//#############################################################################
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;
}
void
HeuristicInnerApproximation::extractInnerApproximation(Bonmin::OsiTMINLPInterface & nlp, OsiSolverInterface &si,
const double * x, bool getObj) {
printf("************ Start extracting inner approx");
int n;
int m;
int nnz_jac_g;
int nnz_h_lag;
Ipopt::TNLP::IndexStyleEnum index_style;
Bonmin::TMINLP2TNLP * problem = nlp.problem();
//Get problem information
problem->get_nlp_info(n, m, nnz_jac_g, nnz_h_lag, index_style);
Bonmin::vector<int> jRow(nnz_jac_g);
Bonmin::vector<int> jCol(nnz_jac_g);
Bonmin::vector<double> jValues(nnz_jac_g);
problem->eval_jac_g(n, NULL, 0, m, nnz_jac_g, jRow(), jCol(), NULL);
if(index_style == Ipopt::TNLP::FORTRAN_STYLE)//put C-style
{
for(int i = 0 ; i < nnz_jac_g ; i++){
jRow[i]--;
jCol[i]--;
}
}
//get Jacobian
problem->eval_jac_g(n, x, 1, m, nnz_jac_g, NULL, NULL,
jValues());
Bonmin::vector<double> g(m);
problem->eval_g(n, x, 1, m, g());
Bonmin::vector<int> nonLinear(m);
//store non linear constraints (which are to be removed from IA)
int numNonLinear = 0;
const double * rowLower = nlp.getRowLower();
const double * rowUpper = nlp.getRowUpper();
const double * colLower = nlp.getColLower();
const double * colUpper = nlp.getColUpper();
assert(m == nlp.getNumRows());
double infty = si.getInfinity();
double nlp_infty = nlp.getInfinity();
Bonmin::vector<Ipopt::TNLP::LinearityType> constTypes(m);
Bonmin::vector<Ipopt::TNLP::LinearityType> varTypes(n);
problem->get_constraints_linearity(m, constTypes());
problem->get_variables_linearity(n, varTypes());
for (int i = 0; i < m; i++) {
if (constTypes[i] == Ipopt::TNLP::NON_LINEAR) {
nonLinear[numNonLinear++] = i;
}
}
Bonmin::vector<double> rowLow(m - numNonLinear);
Bonmin::vector<double> rowUp(m - numNonLinear);
int ind = 0;
for (int i = 0; i < m; i++) {
if (constTypes[i] != Ipopt::TNLP::NON_LINEAR) {
if (rowLower[i] > -nlp_infty) {
// printf("Lower %g ", rowLower[i]);
rowLow[ind] = (rowLower[i]);
} else
rowLow[ind] = -infty;
if (rowUpper[i] < nlp_infty) {
// printf("Upper %g ", rowUpper[i]);
rowUp[ind] = (rowUpper[i]);
} else
rowUp[ind] = infty;
ind++;
}
}
CoinPackedMatrix mat(true, jRow(), jCol(), jValues(), nnz_jac_g);
mat.setDimensions(m, n); // In case matrix was empty, this should be enough
//remove non-linear constraints
mat.deleteRows(numNonLinear, nonLinear());
int numcols = nlp.getNumCols();
Bonmin::vector<double> obj(numcols);
for (int i = 0; i < numcols; i++)
obj[i] = 0.;
si.loadProblem(mat, nlp.getColLower(), nlp.getColUpper(),
obj(), rowLow(), rowUp());
const Bonmin::TMINLP::VariableType* variableType = problem->var_types();
for (int i = 0; i < n; i++) {
if ((variableType[i] == Bonmin::TMINLP::BINARY) || (variableType[i] == Bonmin::TMINLP::INTEGER))
si.setInteger(i);
}
if (getObj) {
bool addObjVar = false;
if (problem->hasLinearObjective()) {
double zero;
Bonmin::vector<double> x0(n, 0.);
problem->eval_f(n, x0(), 1, zero);
si.setDblParam(OsiObjOffset, -zero);
//Copy the linear objective and don't create a dummy variable.
