lemon/karp.h
author Peter Kovacs <kpeter@inf.elte.hu>
Thu, 12 Nov 2009 23:49:05 +0100
changeset 812 4b1b378823dc
parent 771 8452ca46e29a
child 825 75e6020b19b1
permissions -rw-r--r--
Small doc improvements + unifications in MCF classes (#180)
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/* -*- C++ -*-
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 *
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 * This file is a part of LEMON, a generic C++ optimization library
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 *
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 * Copyright (C) 2003-2008
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 * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
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 * (Egervary Research Group on Combinatorial Optimization, EGRES).
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 *
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 * Permission to use, modify and distribute this software is granted
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 * provided that this copyright notice appears in all copies. For
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 * precise terms see the accompanying LICENSE file.
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 *
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 * This software is provided "AS IS" with no warranty of any kind,
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 * express or implied, and with no claim as to its suitability for any
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 * purpose.
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 *
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 */
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#ifndef LEMON_KARP_H
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#define LEMON_KARP_H
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/// \ingroup min_mean_cycle
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///
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/// \file
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/// \brief Karp's algorithm for finding a minimum mean cycle.
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#include <vector>
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#include <limits>
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#include <lemon/core.h>
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#include <lemon/path.h>
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#include <lemon/tolerance.h>
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#include <lemon/connectivity.h>
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namespace lemon {
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  /// \brief Default traits class of Karp algorithm.
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  ///
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  /// Default traits class of Karp algorithm.
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  /// \tparam GR The type of the digraph.
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  /// \tparam LEN The type of the length map.
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  /// It must conform to the \ref concepts::ReadMap "ReadMap" concept.
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#ifdef DOXYGEN
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  template <typename GR, typename LEN>
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#else
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  template <typename GR, typename LEN,
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    bool integer = std::numeric_limits<typename LEN::Value>::is_integer>
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#endif
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  struct KarpDefaultTraits
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  {
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    /// The type of the digraph
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    typedef GR Digraph;
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    /// The type of the length map
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    typedef LEN LengthMap;
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    /// The type of the arc lengths
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    typedef typename LengthMap::Value Value;
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    /// \brief The large value type used for internal computations
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    ///
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    /// The large value type used for internal computations.
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    /// It is \c long \c long if the \c Value type is integer,
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    /// otherwise it is \c double.
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    /// \c Value must be convertible to \c LargeValue.
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    typedef double LargeValue;
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    /// The tolerance type used for internal computations
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    typedef lemon::Tolerance<LargeValue> Tolerance;
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    /// \brief The path type of the found cycles
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    ///
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    /// The path type of the found cycles.
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    /// It must conform to the \ref lemon::concepts::Path "Path" concept
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    /// and it must have an \c addFront() function.
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    typedef lemon::Path<Digraph> Path;
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  };
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  // Default traits class for integer value types
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  template <typename GR, typename LEN>
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  struct KarpDefaultTraits<GR, LEN, true>
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  {
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    typedef GR Digraph;
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    typedef LEN LengthMap;
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    typedef typename LengthMap::Value Value;
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#ifdef LEMON_HAVE_LONG_LONG
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    typedef long long LargeValue;
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#else
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    typedef long LargeValue;
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#endif
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    typedef lemon::Tolerance<LargeValue> Tolerance;
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    typedef lemon::Path<Digraph> Path;
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  };
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  /// \addtogroup min_mean_cycle
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  /// @{
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  /// \brief Implementation of Karp's algorithm for finding a minimum
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  /// mean cycle.
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  ///
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  /// This class implements Karp's algorithm for finding a directed
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  /// cycle of minimum mean length (cost) in a digraph
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  /// \ref amo93networkflows, \ref dasdan98minmeancycle.
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  /// It runs in time O(ne) and uses space O(n<sup>2</sup>+e).
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  ///
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  /// \tparam GR The type of the digraph the algorithm runs on.
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  /// \tparam LEN The type of the length map. The default
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  /// map type is \ref concepts::Digraph::ArcMap "GR::ArcMap<int>".
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#ifdef DOXYGEN
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  template <typename GR, typename LEN, typename TR>
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#else
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  template < typename GR,
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             typename LEN = typename GR::template ArcMap<int>,
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             typename TR = KarpDefaultTraits<GR, LEN> >
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#endif
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  class Karp
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  {
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  public:
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    /// The type of the digraph
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    typedef typename TR::Digraph Digraph;
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    /// The type of the length map
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    typedef typename TR::LengthMap LengthMap;
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    /// The type of the arc lengths
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    typedef typename TR::Value Value;
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    /// \brief The large value type
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    ///
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    /// The large value type used for internal computations.
