318 lines
16 KiB
Python
318 lines
16 KiB
Python
import gurobipy as gp
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from gurobipy import GRB, Model, quicksum
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HYPERGRAPH = { 1, (['Butadien'], ['Butadien']),
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4, (['Butadien', 'Butadien'], ['p_{0,0}', 'p_{0,1}']),
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5, (['Butadien', 'Butadien'], ['Butadien', 'Butadien']),
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7, (['Butadien', 'Butadien'], ['p_{0,2}']),
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9, (['p_{0,0}', 'p_{0,0}'], ['p_{0,0}', 'p_{0,0}']),
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10, (['p_{0,0}', 'p_{0,1}'], ['Butadien', 'Butadien']),
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11, (['p_{0,0}', 'p_{0,1}'], ['p_{0,0}', 'p_{0,1}']),
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13, (['p_{0,0}', 'p_{0,2}'], ['p_{0,3}']),
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14, (['p_{0,0}', 'p_{0,2}'], ['p_{0,0}', 'p_{0,2}']),
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15, (['Butadien', 'p_{0,0}'], ['Butadien', 'p_{0,0}']),
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16, (['p_{0,1}', 'p_{0,1}'], ['p_{0,1}', 'p_{0,1}']),
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18, (['p_{0,1}', 'p_{0,1}'], ['Butadien', 'p_{0,4}']),
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20, (['p_{0,1}', 'p_{0,2}'], ['Butadien', 'p_{0,5}']),
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21, (['Butadien', 'p_{0,1}'], ['Butadien', 'p_{0,1}']),
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22, (['p_{0,1}', 'p_{0,2}'], ['p_{0,1}', 'p_{0,2}']),
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23, (['Butadien', 'p_{0,1}'], ['p_{0,0}', 'p_{0,4}']),
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24, (['p_{0,2}', 'p_{0,2}'], ['p_{0,2}', 'p_{0,2}']),
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25, (['Butadien', 'p_{0,2}'], ['Butadien', 'p_{0,2}']),
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26, (['Butadien', 'p_{0,2}'], ['p_{0,0}', 'p_{0,5}']),
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28, (['p_{0,0}', 'p_{0,1}'], ['p_{0,6}']),
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30, (['Butadien', 'p_{0,0}'], ['p_{0,7}']),
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32, (['Butadien', 'p_{0,1}'], ['p_{0,8}']),
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33, (['Butadien', 'p_{0,1}'], ['p_{0,5}']),
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35, (['Butadien', 'p_{0,1}'], ['p_{0,9}']),
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37, (['Butadien', 'p_{0,1}'], ['p_{0,10}']),
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38, (['p_{0,3}'], ['p_{0,0}', 'p_{0,2}']),
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42, (['p_{0,4}'], ['p_{0,0}', 'p_{0,11}']),
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50, (['p_{0,3}', 'p_{0,3}'], ['p_{0,3}', 'p_{0,3}']),
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51, (['p_{0,3}', 'p_{0,4}'], ['p_{0,3}', 'p_{0,4}']),
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52, (['p_{0,3}', 'p_{0,5}'], ['p_{0,3}', 'p_{0,5}']),
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53, (['p_{0,3}', 'p_{0,6}'], ['p_{0,3}', 'p_{0,6}']),
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54, (['p_{0,3}', 'p_{0,8}'], ['p_{0,3}', 'p_{0,8}']),
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55, (['p_{0,3}', 'p_{0,9}'], ['p_{0,3}', 'p_{0,9}']),
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56, (['p_{0,3}', 'p_{0,10}'], ['p_{0,3}', 'p_{0,10}']),
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57, (['p_{0,2}', 'p_{0,3}'], ['p_{0,2}', 'p_{0,3}']),
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58, (['p_{0,0}', 'p_{0,3}'], ['p_{0,0}', 'p_{0,3}']),
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59, (['Butadien', 'p_{0,3}'], ['Butadien', 'p_{0,3}']),
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60, (['p_{0,1}', 'p_{0,3}'], ['p_{0,1}', 'p_{0,3}']),
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61, (['p_{0,4}', 'p_{0,4}'], ['p_{0,4}', 'p_{0,4}']),
