[877] | 1 | /* -*- mode: C++; indent-tabs-mode: nil; -*- |
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[758] | 2 | * |
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[877] | 3 | * This file is a part of LEMON, a generic C++ optimization library. |
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[758] | 4 | * |
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[877] | 5 | * Copyright (C) 2003-2010 |
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[758] | 6 | * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport |
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| 7 | * (Egervary Research Group on Combinatorial Optimization, EGRES). |
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| 8 | * |
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| 9 | * Permission to use, modify and distribute this software is granted |
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| 10 | * provided that this copyright notice appears in all copies. For |
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| 11 | * precise terms see the accompanying LICENSE file. |
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| 12 | * |
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| 13 | * This software is provided "AS IS" with no warranty of any kind, |
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| 14 | * express or implied, and with no claim as to its suitability for any |
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| 15 | * purpose. |
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| 16 | * |
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| 17 | */ |
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| 18 | |
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[864] | 19 | #ifndef LEMON_HOWARD_MMC_H |
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| 20 | #define LEMON_HOWARD_MMC_H |
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[758] | 21 | |
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[768] | 22 | /// \ingroup min_mean_cycle |
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[758] | 23 | /// |
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| 24 | /// \file |
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| 25 | /// \brief Howard's algorithm for finding a minimum mean cycle. |
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| 26 | |
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| 27 | #include <vector> |
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[763] | 28 | #include <limits> |
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[758] | 29 | #include <lemon/core.h> |
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| 30 | #include <lemon/path.h> |
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| 31 | #include <lemon/tolerance.h> |
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| 32 | #include <lemon/connectivity.h> |
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| 33 | |
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| 34 | namespace lemon { |
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| 35 | |
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[864] | 36 | /// \brief Default traits class of HowardMmc class. |
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[761] | 37 | /// |
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[864] | 38 | /// Default traits class of HowardMmc class. |
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[761] | 39 | /// \tparam GR The type of the digraph. |
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[864] | 40 | /// \tparam CM The type of the cost map. |
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[761] | 41 | /// It must conform to the \ref concepts::ReadMap "ReadMap" concept. |
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| 42 | #ifdef DOXYGEN |
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[864] | 43 | template <typename GR, typename CM> |
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[761] | 44 | #else |
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[864] | 45 | template <typename GR, typename CM, |
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| 46 | bool integer = std::numeric_limits<typename CM::Value>::is_integer> |
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[761] | 47 | #endif |
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[864] | 48 | struct HowardMmcDefaultTraits |
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[761] | 49 | { |
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| 50 | /// The type of the digraph |
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| 51 | typedef GR Digraph; |
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[864] | 52 | /// The type of the cost map |
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| 53 | typedef CM CostMap; |
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| 54 | /// The type of the arc costs |
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| 55 | typedef typename CostMap::Value Cost; |
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[761] | 56 | |
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[864] | 57 | /// \brief The large cost type used for internal computations |
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[761] | 58 | /// |
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[864] | 59 | /// The large cost type used for internal computations. |
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| 60 | /// It is \c long \c long if the \c Cost type is integer, |
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[761] | 61 | /// otherwise it is \c double. |
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[864] | 62 | /// \c Cost must be convertible to \c LargeCost. |
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| 63 | typedef double LargeCost; |
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[761] | 64 | |
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| 65 | /// The tolerance type used for internal computations |
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[864] | 66 | typedef lemon::Tolerance<LargeCost> Tolerance; |
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[761] | 67 | |
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| 68 | /// \brief The path type of the found cycles |
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| 69 | /// |
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| 70 | /// The path type of the found cycles. |
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| 71 | /// It must conform to the \ref lemon::concepts::Path "Path" concept |
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| 72 | /// and it must have an \c addBack() function. |
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| 73 | typedef lemon::Path<Digraph> Path; |
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| 74 | }; |
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| 75 | |
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[864] | 76 | // Default traits class for integer cost types |
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| 77 | template <typename GR, typename CM> |
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| 78 | struct HowardMmcDefaultTraits<GR, CM, true> |
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[761] | 79 | { |
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| 80 | typedef GR Digraph; |
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[864] | 81 | typedef CM CostMap; |
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| 82 | typedef typename CostMap::Value Cost; |
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[761] | 83 | #ifdef LEMON_HAVE_LONG_LONG |
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[864] | 84 | typedef long long LargeCost; |
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[761] | 85 | #else |
