[999] | 1 | /* -*- mode: C++; indent-tabs-mode: nil; -*- |
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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) 2003-2010 |
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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_GROSSO_LOCATELLI_PULLAN_MC_H |
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| 20 | #define LEMON_GROSSO_LOCATELLI_PULLAN_MC_H |
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| 21 | |
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| 22 | /// \ingroup approx_algs |
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| 23 | /// |
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| 24 | /// \file |
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| 25 | /// \brief The iterated local search algorithm of Grosso, Locatelli, and Pullan |
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| 26 | /// for the maximum clique problem |
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| 27 | |
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| 28 | #include <vector> |
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| 29 | #include <limits> |
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| 30 | #include <lemon/core.h> |
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| 31 | #include <lemon/random.h> |
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| 32 | |
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| 33 | namespace lemon { |
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| 34 | |
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| 35 | /// \addtogroup approx_algs |
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| 36 | /// @{ |
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| 37 | |
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| 38 | /// \brief Implementation of the iterated local search algorithm of Grosso, |
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| 39 | /// Locatelli, and Pullan for the maximum clique problem |
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| 40 | /// |
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| 41 | /// \ref GrossoLocatelliPullanMc implements the iterated local search |
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| 42 | /// algorithm of Grosso, Locatelli, and Pullan for solving the \e maximum |
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| 43 | /// \e clique \e problem \ref grosso08maxclique. |
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| 44 | /// It is to find the largest complete subgraph (\e clique) in an |
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| 45 | /// undirected graph, i.e., the largest set of nodes where each |
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| 46 | /// pair of nodes is connected. |
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| 47 | /// |
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| 48 | /// This class provides a simple but highly efficient and robust heuristic |
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| 49 | /// method that quickly finds a large clique, but not necessarily the |
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| 50 | /// largest one. |
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| 51 | /// |
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| 52 | /// \tparam GR The undirected graph type the algorithm runs on. |
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| 53 | /// |
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| 54 | /// \note %GrossoLocatelliPullanMc provides three different node selection |
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| 55 | /// rules, from which the most powerful one is used by default. |
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| 56 | /// For more information, see \ref SelectionRule. |
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| 57 | template <typename GR> |
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| 58 | class GrossoLocatelliPullanMc |
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| 59 | { |
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| 60 | public: |
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| 61 | |
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| 62 | /// \brief Constants for specifying the node selection rule. |
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| 63 | /// |
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| 64 | /// Enum type containing constants for specifying the node selection rule |
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| 65 | /// for the \ref run() function. |
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| 66 | /// |
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| 67 | /// During the algorithm, nodes are selected for addition to the current |
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| 68 | /// clique according to the applied rule. |
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| 69 | /// In general, the PENALTY_BASED rule turned out to be the most powerful |
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| 70 | /// and the most robust, thus it is the default option. |
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| 71 | /// However, another selection rule can be specified using the \ref run() |
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| 72 | /// function with the proper parameter. |
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| 73 | enum SelectionRule { |
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| 74 | |
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| 75 | /// A node is selected randomly without any evaluation at each step. |
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| 76 | RANDOM, |
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| 77 | |
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| 78 | /// A node of maximum degree is selected randomly at each step. |
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| 79 | DEGREE_BASED, |
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| 80 | |
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| 81 | /// A node of minimum penalty is selected randomly at each step. |
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| 82 | /// The node penalties are updated adaptively after each stage of the |
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| 83 | /// search process. |
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| 84 | PENALTY_BASED |
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| 85 | }; |
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| 86 | |
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| 87 | private: |
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| 88 | |
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| 89 | TEMPLATE_GRAPH_TYPEDEFS(GR); |
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| 90 | |
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| 91 | typedef std::vector<int> IntVector; |
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| 92 | typedef std::vector<char> BoolVector; |
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| 93 | typedef std::vector<BoolVector> BoolMatrix; |
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| 94 | // Note: vector<char> is used instead of vector<bool> for efficiency reasons |
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| 95 | |
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| 96 | const GR &_graph; |
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| 97 | IntNodeMap _id; |
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| 98 | |
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| 99 | // Internal matrix representation of the graph |
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| 100 | BoolMatrix _gr; |
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| 101 | int _n; |
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| 102 | |
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| 103 | // The current clique |
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| 104 | BoolVector _clique; |
