//#############################################################################
void
MibSHeuristic::objCutHeuristic()
{
/* Solve the LP relaxation with the new constraint d^2 y <= d^y* */
MibSModel * model = MibSModel_;
//OsiSolverInterface * oSolver = model->origLpSolver_;
OsiSolverInterface * oSolver = model->getSolver();
//OsiSolverInterface * hSolver = new OsiCbcSolverInterface();
OsiSolverInterface * hSolver = new OsiSymSolverInterface();
double objSense(model->getLowerObjSense());
int lCols(model->getLowerDim());
int uCols(model->getUpperDim());
int tCols(lCols + uCols);
int * lColIndices = model->getLowerColInd();
int * uColIndices = model->getUpperColInd();
double * lObjCoeffs = model->getLowerObjCoeffs();
hSolver->loadProblem(*oSolver->getMatrixByCol(),
oSolver->getColLower(), oSolver->getColUpper(),
oSolver->getObjCoefficients(),
oSolver->getRowLower(), oSolver->getRowUpper());
int j(0);
for(j = 0; j < tCols; j++){
if(oSolver->isInteger(j))
hSolver->setInteger(j);
}
double * optLowerSolutionOrd = model->bS_->optLowerSolutionOrd_;
CoinPackedVector objCon;
int i(0), index(0);
double rhs(0.0);
for(i = 0; i < lCols; i++){
index = lColIndices[i];
objCon.insert(index, lObjCoeffs[i] * objSense);
//should this be ordered? and should lObjCoeffs by at index?
//rhs += optLowerSolutionOrd_[i] * lObjCoeffs[i] * objSense;
rhs += optLowerSolutionOrd[i] * lObjCoeffs[i] * objSense;
}
//Hmm, I think this was wrong before...?
// hSolver->addRow(objCon, - hSolver->getInfinity(), rhs);
hSolver->addRow(objCon, rhs, hSolver->getInfinity());
/* optimize w.r.t. to the UL objective with the new row */
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(hSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("max_active_nodes", 1);
}
hSolver->branchAndBound();
if(0)
hSolver->writeLp("objcutheuristic");
if(hSolver->isProvenOptimal()){
MibSSolution *mibSol = NULL;
OsiSolverInterface * lSolver = model->bS_->setUpModel(hSolver, true);
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(lSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("max_active_nodes", 1);
}
lSolver->branchAndBound();
const double * sol = hSolver->getColSolution();
double objVal(lSolver->getObjValue() * objSense);
double etol(etol_);
double lowerObj = getLowerObj(sol, objSense);
double * optUpperSolutionOrd = new double[uCols];
double * optLowerSolutionOrd = new double[lCols];
CoinZeroN(optUpperSolutionOrd, uCols);
CoinZeroN(optLowerSolutionOrd, lCols);
if(fabs(objVal - lowerObj) < etol){
/** Current solution is bilevel feasible **/
mibSol = new MibSSolution(hSolver->getNumCols(),
hSolver->getColSolution(),
hSolver->getObjValue(),
model);
model->storeSolution(BlisSolutionTypeHeuristic, mibSol);
mibSol = NULL;
}
else{
/* solution is not bilevel feasible, create one that is */
const double * uSol = hSolver->getColSolution();
const double * lSol = lSolver->getColSolution();
//int numElements(lSolver->getNumCols());
int numElements(hSolver->getNumCols());
int i(0), pos(0), index(0);
double * lpSolution = new double[numElements];
double upperObj(0.0);
//FIXME: problem is still here. indices may be wrong.
//also is all this necessary, or can we just paste together uSol and lSol?
for(i = 0; i < numElements; i++){
//index = indices[i];
pos = model->bS_->binarySearch(0, lCols - 1, i, lColIndices);
if(pos < 0){
pos = model->bS_->binarySearch(0, uCols - 1, i, uColIndices);
//optUpperSolutionOrd[pos] = values[i];
//optUpperSolutionOrd[pos] = uSol[pos];
if (pos >= 0){
optUpperSolutionOrd[pos] = uSol[i];
}
}
else{
//optLowerSolutionOrd[pos] = lSol[i];
optLowerSolutionOrd[pos] = lSol[pos];
}
}
for(i = 0; i < uCols; i++){
index = uColIndices[i];
lpSolution[index] = optUpperSolutionOrd[i];
upperObj +=
optUpperSolutionOrd[i] * hSolver->getObjCoefficients()[index];
}
for(i = 0; i < lCols; i++){
index = lColIndices[i];
lpSolution[index] = optLowerSolutionOrd[i];
upperObj +=
optLowerSolutionOrd[i] * hSolver->getObjCoefficients()[index];
}
if(model->checkUpperFeasibility(lpSolution)){
mibSol = new MibSSolution(hSolver->getNumCols(),
lpSolution,
upperObj * hSolver->getObjSense(),
model);
model->storeSolution(BlisSolutionTypeHeuristic, mibSol);
mibSol = NULL;
}
delete [] lpSolution;
}
delete lSolver;
}
delete hSolver;
}
//#############################################################################
void
MibSHeuristic::lowerObjHeuristic()
{
/*
optimize wrt to lower-level objective
over current feasible lp feasible region
*/
MibSModel * model = MibSModel_;
OsiSolverInterface * oSolver = model->getSolver();
//OsiSolverInterface * hSolver = new OsiCbcSolverInterface();
OsiSolverInterface* hSolver = new OsiSymSolverInterface();
double objSense(model->getLowerObjSense());
int lCols(model->getLowerDim());
int uCols(model->getUpperDim());
int * lColIndices = model->getLowerColInd();
int * uColIndices = model->getUpperColInd();
double * lObjCoeffs = model->getLowerObjCoeffs();
//int tCols(lCols + uCols);
int tCols(oSolver->getNumCols());
//assert(tCols == oSolver->getNumCols());
hSolver->loadProblem(*oSolver->getMatrixByCol(),
oSolver->getColLower(), oSolver->getColUpper(),
oSolver->getObjCoefficients(),
oSolver->getRowLower(), oSolver->getRowUpper());
int j(0);
for(j = 0; j < tCols; j++){
if(oSolver->isInteger(j))
hSolver->setInteger(j);
}
double * nObjCoeffs = new double[tCols];
int i(0), index(0);
CoinZeroN(nObjCoeffs, tCols);
for(i = 0; i < lCols; i++){
index = lColIndices[i];
nObjCoeffs[index] = lObjCoeffs[i];
}
//MibS objective sense is the opposite of OSI's!
