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 quite large clique, but not necessarily the |
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50 | /// largest one. |
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51 | /// The algorithm performs a certain number of iterations to find several |
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52 | /// cliques and selects the largest one among them. Various limits can be |
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53 | /// specified to control the running time and the effectiveness of the |
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54 | /// search process. |
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55 | /// |
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56 | /// \tparam GR The undirected graph type the algorithm runs on. |
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57 | /// |
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58 | /// \note %GrossoLocatelliPullanMc provides three different node selection |
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59 | /// rules, from which the most powerful one is used by default. |
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60 | /// For more information, see \ref SelectionRule. |
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61 | template <typename GR> |
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62 | class GrossoLocatelliPullanMc |
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63 | { |
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64 | public: |
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65 | |
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66 | /// \brief Constants for specifying the node selection rule. |
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67 | /// |
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68 | /// Enum type containing constants for specifying the node selection rule |
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69 | /// for the \ref run() function. |
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70 | /// |
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71 | /// During the algorithm, nodes are selected for addition to the current |
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72 | /// clique according to the applied rule. |
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73 | /// In general, the PENALTY_BASED rule turned out to be the most powerful |
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74 | /// and the most robust, thus it is the default option. |
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75 | /// However, another selection rule can be specified using the \ref run() |
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76 | /// function with the proper parameter. |
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77 | enum SelectionRule { |
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78 | |
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79 | /// A node is selected randomly without any evaluation at each step. |
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80 | RANDOM, |
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81 | |
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82 | /// A node of maximum degree is selected randomly at each step. |
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83 | DEGREE_BASED, |
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84 | |
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85 | /// A node of minimum penalty is selected randomly at each step. |
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86 | /// The node penalties are updated adaptively after each stage of the |
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87 | /// search process. |
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88 | PENALTY_BASED |
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89 | }; |
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90 | |
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91 | /// \brief Constants for the causes of search termination. |
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92 | /// |
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93 | /// Enum type containing constants for the different causes of search |
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94 | /// termination. The \ref run() function returns one of these values. |
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95 | enum TerminationCause { |
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96 | |
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97 | /// The iteration count limit is reached. |
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98 | ITERATION_LIMIT, |
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99 | |
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100 | /// The step count limit is reached. |
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101 | STEP_LIMIT, |
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102 | |
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103 | /// The clique size limit is reached. |
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104 | SIZE_LIMIT |
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105 | }; |
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106 | |
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107 | private: |
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108 | |
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109 | TEMPLATE_GRAPH_TYPEDEFS(GR); |
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110 | |
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111 | typedef std::vector<int> IntVector; |
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112 | typedef std::vector<char> BoolVector; |
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113 | typedef std::vector<BoolVector> BoolMatrix; |
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114 | // Note: vector<char> is used instead of vector<bool> for efficiency reasons |
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115 | |
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116 | // The underlying graph |
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117 | const GR &_graph; |
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118 | IntNodeMap _id; |
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119 | |
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120 | // Internal matrix representation of the graph |
