LEMON/LEMON-main Changeset - r904:c279b19abc62

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Changeset - r904:c279b19abc62

« 894:24b3f1

905:de428e »

r904:c279b19abc62

Fri, 23 Jul 2010 06:29:37

[Not Reviewed]

0 comments (0 inline)

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

Add a heuristic algorithm for the max clique problem (#380)

0 5 2

default

7 files changed with 879 insertions and 2 deletions:

lemon/grosso_locatelli_pullan_mc.h

680

test/max_clique_test.cc

176

doc/groups.dox

doc/references.bib

lemon/Makefile.am

test/CMakeLists.txt

test/Makefile.am

↑ Collapse diff ↑

lemon/grosso_locatelli_pullan_mc.h

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 		/* -*- mode: C++; indent-tabs-mode: nil; -*-
+		 *
 		 * This file is a part of LEMON, a generic C++ optimization library.
+		 *
 		 * Copyright (C) 2003-2010
 		 * 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_GROSSO_LOCATELLI_PULLAN_MC_H
 		#define LEMON_GROSSO_LOCATELLI_PULLAN_MC_H
 		/// \ingroup approx_algs
 		///
 		/// \file
 		/// \brief The iterated local search algorithm of Grosso, Locatelli, and Pullan
 		/// for the maximum clique problem
 		#include <vector>
 		#include <limits>
 		#include <lemon/core.h>
 		#include <lemon/random.h>
 		namespace lemon {
 		  /// \addtogroup approx_algs
 		  /// @{
 		  /// \brief Implementation of the iterated local search algorithm of Grosso,
 		  /// Locatelli, and Pullan for the maximum clique problem
 		  ///
 		  /// \ref GrossoLocatelliPullanMc implements the iterated local search
 		  /// algorithm of Grosso, Locatelli, and Pullan for solving the \e maximum
 		  /// \e clique \e problem \ref grosso08maxclique.
 		  /// It is to find the largest complete subgraph (\e clique) in an
 		  /// undirected graph, i.e., the largest set of nodes where each
 		  /// pair of nodes is connected.
 		  ///
 		  /// This class provides a simple but highly efficient and robust heuristic
 		  /// method that quickly finds a large clique, but not necessarily the
 		  /// largest one.
 		  ///
 		  /// \tparam GR The undirected graph type the algorithm runs on.
 		  ///
 		  /// \note %GrossoLocatelliPullanMc provides three different node selection
 		  /// rules, from which the most powerful one is used by default.
 		  /// For more information, see \ref SelectionRule.
 		  template <typename GR>
 		  class GrossoLocatelliPullanMc
+		  {
 		  public:
 		    /// \brief Constants for specifying the node selection rule.
 		    ///
 		    /// Enum type containing constants for specifying the node selection rule
 		    /// for the \ref run() function.
 		    ///
 		    /// During the algorithm, nodes are selected for addition to the current
 		    /// clique according to the applied rule.
 		    /// In general, the PENALTY_BASED rule turned out to be the most powerful
 		    /// and the most robust, thus it is the default option.
 		    /// However, another selection rule can be specified using the \ref run()
 		    /// function with the proper parameter.
 		    enum SelectionRule {
 		      /// A node is selected randomly without any evaluation at each step.
 		      RANDOM,
 		      /// A node of maximum degree is selected randomly at each step.
 		      DEGREE_BASED,
 		      /// A node of minimum penalty is selected randomly at each step.
 		      /// The node penalties are updated adaptively after each stage of the
 		      /// search process.
 		      PENALTY_BASED
 		    };
 		  private:
 		    TEMPLATE_GRAPH_TYPEDEFS(GR);
 		    typedef std::vector<int> IntVector;
 		    typedef std::vector<char> BoolVector;
 		    typedef std::vector<BoolVector> BoolMatrix;
 		    // Note: vector<char> is used instead of vector<bool> for efficiency reasons
 		    const GR &_graph;
 		    IntNodeMap _id;
 		    // Internal matrix representation of the graph
 		    BoolMatrix _gr;
 		    int _n;
 		    // The current clique
 		    BoolVector _clique;
 		    int _size;
 		    // The best clique found so far
 		    BoolVector _best_clique;
 		    int _best_size;
 		    // The "distances" of the nodes from the current clique.
 		    // _delta[u] is the number of nodes in the clique that are
 		    // not connected with u.
 		    IntVector _delta;
 		    // The current tabu set
 		    BoolVector _tabu;
 		    // Random number generator
 		    Random _rnd;
 		  private:
 		    // Implementation of the RANDOM node selection rule.
 		    class RandomSelectionRule
+		    {
 		    private:
 		      // References to the algorithm instance
 		      const BoolVector &_clique;
 		      const IntVector  &_delta;
 		      const BoolVector &_tabu;
 		      Random &_rnd;
 		      // Pivot rule data
 		      int _n;
 		    public:
 		      // Constructor
 		      RandomSelectionRule(GrossoLocatelliPullanMc &mc) :
 		        _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu),
 		        _rnd(mc._rnd), _n(mc._n)
 		      {}
 		      // Return a node index for a feasible add move or -1 if no one exists
 		      int nextFeasibleAddNode() const {
 		        int start_node = _rnd[_n];
 		        for (int i = start_node; i != _n; i++) {
 		          if (_delta[i] == 0 && !_tabu[i]) return i;
+		        }
 		        for (int i = 0; i != start_node; i++) {
 		          if (_delta[i] == 0 && !_tabu[i]) return i;
+		        }
 		        return -1;
+		      }
 		      // Return a node index for a feasible swap move or -1 if no one exists
 		      int nextFeasibleSwapNode() const {
 		        int start_node = _rnd[_n];
 		        for (int i = start_node; i != _n; i++) {
 		          if (!_clique[i] && _delta[i] == 1 && !_tabu[i]) return i;
+		        }
 		        for (int i = 0; i != start_node; i++) {
 		          if (!_clique[i] && _delta[i] == 1 && !_tabu[i]) return i;
+		        }
 		        return -1;
+		      }
 		      // Return a node index for an add move or -1 if no one exists
 		      int nextAddNode() const {
 		        int start_node = _rnd[_n];
 		        for (int i = start_node; i != _n; i++) {
 		          if (_delta[i] == 0) return i;
+		        }
 		        for (int i = 0; i != start_node; i++) {
 		          if (_delta[i] == 0) return i;
+		        }
 		        return -1;
+		      }
 		      // Update internal data structures between stages (if necessary)
 		      void update() {}
 		    }; //class RandomSelectionRule
 		    // Implementation of the DEGREE_BASED node selection rule.
 		    class DegreeBasedSelectionRule
+		    {
 		    private:
 		      // References to the algorithm instance
 		      const BoolVector &_clique;
 		      const IntVector  &_delta;
 		      const BoolVector &_tabu;
 		      Random &_rnd;
 		      // Pivot rule data
 		      int _n;
 		      IntVector _deg;
 		    public:
 		      // Constructor
 		      DegreeBasedSelectionRule(GrossoLocatelliPullanMc &mc) :
 		        _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu),
 		        _rnd(mc._rnd), _n(mc._n), _deg(_n)
+		      {
 		        for (int i = 0; i != _n; i++) {
 		          int d = 0;
 		          BoolVector &row = mc._gr[i];
 		          for (int j = 0; j != _n; j++) {
 		            if (row[j]) d++;
+		          }
 		          _deg[i] = d;
+		        }
+		      }
