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 dual simplex algorithm. |
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6 | |
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7 | When dual feasible: |
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8 | |
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9 | If primal feasible, we are optimal. Otherwise choose an infeasible |
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10 | basic variable to leave basis (normally going to nearest bound) (B). We |
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11 | now need to find an incoming variable which will leave problem |
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12 | dual feasible so we get the row of the tableau corresponding to |
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13 | the basic variable (with the correct sign depending if basic variable |
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14 | above or below feasibility region - as that affects whether reduced |
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15 | cost on outgoing variable has to be positive or negative). |
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16 | |
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17 | We now perform a ratio test to determine which incoming variable will |
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18 | preserve dual feasibility (C). If no variable found then problem |
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19 | is infeasible (in primal sense). If there is a variable, we then |
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20 | perform pivot and repeat. Trivial? |
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21 | |
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22 | ------------------------------------------- |
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23 | |
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24 | A) How do we get dual feasible? If all variables have bounds then |
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25 | it is trivial to get feasible by putting non-basic variables to |
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26 | correct bounds. OSL did not have a phase 1/phase 2 approach but |
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27 | instead effectively put fake bounds on variables and this is the |
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28 | approach here, although I had hoped to make it cleaner. |
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29 | |
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30 | If there is a weight of X on getting dual feasible: |
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31 | Non-basic variables with negative reduced costs are put to |
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32 | lesser of their upper bound and their lower bound + X. |
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33 | Similarly, mutatis mutandis, for positive reduced costs. |
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34 | |
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35 | Free variables should normally be in basis, otherwise I have |
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36 | coding which may be able to come out (and may not be correct). |
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37 | |
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38 | In OSL, this weight was changed heuristically, here at present |
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39 | it is only increased if problem looks finished. If problem is |
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40 | feasible I check for unboundedness. If not unbounded we |
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41 | could play with going into primal. As long as weights increase |
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42 | any algorithm would be finite. |
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43 | |
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44 | B) Which outgoing variable to choose is a virtual base class. |
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45 | For difficult problems steepest edge is preferred while for |
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46 | very easy (large) problems we will need partial scan. |
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47 | |
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48 | C) Sounds easy, but this is hardest part of algorithm. |
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49 | 1) Instead of stopping at first choice, we may be able |
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50 | to flip that variable to other bound and if objective |
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51 | still improving choose again. These mini iterations can |
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52 | increase speed by orders of magnitude but we may need to |
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53 | go to more of a bucket choice of variable rather than looking |
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54 | at them one by one (for speed). |
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55 | 2) Accuracy. Reduced costs may be of wrong sign but less than |
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56 | tolerance. Pivoting on these makes objective go backwards. |
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57 | OSL modified cost so a zero move was made, Gill et al |
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58 | (in primal analogue) modified so a strictly positive move was |
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59 | made. It is not quite as neat in dual but that is what we |
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60 | try and do. The two problems are that re-factorizations can |
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61 | change reduced costs above and below tolerances and that when |
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62 | finished we need to reset costs and try again. |
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63 | 3) Degeneracy. Gill et al helps but may not be enough. We |
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64 | may need more. Also it can improve speed a lot if we perturb |
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65 | the costs significantly. |
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66 | |
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67 | References: |
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68 | Forrest and Goldfarb, Steepest-edge simplex algorithms for |
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69 | linear programming - Mathematical Programming 1992 |
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70 | Forrest and Tomlin, Implementing the simplex method for |
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71 | the Optimization Subroutine Library - IBM Systems Journal 1992 |
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72 | Gill, Murray, Saunders, Wright A Practical Anti-Cycling |
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73 | Procedure for Linear and Nonlinear Programming SOL report 1988 |
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74 | |
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75 | |
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76 | TODO: |
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77 | |
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78 | a) Better recovery procedures. At present I never check on forward |
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79 | progress. There is checkpoint/restart with reducing |
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80 | re-factorization frequency, but this is only on singular |
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81 | factorizations. |
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82 | b) Fast methods for large easy problems (and also the option for |
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83 | the code to automatically choose which method). |
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84 | c) We need to be able to stop in various ways for OSI - this |
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85 | is fairly easy. |
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86 | |
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87 | */ |
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88 | |
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89 | #if defined(_MSC_VER) |
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90 | // Turn off compiler warning about long names |
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91 | # pragma warning(disable:4786) |
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92 | #endif |
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93 | |
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94 | #include <math.h> |
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95 | |
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96 | #include "CoinHelperFunctions.hpp" |
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97 | #include "ClpSimplexDual.hpp" |
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98 | #include "ClpFactorization.hpp" |
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99 | #include "OsiPackedMatrix.hpp" |
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100 | #include "OsiIndexedVector.hpp" |
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101 | #include "OsiWarmStartBasis.hpp" |
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102 | #include "ClpDualRowDantzig.hpp" |
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103 | #include "ClpMessage.hpp" |
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104 | #include <cfloat> |
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105 | #include <cassert> |
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106 | #include <string> |
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107 | #include <stdio.h> |
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108 | #include <iostream> |
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109 | // This returns a non const array filled with input from scalar |
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110 | // or actual array |
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111 | template <class T> inline T* |
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112 | copyOfArray( const T * array, const int size, T value) |
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113 | { |
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114 | T * arrayNew = new T[size]; |
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115 | if (array) |
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116 | CoinDisjointCopyN(array,size,arrayNew); |
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117 | else |
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118 | CoinFillN ( arrayNew, size,value); |
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119 | return arrayNew; |
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120 | } |
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121 | |
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122 | // This returns a non const array filled with actual array (or NULL) |
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123 | template <class T> inline T* |
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124 | copyOfArray( const T * array, const int size) |
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125 | { |
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126 | if (array) { |
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127 | T * arrayNew = new T[size]; |
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128 | CoinDisjointCopyN(array,size,arrayNew); |
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129 | return arrayNew; |
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130 | } else { |
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131 | return NULL; |
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132 | } |
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133 | } |
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134 | // dual |
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135 | int ClpSimplexDual::dual ( ) |
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136 | { |
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137 | |
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138 | /* *** Method |
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139 | This is a vanilla version of dual simplex. |
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140 | |
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141 | It tries to be a single phase approach with a weight of 1.0 being |
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142 | given to getting optimal and a weight of dualBound_ being |
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143 | given to getting dual feasible. In this version I have used the |
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144 | idea that this weight can be thought of as a fake bound. If the |
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145 | distance between the lower and upper bounds on a variable is less |
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146 | than the feasibility weight then we are always better off flipping |
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147 | to other bound to make dual feasible. If the distance is greater |
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148 | then we make up a fake bound dualBound_ away from one bound. |
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149 | If we end up optimal or primal infeasible, we check to see if |
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150 | bounds okay. If so we have finished, if not we increase dualBound_ |
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151 | and continue (after checking if unbounded). I am undecided about |
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152 | free variables - there is coding but I am not sure about it. At |
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153 | present I put them in basis anyway. |
