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 | Authors |
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6 | |
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7 | John Forrest |
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8 | |
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9 | */ |
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10 | #ifndef ClpSimplex_H |
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11 | #define ClpSimplex_H |
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12 | |
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13 | #include <iostream> |
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14 | #include <cfloat> |
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15 | #include "ClpModel.hpp" |
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16 | #include "ClpMatrixBase.hpp" |
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17 | #include "ClpSolve.hpp" |
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18 | class ClpDualRowPivot; |
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19 | class ClpPrimalColumnPivot; |
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20 | class ClpFactorization; |
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21 | class CoinIndexedVector; |
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22 | class ClpNonLinearCost; |
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23 | class ClpSimplexProgress; |
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24 | |
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25 | /** This solves LPs using the simplex method |
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26 | |
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27 | It inherits from ClpModel and all its arrays are created at |
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28 | algorithm time. Originally I tried to work with model arrays |
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29 | but for simplicity of coding I changed to single arrays with |
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30 | structural variables then row variables. Some coding is still |
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31 | based on old style and needs cleaning up. |
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32 | |
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33 | For a description of algorithms: |
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34 | |
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35 | for dual see ClpSimplexDual.hpp and at top of ClpSimplexDual.cpp |
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36 | for primal see ClpSimplexPrimal.hpp and at top of ClpSimplexPrimal.cpp |
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37 | |
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38 | There is an algorithm data member. + for primal variations |
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39 | and - for dual variations |
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40 | |
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41 | This file also includes (at end) a very simple class ClpSimplexProgress |
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42 | which is where anti-looping stuff should migrate to |
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43 | |
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44 | */ |
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45 | |
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46 | class ClpSimplex : public ClpModel { |
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47 | friend void ClpSimplexUnitTest(const std::string & mpsDir, |
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48 | const std::string & netlibDir); |
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49 | |
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50 | public: |
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51 | /** enums for status of various sorts. |
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52 | First 4 match CoinWarmStartBasis, |
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53 | isFixed means fixed at lower bound and out of basis |
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54 | */ |
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55 | enum Status { |
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56 | isFree = 0x00, |
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57 | basic = 0x01, |
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58 | atUpperBound = 0x02, |
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59 | atLowerBound = 0x03, |
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60 | superBasic = 0x04, |
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61 | isFixed = 0x05 |
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62 | }; |
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63 | // For Dual |
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64 | enum FakeBound { |
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65 | noFake = 0x00, |
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66 | bothFake = 0x01, |
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67 | upperFake = 0x02, |
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68 | lowerFake = 0x03 |
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69 | }; |
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70 | |
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71 | /**@name Constructors and destructor and copy */ |
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72 | //@{ |
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73 | /// Default constructor |
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74 | ClpSimplex ( ); |
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75 | |
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76 | /// Copy constructor. |
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77 | ClpSimplex(const ClpSimplex &); |
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78 | /// Copy constructor from model. |
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79 | ClpSimplex(const ClpModel &); |
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80 | /** Subproblem constructor. A subset of whole model is created from the |
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81 | row and column lists given. The new order is given by list order and |
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82 | duplicates are allowed. Name and integer information can be dropped |
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83 | */ |
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84 | ClpSimplex (const ClpModel * wholeModel, |
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85 | int numberRows, const int * whichRows, |
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86 | int numberColumns, const int * whichColumns, |
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87 | bool dropNames=true, bool dropIntegers=true); |
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88 | /** This constructor modifies original ClpSimplex and stores |
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89 | original stuff in created ClpSimplex. It is only to be used in |
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90 | conjunction with originalModel */ |
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91 | ClpSimplex (ClpSimplex * wholeModel, |
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92 | int numberColumns, const int * whichColumns); |
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93 | /** This copies back stuff from miniModel and then deletes miniModel. |
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94 | Only to be used with mini constructor */ |
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95 | void originalModel(ClpSimplex * miniModel); |
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96 | /// Assignment operator. This copies the data |
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97 | ClpSimplex & operator=(const ClpSimplex & rhs); |
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98 | /// Destructor |
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99 | ~ClpSimplex ( ); |
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100 | // Ones below are just ClpModel with some changes |
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101 | /** Loads a problem (the constraints on the |
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102 | rows are given by lower and upper bounds). If a pointer is 0 then the |
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103 | following values are the default: |
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104 | <ul> |
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105 | <li> <code>colub</code>: all columns have upper bound infinity |
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106 | <li> <code>collb</code>: all columns have lower bound 0 |
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107 | <li> <code>rowub</code>: all rows have upper bound infinity |
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108 | <li> <code>rowlb</code>: all rows have lower bound -infinity |
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109 | <li> <code>obj</code>: all variables have 0 objective coefficient |
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110 | </ul> |
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111 | */ |
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112 | void loadProblem ( const ClpMatrixBase& matrix, |
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113 | const double* collb, const double* colub, |
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114 | const double* obj, |
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115 | const double* rowlb, const double* rowub, |
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116 | const double * rowObjective=NULL); |
