1 | /* $Id: ClpSimplexDual.hpp 1502 2010-01-29 14:25:07Z forrest $ */ |
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2 | // Copyright (C) 2002, International Business Machines |
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3 | // Corporation and others. All Rights Reserved. |
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4 | |
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5 | /* |
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6 | Authors |
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7 | |
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8 | John Forrest |
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9 | |
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10 | */ |
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11 | #ifndef ClpSimplexDual_H |
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12 | #define ClpSimplexDual_H |
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13 | |
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14 | #include "ClpSimplex.hpp" |
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15 | |
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16 | /** This solves LPs using the dual simplex method |
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17 | |
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18 | It inherits from ClpSimplex. It has no data of its own and |
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19 | is never created - only cast from a ClpSimplex object at algorithm time. |
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20 | |
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21 | */ |
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22 | |
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23 | class ClpSimplexDual : public ClpSimplex { |
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24 | |
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25 | public: |
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26 | |
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27 | /**@name Description of algorithm */ |
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28 | //@{ |
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29 | /** Dual algorithm |
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30 | |
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31 | Method |
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32 | |
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33 | It tries to be a single phase approach with a weight of 1.0 being |
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34 | given to getting optimal and a weight of updatedDualBound_ being |
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35 | given to getting dual feasible. In this version I have used the |
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36 | idea that this weight can be thought of as a fake bound. If the |
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37 | distance between the lower and upper bounds on a variable is less |
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38 | than the feasibility weight then we are always better off flipping |
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39 | to other bound to make dual feasible. If the distance is greater |
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40 | then we make up a fake bound updatedDualBound_ away from one bound. |
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41 | If we end up optimal or primal infeasible, we check to see if |
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42 | bounds okay. If so we have finished, if not we increase updatedDualBound_ |
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43 | and continue (after checking if unbounded). I am undecided about |
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44 | free variables - there is coding but I am not sure about it. At |
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45 | present I put them in basis anyway. |
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46 | |
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47 | The code is designed to take advantage of sparsity so arrays are |
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48 | seldom zeroed out from scratch or gone over in their entirety. |
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49 | The only exception is a full scan to find outgoing variable for |
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50 | Dantzig row choice. For steepest edge we keep an updated list |
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51 | of infeasibilities (actually squares). |
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52 | On easy problems we don't need full scan - just |
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53 | pick first reasonable. |
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54 | |
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55 | One problem is how to tackle degeneracy and accuracy. At present |
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56 | I am using the modification of costs which I put in OSL and some |
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57 | of what I think is the dual analog of Gill et al. |
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58 | I am still not sure of the exact details. |
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59 | |
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60 | The flow of dual is three while loops as follows: |
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61 | |
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62 | while (not finished) { |
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63 | |
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64 | while (not clean solution) { |
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65 | |
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66 | Factorize and/or clean up solution by flipping variables so |
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67 | dual feasible. If looks finished check fake dual bounds. |
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68 | Repeat until status is iterating (-1) or finished (0,1,2) |
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69 | |
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70 | } |
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71 | |
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72 | while (status==-1) { |
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73 | |
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74 | Iterate until no pivot in or out or time to re-factorize. |
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75 | |
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76 | Flow is: |
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77 | |
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78 | choose pivot row (outgoing variable). if none then |
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79 | we are primal feasible so looks as if done but we need to |
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80 | break and check bounds etc. |
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81 | |
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82 | Get pivot row in tableau |
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83 | |
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84 | Choose incoming column. If we don't find one then we look |
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85 | primal infeasible so break and check bounds etc. (Also the |
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86 | pivot tolerance is larger after any iterations so that may be |
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87 | reason) |
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88 | |
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89 | If we do find incoming column, we may have to adjust costs to |
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90 | keep going forwards (anti-degeneracy). Check pivot will be stable |
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91 | and if unstable throw away iteration and break to re-factorize. |
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92 | If minor error re-factorize after iteration. |
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93 | |
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94 | Update everything (this may involve flipping variables to stay |
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95 | dual feasible. |
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96 | |
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97 | } |
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98 | |
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99 | } |
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100 | |
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101 | TODO's (or maybe not) |
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102 | |
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103 | At present we never check we are going forwards. I overdid that in |
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104 | OSL so will try and make a last resort. |
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105 | |
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106 | Needs partial scan pivot out option. |
