreeSet *_tree_set;
 		    IntIntMap *_delta2_index;
 		    BinHeap<Value, IntIntMap> *_delta2;
 		    IntEdgeMap *_delta3_index;
 		    BinHeap<Value, IntEdgeMap> *_delta3;
 		    IntIntMap *_delta4_index;
 		    BinHeap<Value, IntIntMap> *_delta4;
 		    Value _delta_sum;
 		    void createStructures() {
 		      _node_num = countNodes(_graph);
 		      _blossom_num = _node_num * 3 / 2;
 		      if (!_matching) {
 		        _matching = new MatchingMap(_graph);
+		      }
 		      if (!_node_potential) {
 		        _node_potential = new NodePotential(_graph);
+		      }
 		      if (!_blossom_set) {
 		        _blossom_index = new IntNodeMap(_graph);
 		        _blossom_set = new BlossomSet(*_blossom_index);
 		        _blossom_data = new RangeMap<BlossomData>(_blossom_num);
+		      }
 		      if (!_node_index) {
 		        _node_index = new IntNodeMap(_graph);
 		        _node_heap_index = new IntArcMap(_graph);
 		        _node_data = new RangeMap<NodeData>(_node_num,
 		                                            NodeData(*_node_heap_index));
+		      }
 		      if (!_tree_set) {
 		        _tree_set_index = new IntIntMap(_blossom_num);
 		        _tree_set = new TreeSet(*_tree_set_index);
+		      }
 		      if (!_delta2) {
 		        _delta2_index = new IntIntMap(_blossom_num);
 		        _delta2 = new BinHeap<Value, IntIntMap>(*_delta2_index);
+		      }
 		      if (!_delta3) {
 		        _delta3_index = new IntEdgeMap(_graph);
 		        _delta3 = new BinHeap<Value, IntEdgeMap>(*_delta3_index);
+		      }
 		      if (!_delta4) {
 		        _delta4_index = new IntIntMap(_blossom_num);
 		        _delta4 = new BinHeap<Value, IntIntMap>(*_delta4_index);
+		      }
+		    }
 		    void destroyStructures() {
 		      _node_num = countNodes(_graph);
 		      _blossom_num = _node_num * 3 / 2;
 		      if (_matching) {
 		        delete _matching;
+		      }
 		      if (_node_potential) {
 		        delete _node_potential;
+		      }
 		      if (_blossom_set) {
 		        delete _blossom_index;
 		        delete _blossom_set;
 		        delete _blossom_data;
+		      }
 		      if (_node_index) {
 		        delete _node_index;
 		        delete _node_heap_index;
 		        delete _node_data;
+		      }
 		      if (_tree_set) {
 		        delete _tree_set_index;
 		        delete _tree_set;
+		      }
 		      if (_delta2) {
 		        delete _delta2_index;
 		        delete _delta2;
+		      }
 		      if (_delta3) {
 		        delete _delta3_index;
 		        delete _delta3;
+		      }
 		      if (_delta4) {
 		        delete _delta4_index;
 		        delete _delta4;
+		      }
+		    }
 		    void matchedToEven(int blossom, int tree) {
 		      if (_delta2->state(blossom) == _delta2->IN_HEAP) {
 		        _delta2->erase(blossom);
+		      }
 		      if (!_blossom_set->trivial(blossom)) {
 		        (*_blossom_data)[blossom].pot -=
 * (_delta_sum - (*_blossom_data)[blossom].offset);
+		      }
 		      for (typename BlossomSet::ItemIt n(*_blossom_set, blossom);
 		           n != INVALID; ++n) {
 		        _blossom_set->increase(n, std::numeric_limits<Value>::max());
 		        int ni = (*_node_index)[n];
@@ -2629,479 +2696,512 @@
 		      Arc next = (*_blossom_data)[blossom].next;
 		      Arc pred = (*_blossom_data)[blossom].pred;
 		      int tree = _tree_set->find(blossom);
 		      (*_blossom_data)[blossom].status = MATCHED;
 		      oddToMatched(blossom);
 		      if (_delta2->state(blossom) == _delta2->IN_HEAP) {
 		        _delta2->erase(blossom);
+		      }
 		      std::vector<int> subblossoms;
 		      _blossom_set->split(blossom, std::back_inserter(subblossoms));
 		      Value offset = (*_blossom_data)[blossom].offset;
 		      int b = _blossom_set->find(_graph.source(pred));
 		      int d = _blossom_set->find(_graph.source(next));
 		      int ib = -1, id = -1;
 		      for (int i = 0; i < int(subblossoms.size()); ++i) {
 		        if (subblossoms[i] == b) ib = i;
 		        if (subblossoms[i] == d) id = i;
 		        (*_blossom_data)[subblossoms[i]].offset = offset;
 		        if (!_blossom_set->trivial(subblossoms[i])) {
 		          (*_blossom_data)[subblossoms[i]].pot -= 2 * offset;
+		        }
 		        if (_blossom_set->classPrio(subblossoms[i]) !=
 		            std::numeric_limits<Value>::max()) {
 		          _delta2->push(subblossoms[i],
 		                        _blossom_set->classPrio(subblossoms[i]) -
 		                        (*_blossom_data)[subblossoms[i]].offset);
+		        }
+		      }
 		      if (id > ib ? ((id - ib) % 2 == 0) : ((ib - id) % 2 == 1)) {
 		        for (int i = (id + 1) % subblossoms.size();
 		             i != ib; i = (i + 2) % subblossoms.size()) {
 		          int sb = subblossoms[i];
 		          int tb = subblossoms[(i + 1) % subblossoms.size()];
 		          (*_blossom_data)[sb].next =
