diff -r 7c4ba7daaf5f -r 2b6bffe0e7e8 lemon/howard_mmc.h
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lemon/howard_mmc.h	Tue Dec 20 18:15:14 2011 +0100
@@ -0,0 +1,605 @@
+/* -*- 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_HOWARD_MMC_H
+#define LEMON_HOWARD_MMC_H
+
+/// \ingroup min_mean_cycle
+///
+/// \file
+/// \brief Howard's algorithm for finding a minimum mean cycle.
+
+#include <vector>
+#include <limits>
+#include <lemon/core.h>
+#include <lemon/path.h>
+#include <lemon/tolerance.h>
+#include <lemon/connectivity.h>
+
+namespace lemon {
+
+  /// \brief Default traits class of HowardMmc class.
+  ///
+  /// Default traits class of HowardMmc class.
+  /// \tparam GR The type of the digraph.
+  /// \tparam CM The type of the cost map.
+  /// It must conform to the \ref concepts::ReadMap "ReadMap" concept.
+#ifdef DOXYGEN
+  template <typename GR, typename CM>
+#else
+  template <typename GR, typename CM,
+    bool integer = std::numeric_limits<typename CM::Value>::is_integer>
+#endif
+  struct HowardMmcDefaultTraits
+  {
+    /// The type of the digraph
+    typedef GR Digraph;
+    /// The type of the cost map
+    typedef CM CostMap;
+    /// The type of the arc costs
+    typedef typename CostMap::Value Cost;
+
+    /// \brief The large cost type used for internal computations
+    ///
+    /// The large cost type used for internal computations.
+    /// It is \c long \c long if the \c Cost type is integer,
+    /// otherwise it is \c double.
+    /// \c Cost must be convertible to \c LargeCost.
+    typedef double LargeCost;
+
+    /// The tolerance type used for internal computations
+    typedef lemon::Tolerance<LargeCost> Tolerance;
+
+    /// \brief The path type of the found cycles
+    ///
+    /// The path type of the found cycles.
+    /// It must conform to the \ref lemon::concepts::Path "Path" concept
+    /// and it must have an \c addBack() function.
+    typedef lemon::Path<Digraph> Path;
+  };
+
+  // Default traits class for integer cost types
+  template <typename GR, typename CM>
+  struct HowardMmcDefaultTraits<GR, CM, true>
+  {
+    typedef GR Digraph;
+    typedef CM CostMap;
+    typedef typename CostMap::Value Cost;
+#ifdef LEMON_HAVE_LONG_LONG
+    typedef long long LargeCost;
+#else
+    typedef long LargeCost;
+#endif
+    typedef lemon::Tolerance<LargeCost> Tolerance;
+    typedef lemon::Path<Digraph> Path;
+  };
+
+
+  /// \addtogroup min_mean_cycle
+  /// @{
+
+  /// \brief Implementation of Howard's algorithm for finding a minimum
+  /// mean cycle.
+  ///
+  /// This class implements Howard's policy iteration algorithm for finding
+  /// a directed cycle of minimum mean cost in a digraph
+  /// \ref amo93networkflows, \ref dasdan98minmeancycle.
+  /// This class provides the most efficient algorithm for the
+  /// minimum mean cycle problem, though the best known theoretical
+  /// bound on its running time is exponential.
+  ///
+  /// \tparam GR The type of the digraph the algorithm runs on.
+  /// \tparam CM The type of the cost map. The default
+  /// map type is \ref concepts::Digraph::ArcMap "GR::ArcMap<int>".
+  /// \tparam TR The traits class that defines various types used by the
+  /// algorithm. By default, it is \ref HowardMmcDefaultTraits
+  /// "HowardMmcDefaultTraits<GR, CM>".
+  /// In most cases, this parameter should not be set directly,
+  /// consider to use the named template parameters instead.
