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ClpSimplex.cpp
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// Copyright (C) 2002, International Business Machines
// Corporation and others. All Rights Reserved.
// This code is licensed under the terms of the Eclipse Public License (EPL).
//#undef NDEBUG
#include "ClpConfig.h"
#include "CoinPragma.hpp"
#include <math.h>
#if SLIM_CLP == 2
#define SLIM_NOIO
#endif
#include "CoinHelperFunctions.hpp"
#include "CoinFloatEqual.hpp"
#include "ClpSimplex.hpp"
#include "ClpFactorization.hpp"
#include "ClpPackedMatrix.hpp"
#include "CoinIndexedVector.hpp"
#include "ClpDualRowDantzig.hpp"
#include "ClpDualRowSteepest.hpp"
#include "ClpPrimalColumnDantzig.hpp"
#include "ClpPrimalColumnSteepest.hpp"
#include "ClpPEDualRowSteepest.hpp"
#include "ClpPEPrimalColumnSteepest.hpp"
#include "ClpNonLinearCost.hpp"
#include "ClpMessage.hpp"
#include "ClpEventHandler.hpp"
#include "ClpLinearObjective.hpp"
#include "ClpHelperFunctions.hpp"
#include "CoinModel.hpp"
#include "CoinLpIO.hpp"
#include <cfloat>
#if CLP_HAS_ABC
#include "CoinAbcCommon.hpp"
#endif
#include <string>
#include <stdio.h>
#include <iostream>
//#############################################################################
ClpSimplex::ClpSimplex(bool emptyMessages)
:
ClpModel(emptyMessages)
, bestPossibleImprovement_(0.0)
, zeroTolerance_(1.0e-13)
, columnPrimalSequence_(-2)
, rowPrimalSequence_(-2)
, bestObjectiveValue_(-COIN_DBL_MAX)
, moreSpecialOptions_(2)
, baseIteration_(0)
, vectorMode_(0)
, primalToleranceToGetOptimal_(-1.0)
, largeValue_(1.0e15)
, largestPrimalError_(0.0)
, largestDualError_(0.0)
, alphaAccuracy_(-1.0)
, dualBound_(1.0e10)
, alpha_(0.0)
, theta_(0.0)
, lowerIn_(0.0)
, valueIn_(0.0)
, upperIn_(-COIN_DBL_MAX)
, dualIn_(0.0)
, lowerOut_(-1)
, valueOut_(-1)
, upperOut_(-1)
, dualOut_(-1)
, dualTolerance_(1.0e-7)
, primalTolerance_(1.0e-7)
, sumDualInfeasibilities_(0.0)
, sumPrimalInfeasibilities_(0.0)
, infeasibilityCost_(1.0e10)
, sumOfRelaxedDualInfeasibilities_(0.0)
, sumOfRelaxedPrimalInfeasibilities_(0.0)
, acceptablePivot_(1.0e-8)
, lower_(NULL)
, rowLowerWork_(NULL)
, columnLowerWork_(NULL)
, upper_(NULL)
, rowUpperWork_(NULL)
, columnUpperWork_(NULL)
, cost_(NULL)
, rowObjectiveWork_(NULL)
, objectiveWork_(NULL)
, sequenceIn_(-1)
, directionIn_(-1)
, sequenceOut_(-1)
, directionOut_(-1)
, pivotRow_(-1)
, lastGoodIteration_(-100)
, dj_(NULL)
, rowReducedCost_(NULL)
, reducedCostWork_(NULL)
, solution_(NULL)
, rowActivityWork_(NULL)
, columnActivityWork_(NULL)
, numberDualInfeasibilities_(0)
, numberDualInfeasibilitiesWithoutFree_(0)
, numberPrimalInfeasibilities_(100)
, numberRefinements_(0)
, pivotVariable_(NULL)
, factorization_(NULL)
, savedSolution_(NULL)
, numberTimesOptimal_(0)
, disasterArea_(NULL)
, changeMade_(1)
, algorithm_(0)
, forceFactorization_(-1)
, perturbation_(100)
, nonLinearCost_(NULL)
, lastBadIteration_(-999999)
, lastFlaggedIteration_(-999999)
, numberFake_(0)
, numberChanged_(0)
, progressFlag_(0)
, firstFree_(-1)
, numberExtraRows_(0)
, maximumBasic_(0)
, dontFactorizePivots_(0)
, incomingInfeasibility_(1.0)
, allowedInfeasibility_(10.0)
, automaticScale_(0)
, maximumPerturbationSize_(0)
, perturbationArray_(NULL)
, baseModel_(NULL)
#ifdef ABC_INHERIT
, abcSimplex_(NULL)
, abcState_(0)
#endif
, minIntervalProgressUpdate_(0.7)
, lastStatusUpdate_(0.0)
{
int i;
for (i = 0; i < 6; i++) {
rowArray_[i] = NULL;
columnArray_[i] = NULL;
}
for (i = 0; i < 4; i++) {
spareIntArray_[i] = 0;
spareDoubleArray_[i] = 0.0;
}
saveStatus_ = NULL;
// get an empty factorization so we can set tolerances etc
getEmptyFactorization();
// Say sparse
factorization_->sparseThreshold(1);
// say Steepest pricing
