[2017] | 1 | /* -*- C++ -*- |
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| 2 | * |
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| 3 | * This file is a part of LEMON, a generic C++ optimization library |
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| 4 | * |
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[2391] | 5 | * Copyright (C) 2003-2007 |
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[2017] | 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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| 19 | #ifndef LEMON_MIN_COST_ARBORESCENCE_H |
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| 20 | #define LEMON_MIN_COST_ARBORESCENCE_H |
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| 21 | |
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| 22 | ///\ingroup spantree |
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| 23 | ///\file |
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| 24 | ///\brief Minimum Cost Arborescence algorithm. |
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| 25 | |
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| 26 | #include <vector> |
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| 27 | |
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| 28 | #include <lemon/list_graph.h> |
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[2025] | 29 | #include <lemon/bin_heap.h> |
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[2017] | 30 | |
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| 31 | namespace lemon { |
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| 32 | |
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| 33 | |
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| 34 | /// \brief Default traits class of MinCostArborescence class. |
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| 35 | /// |
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| 36 | /// Default traits class of MinCostArborescence class. |
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| 37 | /// \param _Graph Graph type. |
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| 38 | /// \param _CostMap Type of cost map. |
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| 39 | template <class _Graph, class _CostMap> |
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| 40 | struct MinCostArborescenceDefaultTraits{ |
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| 41 | |
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| 42 | /// \brief The graph type the algorithm runs on. |
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| 43 | typedef _Graph Graph; |
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| 44 | |
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| 45 | /// \brief The type of the map that stores the edge costs. |
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| 46 | /// |
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| 47 | /// The type of the map that stores the edge costs. |
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[2260] | 48 | /// It must meet the \ref concepts::ReadMap "ReadMap" concept. |
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[2017] | 49 | typedef _CostMap CostMap; |
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| 50 | |
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| 51 | /// \brief The value type of the costs. |
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| 52 | /// |
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| 53 | /// The value type of the costs. |
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| 54 | typedef typename CostMap::Value Value; |
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| 55 | |
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| 56 | /// \brief The type of the map that stores which edges are |
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| 57 | /// in the arborescence. |
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| 58 | /// |
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| 59 | /// The type of the map that stores which edges are in the arborescence. |
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[2260] | 60 | /// It must meet the \ref concepts::WriteMap "WriteMap" concept. |
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[2259] | 61 | /// Initially it will be set to false on each edge. After it |
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[2025] | 62 | /// will set all arborescence edges once. |
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[2017] | 63 | typedef typename Graph::template EdgeMap<bool> ArborescenceMap; |
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| 64 | |
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| 65 | /// \brief Instantiates a ArborescenceMap. |
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| 66 | /// |
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| 67 | /// This function instantiates a \ref ArborescenceMap. |
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| 68 | /// \param _graph is the graph, to which we would like to define the |
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| 69 | /// ArborescenceMap. |
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| 70 | static ArborescenceMap *createArborescenceMap(const Graph &_graph){ |
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| 71 | return new ArborescenceMap(_graph); |
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| 72 | } |
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| 73 | |
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[2025] | 74 | /// \brief The type of the PredMap |
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| 75 | /// |
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| 76 | /// The type of the PredMap. It is a node map with an edge value type. |
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| 77 | typedef typename Graph::template NodeMap<typename Graph::Edge> PredMap; |
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| 78 | |
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| 79 | /// \brief Instantiates a PredMap. |
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| 80 | /// |
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| 81 | /// This function instantiates a \ref PredMap. |
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| 82 | /// \param _graph is the graph, to which we would like to define the |
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| 83 | /// PredMap. |
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| 84 | static PredMap *createPredMap(const Graph &_graph){ |
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| 85 | return new PredMap(_graph); |
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| 86 | } |
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| 87 | |
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[2017] | 88 | }; |
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| 89 | |
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| 90 | /// \ingroup spantree |
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| 91 | /// |
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| 92 | /// \brief %MinCostArborescence algorithm class. |
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| 93 | /// |
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| 94 | /// This class provides an efficient implementation of |