problem->eval_grad_f(n, x, 1, obj());
si.setObjective(obj());
} else {
addObjVar = true;
}
if (addObjVar) {
nlp.addObjectiveFunction(si, x);
}
}
// Hassan IA initial description
int InnerDesc = 1;
if (InnerDesc == 1) {
OsiCuts cs;
double * p = CoinCopyOfArray(colLower, n);
double * pp = CoinCopyOfArray(colLower, n);
double * up = CoinCopyOfArray(colUpper, n);
for (int i = 0; i < n; i++) {
if (p[i] < -1e3){
p[i] = pp[i] = -1e3;
}
if (up[i] > 1e2){
up[i] = 1e2;
}
}
const int& nbAp = nbAp_;
printf("Generating approximation with %i points.\n", nbAp);
std::vector<double> step(n);
int n_lin = 0;
for (int i = 0; i < n; i++) {
//if ((variableType[i] == Bonmin::TMINLP::BINARY) || (variableType[i] == Bonmin::TMINLP::INTEGER)) {
if (varTypes[i] == Ipopt::TNLP::LINEAR) {
n_lin ++;
step[i] = 0;
p[i] = pp[i] = up[i] = 0;
}
else {
step[i] = (up[i] - p[i]) / (nbAp);
}
}
printf("Number of linears %i\n", n_lin);
for (int j = 1; j < nbAp; j++) {
for (int i = 0; i < n; i++) {
pp[i] += step[i];
}
for (int i = 0; (i < m ); i++) {
if (constTypes[i] == Ipopt::TNLP::LINEAR) continue;
bool status = getMyInnerApproximation(nlp, cs, i, p, pp);// Generate a chord connecting the two points
if(status == false){
printf("Error in generating inner approximation\n");
exit(1);
}
}
std::copy(pp, pp+n, p);
}
for(int i = 0; (i< m); i++) {
if (constTypes[i] == Ipopt::TNLP::LINEAR) continue;
getMyInnerApproximation(nlp, cs, i, p, up);// Generate a chord connecting the two points
}
delete [] p;
delete [] pp;
delete [] up;
si.applyCuts(cs);
}
printf("************ Done extracting inner approx ********");
}
//#############################################################################
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();
}
}
void
HeuristicInnerApproximation::extractInnerApproximation(OsiTMINLPInterface & nlp, OsiSolverInterface &si,
const double * x, bool getObj) {
int n;
int m;
int nnz_jac_g;
int nnz_h_lag;
Ipopt::TNLP::IndexStyleEnum index_style;
TMINLP2TNLP * problem = nlp.problem();
//Get problem information
problem->get_nlp_info(n, m, nnz_jac_g, nnz_h_lag, index_style);
vector<int> jRow(nnz_jac_g);
vector<int> jCol(nnz_jac_g);
vector<double> jValues(nnz_jac_g);
problem->eval_jac_g(n, NULL, 0, m, nnz_jac_g, jRow(), jCol(), NULL);
if(index_style == Ipopt::TNLP::FORTRAN_STYLE)//put C-style
{
for(int i = 0 ; i < nnz_jac_g ; i++){
jRow[i]--;
jCol[i]--;
}
}
//get Jacobian
problem->eval_jac_g(n, x, 1, m, nnz_jac_g, NULL, NULL,
jValues());
vector<double> g(m);
problem->eval_g(n, x, 1, m, g());
vector<int> nonLinear(m);
//store non linear constraints (which are to be removed from IA)
int numNonLinear = 0;
const double * rowLower = nlp.getRowLower();
const double * rowUpper = nlp.getRowUpper();
const double * colLower = nlp.getColLower();
const double * colUpper = nlp.getColUpper();
assert(m == nlp.getNumRows());
double infty = si.getInfinity();
double nlp_infty = nlp.getInfinity();
vector<Ipopt::TNLP::LinearityType> constTypes(m);
problem->get_constraints_linearity(m, constTypes());
for (int i = 0; i < m; i++) {
if (constTypes[i] == Ipopt::TNLP::NON_LINEAR) {
nonLinear[numNonLinear++] = i;
}
}
vector<double> rowLow(m - numNonLinear);
vector<double> rowUp(m - numNonLinear);
int ind = 0;
for (int i = 0; i < m; i++) {
if (constTypes[i] != Ipopt::TNLP::NON_LINEAR) {
if (rowLower[i] > -nlp_infty) {
// printf("Lower %g ", rowLower[i]);
rowLow[ind] = (rowLower[i]);
} else
rowLow[ind] = -infty;
if (rowUpper[i] < nlp_infty) {
// printf("Upper %g ", rowUpper[i]);
rowUp[ind] = (rowUpper[i]);
} else
rowUp[ind] = infty;
ind++;
}
}
CoinPackedMatrix mat(true, jRow(), jCol(), jValues(), nnz_jac_g);
mat.setDimensions(m, n); // In case matrix was empty, this should be enough
//remove non-linear constraints
mat.deleteRows(numNonLinear, nonLinear());
int numcols = nlp.getNumCols();
vector<double> obj(numcols);
for (int i = 0; i < numcols; i++)
obj[i] = 0.;
si.loadProblem(mat, nlp.getColLower(), nlp.getColUpper(),
obj(), rowLow(), rowUp());
const Bonmin::TMINLP::VariableType* variableType = problem->var_types();
for (int i = 0; i < n; i++) {
if ((variableType[i] == TMINLP::BINARY) || (variableType[i]
== TMINLP::INTEGER))
si.setInteger(i);
}
if (getObj) {
bool addObjVar = false;
if (problem->hasLinearObjective()) {
double zero;
vector<double> x0(n, 0.);
problem->eval_f(n, x0(), 1, zero);
si.setDblParam(OsiObjOffset, -zero);
//Copy the linear objective and don't create a dummy variable.
