[1573] | 1 | // $Id: CbcHeuristicVND.cpp 2094 2014-11-18 11:15:36Z forrest $ |
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| 2 | // Copyright (C) 2006, International Business Machines |
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| 3 | // Corporation and others. All Rights Reserved. |
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| 4 | // This code is licensed under the terms of the Eclipse Public License (EPL). |
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| 5 | |
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| 6 | // edwin 12/5/09 carved out of CbcHeuristicRINS |
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| 7 | |
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[1368] | 8 | #if defined(_MSC_VER) |
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| 9 | // Turn off compiler warning about long names |
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| 10 | # pragma warning(disable:4786) |
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| 11 | #endif |
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| 12 | #include <cassert> |
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| 13 | #include <cstdlib> |
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| 14 | #include <cmath> |
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| 15 | #include <cfloat> |
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| 16 | |
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| 17 | #include "OsiSolverInterface.hpp" |
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| 18 | #include "CbcModel.hpp" |
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| 19 | #include "CbcMessage.hpp" |
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| 20 | #include "CbcHeuristicVND.hpp" |
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| 21 | #include "CbcBranchActual.hpp" |
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| 22 | #include "CbcStrategy.hpp" |
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| 23 | #include "CglPreProcess.hpp" |
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| 24 | |
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| 25 | |
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| 26 | // Default Constructor |
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| 27 | CbcHeuristicVND::CbcHeuristicVND() |
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| 28 | : CbcHeuristic() |
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| 29 | { |
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| 30 | numberSolutions_ = 0; |
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| 31 | numberSuccesses_ = 0; |
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| 32 | numberTries_ = 0; |
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| 33 | lastNode_ = -999999; |
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| 34 | howOften_ = 100; |
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| 35 | decayFactor_ = 0.5; |
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| 36 | baseSolution_ = NULL; |
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| 37 | whereFrom_ = 1 + 8 + 255 * 256; |
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| 38 | stepSize_ = 0; |
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| 39 | k_ = 0; |
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| 40 | kmax_ = 0; |
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| 41 | nDifferent_ = 0; |
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| 42 | } |
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| 43 | |
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| 44 | // Constructor with model - assumed before cuts |
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| 45 | |
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| 46 | CbcHeuristicVND::CbcHeuristicVND(CbcModel & model) |
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| 47 | : CbcHeuristic(model) |
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| 48 | { |
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| 49 | numberSolutions_ = 0; |
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| 50 | numberSuccesses_ = 0; |
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| 51 | numberTries_ = 0; |
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| 52 | lastNode_ = -999999; |
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| 53 | howOften_ = 100; |
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| 54 | decayFactor_ = 0.5; |
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| 55 | assert(model.solver()); |
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| 56 | int numberColumns = model.solver()->getNumCols(); |
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| 57 | baseSolution_ = new double [numberColumns]; |
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| 58 | memset(baseSolution_, 0, numberColumns*sizeof(double)); |
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| 59 | whereFrom_ = 1 + 8 + 255 * 256; |
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| 60 | stepSize_ = 0; |
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| 61 | k_ = 0; |
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| 62 | kmax_ = 0; |
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| 63 | nDifferent_ = 0; |
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| 64 | } |
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| 65 | |
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| 66 | // Destructor |
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| 67 | CbcHeuristicVND::~CbcHeuristicVND () |
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| 68 | { |
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| 69 | delete [] baseSolution_; |
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| 70 | } |
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| 71 | |
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| 72 | // Clone |
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| 73 | CbcHeuristic * |
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| 74 | CbcHeuristicVND::clone() const |
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| 75 | { |
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| 76 | return new CbcHeuristicVND(*this); |
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| 77 | } |
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| 78 | |
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| 79 | // Assignment operator |
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| 80 | CbcHeuristicVND & |
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| 81 | CbcHeuristicVND::operator=( const CbcHeuristicVND & rhs) |
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| 82 | { |
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| 83 | if (this != &rhs) { |
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| 84 | CbcHeuristic::operator=(rhs); |
