1 | // Copyright (C) 2002, International Business Machines |
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2 | // Corporation and others. All Rights Reserved. |
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3 | |
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4 | #include "CoinPragma.hpp" |
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5 | #include "ClpSimplex.hpp" |
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6 | #include "ClpDualRowSteepest.hpp" |
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7 | #include "CoinIndexedVector.hpp" |
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8 | #include "ClpFactorization.hpp" |
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9 | #include "CoinHelperFunctions.hpp" |
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10 | #include <cstdio> |
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11 | |
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12 | //############################################################################# |
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13 | // Constructors / Destructor / Assignment |
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14 | //############################################################################# |
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15 | |
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16 | //------------------------------------------------------------------- |
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17 | // Default Constructor |
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18 | //------------------------------------------------------------------- |
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19 | ClpDualRowSteepest::ClpDualRowSteepest (int mode) |
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20 | : ClpDualRowPivot(), |
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21 | state_(-1), |
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22 | mode_(mode), |
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23 | weights_(NULL), |
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24 | infeasible_(NULL), |
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25 | alternateWeights_(NULL), |
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26 | savedWeights_(NULL) |
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27 | { |
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28 | type_=2+64*mode; |
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29 | } |
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30 | |
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31 | //------------------------------------------------------------------- |
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32 | // Copy constructor |
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33 | //------------------------------------------------------------------- |
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34 | ClpDualRowSteepest::ClpDualRowSteepest (const ClpDualRowSteepest & rhs) |
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35 | : ClpDualRowPivot(rhs) |
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36 | { |
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37 | state_=rhs.state_; |
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38 | mode_ = rhs.mode_; |
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39 | model_ = rhs.model_; |
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40 | if (rhs.infeasible_) { |
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41 | infeasible_= new CoinIndexedVector(rhs.infeasible_); |
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42 | } else { |
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43 | infeasible_=NULL; |
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44 | } |
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45 | if (rhs.weights_) { |
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46 | assert(model_); |
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47 | int number = model_->numberRows(); |
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48 | weights_= new double[number]; |
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49 | ClpDisjointCopyN(rhs.weights_,number,weights_); |
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50 | } else { |
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51 | weights_=NULL; |
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52 | } |
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53 | if (rhs.alternateWeights_) { |
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54 | alternateWeights_= new CoinIndexedVector(rhs.alternateWeights_); |
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55 | } else { |
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56 | alternateWeights_=NULL; |
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57 | } |
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58 | if (rhs.savedWeights_) { |
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59 | savedWeights_= new CoinIndexedVector(rhs.savedWeights_); |
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60 | } else { |
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61 | savedWeights_=NULL; |
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62 | } |
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63 | } |
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64 | |
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65 | //------------------------------------------------------------------- |
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66 | // Destructor |
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67 | //------------------------------------------------------------------- |
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68 | ClpDualRowSteepest::~ClpDualRowSteepest () |
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69 | { |
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70 | delete [] weights_; |
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71 | delete infeasible_; |
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72 | delete alternateWeights_; |
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73 | delete savedWeights_; |
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74 | } |
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75 | |
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76 | //---------------------------------------------------------------- |
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77 | // Assignment operator |
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78 | //------------------------------------------------------------------- |
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79 | ClpDualRowSteepest & |
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80 | ClpDualRowSteepest::operator=(const ClpDualRowSteepest& rhs) |
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81 | { |
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82 | if (this != &rhs) { |
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83 | ClpDualRowPivot::operator=(rhs); |
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84 | state_=rhs.state_; |
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85 | mode_ = rhs.mode_; |
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86 | model_ = rhs.model_; |
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87 | delete [] weights_; |
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88 | delete infeasible_; |
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89 | delete alternateWeights_; |
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90 | delete savedWeights_; |
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91 | if (rhs.infeasible_!=NULL) { |
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92 | infeasible_= new CoinIndexedVector(rhs.infeasible_); |
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93 | } else { |
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94 | infeasible_=NULL; |
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95 | } |
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96 | if (rhs.weights_!=NULL) { |
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97 | assert(model_); |
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98 | int number = model_->numberRows(); |
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99 | weights_= new double[number]; |
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100 | ClpDisjointCopyN(rhs.weights_,number,weights_); |
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101 | } else { |
