int main (int argc, const char *argv[])
{
// Define your favorite OsiSolver
OsiClpSolverInterface solver1;
// Read in model using argv[1]
// and assert that it is a clean model
std::string dirsep(1,CoinFindDirSeparator());
std::string mpsFileName;
# if defined(SAMPLEDIR)
mpsFileName = SAMPLEDIR ;
mpsFileName += dirsep+"p0033.mps";
# else
if (argc < 2) {
fprintf(stderr, "Do not know where to find sample MPS files.\n");
exit(1);
}
# endif
if (argc>=2) mpsFileName = argv[1];
int numMpsReadErrors = solver1.readMps(mpsFileName.c_str(),"");
if( numMpsReadErrors != 0 )
{
printf("%d errors reading MPS file\n", numMpsReadErrors);
return numMpsReadErrors;
}
double time1 = CoinCpuTime();
/* Options are:
preprocess to do preprocessing
time in minutes
if 2 parameters and numeric taken as time
*/
bool preProcess=false;
double minutes=-1.0;
int nGoodParam=0;
for (int iParam=2; iParam<argc;iParam++) {
if (!strcmp(argv[iParam],"preprocess")) {
preProcess=true;
nGoodParam++;
} else if (!strcmp(argv[iParam],"time")) {
if (iParam+1<argc&&isdigit(argv[iParam+1][0])) {
minutes=atof(argv[iParam+1]);
if (minutes>=0.0) {
nGoodParam+=2;
iParam++; // skip time
}
}
}
}
if (nGoodParam==0&&argc==3&&isdigit(argv[2][0])) {
// If time is given then stop after that number of minutes
minutes = atof(argv[2]);
if (minutes>=0.0)
nGoodParam=1;
}
if (nGoodParam!=argc-2&&argc>=2) {
printf("Usage <file> [preprocess] [time <minutes>] or <file> <minutes>\n");
exit(1);
}
solver1.initialSolve();
// Reduce printout
solver1.setHintParam(OsiDoReducePrint,true,OsiHintTry);
// See if we want preprocessing
OsiSolverInterface * solver2=&solver1;
#if PREPROCESS==1
CglPreProcess process;
if (preProcess) {
/* Do not try and produce equality cliques and
do up to 5 passes */
solver2 = process.preProcess(solver1,false,5);
if (!solver2) {
printf("Pre-processing says infeasible\n");
exit(2);
}
solver2->resolve();
}
#endif
CbcModel model(*solver2);
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(1);
generator1.setMaxPassRoot(5);
// Number of unsatisfied variables to look at
generator1.setMaxProbe(10);
generator1.setMaxProbeRoot(1000);
// How far to follow the consequences
generator1.setMaxLook(50);
generator1.setMaxLookRoot(500);
// Only look at rows with fewer than this number of elements
generator1.setMaxElements(200);
generator1.setRowCuts(3);
CglGomory generator2;
// try larger limit
generator2.setLimit(300);
CglKnapsackCover generator3;
CglRedSplit generator4;
// try larger limit
generator4.setLimit(200);
CglClique generator5;
generator5.setStarCliqueReport(false);
generator5.setRowCliqueReport(false);
CglMixedIntegerRounding2 mixedGen;
CglFlowCover flowGen;
// Add in generators
// Experiment with -1 and -99 etc
model.addCutGenerator(&generator1,-1,"Probing");
model.addCutGenerator(&generator2,-1,"Gomory");
model.addCutGenerator(&generator3,-1,"Knapsack");
// model.addCutGenerator(&generator4,-1,"RedSplit");
model.addCutGenerator(&generator5,-1,"Clique");
model.addCutGenerator(&flowGen,-1,"FlowCover");
model.addCutGenerator(&mixedGen,-1,"MixedIntegerRounding");
// Say we want timings
int numberGenerators = model.numberCutGenerators();
int iGenerator;
for (iGenerator=0;iGenerator<numberGenerators;iGenerator++) {
CbcCutGenerator * generator = model.cutGenerator(iGenerator);
generator->setTiming(true);
}
OsiClpSolverInterface * osiclp = dynamic_cast< OsiClpSolverInterface*> (model.solver());
// go faster stripes
if (osiclp) {
// Turn this off if you get problems
// Used to be automatically set
osiclp->setSpecialOptions(128);
if(osiclp->getNumRows()<300&&osiclp->getNumCols()<500) {
//osiclp->setupForRepeatedUse(2,0);
osiclp->setupForRepeatedUse(0,0);
}
}
// Uncommenting this should switch off all CBC messages
// model.messagesPointer()->setDetailMessages(10,10000,NULL);
// 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
double objValue = model.solver()->getObjSense()*model.solver()->getObjValue();
double minimumDropA=CoinMin(1.0,fabs(objValue)*1.0e-3+1.0e-4);
double minimumDrop= fabs(objValue)*1.0e-4+1.0e-4;
printf("min drop %g (A %g)\n",minimumDrop,minimumDropA);
model.setMinimumDrop(minimumDrop);
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(10);
//model.setMaximumCutPasses(2);
// Switch off strong branching if wanted
// model.setNumberStrong(0);
// Do more strong branching if small
if (model.getNumCols()<5000)
model.setNumberStrong(10);
model.setNumberStrong(20);
//model.setNumberStrong(5);
model.setNumberBeforeTrust(5);
model.solver()->setIntParam(OsiMaxNumIterationHotStart,100);
// If time is given then stop after that number of minutes
if (minutes>=0.0) {
std::cout<<"Stopping after "<<minutes<<" minutes"<<std::endl;