 		            _graph.oppositeArc((*_blossom_data)[tb].next);
+		        }
 		        for (int i = ib; i != id; i = (i + 2) % subblossoms.size()) {
 		          int sb = subblossoms[i];
 		          int tb = subblossoms[(i + 1) % subblossoms.size()];
 		          int ub = subblossoms[(i + 2) % subblossoms.size()];
 		          (*_blossom_data)[sb].status = ODD;
 		          matchedToOdd(sb);
 		          _tree_set->insert(sb, tree);
 		          (*_blossom_data)[sb].pred = pred;
 		          (*_blossom_data)[sb].next =
 		                           _graph.oppositeArc((*_blossom_data)[tb].next);
 		          pred = (*_blossom_data)[ub].next;
 		          (*_blossom_data)[tb].status = EVEN;
 		          matchedToEven(tb, tree);
 		          _tree_set->insert(tb, tree);
 		          (*_blossom_data)[tb].pred = (*_blossom_data)[tb].next;
+		        }
 		        (*_blossom_data)[subblossoms[id]].status = ODD;
 		        matchedToOdd(subblossoms[id]);
 		        _tree_set->insert(subblossoms[id], tree);
 		        (*_blossom_data)[subblossoms[id]].next = next;
 		        (*_blossom_data)[subblossoms[id]].pred = pred;
 		      } else {
 		        for (int i = (ib + 1) % subblossoms.size();
 		             i != id; i = (i + 2) % subblossoms.size()) {
 		          int sb = subblossoms[i];
 		          int tb = subblossoms[(i + 1) % subblossoms.size()];
 		          (*_blossom_data)[sb].next =
 		            _graph.oppositeArc((*_blossom_data)[tb].next);
+		        }
 		        for (int i = id; i != ib; i = (i + 2) % subblossoms.size()) {
 		          int sb = subblossoms[i];
 		          int tb = subblossoms[(i + 1) % subblossoms.size()];
 		          int ub = subblossoms[(i + 2) % subblossoms.size()];
 		          (*_blossom_data)[sb].status = ODD;
 		          matchedToOdd(sb);
 		          _tree_set->insert(sb, tree);
 		          (*_blossom_data)[sb].next = next;
 		          (*_blossom_data)[sb].pred =
 		            _graph.oppositeArc((*_blossom_data)[tb].next);
 		          (*_blossom_data)[tb].status = EVEN;
 		          matchedToEven(tb, tree);
 		          _tree_set->insert(tb, tree);
 		          (*_blossom_data)[tb].pred =
 		            (*_blossom_data)[tb].next =
 		            _graph.oppositeArc((*_blossom_data)[ub].next);
 		          next = (*_blossom_data)[ub].next;
+		        }
 		        (*_blossom_data)[subblossoms[ib]].status = ODD;
 		        matchedToOdd(subblossoms[ib]);
 		        _tree_set->insert(subblossoms[ib], tree);
 		        (*_blossom_data)[subblossoms[ib]].next = next;
 		        (*_blossom_data)[subblossoms[ib]].pred = pred;
+		      }
 		      _tree_set->erase(blossom);
+		    }
 		    void extractBlossom(int blossom, const Node& base, const Arc& matching) {
 		      if (_blossom_set->trivial(blossom)) {
 		        int bi = (*_node_index)[base];
 		        Value pot = (*_node_data)[bi].pot;
 		        (*_matching)[base] = matching;
 		        _blossom_node_list.push_back(base);
 		        (*_node_potential)[base] = pot;
 		      } else {
 		        Value pot = (*_blossom_data)[blossom].pot;
 		        int bn = _blossom_node_list.size();
 		        std::vector<int> subblossoms;
 		        _blossom_set->split(blossom, std::back_inserter(subblossoms));
 		        int b = _blossom_set->find(base);
 		        int ib = -1;
 		        for (int i = 0; i < int(subblossoms.size()); ++i) {
 		          if (subblossoms[i] == b) { ib = i; break; }
+		        }
 		        for (int i = 1; i < int(subblossoms.size()); i += 2) {
 		          int sb = subblossoms[(ib + i) % subblossoms.size()];
 		          int tb = subblossoms[(ib + i + 1) % subblossoms.size()];
 		          Arc m = (*_blossom_data)[tb].next;
 		          extractBlossom(sb, _graph.target(m), _graph.oppositeArc(m));
 		          extractBlossom(tb, _graph.source(m), m);
+		        }
 		        extractBlossom(subblossoms[ib], base, matching);
 		        int en = _blossom_node_list.size();
 		        _blossom_potential.push_back(BlossomVariable(bn, en, pot));
+		      }
+		    }
 		    void extractMatching() {
 		      std::vector<int> blossoms;
 		      for (typename BlossomSet::ClassIt c(*_blossom_set); c != INVALID; ++c) {
 		        blossoms.push_back(c);
+		      }
 		      for (int i = 0; i < int(blossoms.size()); ++i) {
 		        Value offset = (*_blossom_data)[blossoms[i]].offset;
 		        (*_blossom_data)[blossoms[i]].pot += 2 * offset;
 		        for (typename BlossomSet::ItemIt n(*_blossom_set, blossoms[i]);
 		             n != INVALID; ++n) {
 		          (*_node_data)[(*_node_index)[n]].pot -= offset;
+		        }
 		        Arc matching = (*_blossom_data)[blossoms[i]].next;
 		        Node base = _graph.source(matching);
 		        extractBlossom(blossoms[i], base, matching);
+		      }
+		    }
 		  public:
 		    /// \brief Constructor
 		    ///
 		    /// Constructor.
