1 | // $Id: qmip2.cpp 1898 2013-04-09 18:06:04Z forrest $ |
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2 | // Copyright (C) 2006, 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 | |
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8 | #include "CoinPragma.hpp" |
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9 | |
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10 | // For Branch and bound |
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11 | #include "OsiClpSolverInterface.hpp" |
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12 | #include "CbcModel.hpp" |
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13 | #include "CbcCutGenerator.hpp" |
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14 | #include "CoinHelperFunctions.hpp" |
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15 | #include "CbcStrategy.hpp" |
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16 | |
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17 | // Need stored cuts |
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18 | |
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19 | #include "CglStored.hpp" |
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20 | |
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21 | // For saying about solution validity |
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22 | #include "OsiAuxInfo.hpp" |
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23 | |
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24 | |
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25 | // Time |
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26 | #include "CoinTime.hpp" |
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27 | // Class to disallow strong branching solutions |
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28 | #include "CbcFeasibilityBase.hpp" |
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29 | class CbcFeasibilityNoStrong : public CbcFeasibilityBase{ |
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30 | public: |
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31 | // Default Constructor |
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32 | CbcFeasibilityNoStrong () {} |
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33 | |
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34 | virtual ~CbcFeasibilityNoStrong() {} |
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35 | // Copy constructor |
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36 | CbcFeasibilityNoStrong ( const CbcFeasibilityNoStrong &rhs) {} |
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37 | |
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38 | // Assignment operator |
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39 | CbcFeasibilityNoStrong & operator=( const CbcFeasibilityNoStrong& rhs) |
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40 | { return * this;} |
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41 | |
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42 | /// Clone |
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43 | virtual CbcFeasibilityBase * clone() const |
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44 | { return new CbcFeasibilityNoStrong();} |
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45 | |
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46 | /** |
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47 | On input mode: |
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48 | 0 - called after a solve but before any cuts |
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49 | -1 - called after strong branching |
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50 | Returns : |
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51 | 0 - no opinion |
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52 | -1 pretend infeasible |
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53 | 1 pretend integer solution |
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54 | */ |
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55 | virtual int feasible(CbcModel * model, int mode) |
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56 | {return mode;} |
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57 | }; |
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58 | |
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59 | |
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60 | /************************************************************************ |
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61 | |
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62 | This main program solves the following 0-1 problem: |
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63 | |
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64 | min -x0 - 2x1 - 3x2 - 4x3 |
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65 | |
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66 | subject to |
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67 | |
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68 | x0 + x1 + x2 + x3 <= 2 |
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69 | |
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70 | and quadratic constraints with positive random numbers |
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71 | |
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72 | It does it creating extra yij variables and constraints xi + xj -1 <= yij |
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73 | and putting quadratic elements on y |
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74 | |
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75 | The extra constraints are treated as stored cuts. |
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76 | |
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77 | This is to show how to keep branching even if we have a solution |
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78 | |
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79 | ************************************************************************/ |
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80 | |
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81 | int main (int argc, const char *argv[]) |
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82 | { |
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83 | |
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84 | // Define a Solver which inherits from OsiClpsolverInterface -> OsiSolverInterface |
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85 | |
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86 | OsiClpSolverInterface solver1; |
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87 | |
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88 | int nX=4; |
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89 | |
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90 | int nY = (nX * (nX-1)/2); |
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91 | // All columns |
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92 | double * obj = new double [nX+nY]; |
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93 | double * clo = new double[nX+nY]; |
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94 | double * cup = new double[nX+nY]; |
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95 | int i; |
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96 | for (i=0;i<nX;i++) { |
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97 | obj[i] = -(i+1); |
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98 | clo[i]=0.0; |
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99 | cup[i]=1.0; |
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100 | } |
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101 | for (i=nX;i<nX+nY;i++) { |
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102 | obj[i] = 0.0; |
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103 | clo[i]=0.0; |
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104 | cup[i]=1.0; |
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105 | } |
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106 | // Just ordinary rows |
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107 | int nRow = 1+nX; |
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108 | double * rlo = new double[nRow]; |
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109 | double * rup = new double[nRow]; |
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110 | for (i=0;i<nRow;i++) { |
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111 | rlo[i]=-COIN_DBL_MAX; |
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112 | rup[i]=1.0; |
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113 | } |
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114 | // and first row |
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115 | rup[0]=nX/2.0; |
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116 | // Matrix |
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117 | int nEl = nX+nX*nX; |
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118 | int * row = new int[nEl]; |
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119 | int * col = new int[nEl]; |
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120 | double * el = new double[nEl]; |
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121 | // X |
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122 | nEl=0; |
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123 | // May need scale to make plausible |
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124 | double scaleFactor = 1.0; |
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125 | for (i=0;i<nX;i++) { |
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126 | row[nEl]=0; |