 		      // Return a node index for a feasible add move or -1 if no one exists
 		      int nextFeasibleAddNode() const {
 		        int start_node = _rnd[_n];
 		        int node = -1, max_deg = -1;
 		        for (int i = start_node; i != _n; i++) {
 		          if (_delta[i] == 0 && !_tabu[i] && _deg[i] > max_deg) {
 		            node = i;
 		            max_deg = _deg[i];
+		          }
+		        }
 		        for (int i = 0; i != start_node; i++) {
 		          if (_delta[i] == 0 && !_tabu[i] && _deg[i] > max_deg) {
 		            node = i;
 		            max_deg = _deg[i];
+		          }
+		        }
 		        return node;
+		      }
 		      // Return a node index for a feasible swap move or -1 if no one exists
 		      int nextFeasibleSwapNode() const {
 		        int start_node = _rnd[_n];
 		        int node = -1, max_deg = -1;
 		        for (int i = start_node; i != _n; i++) {
 		          if (!_clique[i] && _delta[i] == 1 && !_tabu[i] &&
 		              _deg[i] > max_deg) {
 		            node = i;
 		            max_deg = _deg[i];
+		          }
+		        }
 		        for (int i = 0; i != start_node; i++) {
 		          if (!_clique[i] && _delta[i] == 1 && !_tabu[i] &&
 		              _deg[i] > max_deg) {
 		            node = i;
 		            max_deg = _deg[i];
+		          }
+		        }
 		        return node;
+		      }
 		      // Return a node index for an add move or -1 if no one exists
 		      int nextAddNode() const {
 		        int start_node = _rnd[_n];
 		        int node = -1, max_deg = -1;
 		        for (int i = start_node; i != _n; i++) {
 		          if (_delta[i] == 0 && _deg[i] > max_deg) {
 		            node = i;
 		            max_deg = _deg[i];
+		          }
+		        }
 		        for (int i = 0; i != start_node; i++) {
 		          if (_delta[i] == 0 && _deg[i] > max_deg) {
 		            node = i;
 		            max_deg = _deg[i];
+		          }
+		        }
 		        return node;
+		      }
 		      // Update internal data structures between stages (if necessary)
 		      void update() {}
 		    }; //class DegreeBasedSelectionRule
 		    // Implementation of the PENALTY_BASED node selection rule.
 		    class PenaltyBasedSelectionRule
+		    {
 		    private:
 		      // References to the algorithm instance
 		      const BoolVector &_clique;
 		      const IntVector  &_delta;
 		      const BoolVector &_tabu;
 		      Random &_rnd;
 		      // Pivot rule data
 		      int _n;
 		      IntVector _penalty;
 		    public:
 		      // Constructor
 		      PenaltyBasedSelectionRule(GrossoLocatelliPullanMc &mc) :
 		        _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu),
 		        _rnd(mc._rnd), _n(mc._n), _penalty(_n, 0)
 		      {}
 		      // Return a node index for a feasible add move or -1 if no one exists
 		      int nextFeasibleAddNode() const {
 		        int start_node = _rnd[_n];
 		        int node = -1, min_p = std::numeric_limits<int>::max();
 		        for (int i = start_node; i != _n; i++) {
 		          if (_delta[i] == 0 && !_tabu[i] && _penalty[i] < min_p) {
 		            node = i;
 		            min_p = _penalty[i];
+		          }
+		        }
 		        for (int i = 0; i != start_node; i++) {
 		          if (_delta[i] == 0 && !_tabu[i] && _penalty[i] < min_p) {
 		            node = i;
 		            min_p = _penalty[i];
+		          }
+		        }
 		        return node;
+		      }
 		      // Return a node index for a feasible swap move or -1 if no one exists
 		      int nextFeasibleSwapNode() const {
 		        int start_node = _rnd[_n];
 		        int node = -1, min_p = std::numeric_limits<int>::max();
 		        for (int i = start_node; i != _n; i++) {
 		          if (!_clique[i] && _delta[i] == 1 && !_tabu[i] &&
 		              _penalty[i] < min_p) {
 		            node = i;
 		            min_p = _penalty[i];
+		          }
+		        }
 		        for (int i = 0; i != start_node; i++) {
 		          if (!_clique[i] && _delta[i] == 1 && !_tabu[i] &&
 		              _penalty[i] < min_p) {
 		            node = i;
 		            min_p = _penalty[i];
+		          }
+		        }
 		        return node;
+		      }
 		      // Return a node index for an add move or -1 if no one exists
 		      int nextAddNode() const {
 		        int start_node = _rnd[_n];
 		        int node = -1, min_p = std::numeric_limits<int>::max();
 		        for (int i = start_node; i != _n; i++) {
 		          if (_delta[i] == 0 && _penalty[i] < min_p) {
 		            node = i;
 		            min_p = _penalty[i];
+		          }
+		        }
 		        for (int i = 0; i != start_node; i++) {
 		          if (_delta[i] == 0 && _penalty[i] < min_p) {
 		            node = i;
 		            min_p = _penalty[i];
+		          }
+		        }
 		        return node;
+		      }
 		      // Update internal data structures between stages (if necessary)
 		      void update() {}
 		    }; //class PenaltyBasedSelectionRule
 		  public:
 		    /// \brief Constructor.
 		    ///
 		    /// Constructor.
 		    /// The global \ref rnd "random number generator instance" is used
 		    /// during the algorithm.
 		    ///
 		    /// \param graph The undirected graph the algorithm runs on.
 		    GrossoLocatelliPullanMc(const GR& graph) :
 		      _graph(graph), _id(_graph), _rnd(rnd)
 		    {}
 		    /// \brief Constructor with random seed.
 		    ///
 		    /// Constructor with random seed.
 		    ///
 		    /// \param graph The undirected graph the algorithm runs on.
 		    /// \param seed Seed value for the internal random number generator
 		    /// that is used during the algorithm.
 		    GrossoLocatelliPullanMc(const GR& graph, int seed) :
 		      _graph(graph), _id(_graph), _rnd(seed)
 		    {}
 		    /// \brief Constructor with random number generator.
 		    ///
 		    /// Constructor with random number generator.
 		    ///
 		    /// \param graph The undirected graph the algorithm runs on.
 		    /// \param random A random number generator that is used during the
 		    /// algorithm.
 		    GrossoLocatelliPullanMc(const GR& graph, const Random& random) :
 		      _graph(graph), _id(_graph), _rnd(random)
 		    {}
 		    /// \name Execution Control
 		    /// @{
 		    /// \brief Runs the algorithm.
 		    ///
 		    /// This function runs the algorithm.
 		    ///
 		    /// \param step_num The maximum number of node selections (steps)
 		    /// during the search process.
 		    /// This parameter controls the running time and the success of the
 		    /// algorithm. For larger values, the algorithm runs slower but it more
 		    /// likely finds larger cliques. For smaller values, the algorithm is
 		    /// faster but probably gives worse results.
 		    /// \param rule The node selection rule. For more information, see
 		    /// \ref SelectionRule.
 		    ///
 		    /// \return The size of the found clique.
 		    int run(int step_num = 100000,
 		            SelectionRule rule = PENALTY_BASED)
+		    {
 		      init();
 		      switch (rule) {
 		        case RANDOM:
 		          return start<RandomSelectionRule>(step_num);
 		        case DEGREE_BASED:
 		          return start<DegreeBasedSelectionRule>(step_num);