hSolver->setObjSense(objSense);
hSolver->setObjective(nObjCoeffs);
//double cutoff(model->getCutoff());
double cutoff(model->getKnowledgeBroker()->getIncumbentValue());
if(model->getNumSolutions()){
CoinPackedVector objCon;
//double rhs(cutoff * objSense);
//double smlTol(1.0);
double rhs(cutoff);
for(i = 0; i < tCols; i++){
objCon.insert(i, oSolver->getObjCoefficients()[i]
* oSolver->getObjSense());
}
hSolver->addRow(objCon, - hSolver->getInfinity(), rhs);
}
if(0)
hSolver->writeLp("lobjheurstic");
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(hSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("max_active_nodes", 1);
}
hSolver->branchAndBound();
if(hSolver->isProvenOptimal()){
double upperObjVal(0.0);
/*****************NEW ******************/
MibSSolution *mibSol = NULL;
OsiSolverInterface * lSolver = model->bS_->setUpModel(hSolver, true);
if(0){
lSolver->writeLp("tmp");
}
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(lSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("max_active_nodes", 1);
}
lSolver->branchAndBound();
if (lSolver->isProvenOptimal()){
const double * sol = hSolver->getColSolution();
double objVal(lSolver->getObjValue() * objSense);
double etol(etol_);
double lowerObj = getLowerObj(sol, objSense);
double * optUpperSolutionOrd = new double[uCols];
double * optLowerSolutionOrd = new double[lCols];
CoinZeroN(optUpperSolutionOrd, uCols);
CoinZeroN(optLowerSolutionOrd, lCols);
if(fabs(objVal - lowerObj) < etol){
/** Current solution is bilevel feasible **/
for(i = 0; i < tCols; i++)
upperObjVal +=
hSolver->getColSolution()[i] * oSolver->getObjCoefficients()[i];
mibSol = new MibSSolution(hSolver->getNumCols(),
hSolver->getColSolution(),
upperObjVal,
model);
model->storeSolution(BlisSolutionTypeHeuristic, mibSol);
mibSol = NULL;
}
else{
/* solution is not bilevel feasible, create one that is */
const double * uSol = hSolver->getColSolution();
const double * lSol = lSolver->getColSolution();
int numElements(hSolver->getNumCols());
int i(0), pos(0), index(0);
double * lpSolution = new double[numElements];
double upperObj(0.0);
//FIXME: problem is still here. indices may be wrong.
//also is all this necessary, or can we just paste together uSol and lSol?
//this may be an old comment
for(i = 0; i < numElements; i++){
pos = model->bS_->binarySearch(0, lCols - 1, i, lColIndices);
if(pos < 0){
pos = model->bS_->binarySearch(0, uCols - 1, i, uColIndices);
if (pos >= 0){
optUpperSolutionOrd[pos] = uSol[i];
}
}
else{
optLowerSolutionOrd[pos] = lSol[pos];
}
}
for(i = 0; i < uCols; i++){
index = uColIndices[i];
lpSolution[index] = optUpperSolutionOrd[i];
upperObj +=
optUpperSolutionOrd[i] * oSolver->getObjCoefficients()[index];
}
for(i = 0; i < lCols; i++){
index = lColIndices[i];
lpSolution[index] = optLowerSolutionOrd[i];
upperObj +=
optLowerSolutionOrd[i] * oSolver->getObjCoefficients()[index];
}
if(model->checkUpperFeasibility(lpSolution)){
mibSol = new MibSSolution(hSolver->getNumCols(),
lpSolution,
upperObj * oSolver->getObjSense(),
model);
model->storeSolution(BlisSolutionTypeHeuristic, mibSol);
mibSol = NULL;
}
delete [] lpSolution;
}
}
delete lSolver;
}
delete hSolver;
}
//#############################################################################
mcSol
MibSHeuristic::solveSubproblem(double beta)
{
/*
optimize wrt to weighted upper-level objective
over current feasible lp feasible region
*/
MibSModel * model = MibSModel_;
OsiSolverInterface * oSolver = model->getSolver();
//OsiSolverInterface * sSolver = new OsiCbcSolverInterface();
OsiSolverInterface* sSolver = new OsiSymSolverInterface();
//sSolver = oSolver->clone();
//OsiSolverInterface * sSolver = tmpSolver;
//OsiSolverInterface * tmpSolver = new OsiSolverInterface(oSolver);
double uObjSense(oSolver->getObjSense());
double lObjSense(model->getLowerObjSense());
int lCols(model->getLowerDim());
int uCols(model->getUpperDim());
int * lColIndices = model->getLowerColInd();
int * uColIndices = model->getUpperColInd();
double * lObjCoeffs = model->getLowerObjCoeffs();
const double * uObjCoeffs = oSolver->getObjCoefficients();
double etol(etol_);
int tCols(uCols + lCols);
assert(tCols == oSolver->getNumCols());
sSolver->loadProblem(*oSolver->getMatrixByCol(),
oSolver->getColLower(), oSolver->getColUpper(),
oSolver->getObjCoefficients(),
oSolver->getRowLower(), oSolver->getRowUpper());
int j(0);
for(j = 0; j < tCols; j++){
if(oSolver->isInteger(j))
sSolver->setInteger(j);
}
double * nObjCoeffs = new double[tCols];
int i(0), index(0);
CoinZeroN(nObjCoeffs, tCols);
/* Multiply the UL columns of the UL objective by beta */
for(i = 0; i < uCols; i++){
index = uColIndices[i];
if(fabs(uObjCoeffs[index]) > etol)
nObjCoeffs[index] = beta * uObjCoeffs[index] * uObjSense;
else
nObjCoeffs[index] = 0.0;
}
/* Multiply the LL columns of the UL objective by beta */
for(i = 0; i < lCols; i++){
index = lColIndices[i];
if(fabs(uObjCoeffs[index]) > etol)
nObjCoeffs[index] = beta* uObjCoeffs[index] * uObjSense;
else
nObjCoeffs[index] = 0.0;
}
/* Add the LL columns of the LL objective multiplied by (1 - beta) */
for(i = 0; i < lCols; i++){
index = lColIndices[i];
if(fabs(lObjCoeffs[i]) > etol)
nObjCoeffs[index] += (1 - beta) * lObjCoeffs[i] * lObjSense;
}
sSolver->setObjective(nObjCoeffs);
//int i(0);
if(0){
for(i = 0; i < sSolver->getNumCols(); i++){
std::cout << "betaobj " << sSolver->getObjCoefficients()[i] << std::endl;
}
}
if(0){
sSolver->writeLp("afterbeta");
//sSolver->writeMps("afterbeta");
}
if(0){
for(i = 0; i < sSolver->getNumCols(); i++){
std::cout << "obj " << sSolver->getObjCoefficients()[i] << std::endl;
std::cout << "upper " << sSolver->getColUpper()[i] << std::endl;
std::cout << "lower " << sSolver->getColLower()[i] << std::endl;
}
}