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| 105 | int _size; |
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| 106 | |
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| 107 | // The best clique found so far |
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| 108 | BoolVector _best_clique; |
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| 109 | int _best_size; |
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| 110 | |
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| 111 | // The "distances" of the nodes from the current clique. |
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| 112 | // _delta[u] is the number of nodes in the clique that are |
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| 113 | // not connected with u. |
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| 114 | IntVector _delta; |
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| 115 | |
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| 116 | // The current tabu set |
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| 117 | BoolVector _tabu; |
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| 118 | |
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| 119 | // Random number generator |
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| 120 | Random _rnd; |
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| 121 | |
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| 122 | private: |
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| 123 | |
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| 124 | // Implementation of the RANDOM node selection rule. |
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| 125 | class RandomSelectionRule |
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| 126 | { |
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| 127 | private: |
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| 128 | |
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| 129 | // References to the algorithm instance |
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| 130 | const BoolVector &_clique; |
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| 131 | const IntVector &_delta; |
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| 132 | const BoolVector &_tabu; |
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| 133 | Random &_rnd; |
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| 134 | |
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| 135 | // Pivot rule data |
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| 136 | int _n; |
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| 137 | |
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| 138 | public: |
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| 139 | |
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| 140 | // Constructor |
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| 141 | RandomSelectionRule(GrossoLocatelliPullanMc &mc) : |
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| 142 | _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu), |
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| 143 | _rnd(mc._rnd), _n(mc._n) |
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| 144 | {} |
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| 145 | |
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| 146 | // Return a node index for a feasible add move or -1 if no one exists |
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| 147 | int nextFeasibleAddNode() const { |
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| 148 | int start_node = _rnd[_n]; |
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| 149 | for (int i = start_node; i != _n; i++) { |
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| 150 | if (_delta[i] == 0 && !_tabu[i]) return i; |
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| 151 | } |
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| 152 | for (int i = 0; i != start_node; i++) { |
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| 153 | if (_delta[i] == 0 && !_tabu[i]) return i; |
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| 154 | } |
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| 155 | return -1; |
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| 156 | } |
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| 157 | |
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| 158 | // Return a node index for a feasible swap move or -1 if no one exists |
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| 159 | int nextFeasibleSwapNode() const { |
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| 160 | int start_node = _rnd[_n]; |
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| 161 | for (int i = start_node; i != _n; i++) { |
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| 162 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i]) return i; |
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| 163 | } |
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| 164 | for (int i = 0; i != start_node; i++) { |
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| 165 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i]) return i; |
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| 166 | } |
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| 167 | return -1; |
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| 168 | } |
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| 169 | |
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| 170 | // Return a node index for an add move or -1 if no one exists |
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| 171 | int nextAddNode() const { |
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| 172 | int start_node = _rnd[_n]; |
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| 173 | for (int i = start_node; i != _n; i++) { |
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| 174 | if (_delta[i] == 0) return i; |
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| 175 | } |
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| 176 | for (int i = 0; i != start_node; i++) { |
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| 177 | if (_delta[i] == 0) return i; |
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| 178 | } |
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| 179 | return -1; |
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| 180 | } |
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| 181 | |
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| 182 | // Update internal data structures between stages (if necessary) |
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| 183 | void update() {} |
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| 184 | |
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| 185 | }; //class RandomSelectionRule |
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| 186 | |
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| 187 | |
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| 188 | // Implementation of the DEGREE_BASED node selection rule. |
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| 189 | class DegreeBasedSelectionRule |
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| 190 | { |
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| 191 | private: |
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| 192 | |
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| 193 | // References to the algorithm instance |
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| 194 | const BoolVector &_clique; |
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| 195 | const IntVector &_delta; |
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| 196 | const BoolVector &_tabu; |
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| 197 | Random &_rnd; |
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| 198 | |
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| 199 | // Pivot rule data |
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| 200 | int _n; |
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| 201 | IntVector _deg; |
