[805] | 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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| 5 | * Copyright (C) 2003-2008 |
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| 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_CAPACITY_SCALING_H |
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| 20 | #define LEMON_CAPACITY_SCALING_H |
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| 21 | |
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[806] | 22 | /// \ingroup min_cost_flow_algs |
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[805] | 23 | /// |
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| 24 | /// \file |
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[806] | 25 | /// \brief Capacity Scaling algorithm for finding a minimum cost flow. |
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[805] | 26 | |
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| 27 | #include <vector> |
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[806] | 28 | #include <limits> |
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| 29 | #include <lemon/core.h> |
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[805] | 30 | #include <lemon/bin_heap.h> |
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| 31 | |
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| 32 | namespace lemon { |
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| 33 | |
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[807] | 34 | /// \brief Default traits class of CapacityScaling algorithm. |
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| 35 | /// |
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| 36 | /// Default traits class of CapacityScaling algorithm. |
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| 37 | /// \tparam GR Digraph type. |
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| 38 | /// \tparam V The value type used for flow amounts, capacity bounds |
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| 39 | /// and supply values. By default it is \c int. |
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| 40 | /// \tparam C The value type used for costs and potentials. |
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| 41 | /// By default it is the same as \c V. |
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| 42 | template <typename GR, typename V = int, typename C = V> |
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| 43 | struct CapacityScalingDefaultTraits |
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| 44 | { |
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| 45 | /// The type of the digraph |
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| 46 | typedef GR Digraph; |
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| 47 | /// The type of the flow amounts, capacity bounds and supply values |
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| 48 | typedef V Value; |
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| 49 | /// The type of the arc costs |
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| 50 | typedef C Cost; |
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| 51 | |
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| 52 | /// \brief The type of the heap used for internal Dijkstra computations. |
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| 53 | /// |
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| 54 | /// The type of the heap used for internal Dijkstra computations. |
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| 55 | /// It must conform to the \ref lemon::concepts::Heap "Heap" concept, |
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| 56 | /// its priority type must be \c Cost and its cross reference type |
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| 57 | /// must be \ref RangeMap "RangeMap<int>". |
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| 58 | typedef BinHeap<Cost, RangeMap<int> > Heap; |
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| 59 | }; |
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| 60 | |
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[806] | 61 | /// \addtogroup min_cost_flow_algs |
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[805] | 62 | /// @{ |
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| 63 | |
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[806] | 64 | /// \brief Implementation of the Capacity Scaling algorithm for |
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| 65 | /// finding a \ref min_cost_flow "minimum cost flow". |
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[805] | 66 | /// |
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| 67 | /// \ref CapacityScaling implements the capacity scaling version |
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[806] | 68 | /// of the successive shortest path algorithm for finding a |
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| 69 | /// \ref min_cost_flow "minimum cost flow". It is an efficient dual |
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| 70 | /// solution method. |
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[805] | 71 | /// |
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[806] | 72 | /// Most of the parameters of the problem (except for the digraph) |
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| 73 | /// can be given using separate functions, and the algorithm can be |
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| 74 | /// executed using the \ref run() function. If some parameters are not |
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| 75 | /// specified, then default values will be used. |
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[805] | 76 | /// |
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[806] | 77 | /// \tparam GR The digraph type the algorithm runs on. |
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| 78 | /// \tparam V The value type used for flow amounts, capacity bounds |
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| 79 | /// and supply values in the algorithm. By default it is \c int. |
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| 80 | /// \tparam C The value type used for costs and potentials in the |
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| 81 | /// algorithm. By default it is the same as \c V. |
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[805] | 82 | /// |
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[806] | 83 | /// \warning Both value types must be signed and all input data must |
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| 84 | /// be integer. |
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| 85 | /// \warning This algorithm does not support negative costs for such |
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| 86 | /// arcs that have infinite upper bound. |
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[807] | 87 | #ifdef DOXYGEN |
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| 88 | template <typename GR, typename V, typename C, typename TR> |
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| 89 | #else |
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| 90 | template < typename GR, typename V = int, typename C = V, |
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| 91 | typename TR = CapacityScalingDefaultTraits<GR, V, C> > |
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| 92 | #endif |
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[805] | 93 | class CapacityScaling |
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| 94 | { |
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[806] | 95 | public: |
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[805] | 96 | |
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[807] | 97 | /// The type of the digraph |