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127 | col[nEl]=i; |
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128 | el[nEl++]=1.0; |
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129 | // and diagonal |
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130 | row[nEl]=i+1; |
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131 | col[nEl]=i; |
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132 | double value = CoinDrand48()*scaleFactor; |
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133 | // make reasonable (so multiples of 0.000001) |
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134 | value *= 1.0e6; |
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135 | int iValue = (int) (value+1.0); |
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136 | value = iValue; |
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137 | value *= 1.0e-6; |
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138 | el[nEl++]=value; |
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139 | } |
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140 | // Y |
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141 | nY = nX; |
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142 | // And stored cuts |
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143 | CglStored stored; |
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144 | double cutEls[3]={1.0,1.0,-1.0}; |
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145 | int cutIndices[3]; |
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146 | for (i=0;i<nX;i++) { |
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147 | cutIndices[0]=i; |
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148 | for (int j=i+1;j<nX;j++) { |
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149 | cutIndices[1]=j; |
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150 | cutIndices[2]=nY; |
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151 | // add cut |
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152 | stored.addCut(-COIN_DBL_MAX,1.0,3,cutIndices,cutEls); |
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153 | row[nEl]=i+1; |
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154 | col[nEl]=nY; |
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155 | double value = CoinDrand48()*scaleFactor; |
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156 | // multiply to make ones with most negative objective violated |
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157 | // make reasonable (so multiples of 0.000001) |
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158 | value *= 1.0e6+1.0e6*j; |
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159 | int iValue = (int) (value+1.0); |
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160 | value = iValue; |
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161 | value *= 1.0e-6; |
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162 | el[nEl++]=value; |
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163 | // and other |
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164 | if (i!=j) { |
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165 | row[nEl]=j+1; |
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166 | col[nEl]=nY; |
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167 | el[nEl++]=value; |
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168 | } |
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169 | nY++; |
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170 | } |
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171 | } |
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172 | // Create model |
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173 | CoinPackedMatrix matrix(true, row, col, el, nEl); |
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174 | solver1.loadProblem(matrix, clo, cup, obj, rlo, rup); |
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175 | delete [] obj; |
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176 | delete [] clo; |
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177 | delete [] cup; |
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178 | delete [] rlo; |
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179 | delete [] rup; |
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180 | delete [] row; |
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181 | delete [] col; |
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182 | delete [] el; |
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183 | // Integers |
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184 | for (i=0;i<nX;i++) |
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185 | solver1.setInteger(i); |
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186 | // Reduce printout |
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187 | solver1.setHintParam(OsiDoReducePrint,true,OsiHintTry); |
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188 | // This clones solver |
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189 | CbcModel model(solver1); |
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190 | // Add stored cuts (making sure at all depths) |
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191 | model.addCutGenerator(&stored,1,"Stored",true,false,false,-100,1,-1); |
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192 | /* You need the next few lines - |
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193 | a) so that cut generator will always be called again if it generated cuts |
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194 | b) it is known that matrix is not enough to define problem so do cuts even |
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195 | if it looks integer feasible at continuous optimum. |
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196 | c) a solution found by strong branching will be ignored. |
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197 | d) don't recompute a solution once found |
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198 | */ |
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199 | // Make sure cut generator called correctly (a) |
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200 | model.cutGenerator(0)->setMustCallAgain(true); |
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201 | // Say cuts needed at continuous (b) |
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202 | OsiBabSolver oddCuts; |
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203 | oddCuts.setSolverType(4); |
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204 | model.passInSolverCharacteristics(&oddCuts); |
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205 | // Say no to all solutions by strong branching (c) |
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206 | CbcFeasibilityNoStrong noStrong; |
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207 | model.setProblemFeasibility(noStrong); |
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208 | // Say don't recompute solution d) |
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209 | model.setSpecialOptions(4); |
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210 | |
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211 | double time1 = CoinCpuTime(); |
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212 | |
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213 | // Do complete search |
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214 | |
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215 | model.branchAndBound(); |
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216 | |
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217 | std::cout<<argv[1]<<" took "<<CoinCpuTime()-time1<<" seconds, " |
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218 | <<model.getNodeCount()<<" nodes with objective " |
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219 | <<model.getObjValue() |
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220 | <<(!model.status() ? " Finished" : " Not finished") |
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221 | <<std::endl; |
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222 | |
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223 | // Print solution if finished - we can't get names from Osi! |
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224 | |
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225 | if (!model.status()&&model.getMinimizationObjValue()<1.0e50) { |
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226 | int numberColumns = model.solver()->getNumCols(); |
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227 | |
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228 | //const double * solution = model.bestSolution(); |
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229 | const double * solution = model.solver()->getColSolution(); |
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230 | |
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231 | int iColumn; |
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232 | for (iColumn=0;iColumn<numberColumns;iColumn++) { |
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233 | double value=solution[iColumn]; |
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234 | if (fabs(value)>1.0e-7&&model.solver()->isInteger(iColumn)) |
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235 | printf("Column %d has value %g\n",iColumn,value); |
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236 | } |
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237 | } |
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238 | return 0; |
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239 | } |
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