problem->eval_grad_f(n, x, 1, obj());
si.setObjective(obj());
} else {
addObjVar = true;
}
if (addObjVar) {
nlp.addObjectiveFunction(si, x);
}
}
// Hassan IA initial description
int InnerDesc = 1;
if (InnerDesc == 1) {
OsiCuts cs;
double * p = CoinCopyOfArray(colLower, n);
double * pp = CoinCopyOfArray(colLower, n);
double * up = CoinCopyOfArray(colUpper, n);
const int& nbAp = nbAp_;
std::vector<int> nbG(m, 0);// Number of generated points for each nonlinear constraint
std::vector<double> step(n);
for (int i = 0; i < n; i++) {
if (colUpper[i] > 1e08) {
up[i] = 0;
}
if (colUpper[i] > 1e08 || colLower[i] < -1e08 || (variableType[i]
== TMINLP::BINARY) || (variableType[i] == TMINLP::INTEGER)) {
step[i] = 0;
} else
step[i] = (up[i] - colLower[i]) / (nbAp);
if (colLower[i] < -1e08) {
p[i] = 0;
pp[i] = 0;
}
}
vector<double> g_p(m);
vector<double> g_pp(m);
for (int j = 1; j <= nbAp; j++) {
for (int i = 0; i < n; i++) {
pp[i] += step[i];
}
problem->eval_g(n, p, 1, m, g_p());
problem->eval_g(n, pp, 1, m, g_pp());
double diff = 0;
int varInd = 0;
for (int i = 0; (i < m && constTypes[i] == Ipopt::TNLP::NON_LINEAR); i++) {
if (varInd == n - 1)
varInd = 0;
diff = std::abs(g_p[i] - g_pp[i]);
if (nbG[i] < nbAp - 1) {
getMyInnerApproximation(nlp, cs, i, p, pp);// Generate a chord connecting the two points
p[varInd] = pp[varInd];
nbG[i]++;
}
varInd++;
}
}
for(int i = 0; (i< m && constTypes[i] == Ipopt::TNLP::NON_LINEAR); i++) {
// getConstraintOuterApproximation(cs, i, colUpper, NULL, true);// Generate Tangents at current point
getMyInnerApproximation(nlp, cs, i, p, up);// Generate a chord connecting the two points
}
delete [] p;
delete [] pp;
delete [] up;
si.applyCuts(cs);
}
}
// mex function usage:
// [x,y,status] = mexosi(n_vars,n_cons,A,x_lb,x_ub,c,Ax_lb,Ax_ub,isMIP,isQP,vartype,Q,options)
// 0 1 2 3 4 5 6 7 8 9 10 11 12
void mexFunction(int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[])
{
// Enable printing in MATLAB
int loglevel = 0;
DerivedHandler *mexprinter = new DerivedHandler(); // assumed open
mexprinter->setLogLevel(loglevel);
// check that we have the right number of inputs
if(nrhs < 10) mexErrMsgTxt("At least 10 inputs required in call to mexosi. Bug in osi.m?...");
// check that we have the right number of outputs
if(nlhs < 3) mexErrMsgTxt("At least 3 ouptuts required in call to mexosi. Bug in osi.m?...");
// Get pointers to input values
const int n_vars = (int)*mxGetPr(prhs[0]);
const int n_cons = (int)*mxGetPr(prhs[1]);
const mxArray *A = prhs[2];
const double *x_lb = mxGetPr(prhs[3]);
const double *x_ub = mxGetPr(prhs[4]);
const double *c = mxGetPr(prhs[5]);
const double *Ax_lb = mxGetPr(prhs[6]);
const double *Ax_ub = mxGetPr(prhs[7]);
const bool isMIP = (bool)*(mxLogical*)mxGetData(prhs[8]);
const bool isQP = (bool)*(mxLogical*)mxGetData(prhs[9]);
mxLogical *isinteger = (mxLogical*)mxGetData(prhs[10]);
const mxArray* Q = prhs[11];
// process the options
int returnStatus = 0;
// extract row/col/value data from A
const mwIndex * A_col_starts = mxGetJc(A);
const mwIndex * A_row_index = mxGetIr(A);
const double * A_data = mxGetPr(A);
// figure out the number of non-zeros in A
int nnz = (int)(A_col_starts[n_vars] - A_col_starts[0]); // number of non-zeros
//mexPrintf("nnz = %d, n_vars = %d, n_cons = %d\n",nnz,n_vars,n_cons);
// we need to convert these into other types of indices for Coin to use them
std::vector<CoinBigIndex> A_col_starts_coin(A_col_starts,A_col_starts+n_vars+1);
std::vector<int> A_row_index_coin(A_row_index,A_row_index+nnz);
// declare the solver
OsiSolverInterface* pSolver;
// initialize the solver
if ( isMIP ) {
pSolver = new OsiCbcSolverInterface;
} else {
pSolver = new OsiClpSolverInterface;
}
// OsiCbcSolverInterface is deprecated and CbcModel should be used instead but don't
// know how to get that working with loadProblem.
// OsiCbcSolverInterface solver1;
// CbcModel model(solver1);
// CbcMain0(model);
// OsiSolverInterface * pSolver = model.solver();
if (nrhs>12) { // get stuff out of the options structure if provided
// Finish me
}
// mexPrintf("Setting Log Level to 0.\n");
// load the problem
mexPrintf("Loading the problem.\n");
pSolver->loadProblem( n_vars, n_cons, // problem size
&A_col_starts_coin[0], &A_row_index_coin[0], A_data, // the A matrix
x_lb, x_ub, c, // the objective and bounds
Ax_lb, Ax_ub ); // the constraint bounds
// pSolver->messageHandler()->setLogLevel(0); // This doesn't seem to work
pSolver->setHintParam(OsiDoReducePrint,true,OsiHintTry);
// deal with integer inputs
if ( isMIP ) {
for(int i=0;i<n_vars;i++) {
if (isinteger[i]) pSolver->setInteger(i);
}
}
if (isQP) {
error("QP is not working yet");
// need to call loadQuadraticObjective here ???