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154 | |
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155 | The code is designed to take advantage of sparsity so arrays are |
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156 | seldom zeroed out from scratch or gone over in their entirety. |
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157 | The only exception is a full scan to find outgoing variable. This |
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158 | will be changed to keep an updated list of infeasibilities (or squares |
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159 | if steepest edge). Also on easy problems we don't need full scan - just |
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160 | pick first reasonable. |
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161 | |
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162 | One problem is how to tackle degeneracy and accuracy. At present |
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163 | I am using the modification of costs which I put in OSL and which was |
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164 | extended by Gill et al. I am still not sure of the exact details. |
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165 | |
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166 | The flow of dual is three while loops as follows: |
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167 | |
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168 | while (not finished) { |
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169 | |
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170 | while (not clean solution) { |
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171 | |
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172 | Factorize and/or clean up solution by flipping variables so |
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173 | dual feasible. If looks finished check fake dual bounds. |
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174 | Repeat until status is iterating (-1) or finished (0,1,2) |
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175 | |
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176 | } |
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177 | |
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178 | while (status==-1) { |
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179 | |
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180 | Iterate until no pivot in or out or time to re-factorize. |
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181 | |
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182 | Flow is: |
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183 | |
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184 | choose pivot row (outgoing variable). if none then |
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185 | we are primal feasible so looks as if done but we need to |
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186 | break and check bounds etc. |
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187 | |
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188 | Get pivot row in tableau |
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189 | |
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190 | Choose incoming column. If we don't find one then we look |
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191 | primal infeasible so break and check bounds etc. (Also the |
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192 | pivot tolerance is larger after any iterations so that may be |
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193 | reason) |
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194 | |
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195 | If we do find incoming column, we may have to adjust costs to |
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196 | keep going forwards (anti-degeneracy). Check pivot will be stable |
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197 | and if unstable throw away iteration (we will need to implement |
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198 | flagging of basic variables sometime) and break to re-factorize. |
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199 | If minor error re-factorize after iteration. |
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200 | |
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201 | Update everything (this may involve flipping variables to stay |
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202 | dual feasible. |
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203 | |
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204 | } |
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205 | |
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206 | } |
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207 | |
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208 | At present we never check we are going forwards. I overdid that in |
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209 | OSL so will try and make a last resort. |
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210 | |
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211 | Needs partial scan pivot out option. |
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212 | Needs dantzig, uninitialized and full steepest edge options (can still |
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213 | use partial scan) |
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214 | |
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215 | May need other anti-degeneracy measures, especially if we try and use |
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216 | loose tolerances as a way to solve in fewer iterations. |
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217 | |
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218 | I like idea of dynamic scaling. This gives opportunity to decouple |
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219 | different implications of scaling for accuracy, iteration count and |
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220 | feasibility tolerance. |
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221 | |
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222 | */ |
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223 | |
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224 | |
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225 | // sanity check |
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226 | assert (numberRows_==matrix_->getNumRows()); |
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227 | assert (numberColumns_==matrix_->getNumCols()); |
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228 | // for moment all arrays must exist |
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229 | assert(columnLower_); |
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230 | assert(columnUpper_); |
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231 | assert(rowLower_); |
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232 | assert(rowUpper_); |
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233 | |
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234 | #ifdef CLP_DEBUG |
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235 | int debugIteration=-1; |
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236 | #endif |
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237 | |
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238 | algorithm_ = -1; |
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239 | dualTolerance_=dblParam_[OsiDualTolerance]; |
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240 | primalTolerance_=dblParam_[OsiPrimalTolerance]; |
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241 | |
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242 | // put in standard form (and make row copy) |
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243 | // create modifiable copies of model rim and do optional scaling |
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244 | createRim(7+8+16,true); |
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245 | |
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246 | // save dual bound |
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247 | double saveDualBound_ = dualBound_; |
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248 | |
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249 | int iRow,iColumn; |
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250 | // Do initial factorization |
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251 | // and set certain stuff |
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252 | // We can either set increasing rows so ...IsBasic gives pivot row |
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253 | // or we can just increment iBasic one by one |
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254 | // for now let ...iBasic give pivot row |
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255 | factorization_->increasingRows(2); |
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256 | // row activities have negative sign |
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257 | factorization_->slackValue(-1.0); |
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258 | factorization_->zeroTolerance(1.0e-13); |
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259 | // save if sparse factorization wanted |
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260 | int saveSparse = factorization_->sparseThreshold(); |
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261 | |
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262 | int factorizationStatus = internalFactorize(0); |
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263 | if (factorizationStatus<0) |
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264 | return 1; // some error |
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265 | else if (factorizationStatus) |
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266 | handler_->message(CLP_SINGULARITIES,messages_) |
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267 | <<factorizationStatus |
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268 | <<OsiMessageEol; |
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269 | |
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270 | // If user asked for perturbation - do it |
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271 | int savePerturbation = perturbation_; |
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272 | |
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273 | if (perturbation_<100) |
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274 | perturb(); |
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275 | |
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276 | double objectiveChange; |
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277 | // for dual we will change bounds using dualBound_ |
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278 | // for this we need clean basis so it is after factorize |
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279 | gutsOfSolution(rowActivityWork_,columnActivityWork_); |
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280 | changeBounds(true,NULL,objectiveChange); |
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281 | |
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282 | problemStatus_ = -1; |
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283 | numberIterations_=0; |
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284 | |
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285 | int lastCleaned=0; // last time objective or bounds cleaned up |
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286 | |
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287 | // number of times we have declared optimality |
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288 | numberTimesOptimal_=0; |
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289 | |
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290 | // This says whether to restore things etc |
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291 | int factorType=0; |
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292 | /* |
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293 | Status of problem: |
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294 | 0 - optimal |
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295 | 1 - infeasible |
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296 | 2 - unbounded |
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297 | -1 - iterating |
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298 | -2 - factorization wanted |
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299 | -3 - redo checking without factorization |
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300 | -4 - looks infeasible |
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301 | */ |
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302 | while (problemStatus_<0) { |
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303 | // clear |
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304 | for (iRow=0;iRow<4;iRow++) { |
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305 | rowArray_[iRow]->clear(); |
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306 | } |
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307 | |
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308 | for (iColumn=0;iColumn<2;iColumn++) { |
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309 | columnArray_[iColumn]->clear(); |
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310 | } |
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311 | |
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312 | // give matrix (and model costs and bounds a chance to be |
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313 | // refreshed (normally null) |
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314 | matrix_->refresh(this); |
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315 | // If getting nowhere - why not give it a kick |
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316 | #if 0 |
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317 | // does not seem to work too well - do some more work |
