1 | // Copyright (C) 2003, 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 Tomlin (with some help from John Forrest) |
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8 | |
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9 | */ |
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10 | #ifndef ClpInterior_H |
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11 | #define ClpInterior_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 ClpInteriorProgress; |
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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 ClpInteriorDual.hpp and at top of ClpInteriorDual.cpp |
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36 | for primal see ClpInteriorPrimal.hpp and at top of ClpInteriorPrimal.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 ClpInteriorProgress |
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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 ClpInterior : public ClpModel { |
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47 | friend void ClpInteriorUnitTest(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 | |
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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 | ClpInterior ( ); |
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75 | |
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76 | /// Copy constructor. |
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77 | ClpInterior(const ClpInterior &); |
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78 | /// Copy constructor from model. |
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79 | ClpInterior(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 | ClpInterior (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 | /// Assignment operator. This copies the data |
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89 | ClpInterior & operator=(const ClpInterior & rhs); |
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90 | /// Destructor |
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91 | ~ClpInterior ( ); |
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92 | // Ones below are just ClpModel with some changes |
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93 | /** Loads a problem (the constraints on the |
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94 | rows are given by lower and upper bounds). If a pointer is 0 then the |
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95 | following values are the default: |
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96 | <ul> |
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97 | <li> <code>colub</code>: all columns have upper bound infinity |
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98 | <li> <code>collb</code>: all columns have lower bound 0 |
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99 | <li> <code>rowub</code>: all rows have upper bound infinity |
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100 | <li> <code>rowlb</code>: all rows have lower bound -infinity |
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101 | <li> <code>obj</code>: all variables have 0 objective coefficient |
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102 | </ul> |
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103 | */ |
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104 | void loadProblem ( const ClpMatrixBase& matrix, |
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105 | const double* collb, const double* colub, |
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106 | const double* obj, |
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107 | const double* rowlb, const double* rowub, |
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108 | const double * rowObjective=NULL); |
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109 | void loadProblem ( const CoinPackedMatrix& matrix, |
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110 | const double* collb, const double* colub, |
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111 | const double* obj, |
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112 | const double* rowlb, const double* rowub, |
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113 | const double * rowObjective=NULL); |
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114 | |
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115 | /** Just like the other loadProblem() method except that the matrix is |
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116 | given in a standard column major ordered format (without gaps). */ |
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117 | void loadProblem ( const int numcols, const int numrows, |
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118 | const CoinBigIndex* start, const int* index, |
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119 | const double* value, |
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120 | const double* collb, const double* colub, |
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121 | const double* obj, |
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122 | const double* rowlb, const double* rowub, |
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123 | const double * rowObjective=NULL); |
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124 | /// This one is for after presolve to save memory |
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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,const int * length, |
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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 | /// Read an mps file from the given filename |
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133 | int readMps(const char *filename, |
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134 | bool keepNames=false, |
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135 | bool ignoreErrors = false); |
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136 | /** Borrow model. This is so we dont have to copy large amounts |
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137 | of data around. It assumes a derived class wants to overwrite |
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138 | an empty model with a real one - while it does an algorithm. |
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139 | This is same as ClpModel one, but sets scaling on etc. */ |
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140 | void borrowModel(ClpModel & otherModel); |
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141 | //@} |
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142 | |
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143 | /**@name Functions most useful to user */ |
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144 | //@{ |
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145 | /** General solve algorithm which can do presolve. |
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146 | See ClpSolve.hpp for options |
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147 | */ |
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148 | int initialSolve(ClpSolve & options); |
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149 | /** Dual algorithm - see ClpInteriorDual.hpp for method */ |
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150 | int dual(int ifValuesPass=0); |
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151 | /** Primal algorithm - see ClpInteriorPrimal.hpp for method */ |
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152 | int primal(int ifValuesPass=0); |
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153 | /** Solves quadratic problem using SLP - may be used as crash |
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154 | for other algorithms when number of iterations small. |
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155 | Also exits if all problematical variables are changing |
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156 | less than deltaTolerance |