+#ifdef DOXYGEN
+  template <typename GR, typename CM, typename TR>
+#else
+  template < typename GR,
+             typename CM = typename GR::template ArcMap<int>,
+             typename TR = HowardMmcDefaultTraits<GR, CM> >
+#endif
+  class HowardMmc
+  {
+  public:
+
+    /// The type of the digraph
+    typedef typename TR::Digraph Digraph;
+    /// The type of the cost map
+    typedef typename TR::CostMap CostMap;
+    /// The type of the arc costs
+    typedef typename TR::Cost Cost;
+
+    /// \brief The large cost type
+    ///
+    /// The large cost type used for internal computations.
+    /// By default, it is \c long \c long if the \c Cost type is integer,
+    /// otherwise it is \c double.
+    typedef typename TR::LargeCost LargeCost;
+
+    /// The tolerance type
+    typedef typename TR::Tolerance Tolerance;
+
+    /// \brief The path type of the found cycles
+    ///
+    /// The path type of the found cycles.
+    /// Using the \ref HowardMmcDefaultTraits "default traits class",
+    /// it is \ref lemon::Path "Path<Digraph>".
+    typedef typename TR::Path Path;
+
+    /// The \ref HowardMmcDefaultTraits "traits class" of the algorithm
+    typedef TR Traits;
+
+  private:
+
+    TEMPLATE_DIGRAPH_TYPEDEFS(Digraph);
+
+    // The digraph the algorithm runs on
+    const Digraph &_gr;
+    // The cost of the arcs
+    const CostMap &_cost;
+
+    // Data for the found cycles
+    bool _curr_found, _best_found;
+    LargeCost _curr_cost, _best_cost;
+    int _curr_size, _best_size;
+    Node _curr_node, _best_node;
+
+    Path *_cycle_path;
+    bool _local_path;
+
+    // Internal data used by the algorithm
+    typename Digraph::template NodeMap<Arc> _policy;
+    typename Digraph::template NodeMap<bool> _reached;
+    typename Digraph::template NodeMap<int> _level;
+    typename Digraph::template NodeMap<LargeCost> _dist;
+
+    // Data for storing the strongly connected components
+    int _comp_num;
+    typename Digraph::template NodeMap<int> _comp;
+    std::vector<std::vector<Node> > _comp_nodes;
+    std::vector<Node>* _nodes;
+    typename Digraph::template NodeMap<std::vector<Arc> > _in_arcs;
+
+    // Queue used for BFS search
+    std::vector<Node> _queue;
+    int _qfront, _qback;
+
+    Tolerance _tolerance;
+
+    // Infinite constant
+    const LargeCost INF;
+
+  public:
+
+    /// \name Named Template Parameters
+    /// @{
+
+    template <typename T>
+    struct SetLargeCostTraits : public Traits {
+      typedef T LargeCost;
+      typedef lemon::Tolerance<T> Tolerance;
+    };
+
+    /// \brief \ref named-templ-param "Named parameter" for setting
+    /// \c LargeCost type.
+    ///
+    /// \ref named-templ-param "Named parameter" for setting \c LargeCost
+    /// type. It is used for internal computations in the algorithm.
+    template <typename T>
+    struct SetLargeCost
+      : public HowardMmc<GR, CM, SetLargeCostTraits<T> > {
+      typedef HowardMmc<GR, CM, SetLargeCostTraits<T> > Create;
+    };
+
+    template <typename T>
+    struct SetPathTraits : public Traits {
+      typedef T Path;
+    };
+
+    /// \brief \ref named-templ-param "Named parameter" for setting
+    /// \c %Path type.
+    ///
+    /// \ref named-templ-param "Named parameter" for setting the \c %Path
+    /// type of the found cycles.
+    /// It must conform to the \ref lemon::concepts::Path "Path" concept
+    /// and it must have an \c addBack() function.
+    template <typename T>
+    struct SetPath
+      : public HowardMmc<GR, CM, SetPathTraits<T> > {
+      typedef HowardMmc<GR, CM, SetPathTraits<T> > Create;
+    };
+
+    /// @}
+
+  protected:
+
+    HowardMmc() {}
+
+  public:
+
+    /// \brief Constructor.
+    ///
+    /// The constructor of the class.
+    ///
+    /// \param digraph The digraph the algorithm runs on.
+    /// \param cost The costs of the arcs.