 		        case PENALTY_BASED:
 		          return start<PenaltyBasedSelectionRule>(step_num);
+		      }
 		      return 0; // avoid warning
+		    }
 		    /// @}
 		    /// \name Query Functions
 		    /// @{
 		    /// \brief The size of the found clique
 		    ///
 		    /// This function returns the size of the found clique.
 		    ///
 		    /// \pre run() must be called before using this function.
 		    int cliqueSize() const {
 		      return _best_size;
+		    }
 		    /// \brief Gives back the found clique in a \c bool node map
 		    ///
 		    /// This function gives back the characteristic vector of the found
 		    /// clique in the given node map.
 		    /// It must be a \ref concepts::WriteMap "writable" node map with
 		    /// \c bool (or convertible) value type.
 		    ///
 		    /// \pre run() must be called before using this function.
 		    template <typename CliqueMap>
 		    void cliqueMap(CliqueMap &map) const {
 		      for (NodeIt n(_graph); n != INVALID; ++n) {
 		        map[n] = static_cast<bool>(_best_clique[_id[n]]);
+		      }
+		    }
 		    /// \brief Iterator to list the nodes of the found clique
 		    ///
 		    /// This iterator class lists the nodes of the found clique.
 		    /// Before using it, you must allocate a GrossoLocatelliPullanMc instance
 		    /// and call its \ref GrossoLocatelliPullanMc::run() "run()" method.
 		    ///
 		    /// The following example prints out the IDs of the nodes in the found
 		    /// clique.
 		    /// \code
 		    ///   GrossoLocatelliPullanMc<Graph> mc(g);
 		    ///   mc.run();
 		    ///   for (GrossoLocatelliPullanMc<Graph>::CliqueNodeIt n(mc);
 		    ///        n != INVALID; ++n)
 		    ///   {
 		    ///     std::cout << g.id(n) << std::endl;
 		    ///   }
 		    /// \endcode
 		    class CliqueNodeIt
+		    {
 		    private:
 		      NodeIt _it;
 		      BoolNodeMap _map;
 		    public:
 		      /// Constructor
 		      /// Constructor.
 		      /// \param mc The algorithm instance.
 		      CliqueNodeIt(const GrossoLocatelliPullanMc &mc)
 		       : _map(mc._graph)
+		      {
 		        mc.cliqueMap(_map);
 		        for (_it = NodeIt(mc._graph); _it != INVALID && !_map[_it]; ++_it) ;
+		      }
 		      /// Conversion to \c Node
 		      operator Node() const { return _it; }
 		      bool operator==(Invalid) const { return _it == INVALID; }
 		      bool operator!=(Invalid) const { return _it != INVALID; }
 		      /// Next node
 		      CliqueNodeIt &operator++() {
 		        for (++_it; _it != INVALID && !_map[_it]; ++_it) ;
 		        return *this;
+		      }
 		      /// Postfix incrementation
 		      /// Postfix incrementation.
 		      ///
 		      /// \warning This incrementation returns a \c Node, not a
 		      /// \c CliqueNodeIt as one may expect.
 		      typename GR::Node operator++(int) {
 		        Node n=*this;
 		        ++(*this);
 		        return n;
+		      }
 		    };
 		    /// @}
 		  private:
 		    // Adds a node to the current clique
 		    void addCliqueNode(int u) {
 		      if (_clique[u]) return;
 		      _clique[u] = true;
 		      _size++;
 		      BoolVector &row = _gr[u];
 		      for (int i = 0; i != _n; i++) {
 		        if (!row[i]) _delta[i]++;
+		      }
+		    }
 		    // Removes a node from the current clique
 		    void delCliqueNode(int u) {
 		      if (!_clique[u]) return;
 		      _clique[u] = false;
 		      _size--;
 		      BoolVector &row = _gr[u];
 		      for (int i = 0; i != _n; i++) {
 		        if (!row[i]) _delta[i]--;
+		      }
+		    }
 		    // Initialize data structures
 		    void init() {
 		      _n = countNodes(_graph);
 		      int ui = 0;
 		      for (NodeIt u(_graph); u != INVALID; ++u) {
 		        _id[u] = ui++;
+		      }
 		      _gr.clear();
 		      _gr.resize(_n, BoolVector(_n, false));
 		      ui = 0;
 		      for (NodeIt u(_graph); u != INVALID; ++u) {
 		        for (IncEdgeIt e(_graph, u); e != INVALID; ++e) {
 		          int vi = _id[_graph.runningNode(e)];
 		          _gr[ui][vi] = true;
 		          _gr[vi][ui] = true;
+		        }
 		        ++ui;
+		      }
 		      _clique.clear();
 		      _clique.resize(_n, false);
 		      _size = 0;
 		      _best_clique.clear();
 		      _best_clique.resize(_n, false);
 		      _best_size = 0;
 		      _delta.clear();
 		      _delta.resize(_n, 0);
 		      _tabu.clear();
 		      _tabu.resize(_n, false);
+		    }
 		    // Executes the algorithm
 		    template <typename SelectionRuleImpl>
 		    int start(int max_select) {
 		      // Options for the restart rule
 		      const bool delta_based_restart = true;
 		      const int restart_delta_limit = 4;
 		      if (_n == 0) return 0;
 		      if (_n == 1) {
 		        _best_clique[0] = true;
 		        _best_size = 1;
 		        return _best_size;
+		      }
 		      // Iterated local search
 		      SelectionRuleImpl sel_method(*this);
 		      int select = 0;
 		      IntVector restart_nodes;
 		      while (select < max_select) {
 		        // Perturbation/restart
 		        if (delta_based_restart) {
 		          restart_nodes.clear();
 		          for (int i = 0; i != _n; i++) {
 		            if (_delta[i] >= restart_delta_limit)
 		              restart_nodes.push_back(i);
+		          }
+		        }
 		        int rs_node = -1;
 		        if (restart_nodes.size() > 0) {
 		          rs_node = restart_nodes[_rnd[restart_nodes.size()]];
 		        } else {
 		          rs_node = _rnd[_n];
+		        }
 		        BoolVector &row = _gr[rs_node];
 		        for (int i = 0; i != _n; i++) {
 		          if (_clique[i] && !row[i]) delCliqueNode(i);
+		        }
 		        addCliqueNode(rs_node);
 		        // Local search
 		        _tabu.clear();
 		        _tabu.resize(_n, false);
 		        bool tabu_empty = true;
 		        int max_swap = _size;
 		        while (select < max_select) {
 		          select++;
 		          int u;
 		          if ((u = sel_method.nextFeasibleAddNode()) != -1) {
 		            // Feasible add move
 		            addCliqueNode(u);
 		            if (tabu_empty) max_swap = _size;
+		          }
 		          else if ((u = sel_method.nextFeasibleSwapNode()) != -1) {
 		            // Feasible swap move
 		            int v = -1;
 		            BoolVector &row = _gr[u];
 		            for (int i = 0; i != _n; i++) {
 		              if (_clique[i] && !row[i]) {
 		                v = i;
 		                break;
+		              }
+		            }
 		            addCliqueNode(u);
 		            delCliqueNode(v);
 		            _tabu[v] = true;
 		            tabu_empty = false;
 		            if (--max_swap <= 0) break;
+		          }
 		          else if ((u = sel_method.nextAddNode()) != -1) {
 		            // Non-feasible add move
 		            addCliqueNode(u);
+		          }
 		          else break;
+		        }
 		        if (_size > _best_size) {
 		          _best_clique = _clique;
 		          _best_size = _size;
 		          if (_best_size == _n) return _best_size;
+		        }
 		        sel_method.update();
+		      }
 		      return _best_size;
+		    }
 		  }; //class GrossoLocatelliPullanMc
 		  ///@}
 		} //namespace lemon
 		#endif //LEMON_GROSSO_LOCATELLI_PULLAN_MC_H