dualRowPivot_ = new ClpDualRowSteepest();
// say Steepest pricing
primalColumnPivot_ = new ClpPrimalColumnSteepest();
solveType_ = 1; // say simplex based life form
eventHandler_->setSimplex(this);
}
// Subproblem constructor
ClpSimplex::ClpSimplex(const ClpModel *rhs,
int numberRows, const int *whichRow,
int numberColumns, const int *whichColumn,
bool dropNames, bool dropIntegers, bool fixOthers)
: ClpModel(rhs, numberRows, whichRow,
numberColumns, whichColumn, dropNames, dropIntegers)
, bestPossibleImprovement_(0.0)
, zeroTolerance_(1.0e-13)
, columnPrimalSequence_(-2)
, rowPrimalSequence_(-2)
, bestObjectiveValue_(-COIN_DBL_MAX)
, moreSpecialOptions_(2)
, baseIteration_(0)
, vectorMode_(0)
, primalToleranceToGetOptimal_(-1.0)
, largeValue_(1.0e15)
, largestPrimalError_(0.0)
, largestDualError_(0.0)
, alphaAccuracy_(-1.0)
, dualBound_(1.0e10)
, alpha_(0.0)
, theta_(0.0)
, lowerIn_(0.0)
, valueIn_(0.0)
, upperIn_(-COIN_DBL_MAX)
, dualIn_(0.0)
, lowerOut_(-1)
, valueOut_(-1)
, upperOut_(-1)
, dualOut_(-1)
, dualTolerance_(1.0e-7)
, primalTolerance_(1.0e-7)
, sumDualInfeasibilities_(0.0)
, sumPrimalInfeasibilities_(0.0)
, infeasibilityCost_(1.0e10)
, sumOfRelaxedDualInfeasibilities_(0.0)
, sumOfRelaxedPrimalInfeasibilities_(0.0)
, acceptablePivot_(1.0e-8)
, lower_(NULL)
, rowLowerWork_(NULL)
, columnLowerWork_(NULL)
, upper_(NULL)
, rowUpperWork_(NULL)
, columnUpperWork_(NULL)
, cost_(NULL)
, rowObjectiveWork_(NULL)
, objectiveWork_(NULL)
, sequenceIn_(-1)
, directionIn_(-1)
, sequenceOut_(-1)
, directionOut_(-1)
, pivotRow_(-1)
, lastGoodIteration_(-100)
, dj_(NULL)
, rowReducedCost_(NULL)
, reducedCostWork_(NULL)
, solution_(NULL)
, rowActivityWork_(NULL)
, columnActivityWork_(NULL)
, numberDualInfeasibilities_(0)
, numberDualInfeasibilitiesWithoutFree_(0)
, numberPrimalInfeasibilities_(100)
, numberRefinements_(0)
, pivotVariable_(NULL)
, factorization_(NULL)
, savedSolution_(NULL)
, numberTimesOptimal_(0)
, disasterArea_(NULL)
, changeMade_(1)
, algorithm_(0)
, forceFactorization_(-1)
, perturbation_(100)
, nonLinearCost_(NULL)
, lastBadIteration_(-999999)
, lastFlaggedIteration_(-999999)
, numberFake_(0)
, numberChanged_(0)
, progressFlag_(0)
, firstFree_(-1)
, numberExtraRows_(0)
, maximumBasic_(0)
, dontFactorizePivots_(0)
, incomingInfeasibility_(1.0)
, allowedInfeasibility_(10.0)
, automaticScale_(0)
, maximumPerturbationSize_(0)
, perturbationArray_(NULL)
, baseModel_(NULL)
#ifdef ABC_INHERIT
, abcSimplex_(NULL)
, abcState_(0)
#endif
, minIntervalProgressUpdate_(0.7)
, lastStatusUpdate_(0.0)
{
int i;
for (i = 0; i < 6; i++) {
rowArray_[i] = NULL;
columnArray_[i] = NULL;
}
for (i = 0; i < 4; i++) {
spareIntArray_[i] = 0;
spareDoubleArray_[i] = 0.0;
}
saveStatus_ = NULL;
// get an empty factorization so we can set tolerances etc
getEmptyFactorization();
// say Steepest pricing
dualRowPivot_ = new ClpDualRowSteepest();
// say Steepest pricing
primalColumnPivot_ = new ClpPrimalColumnSteepest();
solveType_ = 1; // say simplex based life form
eventHandler_->setSimplex(this);
if (fixOthers) {
int numberOtherColumns = rhs->numberColumns();
int numberOtherRows = rhs->numberRows();
double *solution = new double[numberOtherColumns];
CoinZeroN(solution, numberOtherColumns);
int i;
for (i = 0; i < numberColumns; i++) {
int iColumn = whichColumn[i];
if (solution[iColumn])
fixOthers = false; // duplicates
solution[iColumn] = 1.0;
}
if (fixOthers) {
const double *otherSolution = rhs->primalColumnSolution();
const double *objective = rhs->objective();
double offset = 0.0;
for (i = 0; i < numberOtherColumns; i++) {
if (solution[i]) {
solution[i] = 0.0; // in
} else {
solution[i] = otherSolution[i];
offset += objective[i] * otherSolution[i];
}
}
double *rhsModification = new double[numberOtherRows];