}
// CbcModel model(pSolver);
// model.solver()->setHintParam(OsiDoReducePrint,true,OsiHintTry);
// solve the problem
//mexPrintf("Trying to solve the problem.\n");
if (isMIP) {
pSolver->branchAndBound();
// model.branchAndBound();
} else {
// model.initialSolve();
pSolver->initialSolve();
}
// Allocate memory for return data
plhs[0] = mxCreateDoubleMatrix(n_vars,1, mxREAL); // for the solution
plhs[1] = mxCreateDoubleMatrix(n_cons,1, mxREAL); // for the constraint prices
plhs[2] = mxCreateDoubleMatrix(1,1, mxREAL); // for the return status
double *x = mxGetPr(plhs[0]);
double *y = mxGetPr(plhs[1]);
double *returncode = mxGetPr(plhs[2]);
// Copy solutions if available
if ( pSolver->isProvenOptimal() ) {
// if ( model.isProvenOptimal() ) {
// if ( model.solver()->isProvenOptimal() ) {
//mexPrintf("Solution found.\n");
// extract the solutions
const double * solution = pSolver->getColSolution();
const double * dualvars = pSolver->getRowPrice();
// copy the solution to the outpus
memcpy(x,solution,n_vars*sizeof(double));
memcpy(y,dualvars,n_cons*sizeof(double));
*returncode = 1;
} else {
if ( pSolver->isProvenPrimalInfeasible() ) {
mexPrintf("Primal problem is proven infeasible.\n");
*returncode = 0;
} else if ( pSolver->isProvenDualInfeasible() ) {
mexPrintf("Dual problem is proven infeasible.\n");
*returncode = -1;
} else if ( pSolver->isPrimalObjectiveLimitReached() ) {
mexPrintf("The primal objective limit was reached.\n");
*returncode = -2;
} else if ( pSolver->isDualObjectiveLimitReached() ) {
mexPrintf("The dual objective limit was reached.\n");
*returncode = -3;
} else if ( pSolver->isIterationLimitReached() ) {
mexPrintf("The iteration limit was reached\n");
*returncode = -4;
}
}
// clean up memory
if ( mexprinter!= NULL) delete mexprinter;
delete pSolver;
}
int
main(void)
{
// Create a problem pointer. We use the base class here.
OsiSolverInterface *si;
// When we instantiate the object, we need a specific derived class.
si = new OSIXXX;
// Build our own instance from scratch
/*
* This section adapted from Matt Galati's example
* on the COIN-OR Tutorial website.
*
* Problem from Bertsimas, Tsitsiklis page 21
*
* optimal solution: x* = (1,1)
*
* minimize -1 x0 - 1 x1
* s.t 1 x0 + 2 x1 <= 3
* 2 x0 + 1 x1 <= 3
* x0 >= 0
* x1 >= 0
*/
int n_cols = 2;
double *objective = new double[n_cols];//the objective coefficients
double *col_lb = new double[n_cols];//the column lower bounds
double *col_ub = new double[n_cols];//the column upper bounds
//Define the objective coefficients.
//minimize -1 x0 - 1 x1
objective[0] = -1.0;
objective[1] = -1.0;
//Define the variable lower/upper bounds.
// x0 >= 0 => 0 <= x0 <= infinity
// x1 >= 0 => 0 <= x1 <= infinity
col_lb[0] = 0.0;
col_lb[1] = 0.0;
col_ub[0] = si->getInfinity();
col_ub[1] = si->getInfinity();
int n_rows = 2;
double *row_lb = new double[n_rows]; //the row lower bounds
double *row_ub = new double[n_rows]; //the row upper bounds
//Define the constraint matrix.
CoinPackedMatrix *matrix = new CoinPackedMatrix(false,0,0);
matrix->setDimensions(0, n_cols);
//1 x0 + 2 x1 <= 3 => -infinity <= 1 x0 + 2 x2 <= 3
CoinPackedVector row1;
row1.insert(0, 1.0);
row1.insert(1, 2.0);
row_lb[0] = -1.0 * si->getInfinity();
row_ub[0] = 3.0;
matrix->appendRow(row1);
//2 x0 + 1 x1 <= 3 => -infinity <= 2 x0 + 1 x1 <= 3
CoinPackedVector row2;
row2.insert(0, 2.0);
row2.insert(1, 1.0);
row_lb[1] = -1.0 * si->getInfinity();
row_ub[1] = 3.0;
matrix->appendRow(row2);
//load the problem to OSI
si->loadProblem(*matrix, col_lb, col_ub, objective, row_lb, row_ub);
//write the MPS file to a file called example.mps
si->writeMps("example");
// Solve the (relaxation of the) problem
si->initialSolve();
// Check the solution
if ( si->isProvenOptimal() ) {
std::cout << "Found optimal solution!" << std::endl;
std::cout << "Objective value is " << si->getObjValue() << std::endl;
int n = si->getNumCols();
const double *solution;
solution = si->getColSolution();
// We could then print the solution or examine it.