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157 | */ |
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158 | int quadraticSLP(int numberPasses,double deltaTolerance); |
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159 | /// Solves quadratic using Dantzig's algorithm - primal |
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160 | int quadraticPrimal(int phase=2); |
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161 | /// Passes in factorization |
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162 | void setFactorization( ClpFactorization & factorization); |
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163 | /// Sets or unsets scaling, 0 -off, 1 equilibrium, 2 geometric, 3, auto, 4 dynamic(later) |
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164 | void scaling(int mode=1); |
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165 | /// Gets scalingFlag |
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166 | inline int scalingFlag() const {return scalingFlag_;}; |
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167 | /** Tightens primal bounds to make dual faster. Unless |
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168 | fixed, bounds are slightly looser than they could be. |
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169 | This is to make dual go faster and is probably not needed |
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170 | with a presolve. Returns non-zero if problem infeasible. |
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171 | |
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172 | Fudge for branch and bound - put bounds on columns of factor * |
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173 | largest value (at continuous) - should improve stability |
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174 | in branch and bound on infeasible branches (0.0 is off) |
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175 | */ |
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176 | int tightenPrimalBounds(double factor=0.0); |
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177 | /** Crash - at present just aimed at dual, returns |
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178 | -2 if dual preferred and crash basis created |
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179 | -1 if dual preferred and all slack basis preferred |
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180 | 0 if basis going in was not all slack |
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181 | 1 if primal preferred and all slack basis preferred |
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182 | 2 if primal preferred and crash basis created. |
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183 | |
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184 | if gap between bounds <="gap" variables can be flipped |
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185 | |
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186 | If "pivot" is |
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187 | 0 No pivoting (so will just be choice of algorithm) |
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188 | 1 Simple pivoting e.g. gub |
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189 | 2 Mini iterations |
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190 | */ |
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191 | int crash(double gap,int pivot); |
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192 | /// Sets row pivot choice algorithm in dual |
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193 | void setDualRowPivotAlgorithm(ClpDualRowPivot & choice); |
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194 | /// Sets column pivot choice algorithm in primal |
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195 | void setPrimalColumnPivotAlgorithm(ClpPrimalColumnPivot & choice); |
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196 | /** For strong branching. On input lower and upper are new bounds |
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197 | while on output they are change in objective function values |
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198 | (>1.0e50 infeasible). |
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199 | Return code is 0 if nothing interesting, -1 if infeasible both |
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200 | ways and +1 if infeasible one way (check values to see which one(s)) |
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201 | Solutions are filled in as well - even down, odd up - also |
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202 | status and number of iterations |
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203 | */ |
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204 | int strongBranching(int numberVariables,const int * variables, |
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205 | double * newLower, double * newUpper, |
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206 | double ** outputSolution, |
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207 | int * outputStatus, int * outputIterations, |
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208 | bool stopOnFirstInfeasible=true, |
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209 | bool alwaysFinish=false); |
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210 | //@} |
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211 | |
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212 | /**@name Needed for functionality of OsiSimplexInterface */ |
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213 | //@{ |
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214 | /** Pivot in a variable and out a variable. Returns 0 if okay, |
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215 | 1 if inaccuracy forced re-factorization, -1 if would be singular. |
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216 | Also updates primal/dual infeasibilities. |
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217 | Assumes sequenceIn_ and pivotRow_ set and also directionIn and Out. |
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218 | */ |
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219 | int pivot(); |
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220 | |
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221 | /** Pivot in a variable and choose an outgoing one. Assumes primal |
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222 | feasible - will not go through a bound. Returns step length in theta |
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223 | Returns ray in ray_ (or NULL if no pivot) |
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224 | Return codes as before but -1 means no acceptable pivot |
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225 | */ |
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226 | int primalPivotResult(); |
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227 | |
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228 | /** Pivot out a variable and choose an incoing one. Assumes dual |
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229 | feasible - will not go through a reduced cost. |
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230 | Returns step length in theta |
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231 | Returns ray in ray_ (or NULL if no pivot) |
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232 | Return codes as before but -1 means no acceptable pivot |
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233 | */ |
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234 | int dualPivotResult(); |
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235 | |
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236 | /** Common bits of coding for dual and primal. Return s0 if okay, |
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237 | 1 if bad matrix, 2 if very bad factorization |
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238 | */ |
