void* problem_convert_to_osi(Problem *p)
{
int i;
double rowLb, rowUb;
OsiSolverInterface *solver = new OsiClpSolverInterface();
CoinBuild cb;
solver->setIntParam(OsiNameDiscipline, 2);
solver->messageHandler()->setLogLevel(0);
solver->setHintParam(OsiDoReducePrint,true,OsiHintTry);
for(i = 0; i < p->numCols; i++)
{
solver->addCol(0, NULL, NULL, p->colLb[i], p->colUb[i], p->objCoef[i]);
solver->setColName(i, p->colName[i]);
if(p->colType[i] == CONTINUOUS)
solver->setContinuous(i);
else
solver->setInteger(i);
}
for(i = 0; i < p->numRows; i++)
{
switch(p->rowSense[i])
{
case 'E':
rowLb = p->rhs[i];
rowUb = p->rhs[i];
break;
case 'L':
rowLb = -p->infty;
rowUb = p->rhs[i];
break;
case 'G':
rowLb = p->rhs[i];
rowUb = p->infty;
break;
default:
fprintf(stderr, "Error: invalid type of constraint!\n");
exit(EXIT_FAILURE);
}
cb.addRow(p->rowNElements[i], p->idxsByRow[i], p->coefsByRow[i], rowLb, rowUb);
}
solver->addRows(cb);
for(i = 0; i < p->numRows; i++)
solver->setRowName(i, p->rowName[i]);
return solver;
}
int main(int argc, const char *argv[])
{
try {
// Empty model
ClpSimplex model;
// Objective - just nonzeros
int objIndex[] = {0, 2};
double objValue[] = {1.0, 4.0};
// Upper bounds - as dense vector
double upper[] = {2.0, COIN_DBL_MAX, 4.0};
// Create space for 3 columns
model.resize(0, 3);
// Fill in
int i;
// Virtuous way
// First objective
for (i = 0; i < 2; i++)
model.setObjectiveCoefficient(objIndex[i], objValue[i]);
// Now bounds (lower will be zero by default but do again)
for (i = 0; i < 3; i++) {
model.setColumnLower(i, 0.0);
model.setColumnUpper(i, upper[i]);
}
/*
We could also have done in non-virtuous way e.g.
double * objective = model.objective();
and then set directly
*/
// Faster to add rows all at once - but this is easier to show
// Now add row 1 as >= 2.0
int row1Index[] = {0, 2};
double row1Value[] = {1.0, 1.0};
model.addRow(2, row1Index, row1Value,
2.0, COIN_DBL_MAX);
// Now add row 2 as == 1.0
int row2Index[] = {0, 1, 2};
double row2Value[] = {1.0, -5.0, 1.0};
model.addRow(3, row2Index, row2Value,
1.0, 1.0);
// solve
model.dual();
/*
Adding one row at a time has a significant overhead so let's
try a more complicated but faster way
First time adding in 10000 rows one by one
*/
model.allSlackBasis();
ClpSimplex modelSave = model;
double time1 = CoinCpuTime();
int k;
for (k = 0; k < 10000; k++) {
int row2Index[] = {0, 1, 2};
double row2Value[] = {1.0, -5.0, 1.0};
model.addRow(3, row2Index, row2Value,
1.0, 1.0);
}
printf("Time for 10000 addRow is %g\n", CoinCpuTime() - time1);
model.dual();
model = modelSave;
// Now use build
CoinBuild buildObject;
time1 = CoinCpuTime();
for (k = 0; k < 10000; k++) {
int row2Index[] = {0, 1, 2};
double row2Value[] = {1.0, -5.0, 1.0};
buildObject.addRow(3, row2Index, row2Value,
1.0, 1.0);
}
model.addRows(buildObject);
printf("Time for 10000 addRow using CoinBuild is %g\n", CoinCpuTime() - time1);
model.dual();
model = modelSave;
int del[] = {0, 1, 2};
model.deleteRows(2, del);
// Now use build +-1
CoinBuild buildObject2;
time1 = CoinCpuTime();
for (k = 0; k < 10000; k++) {
int row2Index[] = {0, 1, 2};
double row2Value[] = {1.0, -1.0, 1.0};
buildObject2.addRow(3, row2Index, row2Value,
1.0, 1.0);
}
model.addRows(buildObject2, true);
printf("Time for 10000 addRow using CoinBuild+-1 is %g\n", CoinCpuTime() - time1);
model.dual();
model = modelSave;
model.deleteRows(2, del);
// Now use build +-1
CoinModel modelObject2;