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| 202 | |
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| 203 | public: |
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| 204 | |
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| 205 | // Constructor |
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| 206 | DegreeBasedSelectionRule(GrossoLocatelliPullanMc &mc) : |
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| 207 | _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu), |
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| 208 | _rnd(mc._rnd), _n(mc._n), _deg(_n) |
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| 209 | { |
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| 210 | for (int i = 0; i != _n; i++) { |
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| 211 | int d = 0; |
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| 212 | BoolVector &row = mc._gr[i]; |
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| 213 | for (int j = 0; j != _n; j++) { |
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| 214 | if (row[j]) d++; |
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| 215 | } |
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| 216 | _deg[i] = d; |
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| 217 | } |
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| 218 | } |
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| 219 | |
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| 220 | // Return a node index for a feasible add move or -1 if no one exists |
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| 221 | int nextFeasibleAddNode() const { |
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| 222 | int start_node = _rnd[_n]; |
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| 223 | int node = -1, max_deg = -1; |
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| 224 | for (int i = start_node; i != _n; i++) { |
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| 225 | if (_delta[i] == 0 && !_tabu[i] && _deg[i] > max_deg) { |
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| 226 | node = i; |
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| 227 | max_deg = _deg[i]; |
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| 228 | } |
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| 229 | } |
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| 230 | for (int i = 0; i != start_node; i++) { |
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| 231 | if (_delta[i] == 0 && !_tabu[i] && _deg[i] > max_deg) { |
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| 232 | node = i; |
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| 233 | max_deg = _deg[i]; |
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| 234 | } |
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| 235 | } |
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| 236 | return node; |
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| 237 | } |
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| 238 | |
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| 239 | // Return a node index for a feasible swap move or -1 if no one exists |
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| 240 | int nextFeasibleSwapNode() const { |
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| 241 | int start_node = _rnd[_n]; |
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| 242 | int node = -1, max_deg = -1; |
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| 243 | for (int i = start_node; i != _n; i++) { |
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| 244 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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| 245 | _deg[i] > max_deg) { |
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| 246 | node = i; |
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| 247 | max_deg = _deg[i]; |
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| 248 | } |
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| 249 | } |
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| 250 | for (int i = 0; i != start_node; i++) { |
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| 251 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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| 252 | _deg[i] > max_deg) { |
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| 253 | node = i; |
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| 254 | max_deg = _deg[i]; |
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| 255 | } |
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| 256 | } |
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| 257 | return node; |
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| 258 | } |
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| 259 | |
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| 260 | // Return a node index for an add move or -1 if no one exists |
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| 261 | int nextAddNode() const { |
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| 262 | int start_node = _rnd[_n]; |
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| 263 | int node = -1, max_deg = -1; |
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| 264 | for (int i = start_node; i != _n; i++) { |
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| 265 | if (_delta[i] == 0 && _deg[i] > max_deg) { |
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| 266 | node = i; |
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| 267 | max_deg = _deg[i]; |
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| 268 | } |
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| 269 | } |
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| 270 | for (int i = 0; i != start_node; i++) { |
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| 271 | if (_delta[i] == 0 && _deg[i] > max_deg) { |
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| 272 | node = i; |
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| 273 | max_deg = _deg[i]; |
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| 274 | } |
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| 275 | } |
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| 276 | return node; |
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| 277 | } |
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| 278 | |
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| 279 | // Update internal data structures between stages (if necessary) |
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| 280 | void update() {} |
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| 281 | |
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| 282 | }; //class DegreeBasedSelectionRule |
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| 283 | |
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| 284 | |
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| 285 | // Implementation of the PENALTY_BASED node selection rule. |
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| 286 | class PenaltyBasedSelectionRule |
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| 287 | { |
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| 288 | private: |
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| 289 | |
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| 290 | // References to the algorithm instance |
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| 291 | const BoolVector &_clique; |
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| 292 | const IntVector &_delta; |
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| 293 | const BoolVector &_tabu; |
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| 294 | Random &_rnd; |
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| 295 | |
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| 296 | // Pivot rule data |
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| 297 | int _n; |
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| 298 | IntVector _penalty; |
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| 299 | |