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(sSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(sSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(sSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(sSolver)->setSymParam("max_active_nodes", 1);
}
//dynamic_cast<OsiSymSolverInterface *> (sSolver)->branchAndBound();
sSolver->branchAndBound();
if(sSolver->isProvenOptimal()){
if(0){
std::cout << "writing lp file." << std::endl;
sSolver->writeLp("afterbeta");
//sSolver->writeMps("afterbeta");
}
double upperObjVal(0.0);
double lowerObjVal(0.0);
for(i = 0; i < tCols; i++){
upperObjVal +=
sSolver->getColSolution()[i] * oSolver->getObjCoefficients()[i];
if(0){
std::cout << "sSolver->getColSolution()[" << i << "] :"
<< sSolver->getColSolution()[i] << std::endl;
}
}
lowerObjVal = getLowerObj(sSolver->getColSolution(), lObjSense);
if(beta == 1.0){
/*
fix upper-level objective to current value and
reoptimize wrt to lower-level objective
*/
//OsiSolverInterface * nSolver = new OsiCbcSolverInterface();
OsiSolverInterface * nSolver = new OsiSymSolverInterface();
nSolver->loadProblem(*oSolver->getMatrixByCol(),
oSolver->getColLower(), oSolver->getColUpper(),
oSolver->getObjCoefficients(),
oSolver->getRowLower(), oSolver->getRowUpper());
for(j = 0; j < tCols; j++){
if(oSolver->isInteger(j))
nSolver->setInteger(j);
}
CoinZeroN(nObjCoeffs, tCols);
for(i = 0; i < lCols; i++){
index = lColIndices[i];
nObjCoeffs[index] = lObjCoeffs[i] * lObjSense;
}
nSolver->setObjective(nObjCoeffs);
CoinPackedVector objCon;
for(i = 0; i < tCols; i++){
objCon.insert(i, uObjCoeffs[i] * uObjSense);
}
nSolver->addRow(objCon, upperObjVal, upperObjVal);
nSolver->writeLp("beta1");
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(nSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("max_active_nodes", 1);
}
nSolver->branchAndBound();
double * colsol = new double[tCols];
if(nSolver->isProvenOptimal()){
lowerObjVal = nSolver->getObjValue();
CoinCopyN(nSolver->getColSolution(), tCols, colsol);
}
else{
//just take the current solution
lowerObjVal = sSolver->getObjValue();
CoinCopyN(sSolver->getColSolution(), tCols, colsol);
}
delete[] nObjCoeffs;
nObjCoeffs = 0;
delete sSolver;
delete nSolver;
return mcSol(std::make_pair(upperObjVal, lowerObjVal), colsol);
}
else if(beta == 0.0){
/*
fix lower-level objective to current value and
reoptimize wrt to upper-level objective
*/
//OsiSolverInterface * nSolver = new OsiCbcSolverInterface();
OsiSolverInterface * nSolver = new OsiSymSolverInterface();
nSolver->loadProblem(*oSolver->getMatrixByCol(),
oSolver->getColLower(), oSolver->getColUpper(),
oSolver->getObjCoefficients(),
oSolver->getRowLower(), oSolver->getRowUpper());
for(j = 0; j < tCols; j++){
if(oSolver->isInteger(j))
nSolver->setInteger(j);
}
CoinZeroN(nObjCoeffs, tCols);
for(i = 0; i < tCols; i++)
nObjCoeffs[i] = uObjCoeffs[i] * uObjSense;
nSolver->setObjective(nObjCoeffs);
CoinPackedVector objCon;
for(i = 0; i < lCols; i++){
index = lColIndices[i];
objCon.insert(index, lObjCoeffs[i] * lObjSense);
}
nSolver->addRow(objCon, lowerObjVal, lowerObjVal);
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(nSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("max_active_nodes", 1);
}
if(0)
nSolver->writeLp("nSolver");
nSolver->branchAndBound();
double * colsol = new double[tCols];
if(nSolver->isProvenOptimal()){
upperObjVal = nSolver->getObjValue();
CoinCopyN(nSolver->getColSolution(), tCols, colsol);
}
else{
upperObjVal = nSolver->getObjValue();
CoinCopyN(nSolver->getColSolution(), tCols, colsol);
}
delete[] nObjCoeffs;
nObjCoeffs = 0;
delete sSolver;
delete nSolver;
return mcSol(std::make_pair(upperObjVal, lowerObjVal), colsol);
}
else{
//no optimality cut needed here. all solutions are supported.
double * colsol = new double[tCols];
CoinCopyN(sSolver->getColSolution(), tCols, colsol);
delete[] nObjCoeffs;
nObjCoeffs = 0;
delete sSolver;
return mcSol(std::make_pair(upperObjVal, lowerObjVal), colsol);
}
}
else{
//FIXME:SHOULD JUST TAKE THIS OUT. DELETE sSolver and remove it from above
nObjCoeffs = 0;
delete[] nObjCoeffs;
delete sSolver;
std::cout << "Subproblem is not proven optimal." << std::endl;
//return NULL;
//abort();
}
}
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();
/* 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.getModelPtr()->setLogLevel(0);
solver1.messageHandler()->setLogLevel(0);
solver1.initialSolve();
// Reduce printout
solver1.setHintParam(OsiDoReducePrint,true,OsiHintTry);
CbcModel model(solver1);
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");
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(2,1);
osiclp->setupForRepeatedUse(0,1);
}
}
// Uncommenting this should switch off most CBC messages
//model.messagesPointer()->setDetailMessages(10,5,5000);
// 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.setSizeMiniTree(2);
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);
}
// 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);
// Go round adding cuts to cutoff last solution
// Stop after finding 20 best solutions
for (int iPass=0;iPass<20;iPass++) {
time1 = CoinCpuTime();
// 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;
// Stop if infeasible
if (model.isProvenInfeasible())
break;
// Print solution if finished - we can't get names from Osi! - so get from OsiClp
assert (model.getMinimizationObjValue()<1.0e50);
OsiSolverInterface * solver = model.solver();
int numberColumns = solver->getNumCols();
const double * solution = model.bestSolution();
//const double * lower = solver->getColLower();
//const double * upper = solver->getColUpper();
// 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
//<<" "<<lower[iColumn]<<" "<<upper[iColumn]
<<std::endl;
}
std::cout<<"--------------------------------------"<<std::endl;
std::cout<<std::resetiosflags(std::ios::fixed|std::ios::showpoint|std::ios::scientific);
/* Now add cut to reference copy.