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102 | weights_=NULL; |
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103 | } |
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104 | if (rhs.alternateWeights_!=NULL) { |
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105 | alternateWeights_= new CoinIndexedVector(rhs.alternateWeights_); |
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106 | } else { |
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107 | alternateWeights_=NULL; |
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108 | } |
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109 | if (rhs.savedWeights_!=NULL) { |
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110 | savedWeights_= new CoinIndexedVector(rhs.savedWeights_); |
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111 | } else { |
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112 | savedWeights_=NULL; |
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113 | } |
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114 | } |
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115 | return *this; |
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116 | } |
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117 | |
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118 | // Returns pivot row, -1 if none |
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119 | int |
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120 | ClpDualRowSteepest::pivotRow() |
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121 | { |
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122 | assert(model_); |
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123 | int i,iRow; |
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124 | double * infeas = infeasible_->denseVector(); |
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125 | double largest=1.0e-50; |
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126 | int * index = infeasible_->getIndices(); |
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127 | int number = infeasible_->getNumElements(); |
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128 | const int * pivotVariable =model_->pivotVariable(); |
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129 | int chosenRow=-1; |
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130 | int lastPivotRow = model_->pivotRow(); |
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131 | double tolerance=model_->currentPrimalTolerance(); |
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132 | // we can't really trust infeasibilities if there is primal error |
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133 | // this coding has to mimic coding in checkPrimalSolution |
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134 | double error = min(1.0e-3,model_->largestPrimalError()); |
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135 | // allow tolerance at least slightly bigger than standard |
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136 | tolerance = tolerance + error; |
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137 | tolerance *= tolerance; // as we are using squares |
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138 | double * solution = model_->solutionRegion(); |
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139 | double * lower = model_->lowerRegion(); |
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140 | double * upper = model_->upperRegion(); |
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141 | // do last pivot row one here |
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142 | //#define COLUMN_BIAS 4.0 |
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143 | //#define FIXED_BIAS 10.0 |
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144 | if (lastPivotRow>=0) { |
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145 | #ifdef COLUMN_BIAS |
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146 | int numberColumns = model_->numberColumns(); |
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147 | #endif |
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148 | int iPivot=pivotVariable[lastPivotRow]; |
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149 | double value = solution[iPivot]; |
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150 | double lower = model_->lower(iPivot); |
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151 | double upper = model_->upper(iPivot); |
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152 | if (value>upper+tolerance) { |
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153 | value -= upper; |
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154 | value *= value; |
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155 | #ifdef COLUMN_BIAS |
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156 | if (iPivot<numberColumns) |
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157 | value *= COLUMN_BIAS; // bias towards columns |
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158 | #endif |
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159 | // store square in list |
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160 | if (infeas[lastPivotRow]) |
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161 | infeas[lastPivotRow] = value; // already there |
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162 | else |
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163 | infeasible_->quickAdd(lastPivotRow,value); |
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164 | } else if (value<lower-tolerance) { |
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165 | value -= lower; |
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166 | value *= value; |
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167 | #ifdef COLUMN_BIAS |
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168 | if (iPivot<numberColumns) |
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169 | value *= COLUMN_BIAS; // bias towards columns |
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170 | #endif |
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171 | // store square in list |
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172 | if (infeas[lastPivotRow]) |
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173 | infeas[lastPivotRow] = value; // already there |
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174 | else |
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175 | infeasible_->add(lastPivotRow,value); |
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176 | } else { |
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177 | // feasible - was it infeasible - if so set tiny |
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178 | if (infeas[lastPivotRow]) |
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179 | infeas[lastPivotRow] = 1.0e-100; |
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180 | } |
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181 | number = infeasible_->getNumElements(); |
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182 | } |
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183 | for (i=0;i<number;i++) { |
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184 | iRow = index[i]; |
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185 | double value = infeas[iRow]; |
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186 | if (value>largest*weights_[iRow]&&value>tolerance) { |
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187 | // make last pivot row last resort choice |
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188 | if (iRow==lastPivotRow) { |
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189 | if (value*1.0e-10<largest*weights_[iRow]) |
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190 | continue; |
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191 | else |
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192 | value *= 1.0e-10; |
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193 | } |
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194 | int iSequence = pivotVariable[iRow]; |
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195 | if (!model_->flagged(iSequence)) { |
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196 | //#define CLP_DEBUG 1 |
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197 | #ifdef CLP_DEBUG |
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198 | double value2=0.0; |
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199 | if (solution[iSequence]>upper[iSequence]+tolerance) |