 		    MaxWeightedPerfectMatching(const Graph& graph, const WeightMap& weight)
 		      : _graph(graph), _weight(weight), _matching(0),
 		        _node_potential(0), _blossom_potential(), _blossom_node_list(),
 		        _node_num(0), _blossom_num(0),
 		        _blossom_index(0), _blossom_set(0), _blossom_data(0),
 		        _node_index(0), _node_heap_index(0), _node_data(0),
 		        _tree_set_index(0), _tree_set(0),
 		        _delta2_index(0), _delta2(0),
 		        _delta3_index(0), _delta3(0),
 		        _delta4_index(0), _delta4(0),
 		        _delta_sum() {}
 		    ~MaxWeightedPerfectMatching() {
 		      destroyStructures();
+		    }
-		    /// \name Execution control
+		    /// \name Execution Control
 		    /// The simplest way to execute the algorithm is to use the
-		    /// \c run() member function.
+		    /// \ref run() member function.
 		    ///@{
 		    /// \brief Initialize the algorithm
 		    ///
-		    /// Initialize the algorithm
+		    /// This function initializes the algorithm.
 		    void init() {
 		      createStructures();
 		      for (ArcIt e(_graph); e != INVALID; ++e) {
 		        (*_node_heap_index)[e] = BinHeap<Value, IntArcMap>::PRE_HEAP;
+		      }
 		      for (EdgeIt e(_graph); e != INVALID; ++e) {
 		        (*_delta3_index)[e] = _delta3->PRE_HEAP;
+		      }
 		      for (int i = 0; i < _blossom_num; ++i) {
 		        (*_delta2_index)[i] = _delta2->PRE_HEAP;
 		        (*_delta4_index)[i] = _delta4->PRE_HEAP;
+		      }
 		      int index = 0;
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        Value max = - std::numeric_limits<Value>::max();
 		        for (OutArcIt e(_graph, n); e != INVALID; ++e) {
 		          if (_graph.target(e) == n) continue;
 		          if ((dualScale * _weight[e]) / 2 > max) {
 		            max = (dualScale * _weight[e]) / 2;
+		          }
+		        }
 		        (*_node_index)[n] = index;
 		        (*_node_data)[index].pot = max;
 		        int blossom =
 		          _blossom_set->insert(n, std::numeric_limits<Value>::max());
 		        _tree_set->insert(blossom);
 		        (*_blossom_data)[blossom].status = EVEN;
 		        (*_blossom_data)[blossom].pred = INVALID;
 		        (*_blossom_data)[blossom].next = INVALID;
 		        (*_blossom_data)[blossom].pot = 0;
 		        (*_blossom_data)[blossom].offset = 0;
 		        ++index;
+		      }
 		      for (EdgeIt e(_graph); e != INVALID; ++e) {
 		        int si = (*_node_index)[_graph.u(e)];
 		        int ti = (*_node_index)[_graph.v(e)];
 		        if (_graph.u(e) != _graph.v(e)) {
 		          _delta3->push(e, ((*_node_data)[si].pot + (*_node_data)[ti].pot -
 		                            dualScale * _weight[e]) / 2);
+		        }
+		      }
+		    }
-		    /// \brief Starts the algorithm
+		    /// \brief Start the algorithm
 		    ///
-		    /// Starts the algorithm
+		    /// This function starts the algorithm.
 		    ///
 		    /// \pre \ref init() must be called before using this function.
 		    bool start() {
 		      enum OpType {
 		        D2, D3, D4
 		      };
 		      int unmatched = _node_num;
 		      while (unmatched > 0) {
 		        Value d2 = !_delta2->empty() ?