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| 95 | /// %MinCostArborescence algorithm. The arborescence is a tree |
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| 96 | /// which is directed from a given source node of the graph. One or |
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| 97 | /// more sources should be given for the algorithm and it will calculate |
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| 98 | /// the minimum cost subgraph which are union of arborescences with the |
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| 99 | /// given sources and spans all the nodes which are reachable from the |
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[2042] | 100 | /// sources. The time complexity of the algorithm is \f$ O(n^2+e) \f$. |
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[2017] | 101 | /// |
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[2025] | 102 | /// The algorithm provides also an optimal dual solution to arborescence |
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[2042] | 103 | /// that way the optimality of the solution can be proofed easily. |
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[2025] | 104 | /// |
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[2017] | 105 | /// \param _Graph The graph type the algorithm runs on. The default value |
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| 106 | /// is \ref ListGraph. The value of _Graph is not used directly by |
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| 107 | /// MinCostArborescence, it is only passed to |
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| 108 | /// \ref MinCostArborescenceDefaultTraits. |
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| 109 | /// \param _CostMap This read-only EdgeMap determines the costs of the |
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| 110 | /// edges. It is read once for each edge, so the map may involve in |
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| 111 | /// relatively time consuming process to compute the edge cost if |
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| 112 | /// it is necessary. The default map type is \ref |
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[2260] | 113 | /// concepts::Graph::EdgeMap "Graph::EdgeMap<int>". The value |
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[2017] | 114 | /// of _CostMap is not used directly by MinCostArborescence, |
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| 115 | /// it is only passed to \ref MinCostArborescenceDefaultTraits. |
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| 116 | /// \param _Traits Traits class to set various data types used |
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| 117 | /// by the algorithm. The default traits class is |
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| 118 | /// \ref MinCostArborescenceDefaultTraits |
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| 119 | /// "MinCostArborescenceDefaultTraits<_Graph,_CostMap>". See \ref |
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| 120 | /// MinCostArborescenceDefaultTraits for the documentation of a |
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| 121 | /// MinCostArborescence traits class. |
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| 122 | /// |
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| 123 | /// \author Balazs Dezso |
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| 124 | #ifndef DOXYGEN |
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| 125 | template <typename _Graph = ListGraph, |
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| 126 | typename _CostMap = typename _Graph::template EdgeMap<int>, |
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| 127 | typename _Traits = |
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| 128 | MinCostArborescenceDefaultTraits<_Graph, _CostMap> > |
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| 129 | #else |
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| 130 | template <typename _Graph, typename _CostMap, typedef _Traits> |
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| 131 | #endif |
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| 132 | class MinCostArborescence { |
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| 133 | public: |
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| 134 | |
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| 135 | /// \brief \ref Exception for uninitialized parameters. |
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| 136 | /// |
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| 137 | /// This error represents problems in the initialization |
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| 138 | /// of the parameters of the algorithms. |
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| 139 | class UninitializedParameter : public lemon::UninitializedParameter { |
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| 140 | public: |
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[2151] | 141 | virtual const char* what() const throw() { |
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[2017] | 142 | return "lemon::MinCostArborescence::UninitializedParameter"; |
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| 143 | } |
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| 144 | }; |
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| 145 | |
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| 146 | /// The traits. |
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| 147 | typedef _Traits Traits; |
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| 148 | /// The type of the underlying graph. |
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| 149 | typedef typename Traits::Graph Graph; |
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| 150 | /// The type of the map that stores the edge costs. |
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| 151 | typedef typename Traits::CostMap CostMap; |
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| 152 | ///The type of the costs of the edges. |
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| 153 | typedef typename Traits::Value Value; |
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[2025] | 154 | ///The type of the predecessor map. |
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| 155 | typedef typename Traits::PredMap PredMap; |
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[2017] | 156 | ///The type of the map that stores which edges are in the arborescence. |
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| 157 | typedef typename Traits::ArborescenceMap ArborescenceMap; |
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| 158 | |
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| 159 | protected: |
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| 160 | |
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| 161 | typedef typename Graph::Node Node; |
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| 162 | typedef typename Graph::Edge Edge; |
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| 163 | typedef typename Graph::NodeIt NodeIt; |
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| 164 | typedef typename Graph::EdgeIt EdgeIt; |
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| 165 | typedef typename Graph::InEdgeIt InEdgeIt; |
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| 166 | typedef typename Graph::OutEdgeIt OutEdgeIt; |