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121 | BoolMatrix _gr; |
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122 | int _n; |
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123 | |
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124 | // Search options |
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125 | bool _delta_based_restart; |
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126 | int _restart_delta_limit; |
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127 | |
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128 | // Search limits |
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129 | int _iteration_limit; |
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130 | int _step_limit; |
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131 | int _size_limit; |
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132 | |
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133 | // The current clique |
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134 | BoolVector _clique; |
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135 | int _size; |
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136 | |
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137 | // The best clique found so far |
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138 | BoolVector _best_clique; |
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139 | int _best_size; |
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140 | |
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141 | // The "distances" of the nodes from the current clique. |
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142 | // _delta[u] is the number of nodes in the clique that are |
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143 | // not connected with u. |
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144 | IntVector _delta; |
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145 | |
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146 | // The current tabu set |
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147 | BoolVector _tabu; |
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148 | |
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149 | // Random number generator |
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150 | Random _rnd; |
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151 | |
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152 | private: |
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153 | |
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154 | // Implementation of the RANDOM node selection rule. |
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155 | class RandomSelectionRule |
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156 | { |
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157 | private: |
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158 | |
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159 | // References to the algorithm instance |
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160 | const BoolVector &_clique; |
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161 | const IntVector &_delta; |
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162 | const BoolVector &_tabu; |
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163 | Random &_rnd; |
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164 | |
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165 | // Pivot rule data |
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166 | int _n; |
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167 | |
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168 | public: |
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169 | |
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170 | // Constructor |
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171 | RandomSelectionRule(GrossoLocatelliPullanMc &mc) : |
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172 | _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu), |
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173 | _rnd(mc._rnd), _n(mc._n) |
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174 | {} |
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175 | |
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176 | // Return a node index for a feasible add move or -1 if no one exists |
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177 | int nextFeasibleAddNode() const { |
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178 | int start_node = _rnd[_n]; |
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179 | for (int i = start_node; i != _n; i++) { |
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180 | if (_delta[i] == 0 && !_tabu[i]) return i; |
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181 | } |
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182 | for (int i = 0; i != start_node; i++) { |
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183 | if (_delta[i] == 0 && !_tabu[i]) return i; |
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184 | } |
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185 | return -1; |
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186 | } |
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187 | |
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188 | // Return a node index for a feasible swap move or -1 if no one exists |
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189 | int nextFeasibleSwapNode() const { |
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190 | int start_node = _rnd[_n]; |
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191 | for (int i = start_node; i != _n; i++) { |
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192 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i]) return i; |
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193 | } |
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194 | for (int i = 0; i != start_node; i++) { |
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195 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i]) return i; |
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196 | } |
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197 | return -1; |
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198 | } |
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199 | |
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200 | // Return a node index for an add move or -1 if no one exists |