 		          _delta2->prio() : std::numeric_limits<Value>::max();
 		        Value d3 = !_delta3->empty() ?
 		          _delta3->prio() : std::numeric_limits<Value>::max();
 		        Value d4 = !_delta4->empty() ?
 		          _delta4->prio() : std::numeric_limits<Value>::max();
 		        _delta_sum = d2; OpType ot = D2;
 		        if (d3 < _delta_sum) { _delta_sum = d3; ot = D3; }
 		        if (d4 < _delta_sum) { _delta_sum = d4; ot = D4; }
 		        if (_delta_sum == std::numeric_limits<Value>::max()) {
 		          return false;
+		        }
 		        switch (ot) {
 		        case D2:
+		          {
 		            int blossom = _delta2->top();
 		            Node n = _blossom_set->classTop(blossom);
 		            Arc e = (*_node_data)[(*_node_index)[n]].heap.top();
 		            extendOnArc(e);
+		          }
 		          break;
 		        case D3:
+		          {
 		            Edge e = _delta3->top();
 		            int left_blossom = _blossom_set->find(_graph.u(e));
 		            int right_blossom = _blossom_set->find(_graph.v(e));
 		            if (left_blossom == right_blossom) {
 		              _delta3->pop();
 		            } else {
 		              int left_tree = _tree_set->find(left_blossom);
 		              int right_tree = _tree_set->find(right_blossom);
 		              if (left_tree == right_tree) {
 		                shrinkOnEdge(e, left_tree);
 		              } else {
 		                augmentOnEdge(e);
 		                unmatched -= 2;
+		              }
+		            }
 		          } break;
 		        case D4:
 		          splitBlossom(_delta4->top());
 		          break;
+		        }
+		      }
 		      extractMatching();
 		      return true;
+		    }
-		    /// \brief Runs %MaxWeightedPerfectMatching algorithm.
+		    /// \brief Run the algorithm.
 		    ///
 		    /// This method runs the %MaxWeightedPerfectMatching algorithm.
+		    /// This method runs the \c %MaxWeightedPerfectMatching algorithm.
 		    ///
-		    /// \note mwm.run() is just a shortcut of the following code.
+		    /// \note mwpm.run() is just a shortcut of the following code.
 		    /// \code
 		    ///   mwm.init();
 		    ///   mwm.start();
 		    ///   mwpm.init();
 		    ///   mwpm.start();
 		    /// \endcode
 		    bool run() {
 		      init();
 		      return start();
+		    }
 		    /// @}
 		    /// \name Primal solution
 		    /// Functions to get the primal solution, ie. the matching.
 		    /// \name Primal Solution
 		    /// Functions to get the primal solution, i.e. the maximum weighted
 		    /// perfect matching.\n
 		    /// Either \ref run() or \ref start() function should be called before
 		    /// using them.
 		    /// @{
-		    /// \brief Returns the matching value.
+		    /// \brief Return the weight of the matching.
 		    ///
-		    /// Returns the matching value.
+		    /// This function returns the weight of the found matching.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Value matchingValue() const {
 		      Value sum = 0;
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        if ((*_matching)[n] != INVALID) {
 		          sum += _weight[(*_matching)[n]];
+		        }
+		      }
 		      return sum /= 2;
+		    }
-		    /// \brief Returns true when the edge is in the matching.
+		    /// \brief Return \c true if the given edge is in the matching.
 		    ///
-		    /// Returns true when the edge is in the matching.
+		    /// This function returns \c true if the given edge is in the found
 		    /// matching.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    bool matching(const Edge& edge) const {
 		      return static_cast<const Edge&>((*_matching)[_graph.u(edge)]) == edge;
+		    }
-		    /// \brief Returns the incident matching edge.
+		    /// \brief Return the matching arc (or edge) incident to the given node.
 		    ///
-		    /// Returns the incident matching arc from given edge.
+		    /// This function returns the matching arc (or edge) incident to the
 		    /// given node in the found matching or \c INVALID if the node is
 		    /// not covered by the matching.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Arc matching(const Node& node) const {
 		      return (*_matching)[node];
+		    }
-		    /// \brief Returns the mate of the node.
+		    /// \brief Return the mate of the given node.
 		    ///
-		    /// Returns the adjancent node in a mathcing arc.
+		    /// This function returns the mate of the given node in the found
 		    /// matching or \c INVALID if the node is not covered by the matching.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Node mate(const Node& node) const {
 		      return _graph.target((*_matching)[node]);
+		    }
 		    /// @}
 		    /// \name Dual solution
 		    /// Functions to get the dual solution.
 		    /// \name Dual Solution
 		    /// Functions to get the dual solution.\n
 		    /// Either \ref run() or \ref start() function should be called before
 		    /// using them.
 		    /// @{
-		    /// \brief Returns the value of the dual solution.
+		    /// \brief Return the value of the dual solution.