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| 98 | typedef typename TR::Digraph Digraph; |
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[806] | 99 | /// The type of the flow amounts, capacity bounds and supply values |
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[807] | 100 | typedef typename TR::Value Value; |
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[806] | 101 | /// The type of the arc costs |
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[807] | 102 | typedef typename TR::Cost Cost; |
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| 103 | |
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| 104 | /// The type of the heap used for internal Dijkstra computations |
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| 105 | typedef typename TR::Heap Heap; |
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| 106 | |
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| 107 | /// The \ref CapacityScalingDefaultTraits "traits class" of the algorithm |
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| 108 | typedef TR Traits; |
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[805] | 109 | |
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| 110 | public: |
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| 111 | |
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[806] | 112 | /// \brief Problem type constants for the \c run() function. |
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| 113 | /// |
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| 114 | /// Enum type containing the problem type constants that can be |
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| 115 | /// returned by the \ref run() function of the algorithm. |
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| 116 | enum ProblemType { |
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| 117 | /// The problem has no feasible solution (flow). |
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| 118 | INFEASIBLE, |
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| 119 | /// The problem has optimal solution (i.e. it is feasible and |
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| 120 | /// bounded), and the algorithm has found optimal flow and node |
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| 121 | /// potentials (primal and dual solutions). |
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| 122 | OPTIMAL, |
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| 123 | /// The digraph contains an arc of negative cost and infinite |
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| 124 | /// upper bound. It means that the objective function is unbounded |
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| 125 | /// on that arc, however note that it could actually be bounded |
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| 126 | /// over the feasible flows, but this algroithm cannot handle |
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| 127 | /// these cases. |
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| 128 | UNBOUNDED |
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| 129 | }; |
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| 130 | |
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| 131 | private: |
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| 132 | |
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| 133 | TEMPLATE_DIGRAPH_TYPEDEFS(GR); |
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| 134 | |
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| 135 | typedef std::vector<int> IntVector; |
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[811] | 136 | typedef std::vector<char> BoolVector; |
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[806] | 137 | typedef std::vector<Value> ValueVector; |
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| 138 | typedef std::vector<Cost> CostVector; |
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[805] | 139 | |
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| 140 | private: |
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| 141 | |
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[806] | 142 | // Data related to the underlying digraph |
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| 143 | const GR &_graph; |
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| 144 | int _node_num; |
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| 145 | int _arc_num; |
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| 146 | int _res_arc_num; |
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| 147 | int _root; |
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| 148 | |
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| 149 | // Parameters of the problem |
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| 150 | bool _have_lower; |
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| 151 | Value _sum_supply; |
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| 152 | |
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| 153 | // Data structures for storing the digraph |
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| 154 | IntNodeMap _node_id; |
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| 155 | IntArcMap _arc_idf; |
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| 156 | IntArcMap _arc_idb; |
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| 157 | IntVector _first_out; |
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| 158 | BoolVector _forward; |
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| 159 | IntVector _source; |
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| 160 | IntVector _target; |
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| 161 | IntVector _reverse; |
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| 162 | |
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| 163 | // Node and arc data |
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| 164 | ValueVector _lower; |
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| 165 | ValueVector _upper; |
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| 166 | CostVector _cost; |
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| 167 | ValueVector _supply; |
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| 168 | |
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| 169 | ValueVector _res_cap; |
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| 170 | CostVector _pi; |
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| 171 | ValueVector _excess; |
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| 172 | IntVector _excess_nodes; |
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| 173 | IntVector _deficit_nodes; |
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| 174 | |
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| 175 | Value _delta; |
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[810] | 176 | int _factor; |
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[806] | 177 | IntVector _pred; |
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| 178 | |
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| 179 | public: |
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| 180 | |
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| 181 | /// \brief Constant for infinite upper bounds (capacities). |
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[805] | 182 | /// |
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[806] | 183 | /// Constant for infinite upper bounds (capacities). |
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| 184 | /// It is \c std::numeric_limits<Value>::infinity() if available, |
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| 185 | /// \c std::numeric_limits<Value>::max() otherwise. |
