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 | |
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5 | /* Notes on implementation of primal simplex algorithm. |
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6 | |
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7 | When primal feasible(A): |
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8 | |
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9 | If dual feasible, we are optimal. Otherwise choose an infeasible |
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10 | basic variable to enter basis from a bound (B). We now need to find an |
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11 | outgoing variable which will leave problem primal feasible so we get |
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12 | the column of the tableau corresponding to the incoming variable |
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13 | (with the correct sign depending if variable will go up or down). |
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14 | |
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15 | We now perform a ratio test to determine which outgoing variable will |
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16 | preserve primal feasibility (C). If no variable found then problem |
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17 | is unbounded (in primal sense). If there is a variable, we then |
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18 | perform pivot and repeat. Trivial? |
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19 | |
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20 | ------------------------------------------- |
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21 | |
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22 | A) How do we get primal feasible? All variables have fake costs |
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23 | outside their feasible region so it is trivial to declare problem |
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24 | feasible. OSL did not have a phase 1/phase 2 approach but |
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25 | instead effectively put an extra cost on infeasible basic variables |
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26 | I am taking the same approach here, although it is generalized |
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27 | to allow for non-linear costs and dual information. |
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28 | |
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29 | In OSL, this weight was changed heuristically, here at present |
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30 | it is only increased if problem looks finished. If problem is |
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31 | feasible I check for unboundedness. If not unbounded we |
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32 | could play with going into dual. As long as weights increase |
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33 | any algorithm would be finite. |
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34 | |
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35 | B) Which incoming variable to choose is a virtual base class. |
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36 | For difficult problems steepest edge is preferred while for |
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37 | very easy (large) problems we will need partial scan. |
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38 | |
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39 | C) Sounds easy, but this is hardest part of algorithm. |
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40 | 1) Instead of stopping at first choice, we may be able |
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41 | to allow that variable to go through bound and if objective |
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42 | still improving choose again. These mini iterations can |
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43 | increase speed by orders of magnitude but we may need to |
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44 | go to more of a bucket choice of variable rather than looking |
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45 | at them one by one (for speed). |
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46 | 2) Accuracy. Basic infeasibilities may be less than |
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47 | tolerance. Pivoting on these makes objective go backwards. |
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48 | OSL modified cost so a zero move was made, Gill et al |
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49 | modified so a strictly positive move was made. |
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50 | The two problems are that re-factorizations can |
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51 | change rinfeasibilities above and below tolerances and that when |
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52 | finished we need to reset costs and try again. |
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53 | 3) Degeneracy. Gill et al helps but may not be enough. We |
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54 | may need more. Also it can improve speed a lot if we perturb |
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55 | the rhs and bounds significantly. |
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56 | |
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57 | References: |
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58 | Forrest and Goldfarb, Steepest-edge simplex algorithms for |
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59 | linear programming - Mathematical Programming 1992 |
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60 | Forrest and Tomlin, Implementing the simplex method for |
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61 | the Optimization Subroutine Library - IBM Systems Journal 1992 |
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62 | Gill, Murray, Saunders, Wright A Practical Anti-Cycling |
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63 | Procedure for Linear and Nonlinear Programming SOL report 1988 |
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64 | |
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65 | |
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66 | TODO: |
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67 | |
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68 | a) Better recovery procedures. At present I never check on forward |
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69 | progress. There is checkpoint/restart with reducing |
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70 | re-factorization frequency, but this is only on singular |
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71 | factorizations. |
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72 | b) Fast methods for large easy problems (and also the option for |
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73 | the code to automatically choose which method). |
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74 | c) We need to be able to stop in various ways for OSI - this |
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75 | is fairly easy. |
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76 | |
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77 | */ |
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78 | |
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79 | |
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80 | #include "CoinPragma.hpp" |
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81 | |
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82 | #include <math.h> |
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83 | |
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84 | #include "CoinHelperFunctions.hpp" |
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85 | #include "ClpSimplexPrimal.hpp" |
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86 | #include "ClpFactorization.hpp" |
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87 | #include "ClpNonLinearCost.hpp" |
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88 | #include "CoinPackedMatrix.hpp" |
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89 | #include "CoinIndexedVector.hpp" |
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90 | #include "CoinWarmStartBasis.hpp" |
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91 | #include "ClpPrimalColumnPivot.hpp" |
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92 | #include "ClpMessage.hpp" |
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93 | #include <cfloat> |
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94 | #include <cassert> |
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95 | #include <string> |
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96 | #include <stdio.h> |
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97 | #include <iostream> |
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98 | // primal |
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99 | int ClpSimplexPrimal::primal (int ifValuesPass ) |
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100 | { |
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101 | |
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102 | /* |
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103 | Method |
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104 | |
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105 | It tries to be a single phase approach with a weight of 1.0 being |
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106 | given to getting optimal and a weight of infeasibilityCost_ being |
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107 | given to getting primal feasible. In this version I have tried to |
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108 | be clever in a stupid way. The idea of fake bounds in dual |
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109 | seems to work so the primal analogue would be that of getting |
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110 | bounds on reduced costs (by a presolve approach) and using |
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111 | these for being above or below feasible region. I decided to waste |
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112 | memory and keep these explicitly. This allows for non-linear |
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113 | costs! |
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114 | |
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115 | The code is designed to take advantage of sparsity so arrays are |
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116 | seldom zeroed out from scratch or gone over in their entirety. |
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117 | The only exception is a full scan to find incoming variable for |
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118 | Dantzig row choice. For steepest edge we keep an updated list |
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119 | of dual infeasibilities (actually squares). |
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120 | On easy problems we don't need full scan - just |
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121 | pick first reasonable. |
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122 | |
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123 | One problem is how to tackle degeneracy and accuracy. At present |
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124 | I am using the modification of costs which I put in OSL and which was |
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125 | extended by Gill et al. I am still not sure of the exact details. |
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126 | |
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127 | The flow of primal is three while loops as follows: |
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128 | |
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129 | while (not finished) { |
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130 | |
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131 | while (not clean solution) { |
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132 | |
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133 | Factorize and/or clean up solution by changing bounds so |
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134 | primal feasible. If looks finished check fake primal bounds. |
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135 | Repeat until status is iterating (-1) or finished (0,1,2) |
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136 | |
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137 | } |
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138 | |
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139 | while (status==-1) { |
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140 | |
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141 | Iterate until no pivot in or out or time to re-factorize. |
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142 | |
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143 | Flow is: |
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144 | |
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145 | choose pivot column (incoming variable). if none then |
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146 | we are primal feasible so looks as if done but we need to |