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318 | if (perturbation_<101&&numberIterations_>2*(numberRows_+numberColumns_)) |
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319 | perturb(); |
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320 | #endif |
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321 | // may factorize, checks if problem finished |
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322 | statusOfProblemInDual(lastCleaned,factorType); |
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323 | |
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324 | // Say good factorization |
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325 | factorType=1; |
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326 | if (saveSparse) { |
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327 | // use default at present |
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328 | factorization_->sparseThreshold(0); |
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329 | factorization_->goSparse(); |
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330 | } |
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331 | |
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332 | // Do iterations |
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333 | whileIterating(); |
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334 | } |
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335 | |
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336 | // at present we are leaving factorization around |
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337 | // maybe we should empty it |
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338 | deleteRim(); |
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339 | handler_->message(CLP_SIMPLEX_FINISHED+problemStatus_,messages_) |
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340 | <<objectiveValue() |
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341 | <<OsiMessageEol; |
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342 | // Restore any saved stuff |
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343 | perturbation_ = savePerturbation; |
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344 | factorization_->sparseThreshold(saveSparse); |
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345 | dualBound_ = saveDualBound_; |
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346 | return problemStatus_; |
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347 | } |
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348 | void |
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349 | ClpSimplexDual::whileIterating() |
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350 | { |
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351 | // status stays at -1 while iterating, >=0 finished, -2 to invert |
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352 | // status -3 to go to top without an invert |
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353 | while (problemStatus_==-1) { |
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354 | #ifdef CLP_DEBUG |
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355 | { |
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356 | int i; |
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357 | for (i=0;i<4;i++) { |
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358 | rowArray_[i]->checkClear(); |
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359 | } |
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360 | for (i=0;i<2;i++) { |
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361 | columnArray_[i]->checkClear(); |
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362 | } |
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363 | } |
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364 | #endif |
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365 | #if CLP_DEBUG>2 |
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366 | // very expensive |
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367 | if (numberIterations_>0&&numberIterations_<-801) { |
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368 | handler_->setLogLevel(63); |
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369 | double saveValue = objectiveValue_; |
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370 | double * saveRow1 = new double[numberRows_]; |
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371 | double * saveRow2 = new double[numberRows_]; |
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372 | memcpy(saveRow1,rowReducedCost_,numberRows_*sizeof(double)); |
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373 | memcpy(saveRow2,rowActivityWork_,numberRows_*sizeof(double)); |
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374 | double * saveColumn1 = new double[numberColumns_]; |
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375 | double * saveColumn2 = new double[numberColumns_]; |
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376 | memcpy(saveColumn1,reducedCostWork_,numberColumns_*sizeof(double)); |
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377 | memcpy(saveColumn2,columnActivityWork_,numberColumns_*sizeof(double)); |
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378 | gutsOfSolution(rowActivityWork_,columnActivityWork_); |
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379 | printf("xxx %d old obj %g, recomputed %g, sum dual inf %g\n", |
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380 | numberIterations_, |
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381 | saveValue,objectiveValue_,sumDualInfeasibilities_); |
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382 | if (saveValue>objectiveValue_+1.0e-2) |
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383 | printf("**bad**\n"); |
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384 | memcpy(rowReducedCost_,saveRow1,numberRows_*sizeof(double)); |
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385 | memcpy(rowActivityWork_,saveRow2,numberRows_*sizeof(double)); |
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386 | memcpy(reducedCostWork_,saveColumn1,numberColumns_*sizeof(double)); |
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387 | memcpy(columnActivityWork_,saveColumn2,numberColumns_*sizeof(double)); |
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388 | delete [] saveRow1; |
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389 | delete [] saveRow2; |
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390 | delete [] saveColumn1; |
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391 | delete [] saveColumn2; |
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392 | objectiveValue_=saveValue; |
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393 | } |
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394 | #endif |
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395 | #ifdef CLP_DEBUG |
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396 | { |
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397 | int iSequence, number=numberRows_+numberColumns_; |
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398 | for (iSequence=0;iSequence<number;iSequence++) { |
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399 | double lowerValue=lower_[iSequence]; |
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400 | double upperValue=upper_[iSequence]; |
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401 | double value=solution_[iSequence]; |
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402 | if(getStatus(iSequence)!=ClpSimplex::basic) { |
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403 | assert(lowerValue>-1.0e20); |
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404 | assert(upperValue<1.0e20); |
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405 | } |
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406 | switch(getStatus(iSequence)) { |
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407 | |
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408 | case ClpSimplex::basic: |
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409 | break; |
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410 | case ClpSimplex::isFree: |
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411 | case ClpSimplex::superBasic: |
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412 | break; |
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413 | case ClpSimplex::atUpperBound: |
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414 | assert (fabs(value-upperValue)<=primalTolerance_) ; |
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415 | break; |
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416 | case ClpSimplex::atLowerBound: |
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417 | assert (fabs(value-lowerValue)<=primalTolerance_) ; |
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418 | break; |
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419 | } |
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420 | } |
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421 | } |
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422 | if(numberIterations_==debugIteration) { |
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423 | printf("dodgy iteration coming up\n"); |
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424 | } |
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425 | #endif |
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426 | // choose row to go out |
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427 | dualRow(); |
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428 | if (pivotRow_>=0) { |
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429 | // we found a pivot row |
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430 | handler_->message(CLP_SIMPLEX_PIVOTROW,messages_) |
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431 | <<pivotRow_ |
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432 | <<OsiMessageEol; |
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433 | // check accuracy of weights |
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434 | dualRowPivot_->checkAccuracy(); |
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435 | // get sign for finding row of tableau |
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436 | rowArray_[0]->insert(pivotRow_,directionOut_); |
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437 | factorization_->updateColumnTranspose(rowArray_[1],rowArray_[0]); |
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438 | // put row of tableau in rowArray[0] and columnArray[0] |
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439 | matrix_->transposeTimes(this,-1.0, |
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440 | rowArray_[0],columnArray_[1],columnArray_[0]); |
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441 | // rowArray has pi equivalent |
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442 | // do ratio test |
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443 | dualColumn(rowArray_[0],columnArray_[0],columnArray_[1], |
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444 | rowArray_[3]); |
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445 | if (sequenceIn_>=0) { |
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446 | // normal iteration |
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447 | // update the incoming column |
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448 | unpack(rowArray_[1]); |
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449 | factorization_->updateColumn(rowArray_[2],rowArray_[1],true); |
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450 | // and update dual weights (can do in parallel - with extra array) |
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451 | dualRowPivot_->updateWeights(rowArray_[0],rowArray_[2], |
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452 | rowArray_[1]); |
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453 | // see if update stable |
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454 | double btranAlpha = -alpha_*directionOut_; // for check |
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455 | alpha_=(*rowArray_[1])[pivotRow_]; |
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456 | #ifdef CLP_DEBUG |
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457 | if ((handler_->logLevel()&32)) |
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458 | printf("btran alpha %g, ftran alpha %g\n",btranAlpha,alpha_); |
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459 | #endif |
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460 | if (fabs(btranAlpha)<1.0e-12||fabs(alpha_)<1.0e-12|| |
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461 | fabs(btranAlpha-alpha_)>1.0e-7*(1.0+fabs(alpha_))) { |
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462 | handler_->message(CLP_DUAL_CHECK,messages_) |
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463 | <<btranAlpha |
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464 | <<alpha_ |
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465 | <<OsiMessageEol; |
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466 | dualRowPivot_->unrollWeights(); |
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467 | if (factorization_->pivots()) { |
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468 | problemStatus_=-2; // factorize now |
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469 | rowArray_[0]->clear(); |
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470 | rowArray_[1]->clear(); |
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471 | columnArray_[0]->clear(); |
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472 | break; |
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473 | } else { |
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474 | // take on more relaxed criterion |
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475 | if (fabs(btranAlpha)<1.0e-12||fabs(alpha_)<1.0e-12|| |
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476 | fabs(btranAlpha-alpha_)>1.0e-4*(1.0+fabs(alpha_))) { |
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477 | // need to reject something |
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478 | char x = isColumn(sequenceOut_) ? 'C' :'R'; |