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);
}
//model.messageHandler()->setLogLevel(2);
//model.solver()->messageHandler()->setLogLevel(2);
//model.setPrintFrequency(50);
//#define DEBUG_CUTS
#ifdef DEBUG_CUTS
// Set up debugger by name (only if no preprocesing)
if (!preProcess) {
std::string problemName ;
model.solver()->getStrParam(OsiProbName,problemName) ;
model.solver()->activateRowCutDebugger(problemName.c_str()) ;
}
#endif
#if PREPROCESS==2
// Default strategy will leave cut generators as they exist already
// so cutsOnlyAtRoot (1) ignored
// numberStrong (2) is 5 (default)
// numberBeforeTrust (3) is 5 (default is 0)
// printLevel (4) defaults (0)
CbcStrategyDefault strategy(true,5,5);
// Set up pre-processing to find sos if wanted
if (preProcess)
strategy.setupPreProcessing(2);
model.setStrategy(strategy);
#endif
// Do complete search
model.branchAndBound();
std::cout<<mpsFileName<<" 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;
for (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";
if (generator->timing())
std::cout<<" ( "<<generator->timeInCutGenerator()<<" seconds)"<<std::endl;
else
std::cout<<std::endl;
}
// Print solution if finished - we can't get names from Osi! - so get from OsiClp
if (model.getMinimizationObjValue()<1.0e50) {
#if PREPROCESS==1
// post process
OsiSolverInterface * solver;
if (preProcess) {
process.postProcess(*model.solver());
// Solution now back in solver1
solver = & solver1;
} else {
solver = model.solver();
}
#else
OsiSolverInterface * solver = model.solver();
#endif
int numberColumns = solver->getNumCols();
const double * solution = solver->getColSolution();
// Get names from solver1 (as OsiSolverInterface may lose)
std::vector<std::string> columnNames = *solver1.getModelPtr()->columnNames();
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&&solver->isInteger(iColumn))
std::cout<<std::setw(6)<<iColumn<<" "
<<columnNames[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[])
{
CbcSolver3 solver1;
// Read in model using argv[1]
// and assert that it is a clean model
std::string mpsFileName;
if (argc>=2) mpsFileName = argv[1];
int numMpsReadErrors = solver1.readMps(mpsFileName.c_str(),"");
assert(numMpsReadErrors==0);
double time1 = CoinCpuTime();
/* Options are:
preprocess to do preprocessing
time in minutes
if 2 parameters and numeric taken as time
*/
bool preProcess=false;
double minutes=-1.0;
int nGoodParam=0;
for (int iParam=2; iParam<argc;iParam++) {
if (!strcmp(argv[iParam],"preprocess")) {
preProcess=true;
nGoodParam++;
} else if (!strcmp(argv[iParam],"time")) {
if (iParam+1<argc&&isdigit(argv[iParam+1][0])) {
minutes=atof(argv[iParam+1]);
if (minutes>=0.0) {
nGoodParam+=2;
iParam++; // skip time
}
}
}
}
if (nGoodParam==0&&argc==3&&isdigit(argv[2][0])) {
// If time is given then stop after that number of minutes
minutes = atof(argv[2]);
if (minutes>=0.0)
nGoodParam=1;
}
if (nGoodParam!=argc-2) {
printf("Usage <file> [preprocess] [time <minutes>] or <file> <minutes>\n");
exit(1);
}
solver1.initialSolve();
// Reduce printout
solver1.setHintParam(OsiDoReducePrint,true,OsiHintTry);
// Say we want scaling
//solver1.setHintParam(OsiDoScale,true,OsiHintTry);
//solver1.setCleanupScaling(1);
// See if we want preprocessing
OsiSolverInterface * solver2=&solver1;
CglPreProcess process;
if (preProcess) {
/* Do not try and produce equality cliques and
do up to 5 passes */
solver2 = process.preProcess(solver1,false,5);
if (!solver2) {
printf("Pre-processing says infeasible\n");
exit(2);
}
solver2->resolve();
}
CbcModel model(*solver2);
// Point to solver
OsiSolverInterface * solver3 = model.solver();
CbcSolver3 * osiclp = dynamic_cast< CbcSolver3*> (solver3);
assert (osiclp);
const double fractionFix=0.985;
osiclp->initialize(&model,NULL);
osiclp->setAlgorithm(2);
osiclp->setMemory(1000);
osiclp->setNested(fractionFix);
//osiclp->setNested(1.0); //off
// Set up some cut generators and defaults
// Probing first as gets tight bounds on continuous
CglProbing generator1;
generator1.setUsingObjective(true);
generator1.setMaxPass(3);
// Number of unsatisfied variables to look at
generator1.setMaxProbe(10);
// How far to follow the consequences
generator1.setMaxLook(50);
// Only look at rows with fewer than this number of elements
generator1.setMaxElements(200);
generator1.setRowCuts(3);
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;
/* This is same as default constructor -
(1,true,1)
I presume if maxAggregate larger then slower but maybe better
criterion can be 1 through 3
Reference:
Hugues Marchand and Laurence A. Wolsey
Aggregation and Mixed Integer Rounding to Solve MIPs
Operations Research, 49(3), May-June 2001.