test/max_clique_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) 2003-2010
 		 * 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 <sstream>
 		#include <lemon/list_graph.h>
 		#include <lemon/full_graph.h>
 		#include <lemon/grid_graph.h>
 		#include <lemon/lgf_reader.h>
 		#include <lemon/grosso_locatelli_pullan_mc.h>
 		#include "test_tools.h"
 		using namespace lemon;
 		char test_lgf[] =
 		  "@nodes\n"
 		  "label max_clique\n"
 		  "1     0\n"
 		  "2     0\n"
 		  "3     0\n"
 		  "4     1\n"
 		  "5     1\n"
 		  "6     1\n"
 		  "7     1\n"
 		  "@edges\n"
 		  "    label\n"
 		  "1 2     1\n"
 		  "1 3     2\n"
 		  "1 4     3\n"
 		  "1 6     4\n"
 		  "2 3     5\n"
 		  "2 5     6\n"
 		  "2 7     7\n"
 		  "3 4     8\n"
 		  "3 5     9\n"
 		  "4 5    10\n"
 		  "4 6    11\n"
 		  "4 7    12\n"
 		  "5 6    13\n"
 		  "5 7    14\n"
 		  "6 7    15\n";
 		// Check with general graphs
 		template <typename Param>
 		void checkMaxCliqueGeneral(int max_sel, Param rule) {
 		  typedef ListGraph GR;
 		  typedef GrossoLocatelliPullanMc<GR> McAlg;
 		  typedef McAlg::CliqueNodeIt CliqueIt;
 		  // Basic tests
+		  {
 		    GR g;
 		    GR::NodeMap<bool> map(g);
 		    McAlg mc(g);
 		    check(mc.run(max_sel, rule) == 0, "Wrong clique size");
 		    check(mc.cliqueSize() == 0, "Wrong clique size");
 		    check(CliqueIt(mc) == INVALID, "Wrong CliqueNodeIt");
 		    GR::Node u = g.addNode();
 		    check(mc.run(max_sel, rule) == 1, "Wrong clique size");
 		    check(mc.cliqueSize() == 1, "Wrong clique size");
 		    mc.cliqueMap(map);
 		    check(map[u], "Wrong clique map");
 		    CliqueIt it1(mc);
 		    check(static_cast<GR::Node>(it1) == u && ++it1 == INVALID,
 		          "Wrong CliqueNodeIt");
 		    GR::Node v = g.addNode();
 		    check(mc.run(max_sel, rule) == 1, "Wrong clique size");
 		    check(mc.cliqueSize() == 1, "Wrong clique size");
 		    mc.cliqueMap(map);
 		    check((map[u] && !map[v]) || (map[v] && !map[u]), "Wrong clique map");
 		    CliqueIt it2(mc);
 		    check(it2 != INVALID && ++it2 == INVALID, "Wrong CliqueNodeIt");
 		    g.addEdge(u, v);
 		    check(mc.run(max_sel, rule) == 2, "Wrong clique size");
 		    check(mc.cliqueSize() == 2, "Wrong clique size");
 		    mc.cliqueMap(map);
 		    check(map[u] && map[v], "Wrong clique map");
 		    CliqueIt it3(mc);
 		    check(it3 != INVALID && ++it3 != INVALID && ++it3 == INVALID,
 		          "Wrong CliqueNodeIt");
+		  }
 		  // Test graph
+		  {
 		    GR g;
 		    GR::NodeMap<bool> max_clique(g);
 		    GR::NodeMap<bool> map(g);
 		    std::istringstream input(test_lgf);
 		    graphReader(g, input)
 		      .nodeMap("max_clique", max_clique)
 		      .run();
 		    McAlg mc(g);
 		    check(mc.run(max_sel, rule) == 4, "Wrong clique size");
 		    check(mc.cliqueSize() == 4, "Wrong clique size");
 		    mc.cliqueMap(map);
 		    for (GR::NodeIt n(g); n != INVALID; ++n) {
 		      check(map[n] == max_clique[n], "Wrong clique map");
+		    }
 		    int cnt = 0;
 		    for (CliqueIt n(mc); n != INVALID; ++n) {
 		      cnt++;
 		      check(map[n] && max_clique[n], "Wrong CliqueNodeIt");
+		    }
 		    check(cnt == 4, "Wrong CliqueNodeIt");
+		  }
+		}
 		// Check with full graphs
 		template <typename Param>
 		void checkMaxCliqueFullGraph(int max_sel, Param rule) {
 		  typedef FullGraph GR;
 		  typedef GrossoLocatelliPullanMc<FullGraph> McAlg;
 		  typedef McAlg::CliqueNodeIt CliqueIt;
 		  for (int size = 0; size <= 40; size = size * 3 + 1) {
 		    GR g(size);
 		    GR::NodeMap<bool> map(g);
 		    McAlg mc(g);
 		    check(mc.run(max_sel, rule) == size, "Wrong clique size");
 		    check(mc.cliqueSize() == size, "Wrong clique size");
 		    mc.cliqueMap(map);
 		    for (GR::NodeIt n(g); n != INVALID; ++n) {
 		      check(map[n], "Wrong clique map");
+		    }
 		    int cnt = 0;
 		    for (CliqueIt n(mc); n != INVALID; ++n) cnt++;
 		    check(cnt == size, "Wrong CliqueNodeIt");
+		  }
+		}
 		// Check with grid graphs
 		template <typename Param>
 		void checkMaxCliqueGridGraph(int max_sel, Param rule) {
 		  GridGraph g(5, 7);
 		  GridGraph::NodeMap<char> map(g);
 		  GrossoLocatelliPullanMc<GridGraph> mc(g);
 		  check(mc.run(max_sel, rule) == 2, "Wrong clique size");
 		  check(mc.cliqueSize() == 2, "Wrong clique size");
+		}
 		int main() {
 		  checkMaxCliqueGeneral(50, GrossoLocatelliPullanMc<ListGraph>::RANDOM);
 		  checkMaxCliqueGeneral(50, GrossoLocatelliPullanMc<ListGraph>::DEGREE_BASED);
 		  checkMaxCliqueGeneral(50, GrossoLocatelliPullanMc<ListGraph>::PENALTY_BASED);
 		  checkMaxCliqueFullGraph(50, GrossoLocatelliPullanMc<FullGraph>::RANDOM);
 		  checkMaxCliqueFullGraph(50, GrossoLocatelliPullanMc<FullGraph>::DEGREE_BASED);
 		  checkMaxCliqueFullGraph(50, GrossoLocatelliPullanMc<FullGraph>::PENALTY_BASED);
 		  checkMaxCliqueGridGraph(50, GrossoLocatelliPullanMc<GridGraph>::RANDOM);
 		  checkMaxCliqueGridGraph(50, GrossoLocatelliPullanMc<GridGraph>::DEGREE_BASED);
 		  checkMaxCliqueGridGraph(50, GrossoLocatelliPullanMc<GridGraph>::PENALTY_BASED);
 		  return 0;
+		}