} else {
std::cout << "Didn't find optimal solution." << std::endl;
// Could then check other status functions.
}
return 0;
}
//#############################################################################
OsiSolverInterface *
MibSBilevel::setUpModel(OsiSolverInterface * oSolver, bool newOsi,
const double *lpSol)
{
/** Create lower-level model with fixed upper-level vars **/
int probType =
model_->MibSPar_->entry(MibSParams::bilevelProblemType);
bool warmStartLL =
model_->MibSPar_->entry(MibSParams::warmStartLL);
bool doDualFixing =
model_->MibSPar_->entry(MibSParams::doDualFixing);
std::string feasCheckSolver =
model_->MibSPar_->entry(MibSParams::feasCheckSolver);
OsiSolverInterface * nSolver;
double etol(model_->etol_);
int i(0), j(0), index1(0), index2(0);
int uCols(model_->getUpperDim());
int lRows(model_->getLowerRowNum());
int lCols(model_->getLowerDim());
int * uColIndices = model_->getUpperColInd();
int * lColIndices = model_->getLowerColInd();
int * lRowIndices = model_->getLowerRowInd();
double objSense(model_->getLowerObjSense());
double * lObjCoeffs = model_->getLowerObjCoeffs();
const CoinPackedMatrix * matrix = oSolver->getMatrixByRow();
double coeff(0.0);
if (!lpSol){
lpSol = oSolver->getColSolution();
}
const double * origRowLb = model_->getOrigRowLb();
const double * origRowUb = model_->getOrigRowUb();
const double * origColLb = model_->getOrigColLb();
const double * origColUb = model_->getOrigColUb();
double * rowUb = new double[lRows];
double * rowLb = new double[lRows];
double * colUb = new double[lCols];
double * colLb = new double[lCols];
//CoinZeroN(rowUb, lRows);
//CoinZeroN(rowLb, lRows);
//CoinZeroN(colUb, lCols);
//CoinZeroN(colLb, lCols);
/** Set the row bounds **/
for(i = 0; i < lRows; i++){
rowLb[i] = origRowLb[lRowIndices[i]];
rowUb[i] = origRowUb[lRowIndices[i]];
}
for(i = 0; i < lCols; i++){
colLb[i] = origColLb[lColIndices[i]];
colUb[i] = origColUb[lColIndices[i]];
}
if (newOsi){
if (feasCheckSolver == "Cbc"){
nSolver = new OsiCbcSolverInterface();
}else if (feasCheckSolver == "SYMPHONY"){
nSolver = new OsiSymSolverInterface();
}else if (feasCheckSolver == "CPLEX"){
#ifdef USE_CPLEX
nSolver = new OsiCpxSolverInterface();
#else
throw CoinError("CPLEX chosen as solver, but it has not been enabled",
"setUpModel", "MibsBilevel");
#endif
}else{
throw CoinError("Unknown solver chosen",
"setUpModel", "MibsBilevel");
}
int * integerVars = new int[lCols];
double * objCoeffs = new double[lCols];
CoinFillN(integerVars, lCols, 0);
//CoinZeroN(objCoeffs, lCols);
int intCnt(0);
/** Fill in array of lower-level integer variables **/
for(i = 0; i < lCols; i++){
index1 = lColIndices[i];
if(oSolver->isInteger(index1)){
integerVars[intCnt] = i;
intCnt++;
}
}
CoinDisjointCopyN(lObjCoeffs, lCols, objCoeffs);
CoinPackedMatrix * newMat = new CoinPackedMatrix(false, 0, 0);
newMat->setDimensions(0, lCols);
double tmp(0.0);
/*
for(i = 0; i < lRows; i++){
CoinPackedVector row;
index1 = lRowIndices[i];
start = matStarts[index1];
end = start + matrix->getVectorSize(index1);
for(j = start; j < end; j++){
index2 = matIndices[j];
//tmp = findIndex(index, lCols, lColIndices);
tmp = binarySearch(0, lCols - 1, index2, lColIndices);
if(tmp > -1)
row.insert(tmp, matElements[j]);
}
newMat->appendRow(row);
}
*/
for(i = 0; i < lRows; i++){
CoinPackedVector row;
index1 = lRowIndices[i];
for(j = 0; j < lCols; j++){
index2 = lColIndices[j];
tmp = matrix->getCoefficient(index1, index2);
row.insert(j, tmp);
}
newMat->appendRow(row);
}
/*
nSolver->assignProblem(newMat, colLb, colUb,
objCoeffs, rowLb, rowUb);
*/
nSolver->loadProblem(*newMat, colLb, colUb,
objCoeffs, rowLb, rowUb);
for(i = 0; i < intCnt; i++){
nSolver->setInteger(integerVars[i]);
}
//nSolver->setInteger(integerVars, intCnt);
nSolver->setObjSense(objSense); //1 min; -1 max
nSolver->setHintParam(OsiDoReducePrint, true, OsiHintDo);
#if 0
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);
}
#endif
delete [] integerVars;
}else{
nSolver = solver_;
}
#define SYM_VERSION_IS_WS strcmp(SYMPHONY_VERSION, "WS")
#if SYMPHONY_VERSION_IS_WS
if (feasCheckSolver == "SYMPHONY" && probType == 1 && warmStartLL &&
!newOsi && doDualFixing){ //Interdiction