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| 300 | public: |
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| 301 | |
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| 302 | // Constructor |
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| 303 | PenaltyBasedSelectionRule(GrossoLocatelliPullanMc &mc) : |
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| 304 | _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu), |
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| 305 | _rnd(mc._rnd), _n(mc._n), _penalty(_n, 0) |
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| 306 | {} |
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| 307 | |
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| 308 | // Return a node index for a feasible add move or -1 if no one exists |
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| 309 | int nextFeasibleAddNode() const { |
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| 310 | int start_node = _rnd[_n]; |
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| 311 | int node = -1, min_p = std::numeric_limits<int>::max(); |
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| 312 | for (int i = start_node; i != _n; i++) { |
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| 313 | if (_delta[i] == 0 && !_tabu[i] && _penalty[i] < min_p) { |
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| 314 | node = i; |
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| 315 | min_p = _penalty[i]; |
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| 316 | } |
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| 317 | } |
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| 318 | for (int i = 0; i != start_node; i++) { |
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| 319 | if (_delta[i] == 0 && !_tabu[i] && _penalty[i] < min_p) { |
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| 320 | node = i; |
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| 321 | min_p = _penalty[i]; |
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| 322 | } |
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| 323 | } |
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| 324 | return node; |
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| 325 | } |
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| 326 | |
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| 327 | // Return a node index for a feasible swap move or -1 if no one exists |
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| 328 | int nextFeasibleSwapNode() const { |
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| 329 | int start_node = _rnd[_n]; |
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| 330 | int node = -1, min_p = std::numeric_limits<int>::max(); |
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| 331 | for (int i = start_node; i != _n; i++) { |
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| 332 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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| 333 | _penalty[i] < min_p) { |
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| 334 | node = i; |
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| 335 | min_p = _penalty[i]; |
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| 336 | } |
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| 337 | } |
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| 338 | for (int i = 0; i != start_node; i++) { |
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| 339 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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| 340 | _penalty[i] < min_p) { |
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| 341 | node = i; |
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| 342 | min_p = _penalty[i]; |
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| 343 | } |
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| 344 | } |
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| 345 | return node; |
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| 346 | } |
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| 347 | |
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| 348 | // Return a node index for an add move or -1 if no one exists |
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| 349 | int nextAddNode() const { |
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| 350 | int start_node = _rnd[_n]; |
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| 351 | int node = -1, min_p = std::numeric_limits<int>::max(); |
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| 352 | for (int i = start_node; i != _n; i++) { |
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| 353 | if (_delta[i] == 0 && _penalty[i] < min_p) { |
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| 354 | node = i; |
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| 355 | min_p = _penalty[i]; |
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| 356 | } |
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| 357 | } |
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| 358 | for (int i = 0; i != start_node; i++) { |
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| 359 | if (_delta[i] == 0 && _penalty[i] < min_p) { |
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| 360 | node = i; |
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| 361 | min_p = _penalty[i]; |
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| 362 | } |
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| 363 | } |
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| 364 | return node; |
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| 365 | } |
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| 366 | |
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| 367 | // Update internal data structures between stages (if necessary) |
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| 368 | void update() {} |
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| 369 | |
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| 370 | }; //class PenaltyBasedSelectionRule |
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| 371 | |
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| 372 | public: |
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| 373 | |
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| 374 | /// \brief Constructor. |
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| 375 | /// |
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| 376 | /// Constructor. |
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| 377 | /// The global \ref rnd "random number generator instance" is used |
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| 378 | /// during the algorithm. |
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| 379 | /// |
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| 380 | /// \param graph The undirected graph the algorithm runs on. |
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| 381 | GrossoLocatelliPullanMc(const GR& graph) : |
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| 382 | _graph(graph), _id(_graph), _rnd(rnd) |
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| 383 | {} |
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| 384 | |
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| 385 | /// \brief Constructor with random seed. |
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| 386 | /// |
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| 387 | /// Constructor with random seed. |
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| 388 | /// |
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| 389 | /// \param graph The undirected graph the algorithm runs on. |
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| 390 | /// \param seed Seed value for the internal random number generator |
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| 391 | /// that is used during the algorithm. |
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| 392 | GrossoLocatelliPullanMc(const GR& graph, int seed) : |
