int main (int argc, const char *argv[])
{
ClpSimplex model;
int status;
// Keep names
if (argc < 2) {
status = model.readMps("small.mps", true);
} else {
status = model.readMps(argv[1], true);
}
if (status)
exit(10);
/*
This driver implements what I called Sprint. Cplex calls it
"sifting" which is just as silly. When I thought of this trivial idea
it reminded me of an LP code of the 60's called sprint which after
every factorization took a subset of the matrix into memory (all
64K words!) and then iterated very fast on that subset. On the
problems of those days it did not work very well, but it worked very
well on aircrew scheduling problems where there were very large numbers
of columns all with the same flavor.
*/
/* The idea works best if you can get feasible easily. To make it
more general we can add in costed slacks */
int originalNumberColumns = model.numberColumns();
int numberRows = model.numberRows();
// We will need arrays to choose variables. These are too big but ..
double * weight = new double [numberRows+originalNumberColumns];
int * sort = new int [numberRows+originalNumberColumns];
int numberSort = 0;
// Say we are going to add slacks - if you can get a feasible
// solution then do that at the comment - Add in your own coding here
bool addSlacks = true;
if (addSlacks) {
// initial list will just be artificials
// first we will set all variables as close to zero as possible
int iColumn;
const double * columnLower = model.columnLower();
const double * columnUpper = model.columnUpper();
double * columnSolution = model.primalColumnSolution();
for (iColumn = 0; iColumn < originalNumberColumns; iColumn++) {
double value = 0.0;
if (columnLower[iColumn] > 0.0)
value = columnLower[iColumn];
else if (columnUpper[iColumn] < 0.0)
value = columnUpper[iColumn];
columnSolution[iColumn] = value;
}
// now see what that does to row solution
double * rowSolution = model.primalRowSolution();
memset (rowSolution, 0, numberRows * sizeof(double));
model.times(1.0, columnSolution, rowSolution);
int * addStarts = new int [numberRows+1];
int * addRow = new int[numberRows];
double * addElement = new double[numberRows];
const double * lower = model.rowLower();
const double * upper = model.rowUpper();
addStarts[0] = 0;
int numberArtificials = 0;
double * addCost = new double [numberRows];
const double penalty = 1.0e8;
int iRow;
for (iRow = 0; iRow < numberRows; iRow++) {
if (lower[iRow] > rowSolution[iRow]) {
addRow[numberArtificials] = iRow;
addElement[numberArtificials] = 1.0;
addCost[numberArtificials] = penalty;
numberArtificials++;
addStarts[numberArtificials] = numberArtificials;
} else if (upper[iRow] < rowSolution[iRow]) {
addRow[numberArtificials] = iRow;
addElement[numberArtificials] = -1.0;
addCost[numberArtificials] = penalty;
numberArtificials++;
addStarts[numberArtificials] = numberArtificials;
}
}
model.addColumns(numberArtificials, NULL, NULL, addCost,
addStarts, addRow, addElement);
delete [] addStarts;
delete [] addRow;
delete [] addElement;
delete [] addCost;
// Set up initial list
numberSort = numberArtificials;
int i;
for (i = 0; i < numberSort; i++)
sort[i] = i + originalNumberColumns;
} else {
// Get initial list in some magical way
// Add in your own coding here
abort();
}
int numberColumns = model.numberColumns();
const double * columnLower = model.columnLower();
const double * columnUpper = model.columnUpper();
double * fullSolution = model.primalColumnSolution();
// Just do this number of passes
int maxPass = 100;
int iPass;
double lastObjective = 1.0e31;
// Just take this number of columns in small problem
int smallNumberColumns = CoinMin(3 * numberRows, numberColumns);
smallNumberColumns = CoinMax(smallNumberColumns, 3000);
// We will be using all rows
int * whichRows = new int [numberRows];
for (int iRow = 0; iRow < numberRows; iRow++)
whichRows[iRow] = iRow;
double originalOffset;
model.getDblParam(ClpObjOffset, originalOffset);
for (iPass = 0; iPass < maxPass; iPass++) {
printf("Start of pass %d\n", iPass);
//printf("Bug until submodel new version\n");
CoinSort_2(sort, sort + numberSort, weight);
// Create small problem
ClpSimplex small(&model, numberRows, whichRows, numberSort, sort);
// now see what variables left out do to row solution
double * rowSolution = model.primalRowSolution();
memset (rowSolution, 0, numberRows * sizeof(double));
int iRow, iColumn;
// zero out ones in small problem
for (iColumn = 0; iColumn < numberSort; iColumn++) {
int kColumn = sort[iColumn];
fullSolution[kColumn] = 0.0;
}
// Get objective offset
double offset = 0.0;
const double * objective = model.objective();
for (iColumn = 0; iColumn < originalNumberColumns; iColumn++)
offset += fullSolution[iColumn] * objective[iColumn];
small.setDblParam(ClpObjOffset, originalOffset - offset);
model.times(1.0, fullSolution, rowSolution);
double * lower = small.rowLower();
double * upper = small.rowUpper();
for (iRow = 0; iRow < numberRows; iRow++) {
if (lower[iRow] > -1.0e50)
lower[iRow] -= rowSolution[iRow];
if (upper[iRow] < 1.0e50)
upper[iRow] -= rowSolution[iRow];
}
/* For some problems a useful variant is to presolve problem.