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147 | break and check bounds etc. |
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148 | |
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149 | Get pivot column in tableau |
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150 | |
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151 | Choose outgoing row. If we don't find one then we look |
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152 | primal unbounded so break and check bounds etc. (Also the |
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153 | pivot tolerance is larger after any iterations so that may be |
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154 | reason) |
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155 | |
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156 | If we do find outgoing row, we may have to adjust costs to |
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157 | keep going forwards (anti-degeneracy). Check pivot will be stable |
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158 | and if unstable throw away iteration and break to re-factorize. |
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159 | If minor error re-factorize after iteration. |
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160 | |
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161 | Update everything (this may involve changing bounds on |
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162 | variables to stay primal feasible. |
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163 | |
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164 | } |
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165 | |
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166 | } |
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167 | |
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168 | At present we never check we are going forwards. I overdid that in |
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169 | OSL so will try and make a last resort. |
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170 | |
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171 | Needs partial scan pivot in option. |
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172 | |
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173 | May need other anti-degeneracy measures, especially if we try and use |
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174 | loose tolerances as a way to solve in fewer iterations. |
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175 | |
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176 | I like idea of dynamic scaling. This gives opportunity to decouple |
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177 | different implications of scaling for accuracy, iteration count and |
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178 | feasibility tolerance. |
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179 | |
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180 | */ |
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181 | |
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182 | algorithm_ = +1; |
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183 | //specialOptions_ |= 4; |
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184 | |
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185 | // save data |
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186 | ClpDataSave data = saveData(); |
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187 | |
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188 | // Save so can see if doing after dual |
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189 | int initialStatus=problemStatus_; |
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190 | // initialize - maybe values pass and algorithm_ is +1 |
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191 | if (!startup(ifValuesPass)) { |
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192 | |
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193 | int lastCleaned=0; // last time objective or bounds cleaned up |
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194 | |
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195 | // Say no pivot has occurred (for steepest edge and updates) |
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196 | pivotRow_=-2; |
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197 | |
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198 | // This says whether to restore things etc |
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199 | int factorType=0; |
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200 | if (problemStatus_<0&&perturbation_<100) { |
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201 | perturb(0); |
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202 | // Can't get here if values pass |
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203 | assert (!ifValuesPass); |
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204 | gutsOfSolution(NULL,NULL); |
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205 | if (handler_->logLevel()>2) { |
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206 | handler_->message(CLP_SIMPLEX_STATUS,messages_) |
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207 | <<numberIterations_<<objectiveValue(); |
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208 | handler_->printing(sumPrimalInfeasibilities_>0.0) |
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209 | <<sumPrimalInfeasibilities_<<numberPrimalInfeasibilities_; |
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210 | handler_->printing(sumDualInfeasibilities_>0.0) |
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211 | <<sumDualInfeasibilities_<<numberDualInfeasibilities_; |
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212 | handler_->printing(numberDualInfeasibilitiesWithoutFree_ |
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213 | <numberDualInfeasibilities_) |
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214 | <<numberDualInfeasibilitiesWithoutFree_; |
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215 | handler_->message()<<CoinMessageEol; |
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216 | } |
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217 | } |
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218 | /* |
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219 | Status of problem: |
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220 | 0 - optimal |
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221 | 1 - infeasible |
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222 | 2 - unbounded |
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223 | -1 - iterating |
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224 | -2 - factorization wanted |
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225 | -3 - redo checking without factorization |
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226 | -4 - looks infeasible |
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227 | -5 - looks unbounded |
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228 | */ |
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229 | while (problemStatus_<0) { |
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230 | int iRow,iColumn; |
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231 | // clear |
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232 | for (iRow=0;iRow<4;iRow++) { |
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233 | rowArray_[iRow]->clear(); |
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234 | } |
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235 | |
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236 | for (iColumn=0;iColumn<2;iColumn++) { |
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237 | columnArray_[iColumn]->clear(); |
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238 | } |
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239 | |
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240 | // give matrix (and model costs and bounds a chance to be |
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241 | // refreshed (normally null) |
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242 | matrix_->refresh(this); |
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243 | // If getting nowhere - why not give it a kick |
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244 | #if 1 |
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245 | if (perturbation_<101&&numberIterations_>2*(numberRows_+numberColumns_) |
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246 | &&initialStatus!=10) |
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247 | perturb(1); |
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248 | #endif |
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249 | // If we have done no iterations - special |
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250 | if (lastGoodIteration_==numberIterations_&&factorType) |
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251 | factorType=3; |
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252 | // may factorize, checks if problem finished |
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253 | statusOfProblemInPrimal(lastCleaned,factorType,progress_); |
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254 | |
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255 | // Say good factorization |
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256 | factorType=1; |
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257 | |
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258 | // Say no pivot has occurred (for steepest edge and updates) |
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259 | pivotRow_=-2; |
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260 | |
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261 | // exit if victory declared |
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262 | if (problemStatus_>=0) |
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263 | break; |
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264 | |
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265 | // Iterate |
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266 | whileIterating(ifValuesPass); |
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267 | } |
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268 | } |
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269 | // if infeasible get real values |
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270 | if (problemStatus_==1) { |
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271 | infeasibilityCost_=0.0; |
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272 | createRim(7); |
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273 | nonLinearCost_->checkInfeasibilities(0.0); |
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274 | sumPrimalInfeasibilities_=nonLinearCost_->sumInfeasibilities(); |
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275 | numberPrimalInfeasibilities_= nonLinearCost_->numberInfeasibilities(); |
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276 | // and get good feasible duals |
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277 | computeDuals(NULL); |
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278 | } |
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279 | // clean up |
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280 | unflag(); |
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281 | finish(); |
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282 | restoreData(data); |
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283 | return problemStatus_; |
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284 | } |
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285 | /* |
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286 | Reasons to come out: |
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287 | -1 iterations etc |
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288 | -2 inaccuracy |
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289 | -3 slight inaccuracy (and done iterations) |
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290 | -4 end of values pass and done iterations |
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291 | +0 looks optimal (might be infeasible - but we will investigate) |
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292 | +2 looks unbounded |
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293 | +3 max iterations |
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294 | */ |
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295 | int |
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296 | ClpSimplexPrimal::whileIterating(int valuesOption) |
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297 | { |
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298 | // Say if values pass |
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299 | int ifValuesPass=(firstFree_>=0) ? 1 : 0; |
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300 | int returnCode=-1; |
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301 | int superBasicType=1; |
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302 | if (valuesOption>1) |
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303 | superBasicType=3; |
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304 | // status stays at -1 while iterating, >=0 finished, -2 to invert |
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305 | // status -3 to go to top without an invert |