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117 | void loadProblem ( const CoinPackedMatrix& matrix, |
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118 | const double* collb, const double* colub, |
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119 | const double* obj, |
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120 | const double* rowlb, const double* rowub, |
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121 | const double * rowObjective=NULL); |
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122 | |
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123 | /** Just like the other loadProblem() method except that the matrix is |
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124 | given in a standard column major ordered format (without gaps). */ |
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125 | void loadProblem ( const int numcols, const int numrows, |
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126 | const CoinBigIndex* start, const int* index, |
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127 | const double* value, |
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128 | const double* collb, const double* colub, |
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129 | const double* obj, |
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130 | const double* rowlb, const double* rowub, |
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131 | const double * rowObjective=NULL); |
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132 | /// This one is for after presolve to save memory |
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133 | void loadProblem ( const int numcols, const int numrows, |
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134 | const CoinBigIndex* start, const int* index, |
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135 | const double* value,const int * length, |
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136 | const double* collb, const double* colub, |
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137 | const double* obj, |
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138 | const double* rowlb, const double* rowub, |
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139 | const double * rowObjective=NULL); |
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140 | /// Read an mps file from the given filename |
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141 | int readMps(const char *filename, |
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142 | bool keepNames=false, |
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143 | bool ignoreErrors = false); |
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144 | /** Borrow model. This is so we dont have to copy large amounts |
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145 | of data around. It assumes a derived class wants to overwrite |
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146 | an empty model with a real one - while it does an algorithm. |
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147 | This is same as ClpModel one, but sets scaling on etc. */ |
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148 | void borrowModel(ClpModel & otherModel); |
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149 | void borrowModel(ClpSimplex & otherModel); |
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150 | /// Pass in Event handler (cloned and deleted at end) |
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151 | void passInEventHandler(const ClpEventHandler * eventHandler); |
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152 | //@} |
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153 | |
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154 | /**@name Functions most useful to user */ |
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155 | //@{ |
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156 | /** General solve algorithm which can do presolve. |
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157 | See ClpSolve.hpp for options |
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158 | */ |
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159 | int initialSolve(ClpSolve & options); |
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160 | /// Default initial solve |
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161 | int initialSolve(); |
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162 | /// Dual initial solve |
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163 | int initialDualSolve(); |
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164 | /// Primal initial solve |
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165 | int initialPrimalSolve(); |
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166 | /** Dual algorithm - see ClpSimplexDual.hpp for method. |
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167 | ifValuesPass==2 just does values pass and then stops */ |
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168 | int dual(int ifValuesPass=0); |
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169 | /** Primal algorithm - see ClpSimplexPrimal.hpp for method. |
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170 | ifValuesPass==2 just does values pass and then stops */ |
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171 | int primal(int ifValuesPass=0); |
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172 | /** Solves quadratic problem using SLP - may be used as crash |
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173 | for other algorithms when number of iterations small. |
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174 | Also exits if all problematical variables are changing |
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175 | less than deltaTolerance |
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176 | */ |
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177 | int quadraticSLP(int numberPasses,double deltaTolerance); |
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178 | /// Solves quadratic using Dantzig's algorithm - primal |
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179 | int quadraticPrimal(int phase=2); |
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180 | /** Solves using barrier (assumes you have good cholesky factor code). |
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181 | Does crossover to simplex if asked*/ |
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182 | int barrier(bool crossover=true); |
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183 | /** Dual ranging. |
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184 | This computes increase/decrease in cost for each given variable and corresponding |
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185 | sequence numbers which would change basis. Sequence numbers are 0..numberColumns |
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186 | and numberColumns.. for artificials/slacks. |
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187 | For non-basic variables the sequence number will be that of the non-basic variables. |
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188 | The increase/decrease value is always >= 0.0 |
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189 | |
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190 | Up to user to provide correct length arrays. |
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191 | |
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192 | Returns non-zero if infeasible unbounded etc |
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193 | */ |
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194 | int dualRanging(int numberCheck,const int * which, |
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195 | double * costIncrease, int * sequenceIncrease, |
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196 | double * costDecrease, int * sequenceDecrease); |
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197 | /// Passes in factorization |
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198 | void setFactorization( ClpFactorization & factorization); |
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199 | /** Tightens primal bounds to make dual faster. Unless |
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200 | fixed, bounds are slightly looser than they could be. |
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201 | This is to make dual go faster and is probably not needed |
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202 | with a presolve. Returns non-zero if problem infeasible. |
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203 | |
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204 | Fudge for branch and bound - put bounds on columns of factor * |
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205 | largest value (at continuous) - should improve stability |
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206 | in branch and bound on infeasible branches (0.0 is off) |
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207 | */ |
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208 | int tightenPrimalBounds(double factor=0.0); |
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209 | /** Crash - at present just aimed at dual, returns |
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210 | -2 if dual preferred and crash basis created |
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211 | -1 if dual preferred and all slack basis preferred |
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212 | 0 if basis going in was not all slack |
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213 | 1 if primal preferred and all slack basis preferred |
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214 | 2 if primal preferred and crash basis created. |
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215 | |
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216 | if gap between bounds <="gap" variables can be flipped |