resetting to reference copy also gets rid of best solution so we
should either save best solution, reset, add cut OR
add cut to reference copy then reset - this is doing latter
*/
OsiSolverInterface * refSolver = model.referenceSolver();
const double * bestSolution = model.bestSolution();
const double * originalLower = refSolver->getColLower();
const double * originalUpper = refSolver->getColUpper();
CoinPackedVector cut;
double rhs = 1.0;
for (iColumn=0;iColumn<numberColumns;iColumn++) {
double value=bestSolution[iColumn];
if (solver->isInteger(iColumn)) {
// only works for 0-1 variables
assert (originalLower[iColumn]==0.0&&
originalUpper[iColumn]==1.0);
// double check integer
assert (fabs(floor(value+0.5)-value)<1.0e-5);
if (value>0.5) {
// at 1.0
cut.insert(iColumn,-1.0);
rhs -= 1.0;
} else {
// at 0.0
cut.insert(iColumn,1.0);
}
}
}
// now add cut
refSolver->addRow(cut,rhs,COIN_DBL_MAX);
model.resetToReferenceSolver();
}
return 0;
}
int main(int argc, char **argv)
{
VRPH_version();
int i, j, k, n, status, num_attempts, *sol_buff, *IP_sol_buff;
char in_file[200];
double lambda, best_heur_sol=VRP_INFINITY;
bool first_sol=false, bootstrap=false;;
VRPSolution *fresh_solution;
OsiSolverInterface *si;
const double *x;
int last_num_cols=0, route_id=0;
time_t start, stop;
int *orderings[MAX_ROUTES];
for(i=0;i<MAX_ROUTES;i++)
orderings[i]=NULL;
// Set timing counters to 0
heur_time=mip_time=0;
// Check arguments
if(argc<5)
{
fprintf(stderr,"Usage: %s -f input_file -n num_runs [-v,-b,-c max_columns -d cols_to_delete]\n",
argv[0]);
fprintf(stderr,"\t Will solve the problem num_solutions times and add the routes\n");
fprintf(stderr,"\t to a set partitioning problem.\n");
fprintf(stderr,"\t Other options:\n");
fprintf(stderr,"\t -v runs in verbose mode\n");
fprintf(stderr,"\t -b will use bootstrapping where we send the set partitioning\n"
"\t solution back to the metaheuristic solver\n");
fprintf(stderr,"\t -c max_columns will allow this many active columns/variables in the IP.\n");
fprintf(stderr,"\t Default value is max_columns=500\n");
fprintf(stderr,"\t -d num_cols_to_delete will delete this many columns once we have too many\n");
fprintf(stderr,"\t in the IP. Default value is num_cols_to_delete=100\n");
exit(-1);
}
// Set defaults
verbose=false;
max_columns=500;
num_cols_to_delete=100;
// Parse command line
for(i=0;i<argc;i++)
{
if(strcmp(argv[i],"-f")==0)
strcpy(in_file,argv[i+1]);
if(strcmp(argv[i],"-n")==0)
num_attempts=atoi(argv[i+1]);
if(strcmp(argv[i],"-v")==0)
verbose=true;
if(strcmp(argv[i],"-b")==0)
bootstrap=true;
if(strcmp(argv[i],"-c")==0)
max_columns=atoi(argv[i+1]);
if(strcmp(argv[i],"-d")==0)
num_cols_to_delete=atoi(argv[i+1]);
}
// This is the # of non-VRPH_DEPOT nodes
n=VRPGetDimension(in_file);
// This will be used to import/export solutions
fresh_solution = new VRPSolution(n);
// Create buffers for importing solutions
sol_buff= new int[n+2];
IP_sol_buff = new int[n+2];
// Declare an OSI interface
si=new OsiGlpkSolverInterface;
si->setIntParam(OsiNameDiscipline,2);
for(i=0;i<n;i++)
{
si->addRow(0,NULL,NULL,1,1);
}
// Declare a VRP of the right size and import the file
VRP V(n);
ClarkeWright CW(n);
VRPRoute route(n);
V.read_TSPLIB_file(in_file);
// Set up a "route warehouse" to store the routes to be added to the IP
V.route_wh=new VRPRouteWarehouse(HASH_TABLE_SIZE);
// Set up a minimization problem
si->setObjSense(1);
// Set to error only output
si->setHintParam(OsiDoReducePrint,true, OsiHintDo);
// Unfortunately GLPK still prints out something regarding the conflict graph
for(i=0;i<num_attempts;i++)
{
if(i==0 || !bootstrap)
{
lambda=.5+1.5*lcgrand(0);
// Start with a clean VRP object
V.reset();
CW.Construct(&V, lambda, false);
if(verbose)
printf("CW solution %d[%5.3f]: %f\n",i,lambda,V.get_total_route_length()-V.get_total_service_time());
}
else
// Use the solution from the IP
V.import_solution_buff(IP_sol_buff);
// Run VRPH's RTR algorithm to improve the solution
start=clock();
V.RTR_solve(ONE_POINT_MOVE | TWO_POINT_MOVE | TWO_OPT | VRPH_USE_NEIGHBOR_LIST,
30, 5, 2, .01, 30, VRPH_LI_PERTURB, VRPH_FIRST_ACCEPT,false);
stop=clock();
heur_time += (stop-start);
if(verbose)
printf("RTR Metaheuristic found solution %5.3f\n",V.get_total_route_length()-V.get_total_service_time());
// The RTR algorithm keeps a "warehouse" of the best solutions discovered during
// the algorithm's search
// Now go through the solutions in the solution warehouse and add the new routes
// discovered to the IP
for(j=0;j<V.solution_wh->num_sols;j++)
{
// Import solution j from the warehouse
V.import_solution_buff(V.solution_wh->sols[j].sol);
if(V.get_total_route_length()-V.get_total_service_time() < best_heur_sol)
best_heur_sol = V.get_total_route_length()-V.get_total_service_time() ;
// Now add the routes from this solution to the IP
for(k=1;k<=V.get_total_number_of_routes();k++)
{
// Clean up the route by running INTRA_ROUTE optimizations only
// using the route_search method of the different local search
// heuristics, accepting improving moves only (VRPH_DOWNHILL)
OnePointMove OPM;
TwoOpt TO;
ThreeOpt ThO;
while(OPM.route_search(&V,k,k,VRPH_DOWNHILL|VRPH_INTRA_ROUTE_ONLY )){}
while(TO.route_search(&V,k,k,VRPH_DOWNHILL|VRPH_INTRA_ROUTE_ONLY )){};