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    /// Using the \ref KarpDefaultTraits "default traits class",
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    /// it is \c long \c long if the \c Value type is integer,
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    /// otherwise it is \c double.
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    typedef typename TR::LargeValue LargeValue;
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    /// The tolerance type
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    typedef typename TR::Tolerance Tolerance;
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    /// \brief The path type of the found cycles
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    ///
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    /// The path type of the found cycles.
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    /// Using the \ref KarpDefaultTraits "default traits class",
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    /// it is \ref lemon::Path "Path<Digraph>".
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    typedef typename TR::Path Path;
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    /// The \ref KarpDefaultTraits "traits class" of the algorithm
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    typedef TR Traits;
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  private:
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    TEMPLATE_DIGRAPH_TYPEDEFS(Digraph);
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    // Data sturcture for path data
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    struct PathData
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    {
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      LargeValue dist;
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      Arc pred;
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      PathData(LargeValue d, Arc p = INVALID) :
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        dist(d), pred(p) {}
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    };
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    typedef typename Digraph::template NodeMap<std::vector<PathData> >
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      PathDataNodeMap;
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  private:
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    // The digraph the algorithm runs on
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    const Digraph &_gr;
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    // The length of the arcs
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    const LengthMap &_length;
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    // Data for storing the strongly connected components
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    int _comp_num;
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    typename Digraph::template NodeMap<int> _comp;
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    std::vector<std::vector<Node> > _comp_nodes;
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    std::vector<Node>* _nodes;
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    typename Digraph::template NodeMap<std::vector<Arc> > _out_arcs;
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    // Data for the found cycle
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    LargeValue _cycle_length;
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    int _cycle_size;
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    Node _cycle_node;
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    Path *_cycle_path;
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    bool _local_path;
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    // Node map for storing path data
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    PathDataNodeMap _data;
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    // The processed nodes in the last round
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    std::vector<Node> _process;
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    Tolerance _tolerance;
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    // Infinite constant
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    const LargeValue INF;
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  public:
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    /// \name Named Template Parameters
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    /// @{
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    template <typename T>
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    struct SetLargeValueTraits : public Traits {
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      typedef T LargeValue;
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      typedef lemon::Tolerance<T> Tolerance;
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    };
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    /// \brief \ref named-templ-param "Named parameter" for setting
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    /// \c LargeValue type.
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    ///
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    /// \ref named-templ-param "Named parameter" for setting \c LargeValue
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    /// type. It is used for internal computations in the algorithm.
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    template <typename T>
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    struct SetLargeValue
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      : public Karp<GR, LEN, SetLargeValueTraits<T> > {
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      typedef Karp<GR, LEN, SetLargeValueTraits<T> > Create;
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    };
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    template <typename T>
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    struct SetPathTraits : public Traits {
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      typedef T Path;
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    };
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    /// \brief \ref named-templ-param "Named parameter" for setting
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    /// \c %Path type.
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    ///
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    /// \ref named-templ-param "Named parameter" for setting the \c %Path
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    /// type of the found cycles.
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    /// It must conform to the \ref lemon::concepts::Path "Path" concept
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    /// and it must have an \c addFront() function.
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    template <typename T>
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    struct SetPath
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      : public Karp<GR, LEN, SetPathTraits<T> > {
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      typedef Karp<GR, LEN, SetPathTraits<T> > Create;
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    };
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    /// @}
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  public:
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    /// \brief Constructor.
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    ///
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    /// The constructor of the class.
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    ///
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    /// \param digraph The digraph the algorithm runs on.
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    /// \param length The lengths (costs) of the arcs.
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    Karp( const Digraph &digraph,
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          const LengthMap &length ) :
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      _gr(digraph), _length(length), _comp(digraph), _out_arcs(digraph),
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      _cycle_length(0), _cycle_size(1), _cycle_node(INVALID),
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      _cycle_path(NULL), _local_path(false), _data(digraph),
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      INF(std::numeric_limits<LargeValue>::has_infinity ?
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          std::numeric_limits<LargeValue>::infinity() :
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          std::numeric_limits<LargeValue>::max())
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    {}
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    /// Destructor.
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    ~Karp() {
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      if (_local_path) delete _cycle_path;
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    }
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    /// \brief Set the path structure for storing the found cycle.
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    ///
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    /// This function sets an external path structure for storing the
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    /// found cycle.
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    ///
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    /// If you don't call this function before calling \ref run() or
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    /// \ref findMinMean(), it will allocate a local \ref Path "path"
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    /// structure. The destuctor deallocates this automatically
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    /// allocated object, of course.