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| 85 | numberSolutions_ = rhs.numberSolutions_; |
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| 86 | howOften_ = rhs.howOften_; |
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| 87 | numberSuccesses_ = rhs.numberSuccesses_; |
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| 88 | numberTries_ = rhs.numberTries_; |
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| 89 | lastNode_ = rhs.lastNode_; |
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| 90 | delete [] baseSolution_; |
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| 91 | if (model_ && rhs.baseSolution_) { |
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| 92 | int numberColumns = model_->solver()->getNumCols(); |
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| 93 | baseSolution_ = new double [numberColumns]; |
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| 94 | memcpy(baseSolution_, rhs.baseSolution_, numberColumns*sizeof(double)); |
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| 95 | } else { |
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| 96 | baseSolution_ = NULL; |
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| 97 | } |
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| 98 | stepSize_ = rhs.stepSize_; |
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| 99 | k_ = rhs.k_; |
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| 100 | kmax_ = rhs.kmax_; |
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| 101 | nDifferent_ = rhs.nDifferent_; |
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| 102 | } |
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| 103 | return *this; |
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| 104 | } |
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| 105 | |
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| 106 | // Create C++ lines to get to current state |
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| 107 | void |
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| 108 | CbcHeuristicVND::generateCpp( FILE * fp) |
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| 109 | { |
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| 110 | CbcHeuristicVND other; |
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| 111 | fprintf(fp, "0#include \"CbcHeuristicVND.hpp\"\n"); |
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| 112 | fprintf(fp, "3 CbcHeuristicVND heuristicVND(*cbcModel);\n"); |
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| 113 | CbcHeuristic::generateCpp(fp, "heuristicVND"); |
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| 114 | if (howOften_ != other.howOften_) |
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| 115 | fprintf(fp, "3 heuristicVND.setHowOften(%d);\n", howOften_); |
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| 116 | else |
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| 117 | fprintf(fp, "4 heuristicVND.setHowOften(%d);\n", howOften_); |
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| 118 | fprintf(fp, "3 cbcModel->addHeuristic(&heuristicVND);\n"); |
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| 119 | } |
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| 120 | |
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| 121 | // Copy constructor |
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| 122 | CbcHeuristicVND::CbcHeuristicVND(const CbcHeuristicVND & rhs) |
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| 123 | : |
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| 124 | CbcHeuristic(rhs), |
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| 125 | numberSolutions_(rhs.numberSolutions_), |
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| 126 | howOften_(rhs.howOften_), |
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| 127 | numberSuccesses_(rhs.numberSuccesses_), |
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| 128 | numberTries_(rhs.numberTries_), |
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| 129 | lastNode_(rhs.lastNode_) |
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| 130 | { |
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| 131 | if (model_ && rhs.baseSolution_) { |
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| 132 | int numberColumns = model_->solver()->getNumCols(); |
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| 133 | baseSolution_ = new double [numberColumns]; |
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| 134 | memcpy(baseSolution_, rhs.baseSolution_, numberColumns*sizeof(double)); |
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| 135 | } else { |
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| 136 | baseSolution_ = NULL; |
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| 137 | } |
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| 138 | stepSize_ = rhs.stepSize_; |
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| 139 | k_ = rhs.k_; |
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| 140 | kmax_ = rhs.kmax_; |
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| 141 | nDifferent_ = rhs.nDifferent_; |
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| 142 | } |
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| 143 | // Resets stuff if model changes |
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| 144 | void |
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| 145 | CbcHeuristicVND::resetModel(CbcModel * /*model*/) |
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| 146 | { |
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| 147 | //CbcHeuristic::resetModel(model); |
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| 148 | delete [] baseSolution_; |
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| 149 | if (model_ && baseSolution_) { |
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| 150 | int numberColumns = model_->solver()->getNumCols(); |
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| 151 | baseSolution_ = new double [numberColumns]; |
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| 152 | memset(baseSolution_, 0, numberColumns*sizeof(double)); |
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| 153 | } else { |
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| 154 | baseSolution_ = NULL; |
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| 155 | } |
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| 156 | } |
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| 157 | /* |
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| 158 | First tries setting a variable to better value. If feasible then |
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| 159 | tries setting others. If not feasible then tries swaps |
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| 160 | Returns 1 if solution, 0 if not */ |
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| 161 | int |