+    HowardMmc( const Digraph &digraph,
+               const CostMap &cost ) :
+      _gr(digraph), _cost(cost), _best_found(false),
+      _best_cost(0), _best_size(1), _cycle_path(NULL), _local_path(false),
+      _policy(digraph), _reached(digraph), _level(digraph), _dist(digraph),
+      _comp(digraph), _in_arcs(digraph),
+      INF(std::numeric_limits<LargeCost>::has_infinity ?
+          std::numeric_limits<LargeCost>::infinity() :
+          std::numeric_limits<LargeCost>::max())
+    {}
+
+    /// Destructor.
+    ~HowardMmc() {
+      if (_local_path) delete _cycle_path;
+    }
+
+    /// \brief Set the path structure for storing the found cycle.
+    ///
+    /// This function sets an external path structure for storing the
+    /// found cycle.
+    ///
+    /// If you don't call this function before calling \ref run() or
+    /// \ref findCycleMean(), it will allocate a local \ref Path "path"
+    /// structure. The destuctor deallocates this automatically
+    /// allocated object, of course.
+    ///
+    /// \note The algorithm calls only the \ref lemon::Path::addBack()
+    /// "addBack()" function of the given path structure.
+    ///
+    /// \return <tt>(*this)</tt>
+    HowardMmc& cycle(Path &path) {
+      if (_local_path) {
+        delete _cycle_path;
+        _local_path = false;
+      }
+      _cycle_path = &path;
+      return *this;
+    }
+
+    /// \brief Set the tolerance used by the algorithm.
+    ///
+    /// This function sets the tolerance object used by the algorithm.
+    ///
+    /// \return <tt>(*this)</tt>
+    HowardMmc& tolerance(const Tolerance& tolerance) {
+      _tolerance = tolerance;
+      return *this;
+    }
+
+    /// \brief Return a const reference to the tolerance.
+    ///
+    /// This function returns a const reference to the tolerance object
+    /// used by the algorithm.
+    const Tolerance& tolerance() const {
+      return _tolerance;
+    }
+
+    /// \name Execution control
+    /// The simplest way to execute the algorithm is to call the \ref run()
+    /// function.\n
+    /// If you only need the minimum mean cost, you may call
+    /// \ref findCycleMean().
+
+    /// @{
+
+    /// \brief Run the algorithm.
+    ///
+    /// This function runs the algorithm.
+    /// It can be called more than once (e.g. if the underlying digraph
+    /// and/or the arc costs have been modified).
+    ///
+    /// \return \c true if a directed cycle exists in the digraph.
+    ///
+    /// \note <tt>mmc.run()</tt> is just a shortcut of the following code.
+    /// \code
+    ///   return mmc.findCycleMean() && mmc.findCycle();
+    /// \endcode
+    bool run() {
+      return findCycleMean() && findCycle();
+    }
+
+    /// \brief Find the minimum cycle mean.
+    ///
+    /// This function finds the minimum mean cost of the directed
+    /// cycles in the digraph.
+    ///
+    /// \return \c true if a directed cycle exists in the digraph.
+    bool findCycleMean() {
+      // Initialize and find strongly connected components
+      init();
+      findComponents();
+
+      // Find the minimum cycle mean in the components
+      for (int comp = 0; comp < _comp_num; ++comp) {
+        // Find the minimum mean cycle in the current component
+        if (!buildPolicyGraph(comp)) continue;
+        while (true) {
+          findPolicyCycle();
+          if (!computeNodeDistances()) break;
+        }
+        // Update the best cycle (global minimum mean cycle)
+        if ( _curr_found && (!_best_found ||
+             _curr_cost * _best_size < _best_cost * _curr_size) ) {
+          _best_found = true;
+          _best_cost = _curr_cost;
+          _best_size = _curr_size;
+          _best_node = _curr_node;
+        }
+      }
+      return _best_found;
+    }
+
+    /// \brief Find a minimum mean directed cycle.
+    ///
+    /// This function finds a directed cycle of minimum mean cost
+    /// in the digraph using the data computed by findCycleMean().