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    ///
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    /// \note The algorithm calls only the \ref lemon::Path::addFront()
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    /// "addFront()" function of the given path structure.
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    ///
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    /// \return <tt>(*this)</tt>
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    Karp& cycle(Path &path) {
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      if (_local_path) {
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        delete _cycle_path;
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        _local_path = false;
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      }
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      _cycle_path = &path;
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      return *this;
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    }
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    /// \brief Set the tolerance used by the algorithm.
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    ///
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    /// This function sets the tolerance object used by the algorithm.
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    ///
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    /// \return <tt>(*this)</tt>
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    Karp& tolerance(const Tolerance& tolerance) {
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      _tolerance = tolerance;
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      return *this;
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    }
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    /// \brief Return a const reference to the tolerance.
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    ///
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    /// This function returns a const reference to the tolerance object
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    /// used by the algorithm.
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    const Tolerance& tolerance() const {
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      return _tolerance;
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    }
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    /// \name Execution control
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    /// The simplest way to execute the algorithm is to call the \ref run()
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    /// function.\n
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    /// If you only need the minimum mean length, you may call
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    /// \ref findMinMean().
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    /// @{
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    /// \brief Run the algorithm.
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    ///
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    /// This function runs the algorithm.
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    /// It can be called more than once (e.g. if the underlying digraph
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    /// and/or the arc lengths have been modified).
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    ///
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    /// \return \c true if a directed cycle exists in the digraph.
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    ///
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    /// \note <tt>mmc.run()</tt> is just a shortcut of the following code.
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    /// \code
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    ///   return mmc.findMinMean() && mmc.findCycle();
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    /// \endcode
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    bool run() {
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      return findMinMean() && findCycle();
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    }
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    /// \brief Find the minimum cycle mean.
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    ///
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    /// This function finds the minimum mean length of the directed
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    /// cycles in the digraph.
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    ///
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    /// \return \c true if a directed cycle exists in the digraph.
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    bool findMinMean() {
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      // Initialization and find strongly connected components
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      init();
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      findComponents();
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      // Find the minimum cycle mean in the components
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      for (int comp = 0; comp < _comp_num; ++comp) {
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        if (!initComponent(comp)) continue;
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        processRounds();
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        updateMinMean();
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      }
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      return (_cycle_node != INVALID);
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    }
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    /// \brief Find a minimum mean directed cycle.
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    ///
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    /// This function finds a directed cycle of minimum mean length
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    /// in the digraph using the data computed by findMinMean().
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    ///
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    /// \return \c true if a directed cycle exists in the digraph.
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    ///
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    /// \pre \ref findMinMean() must be called before using this function.
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    bool findCycle() {
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      if (_cycle_node == INVALID) return false;
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      IntNodeMap reached(_gr, -1);
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      int r = _data[_cycle_node].size();
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      Node u = _cycle_node;
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      while (reached[u] < 0) {
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        reached[u] = --r;
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        u = _gr.source(_data[u][r].pred);
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      }
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      r = reached[u];
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      Arc e = _data[u][r].pred;
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      _cycle_path->addFront(e);
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      _cycle_length = _length[e];
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      _cycle_size = 1;
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      Node v;
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      while ((v = _gr.source(e)) != u) {
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        e = _data[v][--r].pred;
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        _cycle_path->addFront(e);
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        _cycle_length += _length[e];
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        ++_cycle_size;
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      }
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      return true;
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    }
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    /// @}
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    /// \name Query Functions
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    /// The results of the algorithm can be obtained using these
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    /// functions.\n
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    /// The algorithm should be executed before using them.
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    /// @{
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    /// \brief Return the total length of the found cycle.
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    ///
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    /// This function returns the total length of the found cycle.
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    ///
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    /// \pre \ref run() or \ref findMinMean() must be called before
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    /// using this function.
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    LargeValue cycleLength() const {
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      return _cycle_length;
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    }
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    /// \brief Return the number of arcs on the found cycle.
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    ///
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    /// This function returns the number of arcs on the found cycle.
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    ///
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    /// \pre \ref run() or \ref findMinMean() must be called before
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    /// using this function.
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    int cycleArcNum() const {
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      return _cycle_size;
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    }
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    /// \brief Return the mean length of the found cycle.
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    ///
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    /// This function returns the mean length of the found cycle.
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    ///
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    /// \note <tt>alg.cycleMean()</tt> is just a shortcut of the
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    /// following code.