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239 | int startup(int ifValuesPass); |
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240 | void finish(); |
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241 | |
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242 | /** Factorizes and returns true if optimal. Used by user */ |
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243 | bool statusOfProblem(); |
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244 | //@} |
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245 | |
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246 | /**@name most useful gets and sets */ |
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247 | //@{ |
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248 | /// If problem is primal feasible |
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249 | inline bool primalFeasible() const |
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250 | { return (numberPrimalInfeasibilities_==0);}; |
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251 | /// If problem is dual feasible |
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252 | inline bool dualFeasible() const |
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253 | { return (numberDualInfeasibilities_==0);}; |
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254 | /// factorization |
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255 | inline ClpFactorization * factorization() const |
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256 | { return factorization_;}; |
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257 | /// Sparsity on or off |
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258 | bool sparseFactorization() const; |
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259 | void setSparseFactorization(bool value); |
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260 | /// Factorization frequency |
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261 | int factorizationFrequency() const; |
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262 | void setFactorizationFrequency(int value); |
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263 | /// Dual bound |
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264 | inline double dualBound() const |
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265 | { return dualBound_;}; |
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266 | void setDualBound(double value); |
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267 | /// Infeasibility cost |
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268 | inline double infeasibilityCost() const |
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269 | { return infeasibilityCost_;}; |
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270 | void setInfeasibilityCost(double value); |
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271 | /** Amount of print out: |
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272 | 0 - none |
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273 | 1 - just final |
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274 | 2 - just factorizations |
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275 | 3 - as 2 plus a bit more |
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276 | 4 - verbose |
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277 | above that 8,16,32 etc just for selective debug |
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278 | */ |
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279 | /** Perturbation: |
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280 | -50 to +50 - perturb by this power of ten (-6 sounds good) |
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281 | 100 - auto perturb if takes too long (1.0e-6 largest nonzero) |
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282 | 101 - we are perturbed |
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283 | 102 - don't try perturbing again |
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284 | default is 100 |
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285 | */ |
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286 | inline int perturbation() const |
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287 | { return perturbation_;}; |
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288 | void setPerturbation(int value); |
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289 | /// Current (or last) algorithm |
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290 | inline int algorithm() const |
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291 | {return algorithm_; } ; |
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292 | /// Set algorithm |
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293 | inline void setAlgorithm(int value) |
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294 | {algorithm_=value; } ; |
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295 | /// Sum of dual infeasibilities |
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296 | inline double sumDualInfeasibilities() const |
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297 | { return sumDualInfeasibilities_;} ; |
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298 | /// Number of dual infeasibilities |
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299 | inline int numberDualInfeasibilities() const |
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300 | { return numberDualInfeasibilities_;} ; |
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301 | /// Sum of primal infeasibilities |
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302 | inline double sumPrimalInfeasibilities() const |
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303 | { return sumPrimalInfeasibilities_;} ; |
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304 | /// Number of primal infeasibilities |
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305 | inline int numberPrimalInfeasibilities() const |
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306 | { return numberPrimalInfeasibilities_;} ; |
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307 | /** Save model to file, returns 0 if success. This is designed for |
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308 | use outside algorithms so does not save iterating arrays etc. |
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309 | It does not save any messaging information. |
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310 | Does not save scaling values. |
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311 | It does not know about all types of virtual functions. |
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312 | */ |
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313 | int saveModel(const char * fileName); |
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314 | /** Restore model from file, returns 0 if success, |
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315 | deletes current model */ |
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316 | int restoreModel(const char * fileName); |
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317 | |
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318 | /** Just check solution (for external use) - sets sum of |
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319 | infeasibilities etc */ |
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320 | void checkSolution(); |
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321 | /// Useful row length arrays (0,1,2,3,4,5) |
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322 | inline CoinIndexedVector * rowArray(int index) const |
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323 | { return rowArray_[index];}; |
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324 | /// Useful column length arrays (0,1,2,3,4,5) |
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325 | inline CoinIndexedVector * columnArray(int index) const |