while(ThO.route_search(&V,k,VRPH_DOWNHILL|VRPH_INTRA_ROUTE_ONLY )){};
// Copy route k from the solution to the VRPRoute R
V.update_route(k,&route);
route.create_name();
// Add it to the "route warehouse" - this uses a hash table to keep track
// of duplicate columns
status=V.route_wh->add_route(&route);
if(status!=DUPLICATE_ROUTE)
{
// This route is not currently in the WH and so it cannot be in the
// set partitioning problem
//OSI_add_route(si,&V,&route);
OSI_add_route(si,&V,&route,route_id,orderings);
route_id++;
}
}
// Set the row RHS's if we need to
if(first_sol)
{
first_sol=false;
for(int rownum=0;rownum<n;rownum++)
si->setRowBounds(rownum,1,1);
// Note that changing this to >= would be a set covering problem
// where each customer can be visited by more than one route
}
}
// Now erase all the solutions from the WH
V.solution_wh->liquidate();
if(verbose)
{
printf("Attempt %02d: Solving IP with %d columns\n",i,si->getNumCols());
printf("%d routes in the WH\n",V.route_wh->num_unique_routes);
}
// Solve the current set partitioning problem using the MIP solver
start=clock();
si->branchAndBound();
stop=clock();
mip_time += (stop-start);
double opt=si->getObjValue();
x=si->getColSolution();
last_num_cols=si->getNumCols();
if(verbose)
printf("Optimal solution (%d columns) is %f\n",last_num_cols,opt);
// Now recover the solution from the IP solution
OSI_recover_solution(si, orderings, IP_sol_buff);
if(verbose)
printf("IP solution has obj. function value: %5.2f\n"
"Best heuristic obj. function value: %5.2f\n",
si->getObjValue(),best_heur_sol);
}
if(verbose)
printf(
"\nResults\n"
"--------\n"
"After %d runs\n"
"IP solution has obj. function value: %5.2f\n"
"Best heuristic obj. function value: %5.2f\n",
num_attempts,si->getObjValue(),best_heur_sol);
// print to stderr since GLPK prints "conflict graph" to stdout (a known bug...)
// best_heur_sol best_mip_sol heur_time mip_time
fprintf(stderr,"%5.3f %5.3f %5.3f %5.3f\n", best_heur_sol, si->getObjValue(),
(double)(heur_time)/CLOCKS_PER_SEC, (double)(mip_time)/CLOCKS_PER_SEC);
delete V.route_wh;
delete fresh_solution;
delete [] sol_buff;
delete [] IP_sol_buff;
delete si;
for(i=0;i<MAX_ROUTES;i++)
if(orderings[i])
delete [] orderings[i];
return 0;
}
OsiSolverInterface *
expandKnapsack(CoinModel & model, int * whichColumn, int * knapsackStart,
int * knapsackRow, int &numberKnapsack,
CglStored & stored, int logLevel,
int fixedPriority, int SOSPriority, CoinModel & tightenedModel)
{
int maxTotal = numberKnapsack;
// load from coin model
OsiSolverLink *si = new OsiSolverLink();
OsiSolverInterface * finalModel = NULL;
si->setDefaultMeshSize(0.001);
// need some relative granularity
si->setDefaultBound(100.0);
si->setDefaultMeshSize(0.01);
si->setDefaultBound(100000.0);
si->setIntegerPriority(1000);
si->setBiLinearPriority(10000);
si->load(model, true, logLevel);
// get priorities
const int * priorities = model.priorities();
int numberColumns = model.numberColumns();
if (priorities) {
OsiObject ** objects = si->objects();
int numberObjects = si->numberObjects();
for (int iObj = 0; iObj < numberObjects; iObj++) {
int iColumn = objects[iObj]->columnNumber();
if (iColumn >= 0 && iColumn < numberColumns) {
#ifndef NDEBUG
OsiSimpleInteger * obj =
dynamic_cast <OsiSimpleInteger *>(objects[iObj]) ;
#endif
assert (obj);
int iPriority = priorities[iColumn];
if (iPriority > 0)
objects[iObj]->setPriority(iPriority);
}
}
if (fixedPriority > 0) {
si->setFixedPriority(fixedPriority);
}
if (SOSPriority < 0)
SOSPriority = 100000;
}
CoinModel coinModel = *si->coinModel();
assert(coinModel.numberRows() > 0);
tightenedModel = coinModel;
int numberRows = coinModel.numberRows();
// Mark variables
int * whichKnapsack = new int [numberColumns];
int iRow, iColumn;
for (iColumn = 0; iColumn < numberColumns; iColumn++)
whichKnapsack[iColumn] = -1;
int kRow;
bool badModel = false;
// analyze
if (logLevel > 1) {
for (iRow = 0; iRow < numberRows; iRow++) {
/* Just obvious one at first
positive non unit coefficients
all integer
positive rowUpper
for now - linear (but further down in code may use nonlinear)
column bounds should be tight
*/
//double lower = coinModel.getRowLower(iRow);
double upper = coinModel.getRowUpper(iRow);
if (upper < 1.0e10) {
CoinModelLink triple = coinModel.firstInRow(iRow);
bool possible = true;
int n = 0;
int n1 = 0;
while (triple.column() >= 0) {
int iColumn = triple.column();
const char * el = coinModel.getElementAsString(iRow, iColumn);
if (!strcmp("Numeric", el)) {
if (coinModel.columnLower(iColumn) == coinModel.columnUpper(iColumn)) {
triple = coinModel.next(triple);
continue; // fixed
}
double value = coinModel.getElement(iRow, iColumn);
if (value < 0.0) {
possible = false;
} else {
n++;
if (value == 1.0)
n1++;
if (coinModel.columnLower(iColumn) < 0.0)
possible = false;
if (!coinModel.isInteger(iColumn))
possible = false;
if (whichKnapsack[iColumn] >= 0)
possible = false;
}
} else {
possible = false; // non linear
}
triple = coinModel.next(triple);
}
if (n - n1 > 1 && possible) {
double lower = coinModel.getRowLower(iRow);
double upper = coinModel.getRowUpper(iRow);
CoinModelLink triple = coinModel.firstInRow(iRow);
while (triple.column() >= 0) {
int iColumn = triple.column();
lower -= coinModel.columnLower(iColumn) * triple.value();