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| 186 | const Value INF; |
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| 187 | |
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| 188 | private: |
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| 189 | |
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| 190 | // Special implementation of the Dijkstra algorithm for finding |
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| 191 | // shortest paths in the residual network of the digraph with |
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| 192 | // respect to the reduced arc costs and modifying the node |
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| 193 | // potentials according to the found distance labels. |
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[805] | 194 | class ResidualDijkstra |
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| 195 | { |
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| 196 | private: |
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| 197 | |
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[806] | 198 | int _node_num; |
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[811] | 199 | bool _geq; |
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[806] | 200 | const IntVector &_first_out; |
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| 201 | const IntVector &_target; |
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| 202 | const CostVector &_cost; |
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| 203 | const ValueVector &_res_cap; |
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| 204 | const ValueVector &_excess; |
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| 205 | CostVector &_pi; |
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| 206 | IntVector &_pred; |
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| 207 | |
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| 208 | IntVector _proc_nodes; |
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| 209 | CostVector _dist; |
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| 210 | |
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[805] | 211 | public: |
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| 212 | |
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[806] | 213 | ResidualDijkstra(CapacityScaling& cs) : |
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[811] | 214 | _node_num(cs._node_num), _geq(cs._sum_supply < 0), |
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| 215 | _first_out(cs._first_out), _target(cs._target), _cost(cs._cost), |
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| 216 | _res_cap(cs._res_cap), _excess(cs._excess), _pi(cs._pi), |
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| 217 | _pred(cs._pred), _dist(cs._node_num) |
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[805] | 218 | {} |
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| 219 | |
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[806] | 220 | int run(int s, Value delta = 1) { |
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[807] | 221 | RangeMap<int> heap_cross_ref(_node_num, Heap::PRE_HEAP); |
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[805] | 222 | Heap heap(heap_cross_ref); |
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| 223 | heap.push(s, 0); |
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[806] | 224 | _pred[s] = -1; |
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[805] | 225 | _proc_nodes.clear(); |
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| 226 | |
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[806] | 227 | // Process nodes |
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[805] | 228 | while (!heap.empty() && _excess[heap.top()] > -delta) { |
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[806] | 229 | int u = heap.top(), v; |
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| 230 | Cost d = heap.prio() + _pi[u], dn; |
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[805] | 231 | _dist[u] = heap.prio(); |
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[806] | 232 | _proc_nodes.push_back(u); |
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[805] | 233 | heap.pop(); |
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| 234 | |
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[806] | 235 | // Traverse outgoing residual arcs |
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[811] | 236 | int last_out = _geq ? _first_out[u+1] : _first_out[u+1] - 1; |
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| 237 | for (int a = _first_out[u]; a != last_out; ++a) { |
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[806] | 238 | if (_res_cap[a] < delta) continue; |
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| 239 | v = _target[a]; |
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| 240 | switch (heap.state(v)) { |
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[805] | 241 | case Heap::PRE_HEAP: |
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[806] | 242 | heap.push(v, d + _cost[a] - _pi[v]); |
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| 243 | _pred[v] = a; |
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[805] | 244 | break; |
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| 245 | case Heap::IN_HEAP: |
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[806] | 246 | dn = d + _cost[a] - _pi[v]; |
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| 247 | if (dn < heap[v]) { |
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| 248 | heap.decrease(v, dn); |
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| 249 | _pred[v] = a; |
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[805] | 250 | } |
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| 251 | break; |
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| 252 | case Heap::POST_HEAP: |
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| 253 | break; |
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| 254 | } |
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| 255 | } |
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| 256 | } |
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[806] | 257 | if (heap.empty()) return -1; |
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[805] | 258 | |
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[806] | 259 | // Update potentials of processed nodes |
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| 260 | int t = heap.top(); |
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| 261 | Cost dt = heap.prio(); |
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| 262 | for (int i = 0; i < int(_proc_nodes.size()); ++i) { |
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| 263 | _pi[_proc_nodes[i]] += _dist[_proc_nodes[i]] - dt; |
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| 264 | } |
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[805] | 265 | |
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| 266 | return t; |
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| 267 | } |
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| 268 | |
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| 269 | }; //class ResidualDijkstra |
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| 270 | |
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| 271 | public: |
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| 272 | |
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[807] | 273 | /// \name Named Template Parameters |
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| 274 | /// @{ |
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| 275 | |
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| 276 | template <typename T> |
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| 277 | struct SetHeapTraits : public Traits { |