 		    ///
 		    /// Returns the value of the dual solution. It should be equal to
 		    /// the primal value scaled by \ref dualScale "dual scale".
 		    /// This function returns the value of the dual solution.
 		    /// It should be equal to the primal value scaled by \ref dualScale
 		    /// "dual scale".
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Value dualValue() const {
 		      Value sum = 0;
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        sum += nodeValue(n);
+		      }
 		      for (int i = 0; i < blossomNum(); ++i) {
 		        sum += blossomValue(i) * (blossomSize(i) / 2);
+		      }
 		      return sum;
+		    }
-		    /// \brief Returns the value of the node.
+		    /// \brief Return the dual value (potential) of the given node.
 		    ///
-		    /// Returns the the value of the node.
+		    /// This function returns the dual value (potential) of the given node.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Value nodeValue(const Node& n) const {
 		      return (*_node_potential)[n];
+		    }
-		    /// \brief Returns the number of the blossoms in the basis.
+		    /// \brief Return the number of the blossoms in the basis.
 		    ///
-		    /// Returns the number of the blossoms in the basis.
+		    /// This function returns the number of the blossoms in the basis.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    /// \see BlossomIt
 		    int blossomNum() const {
 		      return _blossom_potential.size();
+		    }
 		    /// \brief Returns the number of the nodes in the blossom.
 		    /// \brief Return the number of the nodes in the given blossom.
 		    ///
-		    /// Returns the number of the nodes in the blossom.
+		    /// This function returns the number of the nodes in the given blossom.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    /// \see BlossomIt
 		    int blossomSize(int k) const {
 		      return _blossom_potential[k].end - _blossom_potential[k].begin;
+		    }
-		    /// \brief Returns the value of the blossom.
+		    /// \brief Return the dual value (ptential) of the given blossom.
 		    ///
 		    /// Returns the the value of the blossom.
 		    /// \see BlossomIt
 		    /// This function returns the dual value (ptential) of the given blossom.
 		    ///
 		    /// \pre Either run() or start() must be called before using this function.
 		    Value blossomValue(int k) const {
 		      return _blossom_potential[k].value;
+		    }
-		    /// \brief Iterator for obtaining the nodes of the blossom.
+		    /// \brief Iterator for obtaining the nodes of a blossom.
 		    ///
 		    /// Iterator for obtaining the nodes of the blossom. This class
 		    /// provides a common lemon style iterator for listing a
-		    /// subset of the nodes.
+		    /// This class provides an iterator for obtaining the nodes of the
 		    /// given blossom. It lists a subset of the nodes.
 		    /// Before using this iterator, you must allocate a
 		    /// MaxWeightedPerfectMatching class and execute it.
 		    class BlossomIt {
 		    public:
 		      /// \brief Constructor.
 		      ///
 		      /// Constructor to get the nodes of the variable.
+		      /// Constructor to get the nodes of the given variable.
 		      ///
 		      /// \pre Either \ref MaxWeightedPerfectMatching::run() "algorithm.run()"
 		      /// or \ref MaxWeightedPerfectMatching::start() "algorithm.start()"
 		      /// must be called before initializing this iterator.
 		      BlossomIt(const MaxWeightedPerfectMatching& algorithm, int variable)
 		        : _algorithm(&algorithm)
+		      {
 		        _index = _algorithm->_blossom_potential[variable].begin;
 		        _last = _algorithm->_blossom_potential[variable].end;
+		      }
-		      /// \brief Conversion to node.
+		      /// \brief Conversion to \c Node.
 		      ///
-		      /// Conversion to node.
+		      /// Conversion to \c Node.
 		      operator Node() const {
 		        return _algorithm->_blossom_node_list[_index];
+		      }
 		      /// \brief Increment operator.
 		      ///
 		      /// Increment operator.
 		      BlossomIt& operator++() {
 		        ++_index;
 		        return *this;
+		      }
 		      /// \brief Validity checking
 		      ///
-		      /// Checks whether the iterator is invalid.
+		      /// This function checks whether the iterator is invalid.
 		      bool operator==(Invalid) const { return _index == _last; }
 		      /// \brief Validity checking
 		      ///
-		      /// Checks whether the iterator is valid.
+		      /// This function checks whether the iterator is valid.
 		      bool operator!=(Invalid) const { return _index != _last; }
 		    private:
 		      const MaxWeightedPerfectMatching* _algorithm;
 		      int _last;
 		      int _index;
 		    };
 		    /// @}
 		  };
 		} //END OF NAMESPACE LEMON
 		#endif //LEMON_MAX_MATCHING_H

test/max_matching_test.cc

Show inline comments

 		/* -*- mode: C++; indent-tabs-mode: nil; -*-
+		 *
 		 * This file is a part of LEMON, a generic C++ optimization library.
+		 *
 		 * Copyright (C) 2003-2009
 		 * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
 		 * (Egervary Research Group on Combinatorial Optimization, EGRES).