In this case you need to adjust smallNumberColumns to get
right size problem. Also you can dispense with creating
small problem and fix variables in large problem and do presolve
on that. */
// Solve
small.primal();
// move solution back
const double * solution = small.primalColumnSolution();
for (iColumn = 0; iColumn < numberSort; iColumn++) {
int kColumn = sort[iColumn];
model.setColumnStatus(kColumn, small.getColumnStatus(iColumn));
fullSolution[kColumn] = solution[iColumn];
}
for (iRow = 0; iRow < numberRows; iRow++)
model.setRowStatus(iRow, small.getRowStatus(iRow));
memcpy(model.primalRowSolution(), small.primalRowSolution(),
numberRows * sizeof(double));
if ((small.objectiveValue() > lastObjective - 1.0e-7 && iPass > 5) ||
!small.numberIterations() ||
iPass == maxPass - 1) {
break; // finished
} else {
lastObjective = small.objectiveValue();
// get reduced cost for large problem
// this assumes minimization
memcpy(weight, model.objective(), numberColumns * sizeof(double));
model.transposeTimes(-1.0, small.dualRowSolution(), weight);
// now massage weight so all basic in plus good djs
for (iColumn = 0; iColumn < numberColumns; iColumn++) {
double dj = weight[iColumn];
double value = fullSolution[iColumn];
if (model.getColumnStatus(iColumn) == ClpSimplex::basic)
dj = -1.0e50;
else if (dj < 0.0 && value < columnUpper[iColumn])
dj = dj;
else if (dj > 0.0 && value > columnLower[iColumn])
dj = -dj;
else if (columnUpper[iColumn] > columnLower[iColumn])
dj = fabs(dj);
else
dj = 1.0e50;
weight[iColumn] = dj;
sort[iColumn] = iColumn;
}
// sort
CoinSort_2(weight, weight + numberColumns, sort);
numberSort = smallNumberColumns;
}
}
if (addSlacks) {
int i;
int numberArtificials = numberColumns - originalNumberColumns;
for (i = 0; i < numberArtificials; i++)
sort[i] = i + originalNumberColumns;
model.deleteColumns(numberArtificials, sort);
}
delete [] weight;
delete [] sort;
delete [] whichRows;
model.primal(1);
return 0;
}
int main(int argc, const char *argv[])
{
{
// Empty model
ClpSimplex model;
// Bounds on rows - as dense vector
double lower[] = {2.0, 1.0};
double upper[] = {COIN_DBL_MAX, 1.0};
// Create space for 2 rows
model.resize(2, 0);
// Fill in
int i;
// Now row bounds
for (i = 0; i < 2; i++) {
model.setRowLower(i, lower[i]);
model.setRowUpper(i, upper[i]);
}
// Now add column 1
int column1Index[] = {0, 1};
double column1Value[] = {1.0, 1.0};
model.addColumn(2, column1Index, column1Value,
0.0, 2, 1.0);
// Now add column 2
int column2Index[] = {1};
double column2Value[] = { -5.0};
model.addColumn(1, column2Index, column2Value,
0.0, COIN_DBL_MAX, 0.0);
// Now add column 3
int column3Index[] = {0, 1};
double column3Value[] = {1.0, 1.0};
model.addColumn(2, column3Index, column3Value,
0.0, 4.0, 4.0);
// solve
model.dual();
/*
Adding one column at a time has a significant overhead so let's
try a more complicated but faster way
First time adding in 10000 columns one by one
*/
model.allSlackBasis();
ClpSimplex modelSave = model;
double time1 = CoinCpuTime();
int k;
for (k = 0; k < 10000; k++) {
int column2Index[] = {0, 1};
double column2Value[] = {1.0, -5.0};
model.addColumn(2, column2Index, column2Value,
0.0, 1.0, 10000.0);
}
printf("Time for 10000 addColumn is %g\n", CoinCpuTime() - time1);
model.dual();
model = modelSave;
// Now use build
CoinBuild buildObject;
time1 = CoinCpuTime();
for (k = 0; k < 100000; k++) {
int column2Index[] = {0, 1};
double column2Value[] = {1.0, -5.0};