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201 | int nextAddNode() const { |
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202 | int start_node = _rnd[_n]; |
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203 | for (int i = start_node; i != _n; i++) { |
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204 | if (_delta[i] == 0) return i; |
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205 | } |
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206 | for (int i = 0; i != start_node; i++) { |
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207 | if (_delta[i] == 0) return i; |
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208 | } |
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209 | return -1; |
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210 | } |
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211 | |
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212 | // Update internal data structures between stages (if necessary) |
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213 | void update() {} |
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214 | |
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215 | }; //class RandomSelectionRule |
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216 | |
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217 | |
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218 | // Implementation of the DEGREE_BASED node selection rule. |
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219 | class DegreeBasedSelectionRule |
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220 | { |
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221 | private: |
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222 | |
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223 | // References to the algorithm instance |
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224 | const BoolVector &_clique; |
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225 | const IntVector &_delta; |
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226 | const BoolVector &_tabu; |
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227 | Random &_rnd; |
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228 | |
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229 | // Pivot rule data |
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230 | int _n; |
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231 | IntVector _deg; |
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232 | |
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233 | public: |
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234 | |
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235 | // Constructor |
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236 | DegreeBasedSelectionRule(GrossoLocatelliPullanMc &mc) : |
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237 | _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu), |
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238 | _rnd(mc._rnd), _n(mc._n), _deg(_n) |
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239 | { |
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240 | for (int i = 0; i != _n; i++) { |
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241 | int d = 0; |
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242 | BoolVector &row = mc._gr[i]; |
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243 | for (int j = 0; j != _n; j++) { |
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244 | if (row[j]) d++; |
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245 | } |
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246 | _deg[i] = d; |
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247 | } |
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248 | } |
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249 | |
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250 | // Return a node index for a feasible add move or -1 if no one exists |
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251 | int nextFeasibleAddNode() const { |
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252 | int start_node = _rnd[_n]; |
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253 | int node = -1, max_deg = -1; |
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254 | for (int i = start_node; i != _n; i++) { |
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255 | if (_delta[i] == 0 && !_tabu[i] && _deg[i] > max_deg) { |
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256 | node = i; |
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257 | max_deg = _deg[i]; |
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258 | } |
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259 | } |
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260 | for (int i = 0; i != start_node; i++) { |
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261 | if (_delta[i] == 0 && !_tabu[i] && _deg[i] > max_deg) { |
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262 | node = i; |
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263 | max_deg = _deg[i]; |
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264 | } |
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265 | } |
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266 | return node; |
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267 | } |
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268 | |
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269 | // Return a node index for a feasible swap move or -1 if no one exists |
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270 | int nextFeasibleSwapNode() const { |
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271 | int start_node = _rnd[_n]; |
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272 | int node = -1, max_deg = -1; |
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273 | for (int i = start_node; i != _n; i++) { |
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274 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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275 | _deg[i] > max_deg) { |
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276 | node = i; |
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277 | max_deg = _deg[i]; |
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278 | } |
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279 | } |