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306 | while (problemStatus_==-1) { |
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307 | #ifdef CLP_DEBUG |
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308 | { |
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309 | int i; |
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310 | // not [1] as has information |
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311 | for (i=0;i<4;i++) { |
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312 | if (i!=1) |
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313 | rowArray_[i]->checkClear(); |
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314 | } |
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315 | for (i=0;i<2;i++) { |
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316 | columnArray_[i]->checkClear(); |
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317 | } |
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318 | } |
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319 | #endif |
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320 | #if CLP_DEBUG>2 |
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321 | // very expensive |
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322 | if (numberIterations_>0&&numberIterations_<-2534) { |
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323 | handler_->setLogLevel(63); |
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324 | double saveValue = objectiveValue_; |
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325 | double * saveRow1 = new double[numberRows_]; |
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326 | double * saveRow2 = new double[numberRows_]; |
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327 | memcpy(saveRow1,rowReducedCost_,numberRows_*sizeof(double)); |
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328 | memcpy(saveRow2,rowActivityWork_,numberRows_*sizeof(double)); |
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329 | double * saveColumn1 = new double[numberColumns_]; |
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330 | double * saveColumn2 = new double[numberColumns_]; |
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331 | memcpy(saveColumn1,reducedCostWork_,numberColumns_*sizeof(double)); |
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332 | memcpy(saveColumn2,columnActivityWork_,numberColumns_*sizeof(double)); |
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333 | createRim(7); |
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334 | gutsOfSolution(NULL,NULL); |
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335 | printf("xxx %d old obj %g, recomputed %g, sum primal inf %g\n", |
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336 | numberIterations_, |
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337 | saveValue,objectiveValue_,sumPrimalInfeasibilities_); |
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338 | memcpy(rowReducedCost_,saveRow1,numberRows_*sizeof(double)); |
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339 | memcpy(rowActivityWork_,saveRow2,numberRows_*sizeof(double)); |
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340 | memcpy(reducedCostWork_,saveColumn1,numberColumns_*sizeof(double)); |
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341 | memcpy(columnActivityWork_,saveColumn2,numberColumns_*sizeof(double)); |
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342 | delete [] saveRow1; |
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343 | delete [] saveRow2; |
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344 | delete [] saveColumn1; |
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345 | delete [] saveColumn2; |
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346 | objectiveValue_=saveValue; |
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347 | } |
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348 | #endif |
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349 | if (!ifValuesPass) { |
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350 | // choose column to come in |
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351 | // can use pivotRow_ to update weights |
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352 | // pass in list of cost changes so can do row updates (rowArray_[1]) |
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353 | // NOTE rowArray_[0] is used by computeDuals which is a |
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354 | // slow way of getting duals but might be used |
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355 | primalColumn(rowArray_[1],rowArray_[2],rowArray_[3], |
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356 | columnArray_[0],columnArray_[1]); |
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357 | } else { |
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358 | // in values pass |
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359 | int sequenceIn=nextSuperBasic(superBasicType,columnArray_[0]); |
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360 | if (valuesOption>1) |
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361 | superBasicType=2; |
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362 | if (sequenceIn<0) { |
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363 | // end of values pass - initialize weights etc |
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364 | handler_->message(CLP_END_VALUES_PASS,messages_) |
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365 | <<numberIterations_; |
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366 | primalColumnPivot_->saveWeights(this,5); |
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367 | problemStatus_=-2; // factorize now |
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368 | pivotRow_=-1; // say no weights update |
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369 | returnCode=-4; |
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370 | // Clean up |
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371 | int i; |
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372 | for (i=0;i<numberRows_+numberColumns_;i++) { |
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373 | if (getColumnStatus(i)==atLowerBound||getColumnStatus(i)==isFixed) |
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374 | solution_[i]=lower_[i]; |
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375 | else if (getColumnStatus(i)==atUpperBound) |
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376 | solution_[i]=upper_[i]; |
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377 | } |
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378 | break; |
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379 | } else { |
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380 | // normal |
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381 | sequenceIn_ = sequenceIn; |
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382 | valueIn_=solution_[sequenceIn_]; |
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383 | lowerIn_=lower_[sequenceIn_]; |
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384 | upperIn_=upper_[sequenceIn_]; |
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385 | dualIn_=dj_[sequenceIn_]; |
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386 | } |
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387 | } |
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388 | pivotRow_=-1; |
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389 | sequenceOut_=-1; |
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390 | rowArray_[1]->clear(); |
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391 | if (sequenceIn_>=0) { |
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392 | // we found a pivot column |
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393 | assert (!flagged(sequenceIn_)); |
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394 | #ifdef CLP_DEBUG |
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395 | if ((handler_->logLevel()&32)) { |
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396 | char x = isColumn(sequenceIn_) ? 'C' :'R'; |
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397 | std::cout<<"pivot column "<< |
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398 | x<<sequenceWithin(sequenceIn_)<<std::endl; |
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399 | } |
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400 | #endif |
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401 | // do second half of iteration |
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402 | returnCode = pivotResult(ifValuesPass); |
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403 | if (returnCode<-1&&returnCode>-5) { |
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404 | problemStatus_=-2; // |
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405 | } else if (returnCode==-5) { |
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406 | // something flagged - continue; |
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407 | } else if (returnCode==2) { |
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408 | problemStatus_=-5; // looks unbounded |
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409 | } else if (returnCode==4) { |
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410 | problemStatus_=-2; // looks unbounded but has iterated |
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411 | } else if (returnCode!=-1) { |
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412 | assert(returnCode==3); |
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413 | problemStatus_=3; |
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414 | } |
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415 | } else { |
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416 | // no pivot column |
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417 | #ifdef CLP_DEBUG |
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418 | if (handler_->logLevel()&32) |
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419 | printf("** no column pivot\n"); |
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420 | #endif |
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421 | if (nonLinearCost_->numberInfeasibilities()) |
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422 | problemStatus_=-4; // might be infeasible |
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423 | returnCode=0; |
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424 | break; |
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425 | } |
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426 | } |
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427 | if (valuesOption>1) |
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428 | columnArray_[0]->setNumElements(0); |
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429 | return returnCode; |
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430 | } |
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431 | /* Checks if finished. Updates status */ |
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432 | void |
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433 | ClpSimplexPrimal::statusOfProblemInPrimal(int & lastCleaned,int type, |
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434 | ClpSimplexProgress * progress) |
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435 | { |
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436 | if (type==2) { |
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437 | // trouble - restore solution |
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438 | memcpy(status_ ,saveStatus_,(numberColumns_+numberRows_)*sizeof(char)); |
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439 | memcpy(rowActivityWork_,savedSolution_+numberColumns_ , |
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440 | numberRows_*sizeof(double)); |
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441 | memcpy(columnActivityWork_,savedSolution_ , |
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442 | numberColumns_*sizeof(double)); |
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443 | forceFactorization_=1; // a bit drastic but .. |
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444 | pivotRow_=-1; // say no weights update |
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445 | changeMade_++; // say change made |
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446 | } |
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447 | int saveThreshold = factorization_->sparseThreshold(); |
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448 | int tentativeStatus = problemStatus_; |
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449 | if (problemStatus_>-3||problemStatus_==-4) { |
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450 | // factorize |
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451 | // later on we will need to recover from singularities |
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452 | // also we could skip if first time |
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453 | // do weights |
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454 | // This may save pivotRow_ for use |
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455 | primalColumnPivot_->saveWeights(this,1); |
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456 | |
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457 | if (type) { |
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458 | // is factorization okay? |
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459 | if (internalFactorize(1)) { |