time1 = CoinCpuTime();
for (k = 0; k < 10000; k++) {
int row2Index[] = {0, 1, 2};
double row2Value[] = {1.0, -1.0, 1.0};
modelObject2.addRow(3, row2Index, row2Value,
1.0, 1.0);
}
model.addRows(modelObject2, true);
printf("Time for 10000 addRow using CoinModel+-1 is %g\n", CoinCpuTime() - time1);
model.dual();
model = ClpSimplex();
// Now use build +-1
CoinModel modelObject3;
time1 = CoinCpuTime();
for (k = 0; k < 10000; k++) {
int row2Index[] = {0, 1, 2};
double row2Value[] = {1.0, -1.0, 1.0};
modelObject3.addRow(3, row2Index, row2Value,
1.0, 1.0);
}
model.loadProblem(modelObject3, true);
printf("Time for 10000 addRow using CoinModel load +-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 < 10000; k++) {
int row2Index[] = {0, 1, 2};
double row2Value[] = {1.0, -5.0, 1.0};
modelObject.addRow(3, row2Index, row2Value,
1.0, 1.0);
}
model.addRows(modelObject);
printf("Time for 10000 addRow using CoinModel is %g\n", CoinCpuTime() - time1);
model.dual();
model.writeMps("b.mps");
// Method using least memory - but most complicated
time1 = CoinCpuTime();
// Assumes we know exact size of model and matrix
// Empty model
ClpSimplex model2;
{
// Create space for 3 columns and 10000 rows
int numberRows = 10000;
int numberColumns = 3;
// This is fully dense - but would not normally be so
int numberElements = numberRows * numberColumns;
// Arrays will be set to default values
model2.resize(numberRows, numberColumns);
double * elements = new double [numberElements];
CoinBigIndex * starts = new CoinBigIndex [numberColumns+1];
int * rows = new int [numberElements];;
int * lengths = new int[numberColumns];
// Now fill in - totally unsafe but ....
// no need as defaults to 0.0 double * columnLower = model2.columnLower();
double * columnUpper = model2.columnUpper();
double * objective = model2.objective();
double * rowLower = model2.rowLower();
double * rowUpper = model2.rowUpper();
// Columns - objective was packed
for (k = 0; k < 2; k++) {
int iColumn = objIndex[k];
objective[iColumn] = objValue[k];
}
for (k = 0; k < numberColumns; k++)
columnUpper[k] = upper[k];
// Rows
for (k = 0; k < numberRows; k++) {
rowLower[k] = 1.0;
rowUpper[k] = 1.0;
}
// Now elements
double row2Value[] = {1.0, -5.0, 1.0};
CoinBigIndex put = 0;
for (k = 0; k < numberColumns; k++) {
starts[k] = put;
lengths[k] = numberRows;
double value = row2Value[k];
for (int i = 0; i < numberRows; i++) {
rows[put] = i;
elements[put] = value;
put++;
}
}
starts[numberColumns] = put;
// assign to matrix
CoinPackedMatrix * matrix = new CoinPackedMatrix(true, 0.0, 0.0);
matrix->assignMatrix(true, numberRows, numberColumns, numberElements,
elements, rows, starts, lengths);
ClpPackedMatrix * clpMatrix = new ClpPackedMatrix(matrix);
model2.replaceMatrix(clpMatrix, true);
printf("Time for 10000 addRow using hand written code is %g\n", CoinCpuTime() - time1);
// If matrix is really big could switch off creation of row copy
// model2.setSpecialOptions(256);
}
model2.dual();
model2.writeMps("a.mps");
// Print column solution
int numberColumns = model.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 << setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
else
std::cout << setiosflags(std::ios::scientific) << std::setw(14) << value;
value = columnDual[iColumn];
if (fabs(value) < 1.0e5)
std::cout << setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
else
std::cout << setiosflags(std::ios::scientific) << std::setw(14) << value;