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280 | for (int i = 0; i != start_node; i++) { |
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281 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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282 | _deg[i] > max_deg) { |
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283 | node = i; |
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284 | max_deg = _deg[i]; |
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285 | } |
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286 | } |
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287 | return node; |
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288 | } |
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289 | |
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290 | // Return a node index for an add move or -1 if no one exists |
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291 | int nextAddNode() const { |
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292 | int start_node = _rnd[_n]; |
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293 | int node = -1, max_deg = -1; |
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294 | for (int i = start_node; i != _n; i++) { |
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295 | if (_delta[i] == 0 && _deg[i] > max_deg) { |
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296 | node = i; |
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297 | max_deg = _deg[i]; |
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298 | } |
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299 | } |
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300 | for (int i = 0; i != start_node; i++) { |
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301 | if (_delta[i] == 0 && _deg[i] > max_deg) { |
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302 | node = i; |
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303 | max_deg = _deg[i]; |
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304 | } |
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305 | } |
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306 | return node; |
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307 | } |
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308 | |
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309 | // Update internal data structures between stages (if necessary) |
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310 | void update() {} |
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311 | |
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312 | }; //class DegreeBasedSelectionRule |
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313 | |
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314 | |
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315 | // Implementation of the PENALTY_BASED node selection rule. |
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316 | class PenaltyBasedSelectionRule |
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317 | { |
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318 | private: |
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319 | |
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320 | // References to the algorithm instance |
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321 | const BoolVector &_clique; |
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322 | const IntVector &_delta; |
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323 | const BoolVector &_tabu; |
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324 | Random &_rnd; |
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325 | |
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326 | // Pivot rule data |
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327 | int _n; |
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328 | IntVector _penalty; |
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329 | |
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330 | public: |
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331 | |
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332 | // Constructor |
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333 | PenaltyBasedSelectionRule(GrossoLocatelliPullanMc &mc) : |
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334 | _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu), |
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335 | _rnd(mc._rnd), _n(mc._n), _penalty(_n, 0) |
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336 | {} |
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337 | |
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338 | // Return a node index for a feasible add move or -1 if no one exists |
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339 | int nextFeasibleAddNode() const { |
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340 | int start_node = _rnd[_n]; |
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341 | int node = -1, min_p = std::numeric_limits<int>::max(); |
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342 | for (int i = start_node; i != _n; i++) { |
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343 | if (_delta[i] == 0 && !_tabu[i] && _penalty[i] < min_p) { |
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344 | node = i; |
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345 | min_p = _penalty[i]; |
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346 | } |
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347 | } |
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348 | for (int i = 0; i != start_node; i++) { |
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349 | if (_delta[i] == 0 && !_tabu[i] && _penalty[i] < min_p) { |
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350 | node = i; |
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351 | min_p = _penalty[i]; |
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352 | } |
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353 | } |
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354 | return node; |
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355 | } |
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356 | |
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357 | // Return a node index for a feasible swap move or -1 if no one exists |
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358 | int nextFeasibleSwapNode() const { |