/** Get upper bound from best known (feasible) lower level solution and try
to fix additional variables by sensitivity analysis **/
std::vector<std::pair<AlpsKnowledge*, double> > solutionPool;
model_->getKnowledgeBroker()->
getAllKnowledges(AlpsKnowledgeTypeSolution, solutionPool);
const double * sol;
double objval, Ub(objSense*nSolver->getInfinity());
BlisSolution* blisSol;
std::vector<std::pair<AlpsKnowledge*, double> >::const_iterator si;
for (si = solutionPool.begin(); si != solutionPool.end(); ++si){
blisSol = dynamic_cast<BlisSolution*>(si->first);
sol = blisSol->getValues();
for (i = 0; i < uCols; i++){
if (lpSol[uColIndices[i]] > 1 - etol &&
sol[lColIndices[i]] > 1-etol){
break;
}
}
if (i == uCols && -objSense*blisSol->getQuality() < Ub){
Ub = -objSense*blisSol->getQuality();
}
}
/** Figure out which variables get fixed by the upper level solution **/
int *newUbInd = new int[uCols];
int *newLbInd = new int[uCols];
double *newUbVal = new double[uCols];
double *newLbVal = new double[uCols];
double newLb;
for (i = 0; i < uCols; i++){
newUbInd[i] = uColIndices[i];
newLbInd[i] = uColIndices[i];
newLbVal[i] = 0;
if (lpSol[uColIndices[i]] > 1 - etol){
newUbVal[i] = 0;
}else{
newUbVal[i] = 1;
}
}
/** If we found an upper bound, then do the dual fixing **/
if (Ub < objSense*nSolver->getInfinity()){
sym_environment *env =
dynamic_cast<OsiSymSolverInterface *>(nSolver)->getSymphonyEnvironment();
for (i = 0; i < uCols; i++){
if (newUbVal[i] == 1){
// Try fixing it to zero
newUbVal[i] = 0;
sym_get_lb_for_new_rhs(env, 0, NULL, NULL, uCols, newLbInd,
newLbVal, uCols, newUbInd, newUbVal,
&newLb);
if (objSense*newLb > Ub + etol){
//Victory! This variable can be fixed to 1 permanently
newLbVal[i] = 1;
}
//Set upper bound back to 1
newUbVal[i] = 1;
if (newLbVal[i] == 0){
// Try fixing it to one
newLbVal[i] = 1;
sym_get_lb_for_new_rhs(env, 0, NULL, NULL, uCols, newLbInd,
newLbVal, uCols, newUbInd, newUbVal,
&newLb);
if (objSense*newLb > Ub + etol){
//Victory! This variable can be fixed to 0 permanently
newUbVal[i] = 0;
}
newLbVal[i] = 0;
}
}
}
}
/** Now set the row bounds to account for fixings **/
/** This is probably very frgaile. Assuming interdiction
rows come last. It would be better to set variable
bounds directly, but this doesn't seem to work right now. **/
int iRowStart = lRows-uCols;
#if 1
for(i = iRowStart; i < lRows; i++){
nSolver->setRowLower(i, newLbVal[i-iRowStart]);
nSolver->setRowUpper(i, newUbVal[i-iRowStart]);
}
#else
for(i = 0; i < uCols; i++){
nSolver->setColLower(i, newLbVal[i]);
nSolver->setColUpper(i, newUbVal[i]);
}
#endif
delete[] newUbInd;
delete[] newLbInd;
delete[] newUbVal;
delete[] newLbVal;
}else{
#endif
//FIXME: NEED TO GET ROW SENSE HERE
/** Get contribution of upper-level columns **/
double * upComp = new double[lRows];
CoinFillN(upComp, lRows, 0.0);
for(i = 0; i < lRows; i++){
index1 = lRowIndices[i];
for(j = 0; j < uCols; j++){
index2 = uColIndices[j];
coeff = matrix->getCoefficient(index1, index2);
if (coeff != 0){
upComp[i] += coeff * lpSol[index2];
}
}
}
/** Correct the row bounds to account for fixed upper-level vars **/
for(i = 0; i < lRows; i++){
nSolver->setRowLower(i, rowLb[i] - upComp[i]);
nSolver->setRowUpper(i, rowUb[i] - upComp[i]);
}
delete [] upComp;
#if SYMPHONY_VERSION_IS_WS
}
#endif
//I don't think this is needed
//if(!getWarmStart())
// setWarmStart(nSolver->getWarmStart());
return nSolver;
}
void
CglLandPUnitTest(
OsiSolverInterface * si,
const std::string &mpsDir)
{
CoinRelFltEq eq(1e-05);
// Test default constructor
{
CglLandP aGenerator;
assert(aGenerator.parameter().pivotLimit==20);
assert(aGenerator.parameter().maxCutPerRound==5000);
assert(aGenerator.parameter().failedPivotLimit==1);
assert(aGenerator.parameter().degeneratePivotLimit==0);
assert(eq(aGenerator.parameter().pivotTol, 1e-04));
assert(eq(aGenerator.parameter().away, 5e-04));
assert(eq(aGenerator.parameter().timeLimit, COIN_DBL_MAX));
assert(eq(aGenerator.parameter().singleCutTimeLimit, COIN_DBL_MAX));
assert(aGenerator.parameter().useTableauRow==true);
assert(aGenerator.parameter().modularize==false);
assert(aGenerator.parameter().strengthen==true);
assert(aGenerator.parameter().perturb==true);