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| 278 | typedef T Heap; |
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| 279 | }; |
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| 280 | |
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| 281 | /// \brief \ref named-templ-param "Named parameter" for setting |
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| 282 | /// \c Heap type. |
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| 283 | /// |
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| 284 | /// \ref named-templ-param "Named parameter" for setting \c Heap |
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| 285 | /// type, which is used for internal Dijkstra computations. |
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| 286 | /// It must conform to the \ref lemon::concepts::Heap "Heap" concept, |
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| 287 | /// its priority type must be \c Cost and its cross reference type |
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| 288 | /// must be \ref RangeMap "RangeMap<int>". |
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| 289 | template <typename T> |
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| 290 | struct SetHeap |
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| 291 | : public CapacityScaling<GR, V, C, SetHeapTraits<T> > { |
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| 292 | typedef CapacityScaling<GR, V, C, SetHeapTraits<T> > Create; |
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| 293 | }; |
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| 294 | |
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| 295 | /// @} |
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| 296 | |
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| 297 | public: |
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| 298 | |
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[806] | 299 | /// \brief Constructor. |
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[805] | 300 | /// |
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[806] | 301 | /// The constructor of the class. |
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[805] | 302 | /// |
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[806] | 303 | /// \param graph The digraph the algorithm runs on. |
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| 304 | CapacityScaling(const GR& graph) : |
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| 305 | _graph(graph), _node_id(graph), _arc_idf(graph), _arc_idb(graph), |
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| 306 | INF(std::numeric_limits<Value>::has_infinity ? |
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| 307 | std::numeric_limits<Value>::infinity() : |
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| 308 | std::numeric_limits<Value>::max()) |
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[805] | 309 | { |
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[806] | 310 | // Check the value types |
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| 311 | LEMON_ASSERT(std::numeric_limits<Value>::is_signed, |
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| 312 | "The flow type of CapacityScaling must be signed"); |
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| 313 | LEMON_ASSERT(std::numeric_limits<Cost>::is_signed, |
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| 314 | "The cost type of CapacityScaling must be signed"); |
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| 315 | |
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| 316 | // Resize vectors |
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| 317 | _node_num = countNodes(_graph); |
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| 318 | _arc_num = countArcs(_graph); |
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| 319 | _res_arc_num = 2 * (_arc_num + _node_num); |
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| 320 | _root = _node_num; |
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| 321 | ++_node_num; |
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| 322 | |
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| 323 | _first_out.resize(_node_num + 1); |
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| 324 | _forward.resize(_res_arc_num); |
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| 325 | _source.resize(_res_arc_num); |
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| 326 | _target.resize(_res_arc_num); |
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| 327 | _reverse.resize(_res_arc_num); |
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| 328 | |
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| 329 | _lower.resize(_res_arc_num); |
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| 330 | _upper.resize(_res_arc_num); |
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| 331 | _cost.resize(_res_arc_num); |
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| 332 | _supply.resize(_node_num); |
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| 333 | |
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| 334 | _res_cap.resize(_res_arc_num); |
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| 335 | _pi.resize(_node_num); |
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| 336 | _excess.resize(_node_num); |
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| 337 | _pred.resize(_node_num); |
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| 338 | |
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| 339 | // Copy the graph |
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| 340 | int i = 0, j = 0, k = 2 * _arc_num + _node_num - 1; |
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| 341 | for (NodeIt n(_graph); n != INVALID; ++n, ++i) { |
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| 342 | _node_id[n] = i; |
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[805] | 343 | } |
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[806] | 344 | i = 0; |
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| 345 | for (NodeIt n(_graph); n != INVALID; ++n, ++i) { |
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| 346 | _first_out[i] = j; |
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| 347 | for (OutArcIt a(_graph, n); a != INVALID; ++a, ++j) { |
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| 348 | _arc_idf[a] = j; |
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| 349 | _forward[j] = true; |
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| 350 | _source[j] = i; |
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| 351 | _target[j] = _node_id[_graph.runningNode(a)]; |
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| 352 | } |
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| 353 | for (InArcIt a(_graph, n); a != INVALID; ++a, ++j) { |
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| 354 | _arc_idb[a] = j; |
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| 355 | _forward[j] = false; |
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| 356 | _source[j] = i; |
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| 357 | _target[j] = _node_id[_graph.runningNode(a)]; |
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| 358 | } |
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| 359 | _forward[j] = false; |
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| 360 | _source[j] = i; |
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| 361 | _target[j] = _root; |
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| 362 | _reverse[j] = k; |
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| 363 | _forward[k] = true; |
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| 364 | _source[k] = _root; |