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460 | if (solveType_==2) { |
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461 | // say odd |
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462 | problemStatus_=5; |
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463 | return; |
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464 | } |
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465 | #if 1 |
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466 | // switch off dense |
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467 | int saveDense = factorization_->denseThreshold(); |
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468 | factorization_->setDenseThreshold(0); |
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469 | internalFactorize(2); |
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470 | factorization_->setDenseThreshold(saveDense); |
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471 | #else |
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472 | |
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473 | // no - restore previous basis |
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474 | assert (type==1); |
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475 | memcpy(status_ ,saveStatus_,(numberColumns_+numberRows_)*sizeof(char)); |
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476 | memcpy(rowActivityWork_,savedSolution_+numberColumns_ , |
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477 | numberRows_*sizeof(double)); |
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478 | memcpy(columnActivityWork_,savedSolution_ , |
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479 | numberColumns_*sizeof(double)); |
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480 | forceFactorization_=1; // a bit drastic but .. |
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481 | type = 2; |
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482 | assert (internalFactorize(1)==0); |
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483 | #endif |
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484 | changeMade_++; // say change made |
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485 | } |
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486 | } |
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487 | if (problemStatus_!=-4) |
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488 | problemStatus_=-3; |
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489 | } |
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490 | // at this stage status is -3 or -5 if looks unbounded |
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491 | // get primal and dual solutions |
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492 | // put back original costs and then check |
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493 | createRim(4); |
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494 | gutsOfSolution(NULL,NULL,(firstFree_>=0)); |
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495 | // Double check reduced costs if no action |
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496 | if (progress->lastIterationNumber(0)==numberIterations_) { |
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497 | if (primalColumnPivot_->looksOptimal()) { |
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498 | numberDualInfeasibilities_ = 0; |
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499 | sumDualInfeasibilities_ = 0.0; |
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500 | } |
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501 | } |
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502 | // Check if looping |
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503 | int loop; |
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504 | if (type!=2) |
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505 | loop = progress->looping(); |
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506 | else |
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507 | loop=-1; |
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508 | if (loop>=0) { |
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509 | problemStatus_ = loop; //exit if in loop |
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510 | return ; |
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511 | } else if (loop<-1) { |
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512 | // something may have changed |
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513 | gutsOfSolution(NULL,NULL); |
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514 | } |
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515 | // Flag to say whether to go to dual to clean up |
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516 | bool goToDual=false; |
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517 | // really for free variables in |
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518 | //if((progressFlag_&2)!=0) |
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519 | //problemStatus_=-1;; |
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520 | progressFlag_ = 0; //reset progress flag |
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521 | |
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522 | handler_->message(CLP_SIMPLEX_STATUS,messages_) |
---|
523 | <<numberIterations_<<objectiveValue(); |
---|
524 | handler_->printing(sumPrimalInfeasibilities_>0.0) |
---|
525 | <<sumPrimalInfeasibilities_<<numberPrimalInfeasibilities_; |
---|
526 | handler_->printing(sumDualInfeasibilities_>0.0) |
---|
527 | <<sumDualInfeasibilities_<<numberDualInfeasibilities_; |
---|
528 | handler_->printing(numberDualInfeasibilitiesWithoutFree_ |
---|
529 | <numberDualInfeasibilities_) |
---|
530 | <<numberDualInfeasibilitiesWithoutFree_; |
---|
531 | handler_->message()<<CoinMessageEol; |
---|
532 | if (!primalFeasible()) { |
---|
533 | nonLinearCost_->checkInfeasibilities(primalTolerance_); |
---|
534 | gutsOfSolution(NULL,NULL); |
---|
535 | } |
---|
536 | // we may wish to say it is optimal even if infeasible |
---|
537 | bool alwaysOptimal = (specialOptions_&1)!=0; |
---|
538 | // give code benefit of doubt |
---|
539 | if (sumOfRelaxedDualInfeasibilities_ == 0.0&& |
---|
540 | sumOfRelaxedPrimalInfeasibilities_ == 0.0) { |
---|
541 | // say optimal (with these bounds etc) |
---|
542 | numberDualInfeasibilities_ = 0; |
---|
543 | sumDualInfeasibilities_ = 0.0; |
---|
544 | numberPrimalInfeasibilities_ = 0; |
---|
545 | sumPrimalInfeasibilities_ = 0.0; |
---|
546 | } |
---|
547 | // had ||(type==3&&problemStatus_!=-5) -- ??? why ???? |
---|
548 | if (dualFeasible()||problemStatus_==-4) { |
---|
549 | if (nonLinearCost_->numberInfeasibilities()&&!alwaysOptimal) { |
---|
550 | //may need infeasiblity cost changed |
---|
551 | // we can see if we can construct a ray |
---|
552 | // make up a new objective |
---|
553 | double saveWeight = infeasibilityCost_; |
---|
554 | // save nonlinear cost as we are going to switch off costs |
---|
555 | ClpNonLinearCost * nonLinear = nonLinearCost_; |
---|
556 | // do twice to make sure Primal solution has settled |
---|
557 | // put non-basics to bounds in case tolerance moved |
---|
558 | // put back original costs |
---|
559 | createRim(4); |
---|
560 | nonLinearCost_->checkInfeasibilities(primalTolerance_); |
---|
561 | gutsOfSolution(NULL,NULL); |
---|
562 | |
---|
563 | infeasibilityCost_=1.0e100; |
---|
564 | // put back original costs |
---|
565 | createRim(4); |
---|
566 | nonLinearCost_->checkInfeasibilities(primalTolerance_); |
---|
567 | // may have fixed infeasibilities - double check |
---|
568 | if (nonLinearCost_->numberInfeasibilities()==0) { |
---|
569 | // carry on |
---|
570 | problemStatus_ = -1; |
---|
571 | infeasibilityCost_=saveWeight; |
---|
572 | } else { |
---|
573 | nonLinearCost_=NULL; |
---|
574 | // scale |
---|
575 | int i; |
---|
576 | for (i=0;i<numberRows_+numberColumns_;i++) |
---|
577 | cost_[i] *= 1.0e-100; |
---|
578 | gutsOfSolution(NULL,NULL); |
---|
579 | nonLinearCost_=nonLinear; |
---|
580 | infeasibilityCost_=saveWeight; |
---|
581 | if ((infeasibilityCost_>=1.0e18|| |
---|
582 | numberDualInfeasibilities_==0)&&perturbation_==101) { |
---|
583 | goToDual=unPerturb(); // stop any further perturbation |
---|
584 | numberDualInfeasibilities_=1; // carry on |
---|
585 | } |
---|
586 | if (infeasibilityCost_>=1.0e20|| |
---|
587 | numberDualInfeasibilities_==0) { |
---|
588 | // we are infeasible - use as ray |
---|
589 | delete [] ray_; |
---|
590 | ray_ = new double [numberRows_]; |
---|
591 | memcpy(ray_,dual_,numberRows_*sizeof(double)); |
---|
592 | // and get feasible duals |
---|
593 | infeasibilityCost_=0.0; |
---|
594 | createRim(4); |
---|
595 | nonLinearCost_->checkInfeasibilities(primalTolerance_); |
---|
596 | gutsOfSolution(NULL,NULL); |
---|
597 | // so will exit |
---|
598 | infeasibilityCost_=1.0e30; |
---|
599 | // reset infeasibilities |
---|
600 | sumPrimalInfeasibilities_=nonLinearCost_->sumInfeasibilities();; |
---|
601 | numberPrimalInfeasibilities_= |
---|
602 | nonLinearCost_->numberInfeasibilities(); |
---|
603 | } |
---|
604 | if (infeasibilityCost_<1.0e20) { |
---|
605 | infeasibilityCost_ *= 5.0; |
---|
606 | changeMade_++; // say change made |
---|
607 | handler_->message(CLP_PRIMAL_WEIGHT,messages_) |
---|
608 | <<infeasibilityCost_ |
---|
609 | <<CoinMessageEol; |
---|
610 | // put back original costs and then check |
---|
611 | createRim(4); |
---|
612 | nonLinearCost_->checkInfeasibilities(0.0); |
---|
613 | gutsOfSolution(NULL,NULL); |
---|
614 | problemStatus_=-1; //continue |
---|
615 | goToDual=false; |
---|
616 | } else { |
---|
617 | // say infeasible |
---|
618 | problemStatus_ = 1; |
---|
619 | } |
---|
620 | } |
---|
621 | } else { |
---|
622 | // may be optimal |
---|
623 | if (perturbation_==101) { |
---|
624 | goToDual=unPerturb(); // stop any further perturbation |
---|
625 | lastCleaned=-1; // carry on |
---|
626 | } |
---|
627 | bool unflagged = unflag(); |
---|
628 | if ( lastCleaned!=numberIterations_||unflagged) { |
---|
629 | handler_->message(CLP_PRIMAL_OPTIMAL,messages_) |
---|
630 | <<primalTolerance_ |
---|
631 | <<CoinMessageEol; |
---|
632 | if (numberTimesOptimal_<4) { |
---|
633 | numberTimesOptimal_++; |
---|
634 | changeMade_++; // say change made |
---|
635 | if (numberTimesOptimal_==1) { |
---|
636 | // better to have small tolerance even if slower |
---|
637 | factorization_->zeroTolerance(1.0e-15); |
---|
638 | } |
---|
639 | lastCleaned=numberIterations_; |
---|
640 | if (primalTolerance_!=dblParam_[ClpPrimalTolerance]) |
---|
641 | handler_->message(CLP_PRIMAL_ORIGINAL,messages_) |
---|
642 | <<CoinMessageEol; |
---|
643 | double oldTolerance = primalTolerance_; |
---|
644 | primalTolerance_=dblParam_[ClpPrimalTolerance]; |
---|
645 | #if 0 |
---|
646 | double * xcost = new double[numberRows_+numberColumns_]; |
---|
647 | double * xlower = new double[numberRows_+numberColumns_]; |
---|
648 | double * xupper = new double[numberRows_+numberColumns_]; |
---|
649 | double * xdj = new double[numberRows_+numberColumns_]; |
---|
650 | double * xsolution = new double[numberRows_+numberColumns_]; |
---|
651 | memcpy(xcost,cost_,(numberRows_+numberColumns_)*sizeof(double)); |
---|
652 | memcpy(xlower,lower_,(numberRows_+numberColumns_)*sizeof(double)); |
---|
653 | memcpy(xupper,upper_,(numberRows_+numberColumns_)*sizeof(double)); |
---|
654 | memcpy(xdj,dj_,(numberRows_+numberColumns_)*sizeof(double)); |
---|
655 | memcpy(xsolution,solution_,(numberRows_+numberColumns_)*sizeof(double)); |
---|
656 | #endif |
---|
657 | // put back original costs and then check |
---|
658 | createRim(4); |
---|
659 | nonLinearCost_->checkInfeasibilities(oldTolerance); |
---|
660 | #if 0 |
---|
661 | int i; |
---|
662 | for (i=0;i<numberRows_+numberColumns_;i++) { |
---|
663 | if (cost_[i]!=xcost[i]) |
---|
664 | printf("** %d old cost %g new %g sol %g\n", |
---|
665 | i,xcost[i],cost_[i],solution_[i]); |
---|
666 | if (lower_[i]!=xlower[i]) |
---|
667 | printf("** %d old lower %g new %g sol %g\n", |
---|
668 | i,xlower[i],lower_[i],solution_[i]); |
---|
669 | if (upper_[i]!=xupper[i]) |
---|
670 | printf("** %d old upper %g new %g sol %g\n", |
---|
671 | i,xupper[i],upper_[i],solution_[i]); |
---|
672 | if (dj_[i]!=xdj[i]) |
---|
673 | printf("** %d old dj %g new %g sol %g\n", |
---|
674 | i,xdj[i],dj_[i],solution_[i]); |
---|
675 | if (solution_[i]!=xsolution[i]) |
---|
676 | printf("** %d old solution %g new %g sol %g\n", |
---|
677 | i,xsolution[i],solution_[i],solution_[i]); |
---|
678 | } |
---|
679 | delete [] xcost; |
---|
680 | delete [] xupper; |
---|
681 | delete [] xlower; |
---|
682 | delete [] xdj; |
---|
683 | delete [] xsolution; |
---|
684 | #endif |
---|
685 | gutsOfSolution(NULL,NULL); |
---|
686 | if (sumOfRelaxedDualInfeasibilities_ == 0.0&& |
---|
687 | sumOfRelaxedPrimalInfeasibilities_ == 0.0) { |
---|
688 | // say optimal (with these bounds etc) |
---|
689 | numberDualInfeasibilities_ = 0; |
---|
690 | sumDualInfeasibilities_ = 0.0; |
---|
691 | numberPrimalInfeasibilities_ = 0; |
---|
692 | sumPrimalInfeasibilities_ = 0.0; |
---|
693 | } |
---|
694 | if (dualFeasible()&&!nonLinearCost_->numberInfeasibilities()&&lastCleaned>=0) |
---|
695 | problemStatus_=0; |
---|
696 | else |
---|
697 | problemStatus_ = -1; |
---|
698 | } else { |
---|
699 | problemStatus_=0; // optimal |
---|
700 | if (lastCleaned<numberIterations_) { |
---|
701 | handler_->message(CLP_SIMPLEX_GIVINGUP,messages_) |
---|
702 | <<CoinMessageEol; |
---|
703 | } |
---|
704 | } |
---|
705 | } else { |
---|
706 | problemStatus_=0; // optimal |
---|
707 | } |
---|
708 | } |
---|
709 | } else { |