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| 167 | |
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| 168 | struct CostEdge { |
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| 169 | |
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| 170 | Edge edge; |
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| 171 | Value value; |
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| 172 | |
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| 173 | CostEdge() {} |
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| 174 | CostEdge(Edge _edge, Value _value) : edge(_edge), value(_value) {} |
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| 175 | |
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| 176 | }; |
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| 177 | |
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[2025] | 178 | const Graph *graph; |
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| 179 | const CostMap *cost; |
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[2017] | 180 | |
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[2025] | 181 | PredMap *_pred; |
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| 182 | bool local_pred; |
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[2017] | 183 | |
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[2025] | 184 | ArborescenceMap *_arborescence; |
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| 185 | bool local_arborescence; |
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| 186 | |
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| 187 | typedef typename Graph::template EdgeMap<int> EdgeOrder; |
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| 188 | EdgeOrder *_edge_order; |
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| 189 | |
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| 190 | typedef typename Graph::template NodeMap<int> NodeOrder; |
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| 191 | NodeOrder *_node_order; |
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[2017] | 192 | |
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| 193 | typedef typename Graph::template NodeMap<CostEdge> CostEdgeMap; |
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| 194 | CostEdgeMap *_cost_edges; |
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| 195 | |
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| 196 | struct StackLevel { |
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| 197 | |
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| 198 | std::vector<CostEdge> edges; |
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| 199 | int node_level; |
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| 200 | |
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| 201 | }; |
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| 202 | |
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| 203 | std::vector<StackLevel> level_stack; |
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| 204 | std::vector<Node> queue; |
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| 205 | |
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[2025] | 206 | typedef std::vector<typename Graph::Node> DualNodeList; |
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| 207 | |
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| 208 | DualNodeList _dual_node_list; |
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| 209 | |
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| 210 | struct DualVariable { |
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| 211 | int begin, end; |
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| 212 | Value value; |
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| 213 | |
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| 214 | DualVariable(int _begin, int _end, Value _value) |
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| 215 | : begin(_begin), end(_end), value(_value) {} |
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| 216 | |
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| 217 | }; |
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| 218 | |
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| 219 | typedef std::vector<DualVariable> DualVariables; |
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| 220 | |
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| 221 | DualVariables _dual_variables; |
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| 222 | |
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| 223 | typedef typename Graph::template NodeMap<int> HeapCrossRef; |
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| 224 | |
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| 225 | HeapCrossRef *_heap_cross_ref; |
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| 226 | |
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[2263] | 227 | typedef BinHeap<int, HeapCrossRef> Heap; |
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[2025] | 228 | |
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| 229 | Heap *_heap; |
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[2017] | 230 | |
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| 231 | public: |
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| 232 | |
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| 233 | /// \name Named template parameters |
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| 234 | |
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| 235 | /// @{ |
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| 236 | |
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| 237 | template <class T> |
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| 238 | struct DefArborescenceMapTraits : public Traits { |
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| 239 | typedef T ArborescenceMap; |
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| 240 | static ArborescenceMap *createArborescenceMap(const Graph &) |
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| 241 | { |
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| 242 | throw UninitializedParameter(); |
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| 243 | } |
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| 244 | }; |
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| 245 | |
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| 246 | /// \brief \ref named-templ-param "Named parameter" for |
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| 247 | /// setting ArborescenceMap type |
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| 248 | /// |
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| 249 | /// \ref named-templ-param "Named parameter" for setting |
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| 250 | /// ArborescenceMap type |
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| 251 | template <class T> |
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| 252 | struct DefArborescenceMap |
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| 253 | : public MinCostArborescence<Graph, CostMap, |
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| 254 | DefArborescenceMapTraits<T> > { |
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| 255 | typedef MinCostArborescence<Graph, CostMap, |
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| 256 | DefArborescenceMapTraits<T> > Create; |