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| 393 | _graph(graph), _id(_graph), _rnd(seed) |
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| 394 | {} |
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| 395 | |
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| 396 | /// \brief Constructor with random number generator. |
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| 397 | /// |
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| 398 | /// Constructor with random number generator. |
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| 399 | /// |
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| 400 | /// \param graph The undirected graph the algorithm runs on. |
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| 401 | /// \param random A random number generator that is used during the |
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| 402 | /// algorithm. |
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| 403 | GrossoLocatelliPullanMc(const GR& graph, const Random& random) : |
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| 404 | _graph(graph), _id(_graph), _rnd(random) |
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| 405 | {} |
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| 406 | |
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| 407 | /// \name Execution Control |
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| 408 | /// @{ |
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| 409 | |
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| 410 | /// \brief Runs the algorithm. |
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| 411 | /// |
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| 412 | /// This function runs the algorithm. |
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| 413 | /// |
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| 414 | /// \param step_num The maximum number of node selections (steps) |
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| 415 | /// during the search process. |
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| 416 | /// This parameter controls the running time and the success of the |
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| 417 | /// algorithm. For larger values, the algorithm runs slower but it more |
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| 418 | /// likely finds larger cliques. For smaller values, the algorithm is |
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| 419 | /// faster but probably gives worse results. |
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| 420 | /// \param rule The node selection rule. For more information, see |
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| 421 | /// \ref SelectionRule. |
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| 422 | /// |
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| 423 | /// \return The size of the found clique. |
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| 424 | int run(int step_num = 100000, |
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| 425 | SelectionRule rule = PENALTY_BASED) |
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| 426 | { |
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| 427 | init(); |
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| 428 | switch (rule) { |
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| 429 | case RANDOM: |
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| 430 | return start<RandomSelectionRule>(step_num); |
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| 431 | case DEGREE_BASED: |
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| 432 | return start<DegreeBasedSelectionRule>(step_num); |
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| 433 | case PENALTY_BASED: |
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| 434 | return start<PenaltyBasedSelectionRule>(step_num); |
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| 435 | } |
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| 436 | return 0; // avoid warning |
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| 437 | } |
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| 438 | |
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| 439 | /// @} |
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| 440 | |
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| 441 | /// \name Query Functions |
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| 442 | /// @{ |
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| 443 | |
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| 444 | /// \brief The size of the found clique |
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| 445 | /// |
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| 446 | /// This function returns the size of the found clique. |
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| 447 | /// |
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| 448 | /// \pre run() must be called before using this function. |
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| 449 | int cliqueSize() const { |
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| 450 | return _best_size; |
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| 451 | } |
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| 452 | |
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| 453 | /// \brief Gives back the found clique in a \c bool node map |
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| 454 | /// |
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| 455 | /// This function gives back the characteristic vector of the found |
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| 456 | /// clique in the given node map. |
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| 457 | /// It must be a \ref concepts::WriteMap "writable" node map with |
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| 458 | /// \c bool (or convertible) value type. |
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| 459 | /// |
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| 460 | /// \pre run() must be called before using this function. |
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| 461 | template <typename CliqueMap> |
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| 462 | void cliqueMap(CliqueMap &map) const { |
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| 463 | for (NodeIt n(_graph); n != INVALID; ++n) { |
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| 464 | map[n] = static_cast<bool>(_best_clique[_id[n]]); |
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| 465 | } |
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| 466 | } |
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| 467 | |
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| 468 | /// \brief Iterator to list the nodes of the found clique |
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| 469 | /// |
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| 470 | /// This iterator class lists the nodes of the found clique. |
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| 471 | /// Before using it, you must allocate a GrossoLocatelliPullanMc instance |
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| 472 | /// and call its \ref GrossoLocatelliPullanMc::run() "run()" method. |
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| 473 | /// |
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| 474 | /// The following example prints out the IDs of the nodes in the found |
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| 475 | /// clique. |
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| 476 | /// \code |
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| 477 | /// GrossoLocatelliPullanMc<Graph> mc(g); |
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| 478 | /// mc.run(); |
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| 479 | /// for (GrossoLocatelliPullanMc<Graph>::CliqueNodeIt n(mc); |
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| 480 | /// n != INVALID; ++n) |
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| 481 | /// { |