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479 | handler_->message(CLP_SIMPLEX_FLAG,messages_) |
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480 | <<x<<sequenceWithin(sequenceOut_) |
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481 | <<OsiMessageEol; |
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482 | setFlagged(sequenceOut_); |
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483 | lastBadIteration_ = numberIterations_; // say be more cautious |
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484 | rowArray_[0]->clear(); |
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485 | rowArray_[1]->clear(); |
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486 | columnArray_[0]->clear(); |
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487 | continue; |
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488 | } |
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489 | } |
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490 | } |
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491 | // update duals BEFORE replaceColumn so can do updateColumn |
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492 | double objectiveChange=0.0; |
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493 | // do duals first as variables may flip bounds |
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494 | // rowArray_[0] and columnArray_[0] may have flips |
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495 | // so use rowArray_[3] for work array from here on |
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496 | int nswapped = |
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497 | updateDualsInDual(rowArray_[0],columnArray_[0],rowArray_[2],theta_, |
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498 | objectiveChange); |
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499 | // which will change basic solution |
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500 | if (nswapped) { |
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501 | #ifdef CLP_DEBUG |
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502 | if ((handler_->logLevel()&16)) |
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503 | printf("old dualOut_ %g, v %g, l %g, u %g - new ", |
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504 | dualOut_,valueOut_,lowerOut_,upperOut_); |
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505 | double oldOut=dualOut_; |
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506 | #endif |
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507 | factorization_->updateColumn(rowArray_[3],rowArray_[2],false); |
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508 | dualRowPivot_->updatePrimalSolution(rowArray_[2], |
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509 | 1.0,objectiveChange); |
---|
510 | |
---|
511 | // recompute dualOut_ |
---|
512 | valueOut_ = solution_[sequenceOut_]; |
---|
513 | if (directionOut_<0) { |
---|
514 | dualOut_ = valueOut_ - upperOut_; |
---|
515 | } else { |
---|
516 | dualOut_ = lowerOut_ - valueOut_; |
---|
517 | } |
---|
518 | #ifdef CLP_DEBUG |
---|
519 | if ((handler_->logLevel()&16)) |
---|
520 | printf("%g\n",dualOut_); |
---|
521 | assert(dualOut_<=oldOut); |
---|
522 | #endif |
---|
523 | if(dualOut_<0.0&&factorization_->pivots()) { |
---|
524 | // going backwards - factorize |
---|
525 | dualRowPivot_->unrollWeights(); |
---|
526 | problemStatus_=-2; // factorize now |
---|
527 | break; |
---|
528 | } |
---|
529 | } |
---|
530 | // amount primal will move |
---|
531 | double movement = -dualOut_*directionOut_/alpha_; |
---|
532 | // so objective should increase by fabs(dj)*movement |
---|
533 | // but we already have objective change - so check will be good |
---|
534 | if (objectiveChange+fabs(movement*dualIn_)<-1.0e-5) { |
---|
535 | #ifdef CLP_DEBUG |
---|
536 | if (handler_->logLevel()&32) |
---|
537 | printf("movement %g, swap change %g, rest %g * %g\n", |
---|
538 | objectiveChange+fabs(movement*dualIn_), |
---|
539 | objectiveChange,movement,dualIn_); |
---|
540 | #endif |
---|
541 | if(factorization_->pivots()>5) { |
---|
542 | // going backwards - factorize |
---|
543 | dualRowPivot_->unrollWeights(); |
---|
544 | problemStatus_=-2; // factorize now |
---|
545 | break; |
---|
546 | } |
---|
547 | } |
---|
548 | // if stable replace in basis |
---|
549 | int updateStatus = factorization_->replaceColumn(rowArray_[2], |
---|
550 | pivotRow_, |
---|
551 | alpha_); |
---|
552 | if (updateStatus==1) { |
---|
553 | // slight error |
---|
554 | if (factorization_->pivots()>5) |
---|
555 | problemStatus_=-2; // factorize now |
---|
556 | } else if (updateStatus==2) { |
---|
557 | // major error |
---|
558 | dualRowPivot_->unrollWeights(); |
---|
559 | // later we may need to unwind more e.g. fake bounds |
---|
560 | if (factorization_->pivots()) { |
---|
561 | problemStatus_=-2; // factorize now |
---|
562 | break; |
---|
563 | } else { |
---|
564 | // need to reject something |
---|
565 | char x = isColumn(sequenceOut_) ? 'C' :'R'; |
---|
566 | handler_->message(CLP_SIMPLEX_FLAG,messages_) |
---|
567 | <<x<<sequenceWithin(sequenceOut_) |
---|
568 | <<OsiMessageEol; |
---|
569 | setFlagged(sequenceOut_); |
---|
570 | lastBadIteration_ = numberIterations_; // say be more cautious |
---|
571 | rowArray_[0]->clear(); |
---|
572 | rowArray_[1]->clear(); |
---|
573 | columnArray_[0]->clear(); |
---|
574 | continue; |
---|
575 | } |
---|
576 | } else if (updateStatus==3) { |
---|
577 | // out of memory |
---|
578 | // increase space if not many iterations |
---|
579 | if (factorization_->pivots()< |
---|
580 | 0.5*factorization_->maximumPivots()&& |
---|
581 | factorization_->pivots()<200) |
---|
582 | factorization_->areaFactor( |
---|
583 | factorization_->areaFactor() * 1.1); |
---|
584 | problemStatus_=-2; // factorize now |
---|
585 | } |
---|
586 | // update primal solution |
---|
587 | if (theta_<0.0) { |
---|
588 | #ifdef CLP_DEBUG |
---|
589 | if (handler_->logLevel()&32) |
---|
590 | printf("negative theta %g\n",theta_); |
---|
591 | #endif |
---|
592 | theta_=0.0; |
---|
593 | } |
---|
594 | // do actual flips |
---|
595 | flipBounds(rowArray_[0],columnArray_[0],theta_); |
---|
596 | dualRowPivot_->updatePrimalSolution(rowArray_[1], |
---|
597 | movement, |
---|
598 | objectiveChange); |
---|
599 | #ifdef CLP_DEBUG |
---|
600 | double oldobj=objectiveValue_; |
---|
601 | #endif |
---|
602 | int whatNext=housekeeping(objectiveChange); |
---|
603 | // and set bounds correctly |
---|
604 | originalBound(sequenceIn_); |
---|
605 | changeBound(sequenceOut_); |
---|
606 | #ifdef CLP_DEBUG |
---|
607 | if (objectiveValue_<oldobj-1.0e-5&&(handler_->logLevel()&16)) |
---|
608 | printf("obj backwards %g %g\n",objectiveValue_,oldobj); |
---|
609 | #endif |
---|
610 | if (whatNext==1) { |
---|
611 | problemStatus_ =-2; // refactorize |
---|
612 | } else if (whatNext==2) { |
---|
613 | // maximum iterations or equivalent |
---|
614 | problemStatus_= 3; |
---|
615 | break; |
---|
616 | } |
---|
617 | } else { |
---|
618 | // no incoming column is valid |
---|
619 | #ifdef CLP_DEBUG |
---|
620 | if (handler_->logLevel()&32) |
---|
621 | printf("** no column pivot\n"); |
---|
622 | #endif |
---|
623 | if (factorization_->pivots()<5) { |
---|
624 | problemStatus_=-4; //say looks infeasible |
---|
625 | // create ray anyway |
---|
626 | delete [] ray_; |
---|
627 | ray_ = new double [ numberRows_]; |
---|
628 | CoinDisjointCopyN(rowArray_[0]->denseVector(),numberRows_,ray_); |
---|
629 | } |
---|
630 | rowArray_[0]->clear(); |
---|
631 | columnArray_[0]->clear(); |
---|
632 | break; |
---|
633 | } |
---|
634 | } else { |
---|
635 | // no pivot row |
---|
636 | #ifdef CLP_DEBUG |
---|
637 | if (handler_->logLevel()&32) |
---|
638 | printf("** no row pivot\n"); |
---|
639 | #endif |
---|
640 | if (!factorization_->pivots()) { |
---|
641 | // may have crept through - so may be optimal |
---|
642 | //problemStatus_=-5; //say looks unbounded |
---|
643 | problemStatus_=0; |
---|
644 | // check any flagged variables |
---|
645 | int iRow; |
---|
646 | for (iRow=0;iRow<numberRows_;iRow++) { |
---|
647 | int iPivot=pivotVariable_[iRow]; |
---|
648 | if (flagged(iPivot)) |
---|
649 | break; |
---|
650 | } |
---|
651 | if (iRow<numberRows_) { |
---|
652 | #ifdef CLP_DEBUG |
---|
653 | std::cerr<<"Flagged variables at end - infeasible?"<<std::endl; |
---|
654 | #endif |
---|
655 | problemStatus_=-4; //say looks infeasible |
---|
656 | // create ray anyway |
---|
657 | delete [] ray_; |
---|
658 | ray_ = new double [ numberRows_]; |
---|
659 | CoinDisjointCopyN(rowArray_[0]->denseVector(),numberRows_,ray_); |
---|
660 | } |
---|
661 | } |
---|
662 | break; |
---|
663 | } |
---|
664 | } |
---|
665 | } |
---|
666 | /* The duals are updated by the given arrays. |
---|
667 | Returns number of infeasibilities. |
---|
668 | rowArray and columnarray will have flipped |
---|
669 | The output vector has movement (row length array) */ |
---|
670 | int |
---|
671 | ClpSimplexDual::updateDualsInDual(OsiIndexedVector * rowArray, |
---|
672 | OsiIndexedVector * columnArray, |
---|
673 | OsiIndexedVector * outputArray, |
---|
674 | double theta, |
---|
675 | double & objectiveChange) |
---|
676 | { |
---|
677 | |
---|
678 | outputArray->clear(); |
---|
679 | |
---|
680 | double * work; |
---|
681 | int number; |
---|
682 | int * which; |
---|
683 | |
---|
684 | int numberInfeasibilities=0; |
---|
685 | int numberRowInfeasibilities=0; |
---|
686 | |
---|
687 | // see whether we will be doing full recompute |
---|
688 | bool fullRecompute= (rowArray->getNumElements()==numberRows_&& |
---|
689 | columnArray->getNumElements()==numberColumns_); |
---|
690 | int numberAtFake=0; |
---|
691 | |
---|
692 | // use a tighter tolerance except for all being okay |
---|
693 | double tolerance = dualTolerance_; |
---|
694 | |
---|
695 | double changeObj=0.0; |
---|
696 | |
---|
697 | int iSection; |
---|
698 | |
---|
699 | for (iSection=0;iSection<2;iSection++) { |
---|
700 | int i; |
---|
701 | double * solution = solutionRegion(iSection); |
---|
702 | double * reducedCost = djRegion(iSection); |
---|
703 | double * lower = lowerRegion(iSection); |
---|
704 | double * upper = upperRegion(iSection); |
---|
705 | double * cost = costRegion(iSection); |
---|
706 | int addSequence; |
---|
707 | if (!iSection) { |
---|
708 | addSequence = numberColumns_; |
---|
709 | work = rowArray->denseVector(); |
---|
710 | number = rowArray->getNumElements(); |
---|
711 | which = rowArray->getIndices(); |
---|
712 | } else { |
---|
713 | // set number of infeasibilities in row array |
---|
714 | addSequence=0; |
---|
715 | numberRowInfeasibilities=numberInfeasibilities; |
---|
716 | rowArray->setNumElements(numberInfeasibilities); |
---|
717 | numberInfeasibilities=0; |
---|
718 | work = columnArray->denseVector(); |
---|
719 | number = columnArray->getNumElements(); |
---|
720 | which = columnArray->getIndices(); |
---|
721 | } |
---|
722 | |
---|
723 | for (i=0;i<number;i++) { |
---|
724 | int iSequence = which[i]; |
---|
725 | double alphaI = work[iSequence]; |
---|
726 | double value = reducedCost[iSequence]-theta*alphaI; |
---|
727 | work[iSequence]=0.0; |
---|
728 | reducedCost[iSequence]=value; |
---|
729 | |
---|
730 | if (!fixed(iSequence+addSequence)) { |
---|
731 | double movement=0.0; |
---|
732 | FakeBound bound = getFakeBound(iSequence+addSequence); |
---|
733 | Status status = getStatus(iSequence+addSequence); |
---|
734 | |
---|
735 | switch(status) { |
---|
736 | |
---|
737 | case ClpSimplex::basic: |
---|
738 | case ClpSimplex::superBasic: |
---|
739 | break; |
---|
740 | case ClpSimplex::isFree: |
---|
741 | if (fabs(value)>tolerance) { |
---|
742 | #ifdef CLP_DEBUG |
---|
743 | if (handler_->logLevel()&32) |
---|
744 | printf("%d %d, free has dj of %g, alpha %g\n", |
---|
745 | iSection,iSequence,value,alphaI); |
---|
746 | #endif |
---|
747 | } |
---|
748 | break; |
---|
749 | case ClpSimplex::atUpperBound: |
---|
750 | if (value>tolerance) { |
---|
751 | // to lower bound (if swap) |
---|
752 | // put back alpha |
---|
753 | which[numberInfeasibilities++]=iSequence; |
---|
754 | work[iSequence]=alphaI; |
---|
755 | movement = lower[iSequence]-upper[iSequence]; |
---|
756 | #ifdef CLP_DEBUG |
---|
757 | if ((handler_->logLevel()&32)) |
---|
758 | printf("%d %d, new dj %g, alpha %g, movement %g\n", |
---|
759 | iSection,iSequence,value,alphaI,movement); |
---|
760 | #endif |
---|
761 | changeObj += movement*cost[iSequence]; |
---|
762 | if (bound==ClpSimplexDual::bothFake|| |
---|
763 | bound==ClpSimplexDual::lowerFake) |
---|
764 | numberAtFake++; |
---|
765 | } else if (fullRecompute) { |
---|
766 | // at correct bound |
---|
767 | if (bound==ClpSimplexDual::bothFake|| |
---|
768 | bound==ClpSimplexDual::upperFake) { |
---|
769 | // but flip if dj would allow |
---|
770 | if (bound==ClpSimplexDual::upperFake&& |
---|
771 | value>=-tolerance) { |
---|
772 | movement = lower[iSequence]-upper[iSequence]; |
---|
773 | setStatus(iSequence+addSequence,ClpSimplex::atLowerBound); |
---|
774 | solution[iSequence] = lower[iSequence]; |
---|
775 | changeObj += movement*cost[iSequence]; |
---|
776 | } else { |
---|
777 | numberAtFake++; |
---|
778 | } |
---|
779 | } |
---|
780 | } |
---|
781 | break; |
---|
782 | case ClpSimplex::atLowerBound: |
---|
783 | if (value<-tolerance) { |
---|
784 | // to upper bound |
---|
785 | // put back alpha |
---|
786 | which[numberInfeasibilities++]=iSequence; |
---|
787 | work[iSequence]=alphaI; |
---|
788 | movement = upper[iSequence] - lower[iSequence]; |
---|
789 | #ifdef CLP_DEBUG |
---|
790 | if ((handler_->logLevel()&32)) |
---|
791 | printf("%d %d, new dj %g, alpha %g, movement %g\n", |
---|
792 | iSection,iSequence,value,alphaI,movement); |
---|
793 | #endif |
---|
794 | changeObj += movement*cost[iSequence]; |
---|
795 | if (bound==ClpSimplexDual::bothFake|| |
---|
796 | bound==ClpSimplexDual::upperFake) |
---|
797 | numberAtFake++; |
---|
798 | } else if (fullRecompute) { |
---|
799 | // at correct bound |
---|
800 | if (bound==ClpSimplexDual::bothFake|| |
---|
801 | bound==ClpSimplexDual::lowerFake) { |
---|
802 | // but flip if dj would allow |
---|
803 | if (bound==ClpSimplexDual::lowerFake&& |
---|
804 | value<=tolerance) { |
---|
805 | movement = upper[iSequence] - lower[iSequence]; |
---|
806 | setStatus(iSequence+addSequence,ClpSimplex::atUpperBound); |
---|
807 | solution[iSequence] = upper[iSequence]; |
---|
808 | changeObj += movement*cost[iSequence]; |
---|
809 | } else { |
---|