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| 257 | }; |
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[2025] | 258 | |
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| 259 | template <class T> |
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| 260 | struct DefPredMapTraits : public Traits { |
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| 261 | typedef T PredMap; |
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| 262 | static PredMap *createPredMap(const Graph &) |
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| 263 | { |
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| 264 | throw UninitializedParameter(); |
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| 265 | } |
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| 266 | }; |
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| 267 | |
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| 268 | /// \brief \ref named-templ-param "Named parameter" for |
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| 269 | /// setting PredMap type |
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| 270 | /// |
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| 271 | /// \ref named-templ-param "Named parameter" for setting |
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| 272 | /// PredMap type |
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| 273 | template <class T> |
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| 274 | struct DefPredMap |
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| 275 | : public MinCostArborescence<Graph, CostMap, DefPredMapTraits<T> > { |
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| 276 | typedef MinCostArborescence<Graph, CostMap, DefPredMapTraits<T> > Create; |
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| 277 | }; |
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[2017] | 278 | |
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| 279 | /// @} |
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| 280 | |
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| 281 | /// \brief Constructor. |
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| 282 | /// |
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| 283 | /// \param _graph The graph the algorithm will run on. |
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| 284 | /// \param _cost The cost map used by the algorithm. |
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| 285 | MinCostArborescence(const Graph& _graph, const CostMap& _cost) |
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[2025] | 286 | : graph(&_graph), cost(&_cost), _pred(0), local_pred(false), |
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| 287 | _arborescence(0), local_arborescence(false), |
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| 288 | _edge_order(0), _node_order(0), _cost_edges(0), |
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| 289 | _heap_cross_ref(0), _heap(0) {} |
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[2017] | 290 | |
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| 291 | /// \brief Destructor. |
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| 292 | ~MinCostArborescence() { |
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| 293 | destroyStructures(); |
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| 294 | } |
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| 295 | |
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| 296 | /// \brief Sets the arborescence map. |
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| 297 | /// |
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| 298 | /// Sets the arborescence map. |
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| 299 | /// \return \c (*this) |
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| 300 | MinCostArborescence& arborescenceMap(ArborescenceMap& m) { |
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[2025] | 301 | if (local_arborescence) { |
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| 302 | delete _arborescence; |
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| 303 | } |
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| 304 | local_arborescence = false; |
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| 305 | _arborescence = &m; |
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| 306 | return *this; |
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| 307 | } |
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| 308 | |
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| 309 | /// \brief Sets the arborescence map. |
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| 310 | /// |
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| 311 | /// Sets the arborescence map. |
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| 312 | /// \return \c (*this) |
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| 313 | MinCostArborescence& predMap(PredMap& m) { |
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| 314 | if (local_pred) { |
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| 315 | delete _pred; |
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| 316 | } |
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| 317 | local_pred = false; |
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| 318 | _pred = &m; |
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[2017] | 319 | return *this; |
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| 320 | } |
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| 321 | |
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| 322 | /// \name Query Functions |
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| 323 | /// The result of the %MinCostArborescence algorithm can be obtained |
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| 324 | /// using these functions.\n |
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| 325 | /// Before the use of these functions, |
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| 326 | /// either run() or start() must be called. |
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| 327 | |
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| 328 | /// @{ |
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| 329 | |
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| 330 | /// \brief Returns a reference to the arborescence map. |
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| 331 | /// |
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| 332 | /// Returns a reference to the arborescence map. |
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| 333 | const ArborescenceMap& arborescenceMap() const { |
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[2025] | 334 | return *_arborescence; |
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[2017] | 335 | } |
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| 336 | |
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| 337 | /// \brief Returns true if the edge is in the arborescence. |
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| 338 | /// |
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| 339 | /// Returns true if the edge is in the arborescence. |
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| 340 | /// \param edge The edge of the graph. |
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| 341 | /// \pre \ref run() must be called before using this function. |