+    ///
+    /// \return \c true if a directed cycle exists in the digraph.
+    ///
+    /// \pre \ref findCycleMean() must be called before using this function.
+    bool findCycle() {
+      if (!_best_found) return false;
+      _cycle_path->addBack(_policy[_best_node]);
+      for ( Node v = _best_node;
+            (v = _gr.target(_policy[v])) != _best_node; ) {
+        _cycle_path->addBack(_policy[v]);
+      }
+      return true;
+    }
+
+    /// @}
+
+    /// \name Query Functions
+    /// The results of the algorithm can be obtained using these
+    /// functions.\n
+    /// The algorithm should be executed before using them.
+
+    /// @{
+
+    /// \brief Return the total cost of the found cycle.
+    ///
+    /// This function returns the total cost of the found cycle.
+    ///
+    /// \pre \ref run() or \ref findCycleMean() must be called before
+    /// using this function.
+    Cost cycleCost() const {
+      return static_cast<Cost>(_best_cost);
+    }
+
+    /// \brief Return the number of arcs on the found cycle.
+    ///
+    /// This function returns the number of arcs on the found cycle.
+    ///
+    /// \pre \ref run() or \ref findCycleMean() must be called before
+    /// using this function.
+    int cycleSize() const {
+      return _best_size;
+    }
+
+    /// \brief Return the mean cost of the found cycle.
+    ///
+    /// This function returns the mean cost of the found cycle.
+    ///
+    /// \note <tt>alg.cycleMean()</tt> is just a shortcut of the
+    /// following code.
+    /// \code
+    ///   return static_cast<double>(alg.cycleCost()) / alg.cycleSize();
+    /// \endcode
+    ///
+    /// \pre \ref run() or \ref findCycleMean() must be called before
+    /// using this function.
+    double cycleMean() const {
+      return static_cast<double>(_best_cost) / _best_size;
+    }
+
+    /// \brief Return the found cycle.
+    ///
+    /// This function returns a const reference to the path structure
+    /// storing the found cycle.
+    ///
+    /// \pre \ref run() or \ref findCycle() must be called before using
+    /// this function.
+    const Path& cycle() const {
+      return *_cycle_path;
+    }
+
+    ///@}
+
+  private:
+
+    // Initialize
+    void init() {
+      if (!_cycle_path) {
+        _local_path = true;
+        _cycle_path = new Path;
+      }
+      _queue.resize(countNodes(_gr));
+      _best_found = false;
+      _best_cost = 0;
+      _best_size = 1;
+      _cycle_path->clear();
+    }
+
+    // Find strongly connected components and initialize _comp_nodes
+    // and _in_arcs
+    void findComponents() {
+      _comp_num = stronglyConnectedComponents(_gr, _comp);
+      _comp_nodes.resize(_comp_num);
+      if (_comp_num == 1) {
+        _comp_nodes[0].clear();
+        for (NodeIt n(_gr); n != INVALID; ++n) {
+          _comp_nodes[0].push_back(n);
+          _in_arcs[n].clear();
+          for (InArcIt a(_gr, n); a != INVALID; ++a) {
+            _in_arcs[n].push_back(a);
+          }
+        }
+      } else {
+        for (int i = 0; i < _comp_num; ++i)
+          _comp_nodes[i].clear();
+        for (NodeIt n(_gr); n != INVALID; ++n) {
+          int k = _comp[n];
+          _comp_nodes[k].push_back(n);
+          _in_arcs[n].clear();
+          for (InArcIt a(_gr, n); a != INVALID; ++a) {
+            if (_comp[_gr.source(a)] == k) _in_arcs[n].push_back(a);
+          }
+        }
+      }
+    }
+
+    // Build the policy graph in the given strongly connected component
+    // (the out-degree of every node is 1)
+    bool buildPolicyGraph(int comp) {
+      _nodes = &(_comp_nodes[comp]);
+      if (_nodes->size() < 1 ||