+		 *
 		 * Permission to use, modify and distribute this software is granted
 		 * provided that this copyright notice appears in all copies. For
 		 * precise terms see the accompanying LICENSE file.
+		 *
 		 * This software is provided "AS IS" with no warranty of any kind,
 		 * express or implied, and with no claim as to its suitability for any
 		 * purpose.
+		 *
 		 */
 		#include <iostream>
 		#include <sstream>
 		#include <vector>
 		#include <queue>
 		#include <lemon/math.h>
 		#include <cstdlib>
 		#include <lemon/max_matching.h>
 		#include <lemon/smart_graph.h>
 		#include <lemon/concepts/graph.h>
 		#include <lemon/concepts/maps.h>
 		#include <lemon/lgf_reader.h>
 		#include <lemon/math.h>
 		#include "test_tools.h"
 		using namespace std;
 		using namespace lemon;
 		GRAPH_TYPEDEFS(SmartGraph);
 		const int lgfn = 3;
 		const std::string lgf[lgfn] = {
 		  "@nodes\n"
 		  "label\n"
 		  "0\n"
 		  "1\n"
 		  "2\n"
 		  "3\n"
 		  "4\n"
 		  "5\n"
 		  "6\n"
 		  "7\n"
 		  "@edges\n"
 		  "     label  weight\n"
 		  "7 4  0      984\n"
 		  "0 7  1      73\n"
 		  "7 1  2      204\n"
 		  "2 3  3      583\n"
 		  "2 7  4      565\n"
 		  "2 1  5      582\n"
 		  "0 4  6      551\n"
 		  "2 5  7      385\n"
 		  "1 5  8      561\n"
 		  "5 3  9      484\n"
 		  "7 5  10     904\n"
 		  "3 6  11     47\n"
 		  "7 6  12     888\n"
 		  "3 0  13     747\n"
 		  "6 1  14     310\n",
 		  "@nodes\n"
 		  "label\n"
 		  "0\n"
 		  "1\n"
 		  "2\n"
 		  "3\n"
 		  "4\n"
 		  "5\n"
 		  "6\n"
 		  "7\n"
 		  "@edges\n"
 		  "     label  weight\n"
 		  "2 5  0      710\n"
 		  "0 5  1      241\n"
 		  "2 4  2      856\n"
 		  "2 6  3      762\n"
 		  "4 1  4      747\n"
 		  "6 1  5      962\n"
 		  "4 7  6      723\n"
 		  "1 7  7      661\n"
 		  "2 3  8      376\n"
 		  "1 0  9      416\n"
 		  "6 7  10     391\n",
 		  "@nodes\n"
 		  "label\n"
 		  "0\n"
 		  "1\n"
 		  "2\n"
 		  "3\n"
 		  "4\n"
 		  "5\n"
 		  "6\n"
 		  "7\n"
 		  "@edges\n"
 		  "     label  weight\n"
 		  "6 2  0      553\n"
 		  "0 7  1      653\n"
 		  "6 3  2      22\n"
 		  "4 7  3      846\n"
 		  "7 2  4      981\n"
 		  "7 6  5      250\n"
 		  "5 2  6      539\n",
 		};
 		void checkMaxMatchingCompile()
+		{
 		  typedef concepts::Graph Graph;
 		  typedef Graph::Node Node;
 		  typedef Graph::Edge Edge;
 		  typedef Graph::EdgeMap<bool> MatMap;
 		  Graph g;
 		  Node n;
 		  Edge e;
 		  MatMap mat(g);
 		  MaxMatching<Graph> mat_test(g);
 		  const MaxMatching<Graph>&
 		    const_mat_test = mat_test;
 		  mat_test.init();
 		  mat_test.greedyInit();
 		  mat_test.matchingInit(mat);
 		  mat_test.startSparse();
 		  mat_test.startDense();
 		  mat_test.run();
 		  const_mat_test.matchingSize();
 		  const_mat_test.matching(e);
 		  const_mat_test.matching(n);
 		  const_mat_test.mate(n);
 		  MaxMatching<Graph>::Status stat =
 		    const_mat_test.decomposition(n);
 		  const_mat_test.barrier(n);
 		  ignore_unused_variable_warning(stat);
+		}
 		void checkMaxWeightedMatchingCompile()
+		{
 		  typedef concepts::Graph Graph;
 		  typedef Graph::Node Node;
 		  typedef Graph::Edge Edge;
 		  typedef Graph::EdgeMap<int> WeightMap;
 		  Graph g;
 		  Node n;
 		  Edge e;
 		  WeightMap w(g);
 		  MaxWeightedMatching<Graph> mat_test(g, w);
 		  const MaxWeightedMatching<Graph>&
 		    const_mat_test = mat_test;
 		  mat_test.init();
 		  mat_test.start();
 		  mat_test.run();
 		  const_mat_test.matchingValue();
 		  const_mat_test.matchingSize();
 		  const_mat_test.matching(e);
 		  const_mat_test.matching(n);
 		  const_mat_test.mate(n);
 		  int k = 0;
 		  const_mat_test.dualValue();
 		  const_mat_test.nodeValue(n);
 		  const_mat_test.blossomNum();