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359 | int start_node = _rnd[_n]; |
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360 | int node = -1, min_p = std::numeric_limits<int>::max(); |
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361 | for (int i = start_node; i != _n; i++) { |
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362 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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363 | _penalty[i] < min_p) { |
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364 | node = i; |
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365 | min_p = _penalty[i]; |
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366 | } |
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367 | } |
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368 | for (int i = 0; i != start_node; i++) { |
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369 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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370 | _penalty[i] < min_p) { |
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371 | node = i; |
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372 | min_p = _penalty[i]; |
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373 | } |
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374 | } |
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375 | return node; |
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376 | } |
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377 | |
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378 | // Return a node index for an add move or -1 if no one exists |
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379 | int nextAddNode() const { |
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380 | int start_node = _rnd[_n]; |
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381 | int node = -1, min_p = std::numeric_limits<int>::max(); |
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382 | for (int i = start_node; i != _n; i++) { |
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383 | if (_delta[i] == 0 && _penalty[i] < min_p) { |
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384 | node = i; |
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385 | min_p = _penalty[i]; |
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386 | } |
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387 | } |
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388 | for (int i = 0; i != start_node; i++) { |
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389 | if (_delta[i] == 0 && _penalty[i] < min_p) { |
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390 | node = i; |
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391 | min_p = _penalty[i]; |
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392 | } |
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393 | } |
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394 | return node; |
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395 | } |
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396 | |
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397 | // Update internal data structures between stages (if necessary) |
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398 | void update() {} |
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399 | |
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400 | }; //class PenaltyBasedSelectionRule |
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401 | |
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402 | public: |
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403 | |
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404 | /// \brief Constructor. |
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405 | /// |
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406 | /// Constructor. |
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407 | /// The global \ref rnd "random number generator instance" is used |
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408 | /// during the algorithm. |
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409 | /// |
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410 | /// \param graph The undirected graph the algorithm runs on. |
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411 | GrossoLocatelliPullanMc(const GR& graph) : |
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412 | _graph(graph), _id(_graph), _rnd(rnd) |
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413 | { |
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414 | initOptions(); |
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415 | } |
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416 | |
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417 | /// \brief Constructor with random seed. |
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418 | /// |
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419 | /// Constructor with random seed. |
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420 | /// |
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421 | /// \param graph The undirected graph the algorithm runs on. |
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422 | /// \param seed Seed value for the internal random number generator |
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423 | /// that is used during the algorithm. |
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424 | GrossoLocatelliPullanMc(const GR& graph, int seed) : |
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425 | _graph(graph), _id(_graph), _rnd(seed) |
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426 | { |
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427 | initOptions(); |
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428 | } |
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429 | |
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430 | /// \brief Constructor with random number generator. |
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431 | /// |
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432 | /// Constructor with random number generator. |
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433 | /// |
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434 | /// \param graph The undirected graph the algorithm runs on. |
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435 | /// \param random A random number generator that is used during the |
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436 | /// algorithm. |