upper -= coinModel.columnLower(iColumn) * triple.value();
triple = coinModel.next(triple);
}
printf("%d is possible %g <=", iRow, lower);
// print
triple = coinModel.firstInRow(iRow);
while (triple.column() >= 0) {
int iColumn = triple.column();
if (coinModel.columnLower(iColumn) != coinModel.columnUpper(iColumn))
printf(" (%d,el %g up %g)", iColumn, triple.value(),
coinModel.columnUpper(iColumn) - coinModel.columnLower(iColumn));
triple = coinModel.next(triple);
}
printf(" <= %g\n", upper);
}
}
}
}
numberKnapsack = 0;
for (kRow = 0; kRow < numberRows; kRow++) {
iRow = kRow;
/* Just obvious one at first
positive non unit coefficients
all integer
positive rowUpper
for now - linear (but further down in code may use nonlinear)
column bounds should be tight
*/
//double lower = coinModel.getRowLower(iRow);
double upper = coinModel.getRowUpper(iRow);
if (upper < 1.0e10) {
CoinModelLink triple = coinModel.firstInRow(iRow);
bool possible = true;
int n = 0;
int n1 = 0;
while (triple.column() >= 0) {
int iColumn = triple.column();
const char * el = coinModel.getElementAsString(iRow, iColumn);
if (!strcmp("Numeric", el)) {
if (coinModel.columnLower(iColumn) == coinModel.columnUpper(iColumn)) {
triple = coinModel.next(triple);
continue; // fixed
}
double value = coinModel.getElement(iRow, iColumn);
if (value < 0.0) {
possible = false;
} else {
n++;
if (value == 1.0)
n1++;
if (coinModel.columnLower(iColumn) < 0.0)
possible = false;
if (!coinModel.isInteger(iColumn))
possible = false;
if (whichKnapsack[iColumn] >= 0)
possible = false;
}
} else {
possible = false; // non linear
}
triple = coinModel.next(triple);
}
if (n - n1 > 1 && possible) {
// try
CoinModelLink triple = coinModel.firstInRow(iRow);
while (triple.column() >= 0) {
int iColumn = triple.column();
if (coinModel.columnLower(iColumn) != coinModel.columnUpper(iColumn))
whichKnapsack[iColumn] = numberKnapsack;
triple = coinModel.next(triple);
}
knapsackRow[numberKnapsack++] = iRow;
}
}
}
if (logLevel > 0)
printf("%d out of %d candidate rows are possible\n", numberKnapsack, numberRows);
// Check whether we can get rid of nonlinearities
/* mark rows
-2 in knapsack and other variables
-1 not involved
n only in knapsack n
*/
int * markRow = new int [numberRows];
for (iRow = 0; iRow < numberRows; iRow++)
markRow[iRow] = -1;
int canDo = 1; // OK and linear
for (iColumn = 0; iColumn < numberColumns; iColumn++) {
CoinModelLink triple = coinModel.firstInColumn(iColumn);
int iKnapsack = whichKnapsack[iColumn];
bool linear = true;
// See if quadratic objective
const char * expr = coinModel.getColumnObjectiveAsString(iColumn);
if (strcmp(expr, "Numeric")) {
linear = false;
}
while (triple.row() >= 0) {
int iRow = triple.row();
if (iKnapsack >= 0) {
if (markRow[iRow] == -1) {
markRow[iRow] = iKnapsack;
} else if (markRow[iRow] != iKnapsack) {
markRow[iRow] = -2;
}
}
const char * expr = coinModel.getElementAsString(iRow, iColumn);
if (strcmp(expr, "Numeric")) {
linear = false;
}
triple = coinModel.next(triple);
}
if (!linear) {
if (whichKnapsack[iColumn] < 0) {
canDo = 0;
break;
} else {
canDo = 2;
}
}
}
int * markKnapsack = NULL;
double * coefficient = NULL;
double * linear = NULL;
int * whichRow = NULL;
int * lookupRow = NULL;
badModel = (canDo == 0);
if (numberKnapsack && canDo) {
/* double check - OK if
no nonlinear
nonlinear only on columns in knapsack
nonlinear only on columns in knapsack * ONE other - same for all in knapsack
AND that is only row connected to knapsack
(theoretically could split knapsack if two other and small numbers)
also ONE could be ONE expression - not just a variable
*/
int iKnapsack;
markKnapsack = new int [numberKnapsack];
coefficient = new double [numberKnapsack];
linear = new double [numberColumns];
for (iKnapsack = 0; iKnapsack < numberKnapsack; iKnapsack++)
markKnapsack[iKnapsack] = -1;
if (canDo == 2) {
for (iRow = -1; iRow < numberRows; iRow++) {
int numberOdd;
CoinPackedMatrix * row = coinModel.quadraticRow(iRow, linear, numberOdd);
if (row) {
// see if valid
const double * element = row->getElements();
const int * column = row->getIndices();
const CoinBigIndex * columnStart = row->getVectorStarts();
const int * columnLength = row->getVectorLengths();
int numberLook = row->getNumCols();
for (int i = 0; i < numberLook; i++) {
int iKnapsack = whichKnapsack[i];
if (iKnapsack < 0) {
// might be able to swap - but for now can't have knapsack in
for (int j = columnStart[i]; j < columnStart[i] + columnLength[i]; j++) {
int iColumn = column[j];
if (whichKnapsack[iColumn] >= 0) {
canDo = 0; // no good
badModel = true;
break;
}
}
} else {
// OK if in same knapsack - or maybe just one
int marked = markKnapsack[iKnapsack];
for (int j = columnStart[i]; j < columnStart[i] + columnLength[i]; j++) {
int iColumn = column[j];
if (whichKnapsack[iColumn] != iKnapsack && whichKnapsack[iColumn] >= 0) {
canDo = 0; // no good
badModel = true;
break;
} else if (marked == -1) {
markKnapsack[iKnapsack] = iColumn;
marked = iColumn;
coefficient[iKnapsack] = element[j];
coinModel.associateElement(coinModel.columnName(iColumn), 1.0);
} else if (marked != iColumn) {
badModel = true;
canDo = 0; // no good
break;
} else {
// could manage with different coefficients - but for now ...