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217 | |
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218 | If "pivot" is |
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219 | 0 No pivoting (so will just be choice of algorithm) |
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220 | 1 Simple pivoting e.g. gub |
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221 | 2 Mini iterations |
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222 | */ |
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223 | int crash(double gap,int pivot); |
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224 | /// Sets row pivot choice algorithm in dual |
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225 | void setDualRowPivotAlgorithm(ClpDualRowPivot & choice); |
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226 | /// Sets column pivot choice algorithm in primal |
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227 | void setPrimalColumnPivotAlgorithm(ClpPrimalColumnPivot & choice); |
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228 | /** For strong branching. On input lower and upper are new bounds |
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229 | while on output they are change in objective function values |
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230 | (>1.0e50 infeasible). |
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231 | Return code is 0 if nothing interesting, -1 if infeasible both |
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232 | ways and +1 if infeasible one way (check values to see which one(s)) |
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233 | Solutions are filled in as well - even down, odd up - also |
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234 | status and number of iterations |
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235 | */ |
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236 | int strongBranching(int numberVariables,const int * variables, |
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237 | double * newLower, double * newUpper, |
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238 | double ** outputSolution, |
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239 | int * outputStatus, int * outputIterations, |
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240 | bool stopOnFirstInfeasible=true, |
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241 | bool alwaysFinish=false); |
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242 | //@} |
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243 | |
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244 | /**@name Needed for functionality of OsiSimplexInterface */ |
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245 | //@{ |
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246 | /** Pivot in a variable and out a variable. Returns 0 if okay, |
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247 | 1 if inaccuracy forced re-factorization, -1 if would be singular. |
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248 | Also updates primal/dual infeasibilities. |
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249 | Assumes sequenceIn_ and pivotRow_ set and also directionIn and Out. |
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250 | */ |
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251 | int pivot(); |
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252 | |
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253 | /** Pivot in a variable and choose an outgoing one. Assumes primal |
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254 | feasible - will not go through a bound. Returns step length in theta |
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255 | Returns ray in ray_ (or NULL if no pivot) |
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256 | Return codes as before but -1 means no acceptable pivot |
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257 | */ |
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258 | int primalPivotResult(); |
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259 | |
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260 | /** Pivot out a variable and choose an incoing one. Assumes dual |
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261 | feasible - will not go through a reduced cost. |
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262 | Returns step length in theta |
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263 | Returns ray in ray_ (or NULL if no pivot) |
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264 | Return codes as before but -1 means no acceptable pivot |
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265 | */ |
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266 | int dualPivotResult(); |
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267 | |
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268 | /** Common bits of coding for dual and primal. Return s0 if okay, |
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269 | 1 if bad matrix, 2 if very bad factorization |
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270 | */ |
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271 | int startup(int ifValuesPass); |
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272 | void finish(); |
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273 | |
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274 | /** Factorizes and returns true if optimal. Used by user */ |
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275 | bool statusOfProblem(); |
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276 | //@} |
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277 | |
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278 | /**@name most useful gets and sets */ |
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279 | //@{ |
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280 | /// If problem is primal feasible |
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281 | inline bool primalFeasible() const |
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282 | { return (numberPrimalInfeasibilities_==0);}; |
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283 | /// If problem is dual feasible |
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284 | inline bool dualFeasible() const |
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285 | { return (numberDualInfeasibilities_==0);}; |
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286 | /// factorization |
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287 | inline ClpFactorization * factorization() const |
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288 | { return factorization_;}; |
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289 | /// Sparsity on or off |
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290 | bool sparseFactorization() const; |
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291 | void setSparseFactorization(bool value); |
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292 | /// Factorization frequency |
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293 | int factorizationFrequency() const; |
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294 | void setFactorizationFrequency(int value); |
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295 | /// Dual bound |
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296 | inline double dualBound() const |
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297 | { return dualBound_;}; |
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298 | void setDualBound(double value); |
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299 | /// Infeasibility cost |
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300 | inline double infeasibilityCost() const |
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301 | { return infeasibilityCost_;}; |
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302 | void setInfeasibilityCost(double value); |
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303 | /** Amount of print out: |
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304 | 0 - none |
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305 | 1 - just final |
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306 | 2 - just factorizations |
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307 | 3 - as 2 plus a bit more |
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308 | 4 - verbose |
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309 | above that 8,16,32 etc just for selective debug |
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310 | */ |
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311 | /** Perturbation: |
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312 | 50 - switch on perturbation |
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313 | 100 - auto perturb if takes too long (1.0e-6 largest nonzero) |
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314 | 101 - we are perturbed |
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315 | 102 - don't try perturbing again |
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316 | default is 100 |
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317 | others are for playing |
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318 | */ |
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319 | inline int perturbation() const |
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320 | { return perturbation_;}; |
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321 | void setPerturbation(int value); |
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322 | /// Current (or last) algorithm |
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323 | inline int algorithm() const |
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324 | {return algorithm_; } ; |
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325 | /// Set algorithm |