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[864] | 86 | typedef long LargeCost; |
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[761] | 87 | #endif |
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[864] | 88 | typedef lemon::Tolerance<LargeCost> Tolerance; |
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[761] | 89 | typedef lemon::Path<Digraph> Path; |
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| 90 | }; |
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| 91 | |
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| 92 | |
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[768] | 93 | /// \addtogroup min_mean_cycle |
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[758] | 94 | /// @{ |
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| 95 | |
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| 96 | /// \brief Implementation of Howard's algorithm for finding a minimum |
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| 97 | /// mean cycle. |
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| 98 | /// |
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[764] | 99 | /// This class implements Howard's policy iteration algorithm for finding |
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[864] | 100 | /// a directed cycle of minimum mean cost in a digraph |
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[771] | 101 | /// \ref amo93networkflows, \ref dasdan98minmeancycle. |
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[768] | 102 | /// This class provides the most efficient algorithm for the |
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| 103 | /// minimum mean cycle problem, though the best known theoretical |
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| 104 | /// bound on its running time is exponential. |
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[758] | 105 | /// |
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| 106 | /// \tparam GR The type of the digraph the algorithm runs on. |
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[864] | 107 | /// \tparam CM The type of the cost map. The default |
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[758] | 108 | /// map type is \ref concepts::Digraph::ArcMap "GR::ArcMap<int>". |
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[825] | 109 | /// \tparam TR The traits class that defines various types used by the |
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[864] | 110 | /// algorithm. By default, it is \ref HowardMmcDefaultTraits |
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| 111 | /// "HowardMmcDefaultTraits<GR, CM>". |
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[825] | 112 | /// In most cases, this parameter should not be set directly, |
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| 113 | /// consider to use the named template parameters instead. |
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[758] | 114 | #ifdef DOXYGEN |
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[864] | 115 | template <typename GR, typename CM, typename TR> |
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[758] | 116 | #else |
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| 117 | template < typename GR, |
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[864] | 118 | typename CM = typename GR::template ArcMap<int>, |
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| 119 | typename TR = HowardMmcDefaultTraits<GR, CM> > |
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[758] | 120 | #endif |
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[864] | 121 | class HowardMmc |
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[758] | 122 | { |
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| 123 | public: |
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[877] | 124 | |
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[761] | 125 | /// The type of the digraph |
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| 126 | typedef typename TR::Digraph Digraph; |
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[864] | 127 | /// The type of the cost map |
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| 128 | typedef typename TR::CostMap CostMap; |
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| 129 | /// The type of the arc costs |
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| 130 | typedef typename TR::Cost Cost; |
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[761] | 131 | |
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[864] | 132 | /// \brief The large cost type |
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[761] | 133 | /// |
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[864] | 134 | /// The large cost type used for internal computations. |
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| 135 | /// By default, it is \c long \c long if the \c Cost type is integer, |
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[761] | 136 | /// otherwise it is \c double. |
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[864] | 137 | typedef typename TR::LargeCost LargeCost; |
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[761] | 138 | |
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| 139 | /// The tolerance type |
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| 140 | typedef typename TR::Tolerance Tolerance; |
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| 141 | |
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| 142 | /// \brief The path type of the found cycles |
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| 143 | /// |
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| 144 | /// The path type of the found cycles. |
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[864] | 145 | /// Using the \ref HowardMmcDefaultTraits "default traits class", |
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[761] | 146 | /// it is \ref lemon::Path "Path<Digraph>". |
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| 147 | typedef typename TR::Path Path; |
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| 148 | |
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[864] | 149 | /// The \ref HowardMmcDefaultTraits "traits class" of the algorithm |
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[761] | 150 | typedef TR Traits; |
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[758] | 151 | |
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| 152 | private: |
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| 153 | |
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| 154 | TEMPLATE_DIGRAPH_TYPEDEFS(Digraph); |
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[877] | 155 | |
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[758] | 156 | // The digraph the algorithm runs on |
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| 157 | const Digraph &_gr; |
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[864] | 158 | // The cost of the arcs |
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| 159 | const CostMap &_cost; |
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[758] | 160 | |
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[760] | 161 | // Data for the found cycles |
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| 162 | bool _curr_found, _best_found; |
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[864] | 163 | LargeCost _curr_cost, _best_cost; |
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[760] | 164 | int _curr_size, _best_size; |
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| 165 | Node _curr_node, _best_node; |
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| 166 | |
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[758] | 167 | Path *_cycle_path; |