CoinZeroN(rhsModification, numberOtherRows);
rhs->matrix()->times(solution, rhsModification);
for (i = 0; i < numberRows; i++) {
int iRow = whichRow[i];
if (rowLower_[i] > -1.0e20)
rowLower_[i] -= rhsModification[iRow];
if (rowUpper_[i] < 1.0e20)
rowUpper_[i] -= rhsModification[iRow];
}
delete[] rhsModification;
setObjectiveOffset(rhs->objectiveOffset() - offset);
// And set objective value to match
setObjectiveValue(rhs->objectiveValue());
}
delete[] solution;
}
}
// Subproblem constructor
ClpSimplex::ClpSimplex(const ClpSimplex *rhs,
int numberRows, const int *whichRow,
int numberColumns, const int *whichColumn,
bool dropNames, bool dropIntegers, bool fixOthers)
: ClpModel(rhs, numberRows, whichRow,
numberColumns, whichColumn, dropNames, dropIntegers)
, bestPossibleImprovement_(0.0)
, zeroTolerance_(1.0e-13)
, columnPrimalSequence_(-2)
, rowPrimalSequence_(-2)
, bestObjectiveValue_(-COIN_DBL_MAX)
, moreSpecialOptions_(2)
, baseIteration_(0)
, vectorMode_(0)
, primalToleranceToGetOptimal_(-1.0)
, largeValue_(1.0e15)
, largestPrimalError_(0.0)
, largestDualError_(0.0)
, alphaAccuracy_(-1.0)
, dualBound_(1.0e10)
, alpha_(0.0)
, theta_(0.0)
, lowerIn_(0.0)
, valueIn_(0.0)
, upperIn_(-COIN_DBL_MAX)
, dualIn_(0.0)
, lowerOut_(-1)
, valueOut_(-1)
, upperOut_(-1)
, dualOut_(-1)
, dualTolerance_(rhs->dualTolerance_)
, primalTolerance_(rhs->primalTolerance_)
, sumDualInfeasibilities_(0.0)
, sumPrimalInfeasibilities_(0.0)
, infeasibilityCost_(1.0e10)
, sumOfRelaxedDualInfeasibilities_(0.0)
, sumOfRelaxedPrimalInfeasibilities_(0.0)
, acceptablePivot_(1.0e-8)
, lower_(NULL)
, rowLowerWork_(NULL)
, columnLowerWork_(NULL)
, upper_(NULL)
, rowUpperWork_(NULL)
, columnUpperWork_(NULL)
, cost_(NULL)
, rowObjectiveWork_(NULL)
, objectiveWork_(NULL)
, sequenceIn_(-1)
, directionIn_(-1)
, sequenceOut_(-1)
, directionOut_(-1)
, pivotRow_(-1)
, lastGoodIteration_(-100)
, dj_(NULL)
, rowReducedCost_(NULL)
, reducedCostWork_(NULL)
, solution_(NULL)
, rowActivityWork_(NULL)
, columnActivityWork_(NULL)
, numberDualInfeasibilities_(0)
, numberDualInfeasibilitiesWithoutFree_(0)
, numberPrimalInfeasibilities_(100)
, numberRefinements_(0)
, pivotVariable_(NULL)
, factorization_(NULL)
, savedSolution_(NULL)
, numberTimesOptimal_(0)
, disasterArea_(NULL)
, changeMade_(1)
, algorithm_(0)
, forceFactorization_(-1)
, perturbation_(100)
, nonLinearCost_(NULL)
, lastBadIteration_(-999999)
, lastFlaggedIteration_(-999999)
, numberFake_(0)
, numberChanged_(0)
, progressFlag_(0)
, firstFree_(-1)
, numberExtraRows_(0)
, maximumBasic_(0)
, dontFactorizePivots_(0)
, incomingInfeasibility_(1.0)
, allowedInfeasibility_(10.0)
, automaticScale_(0)
, maximumPerturbationSize_(0)
, perturbationArray_(NULL)
, baseModel_(NULL)
#ifdef ABC_INHERIT
, abcSimplex_(NULL)
, abcState_(rhs->abcState_)
#endif
, minIntervalProgressUpdate_(rhs->minIntervalProgressUpdate_)
, lastStatusUpdate_(rhs->lastStatusUpdate_)
{
int i;
for (i = 0; i < 6; i++) {
rowArray_[i] = NULL;
columnArray_[i] = NULL;
}
for (i = 0; i < 4; i++) {
spareIntArray_[i] = 0;
spareDoubleArray_[i] = 0.0;
}
saveStatus_ = NULL;
eventHandler_->setSimplex(this);
factorization_ = new ClpFactorization(*rhs->factorization_, -numberRows_);
//factorization_ = new ClpFactorization(*rhs->factorization_,
// rhs->factorization_->goDenseThreshold());
ClpPEDualRowSteepest *pivotDualPE = dynamic_cast< ClpPEDualRowSteepest * >(rhs->dualRowPivot_);
if (pivotDualPE) {
dualRowPivot_ = new ClpPEDualRowSteepest(pivotDualPE->psi());
} else {
ClpDualRowDantzig *pivot = dynamic_cast< ClpDualRowDantzig * >(rhs->dualRowPivot_);
// say Steepest pricing
if (!pivot)
dualRowPivot_ = new ClpDualRowSteepest();
else
dualRowPivot_ = new ClpDualRowDantzig();
}