buildObject.addColumn(2, column2Index, column2Value,
0.0, 1.0, 10000.0);
}
model.addColumns(buildObject);
printf("Time for 100000 addColumn using CoinBuild is %g\n", CoinCpuTime() - time1);
model.dual();
model = modelSave;
// Now use build +-1
int del[] = {0, 1, 2};
model.deleteColumns(3, del);
CoinBuild buildObject2;
time1 = CoinCpuTime();
for (k = 0; k < 10000; k++) {
int column2Index[] = {0, 1};
double column2Value[] = {1.0, 1.0, -1.0};
int bias = k & 1;
buildObject2.addColumn(2, column2Index, column2Value + bias,
0.0, 1.0, 10000.0);
}
model.addColumns(buildObject2, true);
printf("Time for 10000 addColumn using CoinBuild+-1 is %g\n", CoinCpuTime() - time1);
model.dual();
model = modelSave;
// Now use build +-1
model.deleteColumns(3, del);
CoinModel modelObject2;
time1 = CoinCpuTime();
for (k = 0; k < 10000; k++) {
int column2Index[] = {0, 1};
double column2Value[] = {1.0, 1.0, -1.0};
int bias = k & 1;
modelObject2.addColumn(2, column2Index, column2Value + bias,
0.0, 1.0, 10000.0);
}
model.addColumns(modelObject2, true);
printf("Time for 10000 addColumn using CoinModel+-1 is %g\n", CoinCpuTime() - time1);
//model.writeMps("xx.mps");
model.dual();
model = modelSave;
// Now use model
CoinModel modelObject;
time1 = CoinCpuTime();
for (k = 0; k < 100000; k++) {
int column2Index[] = {0, 1};
double column2Value[] = {1.0, -5.0};
modelObject.addColumn(2, column2Index, column2Value,
0.0, 1.0, 10000.0);
}
model.addColumns(modelObject);
printf("Time for 100000 addColumn using CoinModel is %g\n", CoinCpuTime() - time1);
model.dual();
// Print column solution Just first 3 columns
int numberColumns = model.numberColumns();
numberColumns = CoinMin(3, numberColumns);
// Alternatively getColSolution()
double * columnPrimal = model.primalColumnSolution();
// Alternatively getReducedCost()
double * columnDual = model.dualColumnSolution();
// Alternatively getColLower()
double * columnLower = model.columnLower(); // Alternatively getColUpper()
double * columnUpper = model.columnUpper();
// Alternatively getObjCoefficients()
double * columnObjective = model.objective();
int iColumn;
std::cout << " Primal Dual Lower Upper Cost"
<< std::endl;
for (iColumn = 0; iColumn < numberColumns; iColumn++) {
double value;
std::cout << std::setw(6) << iColumn << " ";
value = columnPrimal[iColumn];
if (fabs(value) < 1.0e5)
std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
else
std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
value = columnDual[iColumn];
if (fabs(value) < 1.0e5)
std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
else
std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
value = columnLower[iColumn];
if (fabs(value) < 1.0e5)
std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
else
std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
value = columnUpper[iColumn];
if (fabs(value) < 1.0e5)
std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
else
std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
value = columnObjective[iColumn];
if (fabs(value) < 1.0e5)
std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
else
std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
std::cout << std::endl;
}
std::cout << "--------------------------------------" << std::endl;
}
{
// Now copy a model
ClpSimplex model;
int status;
if (argc < 2) {
#if defined(SAMPLEDIR)
status = model.readMps(SAMPLEDIR "/p0033.mps", true);
#else
fprintf(stderr, "Do not know where to find sample MPS files.\n");
exit(1);
#endif
} else