doc/groups.dox

Show inline comments

@@ -298,465 +298,469 @@
 		\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)
 		\ref clrs01algorithms.
 		*/
 		/**
 		@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 clrs01algorithms.
 		 - \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 spantree Minimum Spanning Tree Algorithms
 		@ingroup algs
 		\brief Algorithms for finding minimum cost spanning trees and arborescences.
 		This group contains the algorithms for finding minimum cost spanning
 		trees and arborescences \ref clrs01algorithms.
 		*/
 		/**
 		@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 \ref clrs01algorithms, \ref amo93networkflows.
 		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_{sv\in A} f(sv) - \sum_{vs\in A} f(vs) \f]
 		\f[ \sum_{uv\in A} f(uv) = \sum_{vu\in A} f(vu)
 		    \quad \forall u\in V\setminus\{s,t\} \f]
 		\f[ 0 \leq f(uv) \leq cap(uv) \quad \forall uv\in A \f]
 		LEMON contains several algorithms for solving maximum flow problems:
 		- \ref EdmondsKarp Edmonds-Karp algorithm
 		  \ref edmondskarp72theoretical.
 		- \ref Preflow Goldberg-Tarjan's preflow push-relabel algorithm
 		  \ref goldberg88newapproach.
 		- \ref DinitzSleatorTarjan Dinitz's blocking flow algorithm with dynamic trees
 		  \ref dinic70algorithm, \ref sleator83dynamic.
 		- \ref GoldbergTarjan !Preflow push-relabel algorithm with dynamic trees
 		  \ref goldberg88newapproach, \ref sleator83dynamic.
 		In most cases the \ref Preflow algorithm provides the
 		fastest method for computing a maximum flow. All implementations
 		also provide functions to query the minimum cut, which is the dual
 		problem of maximum flow.
 		\ref Circulation is a preflow push-relabel algorithm implemented directly
 		for finding feasible circulations, which is a somewhat different problem,
 		but it is strongly related to maximum flow.
 		For more information, see \ref Circulation.
 		*/
 		/**
 		@defgroup min_cost_flow_algs 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 \ref amo93networkflows. For more information about this
 		problem and its dual solution, see \ref min_cost_flow
 		"Minimum Cost Flow Problem".
 		LEMON contains several algorithms for this problem.
 		 - \ref NetworkSimplex Primal Network Simplex algorithm with various
 		   pivot strategies \ref dantzig63linearprog, \ref kellyoneill91netsimplex.
 		 - \ref CostScaling Cost Scaling algorithm based on push/augment and
 		   relabel operations \ref goldberg90approximation, \ref goldberg97efficient,
 		   \ref bunnagel98efficient.
 		 - \ref CapacityScaling Capacity Scaling algorithm based on the successive
 		   shortest path method \ref edmondskarp72theoretical.
 		 - \ref CycleCanceling Cycle-Canceling algorithms, two of which are
 		   strongly polynomial \ref klein67primal, \ref goldberg89cyclecanceling.
 		In general NetworkSimplex is the most efficient implementation,
 		but in special cases other algorithms could be faster.
 		For example, if the total supply and/or capacities are rather small,
 		CapacityScaling is usually the fastest algorithm (without effective scaling).
 		*/
 		/**
 		@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 min_mean_cycle Minimum Mean Cycle Algorithms
 		@ingroup algs
 		\brief Algorithms for finding minimum mean cycles.
 		This group contains the algorithms for finding minimum mean cycles
 		\ref clrs01algorithms, \ref amo93networkflows.
 		The \e minimum \e mean \e cycle \e problem is to find a directed cycle
 		of minimum mean length (cost) in a digraph.
 		The mean length of a cycle is the average length of its arcs, i.e. the
 		ratio between the total length of the cycle and the number of arcs on it.
 		This problem has an important connection to \e conservative \e length
 		\e functions, too. A length function on the arcs of a digraph is called
 		conservative if and only if there is no directed cycle of negative total
 		length. For an arbitrary length function, the negative of the minimum
 		cycle mean is the smallest \f$\epsilon\f$ value so that increasing the
 		arc lengths uniformly by \f$\epsilon\f$ results in a conservative length
 		function.
 		LEMON contains three algorithms for solving the minimum mean cycle problem:
 		- \ref KarpMmc Karp's original algorithm \ref amo93networkflows,
 		  \ref dasdan98minmeancycle.
 		- \ref HartmannOrlinMmc Hartmann-Orlin's algorithm, which is an improved
 		  version of Karp's algorithm \ref dasdan98minmeancycle.
 		- \ref HowardMmc Howard's policy iteration algorithm
 		  \ref dasdan98minmeancycle.
 		In practice, the \ref HowardMmc "Howard" algorithm proved to be by far the
 		most efficient one, though the best known theoretical bound on its running
 		time is exponential.
 		Both \ref KarpMmc "Karp" and \ref HartmannOrlinMmc "Hartmann-Orlin" algorithms
 		run in time O(ne) and use space O(n<sup>2</sup>+e), but the latter one is
 		typically faster due to the applied early termination scheme.
 		*/
 		/**
 		@defgroup matching Matching Algorithms
 		@ingroup algs
 		\brief Algorithms for finding matchings in graphs and bipartite graphs.
 		This group contains the algorithms for calculating
 		matchings in graphs and bipartite graphs. The general matching problem is
 		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.
 		- \ref MaxFractionalMatching Push-relabel algorithm for calculating
 		  maximum cardinality fractional matching in general graphs.
 		- \ref MaxWeightedFractionalMatching Augmenting path algorithm for calculating
 		  maximum weighted fractional matching in general graphs.
 		- \ref MaxWeightedPerfectFractionalMatching
 		  Augmenting path algorithm for calculating maximum weighted
 		  perfect fractional matching in general graphs.
 		\image html matching.png
 		\image latex matching.eps "Min Cost Perfect Matching" width=\textwidth
 		*/
 		/**
 		@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 connected_components.png
 		\image latex connected_components.eps "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 approx Approximation Algorithms
+		@defgroup approx_algs Approximation Algorithms
 		@ingroup algs
 		\brief Approximation algorithms.
 		This group contains the approximation and heuristic algorithms
 		implemented in LEMON.
 		<b>Maximum Clique Problem</b>
 		  - \ref GrossoLocatelliPullanMc An efficient heuristic algorithm of
 		    Grosso, Locatelli, and Pullan.
 		*/
 		/**
 		@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 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.
 		Various LP solvers could be used in the same manner with this
 		high-level interface.
 		The currently supported solvers are \ref glpk, \ref clp, \ref cbc,
 		\ref cplex, \ref soplex.
 		*/
 		/**
 		@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.
 		- These classes declare every functions, <tt>typedef</tt>s etc. an
 		  implementation of the %concepts should provide, however completely
 		  without implementations and real data structures behind the
 		  interface. On the other hand they should provide nothing else. All
 		  the algorithms working on a data structure meeting a certain concept
 		  should compile with these classes. (Though it will not run properly,
 		  of course.) In this way it is easily to check if an algorithm
 		  doesn't use any extra feature of a certain implementation.
 		- The concept descriptor classes also provide a <em>checker class</em>
 		  that makes it possible to check whether a certain implementation of a
 		  concept indeed provides all the required features.
 		- Finally, They can serve as a skeleton of a new implementation of a concept.
 		*/
 		/**
 		@defgroup graph_concepts Graph Structure Concepts
 		@ingroup concept
 		\brief Skeleton and concept checking classes for graph structures
 		This group contains the skeletons and concept checking classes of
 		graph structures.
 		*/
 		/**
 		@defgroup map_concepts Map Concepts
 		@ingroup concept
 		\brief Skeleton and concept checking classes for maps
 		This group contains the skeletons and concept checking classes of maps.
 		*/
 		/**
 		@defgroup tools Standalone Utility Applications
 		Some utility applications are listed here.
 		The standard compilation procedure (<tt>./configure;make</tt>) will compile
 		them, as well.
 		*/
 		/**
 		\anchor demoprograms
 		@defgroup demos Demo Programs
 		Some demo programs are listed here. Their full source codes can be found in
 		the \c demo subdirectory of the source tree.
 		In order to compile them, use the <tt>make demo</tt> or the
 		<tt>make check</tt> commands.
 		*/
+		}