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437 | GrossoLocatelliPullanMc(const GR& graph, const Random& random) : |
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438 | _graph(graph), _id(_graph), _rnd(random) |
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439 | { |
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440 | initOptions(); |
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441 | } |
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442 | |
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443 | /// \name Execution Control |
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444 | /// The \ref run() function can be used to execute the algorithm.\n |
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445 | /// The functions \ref iterationLimit(int), \ref stepLimit(int), and |
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446 | /// \ref sizeLimit(int) can be used to specify various limits for the |
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447 | /// search process. |
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448 | |
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449 | /// @{ |
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450 | |
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451 | /// \brief Sets the maximum number of iterations. |
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452 | /// |
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453 | /// This function sets the maximum number of iterations. |
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454 | /// Each iteration of the algorithm finds a maximal clique (but not |
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455 | /// necessarily the largest one) by performing several search steps |
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456 | /// (node selections). |
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457 | /// |
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458 | /// This limit controls the running time and the success of the |
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459 | /// algorithm. For larger values, the algorithm runs slower, but it more |
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460 | /// likely finds larger cliques. For smaller values, the algorithm is |
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461 | /// faster but probably gives worse results. |
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462 | /// |
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463 | /// The default value is \c 1000. |
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464 | /// \c -1 means that number of iterations is not limited. |
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465 | /// |
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466 | /// \warning You should specify a reasonable limit for the number of |
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467 | /// iterations and/or the number of search steps. |
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468 | /// |
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469 | /// \return <tt>(*this)</tt> |
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470 | /// |
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471 | /// \sa stepLimit(int) |
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472 | /// \sa sizeLimit(int) |
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473 | GrossoLocatelliPullanMc& iterationLimit(int limit) { |
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474 | _iteration_limit = limit; |
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475 | return *this; |
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476 | } |
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477 | |
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478 | /// \brief Sets the maximum number of search steps. |
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479 | /// |
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480 | /// This function sets the maximum number of elementary search steps. |
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481 | /// Each iteration of the algorithm finds a maximal clique (but not |
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482 | /// necessarily the largest one) by performing several search steps |
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483 | /// (node selections). |
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484 | /// |
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485 | /// This limit controls the running time and the success of the |
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486 | /// algorithm. For larger values, the algorithm runs slower, but it more |
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487 | /// likely finds larger cliques. For smaller values, the algorithm is |
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488 | /// faster but probably gives worse results. |
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489 | /// |
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490 | /// The default value is \c -1, which means that number of steps |
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491 | /// is not limited explicitly. However, the number of iterations is |
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492 | /// limited and each iteration performs a finite number of search steps. |
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493 | /// |
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494 | /// \warning You should specify a reasonable limit for the number of |
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495 | /// iterations and/or the number of search steps. |
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496 | /// |
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497 | /// \return <tt>(*this)</tt> |
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498 | /// |
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499 | /// \sa iterationLimit(int) |
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500 | /// \sa sizeLimit(int) |
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501 | GrossoLocatelliPullanMc& stepLimit(int limit) { |
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502 | _step_limit = limit; |
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503 | return *this; |
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504 | } |