status = model.readMps(argv[1]);
if (status) {
printf("errors on input\n");
exit(77);
}
model.initialSolve();
int numberRows = model.numberRows();
int numberColumns = model.numberColumns();
const double * rowLower = model.rowLower();
const double * rowUpper = model.rowUpper();
// Start off model2
ClpSimplex model2;
model2.addRows(numberRows, rowLower, rowUpper, NULL);
// Build object
CoinBuild buildObject;
// Add columns
const double * columnLower = model.columnLower();
const double * columnUpper = model.columnUpper();
const double * objective = model.objective();
CoinPackedMatrix * matrix = model.matrix();
const int * row = matrix->getIndices();
const int * columnLength = matrix->getVectorLengths();
const CoinBigIndex * columnStart = matrix->getVectorStarts();
const double * elementByColumn = matrix->getElements();
for (int iColumn = 0; iColumn < numberColumns; iColumn++) {
CoinBigIndex start = columnStart[iColumn];
buildObject.addColumn(columnLength[iColumn], row + start, elementByColumn + start,
columnLower[iColumn], columnUpper[iColumn],
objective[iColumn]);
}
// add in
model2.addColumns(buildObject);
model2.initialSolve();
}
{
// and again
ClpSimplex model;
int status;
if (argc < 2) {
#if defined(SAMPLEDIR)
status = model.readMps(SAMPLEDIR "/p0033.mps", true);
#else
fprintf(stderr, "Do not know where to find sample MPS files.\n");
exit(1);
#endif
} else
status = model.readMps(argv[1]);
if (status) {
printf("errors on input\n");
exit(77);
}
model.initialSolve();
int numberRows = model.numberRows();
int numberColumns = model.numberColumns();
const double * rowLower = model.rowLower();
const double * rowUpper = model.rowUpper();
// Build object
CoinModel buildObject;
for (int iRow = 0; iRow < numberRows; iRow++)
buildObject.setRowBounds(iRow, rowLower[iRow], rowUpper[iRow]);
// Add columns
const double * columnLower = model.columnLower();
const double * columnUpper = model.columnUpper();
const double * objective = model.objective();
CoinPackedMatrix * matrix = model.matrix();
const int * row = matrix->getIndices();
const int * columnLength = matrix->getVectorLengths();
const CoinBigIndex * columnStart = matrix->getVectorStarts();
const double * elementByColumn = matrix->getElements();
for (int iColumn = 0; iColumn < numberColumns; iColumn++) {
CoinBigIndex start = columnStart[iColumn];
buildObject.addColumn(columnLength[iColumn], row + start, elementByColumn + start,
columnLower[iColumn], columnUpper[iColumn],
objective[iColumn]);
}
// add in
ClpSimplex model2;
model2.loadProblem(buildObject);
model2.initialSolve();
}
return 0;
}
//-----------------------------------------------------------------------------
void CbcSolver2::resolve()
{
int numberColumns = modelPtr_->numberColumns();
if ((count_<10&&algorithm_==2)||!algorithm_) {
OsiClpSolverInterface::resolve();
if (modelPtr_->status()==0) {
count_++;
double * solution = modelPtr_->primalColumnSolution();
int i;
for (i=0;i<numberColumns;i++) {
if (solution[i]>1.0e-6||modelPtr_->getStatus(i)==ClpSimplex::basic) {
node_[i]=CoinMax(count_,node_[i]);
howMany_[i]++;
}
}
} else {
if (!algorithm_==2)
printf("infeasible early on\n");
}
} else {
// use counts
int numberRows=modelPtr_->numberRows();
int * whichRow = new int[numberRows];
int * whichColumn = new int [numberColumns];
int i;
const double * lower = modelPtr_->columnLower();
const double * upper = modelPtr_->columnUpper();
const double * rowUpper = modelPtr_->rowUpper();
bool equality=false;