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| 482 | /// std::cout << g.id(n) << std::endl; |
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| 483 | /// } |
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| 484 | /// \endcode |
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| 485 | class CliqueNodeIt |
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| 486 | { |
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| 487 | private: |
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| 488 | NodeIt _it; |
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| 489 | BoolNodeMap _map; |
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| 490 | |
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| 491 | public: |
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| 492 | |
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| 493 | /// Constructor |
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| 494 | |
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| 495 | /// Constructor. |
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| 496 | /// \param mc The algorithm instance. |
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| 497 | CliqueNodeIt(const GrossoLocatelliPullanMc &mc) |
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| 498 | : _map(mc._graph) |
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| 499 | { |
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| 500 | mc.cliqueMap(_map); |
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| 501 | for (_it = NodeIt(mc._graph); _it != INVALID && !_map[_it]; ++_it) ; |
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| 502 | } |
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| 503 | |
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| 504 | /// Conversion to \c Node |
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| 505 | operator Node() const { return _it; } |
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| 506 | |
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| 507 | bool operator==(Invalid) const { return _it == INVALID; } |
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| 508 | bool operator!=(Invalid) const { return _it != INVALID; } |
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| 509 | |
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| 510 | /// Next node |
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| 511 | CliqueNodeIt &operator++() { |
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| 512 | for (++_it; _it != INVALID && !_map[_it]; ++_it) ; |
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| 513 | return *this; |
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| 514 | } |
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| 515 | |
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| 516 | /// Postfix incrementation |
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| 517 | |
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| 518 | /// Postfix incrementation. |
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| 519 | /// |
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| 520 | /// \warning This incrementation returns a \c Node, not a |
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| 521 | /// \c CliqueNodeIt as one may expect. |
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| 522 | typename GR::Node operator++(int) { |
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| 523 | Node n=*this; |
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| 524 | ++(*this); |
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| 525 | return n; |
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| 526 | } |
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| 527 | |
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| 528 | }; |
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| 529 | |
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| 530 | /// @} |
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| 531 | |
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| 532 | private: |
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| 533 | |
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| 534 | // Adds a node to the current clique |
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| 535 | void addCliqueNode(int u) { |
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| 536 | if (_clique[u]) return; |
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| 537 | _clique[u] = true; |
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| 538 | _size++; |
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| 539 | BoolVector &row = _gr[u]; |
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| 540 | for (int i = 0; i != _n; i++) { |
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| 541 | if (!row[i]) _delta[i]++; |
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| 542 | } |
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| 543 | } |
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| 544 | |
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| 545 | // Removes a node from the current clique |
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| 546 | void delCliqueNode(int u) { |
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| 547 | if (!_clique[u]) return; |
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| 548 | _clique[u] = false; |
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| 549 | _size--; |
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| 550 | BoolVector &row = _gr[u]; |
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| 551 | for (int i = 0; i != _n; i++) { |
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| 552 | if (!row[i]) _delta[i]--; |
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| 553 | } |
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| 554 | } |
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| 555 | |
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| 556 | // Initialize data structures |
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| 557 | void init() { |
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| 558 | _n = countNodes(_graph); |
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| 559 | int ui = 0; |
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| 560 | for (NodeIt u(_graph); u != INVALID; ++u) { |
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| 561 | _id[u] = ui++; |
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| 562 | } |
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| 563 | _gr.clear(); |
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| 564 | _gr.resize(_n, BoolVector(_n, false)); |
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| 565 | ui = 0; |
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| 566 | for (NodeIt u(_graph); u != INVALID; ++u) { |
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| 567 | for (IncEdgeIt e(_graph, u); e != INVALID; ++e) { |
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| 568 | int vi = _id[_graph.runningNode(e)]; |
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| 569 | _gr[ui][vi] = true; |
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| 570 | _gr[vi][ui] = true; |
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| 571 | } |
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| 572 | ++ui; |
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| 573 | } |
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| 574 | |
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| 575 | _clique.clear(); |
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| 576 | _clique.resize(_n, false); |
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| 577 | _size = 0; |
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| 578 | _best_clique.clear(); |
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| 579 | _best_clique.resize(_n, false); |
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| 580 | _best_size = 0; |
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| 581 | _delta.clear(); |