---|
710 | // see if looks unbounded |
---|
711 | if (problemStatus_==-5) { |
---|
712 | if (nonLinearCost_->numberInfeasibilities()) { |
---|
713 | if (infeasibilityCost_>1.0e18&&perturbation_==101) { |
---|
714 | // back off weight |
---|
715 | infeasibilityCost_ = 1.0e13; |
---|
716 | goToDual=unPerturb(); // stop any further perturbation |
---|
717 | } |
---|
718 | //we need infeasiblity cost changed |
---|
719 | if (infeasibilityCost_<1.0e20) { |
---|
720 | infeasibilityCost_ *= 5.0; |
---|
721 | changeMade_++; // say change made |
---|
722 | handler_->message(CLP_PRIMAL_WEIGHT,messages_) |
---|
723 | <<infeasibilityCost_ |
---|
724 | <<CoinMessageEol; |
---|
725 | // put back original costs and then check |
---|
726 | createRim(4); |
---|
727 | gutsOfSolution(NULL,NULL); |
---|
728 | problemStatus_=-1; //continue |
---|
729 | } else { |
---|
730 | // say unbounded |
---|
731 | problemStatus_ = 2; |
---|
732 | } |
---|
733 | } else { |
---|
734 | // say unbounded |
---|
735 | problemStatus_ = 2; |
---|
736 | } |
---|
737 | } else { |
---|
738 | if(type==3&&problemStatus_!=-5) |
---|
739 | unflag(); // odd |
---|
740 | // carry on |
---|
741 | problemStatus_ = -1; |
---|
742 | } |
---|
743 | } |
---|
744 | if (type==0||type==1) { |
---|
745 | if (type!=1||!saveStatus_) { |
---|
746 | // create save arrays |
---|
747 | delete [] saveStatus_; |
---|
748 | delete [] savedSolution_; |
---|
749 | saveStatus_ = new unsigned char [numberRows_+numberColumns_]; |
---|
750 | savedSolution_ = new double [numberRows_+numberColumns_]; |
---|
751 | } |
---|
752 | // save arrays |
---|
753 | memcpy(saveStatus_,status_,(numberColumns_+numberRows_)*sizeof(char)); |
---|
754 | memcpy(savedSolution_+numberColumns_ ,rowActivityWork_, |
---|
755 | numberRows_*sizeof(double)); |
---|
756 | memcpy(savedSolution_ ,columnActivityWork_,numberColumns_*sizeof(double)); |
---|
757 | } |
---|
758 | // restore weights (if saved) - also recompute infeasibility list |
---|
759 | if (tentativeStatus>-3) |
---|
760 | primalColumnPivot_->saveWeights(this,(type <2) ? 2 : 4); |
---|
761 | else |
---|
762 | primalColumnPivot_->saveWeights(this,3); |
---|
763 | if (problemStatus_<0&&!changeMade_) { |
---|
764 | problemStatus_=4; // unknown |
---|
765 | } |
---|
766 | if (saveThreshold) { |
---|
767 | // use default at present |
---|
768 | factorization_->sparseThreshold(0); |
---|
769 | factorization_->goSparse(); |
---|
770 | } |
---|
771 | lastGoodIteration_ = numberIterations_; |
---|
772 | if (goToDual) |
---|
773 | problemStatus_=10; // try dual |
---|
774 | #if 0 |
---|
775 | double thisObj = progress->lastObjective(0); |
---|
776 | double lastObj = progress->lastObjective(1); |
---|
777 | if (lastObj<thisObj-1.0e-7*max(fabs(thisObj),fabs(lastObj))-1.0e-8 |
---|
778 | &&firstFree_<0) { |
---|
779 | int maxFactor = factorization_->maximumPivots(); |
---|
780 | if (maxFactor>10) { |
---|
781 | if (forceFactorization_<0) |
---|
782 | forceFactorization_= maxFactor; |
---|
783 | forceFactorization_ = max (1,(forceFactorization_>>1)); |
---|
784 | printf("Reducing factorization frequency\n"); |
---|
785 | } |
---|
786 | } |
---|
787 | #endif |
---|
788 | } |
---|
789 | /* |
---|
790 | Row array has pivot column |
---|
791 | This chooses pivot row. |
---|
792 | For speed, we may need to go to a bucket approach when many |
---|
793 | variables go through bounds |
---|
794 | On exit rhsArray will have changes in costs of basic variables |
---|
795 | */ |
---|
796 | void |
---|
797 | ClpSimplexPrimal::primalRow(CoinIndexedVector * rowArray, |
---|
798 | CoinIndexedVector * rhsArray, |
---|
799 | CoinIndexedVector * spareArray, |
---|
800 | CoinIndexedVector * spareArray2, |
---|
801 | int valuesPass) |
---|
802 | { |
---|
803 | if (valuesPass) { |
---|
804 | dualIn_ = cost_[sequenceIn_]; |
---|
805 | |
---|
806 | double * work=rowArray->denseVector(); |
---|
807 | int number=rowArray->getNumElements(); |
---|
808 | int * which=rowArray->getIndices(); |
---|
809 | |
---|
810 | int iIndex; |
---|
811 | for (iIndex=0;iIndex<number;iIndex++) { |
---|
812 | |
---|
813 | int iRow = which[iIndex]; |
---|
814 | double alpha = work[iRow]; |
---|
815 | int iPivot=pivotVariable_[iRow]; |
---|
816 | dualIn_ -= alpha*cost(iPivot); |
---|
817 | } |
---|
818 | // determine direction here |
---|
819 | if (dualIn_<-dualTolerance_) { |
---|
820 | directionIn_=1; |
---|
821 | } else if (dualIn_>dualTolerance_) { |
---|
822 | directionIn_=-1; |
---|
823 | } else { |
---|
824 | // towards nearest bound |
---|
825 | if (valueIn_-lowerIn_<upperIn_-valueIn_) { |
---|
826 | directionIn_=-1; |
---|
827 | dualIn_=dualTolerance_; |
---|
828 | } else { |
---|
829 | directionIn_=1; |
---|
830 | dualIn_=-dualTolerance_; |
---|
831 | } |
---|
832 | } |
---|
833 | } |
---|
834 | |
---|
835 | // sequence stays as row number until end |
---|
836 | pivotRow_=-1; |
---|
837 | int numberSwapped=0; |
---|
838 | int numberRemaining=0; |
---|
839 | |
---|
840 | int numberThru =0; // number gone thru a barrier |
---|
841 | int lastThru =0; // number gone thru a barrier on last time |
---|
842 | |
---|
843 | double totalThru=0.0; // for when variables flip |
---|
844 | double acceptablePivot=1.0e-7; |
---|
845 | if (factorization_->pivots()) |
---|
846 | acceptablePivot=1.0e-5; // if we have iterated be more strict |
---|
847 | double bestEverPivot=acceptablePivot; |
---|
848 | int lastPivotRow = -1; |
---|
849 | double lastPivot=0.0; |
---|
850 | double lastTheta=1.0e50; |
---|
851 | int lastNumberSwapped=0; |
---|
852 | |
---|
853 | // use spareArrays to put ones looked at in |
---|
854 | // First one is list of candidates |
---|
855 | // We could compress if we really know we won't need any more |
---|
856 | // Second array has current set of pivot candidates |
---|
857 | // with a backup list saved in double * part of indexed vector |
---|
858 | |
---|
859 | // for zeroing out arrays after |
---|
860 | int maximumSwapped=0; |
---|
861 | // pivot elements |
---|
862 | double * spare; |
---|
863 | // indices |
---|
864 | int * index, * indexSwapped; |
---|
865 | int * saveSwapped; |
---|
866 | spareArray->clear(); |
---|
867 | spareArray2->clear(); |
---|
868 | spare = spareArray->denseVector(); |
---|
869 | index = spareArray->getIndices(); |
---|
870 | saveSwapped = (int *) spareArray2->denseVector(); |
---|
871 | indexSwapped = spareArray2->getIndices(); |
---|
872 | |
---|
873 | // we also need somewhere for effective rhs |
---|
874 | double * rhs=rhsArray->denseVector(); |
---|
875 | |
---|
876 | /* |
---|
877 | First we get a list of possible pivots. We can also see if the |
---|
878 | problem looks unbounded. |
---|
879 | |
---|
880 | At first we increase theta and see what happens. We start |
---|
881 | theta at a reasonable guess. If in right area then we do bit by bit. |
---|
882 | We save possible pivot candidates |
---|
883 | |
---|
884 | */ |
---|
885 | |
---|
886 | // do first pass to get possibles |
---|
887 | // We can also see if unbounded |
---|
888 | // We also re-compute reduced cost |
---|
889 | |
---|
890 | dualIn_ = cost_[sequenceIn_]; |
---|
891 | |
---|
892 | double * work=rowArray->denseVector(); |
---|
893 | int number=rowArray->getNumElements(); |
---|
894 | int * which=rowArray->getIndices(); |
---|
895 | |
---|
896 | // we need to swap sign if coming in from ub |
---|
897 | double way = directionIn_; |
---|
898 | double maximumMovement; |
---|
899 | if (way>0.0) |
---|
900 | maximumMovement = min(1.0e30,upperIn_-valueIn_); |
---|
901 | else |
---|
902 | maximumMovement = min(1.0e30,valueIn_-lowerIn_); |
---|
903 | |
---|
904 | double tentativeTheta = maximumMovement; |
---|
905 | double upperTheta = maximumMovement; |
---|
906 | |
---|
907 | int iIndex; |
---|
908 | #if 0 |
---|
909 | if (numberIterations_<=39) |
---|
910 | handler_->setLogLevel(63); |
---|
911 | else |
---|
912 | handler_->setLogLevel(2); |
---|
913 | if (numberIterations_==38) |
---|
914 | printf("trouble\n"); |
---|
915 | assert (solution_[29176]>-1.0e20); |
---|
916 | #endif |
---|
917 | for (iIndex=0;iIndex<number;iIndex++) { |
---|
918 | |
---|
919 | int iRow = which[iIndex]; |
---|
920 | double alpha = work[iRow]; |
---|
921 | int iPivot=pivotVariable_[iRow]; |
---|
922 | dualIn_ -= alpha*cost(iPivot); |
---|
923 | alpha *= way; |
---|
924 | double oldValue = solution(iPivot); |
---|
925 | // get where in bound sequence |
---|
926 | if (alpha>0.0) { |
---|
927 | // basic variable going towards lower bound |
---|
928 | double bound = lower(iPivot); |
---|
929 | oldValue -= bound; |
---|
930 | } else { |
---|
931 | // basic variable going towards upper bound |
---|
932 | double bound = upper(iPivot); |
---|
933 | oldValue = bound-oldValue; |
---|
934 | } |
---|
935 | |
---|
936 | double value = oldValue-tentativeTheta*fabs(alpha); |
---|
937 | assert (oldValue>=-primalTolerance_*1.002); |
---|
938 | if (value<-primalTolerance_) { |
---|
939 | // add to list |
---|
940 | spare[numberRemaining]=alpha; |
---|
941 | rhs[iRow]=oldValue; |
---|
942 | index[numberRemaining++]=iRow; |
---|
943 | double value=oldValue-upperTheta*fabs(alpha); |
---|
944 | if (value<-primalTolerance_&&fabs(alpha)>=acceptablePivot) |
---|
945 | upperTheta = (oldValue+primalTolerance_)/fabs(alpha); |
---|
946 | } |
---|
947 | } |
---|
948 | #if 0 |
---|
949 | if (numberIterations_>17701) |
---|
950 | handler_->setLogLevel(63); |
---|
951 | if (!valuesPass&&fabs(dualIn_-saveDj)>1.0e-1*(1.0+fabs(saveDj))) { |
---|
952 | double d=0.0; |
---|
953 | for (iIndex=0;iIndex<number;iIndex++) { |
---|
954 | |
---|
955 | int iRow = which[iIndex]; |
---|
956 | double alpha = work[iRow]; |
---|
957 | int iPivot=pivotVariable_[iRow]; |
---|
958 | double value = alpha*cost(iPivot); |
---|
959 | d -= value; |
---|
960 | if (value>1.0e7) |
---|
961 | printf("%d %g\n",iRow,value); |
---|
962 | } |
---|
963 | } |
---|
964 | #endif |
---|
965 | // we need to keep where rhs non-zeros are |
---|
966 | int numberInRhs=numberRemaining; |
---|
967 | memcpy(rhsArray->getIndices(),index,numberInRhs*sizeof(int)); |
---|
968 | rhsArray->setNumElements(numberInRhs); |
---|
969 | |
---|
970 | theta_=maximumMovement; |
---|
971 | |
---|
972 | double dualCheck = fabs(dualIn_); |
---|
973 | // but make a bit more pessimistic |
---|
974 | dualCheck=max(dualCheck-100.0*dualTolerance_,0.99*dualCheck); |
---|
975 | |
---|
976 | bool goBackOne = false; |
---|
977 | |
---|
978 | if (numberRemaining) { |
---|
979 | |
---|
980 | // looks like pivoting |
---|
981 | // now try until reasonable theta |
---|
982 | tentativeTheta = max(10.0*upperTheta,1.0e-7); |
---|
983 | tentativeTheta = min(tentativeTheta,maximumMovement); |
---|
984 | |
---|
985 | // loops increasing tentative theta until can't go through |
---|
986 | |
---|
987 | while (tentativeTheta <= maximumMovement) { |
---|
988 | double thruThis = 0.0; |
---|
989 | |
---|
990 | double bestPivot=acceptablePivot; |
---|
991 | pivotRow_ = -1; |
---|
992 | |
---|
993 | numberSwapped = 0; |
---|
994 | |
---|
995 | upperTheta = maximumMovement; |
---|
996 | |
---|
997 | for (iIndex=0;iIndex<numberRemaining;iIndex++) { |
---|
998 | |
---|
999 | int iRow = index[iIndex]; |
---|
1000 | double alpha = spare[iIndex]; |
---|
1001 | double oldValue = rhs[iRow]; |
---|
1002 | double value = oldValue-tentativeTheta*fabs(alpha); |
---|
1003 | |
---|
1004 | if (value<-primalTolerance_) { |
---|
1005 | // how much would it cost to go thru |
---|
1006 | thruThis += alpha* |
---|
1007 | nonLinearCost_->changeInCost(pivotVariable_[iRow],alpha); |
---|
1008 | // goes on swapped list (also means candidates if too many) |
---|
1009 | indexSwapped[numberSwapped++]=iRow; |
---|
1010 | if (fabs(alpha)>bestPivot) { |
---|
1011 | bestPivot=fabs(alpha); |
---|
1012 | pivotRow_ = iRow; |
---|
1013 | theta_ = oldValue/bestPivot; |
---|
1014 | } |
---|
1015 | } else { |
---|
1016 | value = oldValue-upperTheta*fabs(alpha); |
---|
1017 | if (value<-primalTolerance_ && fabs(alpha)>=acceptablePivot) |
---|
1018 | upperTheta = (oldValue+primalTolerance_)/fabs(alpha); |
---|
1019 | } |
---|
1020 | } |
---|
1021 | |
---|
1022 | maximumSwapped = max(maximumSwapped,numberSwapped); |
---|
1023 | |
---|
1024 | if (totalThru+thruThis>=dualCheck) { |
---|
1025 | // We should be pivoting in this batch |
---|
1026 | // so compress down to this lot |
---|
1027 | |
---|
1028 | int saveNumber = numberRemaining; |
---|
1029 | numberRemaining=0; |
---|
1030 | for (iIndex=0;iIndex<numberSwapped;iIndex++) { |
---|
1031 | int iRow = indexSwapped[iIndex]; |
---|
1032 | spare[numberRemaining]=way*work[iRow]; |
---|
1033 | index[numberRemaining++]=iRow; |
---|
1034 | } |
---|
1035 | memset(spare+numberRemaining,0, |
---|
1036 | (saveNumber-numberRemaining)*sizeof(double)); |
---|
1037 | int iTry; |
---|
1038 | #define MAXTRY 100 |
---|
1039 | // first get ratio with tolerance |
---|
1040 | for (iTry=0;iTry<MAXTRY;iTry++) { |
---|
1041 | |
---|
1042 | upperTheta=maximumMovement; |
---|
1043 | numberSwapped = 0; |
---|
1044 | int iBest=-1; |
---|
1045 | for (iIndex=0;iIndex<numberRemaining;iIndex++) { |
---|
1046 | |
---|
1047 | int iRow = index[iIndex]; |
---|
1048 | double alpha = fabs(spare[iIndex]); |
---|
1049 | double oldValue = rhs[iRow]; |
---|
1050 | double value = oldValue-upperTheta*alpha; |
---|
1051 | |
---|
1052 | if (value<-primalTolerance_ && alpha>=acceptablePivot) { |
---|
1053 | upperTheta = (oldValue+primalTolerance_)/alpha; |
---|
1054 | iBest=iRow; // just in case weird numbers |
---|
1055 | } |
---|
1056 | } |
---|
1057 | |
---|
1058 | // now look at best in this lot |
---|
1059 | bestPivot=acceptablePivot; |
---|
1060 | pivotRow_=-1; |
---|
1061 | for (iIndex=0;iIndex<numberRemaining;iIndex++) { |
---|
1062 | |
---|
1063 | int iRow = index[iIndex]; |
---|
1064 | double alpha = spare[iIndex]; |
---|