810 | numberAtFake++; |
---|
811 | } |
---|
812 | } |
---|
813 | } |
---|
814 | break; |
---|
815 | } |
---|
816 | if (!fullRecompute) { |
---|
817 | if (movement) { |
---|
818 | if (!iSection) { |
---|
819 | // row (sign ?) |
---|
820 | outputArray->quickAdd(iSequence,-movement); |
---|
821 | } else { |
---|
822 | matrix_->add(this,outputArray,iSequence,movement); |
---|
823 | } |
---|
824 | } |
---|
825 | } |
---|
826 | } |
---|
827 | } |
---|
828 | } |
---|
829 | #ifdef CLP_DEBUG |
---|
830 | if (fullRecompute&&numberAtFake&&(handler_->logLevel()&16)!=0) |
---|
831 | printf("%d fake after full update\n",numberAtFake); |
---|
832 | #endif |
---|
833 | outputArray->stopQuickAdd(); |
---|
834 | // set number of infeasibilities |
---|
835 | columnArray->setNumElements(numberInfeasibilities); |
---|
836 | numberInfeasibilities += numberRowInfeasibilities; |
---|
837 | if (fullRecompute) { |
---|
838 | // do actual flips |
---|
839 | flipBounds(rowArray,columnArray,theta); |
---|
840 | } |
---|
841 | objectiveChange += changeObj; |
---|
842 | return numberInfeasibilities; |
---|
843 | } |
---|
844 | /* |
---|
845 | Chooses dual pivot row |
---|
846 | Would be faster with separate region to scan |
---|
847 | and will have this (with square of infeasibility) when steepest |
---|
848 | For easy problems we can just choose one of the first rows we look at |
---|
849 | */ |
---|
850 | void |
---|
851 | ClpSimplexDual::dualRow() |
---|
852 | { |
---|
853 | // get pivot row using whichever method it is |
---|
854 | pivotRow_=dualRowPivot_->pivotRow(); |
---|
855 | if (pivotRow_>=0) { |
---|
856 | int iPivot=pivotVariable_[pivotRow_]; |
---|
857 | sequenceOut_ = iPivot; |
---|
858 | if (iPivot>=numberColumns_) { |
---|
859 | // slack |
---|
860 | iPivot-=numberColumns_; |
---|
861 | valueOut_=rowActivityWork_[iPivot]; |
---|
862 | lowerOut_=rowLowerWork_[iPivot]; |
---|
863 | upperOut_=rowUpperWork_[iPivot]; |
---|
864 | } else { |
---|
865 | // column |
---|
866 | valueOut_=columnActivityWork_[iPivot]; |
---|
867 | lowerOut_=columnLowerWork_[iPivot]; |
---|
868 | upperOut_=columnUpperWork_[iPivot]; |
---|
869 | } |
---|
870 | // if we have problems we could try other way and hope we get a |
---|
871 | // zero pivot? |
---|
872 | if (valueOut_>upperOut_) { |
---|
873 | directionOut_ = -1; |
---|
874 | dualOut_ = valueOut_ - upperOut_; |
---|
875 | } else { |
---|
876 | directionOut_ = 1; |
---|
877 | dualOut_ = lowerOut_ - valueOut_; |
---|
878 | } |
---|
879 | #ifdef CLP_DEBUG |
---|
880 | assert(dualOut_>=0.0); |
---|
881 | #endif |
---|
882 | } |
---|
883 | return ; |
---|
884 | } |
---|
885 | // Checks if any fake bounds active - if so returns number and modifies |
---|
886 | // dualBound_ and everything. |
---|
887 | // Free variables will be left as free |
---|
888 | // Returns number of bounds changed if >=0 |
---|
889 | // Returns -1 if not initialize and no effect |
---|
890 | // Fills in changeVector which can be used to see if unbounded |
---|
891 | // and cost of change vector |
---|
892 | int |
---|
893 | ClpSimplexDual::changeBounds(bool initialize, |
---|
894 | OsiIndexedVector * outputArray, |
---|
895 | double & changeCost) |
---|
896 | { |
---|
897 | if (!initialize) { |
---|
898 | int numberInfeasibilities; |
---|
899 | double newBound; |
---|
900 | newBound = 5.0*dualBound_; |
---|
901 | numberInfeasibilities=0; |
---|
902 | changeCost=0.0; |
---|
903 | // put back original bounds and then check |
---|
904 | createRim(3); |
---|
905 | int iSequence; |
---|
906 | // bounds will get bigger - just look at ones at bounds |
---|
907 | for (iSequence=0;iSequence<numberRows_+numberColumns_;iSequence++) { |
---|
908 | double lowerValue=lower_[iSequence]; |
---|
909 | double upperValue=upper_[iSequence]; |
---|
910 | double value=solution_[iSequence]; |
---|
911 | setFakeBound(iSequence,ClpSimplexDual::noFake); |
---|
912 | switch(getStatus(iSequence)) { |
---|
913 | |
---|
914 | case ClpSimplex::basic: |
---|
915 | break; |
---|
916 | case ClpSimplex::isFree: |
---|
917 | case ClpSimplex::superBasic: |
---|
918 | break; |
---|
919 | case ClpSimplex::atUpperBound: |
---|
920 | if (fabs(value-upperValue)>primalTolerance_) |
---|
921 | numberInfeasibilities++; |
---|
922 | break; |
---|
923 | case ClpSimplex::atLowerBound: |
---|
924 | if (fabs(value-lowerValue)>primalTolerance_) |
---|
925 | numberInfeasibilities++; |
---|
926 | break; |
---|
927 | } |
---|
928 | } |
---|
929 | if (numberInfeasibilities) { |
---|
930 | int iSequence; |
---|
931 | for (iSequence=0;iSequence<numberRows_+numberColumns_;iSequence++) { |
---|
932 | double lowerValue=lower_[iSequence]; |
---|
933 | double upperValue=upper_[iSequence]; |
---|
934 | double newLowerValue; |
---|
935 | double newUpperValue; |
---|
936 | Status status = getStatus(iSequence); |
---|
937 | if (status==ClpSimplex::atUpperBound|| |
---|
938 | status==ClpSimplex::atLowerBound) { |
---|
939 | double value = solution_[iSequence]; |
---|
940 | if (value-lowerValue<=upperValue-value) { |
---|
941 | newLowerValue = max(lowerValue,value-0.666667*newBound); |
---|
942 | newUpperValue = min(upperValue,newLowerValue+newBound); |
---|
943 | } else { |
---|
944 | newUpperValue = min(upperValue,value+0.666667*newBound); |
---|
945 | newLowerValue = max(lowerValue,newUpperValue-newBound); |
---|
946 | } |
---|
947 | lower_[iSequence]=newLowerValue; |
---|
948 | upper_[iSequence]=newUpperValue; |
---|
949 | if (newLowerValue > lowerValue) { |
---|
950 | if (newUpperValue < upperValue) |
---|
951 | setFakeBound(iSequence,ClpSimplexDual::bothFake); |
---|
952 | else |
---|
953 | setFakeBound(iSequence,ClpSimplexDual::lowerFake); |
---|
954 | } else { |
---|
955 | if (newUpperValue < upperValue) |
---|
956 | setFakeBound(iSequence,ClpSimplexDual::upperFake); |
---|
957 | } |
---|
958 | if (status==ClpSimplex::atUpperBound) |
---|
959 | solution_[iSequence] = newUpperValue; |
---|
960 | else |
---|
961 | solution_[iSequence] = newLowerValue; |
---|
962 | double movement = solution_[iSequence] - value; |
---|
963 | if (movement&&outputArray) { |
---|
964 | if (iSequence>=numberColumns_) { |
---|
965 | outputArray->quickAdd(iSequence,-movement); |
---|
966 | changeCost += movement*cost_[iSequence]; |
---|
967 | } else { |
---|
968 | matrix_->add(this,outputArray,iSequence,movement); |
---|
969 | changeCost += movement*cost_[iSequence]; |
---|
970 | } |
---|
971 | } |
---|
972 | } |
---|
973 | } |
---|
974 | dualBound_ = newBound; |
---|
975 | if (outputArray) |
---|
976 | outputArray->stopQuickAdd(); |
---|
977 | } else { |
---|
978 | numberInfeasibilities=-1; |
---|
979 | } |
---|
980 | return numberInfeasibilities; |
---|
981 | } else { |
---|
982 | int iSequence; |
---|
983 | |
---|
984 | for (iSequence=0;iSequence<numberRows_+numberColumns_;iSequence++) { |
---|
985 | Status status = getStatus(iSequence); |
---|
986 | if (status==ClpSimplex::atUpperBound|| |
---|
987 | status==ClpSimplex::atLowerBound) { |
---|
988 | double lowerValue=lower_[iSequence]; |
---|
989 | double upperValue=upper_[iSequence]; |
---|
990 | double value = solution_[iSequence]; |
---|
991 | if (lowerValue>-largeValue_||upperValue<largeValue_) { |
---|
992 | if (lowerValue-value>-0.5*dualBound_|| |
---|
993 | upperValue-value<0.5*dualBound_) { |
---|
994 | if (fabs(lowerValue-value)<=fabs(upperValue-value)) { |
---|
995 | if (upperValue > lowerValue + dualBound_) { |
---|
996 | upper_[iSequence]=lowerValue+dualBound_; |
---|
997 | setFakeBound(iSequence,ClpSimplexDual::upperFake); |
---|
998 | } |
---|
999 | } else { |
---|
1000 | if (lowerValue < upperValue - dualBound_) { |
---|
1001 | lower_[iSequence]=upperValue-dualBound_; |
---|
1002 | setFakeBound(iSequence,ClpSimplexDual::lowerFake); |
---|
1003 | } |
---|
1004 | } |
---|
1005 | } else { |
---|
1006 | lower_[iSequence]=-0.5*dualBound_; |
---|
1007 | upper_[iSequence]= 0.5*dualBound_; |
---|
1008 | setFakeBound(iSequence,ClpSimplexDual::bothFake); |
---|
1009 | } |
---|
1010 | } |
---|
1011 | } |
---|
1012 | } |
---|
1013 | return 1; |
---|
1014 | } |
---|
1015 | } |
---|
1016 | /* |
---|
1017 | Row array has row part of pivot row (as duals so sign may be switched) |
---|
1018 | Column array has column part. |
---|
1019 | This chooses pivot column. |
---|
1020 | Spare array will be needed when we start getting clever. |
---|
1021 | We will check for basic so spare array will never overflow. |
---|
1022 | If necessary will modify costs |
---|
1023 | */ |
---|
1024 | void |
---|
1025 | ClpSimplexDual::dualColumn(OsiIndexedVector * rowArray, |
---|
1026 | OsiIndexedVector * columnArray, |
---|
1027 | OsiIndexedVector * spareArray, |
---|
1028 | OsiIndexedVector * spareArray2) |
---|
1029 | { |
---|
1030 | double * work; |
---|
1031 | int number; |
---|
1032 | int * which; |
---|
1033 | double * reducedCost; |
---|
1034 | |
---|
1035 | int iSection; |
---|
1036 | |
---|
1037 | sequenceIn_=-1; |
---|
1038 | int numberPossiblySwapped=0; |
---|
1039 | int numberRemaining=0; |
---|
1040 | |
---|
1041 | double totalThru=0.0; // for when variables flip |
---|
1042 | double acceptablePivot=1.0e-7; |
---|
1043 | if (factorization_->pivots()) |
---|
1044 | acceptablePivot=1.0e-5; // if we have iterated be more strict |
---|
1045 | double bestEverPivot=acceptablePivot; |
---|
1046 | int lastSequence = -1; |
---|
1047 | double lastPivot=0.0; |
---|
1048 | double upperTheta; |
---|
1049 | double newTolerance = dualTolerance_; |
---|
1050 | // will we need to increase tolerance |
---|
1051 | bool thisIncrease=false; |
---|
1052 | // If we think we need to modify costs (not if something from broad sweep) |
---|
1053 | bool modifyCosts=false; |
---|
1054 | // Increase in objective due to swapping bounds (may be negative) |
---|
1055 | double increaseInObjective=0.0; |
---|
1056 | |
---|
1057 | // use spareArrays to put ones looked at in |
---|
1058 | // we are going to flip flop between |
---|
1059 | int iFlip = 0; |
---|
1060 | // Possible list of pivots |
---|
1061 | int interesting[2]; |
---|
1062 | // where possible swapped ones are |
---|
1063 | int swapped[2]; |
---|
1064 | // for zeroing out arrays after |
---|
1065 | int marker[2][2]; |
---|
1066 | // pivot elements |
---|
1067 | double * array[2], * spare, * spare2; |
---|
1068 | // indices |
---|
1069 | int * indices[2], * index, * index2; |
---|
1070 | spareArray->clear(); |
---|
1071 | spareArray2->clear(); |
---|
1072 | array[0] = spareArray->denseVector(); |
---|
1073 | indices[0] = spareArray->getIndices(); |
---|
1074 | spare = array[0]; |
---|
1075 | index = indices[0]; |
---|
1076 | array[1] = spareArray2->denseVector(); |
---|
1077 | indices[1] = spareArray2->getIndices(); |
---|
1078 | int i; |
---|
1079 | double * lower; |
---|
1080 | double * upper; |
---|
1081 | |
---|
1082 | // initialize lists |
---|
1083 | for (i=0;i<2;i++) { |
---|
1084 | interesting[i]=0; |
---|
1085 | swapped[i]=numberColumns_; |
---|
1086 | marker[i][0]=0; |
---|
1087 | marker[i][1]=numberColumns_; |
---|
1088 | } |
---|
1089 | |
---|
1090 | /* |
---|
1091 | First we get a list of possible pivots. We can also see if the |
---|
1092 | problem looks infeasible or whether we want to pivot in free variable. |
---|
1093 | This may make objective go backwards but can only happen a finite |
---|
1094 | number of times and I do want free variables basic. |
---|
1095 | |
---|
1096 | Then we flip back and forth. At the start of each iteration |
---|
1097 | interesting[iFlip] should have possible candidates and swapped[iFlip] |
---|
1098 | will have pivots if we decide to take a previous pivot. |
---|
1099 | At end of each iteration interesting[1-iFlip] should have |
---|
1100 | candidates if we go through this theta and swapped[1-iFlip] |
---|
1101 | pivots if we don't go through. |
---|
1102 | |
---|
1103 | At first we increase theta and see what happens. We start |
---|
1104 | theta at a reasonable guess. If in right area then we do bit by bit. |
---|
1105 | |
---|
1106 | */ |
---|
1107 | |
---|
1108 | // do first pass to get possibles |
---|
1109 | // We can also see if infeasible or pivoting on free |
---|
1110 | double tentativeTheta = 1.0e22; |
---|
1111 | upperTheta = 1.0e31; |
---|
1112 | double freePivot = acceptablePivot; |
---|
1113 | for (iSection=0;iSection<2;iSection++) { |
---|
1114 | |
---|
1115 | int addSequence; |
---|
1116 | |
---|
1117 | if (!iSection) { |
---|
1118 | lower = rowLowerWork_; |
---|
1119 | upper = rowUpperWork_; |
---|
1120 | work = rowArray->denseVector(); |
---|
1121 | number = rowArray->getNumElements(); |
---|
1122 | which = rowArray->getIndices(); |
---|
1123 | reducedCost = rowReducedCost_; |
---|
1124 | addSequence = numberColumns_; |
---|
1125 | } else { |
---|
1126 | lower = columnLowerWork_; |
---|
1127 | upper = columnUpperWork_; |
---|
1128 | work = columnArray->denseVector(); |
---|
1129 | number = columnArray->getNumElements(); |
---|
1130 | which = columnArray->getIndices(); |
---|
1131 | reducedCost = reducedCostWork_; |
---|
1132 | addSequence = 0; |
---|
1133 | } |
---|
1134 | |
---|
1135 | for (i=0;i<number;i++) { |
---|
1136 | int iSequence = which[i]; |
---|
1137 | double alpha = work[iSequence]; |
---|
1138 | if (fixed(iSequence+addSequence)||!alpha) |
---|
1139 | continue; // skip fixed ones or (zeroed out) |
---|
1140 | double oldValue = reducedCost[iSequence]; |
---|
1141 | double value = oldValue-tentativeTheta*alpha; |
---|
1142 | int keep = 0; |
---|
1143 | |
---|
1144 | switch(getStatus(iSequence+addSequence)) { |
---|
1145 | |
---|
1146 | case ClpSimplex::basic: |
---|
1147 | break; |
---|
1148 | case ClpSimplex::isFree: |
---|
1149 | case ClpSimplex::superBasic: |
---|
1150 | if (oldValue>dualTolerance_) { |
---|
1151 | if (value<-newTolerance) |
---|
1152 | keep = 2; |
---|
1153 | } else if (oldValue<-dualTolerance_) { |
---|
1154 | if (value>newTolerance) |
---|
1155 | keep = 2; |
---|
1156 | } else { |
---|
1157 | if (alpha>=acceptablePivot) |
---|
1158 | keep = 2; |
---|
1159 | else if (-alpha>=acceptablePivot) |
---|
1160 | keep = 2; |
---|
1161 | } |
---|
1162 | break; |
---|
1163 | case ClpSimplex::atUpperBound: |
---|
1164 | assert (oldValue<=dualTolerance_*1.0001); |
---|
1165 | if (value>newTolerance) { |
---|
1166 | keep = 1; |
---|
1167 | value = oldValue-upperTheta*alpha; |
---|
1168 | if (value>newTolerance && -alpha>=acceptablePivot) |
---|
1169 | upperTheta = (oldValue-newTolerance)/alpha; |
---|
1170 | } |
---|
1171 | break; |
---|
1172 | case ClpSimplex::atLowerBound: |
---|
1173 | assert (oldValue>=-dualTolerance_*1.0001); |
---|