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326 | inline void setAlgorithm(int value) |
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327 | {algorithm_=value; } ; |
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328 | /// Sum of dual infeasibilities |
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329 | inline double sumDualInfeasibilities() const |
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330 | { return sumDualInfeasibilities_;} ; |
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331 | inline void setSumDualInfeasibilities(double value) |
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332 | { sumDualInfeasibilities_=value;} ; |
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333 | /// Sum of relaxed dual infeasibilities |
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334 | inline double sumOfRelaxedDualInfeasibilities() const |
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335 | { return sumOfRelaxedDualInfeasibilities_;} ; |
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336 | inline void setSumOfRelaxedDualInfeasibilities(double value) |
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337 | { sumOfRelaxedDualInfeasibilities_=value;} ; |
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338 | /// Number of dual infeasibilities |
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339 | inline int numberDualInfeasibilities() const |
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340 | { return numberDualInfeasibilities_;} ; |
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341 | inline void setNumberDualInfeasibilities(int value) |
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342 | { numberDualInfeasibilities_=value;} ; |
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343 | /// Sum of primal infeasibilities |
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344 | inline double sumPrimalInfeasibilities() const |
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345 | { return sumPrimalInfeasibilities_;} ; |
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346 | inline void setSumPrimalInfeasibilities(double value) |
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347 | { sumPrimalInfeasibilities_=value;} ; |
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348 | /// Sum of relaxed primal infeasibilities |
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349 | inline double sumOfRelaxedPrimalInfeasibilities() const |
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350 | { return sumOfRelaxedPrimalInfeasibilities_;} ; |
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351 | inline void setSumOfRelaxedPrimalInfeasibilities(double value) |
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352 | { sumOfRelaxedPrimalInfeasibilities_=value;} ; |
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353 | /// Number of primal infeasibilities |
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354 | inline int numberPrimalInfeasibilities() const |
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355 | { return numberPrimalInfeasibilities_;} ; |
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356 | inline void setNumberPrimalInfeasibilities(int value) |
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357 | { numberPrimalInfeasibilities_=value;} ; |
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358 | /** Save model to file, returns 0 if success. This is designed for |
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359 | use outside algorithms so does not save iterating arrays etc. |
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360 | It does not save any messaging information. |
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361 | Does not save scaling values. |
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362 | It does not know about all types of virtual functions. |
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363 | */ |
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364 | int saveModel(const char * fileName); |
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365 | /** Restore model from file, returns 0 if success, |
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366 | deletes current model */ |
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367 | int restoreModel(const char * fileName); |
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368 | |
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369 | /** Just check solution (for external use) - sets sum of |
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370 | infeasibilities etc */ |
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371 | void checkSolution(); |
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372 | /// Useful row length arrays (0,1,2,3,4,5) |
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373 | inline CoinIndexedVector * rowArray(int index) const |
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374 | { return rowArray_[index];}; |
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375 | /// Useful column length arrays (0,1,2,3,4,5) |
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376 | inline CoinIndexedVector * columnArray(int index) const |
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377 | { return columnArray_[index];}; |
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378 | //@} |
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379 | |
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380 | /******************** End of most useful part **************/ |
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381 | /**@name Functions less likely to be useful to casual user */ |
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382 | //@{ |
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383 | /** Given an existing factorization computes and checks |
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384 | primal and dual solutions. Uses input arrays for variables at |
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385 | bounds. Returns feasibility states */ |
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386 | int getSolution ( const double * rowActivities, |
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387 | const double * columnActivities); |
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388 | /** Given an existing factorization computes and checks |
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389 | primal and dual solutions. Uses current problem arrays for |
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390 | bounds. Returns feasibility states */ |
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391 | int getSolution (); |
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392 | /** Constructs a non linear cost from list of non-linearities (columns only) |
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393 | First lower of each column is taken as real lower |
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394 | Last lower is taken as real upper and cost ignored |
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395 | |
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396 | Returns nonzero if bad data e.g. lowers not monotonic |
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397 | */ |
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398 | int createPiecewiseLinearCosts(const int * starts, |
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399 | const double * lower, const double * gradient); |
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400 | /** Return model - updates any scalars */ |
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401 | void returnModel(ClpSimplex & otherModel); |
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402 | /** Factorizes using current basis. |
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403 | solveType - 1 iterating, 0 initial, -1 external |
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404 | If 10 added then in primal values pass |
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405 | Return codes are as from ClpFactorization unless initial factorization |
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406 | when total number of singularities is returned |
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407 | */ |
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408 | int internalFactorize(int solveType); |
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409 | /// Save data |
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410 | ClpDataSave saveData() ; |
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411 | /// Restore data |
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412 | void restoreData(ClpDataSave saved); |
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413 | /// Clean up status |
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414 | void cleanStatus(); |
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415 | /// Factorizes using current basis. For external use |
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416 | int factorize(); |
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417 | /** Computes duals from scratch. If givenDjs then |
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418 | allows for nonzero basic djs */ |
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419 | void computeDuals(double * givenDjs); |
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420 | /// Computes primals from scratch |
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421 | void computePrimals ( const double * rowActivities, |
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422 | const double * columnActivities); |
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423 | /** Adds multiple of a column into an array */ |
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424 | void add(double * array, |
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425 | int column, double multiplier) const; |