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[760] | 168 | bool _local_path; |
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[758] | 169 | |
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[760] | 170 | // Internal data used by the algorithm |
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| 171 | typename Digraph::template NodeMap<Arc> _policy; |
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| 172 | typename Digraph::template NodeMap<bool> _reached; |
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| 173 | typename Digraph::template NodeMap<int> _level; |
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[864] | 174 | typename Digraph::template NodeMap<LargeCost> _dist; |
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[758] | 175 | |
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[760] | 176 | // Data for storing the strongly connected components |
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| 177 | int _comp_num; |
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[758] | 178 | typename Digraph::template NodeMap<int> _comp; |
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[760] | 179 | std::vector<std::vector<Node> > _comp_nodes; |
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| 180 | std::vector<Node>* _nodes; |
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| 181 | typename Digraph::template NodeMap<std::vector<Arc> > _in_arcs; |
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[877] | 182 | |
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[760] | 183 | // Queue used for BFS search |
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| 184 | std::vector<Node> _queue; |
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| 185 | int _qfront, _qback; |
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[761] | 186 | |
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| 187 | Tolerance _tolerance; |
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[877] | 188 | |
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[767] | 189 | // Infinite constant |
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[864] | 190 | const LargeCost INF; |
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[767] | 191 | |
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[761] | 192 | public: |
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[877] | 193 | |
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[761] | 194 | /// \name Named Template Parameters |
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| 195 | /// @{ |
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| 196 | |
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| 197 | template <typename T> |
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[864] | 198 | struct SetLargeCostTraits : public Traits { |
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| 199 | typedef T LargeCost; |
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[761] | 200 | typedef lemon::Tolerance<T> Tolerance; |
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| 201 | }; |
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| 202 | |
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| 203 | /// \brief \ref named-templ-param "Named parameter" for setting |
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[864] | 204 | /// \c LargeCost type. |
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[761] | 205 | /// |
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[864] | 206 | /// \ref named-templ-param "Named parameter" for setting \c LargeCost |
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[761] | 207 | /// type. It is used for internal computations in the algorithm. |
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| 208 | template <typename T> |
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[864] | 209 | struct SetLargeCost |
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| 210 | : public HowardMmc<GR, CM, SetLargeCostTraits<T> > { |
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| 211 | typedef HowardMmc<GR, CM, SetLargeCostTraits<T> > Create; |
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[761] | 212 | }; |
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| 213 | |
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| 214 | template <typename T> |
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| 215 | struct SetPathTraits : public Traits { |
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| 216 | typedef T Path; |
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| 217 | }; |
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| 218 | |
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| 219 | /// \brief \ref named-templ-param "Named parameter" for setting |
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| 220 | /// \c %Path type. |
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| 221 | /// |
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| 222 | /// \ref named-templ-param "Named parameter" for setting the \c %Path |
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| 223 | /// type of the found cycles. |
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| 224 | /// It must conform to the \ref lemon::concepts::Path "Path" concept |
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| 225 | /// and it must have an \c addBack() function. |
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| 226 | template <typename T> |
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| 227 | struct SetPath |
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[864] | 228 | : public HowardMmc<GR, CM, SetPathTraits<T> > { |
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| 229 | typedef HowardMmc<GR, CM, SetPathTraits<T> > Create; |
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[761] | 230 | }; |
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[877] | 231 | |
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[761] | 232 | /// @} |
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[758] | 233 | |
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[863] | 234 | protected: |
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| 235 | |
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[864] | 236 | HowardMmc() {} |
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[863] | 237 | |
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[758] | 238 | public: |
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| 239 | |
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| 240 | /// \brief Constructor. |
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| 241 | /// |
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| 242 | /// The constructor of the class. |
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| 243 | /// |
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| 244 | /// \param digraph The digraph the algorithm runs on. |
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[864] | 245 | /// \param cost The costs of the arcs. |
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| 246 | HowardMmc( const Digraph &digraph, |
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| 247 | const CostMap &cost ) : |
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| 248 | _gr(digraph), _cost(cost), _best_found(false), |
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| 249 | _best_cost(0), _best_size(1), _cycle_path(NULL), _local_path(false), |
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[760] | 250 | _policy(digraph), _reached(digraph), _level(digraph), _dist(digraph), |
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[767] | 251 | _comp(digraph), _in_arcs(digraph), |
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[864] | 252 | INF(std::numeric_limits<LargeCost>::has_infinity ? |
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| 253 | std::numeric_limits<LargeCost>::infinity() : |