value = columnLower[iColumn];
if (fabs(value) < 1.0e5)
std::cout << setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
else
std::cout << setiosflags(std::ios::scientific) << std::setw(14) << value;
value = columnUpper[iColumn];
if (fabs(value) < 1.0e5)
std::cout << setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
else
std::cout << setiosflags(std::ios::scientific) << std::setw(14) << value;
value = columnObjective[iColumn];
if (fabs(value) < 1.0e5)
std::cout << setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
else
std::cout << setiosflags(std::ios::scientific) << std::setw(14) << value;
std::cout << std::endl;
}
std::cout << "--------------------------------------" << std::endl;
// Test CoinAssert
std::cout << "If Clp compiled with -g below should give assert, if with -O1 or COIN_ASSERT CoinError" << std::endl;
model = modelSave;
model.deleteRows(2, del);
// Deliberate error
model.deleteColumns(1, del + 2);
// Now use build +-1
CoinBuild buildObject3;
time1 = CoinCpuTime();
for (k = 0; k < 10000; k++) {
int row2Index[] = {0, 1, 2};
double row2Value[] = {1.0, -1.0, 1.0};
buildObject3.addRow(3, row2Index, row2Value,
1.0, 1.0);
}
model.addRows(buildObject3, true);
} catch (CoinError e) {
e.print();
if (e.lineNumber() >= 0)
std::cout << "This was from a CoinAssert" << std::endl;
}
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;
}
int main (int argc, const char *argv[])
{
/* Define your favorite OsiSolver.
CbcModel clones the solver so use solver1 up to the time you pass it
to CbcModel then use a pointer to cloned solver (model.solver())
*/
OsiClpSolverInterface solver1;
/* From now on we can build model in a solver independent way.
You can add rows one at a time but for large problems this is slow so
this example uses CoinBuild or CoinModel
*/
OsiSolverInterface * solver = &solver1;
// Data (is exmip1.mps in Mps/Sample
// Objective
double objValue[]={1.0,2.0,0.0,0.0,0.0,0.0,0.0,-1.0};
// Lower bounds for columns
double columnLower[]={2.5,0.0,0.0,0.0,0.5,0.0,0.0,0.0};
// Upper bounds for columns
double columnUpper[]={COIN_DBL_MAX,4.1,1.0,1.0,4.0,
COIN_DBL_MAX,COIN_DBL_MAX,4.3};
// Lower bounds for row activities
double rowLower[]={2.5,-COIN_DBL_MAX,-COIN_DBL_MAX,1.8,3.0};
// Upper bounds for row activities
double rowUpper[]={COIN_DBL_MAX,2.1,4.0,5.0,15.0};
// Matrix stored packed
int column[] = {0,1,3,4,7,
1,2,
2,5,
3,6,
4,7};
double element[] = {3.0,1.0,-2.0,-1.0,-1.0,
2.0,1.1,
1.0,1.0,
2.8,-1.2,
1.0,1.9};
int starts[]={0,5,7,9,11,13};
// Integer variables (note upper bound already 1.0)
int whichInt[]={2,3};
int numberRows=(int) (sizeof(rowLower)/sizeof(double));
int numberColumns=(int) (sizeof(columnLower)/sizeof(double));
#define BUILD 2
#if BUILD==1
// Using CoinBuild
// First do columns (objective and bounds)
int i;
// We are not adding elements
for (i=0;i<numberColumns;i++) {
solver->addCol(0,NULL,NULL,columnLower[i],columnUpper[i],
objValue[i]);
}
// mark as integer
for (i=0;i<(int) (sizeof(whichInt)/sizeof(int));i++)
solver->setInteger(whichInt[i]);
// Now build rows
CoinBuild build;
for (i=0;i<numberRows;i++) {
int startRow = starts[i];
int numberInRow = starts[i+1]-starts[i];
build.addRow(numberInRow,column+startRow,element+startRow,
rowLower[i],rowUpper[i]);
}
// add rows into solver
solver->addRows(build);
#else
/* using CoinModel - more flexible but still beta.
Can do exactly same way but can mix and match much more.