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426 | /** |
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427 | Unpacks one column of the matrix into indexed array |
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428 | Uses sequenceIn_ |
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429 | Also applies scaling if needed |
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430 | */ |
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431 | void unpack(CoinIndexedVector * rowArray) const ; |
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432 | /** |
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433 | Unpacks one column of the matrix into indexed array |
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434 | Slack if sequence>= numberColumns |
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435 | Also applies scaling if needed |
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436 | */ |
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437 | void unpack(CoinIndexedVector * rowArray,int sequence) const; |
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438 | /** |
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439 | Unpacks one column of the matrix into indexed array |
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440 | ** as packed vector |
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441 | Uses sequenceIn_ |
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442 | Also applies scaling if needed |
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443 | */ |
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444 | void unpackPacked(CoinIndexedVector * rowArray) ; |
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445 | /** |
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446 | Unpacks one column of the matrix into indexed array |
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447 | ** as packed vector |
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448 | Slack if sequence>= numberColumns |
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449 | Also applies scaling if needed |
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450 | */ |
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451 | void unpackPacked(CoinIndexedVector * rowArray,int sequence); |
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452 | |
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453 | /** |
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454 | This does basis housekeeping and does values for in/out variables. |
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455 | Can also decide to re-factorize |
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456 | */ |
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457 | int housekeeping(double objectiveChange); |
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458 | /** This sets largest infeasibility and most infeasible and sum |
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459 | and number of infeasibilities (Primal) */ |
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460 | void checkPrimalSolution(const double * rowActivities=NULL, |
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461 | const double * columnActivies=NULL); |
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462 | /** This sets largest infeasibility and most infeasible and sum |
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463 | and number of infeasibilities (Dual) */ |
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464 | void checkDualSolution(); |
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465 | /** For advanced use. When doing iterative solves things can get |
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466 | nasty so on values pass if incoming solution has largest |
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467 | infeasibility < incomingInfeasibility throw out variables |
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468 | from basis until largest infeasibility < allowedInfeasibility |
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469 | or incoming largest infeasibility. |
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470 | If allowedInfeasibility>= incomingInfeasibility this is |
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471 | always possible altough you may end up with an all slack basis. |
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472 | |
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473 | Defaults are 1.0,10.0 |
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474 | */ |
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475 | void setValuesPassAction(float incomingInfeasibility, |
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476 | float allowedInfeasibility); |
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477 | //@} |
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478 | /**@name most useful gets and sets */ |
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479 | //@{ |
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480 | /// Worst column primal infeasibility |
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481 | inline double columnPrimalInfeasibility() const |
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482 | { return columnPrimalInfeasibility_;} ; |
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483 | /// Sequence of worst (-1 if feasible) |
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484 | inline int columnPrimalSequence() const |
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485 | { return columnPrimalSequence_;} ; |
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486 | /// Worst row primal infeasibility |
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487 | inline double rowPrimalInfeasibility() const |
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488 | { return rowPrimalInfeasibility_;} ; |
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489 | /// Sequence of worst (-1 if feasible) |
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490 | inline int rowPrimalSequence() const |
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491 | { return rowPrimalSequence_;} ; |
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492 | /** Worst column dual infeasibility (note - these may not be as meaningful |
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493 | if the problem is primal infeasible */ |
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494 | inline double columnDualInfeasibility() const |
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495 | { return columnDualInfeasibility_;} ; |
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496 | /// Sequence of worst (-1 if feasible) |
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497 | inline int columnDualSequence() const |
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498 | { return columnDualSequence_;} ; |
---|
499 | /// Worst row dual infeasibility |
---|
500 | inline double rowDualInfeasibility() const |
---|
501 | { return rowDualInfeasibility_;} ; |
---|
502 | /// Sequence of worst (-1 if feasible) |
---|
503 | inline int rowDualSequence() const |
---|
504 | { return rowDualSequence_;} ; |
---|
505 | /// Primal tolerance needed to make dual feasible (<largeTolerance) |
---|
506 | inline double primalToleranceToGetOptimal() const |
---|
507 | { return primalToleranceToGetOptimal_;} ; |
---|
508 | /// Remaining largest dual infeasibility |
---|
509 | inline double remainingDualInfeasibility() const |
---|
510 | { return remainingDualInfeasibility_;} ; |
---|
511 | /// Large bound value (for complementarity etc) |
---|
512 | inline double largeValue() const |
---|
513 | { return largeValue_;} ; |
---|
514 | void setLargeValue( double value) ; |
---|
515 | /// Largest error on Ax-b |
---|
516 | inline double largestPrimalError() const |
---|
517 | { return largestPrimalError_;} ; |
---|
518 | /// Largest error on basic duals |
---|
519 | inline double largestDualError() const |
---|
520 | { return largestDualError_;} ; |
---|
521 | /// Largest difference between input primal solution and computed |
---|
522 | inline double largestSolutionError() const |
---|
523 | { return largestSolutionError_;} ; |
---|
524 | /// Basic variables pivoting on which rows |
---|
525 | inline int * pivotVariable() const |
---|
526 | { return pivotVariable_;}; |
---|
527 | /// Scaling of objective |
---|
528 | inline double objectiveScale() const |
---|
529 | { return objectiveScale_;} ; |
---|
530 | inline void setObjectiveScale(double value) |
---|
531 | { objectiveScale_ = value;} ; |
---|
532 | /// Scaling of rhs and bounds |
---|
533 | inline double rhsScale() const |
---|
534 | { return rhsScale_;} ; |
---|
535 | inline void setRhsScale(double value) |
---|
536 | { rhsScale_ = value;} ; |
---|
537 | /// If automatic scaling on |
---|
538 | inline bool automaticScaling() const |
---|
539 | { return automaticScale_!=0;}; |
---|
540 | inline void setAutomaticScaling(bool onOff) |
---|
541 | { automaticScale_ = onOff ? 1: 0;}; |
---|
542 | /// Current dual tolerance |
---|
543 | inline double currentDualTolerance() const |
---|
544 | { return dualTolerance_;} ; |
---|
545 | inline void setCurrentDualTolerance(double value) |
---|
546 | { dualTolerance_ = value;} ; |
---|
547 | /// Current primal tolerance |
---|
548 | inline double currentPrimalTolerance() const |
---|
549 | { return primalTolerance_;} ; |
---|
550 | inline void setCurrentPrimalTolerance(double value) |
---|
551 | { primalTolerance_ = value;} ; |
---|
552 | /// How many iterative refinements to do |
---|
553 | inline int numberRefinements() const |
---|
554 | { return numberRefinements_;} ; |
---|
555 | void setNumberRefinements( int value) ; |
---|
556 | /// Alpha (pivot element) for use by classes e.g. steepestedge |
---|
557 | inline double alpha() const { return alpha_;}; |
---|
558 | inline void setAlpha(double value) { alpha_ = value;}; |
---|
559 | /// Reduced cost of last incoming for use by classes e.g. steepestedge |