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[2025] | 342 | bool arborescence(Edge edge) const { |
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| 343 | return (*_pred)[graph->target(edge)] == edge; |
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| 344 | } |
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| 345 | |
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| 346 | /// \brief Returns a reference to the pred map. |
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| 347 | /// |
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| 348 | /// Returns a reference to the pred map. |
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| 349 | const PredMap& predMap() const { |
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| 350 | return *_pred; |
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| 351 | } |
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| 352 | |
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| 353 | /// \brief Returns the predecessor edge of the given node. |
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| 354 | /// |
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| 355 | /// Returns the predecessor edge of the given node. |
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| 356 | bool pred(Node node) const { |
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| 357 | return (*_pred)[node]; |
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[2017] | 358 | } |
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| 359 | |
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| 360 | /// \brief Returns the cost of the arborescence. |
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| 361 | /// |
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| 362 | /// Returns the cost of the arborescence. |
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[2025] | 363 | Value arborescenceValue() const { |
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[2017] | 364 | Value sum = 0; |
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| 365 | for (EdgeIt it(*graph); it != INVALID; ++it) { |
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[2025] | 366 | if (arborescence(it)) { |
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[2017] | 367 | sum += (*cost)[it]; |
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| 368 | } |
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| 369 | } |
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| 370 | return sum; |
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| 371 | } |
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| 372 | |
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[2025] | 373 | /// \brief Indicates that a node is reachable from the sources. |
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| 374 | /// |
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| 375 | /// Indicates that a node is reachable from the sources. |
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| 376 | bool reached(Node node) const { |
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| 377 | return (*_node_order)[node] != -3; |
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| 378 | } |
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| 379 | |
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| 380 | /// \brief Indicates that a node is processed. |
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| 381 | /// |
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| 382 | /// Indicates that a node is processed. The arborescence path exists |
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| 383 | /// from the source to the given node. |
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| 384 | bool processed(Node node) const { |
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| 385 | return (*_node_order)[node] == -1; |
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| 386 | } |
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| 387 | |
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| 388 | /// \brief Returns the number of the dual variables in basis. |
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| 389 | /// |
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| 390 | /// Returns the number of the dual variables in basis. |
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| 391 | int dualSize() const { |
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| 392 | return _dual_variables.size(); |
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| 393 | } |
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| 394 | |
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| 395 | /// \brief Returns the value of the dual solution. |
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| 396 | /// |
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| 397 | /// Returns the value of the dual solution. It should be |
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| 398 | /// equal to the arborescence value. |
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| 399 | Value dualValue() const { |
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| 400 | Value sum = 0; |
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[2385] | 401 | for (int i = 0; i < int(_dual_variables.size()); ++i) { |
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[2025] | 402 | sum += _dual_variables[i].value; |
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| 403 | } |
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| 404 | return sum; |
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| 405 | } |
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| 406 | |
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| 407 | /// \brief Returns the number of the nodes in the dual variable. |
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| 408 | /// |
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| 409 | /// Returns the number of the nodes in the dual variable. |
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| 410 | int dualSize(int k) const { |
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| 411 | return _dual_variables[k].end - _dual_variables[k].begin; |
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| 412 | } |
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| 413 | |
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| 414 | /// \brief Returns the value of the dual variable. |
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| 415 | /// |
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| 416 | /// Returns the the value of the dual variable. |
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| 417 | const Value& dualValue(int k) const { |
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| 418 | return _dual_variables[k].value; |
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| 419 | } |
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| 420 | |
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| 421 | /// \brief Lemon iterator for get a dual variable. |
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| 422 | /// |
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| 423 | /// Lemon iterator for get a dual variable. This class provides |
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| 424 | /// a common style lemon iterator which gives back a subset of |
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| 425 | /// the nodes. |