assert(coefficient[iKnapsack] == element[j]);
}
}
}
}
delete row;
}
}
}
if (canDo) {
// for any rows which are cuts
whichRow = new int [numberRows];
lookupRow = new int [numberRows];
bool someNonlinear = false;
double maxCoefficient = 1.0;
for (iKnapsack = 0; iKnapsack < numberKnapsack; iKnapsack++) {
if (markKnapsack[iKnapsack] >= 0) {
someNonlinear = true;
int iColumn = markKnapsack[iKnapsack];
maxCoefficient = CoinMax(maxCoefficient, fabs(coefficient[iKnapsack] * coinModel.columnUpper(iColumn)));
}
}
if (someNonlinear) {
// associate all columns to stop possible error messages
for (iColumn = 0; iColumn < numberColumns; iColumn++) {
coinModel.associateElement(coinModel.columnName(iColumn), 1.0);
}
}
ClpSimplex tempModel;
tempModel.loadProblem(coinModel);
// Create final model - first without knapsacks
int nCol = 0;
int nRow = 0;
for (iRow = 0; iRow < numberRows; iRow++) {
if (markRow[iRow] < 0) {
lookupRow[iRow] = nRow;
whichRow[nRow++] = iRow;
} else {
lookupRow[iRow] = -1;
}
}
for (iColumn = 0; iColumn < numberColumns; iColumn++) {
if (whichKnapsack[iColumn] < 0)
whichColumn[nCol++] = iColumn;
}
ClpSimplex finalModelX(&tempModel, nRow, whichRow, nCol, whichColumn, false, false, false);
OsiClpSolverInterface finalModelY(&finalModelX, true);
finalModel = finalModelY.clone();
finalModelY.releaseClp();
// Put back priorities
const int * priorities = model.priorities();
if (priorities) {
finalModel->findIntegers(false);
OsiObject ** objects = finalModel->objects();
int numberObjects = finalModel->numberObjects();
for (int iObj = 0; iObj < numberObjects; iObj++) {
int iColumn = objects[iObj]->columnNumber();
if (iColumn >= 0 && iColumn < nCol) {
#ifndef NDEBUG
OsiSimpleInteger * obj =
dynamic_cast <OsiSimpleInteger *>(objects[iObj]) ;
#endif
assert (obj);
int iPriority = priorities[whichColumn[iColumn]];
if (iPriority > 0)
objects[iObj]->setPriority(iPriority);
}
}
}
for (iRow = 0; iRow < numberRows; iRow++) {
whichRow[iRow] = iRow;
}
int numberOther = finalModel->getNumCols();
int nLargest = 0;
int nelLargest = 0;
int nTotal = 0;
for (iKnapsack = 0; iKnapsack < numberKnapsack; iKnapsack++) {
iRow = knapsackRow[iKnapsack];
int nCreate = maxTotal;
int nelCreate = coinModel.expandKnapsack(iRow, nCreate, NULL, NULL, NULL, NULL);
if (nelCreate < 0)
badModel = true;
nTotal += nCreate;
nLargest = CoinMax(nLargest, nCreate);
nelLargest = CoinMax(nelLargest, nelCreate);
}
if (nTotal > maxTotal)
badModel = true;
if (!badModel) {
// Now arrays for building
nelLargest = CoinMax(nelLargest, nLargest) + 1;
double * buildObj = new double [nLargest];
double * buildElement = new double [nelLargest];
int * buildStart = new int[nLargest+1];
int * buildRow = new int[nelLargest];
// alow for integers in knapsacks
OsiObject ** object = new OsiObject * [numberKnapsack+nTotal];
int nSOS = 0;
int nObj = numberKnapsack;
for (iKnapsack = 0; iKnapsack < numberKnapsack; iKnapsack++) {
knapsackStart[iKnapsack] = finalModel->getNumCols();
iRow = knapsackRow[iKnapsack];
int nCreate = 10000;
coinModel.expandKnapsack(iRow, nCreate, buildObj, buildStart, buildRow, buildElement);
// Redo row numbers
for (iColumn = 0; iColumn < nCreate; iColumn++) {
for (int j = buildStart[iColumn]; j < buildStart[iColumn+1]; j++) {
int jRow = buildRow[j];
jRow = lookupRow[jRow];
assert (jRow >= 0 && jRow < nRow);
buildRow[j] = jRow;
}
}
finalModel->addCols(nCreate, buildStart, buildRow, buildElement, NULL, NULL, buildObj);
int numberFinal = finalModel->getNumCols();
for (iColumn = numberOther; iColumn < numberFinal; iColumn++) {
if (markKnapsack[iKnapsack] < 0) {
finalModel->setColUpper(iColumn, maxCoefficient);
finalModel->setInteger(iColumn);
} else {
finalModel->setColUpper(iColumn, maxCoefficient + 1.0);
finalModel->setInteger(iColumn);
}
OsiSimpleInteger * sosObject = new OsiSimpleInteger(finalModel, iColumn);
sosObject->setPriority(1000000);
object[nObj++] = sosObject;
buildRow[iColumn-numberOther] = iColumn;
buildElement[iColumn-numberOther] = 1.0;
}
if (markKnapsack[iKnapsack] < 0) {
// convexity row
finalModel->addRow(numberFinal - numberOther, buildRow, buildElement, 1.0, 1.0);
} else {
int iColumn = markKnapsack[iKnapsack];
int n = numberFinal - numberOther;
buildRow[n] = iColumn;
buildElement[n++] = -fabs(coefficient[iKnapsack]);
// convexity row (sort of)
finalModel->addRow(n, buildRow, buildElement, 0.0, 0.0);
OsiSOS * sosObject = new OsiSOS(finalModel, n - 1, buildRow, NULL, 1);
sosObject->setPriority(iKnapsack + SOSPriority);
// Say not integral even if is (switch off heuristics)
sosObject->setIntegerValued(false);
object[nSOS++] = sosObject;
}
numberOther = numberFinal;
}
finalModel->addObjects(nObj, object);