ClpPEPrimalColumnSteepest *pivotPrimalPE = dynamic_cast< ClpPEPrimalColumnSteepest * >(rhs->primalColumnPivot_);
if (pivotPrimalPE) {
primalColumnPivot_ = new ClpPEPrimalColumnSteepest(pivotPrimalPE->psi());
} else {
// say Steepest pricing
primalColumnPivot_ = new ClpPrimalColumnSteepest();
}
solveType_ = 1; // say simplex based life form
if (fixOthers) {
int numberOtherColumns = rhs->numberColumns();
int numberOtherRows = rhs->numberRows();
double *solution = new double[numberOtherColumns];
CoinZeroN(solution, numberOtherColumns);
int i;
for (i = 0; i < numberColumns; i++) {
int iColumn = whichColumn[i];
if (solution[iColumn])
fixOthers = false; // duplicates
solution[iColumn] = 1.0;
}
if (fixOthers) {
const double *otherSolution = rhs->primalColumnSolution();
const double *objective = rhs->objective();
double offset = 0.0;
for (i = 0; i < numberOtherColumns; i++) {
if (solution[i]) {
solution[i] = 0.0; // in
} else {
solution[i] = otherSolution[i];
offset += objective[i] * otherSolution[i];
}
}
double *rhsModification = new double[numberOtherRows];
CoinZeroN(rhsModification, numberOtherRows);
rhs->matrix()->times(solution, rhsModification);
for (i = 0; i < numberRows; i++) {
int iRow = whichRow[i];
if (rowLower_[i] > -1.0e20)
rowLower_[i] -= rhsModification[iRow];
if (rowUpper_[i] < 1.0e20)
rowUpper_[i] -= rhsModification[iRow];
}
delete[] rhsModification;
setObjectiveOffset(rhs->objectiveOffset() - offset);
// And set objective value to match
setObjectiveValue(rhs->objectiveValue());
}
delete[] solution;
}
if (rhs->maximumPerturbationSize_) {
maximumPerturbationSize_ = 2 * numberColumns;
perturbationArray_ = new double[maximumPerturbationSize_];
for (i = 0; i < numberColumns; i++) {
int iColumn = whichColumn[i];
perturbationArray_[2 * i] = rhs->perturbationArray_[2 * iColumn];
perturbationArray_[2 * i + 1] = rhs->perturbationArray_[2 * iColumn + 1];
}
}
}
// Puts solution back small model
void ClpSimplex::getbackSolution(const ClpSimplex &smallModel, const int *whichRow, const int *whichColumn)
{
setSumDualInfeasibilities(smallModel.sumDualInfeasibilities());
setNumberDualInfeasibilities(smallModel.numberDualInfeasibilities());
setSumPrimalInfeasibilities(smallModel.sumPrimalInfeasibilities());
setNumberPrimalInfeasibilities(smallModel.numberPrimalInfeasibilities());
setNumberIterations(smallModel.numberIterations());
setProblemStatus(smallModel.status());
setObjectiveValue(smallModel.objectiveValue());
const double *solution2 = smallModel.primalColumnSolution();
int i;
int numberRows2 = smallModel.numberRows();
int numberColumns2 = smallModel.numberColumns();
const double *dj2 = smallModel.dualColumnSolution();
for (i = 0; i < numberColumns2; i++) {
int iColumn = whichColumn[i];
columnActivity_[iColumn] = solution2[i];
reducedCost_[iColumn] = dj2[i];
setStatus(iColumn, smallModel.getStatus(i));
}
const double *dual2 = smallModel.dualRowSolution();
memset(dual_, 0, numberRows_ * sizeof(double));
for (i = 0; i < numberRows2; i++) {
int iRow = whichRow[i];
setRowStatus(iRow, smallModel.getRowStatus(i));
dual_[iRow] = dual2[i];
}
CoinZeroN(rowActivity_, numberRows_);
#if 0
if (!problemStatus_) {
ClpDisjointCopyN(smallModel.objective(), smallModel.numberColumns_, smallModel.reducedCost_);
smallModel.matrix_->transposeTimes(-1.0, smallModel.dual_, smallModel.reducedCost_);
for (int i = 0; i < smallModel.numberColumns_; i++) {
if (smallModel.getColumnStatus(i) == basic)
assert (fabs(smallModel.reducedCost_[i]) < 1.0e-5);
}
ClpDisjointCopyN(objective(), numberColumns_, reducedCost_);
matrix_->transposeTimes(-1.0, dual_, reducedCost_);
for (int i = 0; i < numberColumns_; i++) {
if (getColumnStatus(i) == basic)
assert (fabs(reducedCost_[i]) < 1.0e-5);
}
}
#endif
matrix()->times(columnActivity_, rowActivity_);