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| 365 | _target[k] = i; |
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| 366 | _reverse[k] = j; |
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| 367 | ++j; ++k; |
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| 368 | } |
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| 369 | _first_out[i] = j; |
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| 370 | _first_out[_node_num] = k; |
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[805] | 371 | for (ArcIt a(_graph); a != INVALID; ++a) { |
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[806] | 372 | int fi = _arc_idf[a]; |
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| 373 | int bi = _arc_idb[a]; |
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| 374 | _reverse[fi] = bi; |
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| 375 | _reverse[bi] = fi; |
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[805] | 376 | } |
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[806] | 377 | |
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| 378 | // Reset parameters |
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| 379 | reset(); |
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[805] | 380 | } |
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| 381 | |
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[806] | 382 | /// \name Parameters |
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| 383 | /// The parameters of the algorithm can be specified using these |
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| 384 | /// functions. |
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| 385 | |
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| 386 | /// @{ |
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| 387 | |
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| 388 | /// \brief Set the lower bounds on the arcs. |
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[805] | 389 | /// |
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[806] | 390 | /// This function sets the lower bounds on the arcs. |
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| 391 | /// If it is not used before calling \ref run(), the lower bounds |
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| 392 | /// will be set to zero on all arcs. |
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[805] | 393 | /// |
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[806] | 394 | /// \param map An arc map storing the lower bounds. |
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| 395 | /// Its \c Value type must be convertible to the \c Value type |
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| 396 | /// of the algorithm. |
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| 397 | /// |
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| 398 | /// \return <tt>(*this)</tt> |
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| 399 | template <typename LowerMap> |
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| 400 | CapacityScaling& lowerMap(const LowerMap& map) { |
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| 401 | _have_lower = true; |
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| 402 | for (ArcIt a(_graph); a != INVALID; ++a) { |
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| 403 | _lower[_arc_idf[a]] = map[a]; |
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| 404 | _lower[_arc_idb[a]] = map[a]; |
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[805] | 405 | } |
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| 406 | return *this; |
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| 407 | } |
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| 408 | |
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[806] | 409 | /// \brief Set the upper bounds (capacities) on the arcs. |
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[805] | 410 | /// |
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[806] | 411 | /// This function sets the upper bounds (capacities) on the arcs. |
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| 412 | /// If it is not used before calling \ref run(), the upper bounds |
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| 413 | /// will be set to \ref INF on all arcs (i.e. the flow value will be |
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| 414 | /// unbounded from above on each arc). |
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[805] | 415 | /// |
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[806] | 416 | /// \param map An arc map storing the upper bounds. |
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| 417 | /// Its \c Value type must be convertible to the \c Value type |
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| 418 | /// of the algorithm. |
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| 419 | /// |
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| 420 | /// \return <tt>(*this)</tt> |
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| 421 | template<typename UpperMap> |
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| 422 | CapacityScaling& upperMap(const UpperMap& map) { |
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| 423 | for (ArcIt a(_graph); a != INVALID; ++a) { |
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| 424 | _upper[_arc_idf[a]] = map[a]; |
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[805] | 425 | } |
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| 426 | return *this; |
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| 427 | } |
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| 428 | |
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[806] | 429 | /// \brief Set the costs of the arcs. |
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| 430 | /// |
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| 431 | /// This function sets the costs of the arcs. |
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| 432 | /// If it is not used before calling \ref run(), the costs |
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| 433 | /// will be set to \c 1 on all arcs. |
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| 434 | /// |
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| 435 | /// \param map An arc map storing the costs. |
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| 436 | /// Its \c Value type must be convertible to the \c Cost type |
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| 437 | /// of the algorithm. |
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| 438 | /// |
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| 439 | /// \return <tt>(*this)</tt> |
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| 440 | template<typename CostMap> |
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| 441 | CapacityScaling& costMap(const CostMap& map) { |
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| 442 | for (ArcIt a(_graph); a != INVALID; ++a) { |
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| 443 | _cost[_arc_idf[a]] = map[a]; |
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| 444 | _cost[_arc_idb[a]] = -map[a]; |
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| 445 | } |
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| 446 | return *this; |
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| 447 | } |
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| 448 | |
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| 449 | /// \brief Set the supply values of the nodes. |
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| 450 | /// |
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| 451 | /// This function sets the supply values of the nodes. |