1174 | if (value<-newTolerance) { |
---|
1175 | keep = 1; |
---|
1176 | value = oldValue-upperTheta*alpha; |
---|
1177 | if (value<-newTolerance && alpha>=acceptablePivot) |
---|
1178 | upperTheta = (oldValue+newTolerance)/alpha; |
---|
1179 | } |
---|
1180 | break; |
---|
1181 | } |
---|
1182 | if (keep) { |
---|
1183 | if (keep==2) { |
---|
1184 | // free - choose largest |
---|
1185 | if (fabs(alpha)>freePivot) { |
---|
1186 | freePivot=fabs(alpha); |
---|
1187 | sequenceIn_ = iSequence + addSequence; |
---|
1188 | theta_=oldValue/alpha; |
---|
1189 | } |
---|
1190 | } else { |
---|
1191 | // add to list |
---|
1192 | spare[numberRemaining]=alpha; |
---|
1193 | index[numberRemaining++]=iSequence+addSequence; |
---|
1194 | } |
---|
1195 | } |
---|
1196 | } |
---|
1197 | } |
---|
1198 | interesting[0]=numberRemaining; |
---|
1199 | marker[0][0] = numberRemaining; |
---|
1200 | |
---|
1201 | if (!numberRemaining) |
---|
1202 | return; // Looks infeasible |
---|
1203 | |
---|
1204 | if (sequenceIn_>=0) { |
---|
1205 | // free variable - always choose |
---|
1206 | } else { |
---|
1207 | |
---|
1208 | theta_=1.0e50; |
---|
1209 | // now flip flop between spare arrays until reasonable theta |
---|
1210 | tentativeTheta = max(10.0*upperTheta,1.0e-7); |
---|
1211 | |
---|
1212 | // loops increasing tentative theta until can't go through |
---|
1213 | |
---|
1214 | while (tentativeTheta < 1.0e22) { |
---|
1215 | double thruThis = 0.0; |
---|
1216 | |
---|
1217 | double bestPivot=acceptablePivot; |
---|
1218 | int bestSequence=-1; |
---|
1219 | |
---|
1220 | numberPossiblySwapped = numberColumns_; |
---|
1221 | numberRemaining = 0; |
---|
1222 | |
---|
1223 | upperTheta = 1.0e50; |
---|
1224 | |
---|
1225 | spare = array[iFlip]; |
---|
1226 | index = indices[iFlip]; |
---|
1227 | spare2 = array[1-iFlip]; |
---|
1228 | index2 = indices[1-iFlip]; |
---|
1229 | |
---|
1230 | // try 3 different ways |
---|
1231 | // 1 bias increase by ones with slightly wrong djs |
---|
1232 | // 2 bias by all |
---|
1233 | // 3 bias by all - tolerance (doesn't seem very good) |
---|
1234 | #define TRYBIAS 1 |
---|
1235 | |
---|
1236 | |
---|
1237 | double increaseInThis=0.0; //objective increase in this loop |
---|
1238 | |
---|
1239 | for (i=0;i<interesting[iFlip];i++) { |
---|
1240 | int iSequence = index[i]; |
---|
1241 | double alpha = spare[i]; |
---|
1242 | double oldValue = dj_[iSequence]; |
---|
1243 | double value = oldValue-tentativeTheta*alpha; |
---|
1244 | |
---|
1245 | if (alpha < 0.0) { |
---|
1246 | //at upper bound |
---|
1247 | if (value>newTolerance) { |
---|
1248 | double range = upper_[iSequence] - lower_[iSequence]; |
---|
1249 | thruThis -= range*alpha; |
---|
1250 | #if TRYBIAS==1 |
---|
1251 | if (oldValue>0.0) |
---|
1252 | increaseInThis -= oldValue*range; |
---|
1253 | #elif TRYBIAS==2 |
---|
1254 | increaseInThis -= oldValue*range; |
---|
1255 | #else |
---|
1256 | increaseInThis -= (oldValue+dualTolerance_)*range; |
---|
1257 | #endif |
---|
1258 | // goes on swapped list (also means candidates if too many) |
---|
1259 | spare2[--numberPossiblySwapped]=alpha; |
---|
1260 | index2[numberPossiblySwapped]=iSequence; |
---|
1261 | if (fabs(alpha)>bestPivot) { |
---|
1262 | bestPivot=fabs(alpha); |
---|
1263 | bestSequence=numberPossiblySwapped; |
---|
1264 | } |
---|
1265 | } else { |
---|
1266 | value = oldValue-upperTheta*alpha; |
---|
1267 | if (value>newTolerance && -alpha>=acceptablePivot) |
---|
1268 | upperTheta = (oldValue-newTolerance)/alpha; |
---|
1269 | spare2[numberRemaining]=alpha; |
---|
1270 | index2[numberRemaining++]=iSequence; |
---|
1271 | } |
---|
1272 | } else { |
---|
1273 | // at lower bound |
---|
1274 | if (value<-newTolerance) { |
---|
1275 | double range = upper_[iSequence] - lower_[iSequence]; |
---|
1276 | thruThis += range*alpha; |
---|
1277 | //?? is this correct - and should we look at good ones |
---|
1278 | #if TRYBIAS==1 |
---|
1279 | if (oldValue<0.0) |
---|
1280 | increaseInThis += oldValue*range; |
---|
1281 | #elif TRYBIAS==2 |
---|
1282 | increaseInThis += oldValue*range; |
---|
1283 | #else |
---|
1284 | increaseInThis += (oldValue-dualTolerance_)*range; |
---|
1285 | #endif |
---|
1286 | // goes on swapped list (also means candidates if too many) |
---|
1287 | spare2[--numberPossiblySwapped]=alpha; |
---|
1288 | index2[numberPossiblySwapped]=iSequence; |
---|
1289 | if (fabs(alpha)>bestPivot) { |
---|
1290 | bestPivot=fabs(alpha); |
---|
1291 | bestSequence=numberPossiblySwapped; |
---|
1292 | } |
---|
1293 | } else { |
---|
1294 | value = oldValue-upperTheta*alpha; |
---|
1295 | if (value<-newTolerance && alpha>=acceptablePivot) |
---|
1296 | upperTheta = (oldValue+newTolerance)/alpha; |
---|
1297 | spare2[numberRemaining]=alpha; |
---|
1298 | index2[numberRemaining++]=iSequence; |
---|
1299 | } |
---|
1300 | } |
---|
1301 | } |
---|
1302 | swapped[1-iFlip]=numberPossiblySwapped; |
---|
1303 | interesting[1-iFlip]=numberRemaining; |
---|
1304 | marker[1-iFlip][0]= max(marker[1-iFlip][0],numberRemaining); |
---|
1305 | marker[1-iFlip][1]= min(marker[1-iFlip][1],numberPossiblySwapped); |
---|
1306 | |
---|
1307 | if (totalThru+thruThis>=fabs(dualOut_)|| |
---|
1308 | increaseInObjective+increaseInThis<0.0) { |
---|
1309 | // We should be pivoting in this batch |
---|
1310 | // so compress down to this lot |
---|
1311 | numberRemaining=0; |
---|
1312 | for (i=numberColumns_-1;i>=swapped[1-iFlip];i--) { |
---|
1313 | spare[numberRemaining]=spare2[i]; |
---|
1314 | index[numberRemaining++]=index2[i]; |
---|
1315 | } |
---|
1316 | interesting[iFlip]=numberRemaining; |
---|
1317 | int iTry; |
---|
1318 | #define MAXTRY 100 |
---|
1319 | // first get ratio with tolerance |
---|
1320 | for (iTry=0;iTry<MAXTRY;iTry++) { |
---|
1321 | |
---|
1322 | upperTheta=1.0e50; |
---|
1323 | numberPossiblySwapped = numberColumns_; |
---|
1324 | numberRemaining = 0; |
---|
1325 | |
---|
1326 | increaseInThis=0.0; //objective increase in this loop |
---|
1327 | |
---|
1328 | thruThis=0.0; |
---|
1329 | |
---|
1330 | spare = array[iFlip]; |
---|
1331 | index = indices[iFlip]; |
---|
1332 | spare2 = array[1-iFlip]; |
---|
1333 | index2 = indices[1-iFlip]; |
---|
1334 | |
---|
1335 | for (i=0;i<interesting[iFlip];i++) { |
---|
1336 | int iSequence=index[i]; |
---|
1337 | double alpha=spare[i]; |
---|
1338 | double oldValue = dj_[iSequence]; |
---|
1339 | double value = oldValue-upperTheta*alpha; |
---|
1340 | |
---|
1341 | if (alpha < 0.0) { |
---|
1342 | //at upper bound |
---|
1343 | if (value>newTolerance) { |
---|
1344 | if (-alpha>=acceptablePivot) { |
---|
1345 | upperTheta = (oldValue-newTolerance)/alpha; |
---|
1346 | } |
---|
1347 | } |
---|
1348 | } else { |
---|
1349 | // at lower bound |
---|
1350 | if (value<-newTolerance) { |
---|
1351 | if (alpha>=acceptablePivot) { |
---|
1352 | upperTheta = (oldValue+newTolerance)/alpha; |
---|
1353 | } |
---|
1354 | } |
---|
1355 | } |
---|
1356 | } |
---|
1357 | bestPivot=acceptablePivot; |
---|
1358 | sequenceIn_=-1; |
---|
1359 | // now choose largest and sum all ones which will go through |
---|
1360 | #define MINIMUMTHETA 1.0e-12 |
---|
1361 | for (i=0;i<interesting[iFlip];i++) { |
---|
1362 | int iSequence=index[i]; |
---|
1363 | double alpha=spare[i]; |
---|
1364 | double value = dj_[iSequence]-upperTheta*alpha; |
---|
1365 | double badDj=0.0; |
---|
1366 | |
---|
1367 | bool addToSwapped=false; |
---|
1368 | |
---|
1369 | if (alpha < 0.0) { |
---|
1370 | //at upper bound |
---|
1371 | if (value>=0.0) { |
---|
1372 | addToSwapped=true; |
---|
1373 | #if TRYBIAS==1 |
---|
1374 | badDj = -max(dj_[iSequence],0.0); |
---|
1375 | #elif TRYBIAS==2 |
---|
1376 | badDj = -dj_[iSequence]; |
---|
1377 | #else |
---|
1378 | badDj = -dj_[iSequence]-dualTolerance_; |
---|
1379 | #endif |
---|
1380 | } |
---|
1381 | } else { |
---|
1382 | // at lower bound |
---|
1383 | if (value<=0.0) { |
---|
1384 | addToSwapped=true; |
---|
1385 | #if TRYBIAS==1 |
---|
1386 | badDj = min(dj_[iSequence],0.0); |
---|
1387 | #elif TRYBIAS==2 |
---|
1388 | badDj = dj_[iSequence]; |
---|
1389 | #else |
---|
1390 | badDj = dj_[iSequence]-dualTolerance_; |
---|
1391 | #endif |
---|
1392 | } |
---|
1393 | } |
---|
1394 | if (!addToSwapped) { |
---|
1395 | // add to list of remaining |
---|
1396 | spare2[numberRemaining]=alpha; |
---|
1397 | index2[numberRemaining++]=iSequence; |
---|
1398 | } else { |
---|
1399 | // add to list of swapped |
---|
1400 | spare2[--numberPossiblySwapped]=alpha; |
---|
1401 | index2[numberPossiblySwapped]=iSequence; |
---|
1402 | // select if largest pivot |
---|
1403 | if (fabs(alpha)>bestPivot) { |
---|
1404 | sequenceIn_ = numberPossiblySwapped; |
---|
1405 | bestPivot = fabs(alpha); |
---|
1406 | theta_ = dj_[iSequence]/alpha; |
---|
1407 | } |
---|
1408 | double range = upper[iSequence] - lower[iSequence]; |
---|
1409 | thruThis += range*fabs(alpha); |
---|
1410 | increaseInThis += badDj*range; |
---|
1411 | } |
---|
1412 | } |
---|
1413 | swapped[1-iFlip]=numberPossiblySwapped; |
---|
1414 | interesting[1-iFlip]=numberRemaining; |
---|
1415 | marker[1-iFlip][0]= max(marker[1-iFlip][0],numberRemaining); |
---|
1416 | marker[1-iFlip][1]= min(marker[1-iFlip][1],numberPossiblySwapped); |
---|
1417 | // If we stop now this will be increase in objective (I think) |
---|
1418 | double increase = (fabs(dualOut_)-totalThru)*theta_; |
---|
1419 | increase += increaseInObjective; |
---|
1420 | if (theta_<0.0) |
---|
1421 | thruThis += fabs(dualOut_); // force using this one |
---|
1422 | if (increaseInObjective<0.0&&increase<0.0&&lastSequence>=0) { |
---|
1423 | // back |
---|
1424 | // We may need to be more careful - we could do by |
---|
1425 | // switch so we always do fine grained? |
---|
1426 | bestPivot=0.0; |
---|
1427 | } else { |
---|
1428 | // add in |
---|
1429 | totalThru += thruThis; |
---|
1430 | increaseInObjective += increaseInThis; |
---|
1431 | } |
---|
1432 | if (bestPivot<0.1*bestEverPivot&& |
---|
1433 | bestEverPivot>1.0e-6&&bestPivot<1.0e-3) { |
---|
1434 | // back to previous one |
---|
1435 | sequenceIn_=lastSequence; |
---|
1436 | // swap regions |
---|
1437 | iFlip = 1-iFlip; |
---|
1438 | break; |
---|
1439 | } else if (sequenceIn_==-1&&upperTheta>largeValue_) { |
---|
1440 | if (lastPivot>acceptablePivot) { |
---|
1441 | // back to previous one |
---|
1442 | sequenceIn_=lastSequence; |
---|
1443 | // swap regions |
---|
1444 | iFlip = 1-iFlip; |
---|
1445 | } else { |
---|
1446 | // can only get here if all pivots too small |
---|
1447 | } |
---|
1448 | break; |
---|
1449 | } else if (totalThru>=fabs(dualOut_)) { |
---|
1450 | modifyCosts=true; // fine grain - we can modify costs |
---|
1451 | break; // no point trying another loop |
---|
1452 | } else { |
---|
1453 | lastSequence=sequenceIn_; |
---|
1454 | if (bestPivot>bestEverPivot) |
---|
1455 | bestEverPivot=bestPivot; |
---|
1456 | iFlip = 1 -iFlip; |
---|
1457 | modifyCosts=true; // fine grain - we can modify costs |
---|
1458 | } |
---|
1459 | } |
---|
1460 | if (iTry==MAXTRY) |
---|
1461 | iFlip = 1-iFlip; // flip back |
---|
1462 | break; |
---|
1463 | } else { |
---|
1464 | // skip this lot |
---|
1465 | if (bestPivot>1.0e-3||bestPivot>bestEverPivot) { |
---|
1466 | bestEverPivot=bestPivot; |
---|
1467 | lastSequence=bestSequence; |
---|
1468 | } else { |
---|
1469 | // keep old swapped |
---|
1470 | memcpy(array[1-iFlip]+swapped[iFlip], |
---|
1471 | array[iFlip]+swapped[iFlip], |
---|
1472 | (numberColumns_-swapped[iFlip])*sizeof(double)); |
---|
1473 | memcpy(indices[1-iFlip]+swapped[iFlip], |
---|
1474 | indices[iFlip]+swapped[iFlip], |
---|
1475 | (numberColumns_-swapped[iFlip])*sizeof(int)); |
---|
1476 | marker[1-iFlip][1] = min(marker[1-iFlip][1],swapped[iFlip]); |
---|
1477 | swapped[1-iFlip]=swapped[iFlip]; |
---|
1478 | } |
---|
1479 | increaseInObjective += increaseInThis; |
---|
1480 | iFlip = 1 - iFlip; // swap regions |
---|
1481 | tentativeTheta = 2.0*upperTheta; |
---|
1482 | totalThru += thruThis; |
---|
1483 | } |
---|
1484 | } |
---|
1485 | |
---|
1486 | // can get here without sequenceIn_ set but with lastSequence |
---|
1487 | if (sequenceIn_<0&&lastSequence>=0) { |
---|
1488 | // back to previous one |
---|
1489 | sequenceIn_=lastSequence; |
---|
1490 | // swap regions |
---|
1491 | iFlip = 1-iFlip; |
---|
1492 | } |
---|
1493 | |
---|
1494 | if (sequenceIn_>=0) { |
---|
1495 | // at this stage sequenceIn_ is just pointer into index array |
---|
1496 | // flip just so we can use iFlip |
---|
1497 | iFlip = 1 -iFlip; |
---|
1498 | spare = array[iFlip]; |
---|
1499 | index = indices[iFlip]; |
---|
1500 | double oldValue; |
---|
1501 | double alpha = spare[sequenceIn_]; |
---|
1502 | sequenceIn_ = indices[iFlip][sequenceIn_]; |
---|
1503 | oldValue = dj_[sequenceIn_]; |
---|
1504 | theta_ = oldValue/alpha; |
---|
1505 | if (theta_<MINIMUMTHETA) { |
---|
1506 | // can't pivot to zero |
---|
1507 | if (oldValue-MINIMUMTHETA*alpha>=-dualTolerance_) { |
---|
1508 | theta_=MINIMUMTHETA; |
---|
1509 | } else if (oldValue-MINIMUMTHETA*alpha>=-newTolerance) { |
---|
1510 | theta_=MINIMUMTHETA; |
---|
1511 | thisIncrease=true; |
---|
1512 | } else { |
---|
1513 | theta_=(oldValue+newTolerance)/alpha; |
---|
1514 | assert(theta_>=0.0); |
---|
1515 | thisIncrease=true; |
---|
1516 | } |
---|
1517 | } |
---|
1518 | // may need to adjust costs so all dual feasible AND pivoted is exactly 0 |
---|
1519 | if (modifyCosts) { |
---|
1520 | int i; |
---|
1521 | double * workRow = rowArray->denseVector(); |
---|
1522 | double * workColumn = columnArray->denseVector(); |
---|
1523 | for (i=numberColumns_-1;i>=swapped[iFlip];i--) { |
---|
1524 | int iSequence=index[i]; |
---|
1525 | double alpha; |
---|
1526 | if (iSequence>=numberColumns_) |
---|
1527 | alpha=workRow[iSequence-numberColumns_]; |
---|
1528 | else |
---|
1529 | alpha=workColumn[iSequence]; |
---|
1530 | double value = dj_[iSequence]-theta_*alpha; |
---|
1531 | |
---|
1532 | // can't be free here |
---|
1533 | |
---|
1534 | if (alpha < 0.0) { |
---|
1535 | //at upper bound |
---|
1536 | if (value>dualTolerance_) { |
---|
1537 | thisIncrease=true; |
---|
1538 | #define MODIFYCOST 2 |
---|
1539 | #if MODIFYCOST |
---|
1540 | // modify cost to hit new tolerance |
---|
1541 | double modification = alpha*theta_-dj_[iSequence] |
---|
1542 | +newTolerance; |
---|
1543 | //modification = min(modification,dualTolerance_); |
---|
1544 | //assert (fabs(modification)<1.0e-7); |
---|
1545 | dj_[iSequence] += modification; |
---|
1546 | cost_[iSequence] += modification; |
---|
1547 | #endif |
---|
1548 | } |