+          (_nodes->size() == 1 && _in_arcs[(*_nodes)[0]].size() == 0)) {
+        return false;
+      }
+      for (int i = 0; i < int(_nodes->size()); ++i) {
+        _dist[(*_nodes)[i]] = INF;
+      }
+      Node u, v;
+      Arc e;
+      for (int i = 0; i < int(_nodes->size()); ++i) {
+        v = (*_nodes)[i];
+        for (int j = 0; j < int(_in_arcs[v].size()); ++j) {
+          e = _in_arcs[v][j];
+          u = _gr.source(e);
+          if (_cost[e] < _dist[u]) {
+            _dist[u] = _cost[e];
+            _policy[u] = e;
+          }
+        }
+      }
+      return true;
+    }
+
+    // Find the minimum mean cycle in the policy graph
+    void findPolicyCycle() {
+      for (int i = 0; i < int(_nodes->size()); ++i) {
+        _level[(*_nodes)[i]] = -1;
+      }
+      LargeCost ccost;
+      int csize;
+      Node u, v;
+      _curr_found = false;
+      for (int i = 0; i < int(_nodes->size()); ++i) {
+        u = (*_nodes)[i];
+        if (_level[u] >= 0) continue;
+        for (; _level[u] < 0; u = _gr.target(_policy[u])) {
+          _level[u] = i;
+        }
+        if (_level[u] == i) {
+          // A cycle is found
+          ccost = _cost[_policy[u]];
+          csize = 1;
+          for (v = u; (v = _gr.target(_policy[v])) != u; ) {
+            ccost += _cost[_policy[v]];
+            ++csize;
+          }
+          if ( !_curr_found ||
+               (ccost * _curr_size < _curr_cost * csize) ) {
+            _curr_found = true;
+            _curr_cost = ccost;
+            _curr_size = csize;
+            _curr_node = u;
+          }
+        }
+      }
+    }
+
+    // Contract the policy graph and compute node distances
+    bool computeNodeDistances() {
+      // Find the component of the main cycle and compute node distances
+      // using reverse BFS
+      for (int i = 0; i < int(_nodes->size()); ++i) {
+        _reached[(*_nodes)[i]] = false;
+      }
+      _qfront = _qback = 0;
+      _queue[0] = _curr_node;
+      _reached[_curr_node] = true;
+      _dist[_curr_node] = 0;
+      Node u, v;
+      Arc e;
+      while (_qfront <= _qback) {
+        v = _queue[_qfront++];
+        for (int j = 0; j < int(_in_arcs[v].size()); ++j) {
+          e = _in_arcs[v][j];
+          u = _gr.source(e);
+          if (_policy[u] == e && !_reached[u]) {
+            _reached[u] = true;
+            _dist[u] = _dist[v] + _cost[e] * _curr_size - _curr_cost;
+            _queue[++_qback] = u;
+          }
+        }
+      }
+
+      // Connect all other nodes to this component and compute node
+      // distances using reverse BFS
+      _qfront = 0;
+      while (_qback < int(_nodes->size())-1) {
+        v = _queue[_qfront++];
+        for (int j = 0; j < int(_in_arcs[v].size()); ++j) {
+          e = _in_arcs[v][j];
+          u = _gr.source(e);
+          if (!_reached[u]) {
+            _reached[u] = true;
+            _policy[u] = e;
+            _dist[u] = _dist[v] + _cost[e] * _curr_size - _curr_cost;
+            _queue[++_qback] = u;
+          }
+        }
+      }
+
+      // Improve node distances
+      bool improved = false;
+      for (int i = 0; i < int(_nodes->size()); ++i) {
+        v = (*_nodes)[i];
+        for (int j = 0; j < int(_in_arcs[v].size()); ++j) {
+          e = _in_arcs[v][j];
+          u = _gr.source(e);
+          LargeCost delta = _dist[v] + _cost[e] * _curr_size - _curr_cost;
+          if (_tolerance.less(delta, _dist[u])) {
+            _dist[u] = delta;
+            _policy[u] = e;
+            improved = true;
+          }
+        }
+      }
+      return improved;
+    }
+
+  }; //class HowardMmc
+
+  ///@}
+
+} //namespace lemon
+
+#endif //LEMON_HOWARD_MMC_H