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    /// \code
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    ///   return static_cast<double>(alg.cycleLength()) / alg.cycleArcNum();
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   413
    /// \endcode
kpeter@765
   414
    ///
kpeter@765
   415
    /// \pre \ref run() or \ref findMinMean() must be called before
kpeter@765
   416
    /// using this function.
kpeter@765
   417
    double cycleMean() const {
kpeter@765
   418
      return static_cast<double>(_cycle_length) / _cycle_size;
kpeter@765
   419
    }
kpeter@765
   420
kpeter@765
   421
    /// \brief Return the found cycle.
kpeter@765
   422
    ///
kpeter@765
   423
    /// This function returns a const reference to the path structure
kpeter@765
   424
    /// storing the found cycle.
kpeter@765
   425
    ///
kpeter@765
   426
    /// \pre \ref run() or \ref findCycle() must be called before using
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   427
    /// this function.
kpeter@765
   428
    const Path& cycle() const {
kpeter@765
   429
      return *_cycle_path;
kpeter@765
   430
    }
kpeter@765
   431
kpeter@765
   432
    ///@}
kpeter@765
   433
kpeter@765
   434
  private:
kpeter@765
   435
kpeter@765
   436
    // Initialization
kpeter@765
   437
    void init() {
kpeter@765
   438
      if (!_cycle_path) {
kpeter@765
   439
        _local_path = true;
kpeter@765
   440
        _cycle_path = new Path;
kpeter@765
   441
      }
kpeter@765
   442
      _cycle_path->clear();
kpeter@765
   443
      _cycle_length = 0;
kpeter@765
   444
      _cycle_size = 1;
kpeter@765
   445
      _cycle_node = INVALID;
kpeter@765
   446
      for (NodeIt u(_gr); u != INVALID; ++u)
kpeter@765
   447
        _data[u].clear();
kpeter@765
   448
    }
kpeter@765
   449
kpeter@765
   450
    // Find strongly connected components and initialize _comp_nodes
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   451
    // and _out_arcs
kpeter@765
   452
    void findComponents() {
kpeter@765
   453
      _comp_num = stronglyConnectedComponents(_gr, _comp);
kpeter@765
   454
      _comp_nodes.resize(_comp_num);
kpeter@765
   455
      if (_comp_num == 1) {
kpeter@765
   456
        _comp_nodes[0].clear();
kpeter@765
   457
        for (NodeIt n(_gr); n != INVALID; ++n) {
kpeter@765
   458
          _comp_nodes[0].push_back(n);
kpeter@765
   459
          _out_arcs[n].clear();
kpeter@765
   460
          for (OutArcIt a(_gr, n); a != INVALID; ++a) {
kpeter@765
   461
            _out_arcs[n].push_back(a);
kpeter@765
   462
          }
kpeter@765
   463
        }
kpeter@765
   464
      } else {
kpeter@765
   465
        for (int i = 0; i < _comp_num; ++i)
kpeter@765
   466
          _comp_nodes[i].clear();
kpeter@765
   467
        for (NodeIt n(_gr); n != INVALID; ++n) {
kpeter@765
   468
          int k = _comp[n];
kpeter@765
   469
          _comp_nodes[k].push_back(n);
kpeter@765
   470
          _out_arcs[n].clear();
kpeter@765
   471
          for (OutArcIt a(_gr, n); a != INVALID; ++a) {
kpeter@765
   472
            if (_comp[_gr.target(a)] == k) _out_arcs[n].push_back(a);
kpeter@765
   473
          }
kpeter@765
   474
        }
kpeter@765
   475
      }
kpeter@765
   476
    }
kpeter@765
   477
kpeter@765
   478
    // Initialize path data for the current component
kpeter@765
   479
    bool initComponent(int comp) {
kpeter@765
   480
      _nodes = &(_comp_nodes[comp]);
kpeter@765
   481
      int n = _nodes->size();
kpeter@765
   482
      if (n < 1 || (n == 1 && _out_arcs[(*_nodes)[0]].size() == 0)) {
kpeter@765
   483
        return false;
kpeter@765
   484
      }      
kpeter@765
   485
      for (int i = 0; i < n; ++i) {
kpeter@767
   486
        _data[(*_nodes)[i]].resize(n + 1, PathData(INF));
kpeter@765
   487
      }
kpeter@765
   488
      return true;
kpeter@765
   489
    }
kpeter@765
   490
kpeter@765
   491
    // Process all rounds of computing path data for the current component.
kpeter@765
   492
    // _data[v][k] is the length of a shortest directed walk from the root
kpeter@765
   493
    // node to node v containing exactly k arcs.