*/
int maxAggregate=1;
bool multiply=true;
int criterion=1;
CglMixedIntegerRounding2 mixedGen2(maxAggregate,multiply,criterion);
CglFlowCover flowGen;
// Add in generators
// Experiment with -1 and -99 etc
model.addCutGenerator(&generator1,-99,"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");
//model.addCutGenerator(&mixedGen2,-1,"MixedIntegerRounding2");
// Say we want timings
int numberGenerators = model.numberCutGenerators();
int iGenerator;
for (iGenerator=0;iGenerator<numberGenerators;iGenerator++) {
CbcCutGenerator * generator = model.cutGenerator(iGenerator);
generator->setTiming(true);
}
// Allow rounding heuristic
CbcRounding heuristic1(model);
model.addHeuristic(&heuristic1);
// And Greedy heuristic
CbcHeuristicGreedyCover heuristic2(model);
// Use original upper and perturb more
heuristic2.setAlgorithm(11);
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);
int iColumn;
int numberColumns = solver3->getNumCols();
// do pseudo costs
CbcObject ** objects = new CbcObject * [numberColumns+1];
const CoinPackedMatrix * matrix = solver3->getMatrixByCol();
// Column copy
const int * columnLength = matrix->getVectorLengths();
const double * objective = model.getObjCoefficients();
int n=0;
for (iColumn=0;iColumn<numberColumns;iColumn++) {
if (solver3->isInteger(iColumn)) {
double costPer = objective[iColumn]/ ((double) columnLength[iColumn]);
CbcSimpleIntegerPseudoCost * newObject =
new CbcSimpleIntegerPseudoCost(&model,n,iColumn,
costPer,costPer);
newObject->setMethod(3);
objects[n++]= newObject;
}
}
// and special fix lots branch
objects[n++]=new CbcBranchToFixLots(&model,-1.0e-6,fractionFix+0.01,1,0,NULL);
model.addObjects(n,objects);
for (iColumn=0;iColumn<n;iColumn++)
delete objects[iColumn];
delete [] objects;
// High priority for odd object
int followPriority=1;
model.passInPriorities(&followPriority,true);
// 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(1);
// Do more strong branching if small
//if (model.getNumCols()<5000)
//model.setNumberStrong(10);
// Switch off strong branching if wanted
model.setNumberStrong(0);
model.solver()->setIntParam(OsiMaxNumIterationHotStart,100);
// If time is given then stop after that number of minutes
if (minutes>=0.0) {
std::cout<<"Stopping after "<<minutes<<" minutes"<<std::endl;
model.setDblParam(CbcModel::CbcMaximumSeconds,60.0*minutes);
}
// Switch off most output
if (model.getNumCols()<300000) {
model.messageHandler()->setLogLevel(1);
//model.solver()->messageHandler()->setLogLevel(0);
} else {
model.messageHandler()->setLogLevel(2);
model.solver()->messageHandler()->setLogLevel(1);
}
//model.messageHandler()->setLogLevel(2);
//model.solver()->messageHandler()->setLogLevel(2);
//model.setPrintFrequency(50);
#define DEBUG_CUTS
#ifdef DEBUG_CUTS
// Set up debugger by name (only if no preprocesing)
if (!preProcess) {
std::string problemName ;
//model.solver()->getStrParam(OsiProbName,problemName) ;
//model.solver()->activateRowCutDebugger(problemName.c_str()) ;
model.solver()->activateRowCutDebugger("cap6000a") ;
}
#endif
// Do complete search
try {
model.branchAndBound();
}
catch (CoinError e) {
e.print();
if (e.lineNumber()>=0)
std::cout<<"This was from a CoinAssert"<<std::endl;
exit(0);
}
//void printHowMany();
//printHowMany();
std::cout<<mpsFileName<<" 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;
for (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";
if (generator->timing())
std::cout<<" ( "<<generator->timeInCutGenerator()<<" seconds)"<<std::endl;
else
std::cout<<std::endl;
}
// Print solution if finished - we can't get names from Osi!