1065 | double oldValue = rhs[iRow]; |
---|
1066 | double value = oldValue-upperTheta*fabs(alpha); |
---|
1067 | |
---|
1068 | if (value<=0||iRow==iBest) { |
---|
1069 | // how much would it cost to go thru |
---|
1070 | totalThru += alpha* |
---|
1071 | nonLinearCost_->changeInCost(pivotVariable_[iRow],alpha); |
---|
1072 | // goes on swapped list (also means candidates if too many) |
---|
1073 | indexSwapped[numberSwapped++]=iRow; |
---|
1074 | if (fabs(alpha)>bestPivot) { |
---|
1075 | bestPivot=fabs(alpha); |
---|
1076 | theta_ = oldValue/bestPivot; |
---|
1077 | pivotRow_=iRow; |
---|
1078 | } |
---|
1079 | } else { |
---|
1080 | value = oldValue-upperTheta*fabs(alpha); |
---|
1081 | if (value<-primalTolerance_ && fabs(alpha)>=acceptablePivot) |
---|
1082 | upperTheta = (oldValue+primalTolerance_)/fabs(alpha); |
---|
1083 | } |
---|
1084 | } |
---|
1085 | |
---|
1086 | maximumSwapped = max(maximumSwapped,numberSwapped); |
---|
1087 | // had (&&bestPivot<1.0e-3||totalThru>0.1*dualCheck) as well |
---|
1088 | if (bestPivot<0.1*bestEverPivot&& |
---|
1089 | bestEverPivot>1.0e-6&& bestPivot<1.0e-3) { |
---|
1090 | // back to previous one |
---|
1091 | goBackOne = true; |
---|
1092 | break; |
---|
1093 | } else if (pivotRow_==-1&&upperTheta>largeValue_) { |
---|
1094 | if (lastPivot>acceptablePivot) { |
---|
1095 | // back to previous one |
---|
1096 | goBackOne = true; |
---|
1097 | //break; |
---|
1098 | } else { |
---|
1099 | // can only get here if all pivots so far too small |
---|
1100 | } |
---|
1101 | break; |
---|
1102 | } else if (totalThru>=dualCheck) { |
---|
1103 | break; // no point trying another loop |
---|
1104 | } else { |
---|
1105 | // skip this lot |
---|
1106 | nonLinearCost_->goThru(numberSwapped,way,indexSwapped, work,rhs); |
---|
1107 | lastPivotRow=pivotRow_; |
---|
1108 | lastTheta = theta_; |
---|
1109 | lastThru = numberThru; |
---|
1110 | numberThru += numberSwapped; |
---|
1111 | lastNumberSwapped = numberSwapped; |
---|
1112 | memcpy(saveSwapped,indexSwapped,lastNumberSwapped*sizeof(int)); |
---|
1113 | if (bestPivot>bestEverPivot) |
---|
1114 | bestEverPivot=bestPivot; |
---|
1115 | } |
---|
1116 | } |
---|
1117 | break; |
---|
1118 | } else { |
---|
1119 | // skip this lot |
---|
1120 | nonLinearCost_->goThru(numberSwapped,way,indexSwapped, work,rhs); |
---|
1121 | lastPivotRow=pivotRow_; |
---|
1122 | lastTheta = theta_; |
---|
1123 | lastThru = numberThru; |
---|
1124 | numberThru += numberSwapped; |
---|
1125 | lastNumberSwapped = numberSwapped; |
---|
1126 | memcpy(saveSwapped,indexSwapped,lastNumberSwapped*sizeof(int)); |
---|
1127 | if (bestPivot>bestEverPivot) |
---|
1128 | bestEverPivot=bestPivot; |
---|
1129 | totalThru += thruThis; |
---|
1130 | tentativeTheta = 2.0*upperTheta; |
---|
1131 | } |
---|
1132 | } |
---|
1133 | // can get here without pivotRow_ set but with lastPivotRow |
---|
1134 | if (goBackOne||(pivotRow_<0&&lastPivotRow>=0)) { |
---|
1135 | // back to previous one |
---|
1136 | pivotRow_=lastPivotRow; |
---|
1137 | theta_ = lastTheta; |
---|
1138 | // undo this lot |
---|
1139 | nonLinearCost_->goBack(lastNumberSwapped,saveSwapped,rhs); |
---|
1140 | memcpy(indexSwapped,saveSwapped,lastNumberSwapped*sizeof(int)); |
---|
1141 | numberSwapped = lastNumberSwapped; |
---|
1142 | } |
---|
1143 | } |
---|
1144 | |
---|
1145 | if (pivotRow_>=0) { |
---|
1146 | |
---|
1147 | alpha_ = work[pivotRow_]; |
---|
1148 | // translate to sequence |
---|
1149 | sequenceOut_ = pivotVariable_[pivotRow_]; |
---|
1150 | valueOut_ = solution(sequenceOut_); |
---|
1151 | lowerOut_=lower_[sequenceOut_]; |
---|
1152 | upperOut_=upper_[sequenceOut_]; |
---|
1153 | #define MINIMUMTHETA 1.0e-12 |
---|
1154 | // Movement should be minimum for anti-degeneracy - unless |
---|
1155 | // fixed variable out |
---|
1156 | double minimumTheta; |
---|
1157 | if (upperOut_>lowerOut_) |
---|
1158 | minimumTheta=MINIMUMTHETA; |
---|
1159 | else |
---|
1160 | minimumTheta=0.0; |
---|
1161 | // will we need to increase tolerance |
---|
1162 | //#define CLP_DEBUG |
---|
1163 | #ifdef CLP_DEBUG |
---|
1164 | bool found=false; |
---|
1165 | #endif |
---|
1166 | double largestInfeasibility = primalTolerance_; |
---|
1167 | if (theta_<minimumTheta&&(specialOptions_&4)==0&&!valuesPass) { |
---|
1168 | theta_=minimumTheta; |
---|
1169 | for (iIndex=0;iIndex<numberSwapped;iIndex++) { |
---|
1170 | int iRow = indexSwapped[iIndex]; |
---|
1171 | #ifdef CLP_DEBUG |
---|
1172 | if (iRow==pivotRow_) |
---|
1173 | found=true; |
---|
1174 | #endif |
---|
1175 | largestInfeasibility = max (largestInfeasibility, |
---|
1176 | -(rhs[iRow]-fabs(work[iRow])*theta_)); |
---|
1177 | } |
---|
1178 | #ifdef CLP_DEBUG |
---|
1179 | assert(found); |
---|
1180 | if (largestInfeasibility>primalTolerance_&&(handler_->logLevel()&32)>-1) |
---|
1181 | printf("Primal tolerance increased from %g to %g\n", |
---|
1182 | primalTolerance_,largestInfeasibility); |
---|
1183 | #endif |
---|
1184 | #undef CLP_DEBUG |
---|
1185 | primalTolerance_ = max(primalTolerance_,largestInfeasibility); |
---|
1186 | } |
---|
1187 | |
---|
1188 | if (way<0.0) |
---|
1189 | theta_ = - theta_; |
---|
1190 | double newValue = valueOut_ - theta_*alpha_; |
---|
1191 | // If 4 bit set - Force outgoing variables to exact bound (primal) |
---|
1192 | if (alpha_*way<0.0) { |
---|
1193 | directionOut_=-1; // to upper bound |
---|
1194 | if (fabs(theta_)>1.0e-6||(specialOptions_&4)!=0) { |
---|
1195 | upperOut_ = nonLinearCost_->nearest(sequenceOut_,newValue); |
---|
1196 | } else { |
---|
1197 | upperOut_ = newValue; |
---|
1198 | } |
---|
1199 | } else { |
---|
1200 | directionOut_=1; // to lower bound |
---|
1201 | if (fabs(theta_)>1.0e-6||(specialOptions_&4)!=0) { |
---|
1202 | lowerOut_ = nonLinearCost_->nearest(sequenceOut_,newValue); |
---|
1203 | } else { |
---|
1204 | lowerOut_ = newValue; |
---|
1205 | } |
---|
1206 | } |
---|
1207 | dualOut_ = reducedCost(sequenceOut_); |
---|
1208 | } else if (maximumMovement<1.0e20) { |
---|
1209 | // flip |
---|
1210 | pivotRow_ = -2; // so we can tell its a flip |
---|
1211 | sequenceOut_ = sequenceIn_; |
---|
1212 | valueOut_ = valueIn_; |
---|
1213 | dualOut_ = dualIn_; |
---|
1214 | lowerOut_ = lowerIn_; |
---|
1215 | upperOut_ = upperIn_; |
---|
1216 | alpha_ = 0.0; |
---|
1217 | if (way<0.0) { |
---|
1218 | directionOut_=1; // to lower bound |
---|
1219 | theta_ = lowerOut_ - valueOut_; |
---|
1220 | } else { |
---|
1221 | directionOut_=-1; // to upper bound |
---|
1222 | theta_ = upperOut_ - valueOut_; |
---|
1223 | } |
---|
1224 | } |
---|
1225 | |
---|
1226 | // clear arrays |
---|
1227 | |
---|
1228 | memset(spare,0,numberRemaining*sizeof(double)); |
---|
1229 | memset(saveSwapped,0,maximumSwapped*sizeof(int)); |
---|
1230 | |
---|
1231 | // put back original bounds etc |
---|
1232 | nonLinearCost_->goBackAll(rhsArray); |
---|
1233 | |
---|
1234 | rhsArray->clear(); |
---|
1235 | |
---|
1236 | } |
---|
1237 | /* |
---|
1238 | Chooses primal pivot column |
---|
1239 | updateArray has cost updates (also use pivotRow_ from last iteration) |
---|
1240 | Would be faster with separate region to scan |
---|
1241 | and will have this (with square of infeasibility) when steepest |
---|
1242 | For easy problems we can just choose one of the first columns we look at |
---|
1243 | */ |
---|
1244 | void |
---|
1245 | ClpSimplexPrimal::primalColumn(CoinIndexedVector * updates, |
---|
1246 | CoinIndexedVector * spareRow1, |
---|
1247 | CoinIndexedVector * spareRow2, |
---|
1248 | CoinIndexedVector * spareColumn1, |
---|
1249 | CoinIndexedVector * spareColumn2) |
---|
1250 | { |
---|
1251 | sequenceIn_ = primalColumnPivot_->pivotColumn(updates,spareRow1, |
---|
1252 | spareRow2,spareColumn1, |
---|
1253 | spareColumn2); |
---|
1254 | if (sequenceIn_>=0) { |
---|
1255 | valueIn_=solution_[sequenceIn_]; |
---|
1256 | dualIn_=dj_[sequenceIn_]; |
---|
1257 | if (nonLinearCost_->lookBothWays()) { |
---|
1258 | // double check |
---|
1259 | ClpSimplex::Status status = getStatus(sequenceIn_); |
---|
1260 | |
---|
1261 | switch(status) { |
---|
1262 | case ClpSimplex::atUpperBound: |
---|
1263 | if (dualIn_<0.0) { |
---|
1264 | // move to other side |
---|
1265 | printf("For %d U (%g, %g, %g) dj changed from %g", |
---|
1266 | sequenceIn_,lower_[sequenceIn_],solution_[sequenceIn_], |
---|
1267 | upper_[sequenceIn_],dualIn_); |
---|
1268 | dualIn_ -= nonLinearCost_->changeUpInCost(sequenceIn_); |
---|
1269 | printf(" to %g\n",dualIn_); |
---|
1270 | nonLinearCost_->setOne(sequenceIn_,upper_[sequenceIn_]+2.0*currentPrimalTolerance()); |
---|
1271 | setStatus(sequenceIn_,ClpSimplex::atLowerBound); |
---|
1272 | } |
---|
1273 | break; |
---|
1274 | case ClpSimplex::atLowerBound: |
---|
1275 | if (dualIn_>0.0) { |
---|
1276 | // move to other side |
---|
1277 | printf("For %d L (%g, %g, %g) dj changed from %g", |
---|
1278 | sequenceIn_,lower_[sequenceIn_],solution_[sequenceIn_], |
---|
1279 | upper_[sequenceIn_],dualIn_); |
---|
1280 | dualIn_ -= nonLinearCost_->changeDownInCost(sequenceIn_); |
---|
1281 | printf(" to %g\n",dualIn_); |
---|
1282 | nonLinearCost_->setOne(sequenceIn_,lower_[sequenceIn_]-2.0*currentPrimalTolerance()); |
---|
1283 | setStatus(sequenceIn_,ClpSimplex::atUpperBound); |
---|
1284 | } |
---|
1285 | break; |
---|
1286 | default: |
---|
1287 | break; |
---|
1288 | } |
---|
1289 | } |
---|
1290 | lowerIn_=lower_[sequenceIn_]; |
---|
1291 | upperIn_=upper_[sequenceIn_]; |
---|
1292 | if (dualIn_>0.0) |
---|
1293 | directionIn_ = -1; |
---|
1294 | else |
---|
1295 | directionIn_ = 1; |
---|
1296 | } else { |
---|
1297 | sequenceIn_ = -1; |
---|
1298 | } |
---|
1299 | } |
---|
1300 | /* The primals are updated by the given array. |
---|
1301 | Returns number of infeasibilities. |
---|
1302 | After rowArray will have list of cost changes |
---|
1303 | */ |
---|
1304 | int |
---|
1305 | ClpSimplexPrimal::updatePrimalsInPrimal(CoinIndexedVector * rowArray, |
---|
1306 | double theta, |
---|
1307 | double & objectiveChange) |
---|
1308 | { |
---|
1309 | double * work=rowArray->denseVector(); |
---|
1310 | int number=rowArray->getNumElements(); |
---|
1311 | int * which=rowArray->getIndices(); |
---|
1312 | |
---|
1313 | int newNumber = 0; |
---|
1314 | |
---|
1315 | nonLinearCost_->setChangeInCost(0.0); |
---|
1316 | int iIndex; |
---|
1317 | |
---|
1318 | for (iIndex=0;iIndex<number;iIndex++) { |
---|
1319 | |
---|
1320 | int iRow = which[iIndex]; |
---|
1321 | double alpha = work[iRow]; |
---|
1322 | int iPivot=pivotVariable_[iRow]; |
---|
1323 | double & value = solutionAddress(iPivot); |
---|
1324 | double change = theta*alpha; |
---|
1325 | value -= change; |
---|
1326 | // But make sure one going out is feasible |
---|
1327 | if (change>0.0) { |
---|
1328 | // going down |
---|
1329 | if (value<lower(iPivot)+primalTolerance_) { |
---|
1330 | if (iPivot==sequenceOut_&&value>lower(iPivot)-1.001*primalTolerance_) |
---|
1331 | value=lower(iPivot); |
---|
1332 | double difference = nonLinearCost_->setOne(iPivot,value); |
---|
1333 | work[iRow] = difference; |
---|
1334 | if (difference) { |
---|
1335 | //change reduced cost on this |
---|
1336 | reducedCostAddress(iPivot) = -difference; |
---|
1337 | which[newNumber++]=iRow; |
---|
1338 | } |
---|
1339 | } else { |
---|
1340 | work[iRow]=0.0; |
---|
1341 | } |
---|
1342 | } else { |
---|
1343 | // going up |
---|
1344 | if (value>upper(iPivot)-primalTolerance_) { |
---|
1345 | if (iPivot==sequenceOut_&&value<upper(iPivot)+1.001*primalTolerance_) |
---|
1346 | value=upper(iPivot); |
---|
1347 | double difference = nonLinearCost_->setOne(iPivot,value); |
---|
1348 | work[iRow] = difference; |
---|
1349 | if (difference) { |
---|
1350 | //change reduced cost on this |
---|
1351 | reducedCostAddress(iPivot) = -difference; |
---|
1352 | which[newNumber++]=iRow; |
---|
1353 | } |
---|
1354 | } else { |
---|
1355 | work[iRow]=0.0; |
---|
1356 | } |
---|
1357 | } |
---|
1358 | } |
---|
1359 | objectiveChange += nonLinearCost_->changeInCost(); |
---|
1360 | rowArray->setNumElements(newNumber); |
---|
1361 | #if 0 |
---|
1362 | if (newNumber>10) { |
---|
1363 | printf("in %d out %d, row %d alpha %g\n", |
---|
1364 | sequenceIn_,sequenceOut_,pivotRow_,alpha_); |
---|
1365 | for (iIndex=0;iIndex<newNumber;iIndex++) { |
---|
1366 | int iRow = which[iIndex]; |
---|
1367 | double alpha = work[iRow]; |
---|
1368 | printf("%d %g\n",iRow,alpha); |
---|
1369 | } |
---|
1370 | } |
---|
1371 | #endif |
---|
1372 | |
---|
1373 | return 0; |
---|
1374 | } |
---|
1375 | // Perturbs problem |
---|
1376 | void |
---|
1377 | ClpSimplexPrimal::perturb(int type) |
---|
1378 | { |
---|
1379 | if (perturbation_>100) |
---|
1380 | return; //perturbed already |
---|
1381 | if (perturbation_==100) |
---|
1382 | perturbation_=50; // treat as normal |
---|
1383 | int i; |
---|
1384 | if (!numberIterations_) |
---|
1385 | cleanStatus(); // make sure status okay |
---|
1386 | if (!numberIterations_&&perturbation_==50) { |
---|
1387 | // See if we need to perturb |
---|
1388 | double * sort = new double[numberRows_]; |
---|
1389 | for (i=0;i<numberRows_;i++) { |
---|
1390 | double lo = fabs(lower_[i]); |
---|
1391 | double up = fabs(upper_[i]); |
---|
1392 | double value=0.0; |
---|
1393 | if (lo&&lo<1.0e20) { |
---|
1394 | if (up&&up<1.0e20) |
---|
1395 | value = 0.5*(lo+up); |
---|
1396 | else |
---|
1397 | value=lo; |
---|
1398 | } else { |
---|
1399 | if (up&&up<1.0e20) |
---|
1400 | value = up; |
---|
1401 | } |
---|
1402 | sort[i]=value; |
---|
1403 | } |
---|
1404 | std::sort(sort,sort+numberRows_); |
---|
1405 | int number=1; |
---|
1406 | double last = sort[0]; |
---|
1407 | for (i=1;i<numberRows_;i++) { |
---|
1408 | if (last!=sort[i]) |
---|
1409 | number++; |
---|
1410 | last=sort[i]; |
---|
1411 | } |
---|
1412 | delete [] sort; |
---|