kpeter@765
   494
    void processRounds() {
kpeter@765
   495
      Node start = (*_nodes)[0];
kpeter@767
   496
      _data[start][0] = PathData(0);
kpeter@765
   497
      _process.clear();
kpeter@765
   498
      _process.push_back(start);
kpeter@765
   499
kpeter@765
   500
      int k, n = _nodes->size();
kpeter@765
   501
      for (k = 1; k <= n && int(_process.size()) < n; ++k) {
kpeter@765
   502
        processNextBuildRound(k);
kpeter@765
   503
      }
kpeter@765
   504
      for ( ; k <= n; ++k) {
kpeter@765
   505
        processNextFullRound(k);
kpeter@765
   506
      }
kpeter@765
   507
    }
kpeter@765
   508
kpeter@765
   509
    // Process one round and rebuild _process
kpeter@765
   510
    void processNextBuildRound(int k) {
kpeter@765
   511
      std::vector<Node> next;
kpeter@765
   512
      Node u, v;
kpeter@765
   513
      Arc e;
kpeter@765
   514
      LargeValue d;
kpeter@765
   515
      for (int i = 0; i < int(_process.size()); ++i) {
kpeter@765
   516
        u = _process[i];
kpeter@765
   517
        for (int j = 0; j < int(_out_arcs[u].size()); ++j) {
kpeter@765
   518
          e = _out_arcs[u][j];
kpeter@765
   519
          v = _gr.target(e);
kpeter@765
   520
          d = _data[u][k-1].dist + _length[e];
kpeter@767
   521
          if (_tolerance.less(d, _data[v][k].dist)) {
kpeter@767
   522
            if (_data[v][k].dist == INF) next.push_back(v);
kpeter@767
   523
            _data[v][k] = PathData(d, e);
kpeter@765
   524
          }
kpeter@765
   525
        }
kpeter@765
   526
      }
kpeter@765
   527
      _process.swap(next);
kpeter@765
   528
    }
kpeter@765
   529
kpeter@765
   530
    // Process one round using _nodes instead of _process
kpeter@765
   531
    void processNextFullRound(int k) {
kpeter@765
   532
      Node u, v;
kpeter@765
   533
      Arc e;
kpeter@765
   534
      LargeValue d;
kpeter@765
   535
      for (int i = 0; i < int(_nodes->size()); ++i) {
kpeter@765
   536
        u = (*_nodes)[i];
kpeter@765
   537
        for (int j = 0; j < int(_out_arcs[u].size()); ++j) {
kpeter@765
   538
          e = _out_arcs[u][j];
kpeter@765
   539
          v = _gr.target(e);
kpeter@765
   540
          d = _data[u][k-1].dist + _length[e];
kpeter@767
   541
          if (_tolerance.less(d, _data[v][k].dist)) {
kpeter@767
   542
            _data[v][k] = PathData(d, e);
kpeter@765
   543
          }
kpeter@765
   544
        }
kpeter@765
   545
      }
kpeter@765
   546
    }
kpeter@765
   547
kpeter@765
   548
    // Update the minimum cycle mean
kpeter@765
   549
    void updateMinMean() {
kpeter@765
   550
      int n = _nodes->size();
kpeter@765
   551
      for (int i = 0; i < n; ++i) {
kpeter@765
   552
        Node u = (*_nodes)[i];
kpeter@767
   553
        if (_data[u][n].dist == INF) continue;
kpeter@765
   554
        LargeValue length, max_length = 0;
kpeter@765
   555
        int size, max_size = 1;
kpeter@765
   556
        bool found_curr = false;
kpeter@765
   557
        for (int k = 0; k < n; ++k) {
kpeter@767
   558
          if (_data[u][k].dist == INF) continue;
kpeter@765
   559
          length = _data[u][n].dist - _data[u][k].dist;
kpeter@765
   560
          size = n - k;
kpeter@765
   561
          if (!found_curr || length * max_size > max_length * size) {
kpeter@765
   562
            found_curr = true;
kpeter@765
   563
            max_length = length;
kpeter@765
   564
            max_size = size;
kpeter@765
   565
          }
kpeter@765
   566
        }
kpeter@765
   567
        if ( found_curr && (_cycle_node == INVALID ||
kpeter@765
   568
             max_length * _cycle_size < _cycle_length * max_size) ) {
kpeter@765
   569
          _cycle_length = max_length;
kpeter@765
   570
          _cycle_size = max_size;
kpeter@765
   571
          _cycle_node = u;
kpeter@765
   572
        }
kpeter@765
   573
      }
kpeter@765
   574
    }
kpeter@765
   575
kpeter@765
   576
  }; //class Karp
kpeter@765
   577
kpeter@765
   578
  ///@}
kpeter@765
   579
kpeter@765
   580
} //namespace lemon
kpeter@765
   581
kpeter@765
   582
#endif //LEMON_KARP_H