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62, (['p_{0,4}', 'p_{0,5}'], ['p_{0,4}', 'p_{0,5}']),
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64, (['p_{0,4}', 'p_{0,6}'], ['p_{0,1}', 'p_{0,13}']),
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65, (['p_{0,2}', 'p_{0,4}'], ['p_{0,1}', 'p_{0,5}']),
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66, (['p_{0,0}', 'p_{0,4}'], ['Butadien', 'p_{0,1}']),
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67, (['Butadien', 'p_{0,4}'], ['p_{0,1}', 'p_{0,1}']),
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68, (['Butadien', 'p_{0,4}'], ['Butadien', 'p_{0,4}']),
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69, (['p_{0,1}', 'p_{0,4}'], ['p_{0,1}', 'p_{0,4}']),
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70, (['p_{0,4}', 'p_{0,6}'], ['p_{0,4}', 'p_{0,6}']),
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71, (['p_{0,4}', 'p_{0,8}'], ['p_{0,4}', 'p_{0,8}']),
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72, (['p_{0,4}', 'p_{0,9}'], ['p_{0,4}', 'p_{0,9}']),
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73, (['p_{0,4}', 'p_{0,10}'], ['p_{0,4}', 'p_{0,10}']),
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74, (['p_{0,2}', 'p_{0,4}'], ['p_{0,2}', 'p_{0,4}']),
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75, (['p_{0,0}', 'p_{0,4}'], ['p_{0,0}', 'p_{0,4}']),
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76, (['p_{0,5}', 'p_{0,5}'], ['p_{0,5}', 'p_{0,5}']),
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77, (['p_{0,5}', 'p_{0,6}'], ['p_{0,2}', 'p_{0,13}']),
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78, (['p_{0,2}', 'p_{0,5}'], ['p_{0,2}', 'p_{0,5}']),
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79, (['p_{0,0}', 'p_{0,5}'], ['Butadien', 'p_{0,2}']),
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80, (['Butadien', 'p_{0,5}'], ['p_{0,1}', 'p_{0,2}']),
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81, (['Butadien', 'p_{0,5}'], ['Butadien', 'p_{0,5}']),
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82, (['p_{0,1}', 'p_{0,5}'], ['p_{0,1}', 'p_{0,5}']),
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83, (['p_{0,1}', 'p_{0,5}'], ['p_{0,2}', 'p_{0,4}']),
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84, (['p_{0,5}', 'p_{0,6}'], ['p_{0,5}', 'p_{0,6}']),
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85, (['p_{0,5}', 'p_{0,8}'], ['p_{0,5}', 'p_{0,8}']),
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86, (['p_{0,5}', 'p_{0,9}'], ['p_{0,5}', 'p_{0,9}']),
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87, (['p_{0,5}', 'p_{0,10}'], ['p_{0,5}', 'p_{0,10}']),
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88, (['p_{0,0}', 'p_{0,5}'], ['p_{0,0}', 'p_{0,5}']),
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89, (['p_{0,6}', 'p_{0,6}'], ['p_{0,6}', 'p_{0,6}']),
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90, (['p_{0,6}', 'p_{0,8}'], ['p_{0,6}', 'p_{0,8}']),
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91, (['p_{0,6}', 'p_{0,9}'], ['p_{0,6}', 'p_{0,9}']),
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92, (['p_{0,6}', 'p_{0,10}'], ['p_{0,6}', 'p_{0,10}']),
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93, (['p_{0,2}', 'p_{0,6}'], ['p_{0,2}', 'p_{0,6}']),
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94, (['p_{0,0}', 'p_{0,6}'], ['p_{0,0}', 'p_{0,6}']),
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95, (['Butadien', 'p_{0,6}'], ['Butadien', 'p_{0,6}']),
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96, (['Butadien', 'p_{0,6}'], ['p_{0,0}', 'p_{0,13}']),
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97, (['p_{0,1}', 'p_{0,6}'], ['Butadien', 'p_{0,13}']),
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98, (['p_{0,1}', 'p_{0,6}'], ['p_{0,1}', 'p_{0,6}']),
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100, (['p_{0,0}', 'p_{0,6}'], ['p_{0,14}']),
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102, (['p_{0,0}', 'p_{0,7}'], ['p_{0,15}']),
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103, (['p_{0,8}', 'p_{0,8}'], ['p_{0,8}', 'p_{0,8}']),
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104, (['p_{0,8}', 'p_{0,9}'], ['p_{0,8}', 'p_{0,9}']),