if (model.getMinimizationObjValue()<1.0e50) {
// post process
if (preProcess)
process.postProcess(*model.solver());
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[])
{
// Define your favorite OsiSolver
OsiClpSolverInterface solver1;
// Read in model using argv[1]
// and assert that it is a clean model
std::string mpsFileName;
#if defined(SAMPLEDIR)
mpsFileName = SAMPLEDIR "/p0033.mps";
#else
if (argc < 2) {
fprintf(stderr, "Do not know where to find sample MPS files.\n");
exit(1);
}
#endif
if (argc>=2) mpsFileName = argv[1];
int numMpsReadErrors = solver1.readMps(mpsFileName.c_str(),"");
assert(numMpsReadErrors==0);
double time1 = CoinCpuTime();
OsiClpSolverInterface solverSave = solver1;
/* Options are:
preprocess to do preprocessing
time in minutes
if 2 parameters and numeric taken as time
*/
bool preProcess=false;
double minutes=-1.0;
int nGoodParam=0;
for (int iParam=2; iParam<argc;iParam++) {
if (!strcmp(argv[iParam],"preprocess")) {
preProcess=true;
nGoodParam++;
} else if (!strcmp(argv[iParam],"time")) {
if (iParam+1<argc&&isdigit(argv[iParam+1][0])) {
minutes=atof(argv[iParam+1]);
if (minutes>=0.0) {
nGoodParam+=2;
iParam++; // skip time
}
}
}
}
if (nGoodParam==0&&argc==3&&isdigit(argv[2][0])) {
// If time is given then stop after that number of minutes
minutes = atof(argv[2]);
if (minutes>=0.0)
nGoodParam=1;
}
if (nGoodParam!=argc-2&&argc>=2) {
printf("Usage <file> [preprocess] [time <minutes>] or <file> <minutes>\n");
exit(1);
}
// Reduce printout
solver1.setHintParam(OsiDoReducePrint,true,OsiHintTry);
// See if we want preprocessing
OsiSolverInterface * solver2=&solver1;
CglPreProcess process;
// Never do preprocessing until dual tests out as can fix incorrectly
preProcess=false;
if (preProcess) {
/* Do not try and produce equality cliques and
do up to 5 passes */
solver2 = process.preProcess(solver1,false,5);
if (!solver2) {
printf("Pre-processing says infeasible\n");
exit(2);
}
solver2->resolve();
}
// Turn L rows into cuts
CglStoredUser stored;
{
int numberRows = solver2->getNumRows();
int * whichRow = new int[numberRows];
// get row copy
const CoinPackedMatrix * rowCopy = solver2->getMatrixByRow();
const int * column = rowCopy->getIndices();
const int * rowLength = rowCopy->getVectorLengths();
const CoinBigIndex * rowStart = rowCopy->getVectorStarts();
const double * rowLower = solver2->getRowLower();
const double * rowUpper = solver2->getRowUpper();
const double * element = rowCopy->getElements();
int iRow,nDelete=0;
for (iRow=0;iRow<numberRows;iRow++) {
if (rowLower[iRow]<-1.0e20||rowUpper[iRow]>1.0e20) {
// take out
whichRow[nDelete++]=iRow;
}
}
// leave some rows to avoid empty problem (Gomory does not like)
nDelete = CoinMax(CoinMin(nDelete,numberRows-5),0);
for (int jRow=0;jRow<nDelete;jRow++) {
iRow=whichRow[jRow];
int start = rowStart[iRow];
stored.addCut(rowLower[iRow],rowUpper[iRow],rowLength[iRow],
column+start,element+start);
}
/* The following is problem specific.
Normally cuts are deleted if slack on cut basic.
On some problems you may wish to leave cuts in as long
as slack value zero
*/
int numberCuts=stored.sizeRowCuts();
for (int iCut=0;iCut<numberCuts;iCut++) {
//stored.mutableRowCutPointer(iCut)->setEffectiveness(1.0e50);
}
solver2->deleteRows(nDelete,whichRow);
delete [] whichRow;
}
CbcModel model(*solver2);
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(1);
generator1.setMaxPassRoot(5);
// Number of unsatisfied variables to look at
generator1.setMaxProbe(10);
generator1.setMaxProbeRoot(1000);
// How far to follow the consequences
generator1.setMaxLook(50);
generator1.setMaxLookRoot(500);
// Only look at rows with fewer than this number of elements
generator1.setMaxElements(200);
generator1.setRowCuts(3);
CglGomory generator2;
// try larger limit
generator2.setLimit(300);
CglKnapsackCover generator3;
CglRedSplit generator4;
// try larger limit
generator4.setLimit(200);
CglClique generator5;
generator5.setStarCliqueReport(false);
generator5.setRowCliqueReport(false);
CglMixedIntegerRounding2 mixedGen;
CglFlowCover flowGen;
// Add in generators
// Experiment with -1 and -99 etc
// This is just for one particular model
model.addCutGenerator(&generator1,-1,"Probing");
//model.addCutGenerator(&generator2,-1,"Gomory");
model.addCutGenerator(&generator2,1,"Gomory");
model.addCutGenerator(&generator3,-1,"Knapsack");
// model.addCutGenerator(&generator4,-1,"RedSplit");
//model.addCutGenerator(&generator5,-1,"Clique");
model.addCutGenerator(&generator5,1,"Clique");
model.addCutGenerator(&flowGen,-1,"FlowCover");
model.addCutGenerator(&mixedGen,-1,"MixedIntegerRounding");
// Add stored cuts (making sure at all depths)
model.addCutGenerator(&stored,1,"Stored",true,false,false,-100,1,-1);
int numberGenerators = model.numberCutGenerators();
int iGenerator;
// Say we want timings
for (iGenerator=0;iGenerator<numberGenerators;iGenerator++) {
CbcCutGenerator * generator = model.cutGenerator(iGenerator);
generator->setTiming(true);
}
OsiClpSolverInterface * osiclp = dynamic_cast< OsiClpSolverInterface*> (model.solver());
// go faster stripes
if (osiclp) {
if(osiclp->getNumRows()<300&&osiclp->getNumCols()<500) {
//osiclp->setupForRepeatedUse(2,0);
osiclp->setupForRepeatedUse(0,0);
}
// Don't allow dual stuff
osiclp->setSpecialOptions(osiclp->specialOptions()|262144);
}
// Uncommenting this should switch off all CBC messages
// model.messagesPointer()->setDetailMessages(10,10000,NULL);
// No heuristics
// Do initial solve to continuous
model.initialSolve();
/* You need the next few lines -
a) so that cut generator will always be called again if it generated cuts
b) it is known that matrix is not enough to define problem so do cuts even
if it looks integer feasible at continuous optimum.