---|
1549 | } else { |
---|
1550 | // at lower bound |
---|
1551 | if (-value>dualTolerance_) { |
---|
1552 | thisIncrease=true; |
---|
1553 | #if MODIFYCOST |
---|
1554 | // modify cost to hit new tolerance |
---|
1555 | double modification = alpha*theta_-dj_[iSequence] |
---|
1556 | -newTolerance; |
---|
1557 | //modification = max(modification,-dualTolerance_); |
---|
1558 | //assert (fabs(modification)<1.0e-7); |
---|
1559 | dj_[iSequence] += modification; |
---|
1560 | cost_[iSequence] += modification; |
---|
1561 | #endif |
---|
1562 | } |
---|
1563 | } |
---|
1564 | } |
---|
1565 | } |
---|
1566 | } |
---|
1567 | } |
---|
1568 | |
---|
1569 | if (sequenceIn_>=0) { |
---|
1570 | if (sequenceIn_>=numberColumns_) { |
---|
1571 | //slack |
---|
1572 | alpha_ = rowArray->denseVector()[sequenceIn_-numberColumns_]; |
---|
1573 | } else { |
---|
1574 | // column |
---|
1575 | alpha_ = columnArray->denseVector()[sequenceIn_]; |
---|
1576 | } |
---|
1577 | lowerIn_ = lower_[sequenceIn_]; |
---|
1578 | upperIn_ = upper_[sequenceIn_]; |
---|
1579 | valueIn_ = solution_[sequenceIn_]; |
---|
1580 | dualIn_ = dj_[sequenceIn_]; |
---|
1581 | |
---|
1582 | if (numberTimesOptimal_) { |
---|
1583 | // can we adjust cost back closer to original |
---|
1584 | //*** add coding |
---|
1585 | } |
---|
1586 | #if MODIFYCOST>1 |
---|
1587 | // modify cost to hit zero exactly |
---|
1588 | // so (dualIn_+modification)==theta_*alpha_ |
---|
1589 | double modification = theta_*alpha_-dualIn_; |
---|
1590 | dualIn_ += modification; |
---|
1591 | dj_[sequenceIn_]=dualIn_; |
---|
1592 | cost_[sequenceIn_] += modification; |
---|
1593 | //assert (fabs(modification)<1.0e-6); |
---|
1594 | #ifdef CLP_DEBUG |
---|
1595 | if ((handler_->logLevel()&32)&&fabs(modification)>1.0e-15) |
---|
1596 | printf("exact %d new cost %g, change %g\n",sequenceIn_, |
---|
1597 | cost_[sequenceIn_],modification); |
---|
1598 | #endif |
---|
1599 | #endif |
---|
1600 | |
---|
1601 | if (alpha_<0.0) { |
---|
1602 | // as if from upper bound |
---|
1603 | directionIn_=-1; |
---|
1604 | upperIn_=valueIn_; |
---|
1605 | } else { |
---|
1606 | // as if from lower bound |
---|
1607 | directionIn_=1; |
---|
1608 | lowerIn_=valueIn_; |
---|
1609 | } |
---|
1610 | } |
---|
1611 | if (thisIncrease) { |
---|
1612 | newTolerance = dualTolerance_+1.0e-4*dblParam_[OsiDualTolerance]; |
---|
1613 | } |
---|
1614 | |
---|
1615 | // clear arrays |
---|
1616 | |
---|
1617 | for (i=0;i<2;i++) { |
---|
1618 | memset(array[i],0,marker[i][0]*sizeof(double)); |
---|
1619 | memset(array[i]+marker[i][1],0, |
---|
1620 | (numberColumns_-marker[i][1])*sizeof(double)); |
---|
1621 | } |
---|
1622 | } |
---|
1623 | /* Checks if tentative optimal actually means unbounded |
---|
1624 | Returns -3 if not, 2 if is unbounded */ |
---|
1625 | int |
---|
1626 | ClpSimplexDual::checkUnbounded(OsiIndexedVector * ray, |
---|
1627 | OsiIndexedVector * spare, |
---|
1628 | double changeCost) |
---|
1629 | { |
---|
1630 | int status=2; // say unbounded |
---|
1631 | factorization_->updateColumn(spare,ray); |
---|
1632 | // get reduced cost |
---|
1633 | int i; |
---|
1634 | int number=ray->getNumElements(); |
---|
1635 | int * index = ray->getIndices(); |
---|
1636 | double * array = ray->denseVector(); |
---|
1637 | for (i=0;i<number;i++) { |
---|
1638 | int iRow=index[i]; |
---|
1639 | int iPivot=pivotVariable_[iRow]; |
---|
1640 | changeCost -= cost(iPivot)*array[iRow]; |
---|
1641 | } |
---|
1642 | double way; |
---|
1643 | if (changeCost>0.0) { |
---|
1644 | //try going down |
---|
1645 | way=1.0; |
---|
1646 | } else if (changeCost<0.0) { |
---|
1647 | //try going up |
---|
1648 | way=-1.0; |
---|
1649 | } else { |
---|
1650 | #ifdef CLP_DEBUG |
---|
1651 | printf("can't decide on up or down\n"); |
---|
1652 | #endif |
---|
1653 | way=0.0; |
---|
1654 | status=-3; |
---|
1655 | } |
---|
1656 | double movement=1.0e10*way; // some largish number |
---|
1657 | double zeroTolerance = 1.0e-14*dualBound_; |
---|
1658 | for (i=0;i<number;i++) { |
---|
1659 | int iRow=index[i]; |
---|
1660 | int iPivot=pivotVariable_[iRow]; |
---|
1661 | double arrayValue = array[iRow]; |
---|
1662 | if (fabs(arrayValue)<zeroTolerance) |
---|
1663 | arrayValue=0.0; |
---|
1664 | double newValue=solution(iPivot)+movement*arrayValue; |
---|
1665 | if (newValue>upper(iPivot)+primalTolerance_|| |
---|
1666 | newValue<lower(iPivot)-primalTolerance_) |
---|
1667 | status=-3; // not unbounded |
---|
1668 | } |
---|
1669 | if (status==2) { |
---|
1670 | // create ray |
---|
1671 | delete [] ray_; |
---|
1672 | ray_ = new double [numberColumns_]; |
---|
1673 | CoinFillN(ray_,numberColumns_,0.0); |
---|
1674 | for (i=0;i<number;i++) { |
---|
1675 | int iRow=index[i]; |
---|
1676 | int iPivot=pivotVariable_[iRow]; |
---|
1677 | double arrayValue = array[iRow]; |
---|
1678 | if (iPivot<numberColumns_&&fabs(arrayValue)>=zeroTolerance) |
---|
1679 | ray_[iPivot] = way* array[iRow]; |
---|
1680 | } |
---|
1681 | } |
---|
1682 | ray->clear(); |
---|
1683 | return status; |
---|
1684 | } |
---|
1685 | /* Checks if finished. Updates status */ |
---|
1686 | void |
---|
1687 | ClpSimplexDual::statusOfProblemInDual(int & lastCleaned,int type) |
---|
1688 | { |
---|
1689 | if (type==2) { |
---|
1690 | // trouble - restore solution |
---|
1691 | memcpy(status_ ,saveStatus_,(numberColumns_+numberRows_)*sizeof(char)); |
---|
1692 | memcpy(rowActivityWork_,savedSolution_+numberColumns_ , |
---|
1693 | numberRows_*sizeof(double)); |
---|
1694 | memcpy(columnActivityWork_,savedSolution_ , |
---|
1695 | numberColumns_*sizeof(double)); |
---|
1696 | forceFactorization_=1; // a bit drastic but .. |
---|
1697 | changeMade_++; // say something changed |
---|
1698 | } |
---|
1699 | int tentativeStatus = problemStatus_; |
---|
1700 | double changeCost; |
---|
1701 | |
---|
1702 | if (problemStatus_>-3) { |
---|
1703 | // factorize |
---|
1704 | // later on we will need to recover from singularities |
---|
1705 | // also we could skip if first time |
---|
1706 | // save dual weights |
---|
1707 | dualRowPivot_->saveWeights(this,1); |
---|
1708 | // is factorization okay? |
---|
1709 | if (internalFactorize(1)) { |
---|
1710 | // no - restore previous basis |
---|
1711 | assert (type==1); |
---|
1712 | changeMade_++; // say something changed |
---|
1713 | memcpy(status_ ,saveStatus_,(numberColumns_+numberRows_)*sizeof(char)); |
---|
1714 | memcpy(rowActivityWork_,savedSolution_+numberColumns_ , |
---|
1715 | numberRows_*sizeof(double)); |
---|
1716 | memcpy(columnActivityWork_,savedSolution_ , |
---|
1717 | numberColumns_*sizeof(double)); |
---|
1718 | // get correct bounds on all variables |
---|
1719 | double dummyChangeCost=0.0; |
---|
1720 | changeBounds(true,rowArray_[2],dummyChangeCost); |
---|
1721 | // throw away change |
---|
1722 | rowArray_[2]->clear(); |
---|
1723 | forceFactorization_=1; // a bit drastic but .. |
---|
1724 | type = 2; |
---|
1725 | assert (internalFactorize(1)==0); |
---|
1726 | } |
---|
1727 | problemStatus_=-3; |
---|
1728 | } |
---|
1729 | // at this stage status is -3 or -4 if looks infeasible |
---|
1730 | // get primal and dual solutions |
---|
1731 | gutsOfSolution(rowActivityWork_,columnActivityWork_); |
---|
1732 | #ifdef CLP_DEBUG |
---|
1733 | if (!rowScale_&&(handler_->logLevel()&32)) { |
---|
1734 | double * objectiveSimplex |
---|
1735 | = copyOfArray(objective_,numberColumns_,0.0); |
---|
1736 | double * rowObjectiveSimplex |
---|
1737 | = copyOfArray(rowObjective_,numberRows_,0.0); |
---|
1738 | int i; |
---|
1739 | double largest; |
---|
1740 | largest=0.0; |
---|
1741 | for (i=0;i<numberRows_;i++) { |
---|
1742 | rowObjectiveSimplex[i] *= optimizationDirection_; |
---|
1743 | double difference = fabs(rowObjectiveWork_[i]-rowObjectiveSimplex[i]); |
---|
1744 | if (difference>largest) |
---|
1745 | largest=difference; |
---|
1746 | } |
---|
1747 | for (i=0;i<numberColumns_;i++) { |
---|
1748 | objectiveSimplex[i] *= optimizationDirection_; |
---|
1749 | double difference = fabs(objectiveWork_[i]-objectiveSimplex[i]); |
---|
1750 | if (difference>largest) |
---|
1751 | largest=difference; |
---|
1752 | } |
---|
1753 | if ((handler_->logLevel()&16)) |
---|
1754 | printf("difference in obj %g\n",largest); |
---|
1755 | delete [] objectiveSimplex; |
---|
1756 | delete [] rowObjectiveSimplex; |
---|
1757 | } |
---|
1758 | #endif |
---|
1759 | handler_->message(CLP_SIMPLEX_STATUS,messages_) |
---|
1760 | <<numberIterations_<<objectiveValue(); |
---|
1761 | handler_->printing(sumPrimalInfeasibilities_>0.0) |
---|
1762 | <<sumPrimalInfeasibilities_<<numberPrimalInfeasibilities_; |
---|
1763 | handler_->printing(sumDualInfeasibilities_>0.0) |
---|
1764 | <<sumDualInfeasibilities_<<numberDualInfeasibilities_; |
---|
1765 | handler_->printing(numberDualInfeasibilitiesWithoutFree_ |
---|
1766 | <numberDualInfeasibilities_) |
---|
1767 | <<numberDualInfeasibilities_- |
---|
1768 | numberDualInfeasibilitiesWithoutFree_; |
---|
1769 | handler_->message()<<OsiMessageEol; |
---|
1770 | while (problemStatus_<=-3) { |
---|
1771 | bool cleanDuals=false; |
---|
1772 | int numberChangedBounds=0; |
---|
1773 | int doOriginalTolerance=0; |
---|
1774 | if ( lastCleaned==numberIterations_) |
---|
1775 | doOriginalTolerance=1; |
---|
1776 | // check optimal |
---|
1777 | if (dualFeasible()||problemStatus_==-4) { |
---|
1778 | if (primalFeasible()) { |
---|
1779 | // may be optimal - or may be bounds are wrong |
---|
1780 | handler_->message(CLP_DUAL_BOUNDS,messages_) |
---|
1781 | <<dualBound_ |
---|
1782 | <<OsiMessageEol; |
---|
1783 | // save solution in case unbounded |
---|
1784 | CoinDisjointCopyN(columnActivityWork_,numberColumns_, |
---|
1785 | columnArray_[0]->denseVector()); |
---|
1786 | CoinDisjointCopyN(rowActivityWork_,numberRows_, |
---|
1787 | rowArray_[2]->denseVector()); |
---|
1788 | numberChangedBounds=changeBounds(false,rowArray_[0],changeCost); |
---|
1789 | if (numberChangedBounds<=0) { |
---|
1790 | //looks optimal - do we need to reset tolerance |
---|
1791 | if (lastCleaned<numberIterations_&&numberTimesOptimal_<4) { |
---|
1792 | doOriginalTolerance=2; |
---|
1793 | numberTimesOptimal_++; |
---|
1794 | changeMade_++; // say something changed |
---|
1795 | if (numberTimesOptimal_==1) { |
---|
1796 | dualTolerance_ = min(dualTolerance_,1.0e-8); |
---|
1797 | // better to have small tolerance even if slower |
---|
1798 | factorization_->zeroTolerance(1.0e-15); |
---|
1799 | } |
---|
1800 | } else { |
---|
1801 | problemStatus_=0; // optimal |
---|
1802 | if (lastCleaned<numberIterations_) { |
---|
1803 | handler_->message(CLP_SIMPLEX_GIVINGUP,messages_) |
---|
1804 | <<OsiMessageEol; |
---|
1805 | } |
---|
1806 | } |
---|
1807 | } else { |
---|
1808 | cleanDuals=true; |
---|
1809 | if (doOriginalTolerance==1) { |
---|
1810 | // check unbounded |
---|
1811 | problemStatus_ = checkUnbounded(rowArray_[0],rowArray_[1], |
---|
1812 | changeCost); |
---|
1813 | if (problemStatus_==2) { |
---|
1814 | // it is unbounded - restore solution |
---|
1815 | // but first add in changes to non-basic |
---|
1816 | int iColumn; |
---|
1817 | double * original = columnArray_[0]->denseVector(); |
---|
1818 | for (iColumn=0;iColumn<numberColumns_;iColumn++) { |
---|
1819 | if(getColumnStatus(iColumn)!= ClpSimplex::basic) |
---|
1820 | ray_[iColumn] += |
---|
1821 | columnActivityWork_[iColumn]-original[iColumn]; |
---|
1822 | columnActivityWork_[iColumn] = original[iColumn]; |
---|
1823 | } |
---|
1824 | CoinDisjointCopyN(rowArray_[2]->denseVector(),numberRows_, |
---|
1825 | rowActivityWork_); |
---|
1826 | } |
---|
1827 | } else { |
---|
1828 | doOriginalTolerance=2; |
---|
1829 | rowArray_[0]->clear(); |
---|
1830 | } |
---|
1831 | } |
---|
1832 | CoinFillN(columnArray_[0]->denseVector(),numberColumns_,0.0); |
---|
1833 | CoinFillN(rowArray_[2]->denseVector(),numberRows_,0.0); |
---|
1834 | } |
---|
1835 | if (problemStatus_==-4) { |
---|
1836 | // may be infeasible - or may be bounds are wrong |
---|
1837 | handler_->message(CLP_DUAL_CHECKB,messages_) |
---|
1838 | <<dualBound_ |
---|
1839 | <<OsiMessageEol; |
---|
1840 | numberChangedBounds=changeBounds(false,NULL,changeCost); |
---|
1841 | if (numberChangedBounds<=0||dualBound_>1.0e20|| |
---|
1842 | (largestPrimalError_>1.0&&dualBound_>1.0e17)) { |
---|
1843 | problemStatus_=1; // infeasible |
---|
1844 | } else { |
---|
1845 | problemStatus_=-1; //iterate |
---|
1846 | cleanDuals=true; |
---|
1847 | doOriginalTolerance=2; |
---|
1848 | // and delete ray which has been created |
---|
1849 | delete [] ray_; |
---|
1850 | ray_ = NULL; |
---|
1851 | } |
---|
1852 | } |
---|
1853 | } else { |
---|
1854 | cleanDuals=true; |
---|
1855 | } |
---|
1856 | if (problemStatus_<0) { |
---|
1857 | if (doOriginalTolerance==2) { |
---|
1858 | // put back original tolerance |
---|
1859 | lastCleaned=numberIterations_; |
---|
1860 | handler_->message(CLP_DUAL_ORIGINAL,messages_) |
---|
1861 | <<OsiMessageEol; |
---|
1862 | |
---|
1863 | perturbation_=102; // stop any perturbations |
---|
1864 | createRim(4); |
---|
1865 | // make sure duals are current |
---|
1866 | computeDuals(); |
---|
1867 | // put back bounds as they were if was optimal |
---|
1868 | if (doOriginalTolerance==2) { |
---|
1869 | changeMade_++; // say something changed |
---|
1870 | changeBounds(true,NULL,changeCost); |
---|
1871 | cleanDuals=true; |
---|
1872 | } |
---|
1873 | } |
---|
1874 | if (cleanDuals) { |
---|
1875 | // make sure dual feasible |
---|
1876 | // look at all rows and columns |
---|
1877 | rowArray_[0]->clear(); |
---|
1878 | CoinIotaN(rowArray_[0]->getIndices(),numberRows_,0); |
---|
1879 | rowArray_[0]->setNumElements(numberRows_); |
---|
1880 | columnArray_[0]->clear(); |
---|
1881 | CoinIotaN(columnArray_[0]->getIndices(),numberColumns_,0); |
---|
1882 | columnArray_[0]->setNumElements(numberColumns_); |
---|
1883 | double objectiveChange=0.0; |
---|
1884 | updateDualsInDual(rowArray_[0],columnArray_[0],rowArray_[1], |
---|
1885 | 0.0,objectiveChange); |
---|
1886 | // for now - recompute all |
---|
1887 | gutsOfSolution(rowActivityWork_,columnActivityWork_); |
---|
1888 | assert(numberDualInfeasibilitiesWithoutFree_==0); |
---|
1889 | if (numberDualInfeasibilities_) { |
---|
1890 | // bad free variables |
---|
1891 | if (primalFeasible()) { |
---|
1892 | std::cerr<<"Free variable problem?"<<std::endl; |
---|
1893 | abort(); // what now |
---|
1894 | } |
---|
1895 | problemStatus_=-1; // carry on as normal |
---|
1896 | } |
---|
1897 | } else { |
---|
1898 | // iterate |
---|