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| 582 | _delta.resize(_n, 0); |
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| 583 | _tabu.clear(); |
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| 584 | _tabu.resize(_n, false); |
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| 585 | } |
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| 586 | |
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| 587 | // Executes the algorithm |
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| 588 | template <typename SelectionRuleImpl> |
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| 589 | int start(int max_select) { |
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| 590 | // Options for the restart rule |
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| 591 | const bool delta_based_restart = true; |
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| 592 | const int restart_delta_limit = 4; |
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| 593 | |
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| 594 | if (_n == 0) return 0; |
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| 595 | if (_n == 1) { |
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| 596 | _best_clique[0] = true; |
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| 597 | _best_size = 1; |
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| 598 | return _best_size; |
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| 599 | } |
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| 600 | |
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| 601 | // Iterated local search |
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| 602 | SelectionRuleImpl sel_method(*this); |
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| 603 | int select = 0; |
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| 604 | IntVector restart_nodes; |
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| 605 | |
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| 606 | while (select < max_select) { |
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| 607 | |
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| 608 | // Perturbation/restart |
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| 609 | if (delta_based_restart) { |
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| 610 | restart_nodes.clear(); |
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| 611 | for (int i = 0; i != _n; i++) { |
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| 612 | if (_delta[i] >= restart_delta_limit) |
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| 613 | restart_nodes.push_back(i); |
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| 614 | } |
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| 615 | } |
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| 616 | int rs_node = -1; |
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| 617 | if (restart_nodes.size() > 0) { |
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| 618 | rs_node = restart_nodes[_rnd[restart_nodes.size()]]; |
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| 619 | } else { |
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| 620 | rs_node = _rnd[_n]; |
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| 621 | } |
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| 622 | BoolVector &row = _gr[rs_node]; |
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| 623 | for (int i = 0; i != _n; i++) { |
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| 624 | if (_clique[i] && !row[i]) delCliqueNode(i); |
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| 625 | } |
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| 626 | addCliqueNode(rs_node); |
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| 627 | |
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| 628 | // Local search |
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| 629 | _tabu.clear(); |
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| 630 | _tabu.resize(_n, false); |
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| 631 | bool tabu_empty = true; |
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| 632 | int max_swap = _size; |
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| 633 | while (select < max_select) { |
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| 634 | select++; |
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| 635 | int u; |
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| 636 | if ((u = sel_method.nextFeasibleAddNode()) != -1) { |
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| 637 | // Feasible add move |
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| 638 | addCliqueNode(u); |
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| 639 | if (tabu_empty) max_swap = _size; |
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| 640 | } |
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| 641 | else if ((u = sel_method.nextFeasibleSwapNode()) != -1) { |
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| 642 | // Feasible swap move |
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| 643 | int v = -1; |
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| 644 | BoolVector &row = _gr[u]; |
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| 645 | for (int i = 0; i != _n; i++) { |
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| 646 | if (_clique[i] && !row[i]) { |
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| 647 | v = i; |
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| 648 | break; |
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| 649 | } |
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| 650 | } |
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| 651 | addCliqueNode(u); |
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| 652 | delCliqueNode(v); |
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| 653 | _tabu[v] = true; |
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| 654 | tabu_empty = false; |
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| 655 | if (--max_swap <= 0) break; |
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| 656 | } |
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| 657 | else if ((u = sel_method.nextAddNode()) != -1) { |
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| 658 | // Non-feasible add move |
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| 659 | addCliqueNode(u); |
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| 660 | } |
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| 661 | else break; |
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| 662 | } |
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| 663 | if (_size > _best_size) { |
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| 664 | _best_clique = _clique; |
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| 665 | _best_size = _size; |
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| 666 | if (_best_size == _n) return _best_size; |
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| 667 | } |
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| 668 | sel_method.update(); |
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| 669 | } |
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| 670 | |
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| 671 | return _best_size; |
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| 672 | } |
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| 673 | |
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| 674 | }; //class GrossoLocatelliPullanMc |
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| 675 | |
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| 676 | ///@} |
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| 677 | |
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| 678 | } //namespace lemon |
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| 679 | |
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| 680 | #endif //LEMON_GROSSO_LOCATELLI_PULLAN_MC_H |
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