 		  const_mat_test.blossomSize(k);
 		  const_mat_test.blossomValue(k);
+		}
 		void checkMaxWeightedPerfectMatchingCompile()
+		{
 		  typedef concepts::Graph Graph;
 		  typedef Graph::Node Node;
 		  typedef Graph::Edge Edge;
 		  typedef Graph::EdgeMap<int> WeightMap;
 		  Graph g;
 		  Node n;
 		  Edge e;
 		  WeightMap w(g);
 		  MaxWeightedPerfectMatching<Graph> mat_test(g, w);
 		  const MaxWeightedPerfectMatching<Graph>&
 		    const_mat_test = mat_test;
 		  mat_test.init();
 		  mat_test.start();
 		  mat_test.run();
 		  const_mat_test.matchingValue();
 		  const_mat_test.matching(e);
 		  const_mat_test.matching(n);
 		  const_mat_test.mate(n);
 		  int k = 0;
 		  const_mat_test.dualValue();
 		  const_mat_test.nodeValue(n);
 		  const_mat_test.blossomNum();
 		  const_mat_test.blossomSize(k);
 		  const_mat_test.blossomValue(k);
+		}
 		void checkMatching(const SmartGraph& graph,
 		                   const MaxMatching<SmartGraph>& mm) {
 		  int num = 0;
 		  IntNodeMap comp_index(graph);
 		  UnionFind<IntNodeMap> comp(comp_index);
 		  int barrier_num = 0;
 		  for (NodeIt n(graph); n != INVALID; ++n) {
 		    check(mm.decomposition(n) == MaxMatching<SmartGraph>::EVEN ||
 		          mm.matching(n) != INVALID, "Wrong Gallai-Edmonds decomposition");
 		    if (mm.decomposition(n) == MaxMatching<SmartGraph>::ODD) {
 		      ++barrier_num;
 		    } else {
 		      comp.insert(n);
+		    }
+		  }
 		  for (EdgeIt e(graph); e != INVALID; ++e) {
 		    if (mm.matching(e)) {
 		      check(e == mm.matching(graph.u(e)), "Wrong matching");
 		      check(e == mm.matching(graph.v(e)), "Wrong matching");
 		      ++num;
+		    }
 		    check(mm.decomposition(graph.u(e)) != MaxMatching<SmartGraph>::EVEN ||
 		          mm.decomposition(graph.v(e)) != MaxMatching<SmartGraph>::MATCHED,
 		          "Wrong Gallai-Edmonds decomposition");
 		    check(mm.decomposition(graph.v(e)) != MaxMatching<SmartGraph>::EVEN ||
 		          mm.decomposition(graph.u(e)) != MaxMatching<SmartGraph>::MATCHED,
 		          "Wrong Gallai-Edmonds decomposition");
 		    if (mm.decomposition(graph.u(e)) != MaxMatching<SmartGraph>::ODD &&
 		        mm.decomposition(graph.v(e)) != MaxMatching<SmartGraph>::ODD) {
 		      comp.join(graph.u(e), graph.v(e));
+		    }
+		  }
 		  std::set<int> comp_root;
 		  int odd_comp_num = 0;
 		  for (NodeIt n(graph); n != INVALID; ++n) {
 		    if (mm.decomposition(n) != MaxMatching<SmartGraph>::ODD) {
 		      int root = comp.find(n);
 		      if (comp_root.find(root) == comp_root.end()) {
 		        comp_root.insert(root);
 		        if (comp.size(n) % 2 == 1) {
 		          ++odd_comp_num;
+		        }
+		      }
+		    }
+		  }
 		  check(mm.matchingSize() == num, "Wrong matching");
 		  check(2 * num == countNodes(graph) - (odd_comp_num - barrier_num),
 		         "Wrong matching");
 		  return;
+		}
 		void checkWeightedMatching(const SmartGraph& graph,
 		                   const SmartGraph::EdgeMap<int>& weight,
 		                   const MaxWeightedMatching<SmartGraph>& mwm) {
 		  for (SmartGraph::EdgeIt e(graph); e != INVALID; ++e) {
 		    if (graph.u(e) == graph.v(e)) continue;
 		    int rw = mwm.nodeValue(graph.u(e)) + mwm.nodeValue(graph.v(e));
 		    for (int i = 0; i < mwm.blossomNum(); ++i) {
 		      bool s = false, t = false;
 		      for (MaxWeightedMatching<SmartGraph>::BlossomIt n(mwm, i);
 		           n != INVALID; ++n) {
 		        if (graph.u(e) == n) s = true;
 		        if (graph.v(e) == n) t = true;
+		      }
 		      if (s == true && t == true) {
 		        rw += mwm.blossomValue(i);
+		      }
+		    }
 		    rw -= weight[e] * mwm.dualScale;
 		    check(rw >= 0, "Negative reduced weight");