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105, (['p_{0,8}', 'p_{0,10}'], ['p_{0,8}', 'p_{0,10}']),
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106, (['p_{0,2}', 'p_{0,8}'], ['p_{0,2}', 'p_{0,8}']),
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107, (['p_{0,0}', 'p_{0,8}'], ['p_{0,0}', 'p_{0,8}']),
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108, (['Butadien', 'p_{0,8}'], ['Butadien', 'p_{0,8}']),
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109, (['p_{0,1}', 'p_{0,8}'], ['p_{0,1}', 'p_{0,8}']),
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110, (['p_{0,9}', 'p_{0,9}'], ['p_{0,9}', 'p_{0,9}']),
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111, (['p_{0,9}', 'p_{0,10}'], ['p_{0,9}', 'p_{0,10}']),
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112, (['p_{0,2}', 'p_{0,9}'], ['p_{0,2}', 'p_{0,9}']),
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113, (['p_{0,0}', 'p_{0,9}'], ['p_{0,0}', 'p_{0,9}']),
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114, (['Butadien', 'p_{0,9}'], ['Butadien', 'p_{0,9}']),
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115, (['p_{0,1}', 'p_{0,9}'], ['p_{0,1}', 'p_{0,9}']),
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116, (['p_{0,10}', 'p_{0,10}'], ['p_{0,10}', 'p_{0,10}']),
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117, (['p_{0,2}', 'p_{0,10}'], ['p_{0,2}', 'p_{0,10}']),
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118, (['p_{0,0}', 'p_{0,10}'], ['p_{0,0}', 'p_{0,10}']),
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119, (['Butadien', 'p_{0,10}'], ['Butadien', 'p_{0,10}']),
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120, (['p_{0,1}', 'p_{0,10}'], ['p_{0,1}', 'p_{0,10}']),
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121, (['p_{0,0}', 'p_{0,4}'], ['p_{0,13}']),
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123, (['p_{0,0}', 'p_{0,4}'], ['p_{0,16}']),
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125, (['Butadien', 'p_{0,7}'], ['p_{0,17}']),
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130, (['p_{0,14}'], ['p_{0,0}', 'p_{0,6}']),
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132, (['p_{0,15}'], ['p_{0,0}', 'p_{0,7}']),
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135, (['p_{0,0}', 'p_{0,11}'], ['p_{0,4}']),
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136, (['p_{0,12}', 'p_{0,12}'], ['p_{0,12}', 'p_{0,12}']),
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137, (['p_{0,12}', 'p_{0,13}'], ['p_{0,12}', 'p_{0,13}']),
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138, (['p_{0,12}', 'p_{0,14}'], ['p_{0,12}', 'p_{0,14}']),
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139, (['p_{0,12}', 'p_{0,15}'], ['p_{0,12}', 'p_{0,15}']),
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140, (['p_{0,12}', 'p_{0,16}'], ['p_{0,12}', 'p_{0,16}']),
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141, (['p_{0,8}', 'p_{0,12}'], ['p_{0,8}', 'p_{0,12}']),
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142, (['p_{0,9}', 'p_{0,12}'], ['p_{0,9}', 'p_{0,12}']),
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143, (['Butadien', 'p_{0,12}'], ['Butadien', 'p_{0,12}']),
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144, (['p_{0,10}', 'p_{0,12}'], ['p_{0,10}', 'p_{0,12}']),
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145, (['p_{0,0}', 'p_{0,12}'], ['p_{0,0}', 'p_{0,12}']),
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146, (['p_{0,1}', 'p_{0,12}'], ['p_{0,1}', 'p_{0,12}']),
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147, (['p_{0,2}', 'p_{0,12}'], ['p_{0,2}', 'p_{0,12}']),
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148, (['p_{0,3}', 'p_{0,12}'], ['p_{0,3}', 'p_{0,12}']),
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149, (['p_{0,4}', 'p_{0,12}'], ['p_{0,4}', 'p_{0,12}']),
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150, (['p_{0,5}', 'p_{0,12}'], ['p_{0,5}', 'p_{0,12}']),
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151, (['p_{0,6}', 'p_{0,12}'], ['p_{0,6}', 'p_{0,12}']),
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152, (['p_{0,13}', 'p_{0,13}'], ['p_{0,13}', 'p_{0,13}']),
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153, (['Butadien', 'p_{0,13}'], ['p_{0,1}', 'p_{0,6}']),