for (i=0;i<numberRows;i++) {
if (rowUpper[i]==1.0) {
equality=true;
break;
}
}
setBasis(basis_,modelPtr_);
int nNewCol=0;
// Column copy
//const double * element = modelPtr_->matrix()->getElements();
const int * row = modelPtr_->matrix()->getIndices();
const CoinBigIndex * columnStart = modelPtr_->matrix()->getVectorStarts();
const int * columnLength = modelPtr_->matrix()->getVectorLengths();
int * rowActivity = new int[numberRows];
memset(rowActivity,0,numberRows*sizeof(int));
int * rowActivity2 = new int[numberRows];
memset(rowActivity2,0,numberRows*sizeof(int));
char * mark = new char[numberColumns];
memset(mark,0,numberColumns);
// Get rows which are satisfied
for (i=0;i<numberColumns;i++) {
if (lower[i]>0.0) {
CoinBigIndex j;
for (j=columnStart[i];
j<columnStart[i]+columnLength[i];j++) {
int iRow=row[j];
rowActivity2[iRow] ++;
}
} else if (!upper[i]) {
mark[i]=2; // no good
}
}
// If equality - check not infeasible
if (equality) {
bool feasible=true;
for (i=0;i<numberRows;i++) {
if (rowActivity2[i]>1) {
feasible=false;
break;
}
}
if (!feasible) {
delete [] rowActivity;
delete [] rowActivity2;
modelPtr_->setProblemStatus(1);
delete [] whichRow;
delete [] whichColumn;
delete [] mark;
printf("infeasible by inspection (over)\n");
return;
}
}
int nNoGood=0;
for (i=0;i<numberColumns;i++) {
if (mark[i]==2) {
nNoGood++;
continue;
}
bool choose;
if (algorithm_==1)
choose = true;
else
choose = (node_[i]>count_-memory_&&node_[i]>0);
bool any;
if (equality) {
// See if forced to be zero
CoinBigIndex j;
any=true;
for (j=columnStart[i];
j<columnStart[i]+columnLength[i];j++) {
int iRow=row[j];
if (rowActivity2[iRow])
any=false; // can't be in
}
} else {
// See if not useful
CoinBigIndex j;
any=false;
for (j=columnStart[i];
j<columnStart[i]+columnLength[i];j++) {
int iRow=row[j];
if (!rowActivity2[iRow])
any=true; // useful
}
}
if (!any&&!lower[i]) {
choose=false;
// and say can't be useful
mark[i]=2;
nNoGood++;
}
if (strategy_&&modelPtr_->getColumnStatus(i)==ClpSimplex::basic)
choose=true;
if (choose||lower[i]>0.0) {
mark[i]=1;
whichColumn[nNewCol++]=i;
CoinBigIndex j;
double value = upper[i];
if (value) {
for (j=columnStart[i];
j<columnStart[i]+columnLength[i];j++) {
int iRow=row[j];
rowActivity[iRow] ++;
}
}
}
}
// If equality add in slacks
CoinModel build;
if (equality) {
int row=0;
for (i=0;i<numberRows;i++) {
// put in all rows if wanted
if(strategy_)
rowActivity2[i]=0;
if (!rowActivity2[i]) {
double element=1.0;
build.addColumn(1,&row,&element,0.0,1.0,1.0e8); // large cost
row++;
}
}
}
int nOK=0;
int nNewRow=0;
for (i=0;i<numberRows;i++) {
if (rowActivity[i])
nOK++;
if (!rowActivity2[i])
whichRow[nNewRow++]=i; // not satisfied
else
modelPtr_->setRowStatus(i,ClpSimplex::basic); // make slack basic
}
if (nOK<numberRows) {
for (i=0;i<numberColumns;i++) {
if (!mark[i]) {
CoinBigIndex j;
int good=0;
for (j=columnStart[i];
j<columnStart[i]+columnLength[i];j++) {
int iRow=row[j];
if (!rowActivity[iRow]) {
rowActivity[iRow] ++;
good++;
}
}
if (good) {
nOK+=good;
whichColumn[nNewCol++]=i;
}
}
}
}
delete [] rowActivity;
delete [] rowActivity2;
if (nOK<numberRows) {
modelPtr_->setProblemStatus(1);
delete [] whichRow;
delete [] whichColumn;
delete [] mark;
printf("infeasible by inspection\n");
return;
}
bool allIn=false;
if (nNewCol+nNoGood+numberRows>numberColumns) {
// add in all
allIn=true;
for (i=0;i<numberColumns;i++) {
if (!mark[i]) {
whichColumn[nNewCol++]=i;
}
}
}
ClpSimplex * temp = new ClpSimplex(modelPtr_,nNewRow,whichRow,nNewCol,whichColumn);