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200 | value2=solution[iSequence]-upper[iSequence]; |
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201 | else if (solution[iSequence]<lower[iSequence]-tolerance) |
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202 | value2=solution[iSequence]-lower[iSequence]; |
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203 | assert(fabs(value2*value2-infeas[iRow])<1.0e-8*min(value2*value2,infeas[iRow])); |
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204 | #endif |
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205 | if (solution[iSequence]>upper[iSequence]+tolerance|| |
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206 | solution[iSequence]<lower[iSequence]-tolerance) { |
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207 | chosenRow=iRow; |
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208 | largest=value/weights_[iRow]; |
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209 | } |
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210 | } |
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211 | } |
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212 | } |
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213 | return chosenRow; |
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214 | } |
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215 | #define TRY_NORM 1.0e-4 |
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216 | // Updates weights |
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217 | void |
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218 | ClpDualRowSteepest::updateWeights(CoinIndexedVector * input, |
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219 | CoinIndexedVector * spare, |
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220 | CoinIndexedVector * updatedColumn) |
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221 | { |
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222 | // clear other region |
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223 | alternateWeights_->clear(); |
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224 | double norm = 0.0; |
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225 | double * work = input->denseVector(); |
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226 | int number = input->getNumElements(); |
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227 | int * which = input->getIndices(); |
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228 | double * work2 = spare->denseVector(); |
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229 | int * which2 = spare->getIndices(); |
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230 | double * work3 = alternateWeights_->denseVector(); |
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231 | int * which3 = alternateWeights_->getIndices(); |
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232 | int i; |
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233 | #if CLP_DEBUG>2 |
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234 | // Very expensive debug |
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235 | { |
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236 | int numberRows = model_->numberRows(); |
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237 | CoinIndexedVector * temp = new CoinIndexedVector(); |
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238 | temp->reserve(numberRows+ |
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239 | model_->factorization()->maximumPivots()); |
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240 | double * array = alternateWeights_->denseVector(); |
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241 | int * which = alternateWeights_->getIndices(); |
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242 | for (i=0;i<numberRows;i++) { |
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243 | double value=0.0; |
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244 | array[i] = 1.0; |
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245 | which[0] = i; |
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246 | alternateWeights_->setNumElements(1); |
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247 | model_->factorization()->updateColumnTranspose(temp, |
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248 | alternateWeights_); |
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249 | int number = alternateWeights_->getNumElements(); |
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250 | int j; |
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251 | for (j=0;j<number;j++) { |
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252 | int iRow=which[j]; |
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253 | value+=array[iRow]*array[iRow]; |
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254 | array[iRow]=0.0; |
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255 | } |
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256 | alternateWeights_->setNumElements(0); |
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257 | if (fabs(weights_[i]-value)>1.0e-4) |
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258 | printf("%d old %g, true %g\n",i,weights_[i],value); |
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259 | } |
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260 | delete temp; |
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261 | } |
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262 | #endif |
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263 | for (i=0;i<number;i++) { |
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264 | int iRow = which[i]; |
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265 | double value = work[iRow]; |
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266 | norm += value*value; |
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267 | work2[iRow]=value; |
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268 | which2[i]=iRow; |
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269 | } |
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270 | spare->setNumElements(number); |
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271 | // ftran |
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272 | model_->factorization()->updateColumn(alternateWeights_,spare); |
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273 | // alternateWeights_ should still be empty |
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274 | int pivotRow = model_->pivotRow(); |
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275 | #ifdef CLP_DEBUG |
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276 | if ( model_->logLevel ( ) >4 && |
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277 | fabs(norm-weights_[pivotRow])>1.0e-3*(1.0+norm)) |
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278 | printf("on row %d, true weight %g, old %g\n", |
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279 | pivotRow,sqrt(norm),sqrt(weights_[pivotRow])); |
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280 | #endif |
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281 | // could re-initialize here (could be expensive) |
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282 | norm /= model_->alpha() * model_->alpha(); |
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283 | |
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284 | assert(norm); |
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285 | if (norm < TRY_NORM) |
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286 | norm = TRY_NORM; |
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287 | if (norm != 0.) { |
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288 | double multiplier = 2.0 / model_->alpha(); |
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289 | // look at updated column |
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290 | work = updatedColumn->denseVector(); |
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291 | number = updatedColumn->getNumElements(); |
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292 | which = updatedColumn->getIndices(); |
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293 | |
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294 | int nSave=0; |
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295 | |
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296 | for (i =0; i < number; i++) { |
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297 | int iRow = which[i]; |