doc/references.bib

Show inline comments

@@ -44,258 +44,271 @@
 		  key =          {LEDA},
 		  title =        {{LEDA} -- {L}ibrary of {E}fficient {D}ata {T}ypes and
 		                  {A}lgorithms},
 		  url =          {http://www.algorithmic-solutions.com/}
+		}
 		@book{ledabook,
 		  author =       {Kurt Mehlhorn and Stefan N{\"a}her},
 		  title =        {{LEDA}: {A} platform for combinatorial and geometric
 		                  computing},
 		  isbn =         {0-521-56329-1},
 		  publisher =    {Cambridge University Press},
 		  address =      {New York, NY, USA},
 		  year =         1999
+		}
 		%%%%% Tools that LEMON depends on %%%%%
 		@misc{cmake,
 		  key =          {CMake},
 		  title =        {{CMake} -- {C}ross {P}latform {M}ake},
 		  url =          {http://www.cmake.org/}
+		}
 		@misc{doxygen,
 		  key =          {Doxygen},
 		  title =        {{Doxygen} -- {S}ource code documentation generator
 		                  tool},
 		  url =          {http://www.doxygen.org/}
+		}
 		%%%%% LP/MIP libraries %%%%%
 		@misc{glpk,
 		  key =          {GLPK},
 		  title =        {{GLPK} -- {GNU} {L}inear {P}rogramming {K}it},
 		  url =          {http://www.gnu.org/software/glpk/}
+		}
 		@misc{clp,
 		  key =          {Clp},
 		  title =        {{Clp} -- {Coin-Or} {L}inear {P}rogramming},
 		  url =          {http://projects.coin-or.org/Clp/}
+		}
 		@misc{cbc,
 		  key =          {Cbc},
 		  title =        {{Cbc} -- {Coin-Or} {B}ranch and {C}ut},
 		  url =          {http://projects.coin-or.org/Cbc/}
+		}
 		@misc{cplex,
 		  key =          {CPLEX},
 		  title =        {{ILOG} {CPLEX}},
 		  url =          {http://www.ilog.com/}
+		}
 		@misc{soplex,
 		  key =          {SoPlex},
 		  title =        {{SoPlex} -- {T}he {S}equential {O}bject-{O}riented
 		                  {S}implex},
 		  url =          {http://soplex.zib.de/}
+		}
 		%%%%% General books %%%%%
 		@book{amo93networkflows,
 		  author =       {Ravindra K. Ahuja and Thomas L. Magnanti and James
 		                  B. Orlin},
 		  title =        {Network Flows: Theory, Algorithms, and Applications},
 		  publisher =    {Prentice-Hall, Inc.},
 		  year =         1993,
 		  month =        feb,
 		  isbn =         {978-0136175490}
+		}
 		@book{schrijver03combinatorial,
 		  author =       {Alexander Schrijver},
 		  title =        {Combinatorial Optimization: Polyhedra and Efficiency},
 		  publisher =    {Springer-Verlag},
 		  year =         2003,
 		  isbn =         {978-3540443896}
+		}
 		@book{clrs01algorithms,
 		  author =       {Thomas H. Cormen and Charles E. Leiserson and Ronald
 		                  L. Rivest and Clifford Stein},
 		  title =        {Introduction to Algorithms},
 		  publisher =    {The MIT Press},
 		  year =         2001,
 		  edition =      {2nd}
+		}
 		@book{stroustrup00cpp,
 		  author =       {Bjarne Stroustrup},
 		  title =        {The C++ Programming Language},
 		  edition =      {3rd},
 		  publisher =    {Addison-Wesley Professional},
 		  isbn =         0201700735,
 		  month =        {February},
 		  year =         2000
+		}
 		%%%%% Maximum flow algorithms %%%%%
 		@article{edmondskarp72theoretical,
 		  author =       {Jack Edmonds and Richard M. Karp},
 		  title =        {Theoretical improvements in algorithmic efficiency
 		                  for network flow problems},
 		  journal =      {Journal of the ACM},
 		  year =         1972,
 		  volume =       19,
 		  number =       2,
 		  pages =        {248-264}
+		}
 		@article{goldberg88newapproach,
 		  author =       {Andrew V. Goldberg and Robert E. Tarjan},
 		  title =        {A new approach to the maximum flow problem},
 		  journal =      {Journal of the ACM},
 		  year =         1988,
 		  volume =       35,
 		  number =       4,
 		  pages =        {921-940}
+		}
 		@article{dinic70algorithm,
 		  author =       {E. A. Dinic},
 		  title =        {Algorithm for solution of a problem of maximum flow
 		                  in a network with power estimation},
 		  journal =      {Soviet Math. Doklady},
 		  year =         1970,
 		  volume =       11,
 		  pages =        {1277-1280}
+		}
 		@article{goldberg08partial,
 		  author =       {Andrew V. Goldberg},
 		  title =        {The Partial Augment-Relabel Algorithm for the
 		                  Maximum Flow Problem},
 		  journal =      {16th Annual European Symposium on Algorithms},
 		  year =         2008,
 		  pages =        {466-477}
+		}
 		@article{sleator83dynamic,
 		  author =       {Daniel D. Sleator and Robert E. Tarjan},
 		  title =        {A data structure for dynamic trees},
 		  journal =      {Journal of Computer and System Sciences},
 		  year =         1983,
 		  volume =       26,
 		  number =       3,
 		  pages =        {362-391}
+		}
 		%%%%% Minimum mean cycle algorithms %%%%%
 		@article{karp78characterization,
 		  author =       {Richard M. Karp},
 		  title =        {A characterization of the minimum cycle mean in a
 		                  digraph},
 		  journal =      {Discrete Math.},
 		  year =         1978,
 		  volume =       23,
 		  pages =        {309-311}
+		}
 		@article{dasdan98minmeancycle,
 		  author =       {Ali Dasdan and Rajesh K. Gupta},
 		  title =        {Faster Maximum and Minimum Mean Cycle Alogrithms for
 		                  System Performance Analysis},
 		  journal =      {IEEE Transactions on Computer-Aided Design of
 		                  Integrated Circuits and Systems},
 		  year =         1998,
 		  volume =       17,
 		  number =       10,
 		  pages =        {889-899}
+		}
 		%%%%% Minimum cost flow algorithms %%%%%
 		@article{klein67primal,
 		  author =       {Morton Klein},
 		  title =        {A primal method for minimal cost flows with
 		                  applications to the assignment and transportation
 		                  problems},
 		  journal =      {Management Science},
 		  year =         1967,
 		  volume =       14,
 		  pages =        {205-220}
+		}
 		@article{goldberg89cyclecanceling,
 		  author =       {Andrew V. Goldberg and Robert E. Tarjan},
 		  title =        {Finding minimum-cost circulations by canceling
 		                  negative cycles},
 		  journal =      {Journal of the ACM},
 		  year =         1989,
 		  volume =       36,
 		  number =       4,
 		  pages =        {873-886}
+		}
 		@article{goldberg90approximation,
 		  author =       {Andrew V. Goldberg and Robert E. Tarjan},
 		  title =        {Finding Minimum-Cost Circulations by Successive
 		                  Approximation},
 		  journal =      {Mathematics of Operations Research},
 		  year =         1990,
 		  volume =       15,
 		  number =       3,
 		  pages =        {430-466}
+		}
 		@article{goldberg97efficient,
 		  author =       {Andrew V. Goldberg},
 		  title =        {An Efficient Implementation of a Scaling
 		                  Minimum-Cost Flow Algorithm},
 		  journal =      {Journal of Algorithms},
 		  year =         1997,
 		  volume =       22,
 		  number =       1,
 		  pages =        {1-29}
+		}
 		@article{bunnagel98efficient,
 		  author =       {Ursula B{\"u}nnagel and Bernhard Korte and Jens
 		                  Vygen},
 		  title =        {Efficient implementation of the {G}oldberg-{T}arjan
 		                  minimum-cost flow algorithm},
 		  journal =      {Optimization Methods and Software},
 		  year =         1998,
 		  volume =       10,
 		  pages =        {157-174}
+		}
 		@book{dantzig63linearprog,
 		  author =       {George B. Dantzig},
 		  title =        {Linear Programming and Extensions},
 		  publisher =    {Princeton University Press},
 		  year =         1963
+		}
 		@mastersthesis{kellyoneill91netsimplex,
 		  author =       {Damian J. Kelly and Garrett M. O'Neill},
 		  title =        {The Minimum Cost Flow Problem and The Network
 		                  Simplex Method},
 		  school =       {University College},
 		  address =      {Dublin, Ireland},
 		  year =         1991,
-		  month =        sep,
 		  month =        sep
+		}
 		%%%%% Other algorithms %%%%%
 		@article{grosso08maxclique,
 		  author =       {Andrea Grosso and Marco Locatelli and Wayne Pullan},
 		  title =        {Simple ingredients leading to very efficient
 		                  heuristics for the maximum clique problem},
 		  journal =      {Journal of Heuristics},
 		  year =         2008,
 		  volume =       14,
 		  number =       6,
 		  pages =        {587--612}
+		}