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| 452 | /// If neither this function nor \ref stSupply() is used before |
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| 453 | /// calling \ref run(), the supply of each node will be set to zero. |
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| 454 | /// |
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| 455 | /// \param map A node map storing the supply values. |
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| 456 | /// Its \c Value type must be convertible to the \c Value type |
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| 457 | /// of the algorithm. |
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| 458 | /// |
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| 459 | /// \return <tt>(*this)</tt> |
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| 460 | template<typename SupplyMap> |
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| 461 | CapacityScaling& supplyMap(const SupplyMap& map) { |
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| 462 | for (NodeIt n(_graph); n != INVALID; ++n) { |
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| 463 | _supply[_node_id[n]] = map[n]; |
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| 464 | } |
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| 465 | return *this; |
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| 466 | } |
---|
| 467 | |
---|
| 468 | /// \brief Set single source and target nodes and a supply value. |
---|
| 469 | /// |
---|
| 470 | /// This function sets a single source node and a single target node |
---|
| 471 | /// and the required flow value. |
---|
| 472 | /// If neither this function nor \ref supplyMap() is used before |
---|
| 473 | /// calling \ref run(), the supply of each node will be set to zero. |
---|
| 474 | /// |
---|
| 475 | /// Using this function has the same effect as using \ref supplyMap() |
---|
| 476 | /// with such a map in which \c k is assigned to \c s, \c -k is |
---|
| 477 | /// assigned to \c t and all other nodes have zero supply value. |
---|
| 478 | /// |
---|
| 479 | /// \param s The source node. |
---|
| 480 | /// \param t The target node. |
---|
| 481 | /// \param k The required amount of flow from node \c s to node \c t |
---|
| 482 | /// (i.e. the supply of \c s and the demand of \c t). |
---|
| 483 | /// |
---|
| 484 | /// \return <tt>(*this)</tt> |
---|
| 485 | CapacityScaling& stSupply(const Node& s, const Node& t, Value k) { |
---|
| 486 | for (int i = 0; i != _node_num; ++i) { |
---|
| 487 | _supply[i] = 0; |
---|
| 488 | } |
---|
| 489 | _supply[_node_id[s]] = k; |
---|
| 490 | _supply[_node_id[t]] = -k; |
---|
| 491 | return *this; |
---|
| 492 | } |
---|
| 493 | |
---|
| 494 | /// @} |
---|
| 495 | |
---|
[805] | 496 | /// \name Execution control |
---|
[807] | 497 | /// The algorithm can be executed using \ref run(). |
---|
[805] | 498 | |
---|
| 499 | /// @{ |
---|
| 500 | |
---|
| 501 | /// \brief Run the algorithm. |
---|
| 502 | /// |
---|
| 503 | /// This function runs the algorithm. |
---|
[806] | 504 | /// The paramters can be specified using functions \ref lowerMap(), |
---|
| 505 | /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(). |
---|
| 506 | /// For example, |
---|
| 507 | /// \code |
---|
| 508 | /// CapacityScaling<ListDigraph> cs(graph); |
---|
| 509 | /// cs.lowerMap(lower).upperMap(upper).costMap(cost) |
---|
| 510 | /// .supplyMap(sup).run(); |
---|
| 511 | /// \endcode |
---|
| 512 | /// |
---|
| 513 | /// This function can be called more than once. All the parameters |
---|
| 514 | /// that have been given are kept for the next call, unless |
---|
| 515 | /// \ref reset() is called, thus only the modified parameters |
---|
| 516 | /// have to be set again. See \ref reset() for examples. |
---|
| 517 | /// However the underlying digraph must not be modified after this |
---|
[810] | 518 | /// class have been constructed, since it copies and extends the graph. |
---|
[805] | 519 | /// |
---|
[810] | 520 | /// \param factor The capacity scaling factor. It must be larger than |
---|
| 521 | /// one to use scaling. If it is less or equal to one, then scaling |
---|
| 522 | /// will be disabled. |
---|
[805] | 523 | /// |
---|
[806] | 524 | /// \return \c INFEASIBLE if no feasible flow exists, |
---|
| 525 | /// \n \c OPTIMAL if the problem has optimal solution |
---|
| 526 | /// (i.e. it is feasible and bounded), and the algorithm has found |
---|
| 527 | /// optimal flow and node potentials (primal and dual solutions), |
---|
| 528 | /// \n \c UNBOUNDED if the digraph contains an arc of negative cost |
---|
| 529 | /// and infinite upper bound. It means that the objective function |
---|
| 530 | /// is unbounded on that arc, however note that it could actually be |
---|
| 531 | /// bounded over the feasible flows, but this algroithm cannot handle |
---|
| 532 | /// these cases. |
---|
| 533 | /// |
---|
| 534 | /// \see ProblemType |
---|
[810] | 535 | ProblemType run(int factor = 4) { |
---|
| 536 | _factor = factor; |
---|
| 537 | ProblemType pt = init(); |
---|
[806] | 538 | if (pt != OPTIMAL) return pt; |
---|
| 539 | return start(); |
---|
| 540 | } |
---|
| 541 | |
---|
| 542 | /// \brief Reset all the parameters that have been given before. |
---|
| 543 | /// |
---|
| 544 | /// This function resets all the paramaters that have been given |
---|
| 545 | /// before using functions \ref lowerMap(), \ref upperMap(), |
---|
| 546 | /// \ref costMap(), \ref supplyMap(), \ref stSupply(). |
---|
| 547 | /// |
---|
| 548 | /// It is useful for multiple run() calls. If this function is not |
---|
| 549 | /// used, all the parameters given before are kept for the next |
---|
| 550 | /// \ref run() call. |
---|
[810] | 551 | /// However, the underlying digraph must not be modified after this |
---|
[806] | 552 | /// class have been constructed, since it copies and extends the graph. |
---|
| 553 | /// |
---|
| 554 | /// For example, |
---|
| 555 | /// \code |
---|
| 556 | /// CapacityScaling<ListDigraph> cs(graph); |
---|
| 557 | /// |
---|
| 558 | /// // First run |
---|
| 559 | /// cs.lowerMap(lower).upperMap(upper).costMap(cost) |
---|
| 560 | /// .supplyMap(sup).run(); |
---|
| 561 | /// |
---|
| 562 | /// // Run again with modified cost map (reset() is not called, |
---|
| 563 | /// // so only the cost map have to be set again) |
---|
| 564 | /// cost[e] += 100; |
---|
| 565 | /// cs.costMap(cost).run(); |
---|
| 566 | /// |
---|
| 567 | /// // Run again from scratch using reset() |
---|
| 568 | /// // (the lower bounds will be set to zero on all arcs) |
---|
| 569 | /// cs.reset(); |
---|
| 570 | /// cs.upperMap(capacity).costMap(cost) |
---|
| 571 | /// .supplyMap(sup).run(); |
---|
| 572 | /// \endcode |
---|
| 573 | /// |
---|
| 574 | /// \return <tt>(*this)</tt> |
---|
| 575 | CapacityScaling& reset() { |
---|
| 576 | for (int i = 0; i != _node_num; ++i) { |
---|
| 577 | _supply[i] = 0; |
---|
| 578 | } |
---|
| 579 | for (int j = 0; j != _res_arc_num; ++j) { |
---|
| 580 | _lower[j] = 0; |
---|
| 581 | _upper[j] = INF; |
---|
| 582 | _cost[j] = _forward[j] ? 1 : -1; |
---|
| 583 | } |
---|
| 584 | _have_lower = false; |
---|
| 585 | return *this; |
---|
[805] | 586 | } |
---|
| 587 | |
---|
| 588 | /// @} |
---|
| 589 | |
---|
| 590 | /// \name Query Functions |
---|
| 591 | /// The results of the algorithm can be obtained using these |
---|
| 592 | /// functions.\n |
---|
[806] | 593 | /// The \ref run() function must be called before using them. |
---|
[805] | 594 | |
---|
| 595 | /// @{ |
---|
| 596 | |
---|
[806] | 597 | /// \brief Return the total cost of the found flow. |
---|
[805] | 598 | /// |
---|
[806] | 599 | /// This function returns the total cost of the found flow. |
---|
| 600 | /// Its complexity is O(e). |
---|
| 601 | /// |
---|
| 602 | /// \note The return type of the function can be specified as a |