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298 | double theta = work[iRow]; |
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299 | if (theta) { |
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300 | double devex = weights_[iRow]; |
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301 | work3[iRow]=devex; // save old |
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302 | which3[nSave++]=iRow; |
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303 | double value = work2[iRow]; |
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304 | devex += theta * (theta*norm+value * multiplier); |
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305 | if (devex < TRY_NORM) |
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306 | devex = TRY_NORM; |
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307 | weights_[iRow]=devex; |
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308 | } |
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309 | } |
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310 | #ifdef CLP_DEBUG |
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311 | assert(work3[pivotRow]&&work[pivotRow]); |
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312 | #endif |
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313 | alternateWeights_->setNumElements(nSave); |
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314 | if (norm < TRY_NORM) |
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315 | norm = TRY_NORM; |
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316 | weights_[pivotRow] = norm; |
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317 | } |
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318 | spare->clear(); |
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319 | #ifdef CLP_DEBUG |
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320 | spare->checkClear(); |
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321 | #endif |
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322 | } |
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323 | |
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324 | /* Updates primal solution (and maybe list of candidates) |
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325 | Uses input vector which it deletes |
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326 | Computes change in objective function |
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327 | */ |
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328 | void |
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329 | ClpDualRowSteepest::updatePrimalSolution( |
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330 | CoinIndexedVector * primalUpdate, |
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331 | double primalRatio, |
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332 | double & objectiveChange) |
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333 | { |
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334 | double * work = primalUpdate->denseVector(); |
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335 | int number = primalUpdate->getNumElements(); |
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336 | int * which = primalUpdate->getIndices(); |
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337 | int i; |
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338 | double changeObj=0.0; |
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339 | double tolerance=model_->currentPrimalTolerance(); |
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340 | const int * pivotVariable = model_->pivotVariable(); |
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341 | double * infeas = infeasible_->denseVector(); |
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342 | int pivotRow = model_->pivotRow(); |
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343 | double * solution = model_->solutionRegion(); |
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344 | #ifdef COLUMN_BIAS |
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345 | int numberColumns = model_->numberColumns(); |
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346 | #endif |
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347 | for (i=0;i<number;i++) { |
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348 | int iRow=which[i]; |
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349 | int iPivot=pivotVariable[iRow]; |
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350 | double value = solution[iPivot]; |
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351 | double cost = model_->cost(iPivot); |
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352 | double change = primalRatio*work[iRow]; |
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353 | value -= change; |
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354 | changeObj -= change*cost; |
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355 | solution[iPivot] = value; |
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356 | double lower = model_->lower(iPivot); |
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357 | double upper = model_->upper(iPivot); |
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358 | // But if pivot row then use value of incoming |
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359 | // Although it is safer to recompute before next selection |
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360 | // in case something odd happens |
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361 | if (iRow==pivotRow) { |
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362 | iPivot = model_->sequenceIn(); |
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363 | lower = model_->lower(iPivot); |
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364 | upper = model_->upper(iPivot); |
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365 | value = model_->valueIncomingDual(); |
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366 | } |
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367 | if (value<lower-tolerance) { |
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368 | value -= lower; |
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369 | value *= value; |
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370 | #ifdef COLUMN_BIAS |
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371 | if (iPivot<numberColumns) |
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372 | value *= COLUMN_BIAS; // bias towards columns |
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373 | #endif |
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374 | #ifdef FIXED_BIAS |
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375 | if (lower==upper) |
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376 | value *= FIXED_BIAS; // bias towards taking out fixed variables |
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377 | #endif |
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378 | // store square in list |
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379 | if (infeas[iRow]) |
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380 | infeas[iRow] = value; // already there |
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381 | else |
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382 | infeasible_->quickAdd(iRow,value); |
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383 | } else if (value>upper+tolerance) { |
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384 | value -= upper; |
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385 | value *= value; |
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386 | #ifdef COLUMN_BIAS |
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387 | if (iPivot<numberColumns) |
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388 | value *= COLUMN_BIAS; // bias towards columns |
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389 | #endif |
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390 | #ifdef FIXED_BIAS |
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391 | if (lower==upper) |
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392 | value *= FIXED_BIAS; // bias towards taking out fixed variables |
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393 | #endif |
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394 | // store square in list |
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395 | if (infeas[iRow]) |
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396 | infeas[iRow] = value; // already there |
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397 | else |