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| 254 | std::numeric_limits<LargeCost>::max()) |
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[758] | 255 | {} |
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| 256 | |
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| 257 | /// Destructor. |
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[864] | 258 | ~HowardMmc() { |
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[758] | 259 | if (_local_path) delete _cycle_path; |
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| 260 | } |
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| 261 | |
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| 262 | /// \brief Set the path structure for storing the found cycle. |
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| 263 | /// |
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| 264 | /// This function sets an external path structure for storing the |
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| 265 | /// found cycle. |
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| 266 | /// |
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| 267 | /// If you don't call this function before calling \ref run() or |
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[864] | 268 | /// \ref findCycleMean(), it will allocate a local \ref Path "path" |
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[758] | 269 | /// structure. The destuctor deallocates this automatically |
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| 270 | /// allocated object, of course. |
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| 271 | /// |
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| 272 | /// \note The algorithm calls only the \ref lemon::Path::addBack() |
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| 273 | /// "addBack()" function of the given path structure. |
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| 274 | /// |
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| 275 | /// \return <tt>(*this)</tt> |
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[864] | 276 | HowardMmc& cycle(Path &path) { |
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[758] | 277 | if (_local_path) { |
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| 278 | delete _cycle_path; |
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| 279 | _local_path = false; |
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| 280 | } |
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| 281 | _cycle_path = &path; |
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| 282 | return *this; |
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| 283 | } |
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| 284 | |
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[769] | 285 | /// \brief Set the tolerance used by the algorithm. |
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| 286 | /// |
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| 287 | /// This function sets the tolerance object used by the algorithm. |
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| 288 | /// |
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| 289 | /// \return <tt>(*this)</tt> |
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[864] | 290 | HowardMmc& tolerance(const Tolerance& tolerance) { |
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[769] | 291 | _tolerance = tolerance; |
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| 292 | return *this; |
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| 293 | } |
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| 294 | |
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| 295 | /// \brief Return a const reference to the tolerance. |
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| 296 | /// |
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| 297 | /// This function returns a const reference to the tolerance object |
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| 298 | /// used by the algorithm. |
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| 299 | const Tolerance& tolerance() const { |
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| 300 | return _tolerance; |
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| 301 | } |
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| 302 | |
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[758] | 303 | /// \name Execution control |
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| 304 | /// The simplest way to execute the algorithm is to call the \ref run() |
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| 305 | /// function.\n |
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[864] | 306 | /// If you only need the minimum mean cost, you may call |
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| 307 | /// \ref findCycleMean(). |
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[758] | 308 | |
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| 309 | /// @{ |
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| 310 | |
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| 311 | /// \brief Run the algorithm. |
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| 312 | /// |
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| 313 | /// This function runs the algorithm. |
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[759] | 314 | /// It can be called more than once (e.g. if the underlying digraph |
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[864] | 315 | /// and/or the arc costs have been modified). |
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[758] | 316 | /// |
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| 317 | /// \return \c true if a directed cycle exists in the digraph. |
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| 318 | /// |
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[759] | 319 | /// \note <tt>mmc.run()</tt> is just a shortcut of the following code. |
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[758] | 320 | /// \code |
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[864] | 321 | /// return mmc.findCycleMean() && mmc.findCycle(); |
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[758] | 322 | /// \endcode |
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| 323 | bool run() { |
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[864] | 324 | return findCycleMean() && findCycle(); |
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[758] | 325 | } |
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| 326 | |
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[759] | 327 | /// \brief Find the minimum cycle mean. |
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[758] | 328 | /// |
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[864] | 329 | /// This function finds the minimum mean cost of the directed |
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[759] | 330 | /// cycles in the digraph. |
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[758] | 331 | /// |
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[759] | 332 | /// \return \c true if a directed cycle exists in the digraph. |
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[864] | 333 | bool findCycleMean() { |
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[760] | 334 | // Initialize and find strongly connected components |
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| 335 | init(); |
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| 336 | findComponents(); |
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[877] | 337 | |
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[759] | 338 | // Find the minimum cycle mean in the components |