---|
560 | inline double dualIn() const { return dualIn_;}; |
---|
561 | /// Pivot Row for use by classes e.g. steepestedge |
---|
562 | inline int pivotRow() const{ return pivotRow_;}; |
---|
563 | inline void setPivotRow(int value) { pivotRow_=value;}; |
---|
564 | /// value of incoming variable (in Dual) |
---|
565 | double valueIncomingDual() const; |
---|
566 | //@} |
---|
567 | |
---|
568 | protected: |
---|
569 | /**@name protected methods */ |
---|
570 | //@{ |
---|
571 | /** May change basis and then returns number changed. |
---|
572 | Computation of solutions may be overriden by given pi and solution |
---|
573 | */ |
---|
574 | int gutsOfSolution ( double * givenDuals, |
---|
575 | const double * givenPrimals, |
---|
576 | bool valuesPass=false); |
---|
577 | /// Does most of deletion (0 = all, 1 = most, 2 most + factorization) |
---|
578 | void gutsOfDelete(int type); |
---|
579 | /// Does most of copying |
---|
580 | void gutsOfCopy(const ClpSimplex & rhs); |
---|
581 | /** puts in format I like (rowLower,rowUpper) also see StandardMatrix |
---|
582 | 1 bit does rows, 2 bit does column bounds, 4 bit does objective(s). |
---|
583 | 8 bit does solution scaling in |
---|
584 | 16 bit does rowArray and columnArray indexed vectors |
---|
585 | and makes row copy if wanted, also sets columnStart_ etc |
---|
586 | Also creates scaling arrays if needed. It does scaling if needed. |
---|
587 | 16 also moves solutions etc in to work arrays |
---|
588 | On 16 returns false if problem "bad" i.e. matrix or bounds bad |
---|
589 | */ |
---|
590 | bool createRim(int what,bool makeRowCopy=false); |
---|
591 | /** releases above arrays and does solution scaling out. May also |
---|
592 | get rid of factorization data - |
---|
593 | 0 get rid of nothing, 1 get rid of arrays, 2 also factorization |
---|
594 | */ |
---|
595 | void deleteRim(int getRidOfFactorizationData=2); |
---|
596 | /// Sanity check on input rim data (after scaling) - returns true if okay |
---|
597 | bool sanityCheck(); |
---|
598 | //@} |
---|
599 | public: |
---|
600 | /**@name public methods */ |
---|
601 | //@{ |
---|
602 | /** Return row or column sections - not as much needed as it |
---|
603 | once was. These just map into single arrays */ |
---|
604 | inline double * solutionRegion(int section) |
---|
605 | { if (!section) return rowActivityWork_; else return columnActivityWork_;}; |
---|
606 | inline double * djRegion(int section) |
---|
607 | { if (!section) return rowReducedCost_; else return reducedCostWork_;}; |
---|
608 | inline double * lowerRegion(int section) |
---|
609 | { if (!section) return rowLowerWork_; else return columnLowerWork_;}; |
---|
610 | inline double * upperRegion(int section) |
---|
611 | { if (!section) return rowUpperWork_; else return columnUpperWork_;}; |
---|
612 | inline double * costRegion(int section) |
---|
613 | { if (!section) return rowObjectiveWork_; else return objectiveWork_;}; |
---|
614 | /// Return region as single array |
---|
615 | inline double * solutionRegion() |
---|
616 | { return solution_;}; |
---|
617 | inline double * djRegion() |
---|
618 | { return dj_;}; |
---|
619 | inline double * lowerRegion() |
---|
620 | { return lower_;}; |
---|
621 | inline double * upperRegion() |
---|
622 | { return upper_;}; |
---|
623 | inline double * costRegion() |
---|
624 | { return cost_;}; |
---|
625 | inline Status getStatus(int sequence) const |
---|
626 | {return static_cast<Status> (status_[sequence]&7);}; |
---|
627 | inline void setStatus(int sequence, Status status) |
---|
628 | { |
---|
629 | unsigned char & st_byte = status_[sequence]; |
---|
630 | st_byte &= ~7; |
---|
631 | st_byte |= status; |
---|
632 | }; |
---|
633 | /** Normally the first factorization does sparse coding because |
---|
634 | the factorization could be singular. This allows initial dense |
---|
635 | factorization when it is known to be safe |
---|
636 | */ |
---|
637 | void setInitialDenseFactorization(bool onOff); |
---|
638 | bool initialDenseFactorization() const; |
---|
639 | /** Return sequence In or Out */ |
---|
640 | inline int sequenceIn() const |
---|
641 | {return sequenceIn_;}; |
---|
642 | inline int sequenceOut() const |
---|
643 | {return sequenceOut_;}; |
---|
644 | /** Set sequenceIn or Out */ |
---|
645 | inline void setSequenceIn(int sequence) |
---|
646 | { sequenceIn_=sequence;}; |
---|
647 | inline void setSequenceOut(int sequence) |
---|
648 | { sequenceOut_=sequence;}; |
---|
649 | /** Return direction In or Out */ |
---|
650 | inline int directionIn() const |
---|
651 | {return directionIn_;}; |
---|
652 | inline int directionOut() const |
---|
653 | {return directionOut_;}; |
---|
654 | /** Set directionIn or Out */ |
---|
655 | inline void setDirectionIn(int direction) |
---|
656 | { directionIn_=direction;}; |
---|
657 | inline void setDirectionOut(int direction) |
---|
658 | { directionOut_=direction;}; |
---|
659 | /// Value of Out variable |
---|
660 | inline double valueOut() const |
---|
661 | { return valueOut_;}; |
---|
662 | /// Returns 1 if sequence indicates column |
---|
663 | inline int isColumn(int sequence) const |
---|
664 | { return sequence<numberColumns_ ? 1 : 0;}; |
---|
665 | /// Returns sequence number within section |
---|
666 | inline int sequenceWithin(int sequence) const |
---|
667 | { return sequence<numberColumns_ ? sequence : sequence-numberColumns_;}; |
---|
668 | /// Return row or column values |
---|
669 | inline double solution(int sequence) |
---|
670 | { return solution_[sequence];}; |
---|
671 | /// Return address of row or column values |
---|
672 | inline double & solutionAddress(int sequence) |
---|
673 | { return solution_[sequence];}; |
---|
674 | inline double reducedCost(int sequence) |
---|
675 | { return dj_[sequence];}; |
---|
676 | inline double & reducedCostAddress(int sequence) |
---|
677 | { return dj_[sequence];}; |
---|
678 | inline double lower(int sequence) |
---|
679 | { return lower_[sequence];}; |
---|
680 | /// Return address of row or column lower bound |
---|
681 | inline double & lowerAddress(int sequence) |
---|
682 | { return lower_[sequence];}; |
---|
683 | inline double upper(int sequence) |
---|
684 | { return upper_[sequence];}; |
---|
685 | /// Return address of row or column upper bound |
---|
686 | inline double & upperAddress(int sequence) |
---|
687 | { return upper_[sequence];}; |
---|
688 | inline double cost(int sequence) |
---|
689 | { return cost_[sequence];}; |
---|
690 | /// Return address of row or column cost |
---|
691 | inline double & costAddress(int sequence) |
---|
692 | { return cost_[sequence];}; |
---|
693 | /// Return original lower bound |
---|
694 | inline double originalLower(int iSequence) const |
---|
695 | { if (iSequence<numberColumns_) return columnLower_[iSequence]; else |
---|
696 | return rowLower_[iSequence-numberColumns_];}; |
---|
697 | /// Return original lower bound |
---|
698 | inline double originalUpper(int iSequence) const |
---|
699 | { if (iSequence<numberColumns_) return columnUpper_[iSequence]; else |
---|
700 | return rowUpper_[iSequence-numberColumns_];}; |
---|
701 | /// Theta (pivot change) |
---|
702 | inline double theta() const |
---|
703 | { return theta_;}; |
---|
704 | /// Return pointer to details of costs |
---|
705 | inline ClpNonLinearCost * nonLinearCost() const |
---|
706 | { return nonLinearCost_;}; |
---|
707 | //@} |
---|
708 | /**@name status methods */ |
---|
709 | //@{ |
---|
710 | inline void setFakeBound(int sequence, FakeBound fakeBound) |
---|
711 | { |
---|
712 | unsigned char & st_byte = status_[sequence]; |
---|
713 | st_byte &= ~24; |
---|
714 | st_byte |= fakeBound<<3; |
---|
715 | }; |
---|
716 | inline FakeBound getFakeBound(int sequence) const |
---|
717 | {return static_cast<FakeBound> ((status_[sequence]>>3)&3);}; |
---|
718 | inline void setRowStatus(int sequence, Status status) |
---|
719 | { |
---|
720 | unsigned char & st_byte = status_[sequence+numberColumns_]; |
---|
721 | st_byte &= ~7; |
---|
722 | st_byte |= status; |
---|
723 | }; |
---|
724 | inline Status getRowStatus(int sequence) const |
---|
725 | {return static_cast<Status> (status_[sequence+numberColumns_]&7);}; |
---|
726 | inline void setColumnStatus(int sequence, Status status) |
---|
727 | { |
---|
728 | unsigned char & st_byte = status_[sequence]; |
---|
729 | st_byte &= ~7; |
---|
730 | st_byte |= status; |
---|
731 | }; |
---|
732 | inline Status getColumnStatus(int sequence) const |
---|
733 | {return static_cast<Status> (status_[sequence]&7);}; |
---|
734 | inline void setPivoted( int sequence) |
---|
735 | { status_[sequence] |= 32;}; |
---|
736 | inline void clearPivoted( int sequence) |
---|
737 | { status_[sequence] &= ~32; }; |
---|
738 | inline bool pivoted(int sequence) const |
---|
739 | {return (((status_[sequence]>>5)&1)!=0);}; |
---|
740 | /// To flag a variable (not inline to allow for column generation) |
---|
741 | void setFlagged( int sequence); |
---|
742 | inline void clearFlagged( int sequence) |
---|
743 | { |
---|
744 | status_[sequence] &= ~64; |
---|
745 | }; |
---|
746 | inline bool flagged(int sequence) const |
---|
747 | {return ((status_[sequence]&64)!=0);}; |
---|
748 | /// To say row active in primal pivot row choice |
---|
749 | inline void setActive( int iRow) |
---|
750 | { |
---|
751 | status_[iRow] |= 128; |
---|
752 | }; |
---|
753 | inline void clearActive( int iRow) |
---|
754 | { |
---|
755 | status_[iRow] &= ~128; |
---|
756 | }; |
---|
757 | inline bool active(int iRow) const |
---|
758 | {return ((status_[iRow]&128)!=0);}; |
---|
759 | /** Set up status array (can be used by OsiClp). |
---|
760 | Also can be used to set up all slack basis */ |
---|
761 | void createStatus() ; |
---|
762 | inline void allSlackBasis() |
---|
763 | { createStatus();}; |
---|
764 | |
---|
765 | /// So we know when to be cautious |
---|
766 | inline int lastBadIteration() const |
---|
767 | {return lastBadIteration_;}; |
---|
768 | /// Progress flag - at present 0 bit says artificials out |
---|
769 | inline int progressFlag() const |