assert(aGenerator.parameter().pivotSelection==CglLandP::mostNegativeRc);
}
// Test copy constructor
{
CglLandP a;
{
CglLandP b;
b.parameter().pivotLimit = 100;
b.parameter().maxCutPerRound = 100;
b.parameter().failedPivotLimit = 10;
b.parameter().degeneratePivotLimit = 10;
b.parameter().pivotTol = 1e-07;
b.parameter().away = 1e-10;
b.parameter().timeLimit = 120;
b.parameter().singleCutTimeLimit = 15;
b.parameter().useTableauRow = true;
b.parameter().modularize = true;
b.parameter().strengthen = false;
b.parameter().perturb = false;
b.parameter().pivotSelection=CglLandP::bestPivot;
//Test Copy
CglLandP c(b);
assert(c.parameter().pivotLimit == 100);
assert(c.parameter().maxCutPerRound == 100);
assert(c.parameter().failedPivotLimit == 10);
assert(c.parameter().degeneratePivotLimit == 10);
assert(c.parameter().pivotTol == 1e-07);
assert(c.parameter().away == 1e-10);
assert(c.parameter().timeLimit == 120);
assert(c.parameter().singleCutTimeLimit == 15);
assert(c.parameter().useTableauRow == true);
assert(c.parameter().modularize == true);
assert(c.parameter().strengthen == false);
assert(c.parameter().perturb == false);
assert(c.parameter().pivotSelection == CglLandP::bestPivot);
a=b;
assert(a.parameter().pivotLimit == 100);
assert(a.parameter().maxCutPerRound == 100);
assert(a.parameter().failedPivotLimit == 10);
assert(a.parameter().degeneratePivotLimit == 10);
assert(a.parameter().pivotTol == 1e-07);
assert(a.parameter().away == 1e-10);
assert(a.parameter().timeLimit == 120);
assert(a.parameter().singleCutTimeLimit == 15);
assert(a.parameter().useTableauRow == true);
assert(a.parameter().modularize == true);
assert(a.parameter().strengthen == false);
assert(a.parameter().perturb == false);
assert(a.parameter().pivotSelection == CglLandP::bestPivot);
}
}
{
// Maximize 2 x2
// s.t.
// 2x1 + 2x2 <= 3
// -2x1 + 2x2 <= 1
// 7x1 + 4x2 <= 8
// -7x1 + 4x2 <= 1
// x1, x2 >= 0 and x1, x2 integer
// Slacks are s1, s2, s3, s4
//Test that problem is correct
// Optimal Basis is x1, x2, s3, s4 with tableau
// x1 0.25 s1 -0.25 s2 = 0.5
// x2 0.25 s1 0.25 s2 = 1
// -2.75 s1 0.75 s2 s3 = 0.5
// 0.75 s1 -2.75 s2 s4 = 0.5
// z= -0.25 s1 -0.25 s2 = -1
// Gomory cut from variable x1 is x2 <= 0.5
// Can be improved by first pivoting s2 in and s4 out, then s1 in and s3 out
// to x2 <= 0.25
{
int start[2] = {0,4};
int length[2] = {4,4};
int rows[8] = {0,1,2,3,0,1,2,3};
double elements[8] = {2.0,-2.0,7.0,-7.0,2.0,2.0,4.0,4.0};
CoinPackedMatrix columnCopy(true,4,2,8,elements,rows,start,length);
double rowLower[4]={-COIN_DBL_MAX,-COIN_DBL_MAX,
-COIN_DBL_MAX,-COIN_DBL_MAX};
double rowUpper[4]={3.,1.,8.,1.};
double colLower[2]={0.0,0.0};
double colUpper[2]={1.0,1.0};
double obj[2]={-1,-1};
int intVar[2]={0,1};
OsiSolverInterface * siP = si->clone();
siP->loadProblem(columnCopy, colLower, colUpper, obj, rowLower, rowUpper);
siP->setInteger(intVar,2);
CglLandP test;
test.setLogLevel(2);
test.parameter().sepSpace = CglLandP::Full;
siP->resolve();
// Test generateCuts method
{
OsiCuts cuts;
test.generateCuts(*siP,cuts);
cuts.printCuts();
assert(cuts.sizeRowCuts()==1);
OsiRowCut aCut = cuts.rowCut(0);
assert(eq(aCut.lb(), -.0714286));
CoinPackedVector row = aCut.row();
if (row.getNumElements() == 1)
{
assert(row.getIndices()[0]==1);
assert(eq(row.getElements()[0], -4*.0714286));
}
else if (row.getNumElements() == 2)
{
assert(row.getIndices()[0]==0);
assert(eq(row.getElements()[0], 0.));
assert(row.getIndices()[1]==1);
assert(eq(row.getElements()[1], -1));
}
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
}
if (0)
{
OsiCuts cuts;
test.generateCuts(*siP,cuts);
cuts.printCuts();
assert(cuts.sizeRowCuts()==1);
OsiRowCut aCut = cuts.rowCut(0);
CoinPackedVector row = aCut.row();
if (row.getNumElements() == 1)
{
assert(row.getIndices()[0]==1);
assert(eq(row.getElements()[0], -1));
}
else if (row.getNumElements() == 2)
{
assert(row.getIndices()[0]==0);
assert(eq(row.getElements()[0], 0.));
assert(row.getIndices()[1]==1);
assert(eq(row.getElements()[1], -1));
}
assert(eq(aCut.lb(), 0.));
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
}
delete siP;
}
}
if (1) //Test on p0033
{
// Setup
OsiSolverInterface * siP = si->clone();