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505 | |
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506 | /// \brief Sets the desired clique size. |
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507 | /// |
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508 | /// This function sets the desired clique size that serves as a search |
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509 | /// limit. If a clique of this size (or a larger one) is found, then the |
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510 | /// algorithm terminates. |
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511 | /// |
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512 | /// This function is especially useful if you know an exact upper bound |
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513 | /// for the size of the cliques in the graph or if any clique above |
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514 | /// a certain size limit is sufficient for your application. |
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515 | /// |
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516 | /// The default value is \c -1, which means that the size limit is set to |
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517 | /// the number of nodes in the graph. |
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518 | /// |
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519 | /// \return <tt>(*this)</tt> |
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520 | /// |
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521 | /// \sa iterationLimit(int) |
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522 | /// \sa stepLimit(int) |
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523 | GrossoLocatelliPullanMc& sizeLimit(int limit) { |
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524 | _size_limit = limit; |
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525 | return *this; |
---|
526 | } |
---|
527 | |
---|
528 | /// \brief The maximum number of iterations. |
---|
529 | /// |
---|
530 | /// This function gives back the maximum number of iterations. |
---|
531 | /// \c -1 means that no limit is specified. |
---|
532 | /// |
---|
533 | /// \sa iterationLimit(int) |
---|
534 | int iterationLimit() const { |
---|
535 | return _iteration_limit; |
---|
536 | } |
---|
537 | |
---|
538 | /// \brief The maximum number of search steps. |
---|
539 | /// |
---|
540 | /// This function gives back the maximum number of search steps. |
---|
541 | /// \c -1 means that no limit is specified. |
---|
542 | /// |
---|
543 | /// \sa stepLimit(int) |
---|
544 | int stepLimit() const { |
---|
545 | return _step_limit; |
---|
546 | } |
---|
547 | |
---|
548 | /// \brief The desired clique size. |
---|
549 | /// |
---|
550 | /// This function gives back the desired clique size that serves as a |
---|
551 | /// search limit. \c -1 means that this limit is set to the number of |
---|
552 | /// nodes in the graph. |
---|
553 | /// |
---|
554 | /// \sa sizeLimit(int) |
---|
555 | int sizeLimit() const { |
---|
556 | return _size_limit; |
---|
557 | } |
---|
558 | |
---|
559 | /// \brief Runs the algorithm. |
---|
560 | /// |
---|
561 | /// This function runs the algorithm. If one of the specified limits |
---|
562 | /// is reached, the search process terminates. |
---|
563 | /// |
---|
564 | /// \param rule The node selection rule. For more information, see |
---|
565 | /// \ref SelectionRule. |
---|
566 | /// |
---|
567 | /// \return The termination cause of the search. For more information, |
---|
568 | /// see \ref TerminationCause. |
---|
569 | TerminationCause run(SelectionRule rule = PENALTY_BASED) |
---|
570 | { |
---|
571 | init(); |
---|
572 | switch (rule) { |
---|
573 | case RANDOM: |
---|
574 | return start<RandomSelectionRule>(); |
---|
575 | case DEGREE_BASED: |
---|
576 | return start<DegreeBasedSelectionRule>(); |
---|
577 | default: |
---|
578 | return start<PenaltyBasedSelectionRule>(); |
---|
579 | } |
---|
580 | } |
---|
581 | |
---|
582 | /// @} |
---|
583 | |
---|
584 | /// \name Query Functions |
---|
585 | /// The results of the algorithm can be obtained using these functions.\n |
---|
586 | /// The run() function must be called before using them. |
---|
587 | |
---|
588 | /// @{ |
---|
589 | |
---|
590 | /// \brief The size of the found clique |
---|
591 | /// |
---|
592 | /// This function returns the size of the found clique. |
---|
593 | /// |
---|
594 | /// \pre run() must be called before using this function. |
---|
595 | int cliqueSize() const { |
---|
596 | return _best_size; |
---|
597 | } |
---|
598 | |
---|
599 | /// \brief Gives back the found clique in a \c bool node map |
---|
600 | /// |
---|
601 | /// This function gives back the characteristic vector of the found |
---|
602 | /// clique in the given node map. |
---|
603 | /// It must be a \ref concepts::WriteMap "writable" node map with |
---|
604 | /// \c bool (or convertible) value type. |
---|
605 | /// |
---|
606 | /// \pre run() must be called before using this function. |
---|
607 | template <typename CliqueMap> |
---|
608 | void cliqueMap(CliqueMap &map) const { |
---|
609 | for (NodeIt n(_graph); n != INVALID; ++n) { |
---|
610 | map[n] = static_cast<bool>(_best_clique[_id[n]]); |
---|
611 | } |
---|
612 | } |
---|
613 | |
---|
614 | /// \brief Iterator to list the nodes of the found clique |
---|
615 | /// |
---|
616 | /// This iterator class lists the nodes of the found clique. |
---|
617 | /// Before using it, you must allocate a GrossoLocatelliPullanMc instance |
---|
618 | /// and call its \ref GrossoLocatelliPullanMc::run() "run()" method. |
---|
619 | /// |
---|
620 | /// The following example prints out the IDs of the nodes in the found |
---|
621 | /// clique. |
---|
622 | /// \code |
---|
623 | /// GrossoLocatelliPullanMc<Graph> mc(g); |
---|
624 | /// mc.run(); |
---|
625 | /// for (GrossoLocatelliPullanMc<Graph>::CliqueNodeIt n(mc); |
---|
626 | /// n != INVALID; ++n) |
---|
627 | /// { |
---|
628 | /// std::cout << g.id(n) << std::endl; |
---|
629 | /// } |
---|
630 | /// \endcode |
---|
631 | class CliqueNodeIt |
---|
632 | { |
---|
633 | private: |
---|
634 | NodeIt _it; |
---|
635 | BoolNodeMap _map; |
---|
636 | |
---|
637 | public: |
---|
638 | |
---|
639 | /// Constructor |
---|
640 | |
---|
641 | /// Constructor. |
---|
642 | /// \param mc The algorithm instance. |
---|
643 | CliqueNodeIt(const GrossoLocatelliPullanMc &mc) |
---|
644 | : _map(mc._graph) |