---|
770 | {return progressFlag_;}; |
---|
771 | /// Force re-factorization early |
---|
772 | inline void forceFactorization(int value) |
---|
773 | { forceFactorization_ = value;}; |
---|
774 | /// Raw objective value (so always minimize in primal) |
---|
775 | inline double rawObjectiveValue() const |
---|
776 | { return objectiveValue_;}; |
---|
777 | /** Number of extra rows. These are ones which will be dynamically created |
---|
778 | each iteration. This is for GUB but may have other uses. |
---|
779 | */ |
---|
780 | inline int numberExtraRows() const |
---|
781 | { return numberExtraRows_;}; |
---|
782 | /** Maximum number of basic variables - can be more than number of rows if GUB |
---|
783 | */ |
---|
784 | inline int maximumBasic() const |
---|
785 | { return maximumBasic_;}; |
---|
786 | /** For advanced options |
---|
787 | 1 - Don't keep changing infeasibility weight |
---|
788 | 2 - Keep nonLinearCost round solves |
---|
789 | 4 - Force outgoing variables to exact bound (primal) |
---|
790 | 8 - Safe to use dense initial factorization |
---|
791 | 16 -Just use basic variables for operation if column generation |
---|
792 | 32 -Clean up with primal before strong branching |
---|
793 | */ |
---|
794 | inline unsigned int specialOptions() const |
---|
795 | { return specialOptions_;}; |
---|
796 | inline void setSpecialOptions(unsigned int value) |
---|
797 | { specialOptions_=value;}; |
---|
798 | //@} |
---|
799 | |
---|
800 | ////////////////// data ////////////////// |
---|
801 | protected: |
---|
802 | |
---|
803 | /**@name data. Many arrays have a row part and a column part. |
---|
804 | There is a single array with both - columns then rows and |
---|
805 | then normally two arrays pointing to rows and columns. The |
---|
806 | single array is the owner of memory |
---|
807 | */ |
---|
808 | //@{ |
---|
809 | /// Worst column primal infeasibility |
---|
810 | double columnPrimalInfeasibility_; |
---|
811 | /// Worst row primal infeasibility |
---|
812 | double rowPrimalInfeasibility_; |
---|
813 | /// Sequence of worst (-1 if feasible) |
---|
814 | int columnPrimalSequence_; |
---|
815 | /// Sequence of worst (-1 if feasible) |
---|
816 | int rowPrimalSequence_; |
---|
817 | /// Worst column dual infeasibility |
---|
818 | double columnDualInfeasibility_; |
---|
819 | /// Worst row dual infeasibility |
---|
820 | double rowDualInfeasibility_; |
---|
821 | /// Sequence of worst (-1 if feasible) |
---|
822 | int columnDualSequence_; |
---|
823 | /// Sequence of worst (-1 if feasible) |
---|
824 | int rowDualSequence_; |
---|
825 | /// Primal tolerance needed to make dual feasible (<largeTolerance) |
---|
826 | double primalToleranceToGetOptimal_; |
---|
827 | /// Remaining largest dual infeasibility |
---|
828 | double remainingDualInfeasibility_; |
---|
829 | /// Large bound value (for complementarity etc) |
---|
830 | double largeValue_; |
---|
831 | /// Scaling of objective |
---|
832 | double objectiveScale_; |
---|
833 | /// Scaling of rhs and bounds |
---|
834 | double rhsScale_; |
---|
835 | /// Largest error on Ax-b |
---|
836 | double largestPrimalError_; |
---|
837 | /// Largest error on basic duals |
---|
838 | double largestDualError_; |
---|
839 | /// Largest difference between input primal solution and computed |
---|
840 | double largestSolutionError_; |
---|
841 | /// Dual bound |
---|
842 | double dualBound_; |
---|
843 | /// Alpha (pivot element) |
---|
844 | double alpha_; |
---|
845 | /// Theta (pivot change) |
---|
846 | double theta_; |
---|
847 | /// Lower Bound on In variable |
---|
848 | double lowerIn_; |
---|
849 | /// Value of In variable |
---|
850 | double valueIn_; |
---|
851 | /// Upper Bound on In variable |
---|
852 | double upperIn_; |
---|
853 | /// Reduced cost of In variable |
---|
854 | double dualIn_; |
---|
855 | /// Lower Bound on Out variable |
---|
856 | double lowerOut_; |
---|
857 | /// Value of Out variable |
---|
858 | double valueOut_; |
---|
859 | /// Upper Bound on Out variable |
---|
860 | double upperOut_; |
---|
861 | /// Infeasibility (dual) or ? (primal) of Out variable |
---|
862 | double dualOut_; |
---|
863 | /// Current dual tolerance for algorithm |
---|
864 | double dualTolerance_; |
---|
865 | /// Current primal tolerance for algorithm |
---|
866 | double primalTolerance_; |
---|
867 | /// Sum of dual infeasibilities |
---|
868 | double sumDualInfeasibilities_; |
---|
869 | /// Sum of primal infeasibilities |
---|
870 | double sumPrimalInfeasibilities_; |
---|
871 | /// Weight assigned to being infeasible in primal |
---|
872 | double infeasibilityCost_; |
---|
873 | /// Sum of Dual infeasibilities using tolerance based on error in duals |
---|
874 | double sumOfRelaxedDualInfeasibilities_; |
---|
875 | /// Sum of Primal infeasibilities using tolerance based on error in primals |
---|
876 | double sumOfRelaxedPrimalInfeasibilities_; |
---|
877 | /// Working copy of lower bounds (Owner of arrays below) |
---|
878 | double * lower_; |
---|
879 | /// Row lower bounds - working copy |
---|
880 | double * rowLowerWork_; |
---|
881 | /// Column lower bounds - working copy |
---|
882 | double * columnLowerWork_; |
---|
883 | /// Working copy of upper bounds (Owner of arrays below) |
---|
884 | double * upper_; |
---|
885 | /// Row upper bounds - working copy |
---|
886 | double * rowUpperWork_; |
---|
887 | /// Column upper bounds - working copy |
---|
888 | double * columnUpperWork_; |
---|
889 | /// Working copy of objective (Owner of arrays below) |
---|
890 | double * cost_; |
---|
891 | /// Row objective - working copy |
---|
892 | double * rowObjectiveWork_; |
---|
893 | /// Column objective - working copy |
---|
894 | double * objectiveWork_; |
---|
895 | /// Useful row length arrays |
---|
896 | CoinIndexedVector * rowArray_[6]; |
---|
897 | /// Useful column length arrays |
---|
898 | CoinIndexedVector * columnArray_[6]; |
---|
899 | /// Sequence of In variable |
---|
900 | int sequenceIn_; |
---|
901 | /// Direction of In, 1 going up, -1 going down, 0 not a clude |
---|
902 | int directionIn_; |
---|
903 | /// Sequence of Out variable |
---|
904 | int sequenceOut_; |
---|
905 | /// Direction of Out, 1 to upper bound, -1 to lower bound, 0 - superbasic |
---|
906 | int directionOut_; |
---|
907 | /// Pivot Row |
---|
908 | int pivotRow_; |
---|
909 | /// Last good iteration (immediately after a re-factorization) |
---|
910 | int lastGoodIteration_; |
---|
911 | /// Working copy of reduced costs (Owner of arrays below) |
---|
912 | double * dj_; |
---|
913 | /// Reduced costs of slacks not same as duals (or - duals) |
---|
914 | double * rowReducedCost_; |
---|
915 | /// Possible scaled reduced costs |
---|
916 | double * reducedCostWork_; |
---|
917 | /// Working copy of primal solution (Owner of arrays below) |
---|
918 | double * solution_; |
---|
919 | /// Row activities - working copy |
---|
920 | double * rowActivityWork_; |
---|
921 | /// Column activities - working copy |
---|
922 | double * columnActivityWork_; |
---|
923 | /// Number of dual infeasibilities |
---|
924 | int numberDualInfeasibilities_; |
---|
925 | /// Number of dual infeasibilities (without free) |
---|
926 | int numberDualInfeasibilitiesWithoutFree_; |
---|
927 | /// Number of primal infeasibilities |
---|
928 | int numberPrimalInfeasibilities_; |
---|
929 | /// How many iterative refinements to do |
---|
930 | int numberRefinements_; |
---|
931 | /// dual row pivot choice |
---|
932 | ClpDualRowPivot * dualRowPivot_; |
---|
933 | /// primal column pivot choice |
---|
934 | ClpPrimalColumnPivot * primalColumnPivot_; |
---|
935 | /// Basic variables pivoting on which rows |
---|
936 | int * pivotVariable_; |
---|
937 | /// factorization |
---|
938 | ClpFactorization * factorization_; |
---|
939 | /// Saved version of solution |
---|
940 | double * savedSolution_; |
---|
941 | /// Number of times code has tentatively thought optimal |
---|
942 | int numberTimesOptimal_; |
---|
943 | /// If change has been made (first attempt at stopping looping) |
---|
944 | int changeMade_; |
---|
945 | /// Algorithm >0 == Primal, <0 == Dual |
---|
946 | int algorithm_; |
---|
947 | /** Now for some reliability aids |
---|
948 | This forces re-factorization early */ |
---|
949 | int forceFactorization_; |
---|
950 | /** Perturbation: |
---|
951 | -50 to +50 - perturb by this power of ten (-6 sounds good) |
---|
952 | 100 - auto perturb if takes too long (1.0e-6 largest nonzero) |
---|
953 | 101 - we are perturbed |
---|
954 | 102 - don't try perturbing again |
---|
955 | default is 100 |
---|
956 | */ |
---|
957 | int perturbation_; |
---|
958 | /// Saved status regions |
---|
959 | unsigned char * saveStatus_; |
---|
960 | /** Very wasteful way of dealing with infeasibilities in primal. |
---|
961 | However it will allow non-linearities and use of dual |
---|
962 | analysis. If it doesn't work it can easily be replaced. |
---|
963 | */ |
---|
964 | ClpNonLinearCost * nonLinearCost_; |
---|
965 | /** For advanced options |
---|
966 | 1 - Don't keep changing infeasibility weight |
---|
967 | 2 - Keep nonLinearCost round solves |
---|
968 | 4 - Force outgoing variables to exact bound (primal) |
---|
969 | 8 - Safe to use dense initial factorization |
---|
970 | 16 -Just use basic variables for operation |
---|
971 | */ |
---|
972 | unsigned int specialOptions_; |
---|
973 | /// So we know when to be cautious |
---|
974 | int lastBadIteration_; |
---|
975 | /// Can be used for count of fake bounds (dual) or fake costs (primal) |
---|
976 | int numberFake_; |
---|
977 | /// Progress flag - at present 0 bit says artificials out, 1 free in |
---|
978 | int progressFlag_; |
---|
979 | /// First free/super-basic variable (-1 if none) |
---|
980 | int firstFree_; |
---|
981 | /** Number of extra rows. These are ones which will be dynamically created |
---|
982 | each iteration. This is for GUB but may have other uses. |
---|
983 | */ |
---|
984 | int numberExtraRows_; |
---|
985 | /** Maximum number of basic variables - can be more than number of rows if GUB |
---|
986 | */ |