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398 | infeasible_->quickAdd(iRow,value); |
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399 | } else { |
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400 | // feasible - was it infeasible - if so set tiny |
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401 | if (infeas[iRow]) |
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402 | infeas[iRow] = 1.0e-100; |
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403 | } |
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404 | work[iRow]=0.0; |
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405 | } |
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406 | primalUpdate->setNumElements(0); |
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407 | objectiveChange += changeObj; |
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408 | } |
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409 | /* Saves any weights round factorization as pivot rows may change |
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410 | 1) before factorization |
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411 | 2) after factorization |
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412 | 3) just redo infeasibilities |
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413 | 4) restore weights |
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414 | */ |
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415 | void |
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416 | ClpDualRowSteepest::saveWeights(ClpSimplex * model,int mode) |
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417 | { |
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418 | // alternateWeights_ is defined as indexed but is treated oddly |
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419 | model_ = model; |
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420 | int numberRows = model_->numberRows(); |
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421 | int numberColumns = model_->numberColumns(); |
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422 | const int * pivotVariable = model_->pivotVariable(); |
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423 | int i; |
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424 | if (mode==1&&weights_) { |
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425 | alternateWeights_->clear(); |
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426 | // change from row numbers to sequence numbers |
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427 | int * which = alternateWeights_->getIndices(); |
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428 | for (i=0;i<numberRows;i++) { |
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429 | int iPivot=pivotVariable[i]; |
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430 | which[i]=iPivot; |
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431 | } |
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432 | state_=1; |
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433 | } else if (mode==2||mode==4||mode==5) { |
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434 | // restore |
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435 | if (!weights_||state_==-1||mode==5) { |
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436 | // initialize weights |
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437 | delete [] weights_; |
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438 | delete alternateWeights_; |
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439 | weights_ = new double[numberRows]; |
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440 | alternateWeights_ = new CoinIndexedVector(); |
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441 | // enough space so can use it for factorization |
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442 | alternateWeights_->reserve(numberRows+ |
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443 | model_->factorization()->maximumPivots()); |
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444 | if (!mode_||mode==5) { |
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445 | // initialize to 1.0 (can we do better?) |
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446 | for (i=0;i<numberRows;i++) { |
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447 | weights_[i]=1.0; |
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448 | } |
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449 | } else { |
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450 | CoinIndexedVector * temp = new CoinIndexedVector(); |
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451 | temp->reserve(numberRows+ |
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452 | model_->factorization()->maximumPivots()); |
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453 | double * array = alternateWeights_->denseVector(); |
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454 | int * which = alternateWeights_->getIndices(); |
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455 | for (i=0;i<numberRows;i++) { |
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456 | double value=0.0; |
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457 | array[i] = 1.0; |
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458 | which[0] = i; |
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459 | alternateWeights_->setNumElements(1); |
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460 | model_->factorization()->updateColumnTranspose(temp, |
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461 | alternateWeights_); |
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462 | int number = alternateWeights_->getNumElements(); |
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463 | int j; |
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464 | for (j=0;j<number;j++) { |
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465 | int iRow=which[j]; |
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466 | value+=array[iRow]*array[iRow]; |
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467 | array[iRow]=0.0; |
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468 | } |
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469 | alternateWeights_->setNumElements(0); |
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470 | weights_[i] = value; |
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471 | } |
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472 | delete temp; |
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473 | } |
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474 | // create saved weights (not really indexedvector) |
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475 | savedWeights_ = new CoinIndexedVector(); |
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476 | savedWeights_->reserve(numberRows); |
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477 | |
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478 | double * array = savedWeights_->denseVector(); |
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479 | int * which = savedWeights_->getIndices(); |
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480 | for (i=0;i<numberRows;i++) { |
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481 | array[i]=weights_[i]; |
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482 | which[i]=pivotVariable[i]; |
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483 | } |
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484 | } else { |
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485 | int * which = alternateWeights_->getIndices(); |
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486 | if (mode!=4) { |
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487 | // save |
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488 | memcpy(savedWeights_->getIndices(),which, |
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489 | numberRows*sizeof(int)); |
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490 | memcpy(savedWeights_->denseVector(),weights_, |
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491 | numberRows*sizeof(double)); |
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492 | } else { |
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493 | // restore |
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494 | memcpy(which,savedWeights_->getIndices(), |
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495 | numberRows*sizeof(int)); |
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496 | memcpy(weights_,savedWeights_->denseVector(), |