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326 | { return columnArray_[index];}; |
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327 | //@} |
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328 | |
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329 | /******************** End of most useful part **************/ |
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330 | /**@name Functions less likely to be useful to casual user */ |
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331 | //@{ |
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332 | /** Given an existing factorization computes and checks |
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333 | primal and dual solutions. Uses input arrays for variables at |
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334 | bounds. Returns feasibility states */ |
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335 | int getSolution ( const double * rowActivities, |
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336 | const double * columnActivities); |
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337 | /** Given an existing factorization computes and checks |
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338 | primal and dual solutions. Uses current problem arrays for |
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339 | bounds. Returns feasibility states */ |
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340 | int getSolution (); |
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341 | /** Constructs a non linear cost from list of non-linearities (columns only) |
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342 | First lower of each column is taken as real lower |
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343 | Last lower is taken as real upper and cost ignored |
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344 | |
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345 | Returns nonzero if bad data e.g. lowers not monotonic |
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346 | */ |
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347 | int createPiecewiseLinearCosts(const int * starts, |
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348 | const double * lower, const double * gradient); |
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349 | /** Return model - updates any scalars */ |
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350 | void returnModel(ClpInterior & otherModel); |
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351 | /** Factorizes using current basis. |
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352 | solveType - 1 iterating, 0 initial, -1 external |
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353 | If 10 added then in primal values pass |
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354 | Return codes are as from ClpFactorization unless initial factorization |
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355 | when total number of singularities is returned |
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356 | */ |
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357 | /// Save data |
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358 | ClpDataSave saveData() ; |
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359 | /// Restore data |
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360 | void restoreData(ClpDataSave saved); |
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361 | int internalFactorize(int solveType); |
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362 | /// Clean up status |
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363 | void cleanStatus(); |
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364 | /// Factorizes using current basis. For external use |
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365 | int factorize(); |
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366 | /** Computes duals from scratch. If givenDjs then |
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367 | allows for nonzero basic djs */ |
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368 | void computeDuals(double * givenDjs); |
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369 | /// Computes primals from scratch |
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370 | void computePrimals ( const double * rowActivities, |
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371 | const double * columnActivities); |
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372 | /** |
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373 | Unpacks one column of the matrix into indexed array |
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374 | Uses sequenceIn_ |
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375 | Also applies scaling if needed |
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376 | */ |
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377 | void unpack(CoinIndexedVector * rowArray) const ; |
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378 | /** |
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379 | Unpacks one column of the matrix into indexed array |
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380 | Slack if sequence>= numberColumns |
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381 | Also applies scaling if needed |
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382 | */ |
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383 | void unpack(CoinIndexedVector * rowArray,int sequence) const; |
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384 | /** |
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385 | Unpacks one column of the matrix into indexed array |
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386 | ** as packed vector |
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387 | Uses sequenceIn_ |
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388 | Also applies scaling if needed |
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389 | */ |
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390 | void unpackPacked(CoinIndexedVector * rowArray) ; |
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391 | /** |
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392 | Unpacks one column of the matrix into indexed array |
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393 | ** as packed vector |
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394 | Slack if sequence>= numberColumns |
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395 | Also applies scaling if needed |
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396 | */ |
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397 | void unpackPacked(CoinIndexedVector * rowArray,int sequence); |
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398 | |
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399 | /** |
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400 | This does basis housekeeping and does values for in/out variables. |
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401 | Can also decide to re-factorize |
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402 | */ |
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403 | int housekeeping(double objectiveChange); |
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404 | /** This sets largest infeasibility and most infeasible and sum |
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405 | and number of infeasibilities (Primal) */ |
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406 | void checkPrimalSolution(const double * rowActivities=NULL, |
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407 | const double * columnActivies=NULL); |
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408 | /** This sets largest infeasibility and most infeasible and sum |
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409 | and number of infeasibilities (Dual) */ |
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410 | void checkDualSolution(); |