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[758] | 339 | for (int comp = 0; comp < _comp_num; ++comp) { |
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[760] | 340 | // Find the minimum mean cycle in the current component |
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| 341 | if (!buildPolicyGraph(comp)) continue; |
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[758] | 342 | while (true) { |
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[760] | 343 | findPolicyCycle(); |
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[758] | 344 | if (!computeNodeDistances()) break; |
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| 345 | } |
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[760] | 346 | // Update the best cycle (global minimum mean cycle) |
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[767] | 347 | if ( _curr_found && (!_best_found || |
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[864] | 348 | _curr_cost * _best_size < _best_cost * _curr_size) ) { |
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[760] | 349 | _best_found = true; |
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[864] | 350 | _best_cost = _curr_cost; |
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[760] | 351 | _best_size = _curr_size; |
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| 352 | _best_node = _curr_node; |
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| 353 | } |
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[758] | 354 | } |
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[760] | 355 | return _best_found; |
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[758] | 356 | } |
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| 357 | |
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| 358 | /// \brief Find a minimum mean directed cycle. |
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| 359 | /// |
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[864] | 360 | /// This function finds a directed cycle of minimum mean cost |
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| 361 | /// in the digraph using the data computed by findCycleMean(). |
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[758] | 362 | /// |
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| 363 | /// \return \c true if a directed cycle exists in the digraph. |
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| 364 | /// |
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[864] | 365 | /// \pre \ref findCycleMean() must be called before using this function. |
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[758] | 366 | bool findCycle() { |
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[760] | 367 | if (!_best_found) return false; |
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| 368 | _cycle_path->addBack(_policy[_best_node]); |
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| 369 | for ( Node v = _best_node; |
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| 370 | (v = _gr.target(_policy[v])) != _best_node; ) { |
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[758] | 371 | _cycle_path->addBack(_policy[v]); |
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| 372 | } |
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| 373 | return true; |
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| 374 | } |
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| 375 | |
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| 376 | /// @} |
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| 377 | |
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| 378 | /// \name Query Functions |
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[759] | 379 | /// The results of the algorithm can be obtained using these |
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[758] | 380 | /// functions.\n |
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| 381 | /// The algorithm should be executed before using them. |
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| 382 | |
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| 383 | /// @{ |
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| 384 | |
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[864] | 385 | /// \brief Return the total cost of the found cycle. |
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[758] | 386 | /// |
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[864] | 387 | /// This function returns the total cost of the found cycle. |
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[758] | 388 | /// |
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[864] | 389 | /// \pre \ref run() or \ref findCycleMean() must be called before |
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[758] | 390 | /// using this function. |
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[864] | 391 | Cost cycleCost() const { |
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| 392 | return static_cast<Cost>(_best_cost); |
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[758] | 393 | } |
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| 394 | |
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| 395 | /// \brief Return the number of arcs on the found cycle. |
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| 396 | /// |
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| 397 | /// This function returns the number of arcs on the found cycle. |
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| 398 | /// |
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[864] | 399 | /// \pre \ref run() or \ref findCycleMean() must be called before |
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[758] | 400 | /// using this function. |
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[864] | 401 | int cycleSize() const { |
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[760] | 402 | return _best_size; |
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[758] | 403 | } |
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| 404 | |
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[864] | 405 | /// \brief Return the mean cost of the found cycle. |
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[758] | 406 | /// |
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[864] | 407 | /// This function returns the mean cost of the found cycle. |
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[758] | 408 | /// |
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[760] | 409 | /// \note <tt>alg.cycleMean()</tt> is just a shortcut of the |
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[758] | 410 | /// following code. |
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| 411 | /// \code |
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[864] | 412 | /// return static_cast<double>(alg.cycleCost()) / alg.cycleSize(); |
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[758] | 413 | /// \endcode |
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| 414 | /// |
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[864] | 415 | /// \pre \ref run() or \ref findCycleMean() must be called before |
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[758] | 416 | /// using this function. |
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| 417 | double cycleMean() const { |
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[864] | 418 | return static_cast<double>(_best_cost) / _best_size; |
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[758] | 419 | } |
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| 420 | |
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| 421 | /// \brief Return the found cycle. |
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| 422 | /// |
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| 423 | /// This function returns a const reference to the path structure |