}
//-----------------------------------------------------------------------------
ClpSimplex::~ClpSimplex()
{
setPersistenceFlag(0);
gutsOfDelete(0);
delete nonLinearCost_;
}
//#############################################################################
void ClpSimplex::setLargeValue(double value)
{
if (value > 0.0 && value < COIN_DBL_MAX)
largeValue_ = value;
}
int ClpSimplex::gutsOfSolution(double *givenDuals,
const double *givenPrimals,
bool valuesPass)
{
// if values pass, save values of basic variables
double *save = NULL;
double oldValue = 0.0;
double oldLargestPrimalError = largestPrimalError_;
double oldLargestDualError = largestDualError_;
if (valuesPass) {
assert(algorithm_ > 0); // only primal at present
assert(nonLinearCost_);
int iRow;
checkPrimalSolution(rowActivityWork_, columnActivityWork_);
// get correct bounds on all variables
nonLinearCost_->checkInfeasibilities(primalTolerance_);
oldValue = nonLinearCost_->largestInfeasibility();
save = new double[numberRows_];
for (iRow = 0; iRow < numberRows_; iRow++) {
int iPivot = pivotVariable_[iRow];
save[iRow] = solution_[iPivot];
}
}
// do work
computePrimals(rowActivityWork_, columnActivityWork_);
// If necessary - override results
if (givenPrimals) {
CoinMemcpyN(givenPrimals, numberColumns_, columnActivityWork_);
memset(rowActivityWork_, 0, numberRows_ * sizeof(double));
times(-1.0, columnActivityWork_, rowActivityWork_);
}
double objectiveModification = 0.0;
if (algorithm_ > 0 && nonLinearCost_ != NULL) {
#ifdef CLP_USER_DRIVEN
eventHandler_->eventWithInfo(ClpEventHandler::goodFactorization, NULL);
#endif
// primal algorithm
// get correct bounds on all variables
// If 4 bit set - Force outgoing variables to exact bound (primal)
if ((specialOptions_ & 4) == 0)
nonLinearCost_->checkInfeasibilities(primalTolerance_);
else
nonLinearCost_->checkInfeasibilities(0.0);
objectiveModification += nonLinearCost_->changeInCost();
if (nonLinearCost_->numberInfeasibilities())
if (handler_->detail(CLP_SIMPLEX_NONLINEAR, messages_) < 100) {
handler_->message(CLP_SIMPLEX_NONLINEAR, messages_)
<< nonLinearCost_->changeInCost()
<< nonLinearCost_->numberInfeasibilities()
<< CoinMessageEol;
}
}
if (valuesPass) {
double badInfeasibility = nonLinearCost_->largestInfeasibility();
#ifdef CLP_DEBUG
std::cout << "Largest given infeasibility " << oldValue
<< " now " << nonLinearCost_->largestInfeasibility() << std::endl;
#endif
int numberOut = 0;
// But may be very large rhs etc
double useError = CoinMin(largestPrimalError_,
1.0e5 / (1.0+maximumAbsElement(solution_, numberRows_ + numberColumns_)));
if ((oldValue < incomingInfeasibility_ || badInfeasibility > (CoinMax(10.0 * allowedInfeasibility_, 100.0 * oldValue)))
&& (badInfeasibility > CoinMax(incomingInfeasibility_, allowedInfeasibility_) || useError > 1.0e-3)) {
if (algorithm_ > 1) {
// nonlinear
//printf("Original largest infeas %g, now %g, primalError %g\n",
// oldValue,nonLinearCost_->largestInfeasibility(),
// largestPrimalError_);
//printf("going to all slack\n");
allSlackBasis(true);
CoinIotaN(pivotVariable_, numberRows_, numberColumns_);
delete [] save;
return 1;
}
//printf("Original largest infeas %g, now %g, primalError %g\n",
// oldValue,nonLinearCost_->largestInfeasibility(),
// largestPrimalError_);
// throw out up to 1000 structurals
int maxOut = (allowedInfeasibility_ == 10.0) ? 1000 : 100;
int iRow;
int *sort = new int[numberRows_];
// first put back solution and store difference
for (iRow = 0; iRow < numberRows_; iRow++) {
int iPivot = pivotVariable_[iRow];
double difference = fabs(solution_[iPivot] - save[iRow]);
solution_[iPivot] = save[iRow];
save[iRow] = difference;
}
int numberBasic = 0;
for (iRow = 0; iRow < numberRows_; iRow++) {