---|
| 603 | /// template parameter. For example, |
---|
| 604 | /// \code |
---|
| 605 | /// cs.totalCost<double>(); |
---|
| 606 | /// \endcode |
---|
| 607 | /// It is useful if the total cost cannot be stored in the \c Cost |
---|
| 608 | /// type of the algorithm, which is the default return type of the |
---|
| 609 | /// function. |
---|
[805] | 610 | /// |
---|
| 611 | /// \pre \ref run() must be called before using this function. |
---|
[806] | 612 | template <typename Number> |
---|
| 613 | Number totalCost() const { |
---|
| 614 | Number c = 0; |
---|
| 615 | for (ArcIt a(_graph); a != INVALID; ++a) { |
---|
| 616 | int i = _arc_idb[a]; |
---|
| 617 | c += static_cast<Number>(_res_cap[i]) * |
---|
| 618 | (-static_cast<Number>(_cost[i])); |
---|
| 619 | } |
---|
| 620 | return c; |
---|
[805] | 621 | } |
---|
| 622 | |
---|
[806] | 623 | #ifndef DOXYGEN |
---|
| 624 | Cost totalCost() const { |
---|
| 625 | return totalCost<Cost>(); |
---|
[805] | 626 | } |
---|
[806] | 627 | #endif |
---|
[805] | 628 | |
---|
| 629 | /// \brief Return the flow on the given arc. |
---|
| 630 | /// |
---|
[806] | 631 | /// This function returns the flow on the given arc. |
---|
[805] | 632 | /// |
---|
| 633 | /// \pre \ref run() must be called before using this function. |
---|
[806] | 634 | Value flow(const Arc& a) const { |
---|
| 635 | return _res_cap[_arc_idb[a]]; |
---|
[805] | 636 | } |
---|
| 637 | |
---|
[806] | 638 | /// \brief Return the flow map (the primal solution). |
---|
[805] | 639 | /// |
---|
[806] | 640 | /// This function copies the flow value on each arc into the given |
---|
| 641 | /// map. The \c Value type of the algorithm must be convertible to |
---|
| 642 | /// the \c Value type of the map. |
---|
[805] | 643 | /// |
---|
| 644 | /// \pre \ref run() must be called before using this function. |
---|
[806] | 645 | template <typename FlowMap> |
---|
| 646 | void flowMap(FlowMap &map) const { |
---|
| 647 | for (ArcIt a(_graph); a != INVALID; ++a) { |
---|
| 648 | map.set(a, _res_cap[_arc_idb[a]]); |
---|
| 649 | } |
---|
[805] | 650 | } |
---|
| 651 | |
---|
[806] | 652 | /// \brief Return the potential (dual value) of the given node. |
---|
[805] | 653 | /// |
---|
[806] | 654 | /// This function returns the potential (dual value) of the |
---|
| 655 | /// given node. |
---|
[805] | 656 | /// |
---|
| 657 | /// \pre \ref run() must be called before using this function. |
---|
[806] | 658 | Cost potential(const Node& n) const { |
---|
| 659 | return _pi[_node_id[n]]; |
---|
| 660 | } |
---|
| 661 | |
---|
| 662 | /// \brief Return the potential map (the dual solution). |
---|
| 663 | /// |
---|
| 664 | /// This function copies the potential (dual value) of each node |
---|
| 665 | /// into the given map. |
---|
| 666 | /// The \c Cost type of the algorithm must be convertible to the |
---|
| 667 | /// \c Value type of the map. |
---|
| 668 | /// |
---|
| 669 | /// \pre \ref run() must be called before using this function. |
---|
| 670 | template <typename PotentialMap> |
---|
| 671 | void potentialMap(PotentialMap &map) const { |
---|
| 672 | for (NodeIt n(_graph); n != INVALID; ++n) { |
---|
| 673 | map.set(n, _pi[_node_id[n]]); |
---|
| 674 | } |
---|
[805] | 675 | } |
---|
| 676 | |
---|
| 677 | /// @} |
---|
| 678 | |
---|
| 679 | private: |
---|
| 680 | |
---|
[806] | 681 | // Initialize the algorithm |
---|
[810] | 682 | ProblemType init() { |
---|
[806] | 683 | if (_node_num == 0) return INFEASIBLE; |
---|
[805] | 684 | |
---|
[806] | 685 | // Check the sum of supply values |
---|
| 686 | _sum_supply = 0; |
---|
| 687 | for (int i = 0; i != _root; ++i) { |
---|
| 688 | _sum_supply += _supply[i]; |
---|
[805] | 689 | } |
---|
[806] | 690 | if (_sum_supply > 0) return INFEASIBLE; |
---|
| 691 | |
---|
[811] | 692 | // Initialize vectors |
---|
[806] | 693 | for (int i = 0; i != _root; ++i) { |
---|
| 694 | _pi[i] = 0; |
---|
| 695 | _excess[i] = _supply[i]; |
---|
[805] | 696 | } |
---|
| 697 | |
---|
[806] | 698 | // Remove non-zero lower bounds |
---|
[811] | 699 | const Value MAX = std::numeric_limits<Value>::max(); |
---|
| 700 | int last_out; |
---|
[806] | 701 | if (_have_lower) { |
---|
| 702 | for (int i = 0; i != _root; ++i) { |
---|
[811] | 703 | last_out = _first_out[i+1]; |
---|
| 704 | for (int j = _first_out[i]; j != last_out; ++j) { |
---|
[806] | 705 | if (_forward[j]) { |
---|
| 706 | Value c = _lower[j]; |
---|
| 707 | if (c >= 0) { |
---|
[811] | 708 | _res_cap[j] = _upper[j] < MAX ? _upper[j] - c : INF; |
---|
[806] | 709 | } else { |
---|
[811] | 710 | _res_cap[j] = _upper[j] < MAX + c ? _upper[j] - c : INF; |
---|
[806] | 711 | } |
---|
| 712 | _excess[i] -= c; |
---|
| 713 | _excess[_target[j]] += c; |
---|
| 714 | } else { |
---|
| 715 | _res_cap[j] = 0; |
---|
| 716 | } |
---|
| 717 | } |
---|
| 718 | } |
---|
| 719 | } else { |
---|
| 720 | for (int j = 0; j != _res_arc_num; ++j) { |
---|
| 721 | _res_cap[j] = _forward[j] ? _upper[j] : 0; |
---|
| 722 | } |
---|
| 723 | } |
---|
[805] | 724 | |
---|
[806] | 725 | // Handle negative costs |
---|
[811] | 726 | for (int i = 0; i != _root; ++i) { |
---|
| 727 | last_out = _first_out[i+1] - 1; |
---|
| 728 | for (int j = _first_out[i]; j != last_out; ++j) { |
---|
| 729 | Value rc = _res_cap[j]; |
---|
| 730 | if (_cost[j] < 0 && rc > 0) { |
---|
| 731 | if (rc >= MAX) return UNBOUNDED; |
---|
| 732 | _excess[i] -= rc; |
---|
| 733 | _excess[_target[j]] += rc; |
---|
| 734 | _res_cap[j] = 0; |
---|
| 735 | _res_cap[_reverse[j]] += rc; |
---|
[806] | 736 | } |
---|
| 737 | } |
---|
| 738 | } |
---|
| 739 | |
---|
| 740 | // Handle GEQ supply type |
---|
| 741 | if (_sum_supply < 0) { |
---|
| 742 | _pi[_root] = 0; |
---|
| 743 | _excess[_root] = -_sum_supply; |
---|
| 744 | for (int a = _first_out[_root]; a != _res_arc_num; ++a) { |
---|
[811] | 745 | int ra = _reverse[a]; |
---|
| 746 | _res_cap[a] = -_sum_supply + 1; |
---|
| 747 | _res_cap[ra] = 0; |
---|
[806] | 748 | _cost[a] = 0; |
---|
[811] | 749 | _cost[ra] = 0; |
---|
[806] | 750 | } |
---|
| 751 | } else { |
---|
| 752 | _pi[_root] = 0; |
---|
| 753 | _excess[_root] = 0; |
---|
| 754 | for (int a = _first_out[_root]; a != _res_arc_num; ++a) { |
---|
[811] | 755 | int ra = _reverse[a]; |
---|
[806] | 756 | _res_cap[a] = 1; |
---|
[811] | 757 | _res_cap[ra] = 0; |
---|
[806] | 758 | _cost[a] = 0; |
---|
[811] | 759 | _cost[ra] = 0; |
---|
[806] | 760 | } |
---|
| 761 | } |
---|
| 762 | |
---|
| 763 | // Initialize delta value |
---|
[810] | 764 | if (_factor > 1) { |
---|
[805] | 765 | // With scaling |
---|
[806] | 766 | Value max_sup = 0, max_dem = 0; |
---|
| 767 | for (int i = 0; i != _node_num; ++i) { |
---|
[811] | 768 | Value ex = _excess[i]; |
---|
| 769 | if ( ex > max_sup) max_sup = ex; |
---|
| 770 | if (-ex > max_dem) max_dem = -ex; |
---|
[805] | 771 | } |
---|
[806] | 772 | Value max_cap = 0; |
---|
| 773 | for (int j = 0; j != _res_arc_num; ++j) { |
---|
| 774 | if (_res_cap[j] > max_cap) max_cap = _res_cap[j]; |
---|
[805] | 775 | } |
---|
| 776 | max_sup = std::min(std::min(max_sup, max_dem), max_cap); |
---|
[810] | 777 | for (_delta = 1; 2 * _delta <= max_sup; _delta *= 2) ; |
---|
[805] | 778 | } else { |
---|
| 779 | // Without scaling |
---|
| 780 | _delta = 1; |
---|
| 781 | } |
---|
| 782 | |
---|
[806] | 783 | return OPTIMAL; |
---|
[805] | 784 | } |
---|
| 785 | |
---|
[806] | 786 | ProblemType start() { |
---|
| 787 | // Execute the algorithm |
---|
| 788 | ProblemType pt; |
---|
[805] | 789 | if (_delta > 1) |
---|
[806] | 790 | pt = startWithScaling(); |
---|
[805] | 791 | else |
---|
[806] | 792 | pt = startWithoutScaling(); |
---|
| 793 | |
---|
| 794 | // Handle non-zero lower bounds |
---|
| 795 | if (_have_lower) { |
---|
[811] | 796 | int limit = _first_out[_root]; |
---|
| 797 | for (int j = 0; j != limit; ++j) { |
---|
[806] | 798 | if (!_forward[j]) _res_cap[j] += _lower[j]; |