lemon/Makefile.am

Show inline comments

 		EXTRA_DIST += \
 			lemon/lemon.pc.in \
 			lemon/CMakeLists.txt \
 			lemon/config.h.cmake
 		pkgconfig_DATA += lemon/lemon.pc
 		lib_LTLIBRARIES += lemon/libemon.la
 		lemon_libemon_la_SOURCES = \
 			lemon/arg_parser.cc \
 			lemon/base.cc \
 			lemon/color.cc \
 			lemon/lp_base.cc \
 			lemon/lp_skeleton.cc \
 			lemon/random.cc \
 			lemon/bits/windows.cc
 		nodist_lemon_HEADERS = lemon/config.h
 		lemon_libemon_la_CXXFLAGS = \
 			$(AM_CXXFLAGS) \
 			$(GLPK_CFLAGS) \
 			$(CPLEX_CFLAGS) \
 			$(SOPLEX_CXXFLAGS) \
 			$(CLP_CXXFLAGS) \
 			$(CBC_CXXFLAGS)
 		lemon_libemon_la_LDFLAGS = \
 			$(GLPK_LIBS) \
 			$(CPLEX_LIBS) \
 			$(SOPLEX_LIBS) \
 			$(CLP_LIBS) \
 			$(CBC_LIBS)
 		if HAVE_GLPK
 		lemon_libemon_la_SOURCES += lemon/glpk.cc
 		endif
 		if HAVE_CPLEX
 		lemon_libemon_la_SOURCES += lemon/cplex.cc
 		endif
 		if HAVE_SOPLEX
 		lemon_libemon_la_SOURCES += lemon/soplex.cc
 		endif
 		if HAVE_CLP
 		lemon_libemon_la_SOURCES += lemon/clp.cc
 		endif
 		if HAVE_CBC
 		lemon_libemon_la_SOURCES += lemon/cbc.cc
 		endif
 		lemon_HEADERS += \
 			lemon/adaptors.h \
 			lemon/arg_parser.h \
 			lemon/assert.h \
 			lemon/bellman_ford.h \
 			lemon/bfs.h \
 			lemon/bin_heap.h \
 			lemon/binomial_heap.h \
 			lemon/bucket_heap.h \
 			lemon/capacity_scaling.h \
 			lemon/cbc.h \
 			lemon/circulation.h \
 			lemon/clp.h \
 			lemon/color.h \
 			lemon/concept_check.h \
 			lemon/connectivity.h \
 			lemon/core.h \
 			lemon/cost_scaling.h \
 			lemon/counter.h \
 			lemon/cplex.h \
 			lemon/cycle_canceling.h \
 			lemon/dfs.h \
 			lemon/dheap.h \
 			lemon/dijkstra.h \
 			lemon/dim2.h \
 			lemon/dimacs.h \
 			lemon/edge_set.h \
 			lemon/elevator.h \
 			lemon/error.h \
 			lemon/euler.h \
 			lemon/fib_heap.h \
 			lemon/fractional_matching.h \
 			lemon/full_graph.h \
 			lemon/glpk.h \
 			lemon/gomory_hu.h \
 			lemon/graph_to_eps.h \
 			lemon/grid_graph.h \
 			lemon/grosso_locatelli_pullan_mc.h \
 			lemon/hartmann_orlin_mmc.h \
 			lemon/howard_mmc.h \
 			lemon/hypercube_graph.h \
 			lemon/karp_mmc.h \
 			lemon/kruskal.h \
 			lemon/hao_orlin.h \
 			lemon/lgf_reader.h \
 			lemon/lgf_writer.h \
 			lemon/list_graph.h \
 			lemon/lp.h \
 			lemon/lp_base.h \
 			lemon/lp_skeleton.h \
 			lemon/maps.h \
 			lemon/matching.h \
 			lemon/math.h \
 			lemon/min_cost_arborescence.h \
 			lemon/nauty_reader.h \
 			lemon/network_simplex.h \
 			lemon/pairing_heap.h \
 			lemon/path.h \
 			lemon/planarity.h \
 			lemon/preflow.h \
 			lemon/quad_heap.h \
 			lemon/radix_heap.h \
 			lemon/radix_sort.h \
 			lemon/random.h \
 			lemon/smart_graph.h \
 			lemon/soplex.h \
 			lemon/static_graph.h \
 			lemon/suurballe.h \
 			lemon/time_measure.h \
 			lemon/tolerance.h \
 			lemon/unionfind.h \
 			lemon/bits/windows.h
 		bits_HEADERS += \
 			lemon/bits/alteration_notifier.h \
 			lemon/bits/array_map.h \
 			lemon/bits/bezier.h \
 			lemon/bits/default_map.h \
 			lemon/bits/edge_set_extender.h \
 			lemon/bits/enable_if.h \
 			lemon/bits/graph_adaptor_extender.h \
 			lemon/bits/graph_extender.h \
 			lemon/bits/map_extender.h \
 			lemon/bits/path_dump.h \
 			lemon/bits/solver_bits.h \
 			lemon/bits/traits.h \
 			lemon/bits/variant.h \
 			lemon/bits/vector_map.h
 		concept_HEADERS += \
 			lemon/concepts/digraph.h \
 			lemon/concepts/graph.h \
 			lemon/concepts/graph_components.h \
 			lemon/concepts/heap.h \
 			lemon/concepts/maps.h \
 			lemon/concepts/path.h