---|
645 | { |
---|
646 | mc.cliqueMap(_map); |
---|
647 | for (_it = NodeIt(mc._graph); _it != INVALID && !_map[_it]; ++_it) ; |
---|
648 | } |
---|
649 | |
---|
650 | /// Conversion to \c Node |
---|
651 | operator Node() const { return _it; } |
---|
652 | |
---|
653 | bool operator==(Invalid) const { return _it == INVALID; } |
---|
654 | bool operator!=(Invalid) const { return _it != INVALID; } |
---|
655 | |
---|
656 | /// Next node |
---|
657 | CliqueNodeIt &operator++() { |
---|
658 | for (++_it; _it != INVALID && !_map[_it]; ++_it) ; |
---|
659 | return *this; |
---|
660 | } |
---|
661 | |
---|
662 | /// Postfix incrementation |
---|
663 | |
---|
664 | /// Postfix incrementation. |
---|
665 | /// |
---|
666 | /// \warning This incrementation returns a \c Node, not a |
---|
667 | /// \c CliqueNodeIt as one may expect. |
---|
668 | typename GR::Node operator++(int) { |
---|
669 | Node n=*this; |
---|
670 | ++(*this); |
---|
671 | return n; |
---|
672 | } |
---|
673 | |
---|
674 | }; |
---|
675 | |
---|
676 | /// @} |
---|
677 | |
---|
678 | private: |
---|
679 | |
---|
680 | // Initialize search options and limits |
---|
681 | void initOptions() { |
---|
682 | // Search options |
---|
683 | _delta_based_restart = true; |
---|
684 | _restart_delta_limit = 4; |
---|
685 | |
---|
686 | // Search limits |
---|
687 | _iteration_limit = 1000; |
---|
688 | _step_limit = -1; // this is disabled by default |
---|
689 | _size_limit = -1; // this is disabled by default |
---|
690 | } |
---|
691 | |
---|
692 | // Adds a node to the current clique |
---|
693 | void addCliqueNode(int u) { |
---|
694 | if (_clique[u]) return; |
---|
695 | _clique[u] = true; |
---|
696 | _size++; |
---|
697 | BoolVector &row = _gr[u]; |
---|
698 | for (int i = 0; i != _n; i++) { |
---|
699 | if (!row[i]) _delta[i]++; |
---|
700 | } |
---|
701 | } |
---|
702 | |
---|
703 | // Removes a node from the current clique |
---|
704 | void delCliqueNode(int u) { |
---|
705 | if (!_clique[u]) return; |
---|
706 | _clique[u] = false; |
---|
707 | _size--; |
---|
708 | BoolVector &row = _gr[u]; |
---|
709 | for (int i = 0; i != _n; i++) { |
---|
710 | if (!row[i]) _delta[i]--; |
---|
711 | } |
---|
712 | } |
---|
713 | |
---|
714 | // Initialize data structures |
---|
715 | void init() { |
---|
716 | _n = countNodes(_graph); |
---|
717 | int ui = 0; |
---|
718 | for (NodeIt u(_graph); u != INVALID; ++u) { |
---|
719 | _id[u] = ui++; |
---|
720 | } |
---|
721 | _gr.clear(); |
---|
722 | _gr.resize(_n, BoolVector(_n, false)); |
---|
723 | ui = 0; |
---|
724 | for (NodeIt u(_graph); u != INVALID; ++u) { |
---|
725 | for (IncEdgeIt e(_graph, u); e != INVALID; ++e) { |
---|
726 | int vi = _id[_graph.runningNode(e)]; |
---|
727 | _gr[ui][vi] = true; |
---|
728 | _gr[vi][ui] = true; |
---|
729 | } |
---|
730 | ++ui; |
---|
731 | } |
---|
732 | |
---|
733 | _clique.clear(); |
---|
734 | _clique.resize(_n, false); |
---|
735 | _size = 0; |
---|
736 | _best_clique.clear(); |
---|
737 | _best_clique.resize(_n, false); |
---|
738 | _best_size = 0; |
---|
739 | _delta.clear(); |
---|
740 | _delta.resize(_n, 0); |
---|
741 | _tabu.clear(); |
---|
742 | _tabu.resize(_n, false); |
---|
743 | } |
---|
744 | |
---|
745 | // Executes the algorithm |
---|
746 | template <typename SelectionRuleImpl> |
---|
747 | TerminationCause start() { |
---|
748 | if (_n == 0) return SIZE_LIMIT; |
---|
749 | if (_n == 1) { |
---|
750 | _best_clique[0] = true; |
---|
751 | _best_size = 1; |
---|
752 | return SIZE_LIMIT; |
---|
753 | } |
---|
754 | |
---|
755 | // Iterated local search algorithm |
---|
756 | const int max_size = _size_limit >= 0 ? _size_limit : _n; |
---|
757 | const int max_restart = _iteration_limit >= 0 ? |
---|
758 | _iteration_limit : std::numeric_limits<int>::max(); |
---|
759 | const int max_select = _step_limit >= 0 ? |
---|
760 | _step_limit : std::numeric_limits<int>::max(); |
---|
761 | |
---|
762 | SelectionRuleImpl sel_method(*this); |
---|
763 | int select = 0, restart = 0; |
---|
764 | IntVector restart_nodes; |
---|
765 | while (select < max_select && restart < max_restart) { |
---|
766 | |
---|
767 | // Perturbation/restart |
---|
768 | restart++; |
---|
769 | if (_delta_based_restart) { |
---|
770 | restart_nodes.clear(); |
---|
771 | for (int i = 0; i != _n; i++) { |
---|
772 | if (_delta[i] >= _restart_delta_limit) |
---|
773 | restart_nodes.push_back(i); |
---|
774 | } |
---|
775 | } |
---|
776 | int rs_node = -1; |
---|
777 | if (restart_nodes.size() > 0) { |
---|
778 | rs_node = restart_nodes[_rnd[restart_nodes.size()]]; |
---|
779 | } else { |
---|
780 | rs_node = _rnd[_n]; |
---|
781 | } |
---|
782 | BoolVector &row = _gr[rs_node]; |
---|
783 | for (int i = 0; i != _n; i++) { |
---|
784 | if (_clique[i] && !row[i]) delCliqueNode(i); |
---|
785 | } |
---|
786 | addCliqueNode(rs_node); |
---|
787 | |
---|
788 | // Local search |
---|
789 | _tabu.clear(); |
---|
790 | _tabu.resize(_n, false); |
---|
791 | bool tabu_empty = true; |
---|
792 | int max_swap = _size; |
---|
793 | while (select < max_select) { |
---|
794 | select++; |
---|
795 | int u; |
---|
796 | if ((u = sel_method.nextFeasibleAddNode()) != -1) { |
---|
797 | // Feasible add move |
---|
798 | addCliqueNode(u); |
---|
799 | if (tabu_empty) max_swap = _size; |
---|
800 | } |
---|
801 | else if ((u = sel_method.nextFeasibleSwapNode()) != -1) { |
---|
802 | // Feasible swap move |
---|
803 | int v = -1; |
---|
804 | BoolVector &row = _gr[u]; |
---|
805 | for (int i = 0; i != _n; i++) { |
---|
806 | if (_clique[i] && !row[i]) { |
---|
807 | v = i; |
---|
808 | break; |
---|
809 | } |
---|
810 | } |
---|
811 | addCliqueNode(u); |
---|
812 | delCliqueNode(v); |
---|
813 | _tabu[v] = true; |
---|
814 | tabu_empty = false; |
---|
815 | if (--max_swap <= 0) break; |
---|
816 | } |
---|
817 | else if ((u = sel_method.nextAddNode()) != -1) { |
---|
818 | // Non-feasible add move |
---|
819 | addCliqueNode(u); |
---|
820 | } |
---|
821 | else break; |
---|
822 | } |
---|
823 | if (_size > _best_size) { |
---|
824 | _best_clique = _clique; |
---|
825 | _best_size = _size; |
---|
826 | if (_best_size >= max_size) return SIZE_LIMIT; |
---|
827 | } |
---|
828 | sel_method.update(); |
---|
829 | } |
---|
830 | |
---|
831 | return (restart >= max_restart ? ITERATION_LIMIT : STEP_LIMIT); |
---|
832 | } |
---|
833 | |
---|
834 | }; //class GrossoLocatelliPullanMc |
---|
835 | |
---|
836 | ///@} |
---|
837 | |
---|
838 | } //namespace lemon |
---|
839 | |
---|
840 | #endif //LEMON_GROSSO_LOCATELLI_PULLAN_MC_H |
---|