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411 | /** For advanced use. When doing iterative solves things can get |
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412 | nasty so on values pass if incoming solution has largest |
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413 | infeasibility < incomingInfeasibility throw out variables |
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414 | from basis until largest infeasibility < allowedInfeasibility |
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415 | or incoming largest infeasibility. |
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416 | If allowedInfeasibility>= incomingInfeasibility this is |
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417 | always possible altough you may end up with an all slack basis. |
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418 | |
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419 | Defaults are 1.0,10.0 |
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420 | */ |
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421 | void setValuesPassAction(float incomingInfeasibility, |
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422 | float allowedInfeasibility); |
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423 | //@} |
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424 | /**@name Matrix times vector methods |
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425 | They can be faster if scalar is +- 1 |
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426 | These are covers so user need not worry about scaling |
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427 | Also for simplex I am not using basic/non-basic split */ |
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428 | //@{ |
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429 | /** Return <code>y + A * x * scalar</code> in <code>y</code>. |
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430 | @pre <code>x</code> must be of size <code>numColumns()</code> |
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431 | @pre <code>y</code> must be of size <code>numRows()</code> */ |
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432 | void times(double scalar, |
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433 | const double * x, double * y) const; |
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434 | /** Return <code>y + x * scalar * A</code> in <code>y</code>. |
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435 | @pre <code>x</code> must be of size <code>numRows()</code> |
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436 | @pre <code>y</code> must be of size <code>numColumns()</code> */ |
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437 | void transposeTimes(double scalar, |
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438 | const double * x, double * y) const ; |
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439 | //@} |
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440 | |
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441 | /**@name most useful gets and sets */ |
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442 | //@{ |
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443 | /// Worst column primal infeasibility |
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444 | inline double columnPrimalInfeasibility() const |
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445 | { return columnPrimalInfeasibility_;} ; |
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446 | /// Sequence of worst (-1 if feasible) |
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447 | inline int columnPrimalSequence() const |
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448 | { return columnPrimalSequence_;} ; |
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449 | /// Worst row primal infeasibility |
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450 | inline double rowPrimalInfeasibility() const |
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451 | { return rowPrimalInfeasibility_;} ; |
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452 | /// Sequence of worst (-1 if feasible) |
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453 | inline int rowPrimalSequence() const |
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454 | { return rowPrimalSequence_;} ; |
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455 | /** Worst column dual infeasibility (note - these may not be as meaningful |
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456 | if the problem is primal infeasible */ |
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457 | inline double columnDualInfeasibility() const |
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458 | { return columnDualInfeasibility_;} ; |
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459 | /// Sequence of worst (-1 if feasible) |
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460 | inline int columnDualSequence() const |
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461 | { return columnDualSequence_;} ; |
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462 | /// Worst row dual infeasibility |
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463 | inline double rowDualInfeasibility() const |
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464 | { return rowDualInfeasibility_;} ; |
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465 | /// Sequence of worst (-1 if feasible) |
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466 | inline int rowDualSequence() const |
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467 | { return rowDualSequence_;} ; |
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468 | /// Primal tolerance needed to make dual feasible (<largeTolerance) |
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469 | inline double primalToleranceToGetOptimal() const |
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470 | { return primalToleranceToGetOptimal_;} ; |
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471 | /// Remaining largest dual infeasibility |
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472 | inline double remainingDualInfeasibility() const |
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473 | { return remainingDualInfeasibility_;} ; |
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474 | /// Large bound value (for complementarity etc) |
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475 | inline double largeValue() const |
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476 | { return largeValue_;} ; |
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477 | void setLargeValue( double value) ; |
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478 | /// Largest error on Ax-b |
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479 | inline double largestPrimalError() const |
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480 | { return largestPrimalError_;} ; |
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481 | /// Largest error on basic duals |
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482 | inline double largestDualError() const |
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483 | { return largestDualError_;} ; |
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484 | /// Largest difference between input primal solution and computed |
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485 | inline double largestSolutionError() const |
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486 | { return largestSolutionError_;} ; |
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487 | /// Basic variables pivoting on which rows |
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488 | inline int * pivotVariable() const |
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489 | { return pivotVariable_;}; |
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490 | /// Scaling of objective 12345.0 (auto), 0.0 (off), other user |
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491 | inline double objectiveScale() const |