 		    check(rw == 0 || !mwm.matching(e),
 		          "Non-zero reduced weight on matching edge");
+		  }
 		  int pv = 0;
 		  for (SmartGraph::NodeIt n(graph); n != INVALID; ++n) {
 		    if (mwm.matching(n) != INVALID) {
 		      check(mwm.nodeValue(n) >= 0, "Invalid node value");
 		      pv += weight[mwm.matching(n)];
 		      SmartGraph::Node o = graph.target(mwm.matching(n));
 		      check(mwm.mate(n) == o, "Invalid matching");
 		      check(mwm.matching(n) == graph.oppositeArc(mwm.matching(o)),
 		            "Invalid matching");
 		    } else {
 		      check(mwm.mate(n) == INVALID, "Invalid matching");
 		      check(mwm.nodeValue(n) == 0, "Invalid matching");
+		    }
+		  }
 		  int dv = 0;
 		  for (SmartGraph::NodeIt n(graph); n != INVALID; ++n) {
 		    dv += mwm.nodeValue(n);
+		  }
 		  for (int i = 0; i < mwm.blossomNum(); ++i) {
 		    check(mwm.blossomValue(i) >= 0, "Invalid blossom value");
 		    check(mwm.blossomSize(i) % 2 == 1, "Even blossom size");
 		    dv += mwm.blossomValue(i) * ((mwm.blossomSize(i) - 1) / 2);
+		  }
 		  check(pv * mwm.dualScale == dv * 2, "Wrong duality");
 		  return;
+		}
 		void checkWeightedPerfectMatching(const SmartGraph& graph,
 		                          const SmartGraph::EdgeMap<int>& weight,
 		                          const MaxWeightedPerfectMatching<SmartGraph>& mwpm) {
 		  for (SmartGraph::EdgeIt e(graph); e != INVALID; ++e) {
 		    if (graph.u(e) == graph.v(e)) continue;
 		    int rw = mwpm.nodeValue(graph.u(e)) + mwpm.nodeValue(graph.v(e));
 		    for (int i = 0; i < mwpm.blossomNum(); ++i) {
 		      bool s = false, t = false;
 		      for (MaxWeightedPerfectMatching<SmartGraph>::BlossomIt n(mwpm, i);
 		           n != INVALID; ++n) {
 		        if (graph.u(e) == n) s = true;
 		        if (graph.v(e) == n) t = true;
+		      }
 		      if (s == true && t == true) {
 		        rw += mwpm.blossomValue(i);
+		      }
+		    }
 		    rw -= weight[e] * mwpm.dualScale;
 		    check(rw >= 0, "Negative reduced weight");
 		    check(rw == 0 || !mwpm.matching(e),
 		          "Non-zero reduced weight on matching edge");
+		  }
 		  int pv = 0;
 		  for (SmartGraph::NodeIt n(graph); n != INVALID; ++n) {
 		    check(mwpm.matching(n) != INVALID, "Non perfect");
 		    pv += weight[mwpm.matching(n)];
 		    SmartGraph::Node o = graph.target(mwpm.matching(n));
 		    check(mwpm.mate(n) == o, "Invalid matching");
 		    check(mwpm.matching(n) == graph.oppositeArc(mwpm.matching(o)),
 		          "Invalid matching");
+		  }
 		  int dv = 0;
 		  for (SmartGraph::NodeIt n(graph); n != INVALID; ++n) {
 		    dv += mwpm.nodeValue(n);
+		  }
 		  for (int i = 0; i < mwpm.blossomNum(); ++i) {
 		    check(mwpm.blossomValue(i) >= 0, "Invalid blossom value");
 		    check(mwpm.blossomSize(i) % 2 == 1, "Even blossom size");
 		    dv += mwpm.blossomValue(i) * ((mwpm.blossomSize(i) - 1) / 2);
+		  }
 		  check(pv * mwpm.dualScale == dv * 2, "Wrong duality");
 		  return;
+		}
 		int main() {
 		  for (int i = 0; i < lgfn; ++i) {
 		    SmartGraph graph;
 		    SmartGraph::EdgeMap<int> weight(graph);
 		    istringstream lgfs(lgf[i]);
 		    graphReader(graph, lgfs).
 		      edgeMap("weight", weight).run();
 		    MaxMatching<SmartGraph> mm(graph);
 		    mm.run();
 		    checkMatching(graph, mm);
 		    MaxWeightedMatching<SmartGraph> mwm(graph, weight);
 		    mwm.run();
 		    checkWeightedMatching(graph, weight, mwm);
 		    MaxWeightedPerfectMatching<SmartGraph> mwpm(graph, weight);
 		    bool perfect = mwpm.run();
 		    check(perfect == (mm.matchingSize() * 2 == countNodes(graph)),
 		          "Perfect matching found");
 		    if (perfect) {

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