c) a solution found by strong branching will be ignored.
d) don't recompute a solution once found
*/
// Make sure cut generator called correctly (a)
iGenerator=numberGenerators-1;
model.cutGenerator(iGenerator)->setMustCallAgain(true);
// Say cuts needed at continuous (b)
OsiBabSolver oddCuts;
oddCuts.setSolverType(4);
// owing to bug must set after initialSolve
model.passInSolverCharacteristics(&oddCuts);
// Say no to all solutions by strong branching (c)
CbcFeasibilityNoStrong noStrong;
model.setProblemFeasibility(noStrong);
// Say don't recompute solution d)
model.setSpecialOptions(4);
// Could tune more
double objValue = model.solver()->getObjSense()*model.solver()->getObjValue();
double minimumDropA=CoinMin(1.0,fabs(objValue)*1.0e-3+1.0e-4);
double minimumDrop= fabs(objValue)*1.0e-4+1.0e-4;
printf("min drop %g (A %g)\n",minimumDrop,minimumDropA);
model.setMinimumDrop(minimumDrop);
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(10);
//model.setMaximumCutPasses(2);
// Switch off strong branching if wanted
// model.setNumberStrong(0);
// Do more strong branching if small
if (model.getNumCols()<5000)
model.setNumberStrong(10);
model.setNumberStrong(20);
//model.setNumberStrong(5);
model.setNumberBeforeTrust(5);
model.solver()->setIntParam(OsiMaxNumIterationHotStart,100);
// If time is given then stop after that number of minutes
if (minutes>=0.0) {
std::cout<<"Stopping after "<<minutes<<" minutes"<<std::endl;
model.setDblParam(CbcModel::CbcMaximumSeconds,60.0*minutes);
}
// Switch off most output
if (model.getNumCols()<30000) {
model.messageHandler()->setLogLevel(1);
//model.solver()->messageHandler()->setLogLevel(0);
} else {
model.messageHandler()->setLogLevel(2);
model.solver()->messageHandler()->setLogLevel(1);
}
//model.messageHandler()->setLogLevel(2);
//model.solver()->messageHandler()->setLogLevel(2);
//model.setPrintFrequency(50);
//#define DEBUG_CUTS
#ifdef DEBUG_CUTS
// Set up debugger by name (only if no preprocesing)
if (!preProcess) {
std::string problemName ;
model.solver()->getStrParam(OsiProbName,problemName) ;
model.solver()->activateRowCutDebugger(problemName.c_str()) ;
}
#endif
// Do complete search
model.branchAndBound();
std::cout<<mpsFileName<<" 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;
for (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";
if (generator->timing())
std::cout<<" ( "<<generator->timeInCutGenerator()<<" seconds)"<<std::endl;
else
std::cout<<std::endl;
}
// Print solution if finished - we can't get names from Osi! - so get from OsiClp
if (model.getMinimizationObjValue()<1.0e50) {
// post process
OsiSolverInterface * solver;
if (preProcess) {
process.postProcess(*model.solver());
// Solution now back in solver1
solver = & solver1;
} else {
solver = model.solver();
}
int numberColumns = solver->getNumCols();
const double * solution = solver->getColSolution();
// Get names from solver1 (as OsiSolverInterface may lose)
std::vector<std::string> columnNames = *solver1.getModelPtr()->columnNames();
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&&solver->isInteger(iColumn)) {
std::cout<<std::setw(6)<<iColumn<<" "
<<columnNames[iColumn]<<" "
<<value<<std::endl;
solverSave.setColLower(iColumn,value);
solverSave.setColUpper(iColumn,value);
}
}
std::cout<<"--------------------------------------"<<std::endl;
std::cout<<std::resetiosflags(std::ios::fixed|std::ios::showpoint|std::ios::scientific);
solverSave.initialSolve();
}
return 0;
}
int doBaCParam (CoinParam *param)
{
assert (param != 0) ;
CbcGenParam *genParam = dynamic_cast<CbcGenParam *>(param) ;
assert (genParam != 0) ;
CbcGenCtlBlk *ctlBlk = genParam->obj() ;
assert (ctlBlk != 0) ;
CbcModel *model = ctlBlk->model_ ;
assert (model != 0) ;
/*
Setup to return nonfatal/fatal error (1/-1) by default.