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154, (['Butadien', 'p_{0,13}'], ['Butadien', 'p_{0,13}']),
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155, (['p_{0,0}', 'p_{0,13}'], ['Butadien', 'p_{0,6}']),
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156, (['p_{0,1}', 'p_{0,13}'], ['p_{0,1}', 'p_{0,13}']),
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157, (['p_{0,1}', 'p_{0,13}'], ['p_{0,4}', 'p_{0,6}']),
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158, (['p_{0,2}', 'p_{0,13}'], ['p_{0,5}', 'p_{0,6}']),
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159, (['p_{0,4}', 'p_{0,13}'], ['p_{0,4}', 'p_{0,13}']),
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160, (['p_{0,5}', 'p_{0,13}'], ['p_{0,5}', 'p_{0,13}']),
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161, (['p_{0,6}', 'p_{0,13}'], ['p_{0,6}', 'p_{0,13}']),
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162, (['p_{0,13}', 'p_{0,14}'], ['p_{0,13}', 'p_{0,14}']),
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163, (['p_{0,13}', 'p_{0,15}'], ['p_{0,13}', 'p_{0,15}']),
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164, (['p_{0,13}', 'p_{0,16}'], ['p_{0,13}', 'p_{0,16}']),
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165, (['p_{0,8}', 'p_{0,13}'], ['p_{0,8}', 'p_{0,13}']),
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166, (['p_{0,9}', 'p_{0,13}'], ['p_{0,9}', 'p_{0,13}']),
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167, (['p_{0,10}', 'p_{0,13}'], ['p_{0,10}', 'p_{0,13}']),
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168, (['p_{0,0}', 'p_{0,13}'], ['p_{0,0}', 'p_{0,13}']),
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169, (['p_{0,2}', 'p_{0,13}'], ['p_{0,2}', 'p_{0,13}']),
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170, (['p_{0,3}', 'p_{0,13}'], ['p_{0,3}', 'p_{0,13}']),
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171, (['p_{0,14}', 'p_{0,14}'], ['p_{0,14}', 'p_{0,14}']),
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172, (['p_{0,14}', 'p_{0,15}'], ['p_{0,14}', 'p_{0,15}']),
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173, (['p_{0,14}', 'p_{0,16}'], ['p_{0,14}', 'p_{0,16}']),
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174, (['p_{0,8}', 'p_{0,14}'], ['p_{0,8}', 'p_{0,14}']),
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175, (['p_{0,9}', 'p_{0,14}'], ['p_{0,9}', 'p_{0,14}']),
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176, (['Butadien', 'p_{0,14}'], ['Butadien', 'p_{0,14}']),
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177, (['p_{0,10}', 'p_{0,14}'], ['p_{0,10}', 'p_{0,14}']),
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178, (['p_{0,0}', 'p_{0,14}'], ['p_{0,0}', 'p_{0,14}']),
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179, (['p_{0,1}', 'p_{0,14}'], ['p_{0,1}', 'p_{0,14}']),
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180, (['p_{0,2}', 'p_{0,14}'], ['p_{0,2}', 'p_{0,14}']),
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181, (['p_{0,3}', 'p_{0,14}'], ['p_{0,3}', 'p_{0,14}']),
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182, (['p_{0,4}', 'p_{0,14}'], ['p_{0,4}', 'p_{0,14}']),
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183, (['p_{0,5}', 'p_{0,14}'], ['p_{0,5}', 'p_{0,14}']),
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184, (['p_{0,6}', 'p_{0,14}'], ['p_{0,6}', 'p_{0,14}']),
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185, (['p_{0,15}', 'p_{0,15}'], ['p_{0,15}', 'p_{0,15}']),
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186, (['p_{0,15}', 'p_{0,16}'], ['p_{0,15}', 'p_{0,16}']),
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187, (['p_{0,8}', 'p_{0,15}'], ['p_{0,8}', 'p_{0,15}']),
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188, (['p_{0,9}', 'p_{0,15}'], ['p_{0,9}', 'p_{0,15}']),
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189, (['Butadien', 'p_{0,15}'], ['Butadien', 'p_{0,15}']),
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190, (['p_{0,10}', 'p_{0,15}'], ['p_{0,10}', 'p_{0,15}']),
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191, (['p_{0,0}', 'p_{0,15}'], ['p_{0,0}', 'p_{0,15}']),
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192, (['p_{0,1}', 'p_{0,15}'], ['p_{0,1}', 'p_{0,15}']),