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492 | { return objectiveScale_;} ; |
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493 | inline void setObjectiveScale(double value) |
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494 | { objectiveScale_ = value;} ; |
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495 | /// Current dual tolerance |
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496 | inline double currentDualTolerance() const |
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497 | { return dualTolerance_;} ; |
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498 | inline void setCurrentDualTolerance(double value) |
---|
499 | { dualTolerance_ = value;} ; |
---|
500 | /// Current primal tolerance |
---|
501 | inline double currentPrimalTolerance() const |
---|
502 | { return primalTolerance_;} ; |
---|
503 | inline void setCurrentPrimalTolerance(double value) |
---|
504 | { primalTolerance_ = value;} ; |
---|
505 | /// How many iterative refinements to do |
---|
506 | inline int numberRefinements() const |
---|
507 | { return numberRefinements_;} ; |
---|
508 | void setNumberRefinements( int value) ; |
---|
509 | /// Alpha (pivot element) for use by classes e.g. steepestedge |
---|
510 | inline double alpha() const { return alpha_;}; |
---|
511 | /// Reduced cost of last incoming for use by classes e.g. steepestedge |
---|
512 | inline double dualIn() const { return dualIn_;}; |
---|
513 | /// Pivot Row for use by classes e.g. steepestedge |
---|
514 | inline int pivotRow() const{ return pivotRow_;}; |
---|
515 | /// value of incoming variable (in Dual) |
---|
516 | double valueIncomingDual() const; |
---|
517 | //@} |
---|
518 | |
---|
519 | protected: |
---|
520 | /**@name protected methods */ |
---|
521 | //@{ |
---|
522 | /** May change basis and then returns number changed. |
---|
523 | Computation of solutions may be overriden by given pi and solution |
---|
524 | */ |
---|
525 | int gutsOfSolution ( double * givenDuals, |
---|
526 | const double * givenPrimals, |
---|
527 | bool valuesPass=false); |
---|
528 | /// Does most of deletion (0 = all, 1 = most, 2 most + factorization) |
---|
529 | void gutsOfDelete(int type); |
---|
530 | /// Does most of copying |
---|
531 | void gutsOfCopy(const ClpInterior & rhs); |
---|
532 | /** puts in format I like (rowLower,rowUpper) also see StandardMatrix |
---|
533 | 1 bit does rows, 2 bit does column bounds, 4 bit does objective(s). |
---|
534 | 8 bit does solution scaling in |
---|
535 | 16 bit does rowArray and columnArray indexed vectors |
---|
536 | and makes row copy if wanted, also sets columnStart_ etc |
---|
537 | Also creates scaling arrays if needed. It does scaling if needed. |
---|
538 | 16 also moves solutions etc in to work arrays |
---|
539 | On 16 returns false if problem "bad" i.e. matrix or bounds bad |
---|
540 | */ |
---|
541 | bool createRim(int what,bool makeRowCopy=false); |
---|
542 | /** releases above arrays and does solution scaling out. May also |
---|
543 | get rid of factorization data - |
---|
544 | 0 get rid of nothing, 1 get rid of arrays, 2 also factorization |
---|
545 | */ |
---|
546 | void deleteRim(int getRidOfFactorizationData=2); |
---|
547 | /// Sanity check on input rim data (after scaling) - returns true if okay |
---|
548 | bool sanityCheck(); |
---|
549 | //@} |
---|
550 | public: |
---|
551 | /**@name public methods */ |
---|
552 | //@{ |
---|
553 | /** Return row or column sections - not as much needed as it |
---|
554 | once was. These just map into single arrays */ |
---|
555 | inline double * solutionRegion(int section) |
---|
556 | { if (!section) return rowActivityWork_; else return columnActivityWork_;}; |
---|
557 | inline double * djRegion(int section) |
---|
558 | { if (!section) return rowReducedCost_; else return reducedCostWork_;}; |
---|
559 | inline double * lowerRegion(int section) |
---|
560 | { if (!section) return rowLowerWork_; else return columnLowerWork_;}; |
---|
561 | inline double * upperRegion(int section) |
---|
562 | { if (!section) return rowUpperWork_; else return columnUpperWork_;}; |
---|
563 | inline double * costRegion(int section) |
---|
564 | { if (!section) return rowObjectiveWork_; else return objectiveWork_;}; |
---|
565 | /// Return region as single array |
---|
566 | inline double * solutionRegion() |
---|
567 | { return solution_;}; |
---|
568 | inline double * djRegion() |
---|
569 | { return dj_;}; |
---|
570 | inline double * lowerRegion() |
---|
571 | { return lower_;}; |
---|
572 | inline double * upperRegion() |
---|
573 | { return upper_;}; |
---|
574 | inline double * costRegion() |
---|
575 | { return cost_;}; |
---|
576 | inline Status getStatus(int sequence) const |
---|
577 | {return static_cast<Status> (status_[sequence]&7);}; |
---|
578 | inline void setStatus(int sequence, Status status) |
---|
579 | { |
---|
580 | unsigned char & st_byte = status_[sequence]; |
---|
581 | st_byte &= ~7; |
---|
582 | st_byte |= status; |
---|
583 | }; |
---|
584 | /** Normally the first factorization does sparse coding because |
---|
585 | the factorization could be singular. This allows initial dense |
---|
586 | factorization when it is known to be safe |
---|
587 | */ |
---|
588 | void setInitialDenseFactorization(bool onOff); |
---|
589 | bool initialDenseFactorization() const; |
---|
590 | /** Return sequence In or Out */ |
---|
591 | inline int sequenceIn() const |
---|
592 | {return sequenceIn_;}; |
---|
593 | inline int sequenceOut() const |
---|
594 | {return sequenceOut_;}; |
---|
595 | /** Set sequenceIn or Out */ |
---|
596 | inline void setSequenceIn(int sequence) |
---|
597 | { sequenceIn_=sequence;}; |
---|
598 | inline void setSequenceOut(int sequence) |
---|
599 | { sequenceOut_=sequence;}; |
---|
600 | /** Return direction In or Out */ |
---|
601 | inline int directionIn() const |
---|
602 | {return directionIn_;}; |
---|
603 | inline int directionOut() const |
---|
604 | {return directionOut_;}; |
---|
605 | /** Set directionIn or Out */ |
---|
606 | inline void setDirectionIn(int direction) |
---|
607 | { directionIn_=direction;}; |
---|
608 | inline void setDirectionOut(int direction) |
---|
609 | { directionOut_=direction;}; |
---|
610 | /// Returns 1 if sequence indicates column |
---|
611 | inline int isColumn(int sequence) const |
---|
612 | { return sequence<numberColumns_ ? 1 : 0;}; |
---|
613 | /// Returns sequence number within section |
---|
614 | inline int sequenceWithin(int sequence) const |
---|
615 | { return sequence<numberColumns_ ? sequence : sequence-numberColumns_;}; |
---|
616 | /// Return row or column values |
---|
617 | inline double solution(int sequence) |
---|
618 | { return solution_[sequence];}; |
---|
619 | /// Return address of row or column values |
---|
620 | inline double & solutionAddress(int sequence) |
---|
621 | { return solution_[sequence];}; |
---|
622 | inline double reducedCost(int sequence) |
---|
623 | { return dj_[sequence];}; |
---|
624 | inline double & reducedCostAddress(int sequence) |
---|
625 | { return dj_[sequence];}; |
---|
626 | inline double lower(int sequence) |
---|
627 | { return lower_[sequence];}; |
---|
628 | /// Return address of row or column lower bound |
---|
629 | inline double & lowerAddress(int sequence) |
---|
630 | { return lower_[sequence];}; |
---|
631 | inline double upper(int sequence) |
---|
632 | { return upper_[sequence];}; |
---|
633 | /// Return address of row or column upper bound |
---|
634 | inline double & upperAddress(int sequence) |
---|
635 | { return upper_[sequence];}; |
---|
636 | inline double cost(int sequence) |
---|
637 | { return cost_[sequence];}; |
---|
638 | /// Return address of row or column cost |
---|
639 | inline double & costAddress(int sequence) |
---|
640 | { return cost_[sequence];}; |
---|
641 | /// Return original lower bound |
---|
642 | inline double originalLower(int iSequence) const |
---|
643 | { if (iSequence<numberColumns_) return columnLower_[iSequence]; else |
---|
644 | return rowLower_[iSequence-numberColumns_];}; |
---|
645 | /// Return original lower bound |
---|