*/
int retval ;
if (CoinParamUtils::isInteractive()) {
retval = 1 ;
} else {
retval = -1 ;
}
ctlBlk->setBaBStatus(CbcGenCtlBlk::BACAbandon, CbcGenCtlBlk::BACmInvalid,
CbcGenCtlBlk::BACwNotStarted, false, 0) ;
/*
We ain't gonna do squat without a good model.
*/
if (!ctlBlk->goodModel_) {
std::cout << "** Current model not valid!" << std::endl ;
return (retval) ;
}
/*
Start the clock ticking.
*/
double time1 = CoinCpuTime() ;
double time2 ;
/*
Create a clone of the model which we can modify with impunity. Extract
the underlying solver for convenient access.
*/
CbcModel babModel(*model) ;
OsiSolverInterface *babSolver = babModel.solver() ;
assert (babSolver != 0) ;
# if CBC_TRACK_SOLVERS > 0
std::cout
<< "doBaCParam: initial babSolver is "
<< std::hex << babSolver << std::dec
<< ", log level " << babSolver->messageHandler()->logLevel()
<< "." << std::endl ;
# endif
/*
Solve the root relaxation. Bail unless it solves to optimality.
*/
if (!solveRelaxation(&babModel)) {
ctlBlk->setBaBStatus(&babModel, CbcGenCtlBlk::BACwBareRoot) ;
return (0) ;
}
# if COIN_CBC_VERBOSITY > 0
std::cout
<< "doBaCParam: initial relaxation z = "
<< babSolver->getObjValue() << "." << std::endl ;
# endif
/*
Are we up for fixing variables based on reduced cost alone?
*/
if (ctlBlk->djFix_.action_ == true) {
reducedCostHack(babSolver, ctlBlk->djFix_.threshold_) ;
}
/*
Time to consider preprocessing. We'll do a bit of setup before getting to
the meat of the issue.
preIppSolver will hold a clone of the unpreprocessed constraint system.
We'll need it when we postprocess. ippSolver holds the preprocessed
constraint system. Again, we clone it and give the clone to babModel for
B&C. Presumably we need an unmodified copy of the preprocessed system to
do postprocessing, but the copy itself is hidden inside the preprocess
object.
*/
OsiSolverInterface *preIppSolver = 0 ;
CglPreProcess ippObj ;
bool didIPP = false ;
int numberChanged = 0 ;
int numberOriginalColumns = babSolver->getNumCols() ;
CbcGenCtlBlk::IPPControl ippAction = ctlBlk->getIPPAction() ;
if (!(ippAction == CbcGenCtlBlk::IPPOff ||
ippAction == CbcGenCtlBlk::IPPStrategy)) {
double timeLeft = babModel.getMaximumSeconds() ;
preIppSolver = babSolver->clone() ;
OsiSolverInterface *ippSolver ;
# if CBC_TRACK_SOLVERS > 0
std::cout
<< "doBaCParam: clone made prior to IPP is "
<< std::hex << preIppSolver << std::dec
<< ", log level " << preIppSolver->messageHandler()->logLevel()
<< "." << std::endl ;
# endif
preIppSolver->setHintParam(OsiDoInBranchAndCut, true, OsiHintDo) ;
ippObj.messageHandler()->setLogLevel(babModel.logLevel()) ;
CglProbing probingGen ;
probingGen.setUsingObjective(true) ;
probingGen.setMaxPass(3) ;
probingGen.setMaxProbeRoot(preIppSolver->getNumCols()) ;
probingGen.setMaxElements(100) ;
probingGen.setMaxLookRoot(50) ;
probingGen.setRowCuts(3) ;
ippObj.addCutGenerator(&probingGen) ;
/*
For preProcessNonDefault, the 2nd parameter controls the conversion of
clique and SOS constraints. 0 does nothing, -1 converts <= to ==, and
2 and 3 form SOS sets under strict and not-so-strict conditions,
respectively.
*/
int convert = 0 ;
if (ippAction == CbcGenCtlBlk::IPPEqual) {
convert = -1 ;
} else if (ippAction == CbcGenCtlBlk::IPPEqualAll) {
convert = -2 ;
} else if (ippAction == CbcGenCtlBlk::IPPSOS) {
convert = 2 ;
} else if (ippAction == CbcGenCtlBlk::IPPTrySOS) {
convert = 3 ;
}
ippSolver = ippObj.preProcessNonDefault(*preIppSolver, convert, 10) ;
# if CBC_TRACK_SOLVERS > 0
std::cout
<< "doBaCParam: solver returned from IPP is "
<< std::hex << ippSolver << std::dec ;
if (ippSolver) {
std::cout
<< ", log level " << ippSolver->messageHandler()->logLevel() ;
}
std::cout << "." << std::endl ;
# endif
/*
ippSolver == 0 is success of a sort --- integer preprocess has found the
problem to be infeasible or unbounded. Need to think about how to indicate
status.