if (equality)
temp->addColumns(build);
temp->setLogLevel(1);
printf("small has %d rows and %d columns (%d impossible to help) %s\n",
nNewRow,nNewCol,nNoGood,allIn ? "all in" : "");
temp->setSpecialOptions(128+512);
temp->setDualObjectiveLimit(1.0e50);
temp->dual();
assert (!temp->status());
double * solution = modelPtr_->primalColumnSolution();
const double * solution2 = temp->primalColumnSolution();
memset(solution,0,numberColumns*sizeof(double));
for (i=0;i<nNewCol;i++) {
int iColumn = whichColumn[i];
solution[iColumn]=solution2[i];
modelPtr_->setStatus(iColumn,temp->getStatus(i));
}
double * rowSolution = modelPtr_->primalRowSolution();
const double * rowSolution2 = temp->primalRowSolution();
double * dual = modelPtr_->dualRowSolution();
const double * dual2 = temp->dualRowSolution();
memset(dual,0,numberRows*sizeof(double));
for (i=0;i<nNewRow;i++) {
int iRow=whichRow[i];
modelPtr_->setRowStatus(iRow,temp->getRowStatus(i));
rowSolution[iRow]=rowSolution2[i];
dual[iRow]=dual2[i];
}
// See if optimal
double * dj = modelPtr_->dualColumnSolution();
// get reduced cost for large problem
// this assumes minimization
memcpy(dj,modelPtr_->objective(),numberColumns*sizeof(double));
modelPtr_->transposeTimes(-1.0,dual,dj);
modelPtr_->setObjectiveValue(temp->objectiveValue());
modelPtr_->setProblemStatus(0);
int nBad=0;
for (i=0;i<numberColumns;i++) {
if (modelPtr_->getStatus(i)==ClpSimplex::atLowerBound
&&upper[i]>lower[i]&&dj[i]<-1.0e-5)
nBad++;
}
//modelPtr_->writeMps("bada.mps");
//temp->writeMps("badb.mps");
delete temp;
if (nBad&&!allIn) {
assert (algorithm_==2);
//printf("%d bad\n",nBad);
timesBad_++;
// just non mark==2
int nAdded=0;
for (i=0;i<numberColumns;i++) {
if (!mark[i]) {
whichColumn[nNewCol++]=i;
nAdded++;
}
}
assert (nAdded);
{
temp = new ClpSimplex(modelPtr_,nNewRow,whichRow,nNewCol,whichColumn);
if (equality)
temp->addColumns(build);
temp->setLogLevel(2);
temp->setSpecialOptions(128+512);
temp->setDualObjectiveLimit(1.0e50);
temp->primal(1);
assert (!temp->status());
double * solution = modelPtr_->primalColumnSolution();
const double * solution2 = temp->primalColumnSolution();
memset(solution,0,numberColumns*sizeof(double));
for (i=0;i<nNewCol;i++) {
int iColumn = whichColumn[i];
solution[iColumn]=solution2[i];
modelPtr_->setStatus(iColumn,temp->getStatus(i));
}
double * rowSolution = modelPtr_->primalRowSolution();
const double * rowSolution2 = temp->primalRowSolution();
double * dual = modelPtr_->dualRowSolution();
const double * dual2 = temp->dualRowSolution();
memset(dual,0,numberRows*sizeof(double));
for (i=0;i<nNewRow;i++) {
int iRow=whichRow[i];
modelPtr_->setRowStatus(iRow,temp->getRowStatus(i));
rowSolution[iRow]=rowSolution2[i];
dual[iRow]=dual2[i];
}
modelPtr_->setObjectiveValue(temp->objectiveValue());
modelPtr_->setProblemStatus(0);
iterationsBad_ += temp->numberIterations();
printf("clean %d\n",temp->numberIterations());
delete temp;
}
}
delete [] mark;
delete [] whichRow;
delete [] whichColumn;
basis_ = getBasis(modelPtr_);
modelPtr_->setSpecialOptions(0);
count_++;
if ((count_%100)==0&&algorithm_==2)
printf("count %d, bad %d - iterations %d\n",count_,timesBad_,iterationsBad_);
for (i=0;i<numberColumns;i++) {
if (solution[i]>1.0e-6||modelPtr_->getStatus(i)==ClpSimplex::basic) {
node_[i]=CoinMax(count_,node_[i]);
howMany_[i]++;
}
}
if (modelPtr_->objectiveValue()>=modelPtr_->dualObjectiveLimit())
modelPtr_->setProblemStatus(1);
}
}