1 | // $Id: longthin.cpp 1898 2013-04-09 18:06:04Z tkr $ |
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2 | // Copyright (C) 2005, International Business Machines |
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3 | // Corporation and others. All Rights Reserved. |
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4 | // This code is licensed under the terms of the Eclipse Public License (EPL). |
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5 | |
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6 | #include <cassert> |
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7 | #include <iomanip> |
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8 | |
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9 | |
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10 | #include "CoinPragma.hpp" |
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11 | // For Branch and bound |
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12 | #include "OsiSolverInterface.hpp" |
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13 | #include "CbcModel.hpp" |
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14 | #include "CbcBranchActual.hpp" |
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15 | #include "CbcBranchUser.hpp" |
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16 | #include "CbcCompareUser.hpp" |
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17 | #include "CbcCutGenerator.hpp" |
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18 | #include "CbcHeuristicGreedy.hpp" |
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19 | #include "CbcSolver2.hpp" |
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20 | #include "CoinModel.hpp" |
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21 | |
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22 | // Cuts |
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23 | |
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24 | #include "CglProbing.hpp" |
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25 | |
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26 | #include "CoinTime.hpp" |
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27 | |
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28 | /************************************************************************ |
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29 | |
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30 | This main program reads in an integer model from an mps file. |
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31 | It expects it to be unit coefficients and unit rhs and long and thin |
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32 | |
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33 | Branching is simple binary branching on integer variables. |
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34 | |
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35 | */ |
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36 | int main (int argc, const char *argv[]) |
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37 | { |
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38 | |
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39 | // Define a Solver for long thin problems |
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40 | |
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41 | CbcSolver2 solver1; |
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42 | |
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43 | // Read in model using argv[1] |
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44 | // and assert that it is a clean model |
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45 | std::string mpsFileName; |
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46 | #if defined(SAMPLEDIR) |
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47 | mpsFileName = SAMPLEDIR "/p0033.mps"; |
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48 | #else |
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49 | if (argc < 2) { |
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50 | fprintf(stderr, "Do not know where to find sample MPS files.\n"); |
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51 | exit(1); |
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52 | } |
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53 | #endif |
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54 | if (argc>=2) mpsFileName = argv[1]; |
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55 | int numMpsReadErrors = solver1.readMps(mpsFileName.c_str(),""); |
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56 | if( numMpsReadErrors != 0 ) |
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57 | { |
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58 | printf("%d errors reading MPS file\n", numMpsReadErrors); |
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59 | return numMpsReadErrors; |
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60 | } |
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61 | double time1 = CoinCpuTime(); |
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62 | |
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63 | solver1.initialSolve(); |
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64 | // Reduce printout |
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65 | solver1.setHintParam(OsiDoReducePrint,true,OsiHintTry); |
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66 | |
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67 | OsiSolverInterface * solver2=&solver1; |
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68 | CbcModel model(*solver2); |
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69 | // Point to solver |
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70 | OsiSolverInterface * solver3 = model.solver(); |
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71 | CbcSolver2 * osiclp = dynamic_cast< CbcSolver2*> (solver3); |
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72 | assert (osiclp); |
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73 | osiclp->initialize(&model,NULL); |
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74 | osiclp->setAlgorithm(2); |
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75 | osiclp->setMemory(1000); |
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76 | // Set up some cut generators and defaults |
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77 | // Probing first as gets tight bounds on continuous |
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78 | |
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79 | CglProbing generator1; |
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80 | generator1.setUsingObjective(true); |
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81 | generator1.setMaxPass(3); |
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82 | // Number of unsatisfied variables to look at |
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83 | generator1.setMaxProbe(10); |
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84 | // How far to follow the consequences |
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85 | generator1.setMaxLook(50); |
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86 | // Only look at rows with fewer than this number of elements |
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87 | generator1.setMaxElements(200); |
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88 | generator1.setRowCuts(3); |
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89 | |
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90 | |
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91 | // Add in generators |
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92 | // Experiment with -1 and -99 etc |
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93 | model.addCutGenerator(&generator1,-99,"Probing"); |
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94 | // Allow rounding heuristic |
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95 | |
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96 | CbcRounding heuristic1(model); |
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97 | model.addHeuristic(&heuristic1); |
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98 | |
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99 | // And Greedy heuristic |
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100 | |
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101 | CbcHeuristicGreedyCover heuristic2(model); |
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102 | // Use original upper and perturb more |
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103 | heuristic2.setAlgorithm(11); |
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104 | model.addHeuristic(&heuristic2); |
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105 | |
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106 | // Redundant definition of default branching (as Default == User) |
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107 | CbcBranchUserDecision branch; |
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108 | model.setBranchingMethod(&branch); |