*/
if (!ippSolver) {
std::cout
<< "Integer preprocess says infeasible or unbounded" << std::endl ;
delete preIppSolver ;
ctlBlk->setBaBStatus(&babModel, CbcGenCtlBlk::BACwIPP) ;
return (0) ;
}
# if COIN_CBC_VERBOSITY > 0
else {
std::cout
<< "After integer preprocessing, model has "
<< ippSolver->getNumRows()
<< " rows, " << ippSolver->getNumCols() << " columns, and "
<< ippSolver->getNumElements() << " elements." << std::endl ;
}
# endif
preIppSolver->setHintParam(OsiDoInBranchAndCut, false, OsiHintDo) ;
ippSolver->setHintParam(OsiDoInBranchAndCut, false, OsiHintDo) ;
if (ippAction == CbcGenCtlBlk::IPPSave) {
ippSolver->writeMps("presolved", "mps", 1.0) ;
std::cout
<< "Integer preprocessed model written to `presolved.mps' "
<< "as minimisation problem." << std::endl ;
}
OsiSolverInterface *osiTmp = ippSolver->clone() ;
babModel.assignSolver(osiTmp) ;
babSolver = babModel.solver() ;
# if CBC_TRACK_SOLVERS > 0
std::cout
<< "doBaCParam: clone of IPP solver passed to babModel is "
<< std::hex << babSolver << std::dec
<< ", log level " << babSolver->messageHandler()->logLevel()
<< "." << std::endl ;
# endif
if (!solveRelaxation(&babModel)) {
delete preIppSolver ;
ctlBlk->setBaBStatus(&babModel, CbcGenCtlBlk::BACwIPPRelax) ;
return (0) ;
}
# if COIN_CBC_VERBOSITY > 0
std::cout
<< "doBaCParam: presolved relaxation z = "
<< babSolver->getObjValue() << "." << std::endl ;
# endif
babModel.setMaximumSeconds(timeLeft - (CoinCpuTime() - time1)) ;
didIPP = true ;
}
/*
At this point, babModel and babSolver hold the constraint system we'll use
for B&C (either the original system or the preprocessed system) and we have
a solution to the lp relaxation.
If we're using the COSTSTRATEGY option, set up priorities here and pass
them to the babModel.
*/
if (ctlBlk->priorityAction_ != CbcGenCtlBlk::BPOff) {
setupPriorities(&babModel, ctlBlk->priorityAction_) ;
}
/*
Install heuristics and cutting planes.
*/
installHeuristics(ctlBlk, &babModel) ;
installCutGenerators(ctlBlk, &babModel) ;
/*
Set up status print frequency for babModel.
*/
if (babModel.getNumCols() > 2000 || babModel.getNumRows() > 1500 ||
babModel.messageHandler()->logLevel() > 1)
babModel.setPrintFrequency(100) ;
/*
If we've read in a known good solution for debugging, activate the row cut
debugger.
*/
if (ctlBlk->debugSol_.values_) {
if (ctlBlk->debugSol_.numCols_ == babModel.getNumCols()) {
babSolver->activateRowCutDebugger(ctlBlk->debugSol_.values_) ;
} else {
std::cout
<< "doBaCParam: debug file has incorrect number of columns."
<< std::endl ;
}
}
/*
Set ratio-based integrality gap, if specified by user.
*/
if (ctlBlk->setByUser_[CbcCbcParam::GAPRATIO] == true) {
double obj = babSolver->getObjValue() ;
double gapRatio = babModel.getDblParam(CbcModel::CbcAllowableFractionGap) ;
double gap = gapRatio * (1.0e-5 + fabs(obj)) ;
babModel.setAllowableGap(gap) ;
std::cout
<< "doBaCParam: Continuous objective = " << obj
<< ", so allowable gap set to " << gap << std::endl ;
}
/*
A bit of mystery code. As best I can figure, setSpecialOptions(2) suppresses
the removal of warm start information when checkSolution runs an lp to check
a solution. John's comment, ``probably faster to use a basis to get integer
solutions'' makes some sense in this context. Didn't try to track down
moreMipOptions just yet.
*/
babModel.setSpecialOptions(babModel.specialOptions() | 2) ;
/*
{ int ndx = whichParam(MOREMIPOPTIONS,numberParameters,parameters) ;
int moreMipOptions = parameters[ndx].intValue() ;
if (moreMipOptions >= 0)
{ printf("more mip options %d\n",moreMipOptions);
babModel.setSearchStrategy(moreMipOptions); } }
*/
/*
Begin the final run-up to branch-and-cut.
Make sure that objects are set up in the solver. It's possible that whoever
loaded the model into the solver also set up objects. But it's also
entirely likely that none exist to this point (and interesting to note that
IPP doesn't need to know anything about objects).