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107 | |
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108 | May need other anti-degeneracy measures, especially if we try and use |
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109 | loose tolerances as a way to solve in fewer iterations. |
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110 | |
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111 | I like idea of dynamic scaling. This gives opportunity to decouple |
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112 | different implications of scaling for accuracy, iteration count and |
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113 | feasibility tolerance. |
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114 | |
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115 | for use of exotic parameter startFinishoptions see Clpsimplex.hpp |
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116 | */ |
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117 | |
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118 | int dual(int ifValuesPass, int startFinishOptions = 0); |
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119 | /** For strong branching. On input lower and upper are new bounds |
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120 | while on output they are change in objective function values |
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121 | (>1.0e50 infeasible). |
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122 | Return code is 0 if nothing interesting, -1 if infeasible both |
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123 | ways and +1 if infeasible one way (check values to see which one(s)) |
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124 | Solutions are filled in as well - even down, odd up - also |
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125 | status and number of iterations |
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126 | */ |
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127 | int strongBranching(int numberVariables, const int * variables, |
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128 | double * newLower, double * newUpper, |
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129 | double ** outputSolution, |
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130 | int * outputStatus, int * outputIterations, |
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131 | bool stopOnFirstInfeasible = true, |
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132 | bool alwaysFinish = false, |
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133 | int startFinishOptions = 0); |
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134 | /// This does first part of StrongBranching |
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135 | ClpFactorization * setupForStrongBranching(char * arrays, int numberRows, |
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136 | int numberColumns, bool solveLp = false); |
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137 | /// This cleans up after strong branching |
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138 | void cleanupAfterStrongBranching(ClpFactorization * factorization); |
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139 | //@} |
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140 | |
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141 | /**@name Functions used in dual */ |
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142 | //@{ |
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143 | /** This has the flow between re-factorizations |
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144 | Broken out for clarity and will be used by strong branching |
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145 | |
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146 | Reasons to come out: |
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147 | -1 iterations etc |
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148 | -2 inaccuracy |
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149 | -3 slight inaccuracy (and done iterations) |
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150 | +0 looks optimal (might be unbounded - but we will investigate) |
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151 | +1 looks infeasible |
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152 | +3 max iterations |
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153 | |
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154 | If givenPi not NULL then in values pass |
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155 | */ |
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156 | int whileIterating(double * & givenPi, int ifValuesPass); |
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157 | /** The duals are updated by the given arrays. |
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158 | Returns number of infeasibilities. |
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159 | After rowArray and columnArray will just have those which |
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160 | have been flipped. |
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161 | Variables may be flipped between bounds to stay dual feasible. |
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162 | The output vector has movement of primal |
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163 | solution (row length array) */ |
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164 | int updateDualsInDual(CoinIndexedVector * rowArray, |
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165 | CoinIndexedVector * columnArray, |
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166 | CoinIndexedVector * outputArray, |
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167 | double theta, |
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168 | double & objectiveChange, |
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169 | bool fullRecompute); |
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170 | /** The duals are updated by the given arrays. |
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171 | This is in values pass - so no changes to primal is made |
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172 | */ |
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173 | void updateDualsInValuesPass(CoinIndexedVector * rowArray, |
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174 | CoinIndexedVector * columnArray, |
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175 | double theta); |
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176 | /** While updateDualsInDual sees what effect is of flip |
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177 | this does actual flipping. |
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178 | */ |
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179 | void flipBounds(CoinIndexedVector * rowArray, |
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180 | CoinIndexedVector * columnArray); |
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181 | /** |
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182 | Row array has row part of pivot row |
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183 | Column array has column part. |
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184 | This chooses pivot column. |
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185 | Spare arrays are used to save pivots which will go infeasible |
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186 | We will check for basic so spare array will never overflow. |
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187 | If necessary will modify costs |
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188 | For speed, we may need to go to a bucket approach when many |
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189 | variables are being flipped. |
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190 | Returns best possible pivot value |
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191 | */ |
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192 | double dualColumn(CoinIndexedVector * rowArray, |
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193 | CoinIndexedVector * columnArray, |
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194 | CoinIndexedVector * spareArray, |
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195 | CoinIndexedVector * spareArray2, |
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196 | double accpetablePivot, |
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197 | CoinBigIndex * dubiousWeights); |
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198 | /// Does first bit of dualColumn |
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199 | int dualColumn0(const CoinIndexedVector * rowArray, |
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200 | const CoinIndexedVector * columnArray, |
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201 | CoinIndexedVector * spareArray, |
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202 | double acceptablePivot, |
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203 | double & upperReturn, double &bestReturn, double & badFree); |
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204 | /** |