1413 | if (number*4>numberRows_) |
---|
1414 | return; // good enough |
---|
1415 | } |
---|
1416 | // primal perturbation |
---|
1417 | double perturbation=1.0e-20; |
---|
1418 | int numberNonZero=0; |
---|
1419 | // maximum fraction of rhs/bounds to perturb |
---|
1420 | double maximumFraction = 1.0e-7; |
---|
1421 | if (perturbation_>=50) { |
---|
1422 | perturbation = 1.0e-4; |
---|
1423 | for (i=0;i<numberColumns_+numberRows_;i++) { |
---|
1424 | if (upper_[i]>lower_[i]+primalTolerance_) { |
---|
1425 | double lowerValue, upperValue; |
---|
1426 | if (lower_[i]>-1.0e20) |
---|
1427 | lowerValue = fabs(lower_[i]); |
---|
1428 | else |
---|
1429 | lowerValue=0.0; |
---|
1430 | if (upper_[i]<1.0e20) |
---|
1431 | upperValue = fabs(upper_[i]); |
---|
1432 | else |
---|
1433 | upperValue=0.0; |
---|
1434 | double value = max(fabs(lowerValue),fabs(upperValue)); |
---|
1435 | value = min(value,upper_[i]-lower_[i]); |
---|
1436 | #if 1 |
---|
1437 | if (value) { |
---|
1438 | perturbation += value; |
---|
1439 | numberNonZero++; |
---|
1440 | } |
---|
1441 | #else |
---|
1442 | perturbation = max(perturbation,value); |
---|
1443 | #endif |
---|
1444 | } |
---|
1445 | } |
---|
1446 | if (numberNonZero) |
---|
1447 | perturbation /= (double) numberNonZero; |
---|
1448 | else |
---|
1449 | perturbation = 1.0e-1; |
---|
1450 | } else if (perturbation_<100) { |
---|
1451 | perturbation = pow(10.0,perturbation_); |
---|
1452 | // user is in charge |
---|
1453 | maximumFraction = 1.0; |
---|
1454 | } |
---|
1455 | double largestZero=0.0; |
---|
1456 | double largest=0.0; |
---|
1457 | double largestPerCent=0.0; |
---|
1458 | bool printOut=(handler_->logLevel()==63); |
---|
1459 | printOut=false; //off |
---|
1460 | // Check if all slack |
---|
1461 | int number=0; |
---|
1462 | int iSequence; |
---|
1463 | for (iSequence=0;iSequence<numberRows_;iSequence++) { |
---|
1464 | if (getRowStatus(iSequence)==basic) |
---|
1465 | number++; |
---|
1466 | } |
---|
1467 | if (number!=numberRows_) |
---|
1468 | type=1; |
---|
1469 | // modify bounds |
---|
1470 | if (type==1) { |
---|
1471 | double multiplier = perturbation*maximumFraction; |
---|
1472 | for (iSequence=0;iSequence<numberRows_+numberColumns_;iSequence++) { |
---|
1473 | if (getStatus(iSequence)==basic) { |
---|
1474 | double solutionValue = solution_[iSequence]; |
---|
1475 | double lowerValue = lower_[iSequence]; |
---|
1476 | double upperValue = upper_[iSequence]; |
---|
1477 | double difference = upperValue-lowerValue; |
---|
1478 | difference = min(difference,perturbation); |
---|
1479 | difference = min(difference,fabs(solutionValue)+1.0); |
---|
1480 | double value = CoinDrand48()*multiplier*(difference+1.0); |
---|
1481 | if (solutionValue-lowerValue<=primalTolerance_) { |
---|
1482 | lower_[iSequence] -= value; |
---|
1483 | } else if (upperValue-solutionValue<=primalTolerance_) { |
---|
1484 | upper_[iSequence] += value; |
---|
1485 | } else { |
---|
1486 | #if 0 |
---|
1487 | if (iSequence>=numberColumns_) { |
---|
1488 | // may not be at bound - but still perturb (unless free) |
---|
1489 | if (upperValue>1.0e30&&lowerValue<-1.0e30) |
---|
1490 | value=0.0; |
---|
1491 | else |
---|
1492 | value = - value; // as -1.0 in matrix |
---|
1493 | } else { |
---|
1494 | value = 0.0; |
---|
1495 | } |
---|
1496 | #else |
---|
1497 | value=0.0; |
---|
1498 | #endif |
---|
1499 | } |
---|
1500 | if (value) { |
---|
1501 | if (printOut) |
---|
1502 | printf("col %d lower from %g to %g, upper from %g to %g\n", |
---|
1503 | iSequence,lower_[iSequence],lowerValue,upper_[iSequence],upperValue); |
---|
1504 | if (solutionValue) { |
---|
1505 | largest = max(largest,value); |
---|
1506 | if (value>(fabs(solutionValue)+1.0)*largestPerCent) |
---|
1507 | largestPerCent=value/(fabs(solutionValue)+1.0); |
---|
1508 | } else { |
---|
1509 | largestZero = max(largestZero,value); |
---|
1510 | } |
---|
1511 | } |
---|
1512 | } |
---|
1513 | } |
---|
1514 | } else { |
---|
1515 | for (i=0;i<numberColumns_;i++) { |
---|
1516 | double lowerValue=lower_[i], upperValue=upper_[i]; |
---|
1517 | if (upperValue>lowerValue+primalTolerance_) { |
---|
1518 | double value = CoinDrand48()*perturbation*maximumFraction; |
---|
1519 | if (lowerValue>-1.0e20&&lowerValue) |
---|
1520 | lowerValue -= value * (max(1.0,1.0e-5*fabs(lowerValue))); |
---|
1521 | if (upperValue<1.0e20&&upperValue) |
---|
1522 | upperValue += value * (max(1.0,1.0e-5*fabs(upperValue))); |
---|
1523 | if (lowerValue!=lower_[i]) { |
---|
1524 | double difference = fabs(lowerValue-lower_[i]); |
---|
1525 | largest = max(largest,difference); |
---|
1526 | if (difference>fabs(lower_[i])*largestPerCent) |
---|
1527 | largestPerCent=fabs(difference/lower_[i]); |
---|
1528 | } |
---|
1529 | if (upperValue!=upper_[i]) { |
---|
1530 | double difference = fabs(upperValue-upper_[i]); |
---|
1531 | largest = max(largest,difference); |
---|
1532 | if (difference>fabs(upper_[i])*largestPerCent) |
---|
1533 | largestPerCent=fabs(difference/upper_[i]); |
---|
1534 | } |
---|
1535 | if (printOut) |
---|
1536 | printf("col %d lower from %g to %g, upper from %g to %g\n", |
---|
1537 | i,lower_[i],lowerValue,upper_[i],upperValue); |
---|
1538 | } |
---|
1539 | lower_[i]=lowerValue; |
---|
1540 | upper_[i]=upperValue; |
---|
1541 | } |
---|
1542 | for (;i<numberColumns_+numberRows_;i++) { |
---|
1543 | double lowerValue=lower_[i], upperValue=upper_[i]; |
---|
1544 | double value = CoinDrand48()*perturbation*maximumFraction; |
---|
1545 | if (upperValue>lowerValue+primalTolerance_) { |
---|
1546 | if (lowerValue>-1.0e20&&lowerValue) |
---|
1547 | lowerValue -= value * (max(1.0,1.0e-5*fabs(lowerValue))); |
---|
1548 | if (upperValue<1.0e20&&upperValue) |
---|
1549 | upperValue += value * (max(1.0,1.0e-5*fabs(upperValue))); |
---|
1550 | } else if (upperValue>0.0) { |
---|
1551 | upperValue -= value * (max(1.0,1.0e-5*fabs(lowerValue))); |
---|
1552 | lowerValue -= value * (max(1.0,1.0e-5*fabs(lowerValue))); |
---|
1553 | } else if (upperValue<0.0) { |
---|
1554 | upperValue += value * (max(1.0,1.0e-5*fabs(lowerValue))); |
---|
1555 | lowerValue += value * (max(1.0,1.0e-5*fabs(lowerValue))); |
---|
1556 | } else { |
---|
1557 | } |
---|
1558 | if (lowerValue!=lower_[i]) { |
---|
1559 | double difference = fabs(lowerValue-lower_[i]); |
---|
1560 | largest = max(largest,difference); |
---|
1561 | if (difference>fabs(lower_[i])*largestPerCent) |
---|
1562 | largestPerCent=fabs(difference/lower_[i]); |
---|
1563 | } |
---|
1564 | if (upperValue!=upper_[i]) { |
---|
1565 | double difference = fabs(upperValue-upper_[i]); |
---|
1566 | largest = max(largest,difference); |
---|
1567 | if (difference>fabs(upper_[i])*largestPerCent) |
---|
1568 | largestPerCent=fabs(difference/upper_[i]); |
---|
1569 | } |
---|
1570 | if (printOut) |
---|
1571 | printf("row %d lower from %g to %g, upper from %g to %g\n", |
---|
1572 | i-numberColumns_,lower_[i],lowerValue,upper_[i],upperValue); |
---|
1573 | lower_[i]=lowerValue; |
---|
1574 | upper_[i]=upperValue; |
---|
1575 | } |
---|
1576 | } |
---|
1577 | // Clean up |
---|
1578 | for (i=0;i<numberColumns_+numberRows_;i++) { |
---|
1579 | switch(getStatus(i)) { |
---|
1580 | |
---|
1581 | case basic: |
---|
1582 | break; |
---|
1583 | case atUpperBound: |
---|
1584 | solution_[i]=upper_[i]; |
---|
1585 | break; |
---|
1586 | case isFixed: |
---|
1587 | case atLowerBound: |
---|
1588 | solution_[i]=lower_[i]; |
---|
1589 | break; |
---|
1590 | case isFree: |
---|
1591 | break; |
---|
1592 | case superBasic: |
---|
1593 | break; |
---|
1594 | } |
---|
1595 | } |
---|
1596 | handler_->message(CLP_SIMPLEX_PERTURB,messages_) |
---|
1597 | <<100.0*maximumFraction<<perturbation<<largest<<100.0*largestPerCent<<largestZero |
---|
1598 | <<CoinMessageEol; |
---|
1599 | // redo nonlinear costs |
---|
1600 | // say perturbed |
---|
1601 | perturbation_=101; |
---|
1602 | } |
---|
1603 | // un perturb |
---|
1604 | bool |
---|
1605 | ClpSimplexPrimal::unPerturb() |
---|
1606 | { |
---|
1607 | if (perturbation_!=101) |
---|
1608 | return false; |
---|
1609 | // put back original bounds and costs |
---|
1610 | createRim(7); |
---|
1611 | // unflag |
---|
1612 | unflag(); |
---|
1613 | // get a valid nonlinear cost function |
---|
1614 | delete nonLinearCost_; |
---|
1615 | nonLinearCost_= new ClpNonLinearCost(this); |
---|
1616 | perturbation_ = 102; // stop any further perturbation |
---|
1617 | // move non basic variables to new bounds |
---|
1618 | nonLinearCost_->checkInfeasibilities(0.0); |
---|
1619 | #if 1 |
---|
1620 | // Try using dual |
---|
1621 | return true; |
---|
1622 | #else |
---|
1623 | gutsOfSolution(NULL,NULL); |
---|
1624 | return false; |
---|
1625 | #endif |
---|
1626 | |
---|
1627 | } |
---|
1628 | // Unflag all variables and return number unflagged |
---|
1629 | int |
---|
1630 | ClpSimplexPrimal::unflag() |
---|
1631 | { |
---|
1632 | int i; |
---|
1633 | int number = numberRows_+numberColumns_; |
---|
1634 | int numberFlagged=0; |
---|
1635 | for (i=0;i<number;i++) { |
---|
1636 | if (flagged(i)) { |
---|
1637 | clearFlagged(i); |
---|
1638 | numberFlagged++; |
---|
1639 | } |
---|
1640 | } |
---|
1641 | return numberFlagged; |
---|
1642 | } |
---|
1643 | // Do not change infeasibility cost and always say optimal |
---|
1644 | void |
---|
1645 | ClpSimplexPrimal::alwaysOptimal(bool onOff) |
---|
1646 | { |
---|
1647 | if (onOff) |
---|
1648 | specialOptions_ |= 1; |
---|
1649 | else |
---|
1650 | specialOptions_ &= ~1; |
---|
1651 | } |
---|
1652 | bool |
---|
1653 | ClpSimplexPrimal::alwaysOptimal() const |
---|
1654 | { |
---|
1655 | return (specialOptions_&1)!=0; |
---|
1656 | } |
---|
1657 | // Flatten outgoing variables i.e. - always to exact bound |
---|
1658 | void |
---|
1659 | ClpSimplexPrimal::exactOutgoing(bool onOff) |
---|
1660 | { |
---|
1661 | if (onOff) |
---|
1662 | specialOptions_ |= 4; |
---|
1663 | else |
---|
1664 | specialOptions_ &= ~4; |
---|
1665 | } |
---|
1666 | bool |
---|
1667 | ClpSimplexPrimal::exactOutgoing() const |
---|
1668 | { |
---|
1669 | return (specialOptions_&4)!=0; |
---|
1670 | } |
---|
1671 | /* |
---|
1672 | Reasons to come out (normal mode/user mode): |
---|
1673 | -1 normal |
---|
1674 | -2 factorize now - good iteration/ NA |
---|
1675 | -3 slight inaccuracy - refactorize - iteration done/ same but factor done |
---|
1676 | -4 inaccuracy - refactorize - no iteration/ NA |
---|
1677 | -5 something flagged - go round again/ pivot not possible |
---|
1678 | +2 looks unbounded |
---|
1679 | +3 max iterations (iteration done) |
---|
1680 | */ |
---|
1681 | int |
---|
1682 | ClpSimplexPrimal::pivotResult(int ifValuesPass) |
---|
1683 | { |
---|
1684 | |
---|
1685 | bool roundAgain=true; |
---|
1686 | int returnCode=-1; |
---|
1687 | |
---|
1688 | // loop round if user setting and doing refactorization |
---|
1689 | while (roundAgain) { |
---|
1690 | roundAgain=false; |
---|
1691 | returnCode=-1; |
---|
1692 | pivotRow_=-1; |
---|
1693 | sequenceOut_=-1; |
---|
1694 | rowArray_[1]->clear(); |
---|
1695 | // we found a pivot column |
---|
1696 | // update the incoming column |
---|
1697 | unpack(rowArray_[1]); |
---|
1698 | // save reduced cost |
---|
1699 | double saveDj = dualIn_; |
---|
1700 | factorization_->updateColumnFT(rowArray_[2],rowArray_[1]); |
---|
1701 | // do ratio test and re-compute dj |
---|
1702 | primalRow(rowArray_[1],rowArray_[3],rowArray_[2],rowArray_[0], |
---|
1703 | ifValuesPass); |
---|
1704 | if (ifValuesPass) { |
---|
1705 | saveDj=dualIn_; |
---|
1706 | if (pivotRow_==-1||(pivotRow_>=0&&fabs(alpha_)<1.0e-5)) { |
---|
1707 | if(fabs(dualIn_)<1.0e2*dualTolerance_) { |
---|
1708 | // try other way |
---|
1709 | directionIn_=-directionIn_; |
---|
1710 | primalRow(rowArray_[1],rowArray_[3],rowArray_[2],rowArray_[0], |
---|
1711 | 0); |
---|
1712 | } |
---|
1713 | if (pivotRow_==-1||(pivotRow_>=0&&fabs(alpha_)<1.0e-5)) { |
---|
1714 | if (solveType_==1) { |
---|
1715 | // reject it |
---|
1716 | char x = isColumn(sequenceIn_) ? 'C' :'R'; |
---|
1717 | handler_->message(CLP_SIMPLEX_FLAG,messages_) |
---|
1718 | <<x<<sequenceWithin(sequenceIn_) |
---|
1719 | <<CoinMessageEol; |
---|
1720 | setFlagged(sequenceIn_); |
---|
1721 | lastBadIteration_ = numberIterations_; // say be more cautious |
---|
1722 | rowArray_[1]->clear(); |
---|
1723 | pivotRow_=-1; |
---|
1724 | } |
---|
1725 | returnCode=-5; |
---|
1726 | break; |
---|
1727 | } |
---|
1728 | } |
---|
1729 | } |
---|
1730 | double checkValue=1.0e-2; |
---|
1731 | if (largestDualError_>1.0e-5) |
---|
1732 | checkValue=1.0e-1; |
---|
1733 | if (solveType_==1&&((saveDj*dualIn_<1.0e-20&&!ifValuesPass)|| |
---|
1734 | fabs(saveDj-dualIn_)>checkValue*(1.0+fabs(saveDj)))) { |
---|
1735 | handler_->message(CLP_PRIMAL_DJ,messages_) |
---|
1736 | <<saveDj<<dualIn_ |
---|
1737 | <<CoinMessageEol; |
---|
1738 | if(lastGoodIteration_ != numberIterations_) { |
---|
1739 | rowArray_[1]->clear(); |
---|
1740 | pivotRow_=-1; // say no weights update |
---|
1741 | returnCode=-4; |
---|
1742 | if(lastGoodIteration_+1 == numberIterations_) { |
---|
1743 | // not looking wonderful - try cleaning bounds |
---|
1744 | // put non-basics to bounds in case tolerance moved |
---|
1745 | nonLinearCost_->checkInfeasibilities(0.0); |
---|
1746 | } |
---|
1747 | sequenceOut_=-1; |
---|
1748 | break; |
---|
1749 | } else { |
---|
1750 | // take on more relaxed criterion |
---|
1751 | if (saveDj*dualIn_<1.0e-20|| |
---|
1752 | fabs(saveDj-dualIn_)>2.0e-1*(1.0+fabs(dualIn_))) { |
---|
1753 | // need to reject something |
---|
1754 | char x = isColumn(sequenceIn_) ? 'C' :'R'; |
---|
1755 | handler_->message(CLP_SIMPLEX_FLAG,messages_) |
---|
1756 | <<x<<sequenceWithin(sequenceIn_) |
---|
1757 | <<CoinMessageEol; |
---|
1758 | setFlagged(sequenceIn_); |
---|
1759 | lastBadIteration_ = numberIterations_; // say be more cautious |