*/
setupObjects(babSolver, didIPP, &ippObj) ;
/*
Set the branching method. We can't do this until we establish objects,
because the constructor will set up arrays based on the number of objects,
and there's no provision to set this information after creation. Arguably not
good --- it'd be nice to set this in the prototype model that's cloned for
this routine. In CoinSolve, shadowPriceMode is handled with the TESTOSI
option.
*/
OsiChooseStrong strong(babSolver) ;
strong.setNumberBeforeTrusted(babModel.numberBeforeTrust()) ;
strong.setNumberStrong(babModel.numberStrong()) ;
strong.setShadowPriceMode(ctlBlk->chooseStrong_.shadowPriceMode_) ;
CbcBranchDefaultDecision decision ;
decision.setChooseMethod(strong) ;
babModel.setBranchingMethod(decision) ;
/*
Here I've deleted a huge block of code that deals with external priorities,
branch direction, pseudocosts, and solution. (PRIORITYIN) Also a block of
code that generates C++ code.
*/
/*
Set up strategy for branch-and-cut. Note that the integer code supplied to
setupPreProcessing is *not* compatible with the IPPAction enum. But at least
it's documented. See desiredPreProcess_ in CbcStrategyDefault. `1' is
accidentally equivalent to IPPOn.
*/
if (ippAction == CbcGenCtlBlk::IPPStrategy) {
CbcStrategyDefault strategy(true, 5, 5) ;
strategy.setupPreProcessing(1) ;
babModel.setStrategy(strategy) ;
}
/*
Yes! At long last, we're ready for the big call. Do branch and cut. In
general, the solver used to return the solution will not be the solver we
passed in, so reset babSolver here.
*/
int statistics = (ctlBlk->printOpt_ > 0) ? ctlBlk->printOpt_ : 0 ;
# if CBC_TRACK_SOLVERS > 0
std::cout
<< "doBaCParam: solver at call to branchAndBound is "
<< std::hex << babModel.solver() << std::dec
<< ", log level " << babModel.solver()->messageHandler()->logLevel()
<< "." << std::endl ;
# endif
babModel.branchAndBound(statistics) ;
babSolver = babModel.solver() ;
# if CBC_TRACK_SOLVERS > 0
std::cout
<< "doBaCParam: solver at return from branchAndBound is "
<< std::hex << babModel.solver() << std::dec
<< ", log level " << babModel.solver()->messageHandler()->logLevel()
<< "." << std::endl ;
# endif
/*
Write out solution to preprocessed model.
*/
if (ctlBlk->debugCreate_ == "createAfterPre" &&
babModel.bestSolution()) {
CbcGenParamUtils::saveSolution(babSolver, "debug.file") ;
}
/*
Print some information about branch-and-cut.
*/
# if COIN_CBC_VERBOSITY > 0
std::cout
<< "Cuts at root node changed objective from "
<< babModel.getContinuousObjective()
<< " to " << babModel.rootObjectiveAfterCuts() << std::endl ;
for (int iGen = 0 ; iGen < babModel.numberCutGenerators() ; iGen++) {
CbcCutGenerator *generator = babModel.cutGenerator(iGen) ;
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" ;
if (generator->timing()) {
std::cout << " ( " << generator->timeInCutGenerator() << " seconds)" ;
}
std::cout << std::endl ;
}
# endif
time2 = CoinCpuTime();
ctlBlk->totalTime_ += time2 - time1;
/*
If we performed integer preprocessing, time to back it out.
*/
if (ippAction != CbcGenCtlBlk::IPPOff) {
# if CBC_TRACK_SOLVERS > 0
std::cout
<< "doBaCParam: solver passed to IPP postprocess is "
<< std::hex << babSolver << std::dec << "." << std::endl ;
# endif
ippObj.postProcess(*babSolver);
babModel.assignSolver(preIppSolver) ;
babSolver = babModel.solver() ;
# if CBC_TRACK_SOLVERS > 0
std::cout
<< "doBaCParam: solver in babModel after IPP postprocess is "
<< std::hex << babSolver << std::dec << "." << std::endl ;
# endif
}
/*
Write out postprocessed solution to debug file, if requested.
*/
if (ctlBlk->debugCreate_ == "create" && babModel.bestSolution()) {
CbcGenParamUtils::saveSolution(babSolver, "debug.file") ;
}
/*
If we have a good solution, detach the solver with the answer. Fill in the
rest of the status information for the benefit of the wider world.
*/
bool keepAnswerSolver = false ;
OsiSolverInterface *answerSolver = 0 ;
if (babModel.bestSolution()) {
babModel.setModelOwnsSolver(false) ;
keepAnswerSolver = true ;
answerSolver = babSolver ;
}
ctlBlk->setBaBStatus(&babModel, CbcGenCtlBlk::BACwBAC,
keepAnswerSolver, answerSolver) ;
/*
And one last bit of information & statistics.
*/
ctlBlk->printBaBStatus() ;
std::cout << " " ;
if (keepAnswerSolver) {
std::cout
<< "objective " << babModel.getObjValue() << "; " ;
}
std::cout
<< babModel.getNodeCount() << " nodes and "
<< babModel.getIterationCount() << " iterations - took "
<< time2 - time1 << " seconds" << std::endl ;
return (0) ;
}