646 | inline double originalUpper(int iSequence) const |
---|
647 | { if (iSequence<numberColumns_) return columnUpper_[iSequence]; else |
---|
648 | return rowUpper_[iSequence-numberColumns_];}; |
---|
649 | /// Theta (pivot change) |
---|
650 | inline double theta() const |
---|
651 | { return theta_;}; |
---|
652 | /// Scaling |
---|
653 | const double * rowScale() const {return rowScale_;}; |
---|
654 | const double * columnScale() const {return columnScale_;}; |
---|
655 | void setRowScale(double * scale) { rowScale_ = scale;}; |
---|
656 | void setColumnScale(double * scale) { columnScale_ = scale;}; |
---|
657 | /// Return pointer to details of costs |
---|
658 | inline ClpNonLinearCost * nonLinearCost() const |
---|
659 | { return nonLinearCost_;}; |
---|
660 | //@} |
---|
661 | /**@name status methods */ |
---|
662 | //@{ |
---|
663 | inline void setFakeBound(int sequence, FakeBound fakeBound) |
---|
664 | { |
---|
665 | unsigned char & st_byte = status_[sequence]; |
---|
666 | st_byte &= ~24; |
---|
667 | st_byte |= fakeBound<<3; |
---|
668 | }; |
---|
669 | inline FakeBound getFakeBound(int sequence) const |
---|
670 | {return static_cast<FakeBound> ((status_[sequence]>>3)&3);}; |
---|
671 | inline void setRowStatus(int sequence, Status status) |
---|
672 | { |
---|
673 | unsigned char & st_byte = status_[sequence+numberColumns_]; |
---|
674 | st_byte &= ~7; |
---|
675 | st_byte |= status; |
---|
676 | }; |
---|
677 | inline Status getRowStatus(int sequence) const |
---|
678 | {return static_cast<Status> (status_[sequence+numberColumns_]&7);}; |
---|
679 | inline void setColumnStatus(int sequence, Status status) |
---|
680 | { |
---|
681 | unsigned char & st_byte = status_[sequence]; |
---|
682 | st_byte &= ~7; |
---|
683 | st_byte |= status; |
---|
684 | }; |
---|
685 | inline Status getColumnStatus(int sequence) const |
---|
686 | {return static_cast<Status> (status_[sequence]&7);}; |
---|
687 | inline void setPivoted( int sequence) |
---|
688 | { status_[sequence] |= 32;}; |
---|
689 | inline void clearPivoted( int sequence) |
---|
690 | { status_[sequence] &= ~32; }; |
---|
691 | inline bool pivoted(int sequence) const |
---|
692 | {return (((status_[sequence]>>5)&1)!=0);}; |
---|
693 | inline void setFlagged( int sequence) |
---|
694 | { |
---|
695 | status_[sequence] |= 64; |
---|
696 | }; |
---|
697 | inline void clearFlagged( int sequence) |
---|
698 | { |
---|
699 | status_[sequence] &= ~64; |
---|
700 | }; |
---|
701 | inline bool flagged(int sequence) const |
---|
702 | {return (((status_[sequence]>>6)&1)!=0);}; |
---|
703 | /** Set up status array (can be used by OsiClp). |
---|
704 | Also can be used to set up all slack basis */ |
---|
705 | void createStatus() ; |
---|
706 | inline void allSlackBasis() |
---|
707 | { createStatus();}; |
---|
708 | |
---|
709 | /// So we know when to be cautious |
---|
710 | inline int lastBadIteration() const |
---|
711 | {return lastBadIteration_;}; |
---|
712 | /// Progress flag - at present 0 bit says artificials out |
---|
713 | inline int progressFlag() const |
---|
714 | {return progressFlag_;}; |
---|
715 | /// Force re-factorization early |
---|
716 | inline void forceFactorization(int value) |
---|
717 | { forceFactorization_ = value;}; |
---|
718 | /// Raw objective value (so always minimize in primal) |
---|
719 | inline double rawObjectiveValue() const |
---|
720 | { return objectiveValue_;}; |
---|
721 | //@} |
---|
722 | |
---|
723 | ////////////////// data ////////////////// |
---|
724 | protected: |
---|
725 | |
---|
726 | /**@name data. Many arrays have a row part and a column part. |
---|
727 | There is a single array with both - columns then rows and |
---|
728 | then normally two arrays pointing to rows and columns. The |
---|
729 | single array is the owner of memory |
---|
730 | */ |
---|
731 | //@{ |
---|
732 | /// Worst column primal infeasibility |
---|
733 | double columnPrimalInfeasibility_; |
---|
734 | /// Worst row primal infeasibility |
---|
735 | double rowPrimalInfeasibility_; |
---|
736 | /// Sequence of worst (-1 if feasible) |
---|
737 | int columnPrimalSequence_; |
---|
738 | /// Sequence of worst (-1 if feasible) |
---|
739 | int rowPrimalSequence_; |
---|
740 | /// Worst column dual infeasibility |
---|
741 | double columnDualInfeasibility_; |
---|
742 | /// Worst row dual infeasibility |
---|
743 | double rowDualInfeasibility_; |
---|
744 | /// Sequence of worst (-1 if feasible) |
---|
745 | int columnDualSequence_; |
---|
746 | /// Sequence of worst (-1 if feasible) |
---|
747 | int rowDualSequence_; |
---|
748 | /// Primal tolerance needed to make dual feasible (<largeTolerance) |
---|
749 | double primalToleranceToGetOptimal_; |
---|
750 | /// Remaining largest dual infeasibility |
---|
751 | double remainingDualInfeasibility_; |
---|
752 | /// Large bound value (for complementarity etc) |
---|
753 | double largeValue_; |
---|
754 | /// Scaling of objective |
---|
755 | double objectiveScale_; |
---|
756 | /// Largest error on Ax-b |
---|
757 | double largestPrimalError_; |
---|
758 | /// Largest error on basic duals |
---|
759 | double largestDualError_; |
---|
760 | /// Largest difference between input primal solution and computed |
---|
761 | double largestSolutionError_; |
---|
762 | /// Dual bound |
---|
763 | double dualBound_; |
---|
764 | /// Alpha (pivot element) |
---|
765 | double alpha_; |
---|
766 | /// Theta (pivot change) |
---|
767 | double theta_; |
---|
768 | /// Lower Bound on In variable |
---|
769 | double lowerIn_; |
---|
770 | /// Value of In variable |
---|
771 | double valueIn_; |
---|
772 | /// Upper Bound on In variable |
---|
773 | double upperIn_; |
---|
774 | /// Reduced cost of In variable |
---|
775 | double dualIn_; |
---|
776 | /// Lower Bound on Out variable |
---|
777 | double lowerOut_; |
---|
778 | /// Value of Out variable |
---|
779 | double valueOut_; |
---|
780 | /// Upper Bound on Out variable |
---|
781 | double upperOut_; |
---|
782 | /// Infeasibility (dual) or ? (primal) of Out variable |
---|
783 | double dualOut_; |
---|
784 | /// Current dual tolerance for algorithm |
---|
785 | double dualTolerance_; |
---|
786 | /// Current primal tolerance for algorithm |
---|
787 | double primalTolerance_; |
---|
788 | /// Sum of dual infeasibilities |
---|
789 | double sumDualInfeasibilities_; |
---|
790 | /// Sum of primal infeasibilities |
---|
791 | double sumPrimalInfeasibilities_; |
---|
792 | /// Weight assigned to being infeasible in primal |
---|
793 | double infeasibilityCost_; |
---|
794 | /// Sum of Dual infeasibilities using tolerance based on error in duals |
---|
795 | double sumOfRelaxedDualInfeasibilities_; |
---|
796 | /// Sum of Primal infeasibilities using tolerance based on error in primals |
---|
797 | double sumOfRelaxedPrimalInfeasibilities_; |
---|
798 | /// Working copy of lower bounds (Owner of arrays below) |
---|
799 | double * lower_; |
---|
800 | /// Row lower bounds - working copy |
---|
801 | double * rowLowerWork_; |
---|
802 | /// Column lower bounds - working copy |
---|
803 | double * columnLowerWork_; |
---|
804 | /// Working copy of upper bounds (Owner of arrays below) |
---|
805 | double * upper_; |
---|
806 | /// Row upper bounds - working copy |
---|
807 | double * rowUpperWork_; |
---|
808 | /// Column upper bounds - working copy |
---|
809 | double * columnUpperWork_; |
---|
810 | /// Working copy of objective (Owner of arrays below) |
---|
811 | double * cost_; |
---|
812 | /// Row objective - working copy |
---|
813 | double * rowObjectiveWork_; |
---|
814 | /// Column objective - working copy |
---|
815 | double * objectiveWork_; |
---|
816 | /// Useful row length arrays |
---|
817 | CoinIndexedVector * rowArray_[6]; |
---|
818 | /// Useful column length arrays |
---|
819 | CoinIndexedVector * columnArray_[6]; |
---|
820 | /// Sequence of In variable |
---|
821 | int sequenceIn_; |
---|
822 | /// Direction of In, 1 going up, -1 going down, 0 not a clude |
---|
823 | int directionIn_; |
---|
824 | /// Sequence of Out variable |
---|
825 | int sequenceOut_; |
---|
826 | /// Direction of Out, 1 to upper bound, -1 to lower bound, 0 - superbasic |