for (iKnapsack = 0; iKnapsack < nObj; iKnapsack++)
delete object[iKnapsack];
delete [] object;
// Can we move any rows to cuts
const int * cutMarker = coinModel.cutMarker();
if (cutMarker && 0) {
printf("AMPL CUTS OFF until global cuts fixed\n");
cutMarker = NULL;
}
if (cutMarker) {
// Row copy
const CoinPackedMatrix * matrixByRow = finalModel->getMatrixByRow();
const double * elementByRow = matrixByRow->getElements();
const int * column = matrixByRow->getIndices();
const CoinBigIndex * rowStart = matrixByRow->getVectorStarts();
const int * rowLength = matrixByRow->getVectorLengths();
const double * rowLower = finalModel->getRowLower();
const double * rowUpper = finalModel->getRowUpper();
int nDelete = 0;
for (iRow = 0; iRow < numberRows; iRow++) {
if (cutMarker[iRow] && lookupRow[iRow] >= 0) {
int jRow = lookupRow[iRow];
whichRow[nDelete++] = jRow;
int start = rowStart[jRow];
stored.addCut(rowLower[jRow], rowUpper[jRow],
rowLength[jRow], column + start, elementByRow + start);
}
}
finalModel->deleteRows(nDelete, whichRow);
}
knapsackStart[numberKnapsack] = finalModel->getNumCols();
delete [] buildObj;
delete [] buildElement;
delete [] buildStart;
delete [] buildRow;
finalModel->writeMps("full");
}
}
}
delete [] whichKnapsack;
delete [] markRow;
delete [] markKnapsack;
delete [] coefficient;
delete [] linear;
delete [] whichRow;
delete [] lookupRow;
delete si;
si = NULL;
if (!badModel && finalModel) {
finalModel->setDblParam(OsiObjOffset, coinModel.objectiveOffset());
return finalModel;
} else {
delete finalModel;
printf("can't make knapsacks - did you set fixedPriority (extra1)\n");
return NULL;
}
}
OsiSolverInterface* cpropagation_preprocess(CPropagation *cp, int nindexes[])
{
if(cp->varsToFix == 0)
{
/* printf("There are no variables to remove from the problem!\n"); */
return NULL; /* returns a pointer to original solver */
}
const double *colLb = problem_vars_lower_bound(cp->problem), *colUb = problem_vars_upper_bound(cp->problem);
const double *objCoef = problem_vars_obj_coefs(cp->problem);
const char *ctype = problem_vars_type(cp->problem);
double sumFixedObj = 0.0; /* stores the sum of objective coefficients of all variables fixed to 1 */
OsiSolverInterface *preProcSolver = new OsiClpSolverInterface();
preProcSolver->setIntParam(OsiNameDiscipline, 2);
preProcSolver->messageHandler()->setLogLevel(0);
preProcSolver->setHintParam(OsiDoReducePrint,true,OsiHintTry);
//preProcSolver->setObjName(cp->solver->getObjName());
for(int i = 0, j = 0; i < problem_num_cols(cp->problem); i++)
{
nindexes[i] = -1;
if(cp->isToFix[i] == UNFIXED)
{
preProcSolver->addCol(0, NULL, NULL, colLb[i], colUb[i], objCoef[i]);
preProcSolver->setColName(j, problem_var_name(cp->problem, i));
if(problem_var_type(cp->problem, i) == CONTINUOUS)
preProcSolver->setContinuous(j);
else
preProcSolver->setInteger(j);
nindexes[i] = j++;
}
else if(cp->isToFix[i] == ACTIVATE)
sumFixedObj += objCoef[i];
}
if(fabs(sumFixedObj) > EPS)
{
/* adding a variable with cost equals to the sum of all coefficients of variables fixed to 1 */
preProcSolver->addCol(0, NULL, NULL, 1.0, 1.0, sumFixedObj);
preProcSolver->setColName(preProcSolver->getNumCols()-1, "sumFixedObj");
preProcSolver->setInteger(preProcSolver->getNumCols()-1);
}
for(int idxRow = 0; idxRow < problem_num_rows(cp->problem); idxRow++)
{
const int nElements = problem_row_size(cp->problem, idxRow);
const int *idxs = problem_row_idxs(cp->problem, idxRow);
const double *coefs = problem_row_coefs(cp->problem, idxRow);
vector< int > vidx; vidx.reserve(problem_num_cols(cp->problem));
vector< double > vcoef; vcoef.reserve(problem_num_cols(cp->problem));
double activeCoefs = 0.0;
for(int i = 0; i < nElements; i++)
{
if(cp->isToFix[idxs[i]] == UNFIXED)
{
assert(nindexes[idxs[i]] >= 0 && nindexes[idxs[i]] < problem_num_cols(cp->problem));
vidx.push_back(nindexes[idxs[i]]);
vcoef.push_back(coefs[i]);
}
else if(cp->isToFix[idxs[i]] == ACTIVATE)
activeCoefs += coefs[i];
}
if(!vidx.empty())
{
double rlb, rub;
const char sense = problem_row_sense(cp->problem, idxRow);
if(sense == 'E')
{
rlb = problem_row_rhs(cp->problem, idxRow) - activeCoefs;
rub = problem_row_rhs(cp->problem, idxRow) - activeCoefs;
}
else if(sense == 'L')
{
rlb = preProcSolver->getInfinity();
rub = problem_row_rhs(cp->problem, idxRow) - activeCoefs;
}
else if(sense == 'G')
{
rlb = problem_row_rhs(cp->problem, idxRow) - activeCoefs;
rub = preProcSolver->getInfinity();
}
else
{
fprintf(stderr, "Error: invalid type of constraint!\n");
exit(EXIT_FAILURE);
}
preProcSolver->addRow((int)vcoef.size(), &vidx[0], &vcoef[0], rlb, rub);
preProcSolver->setRowName(idxRow, problem_row_name(cp->problem, idxRow));
}
}
return preProcSolver;
}