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193, (['p_{0,2}', 'p_{0,15}'], ['p_{0,2}', 'p_{0,15}']),
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194, (['p_{0,3}', 'p_{0,15}'], ['p_{0,3}', 'p_{0,15}']),
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195, (['p_{0,4}', 'p_{0,15}'], ['p_{0,4}', 'p_{0,15}']),
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196, (['p_{0,5}', 'p_{0,15}'], ['p_{0,5}', 'p_{0,15}']),
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197, (['p_{0,6}', 'p_{0,15}'], ['p_{0,6}', 'p_{0,15}']),
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198, (['p_{0,16}', 'p_{0,16}'], ['p_{0,16}', 'p_{0,16}']),
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199, (['p_{0,8}', 'p_{0,16}'], ['p_{0,8}', 'p_{0,16}']),
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200, (['p_{0,9}', 'p_{0,16}'], ['p_{0,9}', 'p_{0,16}']),
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201, (['Butadien', 'p_{0,16}'], ['Butadien', 'p_{0,16}']),
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202, (['p_{0,10}', 'p_{0,16}'], ['p_{0,10}', 'p_{0,16}']),
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203, (['p_{0,0}', 'p_{0,16}'], ['p_{0,0}', 'p_{0,16}']),
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204, (['p_{0,1}', 'p_{0,16}'], ['p_{0,1}', 'p_{0,16}']),
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205, (['p_{0,2}', 'p_{0,16}'], ['p_{0,2}', 'p_{0,16}']),
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206, (['p_{0,3}', 'p_{0,16}'], ['p_{0,3}', 'p_{0,16}']),
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207, (['p_{0,4}', 'p_{0,16}'], ['p_{0,4}', 'p_{0,16}']),
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208, (['p_{0,5}', 'p_{0,16}'], ['p_{0,5}', 'p_{0,16}']),
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209, (['p_{0,6}', 'p_{0,16}'], ['p_{0,6}', 'p_{0,16}']),
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211, (['Butadien', 'p_{0,11}'], ['p_{0,18}']),
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8, (['p_{0,1}'], ['p_{0,1}']),
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39, (['p_{0,3}'], ['p_{0,3}']),
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40, (['p_{0,4}'], ['p_{0,4}']),
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44, (['p_{0,5}'], ['p_{0,12}']),
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45, (['p_{0,5}'], ['p_{0,5}']),
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46, (['p_{0,6}'], ['p_{0,6}']),
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47, (['p_{0,8}'], ['p_{0,8}']),
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48, (['p_{0,9}'], ['p_{0,9}']),
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49, (['p_{0,10}'], ['p_{0,10}']),
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126, (['p_{0,11}'], ['p_{0,11}']),
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127, (['p_{0,12}'], ['p_{0,5}']),
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128, (['p_{0,12}'], ['p_{0,12}']),
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129, (['p_{0,13}'], ['p_{0,13}']),
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131, (['p_{0,14}'], ['p_{0,14}']),
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133, (['p_{0,15}'], ['p_{0,15}']),
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134, (['p_{0,16}'], ['p_{0,16}']),
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212, (['p_{0,18}'], ['p_{0,18}']),
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}
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VERTICES = ['Butadien', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', 'p_{0,5}', 'p_{0,6}', 'p_{0,7}', 'p_{0,8}', 'p_{0,9}', 'p_{0,10}', 'p_{0,11}', 'p_{0,12}', 'p_{0,13}', 'p_{0,14}', 'p_{0,15}', 'p_{0,16}', 'p_{0,17}', 'p_{0,18}']
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#Vergleich mit dem NMR von Ethylen und Hexatrien
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NMR1 = [0.32, 0.5, 0.87, 0.0, 0.11, 0.58, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.09, 0.0, 0.0, 0.17, 0.0, 0.09]
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#Vergleich mit dem NMR von Ethylen und Octrien