---|
827 | int directionOut_; |
---|
828 | /// Pivot Row |
---|
829 | int pivotRow_; |
---|
830 | /// Last good iteration (immediately after a re-factorization) |
---|
831 | int lastGoodIteration_; |
---|
832 | /// Working copy of reduced costs (Owner of arrays below) |
---|
833 | double * dj_; |
---|
834 | /// Reduced costs of slacks not same as duals (or - duals) |
---|
835 | double * rowReducedCost_; |
---|
836 | /// Possible scaled reduced costs |
---|
837 | double * reducedCostWork_; |
---|
838 | /// Working copy of primal solution (Owner of arrays below) |
---|
839 | double * solution_; |
---|
840 | /// Row activities - working copy |
---|
841 | double * rowActivityWork_; |
---|
842 | /// Column activities - working copy |
---|
843 | double * columnActivityWork_; |
---|
844 | /// Number of dual infeasibilities |
---|
845 | int numberDualInfeasibilities_; |
---|
846 | /// Number of dual infeasibilities (without free) |
---|
847 | int numberDualInfeasibilitiesWithoutFree_; |
---|
848 | /// Number of primal infeasibilities |
---|
849 | int numberPrimalInfeasibilities_; |
---|
850 | /// How many iterative refinements to do |
---|
851 | int numberRefinements_; |
---|
852 | /// dual row pivot choice |
---|
853 | ClpDualRowPivot * dualRowPivot_; |
---|
854 | /// primal column pivot choice |
---|
855 | ClpPrimalColumnPivot * primalColumnPivot_; |
---|
856 | /// Basic variables pivoting on which rows |
---|
857 | int * pivotVariable_; |
---|
858 | /// factorization |
---|
859 | ClpFactorization * factorization_; |
---|
860 | /// Row scale factors for matrix |
---|
861 | // ****** get working simply then make coding more efficient |
---|
862 | // on full matrix operations |
---|
863 | double * rowScale_; |
---|
864 | /// Saved version of solution |
---|
865 | double * savedSolution_; |
---|
866 | /// Column scale factors |
---|
867 | double * columnScale_; |
---|
868 | /// Scale flag, 0 none, 1 equilibrium, 2 geometric, 3, auto, 4 dynamic |
---|
869 | int scalingFlag_; |
---|
870 | /// Number of times code has tentatively thought optimal |
---|
871 | int numberTimesOptimal_; |
---|
872 | /// If change has been made (first attempt at stopping looping) |
---|
873 | int changeMade_; |
---|
874 | /// Algorithm >0 == Primal, <0 == Dual |
---|
875 | int algorithm_; |
---|
876 | /** Now for some reliability aids |
---|
877 | This forces re-factorization early */ |
---|
878 | int forceFactorization_; |
---|
879 | /** Perturbation: |
---|
880 | -50 to +50 - perturb by this power of ten (-6 sounds good) |
---|
881 | 100 - auto perturb if takes too long (1.0e-6 largest nonzero) |
---|
882 | 101 - we are perturbed |
---|
883 | 102 - don't try perturbing again |
---|
884 | default is 100 |
---|
885 | */ |
---|
886 | int perturbation_; |
---|
887 | /// Saved status regions |
---|
888 | unsigned char * saveStatus_; |
---|
889 | /** Very wasteful way of dealing with infeasibilities in primal. |
---|
890 | However it will allow non-linearities and use of dual |
---|
891 | analysis. If it doesn't work it can easily be replaced. |
---|
892 | */ |
---|
893 | ClpNonLinearCost * nonLinearCost_; |
---|
894 | /** For advanced options |
---|
895 | 1 - Don't keep changing infeasibility weight |
---|
896 | 2 - Keep nonLinearCost round solves |
---|
897 | 4 - Force outgoing variables to exact bound (primal) |
---|
898 | 8 - Safe to use dense initial factorization |
---|
899 | */ |
---|
900 | unsigned int specialOptions_; |
---|
901 | /// So we know when to be cautious |
---|
902 | int lastBadIteration_; |
---|
903 | /// Can be used for count of fake bounds (dual) or fake costs (primal) |
---|
904 | int numberFake_; |
---|
905 | /// Progress flag - at present 0 bit says artificials out, 1 free in |
---|
906 | int progressFlag_; |
---|
907 | /// First free/super-basic variable (-1 if none) |
---|
908 | int firstFree_; |
---|
909 | /** For advanced use. When doing iterative solves things can get |
---|
910 | nasty so on values pass if incoming solution has largest |
---|
911 | infeasibility < incomingInfeasibility throw out variables |
---|
912 | from basis until largest infeasibility < allowedInfeasibility. |
---|
913 | if allowedInfeasibility>= incomingInfeasibility this is |
---|
914 | always possible altough you may end up with an all slack basis. |
---|
915 | |
---|
916 | Defaults are 1.0,10.0 |
---|
917 | */ |
---|
918 | float incomingInfeasibility_; |
---|
919 | float allowedInfeasibility_; |
---|
920 | /// For dealing with all issues of cycling etc |
---|
921 | ClpInteriorProgress * progress_; |
---|
922 | //@} |
---|
923 | }; |
---|
924 | //############################################################################# |
---|
925 | /** A function that tests the methods in the ClpInterior class. The |
---|
926 | only reason for it not to be a member method is that this way it doesn't |
---|
927 | have to be compiled into the library. And that's a gain, because the |
---|
928 | library should be compiled with optimization on, but this method should be |
---|
929 | compiled with debugging. |
---|
930 | |
---|
931 | It also does some testing of ClpFactorization class |
---|
932 | */ |
---|
933 | void |
---|
934 | ClpInteriorUnitTest(const std::string & mpsDir, |
---|
935 | const std::string & netlibDir); |
---|
936 | |
---|
937 | |
---|
938 | /// For saving extra information to see if looping. |
---|
939 | class ClpInteriorProgress { |
---|
940 | |
---|
941 | public: |
---|
942 | |
---|
943 | |
---|
944 | /**@name Constructors and destructor and copy */ |
---|
945 | //@{ |
---|
946 | /// Default constructor |
---|
947 | ClpInteriorProgress ( ); |
---|
948 | |
---|
949 | /// Constructor from model |
---|
950 | ClpInteriorProgress ( ClpInterior * model ); |
---|
951 | |
---|
952 | /// Copy constructor. |
---|
953 | ClpInteriorProgress(const ClpInteriorProgress &); |
---|
954 | |
---|
955 | /// Assignment operator. This copies the data |
---|
956 | ClpInteriorProgress & operator=(const ClpInteriorProgress & rhs); |
---|
957 | /// Destructor |
---|
958 | ~ClpInteriorProgress ( ); |
---|
959 | //@} |
---|
960 | |
---|
961 | /**@name Check progress */ |
---|
962 | //@{ |
---|
963 | /** Returns -1 if okay, -n+1 (n number of times bad) if bad but action taken, |
---|
964 | >=0 if give up and use as problem status |
---|
965 | */ |
---|
966 | int looping ( ); |
---|
967 | /// Start check at beginning of whileIterating |
---|
968 | void startCheck(); |
---|
969 | /// Returns cycle length in whileIterating |
---|
970 | int cycle(int in, int out,int wayIn,int wayOut); |
---|
971 | |
---|
972 | /// Returns previous objective (if -1) - current if (0) |
---|
973 | double lastObjective(int back=1) const; |
---|
974 | /// Modify objective e.g. if dual infeasible in dual |
---|
975 | void modifyObjective(double value); |
---|
976 | /// Returns previous iteration number (if -1) - current if (0) |
---|
977 | int lastIterationNumber(int back=1) const; |
---|
978 | |
---|
979 | //@} |
---|
980 | /**@name Data */ |
---|
981 | #define CLP_PROGRESS 5 |
---|
982 | //@{ |
---|
983 | /// Objective values |
---|
984 | double objective_[CLP_PROGRESS]; |
---|
985 | /// Sum of infeasibilities for algorithm |
---|
986 | double infeasibility_[CLP_PROGRESS]; |
---|
987 | /// Pointer back to model so we can get information |
---|
988 | ClpInterior * model_; |
---|
989 | /// Number of infeasibilities |
---|
990 | int numberInfeasibilities_[CLP_PROGRESS]; |
---|
991 | /// Iteration number at which occurred |
---|
992 | int iterationNumber_[CLP_PROGRESS]; |
---|
993 | /// Number of times checked (so won't stop too early) |
---|
994 | int numberTimes_; |
---|
995 | /// Number of times it looked like loop |
---|
996 | int numberBadTimes_; |
---|
997 | #define CLP_CYCLE 12 |
---|
998 | /// For cycle checking |
---|
999 | int in_[CLP_CYCLE]; |
---|
1000 | int out_[CLP_CYCLE]; |
---|
1001 | char way_[CLP_CYCLE]; |
---|
1002 | //@} |
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1003 | }; |
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1004 | #endif |
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