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| 426 | class DualIt { |
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| 427 | public: |
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| 428 | |
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| 429 | /// \brief Constructor. |
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| 430 | /// |
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| 431 | /// Constructor for get the nodeset of the variable. |
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| 432 | DualIt(const MinCostArborescence& algorithm, int variable) |
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| 433 | : _algorithm(&algorithm), _variable(variable) |
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| 434 | { |
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| 435 | _index = _algorithm->_dual_variables[_variable].begin; |
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| 436 | } |
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| 437 | |
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| 438 | /// \brief Invalid constructor. |
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| 439 | /// |
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| 440 | /// Invalid constructor. |
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| 441 | DualIt(Invalid) : _algorithm(0) {} |
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| 442 | |
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| 443 | /// \brief Conversion to node. |
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| 444 | /// |
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| 445 | /// Conversion to node. |
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| 446 | operator Node() const { |
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| 447 | return _algorithm ? _algorithm->_dual_node_list[_index] : INVALID; |
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| 448 | } |
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| 449 | |
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| 450 | /// \brief Increment operator. |
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| 451 | /// |
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| 452 | /// Increment operator. |
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| 453 | DualIt& operator++() { |
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| 454 | ++_index; |
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| 455 | if (_algorithm->_dual_variables[_variable].end == _index) { |
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| 456 | _algorithm = 0; |
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| 457 | } |
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| 458 | return *this; |
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| 459 | } |
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| 460 | |
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| 461 | bool operator==(const DualIt& it) const { |
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[2385] | 462 | return static_cast<Node>(*this) == static_cast<Node>(it); |
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[2025] | 463 | } |
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| 464 | bool operator!=(const DualIt& it) const { |
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[2385] | 465 | return static_cast<Node>(*this) != static_cast<Node>(it); |
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[2025] | 466 | } |
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| 467 | bool operator<(const DualIt& it) const { |
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[2385] | 468 | return static_cast<Node>(*this) < static_cast<Node>(it); |
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[2025] | 469 | } |
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| 470 | |
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| 471 | private: |
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| 472 | const MinCostArborescence* _algorithm; |
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| 473 | int _variable; |
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| 474 | int _index; |
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| 475 | }; |
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| 476 | |
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[2017] | 477 | /// @} |
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| 478 | |
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| 479 | /// \name Execution control |
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| 480 | /// The simplest way to execute the algorithm is to use |
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| 481 | /// one of the member functions called \c run(...). \n |
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| 482 | /// If you need more control on the execution, |
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| 483 | /// first you must call \ref init(), then you can add several |
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| 484 | /// source nodes with \ref addSource(). |
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[2025] | 485 | /// Finally \ref start() will perform the arborescence |
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[2017] | 486 | /// computation. |
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| 487 | |
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| 488 | ///@{ |
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| 489 | |
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| 490 | /// \brief Initializes the internal data structures. |
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| 491 | /// |
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| 492 | /// Initializes the internal data structures. |
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| 493 | /// |
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| 494 | void init() { |
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| 495 | initStructures(); |
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[2025] | 496 | _heap->clear(); |
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[2017] | 497 | for (NodeIt it(*graph); it != INVALID; ++it) { |
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| 498 | (*_cost_edges)[it].edge = INVALID; |
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[2025] | 499 | _node_order->set(it, -3); |
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| 500 | _heap_cross_ref->set(it, Heap::PRE_HEAP); |
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[2385] | 501 | _pred->set(it, INVALID); |
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[2017] | 502 | } |
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| 503 | for (EdgeIt it(*graph); it != INVALID; ++it) { |
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[2025] | 504 | _arborescence->set(it, false); |
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| 505 | _edge_order->set(it, -1); |
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[2017] | 506 | } |
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[2025] | 507 | _dual_node_list.clear(); |
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| 508 | _dual_variables.clear(); |
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[2017] | 509 | } |
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| 510 | |
---|
| 511 | /// \brief Adds a new source node. |