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NMR2 = [0.33, 0.43, 0.6, 0.0, 0.13, 0.9, 0.07, 0.01, 0.0, 0.0, 0.01, 0.0, 0.0, 0.01, 0.1, 0.0, 0.0, 0.22, 0.0, 0.13]
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#Vergleich mit dem NMR von Ethylen und Benzol
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NMR3 = [0.0, 0.32, 0.0, 0.0, 0.0, 0.0, 0.05, 0.0, 0.0, 0.02, 0.0, 0.29, 0.95, 0.17, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05]
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VERTICESSMILES = ['C=CC=C', 'C=C', 'C=CC=CC=C', 'C1CCC(C=C)CC=1', 'C(CCC(C=C)CC=C)=C', 'C=CC=CC=CC=C', 'C1CCC(C=CC=C)CC=1', 'C=CC1C=CCCC1', 'C1CCCCC=1', 'C=CC1CC=CCC1C=C', 'C1CCC(C=C)C(C=C)C=1', 'C(C1CC(C=C)C=CC1)=C', 'C1C=CC=CC=1', 'C(CCC1C=CC=CC1)=C', 'C1C(C=CC=C)CCCC=1', 'C=CC(C=C)CCCC=C', 'C=CCCCCC=C', 'C=CC1C=CC(C=C)CC1', 'C1CC2CCCCC2CC=1', 'C1CC2C=CC=CC2CC=1']
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FIXED_FLOWS = {
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#1: 1,
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}
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def build_model(name, hyperedges, vertices, nmrlikelihoods1, nmrlikelihoods2, nmrlikelihoods3, excluded_support=None):
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model = Model(name)
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x = {e_id: model.addVar(vtype=GRB.INTEGER, lb = 0, name = f"x_{e_id}") for e_id in hyperedges}
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b = {e_id: model.addVar(vtype=GRB.BINARY, name = f"b_{e_id}") for e_id in hyperedges}
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n1 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr1")
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n2 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr2")
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n3 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr3")
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en1 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 2.0, name = "edgenmr1")
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en2 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 2.0, name = "edgenmr2")
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en3 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 2.0, name = "edgenmr3")
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#Assigns every Molecule the likelihood compared to the three different reference spectra
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for v, nmr1, nmr2, nmr3 in zip(vertices, nmrlikelihoods1, nmrlikelihoods2, nmrlikelihoods3):
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n1[v] = nmr1
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n2[v] = nmr2
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n3[v] = nmr3
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#Assigns the edges a likelihood based on the products
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for e, (_, heads) in hyperedges.items():
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en1[e] = quicksum(n1[head] for head in heads)
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en2[e] = quicksum(n2[head] for head in heads)
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en3[e] = quicksum(n3[head] for head in heads)
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print(en1[7])
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vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
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for v in vertices:
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inflow = quicksum(x[e_id] for e_id, (_, heads) in hyperedges.items() if v in heads)
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outflow = quicksum(x[e_id] for e_id, (tails, _) in hyperedges.items() if v in tails)
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model.addConstr(inflow == outflow, name = f"flow_conservation_{v}")
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for e_id, value in FIXED_FLOWS.items():
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model.addConstr(x[e_id] == value, name = f"fixed_flow_{e_id}")
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for e_id in hyperedges:
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model.addGenConstrIndicator(b[e_id], 0, x[e_id] == 0, name = f"unused_implies_zero_{e_id}")