Also all operations are on building object
*/
CoinModel build;
// First do columns (objective and bounds)
int i;
for (i=0;i<numberColumns;i++) {
build.setColumnBounds(i,columnLower[i],columnUpper[i]);
build.setObjective(i,objValue[i]);
}
// mark as integer
for (i=0;i<(int) (sizeof(whichInt)/sizeof(int));i++)
build.setInteger(whichInt[i]);
// Now build rows
for (i=0;i<numberRows;i++) {
int startRow = starts[i];
int numberInRow = starts[i+1]-starts[i];
build.addRow(numberInRow,column+startRow,element+startRow,
rowLower[i],rowUpper[i]);
}
// add rows into solver
solver->loadFromCoinModel(build);
#endif
// Pass to solver
CbcModel model(*solver);
model.solver()->setHintParam(OsiDoReducePrint,true,OsiHintTry);
// Set up some cut generators and defaults
// Probing first as gets tight bounds on continuous
CglProbing generator1;
generator1.setUsingObjective(true);
generator1.setMaxPass(3);
generator1.setMaxProbe(100);
generator1.setMaxLook(50);
generator1.setRowCuts(3);
// generator1.snapshot(*model.solver());
//generator1.createCliques(*model.solver(),2,1000,true);
//generator1.setMode(0);
CglGomory generator2;
// try larger limit
generator2.setLimit(300);
CglKnapsackCover generator3;
CglOddHole generator4;
generator4.setMinimumViolation(0.005);
generator4.setMinimumViolationPer(0.00002);
// try larger limit
generator4.setMaximumEntries(200);
CglClique generator5;
generator5.setStarCliqueReport(false);
generator5.setRowCliqueReport(false);
CglMixedIntegerRounding mixedGen;
CglFlowCover flowGen;
// Add in generators
model.addCutGenerator(&generator1,-1,"Probing");
model.addCutGenerator(&generator2,-1,"Gomory");
model.addCutGenerator(&generator3,-1,"Knapsack");
model.addCutGenerator(&generator4,-1,"OddHole");
model.addCutGenerator(&generator5,-1,"Clique");
model.addCutGenerator(&flowGen,-1,"FlowCover");
model.addCutGenerator(&mixedGen,-1,"MixedIntegerRounding");
OsiClpSolverInterface * osiclp = dynamic_cast< OsiClpSolverInterface*> (model.solver());
// go faster stripes
if (osiclp->getNumRows()<300&&osiclp->getNumCols()<500) {
osiclp->setupForRepeatedUse(2,0);
printf("trying slightly less reliable but faster version (? Gomory cuts okay?)\n");
printf("may not be safe if doing cuts in tree which need accuracy (level 2 anyway)\n");
}
// Allow rounding heuristic
CbcRounding heuristic1(model);
model.addHeuristic(&heuristic1);
// And local search when new solution found
CbcHeuristicLocal heuristic2(model);
model.addHeuristic(&heuristic2);
// Redundant definition of default branching (as Default == User)
CbcBranchUserDecision branch;
model.setBranchingMethod(&branch);
// Definition of node choice
CbcCompareUser compare;
model.setNodeComparison(compare);
// Do initial solve to continuous
model.initialSolve();
// Could tune more
model.setMinimumDrop(CoinMin(1.0,
fabs(model.getMinimizationObjValue())*1.0e-3+1.0e-4));
if (model.getNumCols()<500)
model.setMaximumCutPassesAtRoot(-100); // always do 100 if possible
else if (model.getNumCols()<5000)
model.setMaximumCutPassesAtRoot(100); // use minimum drop
else
model.setMaximumCutPassesAtRoot(20);
//model.setMaximumCutPasses(5);
// Switch off strong branching if wanted
// model.setNumberStrong(0);
// Do more strong branching if small
if (model.getNumCols()<5000)
model.setNumberStrong(10);
model.solver()->setIntParam(OsiMaxNumIterationHotStart,100);
// If time is given then stop after that number of minutes
if (argc>2) {