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| 162 | CbcHeuristicVND::solution(double & solutionValue, |
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| 163 | double * betterSolution) |
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| 164 | { |
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| 165 | numCouldRun_++; |
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| 166 | int returnCode = 0; |
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| 167 | const double * bestSolution = model_->bestSolution(); |
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| 168 | if (!bestSolution) |
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| 169 | return 0; // No solution found yet |
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[2094] | 170 | #ifdef HEURISTIC_INFORM |
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| 171 | printf("Entering heuristic %s - nRuns %d numCould %d when %d\n", |
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| 172 | heuristicName(),numRuns_,numCouldRun_,when_); |
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| 173 | #endif |
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[1368] | 174 | if (numberSolutions_ < model_->getSolutionCount()) { |
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| 175 | // new solution - add info |
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| 176 | numberSolutions_ = model_->getSolutionCount(); |
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| 177 | |
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| 178 | int numberIntegers = model_->numberIntegers(); |
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| 179 | const int * integerVariable = model_->integerVariable(); |
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| 180 | |
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| 181 | int i; |
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| 182 | for (i = 0; i < numberIntegers; i++) { |
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| 183 | int iColumn = integerVariable[i]; |
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| 184 | const OsiObject * object = model_->object(i); |
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| 185 | // get original bounds |
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| 186 | double originalLower; |
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| 187 | double originalUpper; |
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| 188 | getIntegerInformation( object, originalLower, originalUpper); |
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| 189 | double value = bestSolution[iColumn]; |
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| 190 | if (value < originalLower) { |
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| 191 | value = originalLower; |
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| 192 | } else if (value > originalUpper) { |
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| 193 | value = originalUpper; |
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| 194 | } |
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| 195 | } |
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| 196 | } |
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| 197 | int numberNodes = model_->getNodeCount(); |
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| 198 | if (howOften_ == 100) { |
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| 199 | if (numberNodes < lastNode_ + 12) |
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| 200 | return 0; |
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| 201 | // Do at 50 and 100 |
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| 202 | if ((numberNodes > 40 && numberNodes <= 50) || (numberNodes > 90 && numberNodes < 100)) |
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| 203 | numberNodes = howOften_; |
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| 204 | } |
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[2093] | 205 | if ((numberNodes % howOften_) == 0 && (model_->getCurrentPassNumber() <= 1 || |
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[1368] | 206 | model_->getCurrentPassNumber() == 999999)) { |
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| 207 | lastNode_ = model_->getNodeCount(); |
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| 208 | OsiSolverInterface * solver = model_->solver(); |
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| 209 | |
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| 210 | int numberIntegers = model_->numberIntegers(); |
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| 211 | const int * integerVariable = model_->integerVariable(); |
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| 212 | |
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| 213 | const double * currentSolution = solver->getColSolution(); |
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| 214 | OsiSolverInterface * newSolver = cloneBut(3); // was model_->continuousSolver()->clone(); |
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| 215 | //const double * colLower = newSolver->getColLower(); |
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| 216 | //const double * colUpper = newSolver->getColUpper(); |
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| 217 | |
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| 218 | double primalTolerance; |
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| 219 | solver->getDblParam(OsiPrimalTolerance, primalTolerance); |
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| 220 | |
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| 221 | // Sort on distance |
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| 222 | double * distance = new double [numberIntegers]; |
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| 223 | int * which = new int [numberIntegers]; |
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| 224 | |
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| 225 | int i; |
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| 226 | int nFix = 0; |
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| 227 | double tolerance = 10.0 * primalTolerance; |
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| 228 | for (i = 0; i < numberIntegers; i++) { |
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| 229 | int iColumn = integerVariable[i]; |
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| 230 | const OsiObject * object = model_->object(i); |
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| 231 | // get original bounds |
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| 232 | double originalLower; |
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| 233 | double originalUpper; |
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| 234 | getIntegerInformation( object, originalLower, originalUpper); |