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497 | numberRows*sizeof(double)); |
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498 | } |
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499 | // restore (a bit slow - but only every re-factorization) |
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500 | double * array = new double[numberRows+numberColumns]; |
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501 | for (i=0;i<numberRows;i++) { |
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502 | int iSeq=which[i]; |
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503 | array[iSeq]=weights_[i]; |
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504 | } |
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505 | for (i=0;i<numberRows;i++) { |
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506 | int iPivot=pivotVariable[i]; |
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507 | weights_[i]=array[iPivot]; |
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508 | if (weights_[i]<TRY_NORM) |
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509 | weights_[i] = TRY_NORM; // may need to check more |
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510 | } |
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511 | delete [] array; |
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512 | } |
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513 | state_=0; |
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514 | // set up infeasibilities |
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515 | if (!infeasible_) { |
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516 | infeasible_ = new CoinIndexedVector(); |
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517 | infeasible_->reserve(numberRows); |
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518 | } |
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519 | } |
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520 | if (mode>=2) { |
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521 | infeasible_->clear(); |
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522 | int iRow; |
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523 | const int * pivotVariable = model_->pivotVariable(); |
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524 | double tolerance=model_->currentPrimalTolerance(); |
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525 | for (iRow=0;iRow<numberRows;iRow++) { |
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526 | int iPivot=pivotVariable[iRow]; |
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527 | double value = model_->solution(iPivot); |
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528 | double lower = model_->lower(iPivot); |
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529 | double upper = model_->upper(iPivot); |
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530 | if (value<lower-tolerance) { |
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531 | value -= lower; |
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532 | value *= value; |
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533 | #ifdef COLUMN_BIAS |
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534 | if (iPivot<numberColumns) |
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535 | value *= COLUMN_BIAS; // bias towards columns |
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536 | #endif |
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537 | #ifdef FIXED_BIAS |
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538 | if (lower==upper) |
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539 | value *= FIXED_BIAS; // bias towards taking out fixed variables |
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540 | #endif |
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541 | // store square in list |
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542 | infeasible_->quickAdd(iRow,value); |
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543 | } else if (value>upper+tolerance) { |
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544 | value -= upper; |
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545 | value *= value; |
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546 | #ifdef COLUMN_BIAS |
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547 | if (iPivot<numberColumns) |
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548 | value *= COLUMN_BIAS; // bias towards columns |
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549 | #endif |
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550 | #ifdef FIXED_BIAS |
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551 | if (lower==upper) |
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552 | value *= FIXED_BIAS; // bias towards taking out fixed variables |
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553 | #endif |
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554 | // store square in list |
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555 | infeasible_->quickAdd(iRow,value); |
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556 | } |
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557 | } |
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558 | } |
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559 | } |
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560 | // Gets rid of last update |
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561 | void |
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562 | ClpDualRowSteepest::unrollWeights() |
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563 | { |
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564 | double * saved = alternateWeights_->denseVector(); |
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565 | int number = alternateWeights_->getNumElements(); |
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566 | int * which = alternateWeights_->getIndices(); |
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567 | int i; |
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568 | for (i=0;i<number;i++) { |
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569 | int iRow = which[i]; |
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570 | weights_[iRow]=saved[iRow]; |
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571 | saved[iRow]=0.0; |
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572 | } |
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573 | alternateWeights_->setNumElements(0); |
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574 | } |
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575 | //------------------------------------------------------------------- |
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576 | // Clone |
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577 | //------------------------------------------------------------------- |
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578 | ClpDualRowPivot * ClpDualRowSteepest::clone(bool CopyData) const |
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579 | { |
---|
580 | if (CopyData) { |
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581 | return new ClpDualRowSteepest(*this); |
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582 | } else { |
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583 | return new ClpDualRowSteepest(); |
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584 | } |
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585 | } |
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586 | // Gets rid of all arrays |
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587 | void |
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588 | ClpDualRowSteepest::clearArrays() |
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589 | { |
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590 | delete [] weights_; |
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591 | weights_=NULL; |
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592 | delete infeasible_; |
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593 | infeasible_ = NULL; |
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594 | delete alternateWeights_; |
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595 | alternateWeights_ = NULL; |
---|
596 | delete savedWeights_; |
---|
597 | savedWeights_ = NULL; |
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598 | state_ =-1; |
---|
599 | } |
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600 | |
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