---|
987 | int maximumBasic_; |
---|
988 | /** For advanced use. When doing iterative solves things can get |
---|
989 | nasty so on values pass if incoming solution has largest |
---|
990 | infeasibility < incomingInfeasibility throw out variables |
---|
991 | from basis until largest infeasibility < allowedInfeasibility. |
---|
992 | if allowedInfeasibility>= incomingInfeasibility this is |
---|
993 | always possible altough you may end up with an all slack basis. |
---|
994 | |
---|
995 | Defaults are 1.0,10.0 |
---|
996 | */ |
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997 | float incomingInfeasibility_; |
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998 | float allowedInfeasibility_; |
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999 | /// Automatic scaling of objective and rhs and bounds |
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1000 | int automaticScale_; |
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1001 | /// For dealing with all issues of cycling etc |
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1002 | ClpSimplexProgress * progress_; |
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1003 | //@} |
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1004 | }; |
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1005 | //############################################################################# |
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1006 | /** A function that tests the methods in the ClpSimplex class. The |
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1007 | only reason for it not to be a member method is that this way it doesn't |
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1008 | have to be compiled into the library. And that's a gain, because the |
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1009 | library should be compiled with optimization on, but this method should be |
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1010 | compiled with debugging. |
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1011 | |
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1012 | It also does some testing of ClpFactorization class |
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1013 | */ |
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1014 | void |
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1015 | ClpSimplexUnitTest(const std::string & mpsDir, |
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1016 | const std::string & netlibDir); |
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1017 | |
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1018 | |
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1019 | /// For saving extra information to see if looping. |
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1020 | class ClpSimplexProgress { |
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1021 | |
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1022 | public: |
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1023 | |
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1024 | |
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1025 | /**@name Constructors and destructor and copy */ |
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1026 | //@{ |
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1027 | /// Default constructor |
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1028 | ClpSimplexProgress ( ); |
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1029 | |
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1030 | /// Constructor from model |
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1031 | ClpSimplexProgress ( ClpSimplex * model ); |
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1032 | |
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1033 | /// Copy constructor. |
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1034 | ClpSimplexProgress(const ClpSimplexProgress &); |
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1035 | |
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1036 | /// Assignment operator. This copies the data |
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1037 | ClpSimplexProgress & operator=(const ClpSimplexProgress & rhs); |
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1038 | /// Destructor |
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1039 | ~ClpSimplexProgress ( ); |
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1040 | //@} |
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1041 | |
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1042 | /**@name Check progress */ |
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1043 | //@{ |
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1044 | /** Returns -1 if okay, -n+1 (n number of times bad) if bad but action taken, |
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1045 | >=0 if give up and use as problem status |
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1046 | */ |
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1047 | int looping ( ); |
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1048 | /// Start check at beginning of whileIterating |
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1049 | void startCheck(); |
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1050 | /// Returns cycle length in whileIterating |
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1051 | int cycle(int in, int out,int wayIn,int wayOut); |
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1052 | |
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1053 | /// Returns previous objective (if -1) - current if (0) |
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1054 | double lastObjective(int back=1) const; |
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1055 | /// Modify objective e.g. if dual infeasible in dual |
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1056 | void modifyObjective(double value); |
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1057 | /// Returns previous iteration number (if -1) - current if (0) |
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1058 | int lastIterationNumber(int back=1) const; |
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1059 | /// Odd state |
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1060 | inline void newOddState() |
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1061 | { oddState_= - oddState_-1;}; |
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1062 | inline void endOddState() |
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1063 | { oddState_=abs(oddState_);}; |
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1064 | inline void clearOddState() |
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1065 | { oddState_=0;}; |
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1066 | inline int oddState() const |
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1067 | { return oddState_;}; |
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1068 | /// number of bad times |
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1069 | inline int badTimes() const |
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1070 | { return numberBadTimes_;}; |
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1071 | inline void clearBadTimes() |
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1072 | { numberBadTimes_=0;}; |
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1073 | |
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1074 | //@} |
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1075 | /**@name Data */ |
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1076 | #define CLP_PROGRESS 5 |
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1077 | //@{ |
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1078 | /// Objective values |
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1079 | double objective_[CLP_PROGRESS]; |
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1080 | /// Sum of infeasibilities for algorithm |
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1081 | double infeasibility_[CLP_PROGRESS]; |
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1082 | #define CLP_CYCLE 12 |
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1083 | /// For cycle checking |
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1084 | //double obj_[CLP_CYCLE]; |
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1085 | int in_[CLP_CYCLE]; |
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1086 | int out_[CLP_CYCLE]; |
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1087 | char way_[CLP_CYCLE]; |
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1088 | /// Pointer back to model so we can get information |
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1089 | ClpSimplex * model_; |
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1090 | /// Number of infeasibilities |
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1091 | int numberInfeasibilities_[CLP_PROGRESS]; |
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1092 | /// Iteration number at which occurred |
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1093 | int iterationNumber_[CLP_PROGRESS]; |
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1094 | /// Number of times checked (so won't stop too early) |
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1095 | int numberTimes_; |
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1096 | /// Number of times it looked like loop |
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1097 | int numberBadTimes_; |
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1098 | /// If things are in an odd state |
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1099 | int oddState_; |
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1100 | //@} |
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1101 | }; |
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1102 | #endif |
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