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model.addConstr(x[e_id] >= b[e_id], name = f"used_implies_positive_flow_{e_id}")
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reaction_path = {}
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if excluded_support:
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model.addConstr(quicksum(b[e_id] for e_id in excluded_support) <= len(excluded_support) - 1, name = "different_hyperedges",)
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#Multiplizier den node Wert mit infow + outflow
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model.ModelSense = GRB.MAXIMIZE
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|
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#Multiply node value with infow or outflow
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|
model.setObjectiveN(
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quicksum(n[t_id[0]] * x[e_id] for e_id, (_, t_id) in hyperedges.items() if t_id != []),
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index = 0,
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|
priority = 2,
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|
name = "maximize_nmr_similarity",
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|
)
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|
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model.setObjectiveN(
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|
quicksum(-1 * x[e_id] for e_id in hyperedges),
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|
index=1,
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|
priority=1,
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|
name="minimize_used_hyperedges",
|
|
)
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|
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return model, x, b
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def positive_entries(variable_dict, threshold = 0.5):
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|
return {e_id: var.X for e_id, var in variable_dict.items() if var.X > threshold}
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|
|
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def print_solution(title, flow_solution, binary_solution, hyperedges):
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|
print(f"\n{title}:")
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|
for e_id in sorted(flow_solution):
|
|
flow = flow_solution[e_id]
|
|
tails, heads = hyperedges[e_id]
|
|
print(f"Hyperedge {e_id}: Flow = {flow}, Tails = {tails}, Heads = {heads}")
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|
print("\nBinary Variables:")
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|
for e_id in sorted(binary_solution):
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|
print(f"Binary Variable b_{e_id} = {binary_solution[e_id]}")
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|
|
|
print(f"\nTotal flow: {sum(flow_solution.values())}")
|
|
print(f"Number of used hyperedges: {len(binary_solution)}")
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|
|
|
def main():
|
|
model, x, b = build_model("HypergraphFlow", HYPEREDGES, VERTICES, NMR1, NMR2, NMR3)
|
|
model.optimize()
|
|
if model.status != GRB.Status.OPTIMAL:
|
|
print("No optimal solution found for the first model.")
|
|
return
|
|
optimal_solution = positive_entries(x)
|
|
optimal_binary_solution = positive_entries(b)
|
|
print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, HYPEREDGES)
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|
|
|
""" excluded_support = list(optimal_binary_solution.keys())
|
|
second_model, x2, b2 = build_model("SecondBestHypergraphFlow", HYPEREDGES, excluded_support=excluded_support,)
|
|
|
|
second_model.optimize()
|
|
if second_model.status == GRB.Status.OPTIMAL:
|
|
second_solution = positive_entries(x2)
|
|
second_binary_solution = positive_entries(b2)
|
|
print_solution("Second Best Solution", second_solution, second_binary_solution, HYPEREDGES, VERTICES)
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|
else:
|
|
print("No optimal solution found for the second best model.")
|
|
"""
|
|
if __name__ == "__main__":
|
|
main() |