test/CMakeLists.txt

Show inline comments

 		INCLUDE_DIRECTORIES(
 		  ${PROJECT_SOURCE_DIR}
 		  ${PROJECT_BINARY_DIR}
+		)
 		LINK_DIRECTORIES(
 		  ${PROJECT_BINARY_DIR}/lemon
+		)
 		SET(TESTS
 		  adaptors_test
 		  bellman_ford_test
 		  bfs_test
 		  circulation_test
 		  connectivity_test
 		  counter_test
 		  dfs_test
 		  digraph_test
 		  dijkstra_test
 		  dim_test
 		  edge_set_test
 		  error_test
 		  euler_test
 		  fractional_matching_test
 		  gomory_hu_test
 		  graph_copy_test
 		  graph_test
 		  graph_utils_test
 		  hao_orlin_test
 		  heap_test
 		  kruskal_test
 		  maps_test
 		  matching_test
 		  max_clique_test
 		  min_cost_arborescence_test
 		  min_cost_flow_test
 		  min_mean_cycle_test
 		  path_test
 		  planarity_test
 		  preflow_test
 		  radix_sort_test
 		  random_test
 		  suurballe_test
 		  time_measure_test
 		  unionfind_test
+		)
 		IF(LEMON_HAVE_LP)
 		  ADD_EXECUTABLE(lp_test lp_test.cc)
 		  SET(LP_TEST_LIBS lemon)
 		  IF(LEMON_HAVE_GLPK)
 		    SET(LP_TEST_LIBS ${LP_TEST_LIBS} ${GLPK_LIBRARIES})
 		  ENDIF()
 		  IF(LEMON_HAVE_CPLEX)
 		    SET(LP_TEST_LIBS ${LP_TEST_LIBS} ${CPLEX_LIBRARIES})
 		  ENDIF()
 		  IF(LEMON_HAVE_CLP)
 		    SET(LP_TEST_LIBS ${LP_TEST_LIBS} ${COIN_CLP_LIBRARIES})
 		  ENDIF()
 		  TARGET_LINK_LIBRARIES(lp_test ${LP_TEST_LIBS})
 		  ADD_TEST(lp_test lp_test)
 		  IF(WIN32 AND LEMON_HAVE_GLPK)
 		    GET_TARGET_PROPERTY(TARGET_LOC lp_test LOCATION)
 		    GET_FILENAME_COMPONENT(TARGET_PATH ${TARGET_LOC} PATH)
 		    ADD_CUSTOM_COMMAND(TARGET lp_test POST_BUILD
 		      COMMAND ${CMAKE_COMMAND} -E copy ${GLPK_BIN_DIR}/glpk.dll ${TARGET_PATH}
 		      COMMAND ${CMAKE_COMMAND} -E copy ${GLPK_BIN_DIR}/libltdl3.dll ${TARGET_PATH}
 		      COMMAND ${CMAKE_COMMAND} -E copy ${GLPK_BIN_DIR}/zlib1.dll ${TARGET_PATH}
+		    )
 		  ENDIF()
 		  IF(WIN32 AND LEMON_HAVE_CPLEX)
 		    GET_TARGET_PROPERTY(TARGET_LOC lp_test LOCATION)
 		    GET_FILENAME_COMPONENT(TARGET_PATH ${TARGET_LOC} PATH)
 		    ADD_CUSTOM_COMMAND(TARGET lp_test POST_BUILD
 		      COMMAND ${CMAKE_COMMAND} -E copy ${CPLEX_BIN_DIR}/cplex91.dll ${TARGET_PATH}
+		    )
 		  ENDIF()
 		ENDIF()
 		IF(LEMON_HAVE_MIP)
 		  ADD_EXECUTABLE(mip_test mip_test.cc)
 		  SET(MIP_TEST_LIBS lemon)
 		  IF(LEMON_HAVE_GLPK)
 		    SET(MIP_TEST_LIBS ${MIP_TEST_LIBS} ${GLPK_LIBRARIES})
 		  ENDIF()
 		  IF(LEMON_HAVE_CPLEX)
 		    SET(MIP_TEST_LIBS ${MIP_TEST_LIBS} ${CPLEX_LIBRARIES})
 		  ENDIF()
 		  IF(LEMON_HAVE_CBC)
 		    SET(MIP_TEST_LIBS ${MIP_TEST_LIBS} ${COIN_CBC_LIBRARIES})
 		  ENDIF()
 		  TARGET_LINK_LIBRARIES(mip_test ${MIP_TEST_LIBS})
 		  ADD_TEST(mip_test mip_test)
 		  IF(WIN32 AND LEMON_HAVE_GLPK)
 		    GET_TARGET_PROPERTY(TARGET_LOC mip_test LOCATION)
 		    GET_FILENAME_COMPONENT(TARGET_PATH ${TARGET_LOC} PATH)
 		    ADD_CUSTOM_COMMAND(TARGET mip_test POST_BUILD
 		      COMMAND ${CMAKE_COMMAND} -E copy ${GLPK_BIN_DIR}/glpk.dll ${TARGET_PATH}
 		      COMMAND ${CMAKE_COMMAND} -E copy ${GLPK_BIN_DIR}/libltdl3.dll ${TARGET_PATH}
 		      COMMAND ${CMAKE_COMMAND} -E copy ${GLPK_BIN_DIR}/zlib1.dll ${TARGET_PATH}
+		    )
 		  ENDIF()
 		  IF(WIN32 AND LEMON_HAVE_CPLEX)
 		    GET_TARGET_PROPERTY(TARGET_LOC mip_test LOCATION)
 		    GET_FILENAME_COMPONENT(TARGET_PATH ${TARGET_LOC} PATH)
 		    ADD_CUSTOM_COMMAND(TARGET mip_test POST_BUILD
 		      COMMAND ${CMAKE_COMMAND} -E copy ${CPLEX_BIN_DIR}/cplex91.dll ${TARGET_PATH}
+		    )
 		  ENDIF()
 		ENDIF()
 		FOREACH(TEST_NAME ${TESTS})
 		  ADD_EXECUTABLE(${TEST_NAME} ${TEST_NAME}.cc)
 		  TARGET_LINK_LIBRARIES(${TEST_NAME} lemon)
 		  ADD_TEST(${TEST_NAME} ${TEST_NAME})
 		ENDFOREACH()

test/Makefile.am

Show inline comments

 		if USE_VALGRIND
 		TESTS_ENVIRONMENT=$(top_srcdir)/scripts/valgrind-wrapper.sh
 		endif
 		EXTRA_DIST += \
 			test/CMakeLists.txt
 		noinst_HEADERS += \
 			test/graph_test.h \
 			test/test_tools.h
 		check_PROGRAMS += \
 			test/adaptors_test \
 			test/bellman_ford_test \
 			test/bfs_test \
 			test/circulation_test \
 			test/connectivity_test \
 			test/counter_test \
 			test/dfs_test \
 			test/digraph_test \
 			test/dijkstra_test \
 			test/dim_test \
 			test/edge_set_test \
 			test/error_test \
 			test/euler_test \
 			test/fractional_matching_test \
 			test/gomory_hu_test \
 			test/graph_copy_test \
 			test/graph_test \
 			test/graph_utils_test \
 			test/hao_orlin_test \
 			test/heap_test \
 			test/kruskal_test \
 			test/maps_test \
 			test/matching_test \
 			test/max_clique_test \
 			test/min_cost_arborescence_test \
 			test/min_cost_flow_test \
 			test/min_mean_cycle_test \
 			test/path_test \
 			test/planarity_test \
 			test/preflow_test \
 			test/radix_sort_test \
 			test/random_test \
 			test/suurballe_test \
 			test/test_tools_fail \
 			test/test_tools_pass \
 			test/time_measure_test \
 			test/unionfind_test
 		test_test_tools_pass_DEPENDENCIES = demo
 		if HAVE_LP
 		check_PROGRAMS += test/lp_test
 		endif HAVE_LP
 		if HAVE_MIP
 		check_PROGRAMS += test/mip_test
 		endif HAVE_MIP
 		TESTS += $(check_PROGRAMS)
 		XFAIL_TESTS += test/test_tools_fail$(EXEEXT)
 		test_adaptors_test_SOURCES = test/adaptors_test.cc
 		test_bellman_ford_test_SOURCES = test/bellman_ford_test.cc
 		test_bfs_test_SOURCES = test/bfs_test.cc
 		test_circulation_test_SOURCES = test/circulation_test.cc
 		test_counter_test_SOURCES = test/counter_test.cc
 		test_connectivity_test_SOURCES = test/connectivity_test.cc
 		test_dfs_test_SOURCES = test/dfs_test.cc
 		test_digraph_test_SOURCES = test/digraph_test.cc
 		test_dijkstra_test_SOURCES = test/dijkstra_test.cc
 		test_dim_test_SOURCES = test/dim_test.cc
 		test_edge_set_test_SOURCES = test/edge_set_test.cc
 		test_error_test_SOURCES = test/error_test.cc
 		test_euler_test_SOURCES = test/euler_test.cc
 		test_fractional_matching_test_SOURCES = test/fractional_matching_test.cc
 		test_gomory_hu_test_SOURCES = test/gomory_hu_test.cc
 		test_graph_copy_test_SOURCES = test/graph_copy_test.cc
 		test_graph_test_SOURCES = test/graph_test.cc
 		test_graph_utils_test_SOURCES = test/graph_utils_test.cc
 		test_heap_test_SOURCES = test/heap_test.cc
 		test_kruskal_test_SOURCES = test/kruskal_test.cc
 		test_hao_orlin_test_SOURCES = test/hao_orlin_test.cc
 		test_lp_test_SOURCES = test/lp_test.cc
 		test_maps_test_SOURCES = test/maps_test.cc
 		test_mip_test_SOURCES = test/mip_test.cc
 		test_matching_test_SOURCES = test/matching_test.cc
 		test_max_clique_test_SOURCES = test/max_clique_test.cc
 		test_min_cost_arborescence_test_SOURCES = test/min_cost_arborescence_test.cc
 		test_min_cost_flow_test_SOURCES = test/min_cost_flow_test.cc
 		test_min_mean_cycle_test_SOURCES = test/min_mean_cycle_test.cc
 		test_path_test_SOURCES = test/path_test.cc
 		test_planarity_test_SOURCES = test/planarity_test.cc
 		test_preflow_test_SOURCES = test/preflow_test.cc
 		test_radix_sort_test_SOURCES = test/radix_sort_test.cc
 		test_suurballe_test_SOURCES = test/suurballe_test.cc
 		test_random_test_SOURCES = test/random_test.cc
 		test_test_tools_fail_SOURCES = test/test_tools_fail.cc
 		test_test_tools_pass_SOURCES = test/test_tools_pass.cc
 		test_time_measure_test_SOURCES = test/time_measure_test.cc
 		test_unionfind_test_SOURCES = test/unionfind_test.cc

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