1899 | problemStatus_=-1; |
---|
1900 | } |
---|
1901 | } |
---|
1902 | } |
---|
1903 | if (type==0||type==1) { |
---|
1904 | if (!type) { |
---|
1905 | // create save arrays |
---|
1906 | delete [] saveStatus_; |
---|
1907 | delete [] savedSolution_; |
---|
1908 | saveStatus_ = new unsigned char [numberRows_+numberColumns_]; |
---|
1909 | savedSolution_ = new double [numberRows_+numberColumns_]; |
---|
1910 | } |
---|
1911 | // save arrays |
---|
1912 | memcpy(saveStatus_,status_,(numberColumns_+numberRows_)*sizeof(char)); |
---|
1913 | memcpy(savedSolution_+numberColumns_ ,rowActivityWork_, |
---|
1914 | numberRows_*sizeof(double)); |
---|
1915 | memcpy(savedSolution_ ,columnActivityWork_,numberColumns_*sizeof(double)); |
---|
1916 | } |
---|
1917 | |
---|
1918 | // restore weights (if saved) - also recompute infeasibility list |
---|
1919 | if (tentativeStatus>-3) |
---|
1920 | dualRowPivot_->saveWeights(this,(type <2) ? 2 : 4); |
---|
1921 | else |
---|
1922 | dualRowPivot_->saveWeights(this,3); |
---|
1923 | // unflag all variables (we may want to wait a bit?) |
---|
1924 | int iRow; |
---|
1925 | for (iRow=0;iRow<numberRows_;iRow++) { |
---|
1926 | int iPivot=pivotVariable_[iRow]; |
---|
1927 | clearFlagged(iPivot); |
---|
1928 | } |
---|
1929 | if (problemStatus_<0&&!changeMade_) { |
---|
1930 | problemStatus_=4; // unknown |
---|
1931 | } |
---|
1932 | |
---|
1933 | } |
---|
1934 | /* While updateDualsInDual sees what effect is of flip |
---|
1935 | this does actuall flipping. |
---|
1936 | If change >0.0 then value in array >0.0 => from lower to upper |
---|
1937 | */ |
---|
1938 | void |
---|
1939 | ClpSimplexDual::flipBounds(OsiIndexedVector * rowArray, |
---|
1940 | OsiIndexedVector * columnArray, |
---|
1941 | double change) |
---|
1942 | { |
---|
1943 | double * work; |
---|
1944 | int number; |
---|
1945 | int * which; |
---|
1946 | |
---|
1947 | int iSection; |
---|
1948 | |
---|
1949 | for (iSection=0;iSection<2;iSection++) { |
---|
1950 | int i; |
---|
1951 | double * solution = solutionRegion(iSection); |
---|
1952 | double * lower = lowerRegion(iSection); |
---|
1953 | double * upper = upperRegion(iSection); |
---|
1954 | int addSequence; |
---|
1955 | if (!iSection) { |
---|
1956 | work = rowArray->denseVector(); |
---|
1957 | number = rowArray->getNumElements(); |
---|
1958 | which = rowArray->getIndices(); |
---|
1959 | addSequence = numberColumns_; |
---|
1960 | } else { |
---|
1961 | work = columnArray->denseVector(); |
---|
1962 | number = columnArray->getNumElements(); |
---|
1963 | which = columnArray->getIndices(); |
---|
1964 | addSequence = 0; |
---|
1965 | } |
---|
1966 | |
---|
1967 | for (i=0;i<number;i++) { |
---|
1968 | int iSequence = which[i]; |
---|
1969 | #ifndef NDEBUG |
---|
1970 | double value = work[iSequence]*change; |
---|
1971 | #endif |
---|
1972 | work[iSequence]=0.0; |
---|
1973 | Status status = getStatus(iSequence+addSequence); |
---|
1974 | |
---|
1975 | switch(status) { |
---|
1976 | |
---|
1977 | case ClpSimplex::basic: |
---|
1978 | case ClpSimplex::isFree: |
---|
1979 | case ClpSimplex::superBasic: |
---|
1980 | break; |
---|
1981 | case ClpSimplex::atUpperBound: |
---|
1982 | assert (value<=0.0); |
---|
1983 | // to lower bound |
---|
1984 | setStatus(iSequence+addSequence,ClpSimplex::atLowerBound); |
---|
1985 | solution[iSequence] = lower[iSequence]; |
---|
1986 | break; |
---|
1987 | case ClpSimplex::atLowerBound: |
---|
1988 | assert (value>=0.0); |
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1989 | // to upper bound |
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1990 | setStatus(iSequence+addSequence,ClpSimplex::atUpperBound); |
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1991 | solution[iSequence] = upper[iSequence]; |
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1992 | break; |
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1993 | } |
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1994 | } |
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1995 | } |
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1996 | rowArray->setNumElements(0); |
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1997 | columnArray->setNumElements(0); |
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1998 | } |
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1999 | // Restores bound to original bound |
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2000 | void |
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2001 | ClpSimplexDual::originalBound( int iSequence) |
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2002 | { |
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2003 | if (iSequence>=numberColumns_) { |
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2004 | // rows |
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2005 | int iRow = iSequence-numberColumns_; |
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2006 | rowLowerWork_[iRow]=rowLower_[iRow]; |
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2007 | rowUpperWork_[iRow]=rowUpper_[iRow]; |
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2008 | if (rowScale_) { |
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2009 | if (rowLowerWork_[iRow]>-1.0e50) |
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2010 | rowLowerWork_[iRow] *= rowScale_[iRow]; |
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2011 | if (rowUpperWork_[iRow]<1.0e50) |
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2012 | rowUpperWork_[iRow] *= rowScale_[iRow]; |
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2013 | } |
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2014 | } else { |
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2015 | // columns |
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2016 | columnLowerWork_[iSequence]=columnLower_[iSequence]; |
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2017 | columnUpperWork_[iSequence]=columnUpper_[iSequence]; |
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2018 | if (rowScale_) { |
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2019 | double multiplier = 1.0/columnScale_[iSequence]; |
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2020 | if (columnLowerWork_[iSequence]>-1.0e50) |
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2021 | columnLowerWork_[iSequence] *= multiplier; |
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2022 | if (columnUpperWork_[iSequence]<1.0e50) |
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2023 | columnUpperWork_[iSequence] *= multiplier; |
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2024 | } |
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2025 | } |
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2026 | setFakeBound(iSequence,ClpSimplex::noFake); |
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2027 | } |
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2028 | /* As changeBounds but just changes new bounds for a single variable. |
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2029 | Returns true if change */ |
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2030 | bool |
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2031 | ClpSimplexDual::changeBound( int iSequence) |
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2032 | { |
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2033 | // old values |
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2034 | double oldLower=lower_[iSequence]; |
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2035 | double oldUpper=upper_[iSequence]; |
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2036 | double value=solution_[iSequence]; |
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2037 | bool modified=false; |
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2038 | originalBound(iSequence); |
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2039 | // original values |
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2040 | double lowerValue=lower_[iSequence]; |
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2041 | double upperValue=upper_[iSequence]; |
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2042 | // back to altered values |
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2043 | lower_[iSequence] = oldLower; |
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2044 | upper_[iSequence] = oldUpper; |
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2045 | if (value==oldLower) { |
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2046 | if (upperValue > oldLower + dualBound_) { |
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2047 | upper_[iSequence]=oldLower+dualBound_; |
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2048 | setFakeBound(iSequence,ClpSimplex::upperFake); |
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2049 | modified=true; |
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2050 | } |
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2051 | } else if (value==oldUpper) { |
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2052 | if (lowerValue < oldUpper - dualBound_) { |
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2053 | lower_[iSequence]=oldUpper-dualBound_; |
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2054 | setFakeBound(iSequence,ClpSimplex::lowerFake); |
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2055 | modified=true; |
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2056 | } |
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2057 | } else { |
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2058 | assert(value==oldLower||value==oldUpper); |
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2059 | } |
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2060 | return modified; |
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2061 | } |
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2062 | // Perturbs problem |
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2063 | void |
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2064 | ClpSimplexDual::perturb() |
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2065 | { |
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2066 | if (perturbation_>100) |
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2067 | return; //perturbed already |
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2068 | int iRow,iColumn; |
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2069 | // dual perturbation |
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2070 | double perturbation=1.0e-20; |
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2071 | // maximum fraction of cost to perturb |
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2072 | double maximumFraction = 1.0e-4; |
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2073 | if (perturbation_==100) { |
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2074 | perturbation = 1.0e-4; |
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2075 | for (iRow=0;iRow<numberRows_;iRow++) { |
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2076 | double value = fabs(rowActivityWork_[iRow]*rowObjectiveWork_[iRow]); |
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2077 | perturbation = max(perturbation,value); |
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2078 | } |
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2079 | for (iColumn=0;iColumn<numberColumns_;iColumn++) { |
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2080 | double value = |
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2081 | fabs(columnActivityWork_[iColumn]*objectiveWork_[iColumn]); |
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2082 | perturbation = max(perturbation,value); |
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2083 | } |
---|
2084 | perturbation *= 1.0e-8; |
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2085 | } else if (perturbation_<100) { |
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2086 | perturbation = pow(10.0,perturbation_); |
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2087 | // user is in charge |
---|
2088 | maximumFraction = 1.0e100; |
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2089 | } |
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2090 | // modify costs |
---|
2091 | handler_->message(CLP_SIMPLEX_PERTURB,messages_) |
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2092 | <<perturbation |
---|
2093 | <<OsiMessageEol; |
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2094 | for (iRow=0;iRow<numberRows_;iRow++) { |
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2095 | double value = perturbation; |
---|
2096 | double currentValue = rowObjectiveWork_[iRow]; |
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2097 | value = min(value,maximumFraction*fabs(currentValue)+1.0e-6); |
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2098 | if (rowLowerWork_[iRow]>-largeValue_) { |
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2099 | if (fabs(rowLowerWork_[iRow])<fabs(rowUpperWork_[iRow])) |
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2100 | value *= drand48(); |
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2101 | else |
---|
2102 | value *= -drand48(); |
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2103 | } else if (rowUpperWork_[iRow]<largeValue_) { |
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2104 | value *= -drand48(); |
---|
2105 | } else { |
---|
2106 | value=0.0; |
---|
2107 | } |
---|
2108 | rowObjectiveWork_[iRow] += value; |
---|
2109 | } |
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2110 | for (iColumn=0;iColumn<numberColumns_;iColumn++) { |
---|
2111 | double value = perturbation; |
---|
2112 | double currentValue = objectiveWork_[iColumn]; |
---|
2113 | value = min(value,maximumFraction*fabs(currentValue)+1.0e-6); |
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2114 | if (columnLowerWork_[iColumn]>-largeValue_) { |
---|
2115 | if (fabs(columnLowerWork_[iColumn])< |
---|
2116 | fabs(columnUpperWork_[iColumn])) |
---|
2117 | value *= drand48(); |
---|
2118 | else |
---|
2119 | value *= -drand48(); |
---|
2120 | } else if (columnUpperWork_[iColumn]<largeValue_) { |
---|
2121 | value *= -drand48(); |
---|
2122 | } else { |
---|
2123 | value=0.0; |
---|
2124 | } |
---|
2125 | objectiveWork_[iColumn] += value; |
---|
2126 | } |
---|
2127 | // say perturbed |
---|
2128 | perturbation_=102; |
---|
2129 | |
---|
2130 | } |
---|
2131 | |
---|