---|
1760 | rowArray_[1]->clear(); |
---|
1761 | pivotRow_=-1; |
---|
1762 | returnCode=-5; |
---|
1763 | sequenceOut_=-1; |
---|
1764 | break; |
---|
1765 | } |
---|
1766 | } |
---|
1767 | } |
---|
1768 | if (pivotRow_>=0) { |
---|
1769 | if (solveType_==2) { |
---|
1770 | // **** Coding for user interface |
---|
1771 | // do ray |
---|
1772 | primalRay(rowArray_[1]); |
---|
1773 | // update duals |
---|
1774 | if (pivotRow_>=0) { |
---|
1775 | alpha_ = rowArray_[1]->denseVector()[pivotRow_]; |
---|
1776 | assert (fabs(alpha_)>1.0e-8); |
---|
1777 | double multiplier = dualIn_/alpha_; |
---|
1778 | rowArray_[0]->insert(pivotRow_,multiplier); |
---|
1779 | factorization_->updateColumnTranspose(rowArray_[2],rowArray_[0]); |
---|
1780 | // put row of tableau in rowArray[0] and columnArray[0] |
---|
1781 | matrix_->transposeTimes(this,-1.0, |
---|
1782 | rowArray_[0],columnArray_[1],columnArray_[0]); |
---|
1783 | // update column djs |
---|
1784 | int i; |
---|
1785 | int * index = columnArray_[0]->getIndices(); |
---|
1786 | int number = columnArray_[0]->getNumElements(); |
---|
1787 | double * element = columnArray_[0]->denseVector(); |
---|
1788 | for (i=0;i<number;i++) { |
---|
1789 | int ii = index[i]; |
---|
1790 | dj_[ii] += element[ii]; |
---|
1791 | element[ii]=0.0; |
---|
1792 | } |
---|
1793 | columnArray_[0]->setNumElements(0); |
---|
1794 | // and row djs |
---|
1795 | index = rowArray_[0]->getIndices(); |
---|
1796 | number = rowArray_[0]->getNumElements(); |
---|
1797 | element = rowArray_[0]->denseVector(); |
---|
1798 | for (i=0;i<number;i++) { |
---|
1799 | int ii = index[i]; |
---|
1800 | dj_[ii+numberColumns_] += element[ii]; |
---|
1801 | dual_[ii] = dj_[ii+numberColumns_]; |
---|
1802 | element[ii]=0.0; |
---|
1803 | } |
---|
1804 | rowArray_[0]->setNumElements(0); |
---|
1805 | // check incoming |
---|
1806 | assert (fabs(dj_[sequenceIn_])<1.0e-6); |
---|
1807 | } |
---|
1808 | } |
---|
1809 | // if stable replace in basis |
---|
1810 | int updateStatus = factorization_->replaceColumn(rowArray_[2], |
---|
1811 | pivotRow_, |
---|
1812 | alpha_); |
---|
1813 | // if no pivots, bad update but reasonable alpha - take and invert |
---|
1814 | if (updateStatus==2&& |
---|
1815 | lastGoodIteration_==numberIterations_&&fabs(alpha_)>1.0e-5) |
---|
1816 | updateStatus=4; |
---|
1817 | if (updateStatus==1||updateStatus==4) { |
---|
1818 | // slight error |
---|
1819 | if (factorization_->pivots()>5||updateStatus==4) { |
---|
1820 | returnCode=-3; |
---|
1821 | } |
---|
1822 | } else if (updateStatus==2) { |
---|
1823 | // major error |
---|
1824 | // better to have small tolerance even if slower |
---|
1825 | factorization_->zeroTolerance(1.0e-15); |
---|
1826 | int maxFactor = factorization_->maximumPivots(); |
---|
1827 | if (maxFactor>10) { |
---|
1828 | if (forceFactorization_<0) |
---|
1829 | forceFactorization_= maxFactor; |
---|
1830 | forceFactorization_ = max (1,(forceFactorization_>>1)); |
---|
1831 | } |
---|
1832 | // later we may need to unwind more e.g. fake bounds |
---|
1833 | if(lastGoodIteration_ != numberIterations_) { |
---|
1834 | rowArray_[1]->clear(); |
---|
1835 | pivotRow_=-1; |
---|
1836 | if (solveType_==1) { |
---|
1837 | returnCode=-4; |
---|
1838 | break; |
---|
1839 | } else { |
---|
1840 | // user in charge - re-factorize |
---|
1841 | int lastCleaned; |
---|
1842 | ClpSimplexProgress dummyProgress; |
---|
1843 | if (saveStatus_) |
---|
1844 | statusOfProblemInPrimal(lastCleaned,1,&dummyProgress); |
---|
1845 | else |
---|
1846 | statusOfProblemInPrimal(lastCleaned,0,&dummyProgress); |
---|
1847 | roundAgain=true; |
---|
1848 | continue; |
---|
1849 | } |
---|
1850 | } else { |
---|
1851 | // need to reject something |
---|
1852 | if (solveType_==1) { |
---|
1853 | char x = isColumn(sequenceIn_) ? 'C' :'R'; |
---|
1854 | handler_->message(CLP_SIMPLEX_FLAG,messages_) |
---|
1855 | <<x<<sequenceWithin(sequenceIn_) |
---|
1856 | <<CoinMessageEol; |
---|
1857 | setFlagged(sequenceIn_); |
---|
1858 | } |
---|
1859 | lastBadIteration_ = numberIterations_; // say be more cautious |
---|
1860 | rowArray_[1]->clear(); |
---|
1861 | pivotRow_=-1; |
---|
1862 | sequenceOut_=-1; |
---|
1863 | returnCode = -5; |
---|
1864 | break; |
---|
1865 | |
---|
1866 | } |
---|
1867 | } else if (updateStatus==3) { |
---|
1868 | // out of memory |
---|
1869 | // increase space if not many iterations |
---|
1870 | if (factorization_->pivots()< |
---|
1871 | 0.5*factorization_->maximumPivots()&& |
---|
1872 | factorization_->pivots()<200) |
---|
1873 | factorization_->areaFactor( |
---|
1874 | factorization_->areaFactor() * 1.1); |
---|
1875 | returnCode =-2; // factorize now |
---|
1876 | } else if (updateStatus==5) { |
---|
1877 | problemStatus_=-2; // factorize now |
---|
1878 | } |
---|
1879 | // here do part of steepest - ready for next iteration |
---|
1880 | if (!ifValuesPass) |
---|
1881 | primalColumnPivot_->updateWeights(rowArray_[1]); |
---|
1882 | } else { |
---|
1883 | if (pivotRow_==-1) { |
---|
1884 | // no outgoing row is valid |
---|
1885 | rowArray_[0]->clear(); |
---|
1886 | if (!factorization_->pivots()) { |
---|
1887 | returnCode = 2; //say looks unbounded |
---|
1888 | // do ray |
---|
1889 | primalRay(rowArray_[1]); |
---|
1890 | } else if (solveType_==2) { |
---|
1891 | // refactorize |
---|
1892 | int lastCleaned; |
---|
1893 | ClpSimplexProgress dummyProgress; |
---|
1894 | if (saveStatus_) |
---|
1895 | statusOfProblemInPrimal(lastCleaned,1,&dummyProgress); |
---|
1896 | else |
---|
1897 | statusOfProblemInPrimal(lastCleaned,0,&dummyProgress); |
---|
1898 | roundAgain=true; |
---|
1899 | continue; |
---|
1900 | } else { |
---|
1901 | returnCode = 4; //say looks unbounded but has iterated |
---|
1902 | } |
---|
1903 | break; |
---|
1904 | } else { |
---|
1905 | // flipping from bound to bound |
---|
1906 | } |
---|
1907 | } |
---|
1908 | |
---|
1909 | |
---|
1910 | // update primal solution |
---|
1911 | |
---|
1912 | double objectiveChange=0.0; |
---|
1913 | // Cost on pivot row may change - may need to change dualIn |
---|
1914 | double oldCost=0.0; |
---|
1915 | if (pivotRow_>=0) |
---|
1916 | oldCost = cost(pivotVariable_[pivotRow_]); |
---|
1917 | // rowArray_[1] is not empty - used to update djs |
---|
1918 | updatePrimalsInPrimal(rowArray_[1],theta_, objectiveChange); |
---|
1919 | if (pivotRow_>=0) |
---|
1920 | dualIn_ += (oldCost-cost(pivotVariable_[pivotRow_])); |
---|
1921 | double oldValue = valueIn_; |
---|
1922 | if (directionIn_==-1) { |
---|
1923 | // as if from upper bound |
---|
1924 | if (sequenceIn_!=sequenceOut_) { |
---|
1925 | // variable becoming basic |
---|
1926 | valueIn_ -= fabs(theta_); |
---|
1927 | } else { |
---|
1928 | valueIn_=lowerIn_; |
---|
1929 | } |
---|
1930 | } else { |
---|
1931 | // as if from lower bound |
---|
1932 | if (sequenceIn_!=sequenceOut_) { |
---|
1933 | // variable becoming basic |
---|
1934 | valueIn_ += fabs(theta_); |
---|
1935 | } else { |
---|
1936 | valueIn_=upperIn_; |
---|
1937 | } |
---|
1938 | } |
---|
1939 | objectiveChange += dualIn_*(valueIn_-oldValue); |
---|
1940 | // outgoing |
---|
1941 | if (sequenceIn_!=sequenceOut_) { |
---|
1942 | if (directionOut_>0) { |
---|
1943 | valueOut_ = lowerOut_; |
---|
1944 | } else { |
---|
1945 | valueOut_ = upperOut_; |
---|
1946 | } |
---|
1947 | assert(valueOut_>=lower_[sequenceOut_]-primalTolerance_&& |
---|
1948 | valueOut_<=upper_[sequenceOut_]+primalTolerance_); |
---|
1949 | // may not be exactly at bound and bounds may have changed |
---|
1950 | // Make sure outgoing looks feasible |
---|
1951 | directionOut_=nonLinearCost_->setOneOutgoing(sequenceOut_,valueOut_); |
---|
1952 | solution_[sequenceOut_]=valueOut_; |
---|
1953 | } |
---|
1954 | // change cost and bounds on incoming if primal |
---|
1955 | nonLinearCost_->setOne(sequenceIn_,valueIn_); |
---|
1956 | int whatNext=housekeeping(objectiveChange); |
---|
1957 | #if 0 |
---|
1958 | if (numberIterations_==1148) |
---|
1959 | whatNext=1; |
---|
1960 | if (numberIterations_>1200) |
---|
1961 | exit(0); |
---|
1962 | #endif |
---|
1963 | if (whatNext==1) { |
---|
1964 | returnCode =-2; // refactorize |
---|
1965 | } else if (whatNext==2) { |
---|
1966 | // maximum iterations or equivalent |
---|
1967 | returnCode=3; |
---|
1968 | } else if(numberIterations_ == lastGoodIteration_ |
---|
1969 | + 2 * factorization_->maximumPivots()) { |
---|
1970 | // done a lot of flips - be safe |
---|
1971 | returnCode =-2; // refactorize |
---|
1972 | } |
---|
1973 | } |
---|
1974 | if (solveType_==2&&(returnCode == -2||returnCode==-3)) { |
---|
1975 | // refactorize here |
---|
1976 | int lastCleaned; |
---|
1977 | ClpSimplexProgress dummyProgress; |
---|
1978 | if (saveStatus_) |
---|
1979 | statusOfProblemInPrimal(lastCleaned,1,&dummyProgress); |
---|
1980 | else |
---|
1981 | statusOfProblemInPrimal(lastCleaned,0,&dummyProgress); |
---|
1982 | if (problemStatus_==5) { |
---|
1983 | printf("Singular basis\n"); |
---|
1984 | problemStatus_=-1; |
---|
1985 | returnCode=5; |
---|
1986 | } |
---|
1987 | } |
---|
1988 | #ifdef CLP_DEBUG |
---|
1989 | { |
---|
1990 | int i; |
---|
1991 | // not [1] as may have information |
---|
1992 | for (i=0;i<4;i++) { |
---|
1993 | if (i!=1) |
---|
1994 | rowArray_[i]->checkClear(); |
---|
1995 | } |
---|
1996 | for (i=0;i<2;i++) { |
---|
1997 | columnArray_[i]->checkClear(); |
---|
1998 | } |
---|
1999 | } |
---|
2000 | #endif |
---|
2001 | return returnCode; |
---|
2002 | } |
---|
2003 | // Create primal ray |
---|
2004 | void |
---|
2005 | ClpSimplexPrimal::primalRay(CoinIndexedVector * rowArray) |
---|
2006 | { |
---|
2007 | delete [] ray_; |
---|
2008 | ray_ = new double [numberColumns_]; |
---|
2009 | ClpFillN(ray_,numberColumns_,0.0); |
---|
2010 | int number=rowArray->getNumElements(); |
---|
2011 | int * index = rowArray->getIndices(); |
---|
2012 | double * array = rowArray->denseVector(); |
---|
2013 | double way=-directionIn_; |
---|
2014 | int i; |
---|
2015 | double zeroTolerance=1.0e-12; |
---|
2016 | if (sequenceIn_<numberColumns_) |
---|
2017 | ray_[sequenceIn_]=directionIn_; |
---|
2018 | for (i=0;i<number;i++) { |
---|
2019 | int iRow=index[i]; |
---|
2020 | int iPivot=pivotVariable_[iRow]; |
---|
2021 | double arrayValue = array[iRow]; |
---|
2022 | if (iPivot<numberColumns_&&fabs(arrayValue)>=zeroTolerance) |
---|
2023 | ray_[iPivot] = way* array[iRow]; |
---|
2024 | } |
---|
2025 | } |
---|
2026 | /* Get next superbasic -1 if none, |
---|
2027 | Normal type is 1 |
---|
2028 | If type is 3 then initializes sorted list |
---|
2029 | if 2 uses list. |
---|
2030 | */ |
---|
2031 | int |
---|
2032 | ClpSimplexPrimal::nextSuperBasic(int superBasicType,CoinIndexedVector * columnArray) |
---|
2033 | { |
---|
2034 | if (firstFree_>=0&&superBasicType) { |
---|
2035 | int returnValue=firstFree_; |
---|
2036 | int iColumn=firstFree_+1; |
---|
2037 | if (superBasicType>1) { |
---|
2038 | if (superBasicType>2) { |
---|
2039 | // Initialize list |
---|
2040 | // Wild guess that lower bound more natural than upper |
---|
2041 | int number=0; |
---|
2042 | double * work=columnArray->denseVector(); |
---|
2043 | int * which=columnArray->getIndices(); |
---|
2044 | for (iColumn=0;iColumn<numberRows_+numberColumns_;iColumn++) { |
---|
2045 | if (getStatus(iColumn)==superBasic) { |
---|
2046 | if (fabs(solution_[iColumn]-lower_[iColumn])<=primalTolerance_) { |
---|
2047 | solution_[iColumn]=lower_[iColumn]; |
---|
2048 | setStatus(iColumn,atLowerBound); |
---|
2049 | } else if (fabs(solution_[iColumn]-upper_[iColumn]) |
---|
2050 | <=primalTolerance_) { |
---|
2051 | solution_[iColumn]=upper_[iColumn]; |
---|
2052 | setStatus(iColumn,atUpperBound); |
---|
2053 | } else if (lower_[iColumn]<-1.0e20&&upper_[iColumn]>1.0e20) { |
---|
2054 | setStatus(iColumn,isFree); |
---|
2055 | break; |
---|
2056 | } else if (!flagged(iColumn)) { |
---|
2057 | // put ones near bounds at end after sorting |
---|
2058 | work[number]= - min(0.1*(solution_[iColumn]-lower_[iColumn]), |
---|
2059 | upper_[iColumn]-solution_[iColumn]); |
---|
2060 | which[number++] = iColumn; |
---|
2061 | } |
---|
2062 | } |
---|
2063 | } |
---|
2064 | CoinSort_2(work,work+number,which); |
---|
2065 | columnArray->setNumElements(number); |
---|
2066 | memset(work,0,number*sizeof(double)); |
---|
2067 | } |
---|
2068 | int * which=columnArray->getIndices(); |
---|
2069 | int number = columnArray->getNumElements(); |
---|
2070 | if (!number) { |
---|
2071 | // finished |
---|
2072 | iColumn = numberRows_+numberColumns_; |
---|
2073 | returnValue=-1; |
---|
2074 | } else { |
---|
2075 | number--; |
---|
2076 | returnValue=which[number]; |
---|
2077 | iColumn=returnValue; |
---|
2078 | columnArray->setNumElements(number); |
---|
2079 | } |
---|
2080 | } else { |
---|
2081 | for (;iColumn<numberRows_+numberColumns_;iColumn++) { |
---|
2082 | if (getStatus(iColumn)==superBasic) { |
---|
2083 | if (fabs(solution_[iColumn]-lower_[iColumn])<=primalTolerance_) { |
---|
2084 | solution_[iColumn]=lower_[iColumn]; |
---|
2085 | setStatus(iColumn,atLowerBound); |
---|
2086 | } else if (fabs(solution_[iColumn]-upper_[iColumn]) |
---|
2087 | <=primalTolerance_) { |
---|
2088 | solution_[iColumn]=upper_[iColumn]; |
---|
2089 | setStatus(iColumn,atUpperBound); |
---|
2090 | } else if (lower_[iColumn]<-1.0e20&&upper_[iColumn]>1.0e20) { |
---|
2091 | setStatus(iColumn,isFree); |
---|
2092 | break; |
---|
2093 | } else { |
---|
2094 | break; |
---|
2095 | } |
---|
2096 | } |
---|
2097 | } |
---|
2098 | } |
---|
2099 | firstFree_ = iColumn; |
---|
2100 | if (firstFree_==numberRows_+numberColumns_) |
---|
2101 | firstFree_=-1; |
---|
2102 | return returnValue; |
---|
2103 | } else { |
---|
2104 | return -1; |
---|
2105 | } |
---|
2106 | } |
---|
2107 | |
---|
2108 | |
---|
2109 | |
---|