---|
| 799 | } |
---|
| 800 | } |
---|
| 801 | |
---|
| 802 | // Shift potentials if necessary |
---|
| 803 | Cost pr = _pi[_root]; |
---|
| 804 | if (_sum_supply < 0 || pr > 0) { |
---|
| 805 | for (int i = 0; i != _node_num; ++i) { |
---|
| 806 | _pi[i] -= pr; |
---|
| 807 | } |
---|
| 808 | } |
---|
| 809 | |
---|
| 810 | return pt; |
---|
[805] | 811 | } |
---|
| 812 | |
---|
[806] | 813 | // Execute the capacity scaling algorithm |
---|
| 814 | ProblemType startWithScaling() { |
---|
[807] | 815 | // Perform capacity scaling phases |
---|
[806] | 816 | int s, t; |
---|
| 817 | ResidualDijkstra _dijkstra(*this); |
---|
[805] | 818 | while (true) { |
---|
[806] | 819 | // Saturate all arcs not satisfying the optimality condition |
---|
[811] | 820 | int last_out; |
---|
[806] | 821 | for (int u = 0; u != _node_num; ++u) { |
---|
[811] | 822 | last_out = _sum_supply < 0 ? |
---|
| 823 | _first_out[u+1] : _first_out[u+1] - 1; |
---|
| 824 | for (int a = _first_out[u]; a != last_out; ++a) { |
---|
[806] | 825 | int v = _target[a]; |
---|
| 826 | Cost c = _cost[a] + _pi[u] - _pi[v]; |
---|
| 827 | Value rc = _res_cap[a]; |
---|
| 828 | if (c < 0 && rc >= _delta) { |
---|
| 829 | _excess[u] -= rc; |
---|
| 830 | _excess[v] += rc; |
---|
| 831 | _res_cap[a] = 0; |
---|
| 832 | _res_cap[_reverse[a]] += rc; |
---|
| 833 | } |
---|
[805] | 834 | } |
---|
| 835 | } |
---|
| 836 | |
---|
[806] | 837 | // Find excess nodes and deficit nodes |
---|
[805] | 838 | _excess_nodes.clear(); |
---|
| 839 | _deficit_nodes.clear(); |
---|
[806] | 840 | for (int u = 0; u != _node_num; ++u) { |
---|
[811] | 841 | Value ex = _excess[u]; |
---|
| 842 | if (ex >= _delta) _excess_nodes.push_back(u); |
---|
| 843 | if (ex <= -_delta) _deficit_nodes.push_back(u); |
---|
[805] | 844 | } |
---|
| 845 | int next_node = 0, next_def_node = 0; |
---|
| 846 | |
---|
[806] | 847 | // Find augmenting shortest paths |
---|
[805] | 848 | while (next_node < int(_excess_nodes.size())) { |
---|
[806] | 849 | // Check deficit nodes |
---|
[805] | 850 | if (_delta > 1) { |
---|
| 851 | bool delta_deficit = false; |
---|
| 852 | for ( ; next_def_node < int(_deficit_nodes.size()); |
---|
| 853 | ++next_def_node ) { |
---|
| 854 | if (_excess[_deficit_nodes[next_def_node]] <= -_delta) { |
---|
| 855 | delta_deficit = true; |
---|
| 856 | break; |
---|
| 857 | } |
---|
| 858 | } |
---|
| 859 | if (!delta_deficit) break; |
---|
| 860 | } |
---|
| 861 | |
---|
[806] | 862 | // Run Dijkstra in the residual network |
---|
[805] | 863 | s = _excess_nodes[next_node]; |
---|
[806] | 864 | if ((t = _dijkstra.run(s, _delta)) == -1) { |
---|
[805] | 865 | if (_delta > 1) { |
---|
| 866 | ++next_node; |
---|
| 867 | continue; |
---|
| 868 | } |
---|
[806] | 869 | return INFEASIBLE; |
---|
[805] | 870 | } |
---|
| 871 | |
---|
[806] | 872 | // Augment along a shortest path from s to t |
---|
| 873 | Value d = std::min(_excess[s], -_excess[t]); |
---|
| 874 | int u = t; |
---|
| 875 | int a; |
---|
[805] | 876 | if (d > _delta) { |
---|
[806] | 877 | while ((a = _pred[u]) != -1) { |
---|
| 878 | if (_res_cap[a] < d) d = _res_cap[a]; |
---|
| 879 | u = _source[a]; |
---|
[805] | 880 | } |
---|
| 881 | } |
---|
| 882 | u = t; |
---|
[806] | 883 | while ((a = _pred[u]) != -1) { |
---|
| 884 | _res_cap[a] -= d; |
---|
| 885 | _res_cap[_reverse[a]] += d; |
---|
| 886 | u = _source[a]; |
---|
[805] | 887 | } |
---|
| 888 | _excess[s] -= d; |
---|
| 889 | _excess[t] += d; |
---|
| 890 | |
---|
| 891 | if (_excess[s] < _delta) ++next_node; |
---|
| 892 | } |
---|
| 893 | |
---|
| 894 | if (_delta == 1) break; |
---|
[810] | 895 | _delta = _delta <= _factor ? 1 : _delta / _factor; |
---|
[805] | 896 | } |
---|
| 897 | |
---|
[806] | 898 | return OPTIMAL; |
---|
[805] | 899 | } |
---|
| 900 | |
---|
[806] | 901 | // Execute the successive shortest path algorithm |
---|
| 902 | ProblemType startWithoutScaling() { |
---|
| 903 | // Find excess nodes |
---|
| 904 | _excess_nodes.clear(); |
---|
| 905 | for (int i = 0; i != _node_num; ++i) { |
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| 906 | if (_excess[i] > 0) _excess_nodes.push_back(i); |
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| 907 | } |
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| 908 | if (_excess_nodes.size() == 0) return OPTIMAL; |
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[805] | 909 | int next_node = 0; |
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| 910 | |
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[806] | 911 | // Find shortest paths |
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| 912 | int s, t; |
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| 913 | ResidualDijkstra _dijkstra(*this); |
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[805] | 914 | while ( _excess[_excess_nodes[next_node]] > 0 || |
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| 915 | ++next_node < int(_excess_nodes.size()) ) |
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| 916 | { |
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[806] | 917 | // Run Dijkstra in the residual network |
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[805] | 918 | s = _excess_nodes[next_node]; |
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[806] | 919 | if ((t = _dijkstra.run(s)) == -1) return INFEASIBLE; |
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[805] | 920 | |
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[806] | 921 | // Augment along a shortest path from s to t |
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| 922 | Value d = std::min(_excess[s], -_excess[t]); |
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| 923 | int u = t; |
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| 924 | int a; |
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[805] | 925 | if (d > 1) { |
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[806] | 926 | while ((a = _pred[u]) != -1) { |
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| 927 | if (_res_cap[a] < d) d = _res_cap[a]; |
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| 928 | u = _source[a]; |
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[805] | 929 | } |
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| 930 | } |
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| 931 | u = t; |
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[806] | 932 | while ((a = _pred[u]) != -1) { |
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| 933 | _res_cap[a] -= d; |
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| 934 | _res_cap[_reverse[a]] += d; |
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| 935 | u = _source[a]; |
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[805] | 936 | } |
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| 937 | _excess[s] -= d; |
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| 938 | _excess[t] += d; |
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| 939 | } |
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| 940 | |
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[806] | 941 | return OPTIMAL; |
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[805] | 942 | } |
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| 943 | |
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| 944 | }; //class CapacityScaling |
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| 945 | |
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| 946 | ///@} |
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| 947 | |
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| 948 | } //namespace lemon |
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| 949 | |
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| 950 | #endif //LEMON_CAPACITY_SCALING_H |
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