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| 424 | /// storing the found cycle. |
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| 425 | /// |
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| 426 | /// \pre \ref run() or \ref findCycle() must be called before using |
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| 427 | /// this function. |
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| 428 | const Path& cycle() const { |
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| 429 | return *_cycle_path; |
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| 430 | } |
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| 431 | |
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| 432 | ///@} |
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| 433 | |
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| 434 | private: |
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| 435 | |
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[760] | 436 | // Initialize |
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| 437 | void init() { |
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| 438 | if (!_cycle_path) { |
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| 439 | _local_path = true; |
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| 440 | _cycle_path = new Path; |
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[758] | 441 | } |
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[760] | 442 | _queue.resize(countNodes(_gr)); |
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| 443 | _best_found = false; |
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[864] | 444 | _best_cost = 0; |
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[760] | 445 | _best_size = 1; |
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| 446 | _cycle_path->clear(); |
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| 447 | } |
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[877] | 448 | |
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[760] | 449 | // Find strongly connected components and initialize _comp_nodes |
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| 450 | // and _in_arcs |
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| 451 | void findComponents() { |
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| 452 | _comp_num = stronglyConnectedComponents(_gr, _comp); |
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| 453 | _comp_nodes.resize(_comp_num); |
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| 454 | if (_comp_num == 1) { |
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| 455 | _comp_nodes[0].clear(); |
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| 456 | for (NodeIt n(_gr); n != INVALID; ++n) { |
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| 457 | _comp_nodes[0].push_back(n); |
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| 458 | _in_arcs[n].clear(); |
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| 459 | for (InArcIt a(_gr, n); a != INVALID; ++a) { |
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| 460 | _in_arcs[n].push_back(a); |
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| 461 | } |
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| 462 | } |
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| 463 | } else { |
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| 464 | for (int i = 0; i < _comp_num; ++i) |
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| 465 | _comp_nodes[i].clear(); |
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| 466 | for (NodeIt n(_gr); n != INVALID; ++n) { |
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| 467 | int k = _comp[n]; |
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| 468 | _comp_nodes[k].push_back(n); |
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| 469 | _in_arcs[n].clear(); |
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| 470 | for (InArcIt a(_gr, n); a != INVALID; ++a) { |
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| 471 | if (_comp[_gr.source(a)] == k) _in_arcs[n].push_back(a); |
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| 472 | } |
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| 473 | } |
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[758] | 474 | } |
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[760] | 475 | } |
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| 476 | |
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| 477 | // Build the policy graph in the given strongly connected component |
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| 478 | // (the out-degree of every node is 1) |
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| 479 | bool buildPolicyGraph(int comp) { |
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| 480 | _nodes = &(_comp_nodes[comp]); |
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| 481 | if (_nodes->size() < 1 || |
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| 482 | (_nodes->size() == 1 && _in_arcs[(*_nodes)[0]].size() == 0)) { |
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| 483 | return false; |
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[758] | 484 | } |
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[760] | 485 | for (int i = 0; i < int(_nodes->size()); ++i) { |
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[767] | 486 | _dist[(*_nodes)[i]] = INF; |
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[760] | 487 | } |
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| 488 | Node u, v; |
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| 489 | Arc e; |
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| 490 | for (int i = 0; i < int(_nodes->size()); ++i) { |
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| 491 | v = (*_nodes)[i]; |
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| 492 | for (int j = 0; j < int(_in_arcs[v].size()); ++j) { |
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| 493 | e = _in_arcs[v][j]; |
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| 494 | u = _gr.source(e); |
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[864] | 495 | if (_cost[e] < _dist[u]) { |
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| 496 | _dist[u] = _cost[e]; |
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[760] | 497 | _policy[u] = e; |
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| 498 | } |
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[758] | 499 | } |
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| 500 | } |
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| 501 | return true; |
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| 502 | } |
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| 503 | |
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[760] | 504 | // Find the minimum mean cycle in the policy graph |
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| 505 | void findPolicyCycle() { |
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| 506 | for (int i = 0; i < int(_nodes->size()); ++i) { |
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| 507 | _level[(*_nodes)[i]] = -1; |
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| 508 | } |
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[864] | 509 | LargeCost ccost; |
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[758] | 510 | int csize; |
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| 511 | Node u, v; |
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[760] | 512 | _curr_found = false; |
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| 513 | for (int i = 0; i < int(_nodes->size()); ++i) { |
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| 514 | u = (*_nodes)[i]; |