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205 | Row array has row part of pivot row |
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206 | Column array has column part. |
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207 | This sees what is best thing to do in dual values pass |
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208 | if sequenceIn==sequenceOut can change dual on chosen row and leave variable in basis |
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209 | */ |
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210 | void checkPossibleValuesMove(CoinIndexedVector * rowArray, |
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211 | CoinIndexedVector * columnArray, |
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212 | double acceptablePivot); |
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213 | /** |
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214 | Row array has row part of pivot row |
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215 | Column array has column part. |
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216 | This sees what is best thing to do in branch and bound cleanup |
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217 | If sequenceIn_ < 0 then can't do anything |
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218 | */ |
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219 | void checkPossibleCleanup(CoinIndexedVector * rowArray, |
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220 | CoinIndexedVector * columnArray, |
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221 | double acceptablePivot); |
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222 | /** |
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223 | This sees if we can move duals in dual values pass. |
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224 | This is done before any pivoting |
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225 | */ |
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226 | void doEasyOnesInValuesPass(double * givenReducedCosts); |
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227 | /** |
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228 | Chooses dual pivot row |
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229 | Would be faster with separate region to scan |
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230 | and will have this (with square of infeasibility) when steepest |
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231 | For easy problems we can just choose one of the first rows we look at |
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232 | |
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233 | If alreadyChosen >=0 then in values pass and that row has been |
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234 | selected |
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235 | */ |
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236 | void dualRow(int alreadyChosen); |
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237 | /** Checks if any fake bounds active - if so returns number and modifies |
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238 | updatedDualBound_ and everything. |
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239 | Free variables will be left as free |
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240 | Returns number of bounds changed if >=0 |
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241 | Returns -1 if not initialize and no effect |
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242 | Fills in changeVector which can be used to see if unbounded |
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243 | and cost of change vector |
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244 | If 2 sets to original (just changed) |
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245 | */ |
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246 | int changeBounds(int initialize, CoinIndexedVector * outputArray, |
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247 | double & changeCost); |
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248 | /** As changeBounds but just changes new bounds for a single variable. |
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249 | Returns true if change */ |
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250 | bool changeBound( int iSequence); |
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251 | /// Restores bound to original bound |
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252 | void originalBound(int iSequence); |
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253 | /** Checks if tentative optimal actually means unbounded in dual |
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254 | Returns -3 if not, 2 if is unbounded */ |
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255 | int checkUnbounded(CoinIndexedVector * ray, CoinIndexedVector * spare, |
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256 | double changeCost); |
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257 | /** Refactorizes if necessary |
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258 | Checks if finished. Updates status. |
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259 | lastCleaned refers to iteration at which some objective/feasibility |
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260 | cleaning too place. |
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261 | |
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262 | type - 0 initial so set up save arrays etc |
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263 | - 1 normal -if good update save |
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264 | - 2 restoring from saved |
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265 | */ |
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266 | void statusOfProblemInDual(int & lastCleaned, int type, |
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267 | double * givenDjs, ClpDataSave & saveData, |
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268 | int ifValuesPass); |
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269 | /** Perturbs problem (method depends on perturbation()) |
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270 | returns nonzero if should go to dual */ |
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271 | int perturb(); |
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272 | /** Fast iterations. Misses out a lot of initialization. |
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273 | Normally stops on maximum iterations, first re-factorization |
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274 | or tentative optimum. If looks interesting then continues as |
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275 | normal. Returns 0 if finished properly, 1 otherwise. |
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276 | */ |
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277 | int fastDual(bool alwaysFinish = false); |
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278 | /** Checks number of variables at fake bounds. This is used by fastDual |
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279 | so can exit gracefully before end */ |
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280 | int numberAtFakeBound(); |
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281 | |
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282 | /** Pivot in a variable and choose an outgoing one. Assumes dual |
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283 | feasible - will not go through a reduced cost. Returns step length in theta |
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284 | Returns ray in ray_ (or NULL if no pivot) |
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285 | Return codes as before but -1 means no acceptable pivot |
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286 | */ |
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287 | int pivotResult(); |
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288 | /** Get next free , -1 if none */ |
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289 | int nextSuperBasic(); |
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290 | /** Startup part of dual (may be extended to other algorithms) |
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291 | returns 0 if good, 1 if bad */ |
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292 | int startupSolve(int ifValuesPass, double * saveDuals, int startFinishOptions); |
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293 | void finishSolve(int startFinishOptions); |
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294 | void gutsOfDual(int ifValuesPass, double * & saveDuals, int initialStatus, |
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295 | ClpDataSave & saveData); |
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296 | //int dual2(int ifValuesPass,int startFinishOptions=0); |
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297 | void resetFakeBounds(int type); |
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298 | |
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299 | //@} |
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300 | }; |
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301 | #endif |
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