int iPivot = pivotVariable_[iRow];
if (iPivot < numberColumns_) {
// column
double difference = save[iRow];
if (difference > 1.0e-4) {
sort[numberOut] = iRow;
save[numberOut++] = -difference;
if (getStatus(iPivot) == basic)
numberBasic++;
}
}
}
if (!numberBasic) {
//printf("no errors on basic - going to all slack - numberOut %d\n",numberOut);
#if 0
allSlackBasis(true);
CoinIotaN(pivotVariable_, numberRows_, numberColumns_);
#else
// allow
numberOut = 0;
#endif
}
CoinSort_2(save, save + numberOut, sort);
numberOut = CoinMin(maxOut, numberOut);
for (iRow = 0; iRow < numberOut; iRow++) {
int jRow = sort[iRow];
int iColumn = pivotVariable_[jRow];
setColumnStatus(iColumn, superBasic);
setRowStatus(jRow, basic);
pivotVariable_[jRow] = jRow + numberColumns_;
if (fabs(solution_[iColumn]) > 1.0e10) {
if (upper_[iColumn] < 0.0) {
solution_[iColumn] = upper_[iColumn];
} else if (lower_[iColumn] > 0.0) {
solution_[iColumn] = lower_[iColumn];
} else {
solution_[iColumn] = 0.0;
}
}
}
delete[] sort;
}
delete[] save;
save = NULL;
if (numberOut)
return numberOut;
}
delete [] save;
if ((moreSpecialOptions_ & 128) != 0 && !numberIterations_) {
//printf("trying feas pump\n");
const char *integerType = integerInformation();
assert(integerType);
assert(perturbationArray_);
CoinZeroN(cost_, numberRows_ + numberColumns_);
for (int i = 0; i < numberRows_ - numberRows_; i++) {
int iSequence = pivotVariable_[i];
if (iSequence < numberColumns_ && integerType[iSequence]) {
double lower = lower_[iSequence];
double upper = upper_[iSequence];
double value = solution_[iSequence];
if (value >= lower - primalTolerance_ && value <= upper + primalTolerance_) {
double sign;
if (value - lower < upper - value)
sign = 1.0;
else
sign = -1.0;
cost_[iSequence] = sign * perturbationArray_[iSequence];
}
}
}
}
#if CAN_HAVE_ZERO_OBJ > 1
if ((specialOptions_ & 16777216) == 0) {
#endif
computeDuals(givenDuals);
if ((moreSpecialOptions_ & 128) != 0 && !numberIterations_) {
const char *integerType = integerInformation();
// Need to do columns and rows to stay dual feasible
for (int iSequence = 0; iSequence < numberColumns_; iSequence++) {
if (integerType[iSequence] && getStatus(iSequence) != basic) {
double djValue = dj_[iSequence];
double change = 0.0;
if (getStatus(iSequence) == atLowerBound)
change = CoinMax(-djValue, 10.0 * perturbationArray_[iSequence]);
else if (getStatus(iSequence) == atUpperBound)
change = CoinMin(-djValue, -10.0 * perturbationArray_[iSequence]);
cost_[iSequence] = change;
dj_[iSequence] += change;
}
}
}
// now check solutions
//checkPrimalSolution( rowActivityWork_, columnActivityWork_);
//checkDualSolution();
checkBothSolutions();
objectiveValue_ += objectiveModification / (objectiveScale_ * rhsScale_);
#if CAN_HAVE_ZERO_OBJ > 1
} else {
checkPrimalSolution(rowActivityWork_, columnActivityWork_);
#ifndef COIN_REUSE_RANDOM
memset(dj_, 0, (numberRows_ + numberColumns_) * sizeof(double));
#else
for (int iSequence = 0; iSequence < numberRows_ + numberColumns_; iSequence++) {
double value;
switch (getStatus(iSequence)) {
case atLowerBound:
value = 1.0e-9 * (1.0 + randomNumberGenerator_.randomDouble());
break;
case atUpperBound:
value = -1.0e-9 * (1.0 + randomNumberGenerator_.randomDouble());
break;
default:
value = 0.0;
break;
}
}
#endif
objectiveValue_ = 0.0;
}
#endif
if (handler_->logLevel() > 3 || (largestPrimalError_ > 1.0e-2 || largestDualError_ > 1.0e-2))
handler_->message(CLP_SIMPLEX_ACCURACY, messages_)
<< largestPrimalError_
<< largestDualError_
<< CoinMessageEol;
if (largestPrimalError_ > 1.0e-1 && numberRows_ > 100 && numberIterations_) {
// Change factorization tolerance
if (factorization_->zeroTolerance() > 1.0e-18)
factorization_->zeroTolerance(1.0e-18);
}
int returnCode = 0;