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| 515 | if (_level[u] >= 0) continue; |
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| 516 | for (; _level[u] < 0; u = _gr.target(_policy[u])) { |
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| 517 | _level[u] = i; |
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| 518 | } |
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| 519 | if (_level[u] == i) { |
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| 520 | // A cycle is found |
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[864] | 521 | ccost = _cost[_policy[u]]; |
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[760] | 522 | csize = 1; |
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| 523 | for (v = u; (v = _gr.target(_policy[v])) != u; ) { |
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[864] | 524 | ccost += _cost[_policy[v]]; |
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[760] | 525 | ++csize; |
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[758] | 526 | } |
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[760] | 527 | if ( !_curr_found || |
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[864] | 528 | (ccost * _curr_size < _curr_cost * csize) ) { |
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[760] | 529 | _curr_found = true; |
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[864] | 530 | _curr_cost = ccost; |
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[760] | 531 | _curr_size = csize; |
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| 532 | _curr_node = u; |
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[758] | 533 | } |
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| 534 | } |
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| 535 | } |
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| 536 | } |
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| 537 | |
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[760] | 538 | // Contract the policy graph and compute node distances |
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[758] | 539 | bool computeNodeDistances() { |
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[760] | 540 | // Find the component of the main cycle and compute node distances |
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| 541 | // using reverse BFS |
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| 542 | for (int i = 0; i < int(_nodes->size()); ++i) { |
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| 543 | _reached[(*_nodes)[i]] = false; |
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| 544 | } |
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| 545 | _qfront = _qback = 0; |
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| 546 | _queue[0] = _curr_node; |
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| 547 | _reached[_curr_node] = true; |
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| 548 | _dist[_curr_node] = 0; |
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[758] | 549 | Node u, v; |
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[760] | 550 | Arc e; |
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| 551 | while (_qfront <= _qback) { |
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| 552 | v = _queue[_qfront++]; |
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| 553 | for (int j = 0; j < int(_in_arcs[v].size()); ++j) { |
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| 554 | e = _in_arcs[v][j]; |
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[758] | 555 | u = _gr.source(e); |
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[760] | 556 | if (_policy[u] == e && !_reached[u]) { |
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| 557 | _reached[u] = true; |
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[864] | 558 | _dist[u] = _dist[v] + _cost[e] * _curr_size - _curr_cost; |
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[760] | 559 | _queue[++_qback] = u; |
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[758] | 560 | } |
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| 561 | } |
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| 562 | } |
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[760] | 563 | |
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| 564 | // Connect all other nodes to this component and compute node |
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| 565 | // distances using reverse BFS |
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| 566 | _qfront = 0; |
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| 567 | while (_qback < int(_nodes->size())-1) { |
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| 568 | v = _queue[_qfront++]; |
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| 569 | for (int j = 0; j < int(_in_arcs[v].size()); ++j) { |
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| 570 | e = _in_arcs[v][j]; |
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| 571 | u = _gr.source(e); |
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| 572 | if (!_reached[u]) { |
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| 573 | _reached[u] = true; |
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| 574 | _policy[u] = e; |
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[864] | 575 | _dist[u] = _dist[v] + _cost[e] * _curr_size - _curr_cost; |
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[760] | 576 | _queue[++_qback] = u; |
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| 577 | } |
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| 578 | } |
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| 579 | } |
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| 580 | |
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| 581 | // Improve node distances |
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[758] | 582 | bool improved = false; |
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[760] | 583 | for (int i = 0; i < int(_nodes->size()); ++i) { |
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| 584 | v = (*_nodes)[i]; |
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| 585 | for (int j = 0; j < int(_in_arcs[v].size()); ++j) { |
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| 586 | e = _in_arcs[v][j]; |
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| 587 | u = _gr.source(e); |
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[864] | 588 | LargeCost delta = _dist[v] + _cost[e] * _curr_size - _curr_cost; |
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[761] | 589 | if (_tolerance.less(delta, _dist[u])) { |
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[760] | 590 | _dist[u] = delta; |
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| 591 | _policy[u] = e; |
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| 592 | improved = true; |
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| 593 | } |
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[758] | 594 | } |
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| 595 | } |
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| 596 | return improved; |
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| 597 | } |
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| 598 | |
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[864] | 599 | }; //class HowardMmc |
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[758] | 600 | |
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| 601 | ///@} |
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| 602 | |
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| 603 | } //namespace lemon |
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| 604 | |
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[864] | 605 | #endif //LEMON_HOWARD_MMC_H |
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