int minutes = atoi(argv[2]);
std::cout<<"Stopping after "<<minutes<<" minutes"<<std::endl;
assert (minutes>=0);
model.setDblParam(CbcModel::CbcMaximumSeconds,60.0*minutes);
}
// Switch off most output
if (model.getNumCols()<3000) {
model.messageHandler()->setLogLevel(1);
//model.solver()->messageHandler()->setLogLevel(0);
} else {
model.messageHandler()->setLogLevel(2);
model.solver()->messageHandler()->setLogLevel(1);
}
double time1 = CoinCpuTime();
// Do complete search
model.branchAndBound();
std::cout<<" Branch and cut took "<<CoinCpuTime()-time1<<" seconds, "
<<model.getNodeCount()<<" nodes with objective "
<<model.getObjValue()
<<(!model.status() ? " Finished" : " Not finished")
<<std::endl;
// Print more statistics
std::cout<<"Cuts at root node changed objective from "<<model.getContinuousObjective()
<<" to "<<model.rootObjectiveAfterCuts()<<std::endl;
int numberGenerators = model.numberCutGenerators();
for (int iGenerator=0;iGenerator<numberGenerators;iGenerator++) {
CbcCutGenerator * generator = model.cutGenerator(iGenerator);
std::cout<<generator->cutGeneratorName()<<" was tried "
<<generator->numberTimesEntered()<<" times and created "
<<generator->numberCutsInTotal()<<" cuts of which "
<<generator->numberCutsActive()<<" were active after adding rounds of cuts"
<<std::endl;
}
// Print solution if any - we can't get names from Osi!
if (model.getMinimizationObjValue()<1.0e50) {
int numberColumns = model.solver()->getNumCols();
const double * solution = model.solver()->getColSolution();
int iColumn;
std::cout<<std::setiosflags(std::ios::fixed|std::ios::showpoint)<<std::setw(14);
std::cout<<"--------------------------------------"<<std::endl;
for (iColumn=0;iColumn<numberColumns;iColumn++) {
double value=solution[iColumn];
if (fabs(value)>1.0e-7&&model.solver()->isInteger(iColumn))
std::cout<<std::setw(6)<<iColumn<<" "<<value<<std::endl;
}
std::cout<<"--------------------------------------"<<std::endl;
std::cout<<std::resetiosflags(std::ios::fixed|std::ios::showpoint|std::ios::scientific);
}
return 0;
}
int main(int argc, const char *argv[])
{
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);
}
{
// check if we need to change bounds to rows
int numberColumns = model.numberColumns();
const double * columnLower = model.columnLower();
const double * columnUpper = model.columnUpper();
int iColumn;
CoinBuild build;
double one = 1.0;
for (iColumn = 0; iColumn < numberColumns; iColumn++) {
if (columnUpper[iColumn] < 1.0e20 &&
columnLower[iColumn] > -1.0e20) {
if (fabs(columnLower[iColumn]) < fabs(columnUpper[iColumn])) {
double value = columnUpper[iColumn];
model.setColumnUpper(iColumn, COIN_DBL_MAX);
build.addRow(1, &iColumn, &one, -COIN_DBL_MAX, value);
} else {
double value = columnLower[iColumn];
model.setColumnLower(iColumn, -COIN_DBL_MAX);
build.addRow(1, &iColumn, &one, value, COIN_DBL_MAX);
}
}
}
if (build.numberRows())
model.addRows(build);
}
int numberColumns = model.numberColumns();
const double * columnLower = model.columnLower();
const double * columnUpper = model.columnUpper();
int numberRows = model.numberRows();
double * rowLower = CoinCopyOfArray(model.rowLower(), numberRows);
double * rowUpper = CoinCopyOfArray(model.rowUpper(), numberRows);
const double * objective = model.objective();
CoinPackedMatrix * matrix = model.matrix();
// get transpose
CoinPackedMatrix rowCopy = *matrix;
int iRow, iColumn;
int numberExtraRows = 0;