bool notChanged = true;
if (numberIterations_ && (forceFactorization_ > 2 || forceFactorization_ < 0 || factorization_->pivotTolerance() < 0.9899999999) && (oldLargestDualError || oldLargestPrimalError)) {
double useOldDualError = oldLargestDualError;
double useDualError = largestDualError_;
if (algorithm_ > 0 && nonLinearCost_ && nonLinearCost_->sumInfeasibilities()) {
double factor = CoinMax(1.0, CoinMin(1.0e3, infeasibilityCost_ * 1.0e-6));
useOldDualError /= factor;
useDualError /= factor;
}
if ((largestPrimalError_ > 1.0e3 && oldLargestPrimalError * 1.0e2 < largestPrimalError_) || (useDualError > 1.0e3 && useOldDualError * 1.0e2 < useDualError)) {
double pivotTolerance = factorization_->pivotTolerance();
double factor = (largestPrimalError_ > 1.0e10 || largestDualError_ > 1.0e10)
? 2.0
: 1.2;
if (pivotTolerance < 0.1)
factorization_->pivotTolerance(0.1);
else if (pivotTolerance < 0.98999999)
factorization_->pivotTolerance(CoinMin(0.99, pivotTolerance * factor));
notChanged = pivotTolerance == factorization_->pivotTolerance();
#ifdef CLP_USEFUL_PRINTOUT
if (pivotTolerance < 0.9899999) {
printf("Changing pivot tolerance from %g to %g and backtracking\n",
pivotTolerance, factorization_->pivotTolerance());
}
printf("because old,new primal error %g,%g - dual %g,%g pivot_tol %g\n",
oldLargestPrimalError, largestPrimalError_,
oldLargestDualError, largestDualError_,
pivotTolerance);
#endif
if (pivotTolerance < 0.9899999) {
largestPrimalError_ = 0.0;
largestDualError_ = 0.0;
returnCode = -123456789;
}
}
}
if (progress_.iterationNumber_[0] > 0 && progress_.iterationNumber_[CLP_PROGRESS - 1] - progress_.iterationNumber_[0] < CLP_PROGRESS * 3 && factorization_->pivotTolerance() < 0.25 && notChanged) {
double pivotTolerance = factorization_->pivotTolerance();
factorization_->pivotTolerance(pivotTolerance * 1.5);
#ifdef CLP_USEFUL_PRINTOUT
printf("Changing pivot tolerance from %g to %g - inverting too often\n",
pivotTolerance, factorization_->pivotTolerance());
#endif
}
// Switch off false values pass indicator
if (!valuesPass && algorithm_ > 0)
firstFree_ = -1;
if (handler_->logLevel() == 63)
printf("end getsolution algorithm %d status %d npinf %d sum,relaxed %g,%g ndinf %d sum,relaxed %g,%g\n",
algorithm_, problemStatus_,
numberPrimalInfeasibilities_, sumPrimalInfeasibilities_, sumOfRelaxedPrimalInfeasibilities_,
numberDualInfeasibilities_, sumDualInfeasibilities_, sumOfRelaxedDualInfeasibilities_);
if ((moreSpecialOptions_ & 8388608) != 0) {
if (algorithm_ < 0) {
bool doneFiddling = false;
// Optimization may make exact test iffy
double testTolerance = minimumPrimalTolerance_ + 1.0e-15;
while (!doneFiddling) {
doneFiddling = true;
while (!sumOfRelaxedPrimalInfeasibilities_ && primalTolerance_ > testTolerance) {
// feasible - adjust tolerance
double saveTolerance = primalTolerance_;
primalTolerance_ = CoinMax(0.25 * primalTolerance_,
minimumPrimalTolerance_);
printf("Resetting primal tolerance from %g to %g\n",
saveTolerance, primalTolerance_);
dblParam_[ClpPrimalTolerance] = primalTolerance_;
moreSpecialOptions_ &= ~8388608;
// redo with switch off
returnCode = gutsOfSolution(givenDuals, givenPrimals, valuesPass);
}
if (primalTolerance_ > testTolerance)
moreSpecialOptions_ |= 8388608; // back on
if ((moreSpecialOptions_ & 8388608) != 0) {
assert(numberPrimalInfeasibilities_);
// average infeasibility
double average = sumPrimalInfeasibilities_ / numberPrimalInfeasibilities_;
double minimum = COIN_DBL_MAX;
double averageTotal = average;
bool firstTime = averageInfeasibility_[0] == COIN_DBL_MAX;
for (int i = 0; i < CLP_INFEAS_SAVE - 1; i++) {
double value = averageInfeasibility_[i + 1];
averageTotal += value;
averageInfeasibility_[i] = value;
minimum = CoinMin(minimum, value);