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| 512 | /// |
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| 513 | /// Adds a new source node to the algorithm. |
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| 514 | void addSource(Node source) { |
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| 515 | std::vector<Node> nodes; |
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| 516 | nodes.push_back(source); |
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| 517 | while (!nodes.empty()) { |
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| 518 | Node node = nodes.back(); |
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| 519 | nodes.pop_back(); |
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| 520 | for (OutEdgeIt it(*graph, node); it != INVALID; ++it) { |
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[2025] | 521 | Node target = graph->target(it); |
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| 522 | if ((*_node_order)[target] == -3) { |
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| 523 | (*_node_order)[target] = -2; |
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| 524 | nodes.push_back(target); |
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| 525 | queue.push_back(target); |
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[2017] | 526 | } |
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| 527 | } |
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| 528 | } |
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[2025] | 529 | (*_node_order)[source] = -1; |
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[2017] | 530 | } |
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| 531 | |
---|
| 532 | /// \brief Processes the next node in the priority queue. |
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| 533 | /// |
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| 534 | /// Processes the next node in the priority queue. |
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| 535 | /// |
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| 536 | /// \return The processed node. |
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| 537 | /// |
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| 538 | /// \warning The queue must not be empty! |
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| 539 | Node processNextNode() { |
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| 540 | Node node = queue.back(); |
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| 541 | queue.pop_back(); |
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[2025] | 542 | if ((*_node_order)[node] == -2) { |
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[2017] | 543 | Edge edge = prepare(node); |
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[2025] | 544 | Node source = graph->source(edge); |
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| 545 | while ((*_node_order)[source] != -1) { |
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| 546 | if ((*_node_order)[source] >= 0) { |
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| 547 | edge = contract(source); |
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[2017] | 548 | } else { |
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[2025] | 549 | edge = prepare(source); |
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[2017] | 550 | } |
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[2025] | 551 | source = graph->source(edge); |
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[2017] | 552 | } |
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[2025] | 553 | finalize(edge); |
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[2017] | 554 | level_stack.clear(); |
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| 555 | } |
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| 556 | return node; |
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| 557 | } |
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| 558 | |
---|
| 559 | /// \brief Returns the number of the nodes to be processed. |
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| 560 | /// |
---|
| 561 | /// Returns the number of the nodes to be processed. |
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| 562 | int queueSize() const { |
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| 563 | return queue.size(); |
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| 564 | } |
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| 565 | |
---|
| 566 | /// \brief Returns \c false if there are nodes to be processed. |
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| 567 | /// |
---|
| 568 | /// Returns \c false if there are nodes to be processed. |
---|
| 569 | bool emptyQueue() const { |
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| 570 | return queue.empty(); |
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| 571 | } |
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| 572 | |
---|
| 573 | /// \brief Executes the algorithm. |
---|
| 574 | /// |
---|
| 575 | /// Executes the algorithm. |
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| 576 | /// |
---|
| 577 | /// \pre init() must be called and at least one node should be added |
---|
| 578 | /// with addSource() before using this function. |
---|
| 579 | /// |
---|
| 580 | ///\note mca.start() is just a shortcut of the following code. |
---|
| 581 | ///\code |
---|
| 582 | ///while (!mca.emptyQueue()) { |
---|
| 583 | /// mca.processNextNode(); |
---|
| 584 | ///} |
---|
| 585 | ///\endcode |
---|
| 586 | void start() { |
---|
| 587 | while (!emptyQueue()) { |
---|
| 588 | processNextNode(); |
---|
| 589 | } |
---|
| 590 | } |
---|
| 591 | |
---|
| 592 | /// \brief Runs %MinCostArborescence algorithm from node \c s. |
---|
| 593 | /// |
---|
| 594 | /// This method runs the %MinCostArborescence algorithm from |
---|
| 595 | /// a root node \c s. |
---|
| 596 | /// |
---|
| 597 | ///\note mca.run(s) is just a shortcut of the following code. |
---|
| 598 | ///\code |
---|
| 599 | ///mca.init(); |
---|
| 600 | ///mca.addSource(s); |
---|
| 601 | ///mca.start(); |
---|
| 602 | ///\endcode |
---|
| 603 | void run(Node node) { |
---|
| 604 | init(); |
---|
| 605 | addSource(node); |
---|
| 606 | start(); |
---|
| 607 | } |
---|
| 608 | |
---|
| 609 | ///@} |
---|
| 610 | |
---|
| 611 | protected: |
---|
| 612 | |
---|
| 613 | void initStructures() { |
---|
[2025] | 614 | if (!_pred) { |
---|
| 615 | local_pred = true; |
---|
| 616 | _pred = Traits::createPredMap(*graph); |
---|
[2017] | 617 | } |
---|
[2025] | 618 | if (!_arborescence) { |
---|
| 619 | local_arborescence = true; |
---|
| 620 | _arborescence = Traits::createArborescenceMap(*graph); |
---|
| 621 | } |
---|
| 622 | if (!_edge_order) { |
---|
| 623 | _edge_order = new EdgeOrder(*graph); |
---|
| 624 | } |
---|
| 625 | if (!_node_order) { |
---|
| 626 | _node_order = new NodeOrder(*graph); |
---|
[2017] | 627 | } |
---|
| 628 | if (!_cost_edges) { |
---|
| 629 | _cost_edges = new CostEdgeMap(*graph); |
---|
| 630 | } |
---|
[2025] | 631 | if (!_heap_cross_ref) { |
---|
| 632 | _heap_cross_ref = new HeapCrossRef(*graph, -1); |
---|
| 633 | } |
---|
| 634 | if (!_heap) { |
---|
| 635 | _heap = new Heap(*_heap_cross_ref); |
---|
| 636 | } |
---|
[2017] | 637 | } |
---|
| 638 | |
---|
| 639 | void destroyStructures() { |
---|
[2025] | 640 | if (local_arborescence) { |
---|
| 641 | delete _arborescence; |
---|