for (iRow = 0; iRow < numberRows; iRow++) {
if (rowLower[iRow] <= -1.0e20) {
} else if (rowUpper[iRow] >= 1.0e20) {
} else {
if (rowUpper[iRow] != rowLower[iRow])
numberExtraRows++;
}
}
const int * row = matrix->getIndices();
const int * columnLength = matrix->getVectorLengths();
const CoinBigIndex * columnStart = matrix->getVectorStarts();
const double * elementByColumn = matrix->getElements();
double objOffset = 0.0;
for (iColumn = 0; iColumn < numberColumns; iColumn++) {
double offset = 0.0;
if (columnUpper[iColumn] >= 1.0e20) {
if (columnLower[iColumn] > -1.0e20)
offset = columnLower[iColumn];
} else if (columnLower[iColumn] <= -1.0e20) {
offset = columnUpper[iColumn];
} else {
// taken care of before
abort();
}
if (offset) {
objOffset += offset * objective[iColumn];
for (CoinBigIndex j = columnStart[iColumn];
j < columnStart[iColumn] + columnLength[iColumn]; j++) {
int iRow = row[j];
if (rowLower[iRow] > -1.0e20)
rowLower[iRow] -= offset * elementByColumn[j];
if (rowUpper[iRow] < 1.0e20)
rowUpper[iRow] -= offset * elementByColumn[j];
}
}
}
int * which = new int[numberRows+numberExtraRows];
rowCopy.reverseOrdering();
rowCopy.transpose();
double * fromRowsLower = new double[numberRows+numberExtraRows];
double * fromRowsUpper = new double[numberRows+numberExtraRows];
double * newObjective = new double[numberRows+numberExtraRows];
double * fromColumnsLower = new double[numberColumns];
double * fromColumnsUpper = new double[numberColumns];
for (iColumn = 0; iColumn < numberColumns; iColumn++) {
// Offset is already in
if (columnUpper[iColumn] >= 1.0e20) {
if (columnLower[iColumn] > -1.0e20) {
fromColumnsLower[iColumn] = -COIN_DBL_MAX;
fromColumnsUpper[iColumn] = objective[iColumn];
} else {
// free
fromColumnsLower[iColumn] = objective[iColumn];
fromColumnsUpper[iColumn] = objective[iColumn];
}
} else if (columnLower[iColumn] <= -1.0e20) {
fromColumnsLower[iColumn] = objective[iColumn];
fromColumnsUpper[iColumn] = COIN_DBL_MAX;
} else {
abort();
}
}
int kRow = 0;
for (iRow = 0; iRow < numberRows; iRow++) {
if (rowLower[iRow] <= -1.0e20) {
assert(rowUpper[iRow] < 1.0e20);
newObjective[kRow] = -rowUpper[iRow];
fromRowsLower[kRow] = -COIN_DBL_MAX;
fromRowsUpper[kRow] = 0.0;
which[kRow] = iRow;
kRow++;
} else if (rowUpper[iRow] >= 1.0e20) {
newObjective[kRow] = -rowLower[iRow];
fromRowsLower[kRow] = 0.0;
fromRowsUpper[kRow] = COIN_DBL_MAX;
which[kRow] = iRow;
kRow++;
} else {
if (rowUpper[iRow] == rowLower[iRow]) {
newObjective[kRow] = -rowLower[iRow];
fromRowsLower[kRow] = -COIN_DBL_MAX;;
fromRowsUpper[kRow] = COIN_DBL_MAX;
which[kRow] = iRow;
kRow++;
} else {
// range
newObjective[kRow] = -rowUpper[iRow];
fromRowsLower[kRow] = -COIN_DBL_MAX;
fromRowsUpper[kRow] = 0.0;
which[kRow] = iRow;
kRow++;
newObjective[kRow] = -rowLower[iRow];
fromRowsLower[kRow] = 0.0;
fromRowsUpper[kRow] = COIN_DBL_MAX;
which[kRow] = iRow;
kRow++;
}
}
}
if (numberExtraRows) {
CoinPackedMatrix newCopy;
newCopy.submatrixOfWithDuplicates(rowCopy, kRow, which);
rowCopy = newCopy;
}
ClpSimplex modelDual;
modelDual.loadProblem(rowCopy, fromRowsLower, fromRowsUpper, newObjective,
fromColumnsLower, fromColumnsUpper);
modelDual.setObjectiveOffset(objOffset);
delete [] fromRowsLower;
delete [] fromRowsUpper;
delete [] fromColumnsLower;
delete [] fromColumnsUpper;
delete [] newObjective;
delete [] which;
delete [] rowLower;
delete [] rowUpper;
modelDual.writeMps("dual.mps");
return 0;
}