}
averageInfeasibility_[CLP_INFEAS_SAVE - 1] = average;
averageTotal /= CLP_INFEAS_SAVE;
double oldTolerance = primalTolerance_;
if (averageInfeasibility_[0] != COIN_DBL_MAX) {
if (firstTime) {
primalTolerance_ = CoinMin(0.1, 0.1 * averageTotal);
primalTolerance_ = CoinMin(primalTolerance_, average);
} else if (primalTolerance_ > 0.1 * minimum) {
primalTolerance_ = 0.1 * minimum;
}
primalTolerance_ = CoinMax(primalTolerance_, minimumPrimalTolerance_);
}
if (primalTolerance_ != oldTolerance) {
printf("Changing primal tolerance from %g to %g\n",
oldTolerance, primalTolerance_);
moreSpecialOptions_ &= ~8388608;
// redo with switch off
returnCode = gutsOfSolution(givenDuals, givenPrimals, valuesPass);
if (primalTolerance_ > testTolerance)
moreSpecialOptions_ |= 8388608 | 4194304;
if (!sumOfRelaxedPrimalInfeasibilities_)
doneFiddling = false; // over done it
}
}
}
}
}
return returnCode;
}
void ClpSimplex::computePrimals(const double *rowActivities,
const double *columnActivities)
{
//work space
CoinIndexedVector *workSpace = rowArray_[0];
CoinIndexedVector *arrayVector = rowArray_[1];
arrayVector->clear();
CoinIndexedVector *previousVector = rowArray_[2];
previousVector->clear();
// accumulate non basic stuff
int iRow;
// order is this way for scaling
if (columnActivities != columnActivityWork_)
ClpDisjointCopyN(columnActivities, numberColumns_, columnActivityWork_);
if (rowActivities != rowActivityWork_)
ClpDisjointCopyN(rowActivities, numberRows_, rowActivityWork_);
double *array = arrayVector->denseVector();
int *index = arrayVector->getIndices();
int number = 0;
const double *rhsOffset = matrix_->rhsOffset(this, false, true);
if (!rhsOffset) {
// Use whole matrix every time to make it easier for ClpMatrixBase
// So zero out basic
for (iRow = 0; iRow < numberRows_; iRow++) {
int iPivot = pivotVariable_[iRow];
assert(iPivot >= 0);
solution_[iPivot] = 0.0;
#ifdef CLP_INVESTIGATE
assert(getStatus(iPivot) == basic);
#endif
}
// Extended solution before "update"
matrix_->primalExpanded(this, 0);
times(-1.0, columnActivityWork_, array);
for (iRow = 0; iRow < numberRows_; iRow++) {
double value = array[iRow] + rowActivityWork_[iRow];
if (value) {
array[iRow] = value;
index[number++] = iRow;
} else {
array[iRow] = 0.0;
}
}
} else {
// we have an effective rhs lying around
// zero out basic (really just for slacks)
for (iRow = 0; iRow < numberRows_; iRow++) {
int iPivot = pivotVariable_[iRow];
solution_[iPivot] = 0.0;
}
for (iRow = 0; iRow < numberRows_; iRow++) {
double value = rhsOffset[iRow] + rowActivityWork_[iRow];
if (value) {
array[iRow] = value;
index[number++] = iRow;
} else {
array[iRow] = 0.0;
}
}
}
arrayVector->setNumElements(number);
#ifdef CLP_DEBUG
if (numberIterations_ == -3840) {
int i;
for (i = 0; i < numberRows_ + numberColumns_; i++)
printf("%d status %d\n", i, status_[i]);
printf("xxxxx1\n");
for (i = 0; i < numberRows_; i++)
if (array[i])
printf("%d rhs %g\n", i, array[i]);
printf("xxxxx2\n");
for (i = 0; i < numberRows_ + numberColumns_; i++)
if (getStatus(i) != basic)
printf("%d non basic %g %g %g\n", i, lower_[i], solution_[i], upper_[i]);
printf("xxxxx3\n");
}
#endif
// Ftran adjusted RHS and iterate to improve accuracy
double lastError = COIN_DBL_MAX;
int iRefine;
CoinIndexedVector *thisVector = arrayVector;
CoinIndexedVector *lastVector = previousVector;
#if 0
static double * xsave=NULL;
{
double * xx = thisVector->denseVector();
double largest=0.0;
int iLargest=-1;
for (int i=0;i<numberRows_;i++) {
if (fabs(xx[i])>largest) {
largest=fabs(xx[i]);
iLargest=i;
}
}
printf("largest incoming rhs %g on row %d\n",largest,iLargest);
}
if (numberIterations_<-40722) {
double * xx = thisVector->denseVector();
if (xsave) {
double * sol = xsave+numberRows_;