| 642 | } |
---|
| 643 | if (local_pred) { |
---|
| 644 | delete _pred; |
---|
| 645 | } |
---|
| 646 | if (!_edge_order) { |
---|
| 647 | delete _edge_order; |
---|
| 648 | } |
---|
| 649 | if (_node_order) { |
---|
| 650 | delete _node_order; |
---|
[2017] | 651 | } |
---|
| 652 | if (!_cost_edges) { |
---|
| 653 | delete _cost_edges; |
---|
| 654 | } |
---|
[2025] | 655 | if (!_heap) { |
---|
| 656 | delete _heap; |
---|
| 657 | } |
---|
| 658 | if (!_heap_cross_ref) { |
---|
| 659 | delete _heap_cross_ref; |
---|
[2017] | 660 | } |
---|
| 661 | } |
---|
| 662 | |
---|
| 663 | Edge prepare(Node node) { |
---|
| 664 | std::vector<Node> nodes; |
---|
[2025] | 665 | (*_node_order)[node] = _dual_node_list.size(); |
---|
| 666 | StackLevel level; |
---|
| 667 | level.node_level = _dual_node_list.size(); |
---|
| 668 | _dual_node_list.push_back(node); |
---|
[2017] | 669 | for (InEdgeIt it(*graph, node); it != INVALID; ++it) { |
---|
| 670 | Edge edge = it; |
---|
[2025] | 671 | Node source = graph->source(edge); |
---|
[2017] | 672 | Value value = (*cost)[it]; |
---|
[2025] | 673 | if (source == node || (*_node_order)[source] == -3) continue; |
---|
| 674 | if ((*_cost_edges)[source].edge == INVALID) { |
---|
| 675 | (*_cost_edges)[source].edge = edge; |
---|
| 676 | (*_cost_edges)[source].value = value; |
---|
| 677 | nodes.push_back(source); |
---|
[2017] | 678 | } else { |
---|
[2025] | 679 | if ((*_cost_edges)[source].value > value) { |
---|
| 680 | (*_cost_edges)[source].edge = edge; |
---|
| 681 | (*_cost_edges)[source].value = value; |
---|
[2017] | 682 | } |
---|
| 683 | } |
---|
| 684 | } |
---|
| 685 | CostEdge minimum = (*_cost_edges)[nodes[0]]; |
---|
[2385] | 686 | for (int i = 1; i < int(nodes.size()); ++i) { |
---|
[2017] | 687 | if ((*_cost_edges)[nodes[i]].value < minimum.value) { |
---|
| 688 | minimum = (*_cost_edges)[nodes[i]]; |
---|
| 689 | } |
---|
| 690 | } |
---|
[2025] | 691 | _edge_order->set(minimum.edge, _dual_variables.size()); |
---|
| 692 | DualVariable var(_dual_node_list.size() - 1, |
---|
| 693 | _dual_node_list.size(), minimum.value); |
---|
| 694 | _dual_variables.push_back(var); |
---|
[2385] | 695 | for (int i = 0; i < int(nodes.size()); ++i) { |
---|
[2017] | 696 | (*_cost_edges)[nodes[i]].value -= minimum.value; |
---|
| 697 | level.edges.push_back((*_cost_edges)[nodes[i]]); |
---|
| 698 | (*_cost_edges)[nodes[i]].edge = INVALID; |
---|
| 699 | } |
---|
| 700 | level_stack.push_back(level); |
---|
| 701 | return minimum.edge; |
---|
| 702 | } |
---|
| 703 | |
---|
[2025] | 704 | Edge contract(Node node) { |
---|
| 705 | int node_bottom = bottom(node); |
---|
[2017] | 706 | std::vector<Node> nodes; |
---|
| 707 | while (!level_stack.empty() && |
---|
| 708 | level_stack.back().node_level >= node_bottom) { |
---|
[2385] | 709 | for (int i = 0; i < int(level_stack.back().edges.size()); ++i) { |
---|
[2017] | 710 | Edge edge = level_stack.back().edges[i].edge; |
---|
[2025] | 711 | Node source = graph->source(edge); |
---|
[2017] | 712 | Value value = level_stack.back().edges[i].value; |
---|
[2025] | 713 | if ((*_node_order)[source] >= node_bottom) continue; |
---|
| 714 | if ((*_cost_edges)[source].edge == INVALID) { |
---|
| 715 | (*_cost_edges)[source].edge = edge; |
---|
| 716 | (*_cost_edges)[source].value = value; |
---|
| 717 | nodes.push_back(source); |
---|
[2017] | 718 | } else { |
---|
[2025] | 719 | if ((*_cost_edges)[source].value > value) { |
---|
| 720 | (*_cost_edges)[source].edge = edge; |
---|
| 721 | (*_cost_edges)[source].value = value; |
---|
[2017] | 722 | } |
---|
| 723 | } |
---|
| 724 | } |
---|
| 725 | level_stack.pop_back(); |
---|
| 726 | } |
---|
| 727 | CostEdge minimum = (*_cost_edges)[nodes[0]]; |
---|
[2385] | 728 | for (int i = 1; i < int(nodes.size()); ++i) { |
---|
[2017] | 729 | if ((*_cost_edges)[nodes[i]].value < minimum.value) { |
---|
| 730 | minimum = (*_cost_edges)[nodes[i]]; |
---|
| 731 | } |
---|
| 732 | } |
---|
[2025] | 733 | _edge_order->set(minimum.edge, _dual_variables.size()); |
---|
| 734 | DualVariable var(node_bottom, _dual_node_list.size(), minimum.value); |
---|
| 735 | _dual_variables.push_back(var); |
---|
[2017] | 736 | StackLevel level; |
---|
| 737 | level.node_level = node_bottom; |
---|
[2385] | 738 | for (int i = 0; i < int(nodes.size()); ++i) { |
---|
[2017] | 739 | (*_cost_edges)[nodes[i]].value -= minimum.value; |
---|
| 740 | level.edges.push_back((*_cost_edges)[nodes[i]]); |
---|
| 741 | (*_cost_edges)[nodes[i]].edge = INVALID; |
---|
| 742 | } |
---|
| 743 | level_stack.push_back(level); |
---|
| 744 | return minimum.edge; |
---|
| 745 | } |
---|
| 746 | |
---|
[2025] | 747 | int bottom(Node node) { |
---|
[2017] | 748 | int k = level_stack.size() - 1; |
---|
[2025] | 749 | while (level_stack[k].node_level > (*_node_order)[node]) { |
---|
[2017] | 750 | --k; |
---|
| 751 | } |
---|
| 752 | return level_stack[k].node_level; |
---|
| 753 | } |
---|
| 754 | |
---|
[2025] | 755 | void finalize(Edge edge) { |
---|
| 756 | Node node = graph->target(edge); |
---|
| 757 | _heap->push(node, (*_edge_order)[edge]); |
---|
| 758 | _pred->set(node, edge); |
---|
| 759 | while (!_heap->empty()) { |
---|
| 760 | Node source = _heap->top(); |
---|
| 761 | _heap->pop(); |
---|
| 762 | _node_order->set(source, -1); |
---|
| 763 | for (OutEdgeIt it(*graph, source); it != INVALID; ++it) { |
---|
| 764 | if ((*_edge_order)[it] < 0) continue; |
---|
| 765 | Node target = graph->target(it); |
---|
| 766 | switch(_heap->state(target)) { |
---|
| 767 | case Heap::PRE_HEAP: |
---|
| 768 | _heap->push(target, (*_edge_order)[it]); |
---|
| 769 | _pred->set(target, it); |
---|
| 770 | break; |
---|
| 771 | case Heap::IN_HEAP: |
---|
| 772 | if ((*_edge_order)[it] < (*_heap)[target]) { |
---|
| 773 | _heap->decrease(target, (*_edge_order)[it]); |
---|
| 774 | _pred->set(target, it); |
---|
| 775 | } |
---|
| 776 | break; |
---|
| 777 | case Heap::POST_HEAP: |
---|
| 778 | break; |
---|
[2017] | 779 | } |
---|
| 780 | } |
---|
[2025] | 781 | _arborescence->set((*_pred)[source], true); |
---|
[2017] | 782 | } |
---|
| 783 | } |
---|
| 784 | |
---|
| 785 | }; |
---|
| 786 | |
---|
| 787 | /// \ingroup spantree |
---|
| 788 | /// |
---|
| 789 | /// \brief Function type interface for MinCostArborescence algorithm. |
---|
| 790 | /// |
---|
| 791 | /// Function type interface for MinCostArborescence algorithm. |
---|
| 792 | /// \param graph The Graph that the algorithm runs on. |
---|
| 793 | /// \param cost The CostMap of the edges. |
---|
| 794 | /// \param source The source of the arborescence. |
---|
| 795 | /// \retval arborescence The bool EdgeMap which stores the arborescence. |
---|
| 796 | /// \return The cost of the arborescence. |
---|
| 797 | /// |
---|
| 798 | /// \sa MinCostArborescence |
---|
| 799 | template <typename Graph, typename CostMap, typename ArborescenceMap> |
---|
| 800 | typename CostMap::Value minCostArborescence(const Graph& graph, |
---|
| 801 | const CostMap& cost, |
---|
| 802 | typename Graph::Node source, |
---|
| 803 | ArborescenceMap& arborescence) { |
---|
| 804 | typename MinCostArborescence<Graph, CostMap> |
---|
| 805 | ::template DefArborescenceMap<ArborescenceMap> |
---|
| 806 | ::Create mca(graph, cost); |
---|
| 807 | mca.arborescenceMap(arborescence); |
---|
| 808 | mca.run(source); |
---|
[2025] | 809 | return mca.arborescenceValue(); |
---|
[2017] | 810 | } |
---|
| 811 | |
---|
| 812 | } |
---|
| 813 | |
---|
| 814 | #endif |
---|