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109 | |
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110 | // Definition of node choice |
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111 | CbcCompareUser compare; |
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112 | model.setNodeComparison(compare); |
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113 | |
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114 | int iColumn; |
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115 | int numberColumns = solver3->getNumCols(); |
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116 | // do pseudo costs |
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117 | CbcObject ** objects = new CbcObject * [numberColumns]; |
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118 | const CoinPackedMatrix * matrix = solver3->getMatrixByCol(); |
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119 | // Column copy |
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120 | const int * columnLength = matrix->getVectorLengths(); |
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121 | const double * objective = model.getObjCoefficients(); |
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122 | int numberIntegers=0; |
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123 | for (iColumn=0;iColumn<numberColumns;iColumn++) { |
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124 | if (solver3->isInteger(iColumn)) { |
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125 | /* Branching up gets us much closer to an integer solution so we want |
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126 | to encourage up - so we will branch up if variable value > 0.333333. |
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127 | The expected cost of going up obviously depends on the cost of the |
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128 | variable so we just choose pseudo costs to reflect that. We could also |
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129 | decide to try and use the pseudo costs to make it more likely to branch |
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130 | on a variable with many coefficients. This leads to the computation below. |
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131 | */ |
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132 | double cost = objective[iColumn]*(1.0 + 0.2*((double) columnLength[iColumn])); |
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133 | CbcSimpleIntegerPseudoCost * newObject = |
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134 | new CbcSimpleIntegerPseudoCost(&model,iColumn, |
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135 | 2.0*cost,cost); |
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136 | newObject->setMethod(3); |
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137 | objects[numberIntegers++]= newObject; |
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138 | } |
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139 | } |
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140 | model.addObjects(numberIntegers,objects); |
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141 | for (iColumn=0;iColumn<numberIntegers;iColumn++) |
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142 | delete objects[iColumn]; |
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143 | delete [] objects; |
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144 | |
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145 | // Do initial solve to continuous |
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146 | model.initialSolve(); |
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147 | |
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148 | // Do more strong branching if small |
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149 | // Switch off strong branching if wanted |
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150 | model.setNumberStrong(5); |
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151 | |
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152 | // say use resolve for strong branching |
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153 | osiclp->setSpecialOptions(16); |
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154 | // We had better allow a lot |
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155 | model.solver()->setIntParam(OsiMaxNumIterationHotStart,10000); |
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156 | // So use strategy to keep rows |
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157 | osiclp->setStrategy(1); |
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158 | |
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159 | // Switch off most output |
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160 | if (model.getNumCols()<3000) { |
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161 | model.messageHandler()->setLogLevel(1); |
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162 | //model.solver()->messageHandler()->setLogLevel(0); |
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163 | } else { |
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164 | model.messageHandler()->setLogLevel(2); |
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165 | model.solver()->messageHandler()->setLogLevel(1); |
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166 | } |
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167 | //model.setPrintFrequency(50); |
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168 | |
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169 | // Do complete search |
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170 | try { |
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171 | model.branchAndBound(); |
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172 | } |
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173 | catch (CoinError e) { |
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174 | e.print(); |
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175 | if (e.lineNumber()>=0) |
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176 | std::cout<<"This was from a CoinAssert"<<std::endl; |
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177 | exit(0); |
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178 | } |
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179 | //void printHowMany(); |
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180 | //printHowMany(); |
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181 | std::cout<<mpsFileName<<" took "<<CoinCpuTime()-time1<<" seconds, " |
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182 | <<model.getNodeCount()<<" nodes with objective " |
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183 | <<model.getObjValue() |
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184 | <<(!model.status() ? " Finished" : " Not finished") |
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185 | <<std::endl; |
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186 | |
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187 | // Print solution if finished - we can't get names from Osi! |
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188 | |
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189 | if (model.getMinimizationObjValue()<1.0e50) { |
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190 | int numberColumns = model.solver()->getNumCols(); |
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191 | |
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192 | const double * solution = model.solver()->getColSolution(); |
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193 | |
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194 | int iColumn; |
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195 | std::cout<<std::setiosflags(std::ios::fixed|std::ios::showpoint)<<std::setw(14); |
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196 | |
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197 | std::cout<<"--------------------------------------"<<std::endl; |
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198 | for (iColumn=0;iColumn<numberColumns;iColumn++) { |
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199 | double value=solution[iColumn]; |
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200 | if (fabs(value)>1.0e-7&&model.solver()->isInteger(iColumn)) |
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201 | std::cout<<std::setw(6)<<iColumn<<" "<<value<<std::endl; |
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202 | } |
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203 | std::cout<<"--------------------------------------"<<std::endl; |
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204 | |
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205 | std::cout<<std::resetiosflags(std::ios::fixed|std::ios::showpoint|std::ios::scientific); |
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206 | } |
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207 | return 0; |
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208 | } |
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