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| 235 | double valueInt = bestSolution[iColumn]; |
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| 236 | if (valueInt < originalLower) { |
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| 237 | valueInt = originalLower; |
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| 238 | } else if (valueInt > originalUpper) { |
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| 239 | valueInt = originalUpper; |
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| 240 | } |
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| 241 | baseSolution_[iColumn] = currentSolution[iColumn]; |
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| 242 | distance[i] = fabs(currentSolution[iColumn] - valueInt); |
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| 243 | which[i] = i; |
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| 244 | if (fabs(currentSolution[iColumn] - valueInt) < tolerance) |
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| 245 | nFix++; |
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| 246 | } |
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| 247 | CoinSort_2(distance, distance + numberIntegers, which); |
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| 248 | nDifferent_ = numberIntegers - nFix; |
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| 249 | stepSize_ = nDifferent_ / 10; |
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| 250 | k_ = stepSize_; |
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| 251 | //nFix = numberIntegers-stepSize_; |
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| 252 | for (i = 0; i < nFix; i++) { |
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| 253 | int j = which[i]; |
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| 254 | int iColumn = integerVariable[j]; |
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| 255 | const OsiObject * object = model_->object(i); |
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| 256 | // get original bounds |
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| 257 | double originalLower; |
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| 258 | double originalUpper; |
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| 259 | getIntegerInformation( object, originalLower, originalUpper); |
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| 260 | double valueInt = bestSolution[iColumn]; |
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| 261 | if (valueInt < originalLower) { |
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| 262 | valueInt = originalLower; |
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| 263 | } else if (valueInt > originalUpper) { |
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| 264 | valueInt = originalUpper; |
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| 265 | } |
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| 266 | double nearest = floor(valueInt + 0.5); |
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| 267 | newSolver->setColLower(iColumn, nearest); |
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| 268 | newSolver->setColUpper(iColumn, nearest); |
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| 269 | } |
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| 270 | delete [] distance; |
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| 271 | delete [] which; |
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| 272 | if (nFix > numberIntegers / 5) { |
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| 273 | //printf("%d integers have samish value\n",nFix); |
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| 274 | returnCode = smallBranchAndBound(newSolver, numberNodes_, betterSolution, solutionValue, |
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| 275 | model_->getCutoff(), "CbcHeuristicVND"); |
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| 276 | if (returnCode < 0) |
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| 277 | returnCode = 0; // returned on size |
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| 278 | else |
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| 279 | numRuns_++; |
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| 280 | if ((returnCode&1) != 0) |
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| 281 | numberSuccesses_++; |
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| 282 | //printf("return code %d",returnCode); |
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| 283 | if ((returnCode&2) != 0) { |
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| 284 | // could add cut |
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| 285 | returnCode &= ~2; |
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| 286 | //printf("could add cut with %d elements (if all 0-1)\n",nFix); |
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| 287 | } else { |
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| 288 | //printf("\n"); |
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| 289 | } |
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| 290 | numberTries_++; |
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| 291 | if ((numberTries_ % 10) == 0 && numberSuccesses_*3 < numberTries_) |
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| 292 | howOften_ += static_cast<int> (howOften_ * decayFactor_); |
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| 293 | } |
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| 294 | |
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| 295 | delete newSolver; |
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| 296 | } |
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| 297 | return returnCode; |
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| 298 | } |
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| 299 | // update model |
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| 300 | void CbcHeuristicVND::setModel(CbcModel * model) |
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| 301 | { |
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| 302 | model_ = model; |
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| 303 | // Get a copy of original matrix |
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| 304 | assert(model_->solver()); |
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| 305 | delete [] baseSolution_; |
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| 306 | int numberColumns = model->solver()->getNumCols(); |
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| 307 | baseSolution_ = new double [numberColumns]; |
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| 308 | memset(baseSolution_, 0, numberColumns*sizeof(double)); |
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| 309 | } |
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[1432] | 310 | |
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