std::string fn(mpsDir+"p0033");
siP->readMps(fn.c_str(),"mps");
siP->activateRowCutDebugger("p0033");
CglLandP test;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 2520.5717391304347) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
test.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
//assert( eq(lpRelaxAfter, 2592.1908295194507) );
std::cout<<"Relaxation after "<<lpRelaxAfter<<std::endl;
assert( lpRelaxAfter> 2840. );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
delete siP;
}
if (1) //test again with modularization
{
// Setup
OsiSolverInterface * siP = si->clone();
std::string fn(mpsDir+"p0033");
siP->readMps(fn.c_str(),"mps");
siP->activateRowCutDebugger("p0033");
CglLandP test;
test.parameter().modularize = true;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 2520.5717391304347) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
test.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
//assert( eq(lpRelaxAfter, 2592.1908295194507) );
std::cout<<"Relaxation after "<<lpRelaxAfter<<std::endl;
assert( lpRelaxAfter> 2840. );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
delete siP;
}
if (1) //test again with alternate pivoting rule
{
// Setup
OsiSolverInterface * siP = si->clone();
std::string fn(mpsDir+"p0033");
siP->readMps(fn.c_str(),"mps");
siP->activateRowCutDebugger("p0033");
CglLandP test;
test.parameter().pivotSelection = CglLandP::bestPivot;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 2520.5717391304347) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
test.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
//assert( eq(lpRelaxAfter, 2592.1908295194507) );
std::cout<<"Relaxation after "<<lpRelaxAfter<<std::endl;
assert( lpRelaxAfter> 2840. );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
delete siP;
}
if (1) //Finally test code in documentation
{
// Setup
OsiSolverInterface * siP = si->clone();
std::string fn(mpsDir+"p0033");
siP->readMps(fn.c_str(),"mps");
siP->activateRowCutDebugger("p0033");
CglLandP landpGen;
landpGen.parameter().timeLimit = 10.;
landpGen.parameter().pivotLimit = 2;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 2520.5717391304347) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
landpGen.generateCuts(*siP, cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
//assert( eq(lpRelaxAfter, 2592.1908295194507) );
std::cout<<"Relaxation after "<<lpRelaxAfter<<std::endl;
assert( lpRelaxAfter> 2840. );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
delete siP;
}
}
//--------------------------------------------------------------------------
void
CglKnapsackCoverUnitTest(
const OsiSolverInterface * baseSiP,
const std::string mpsDir )
{
int i;
CoinRelFltEq eq(0.000001);
// Test default constructor
{
CglKnapsackCover kccGenerator;
}
// Test copy & assignment
{
CglKnapsackCover rhs;
{
CglKnapsackCover kccGenerator;
CglKnapsackCover cgC(kccGenerator);
rhs=kccGenerator;
}
}
// test exactSolveKnapsack
{
CglKnapsackCover kccg;
const int n=7;
double c=50;
double p[n] = {70,20,39,37,7,5,10};
double w[n] = {31, 10, 20, 19, 4, 3, 6};
double z;
int x[n];
int exactsol = kccg.exactSolveKnapsack(n, c, p, w, z, x);
assert(exactsol==1);
assert (z == 107);
assert (x[0]==1);
assert (x[1]==0);
assert (x[2]==0);
assert (x[3]==1);
assert (x[4]==0);
assert (x[5]==0);
assert (x[6]==0);
}
/*
// Testcase /u/rlh/osl2/mps/scOneInt.mps
// Model has 3 continous, 2 binary, and 1 general
// integer variable.
{
OsiSolverInterface * siP = baseSiP->clone();
int * complement=NULL;
double * xstar=NULL;
siP->readMps("../Mps/scOneInt","mps");
CglKnapsackCover kccg;
int nCols=siP->getNumCols();
// Test the siP methods for detecting
// variable type
int numCont=0, numBinary=0, numIntNonBinary=0, numInt=0;
for (int thisCol=0; thisCol<nCols; thisCol++) {
if ( siP->isContinuous(thisCol) ) numCont++;
if ( siP->isBinary(thisCol) ) numBinary++;
if ( siP->isIntegerNonBinary(thisCol) ) numIntNonBinary++;
if ( siP->isInteger(thisCol) ) numInt++;
}
assert(numCont==3);
assert(numBinary==2);
assert(numIntNonBinary==1);
assert(numInt==3);
// Test initializeCutGenerator
siP->initialSolve();
assert(xstar !=NULL);
for (i=0; i<nCols; i++){
assert(complement[i]==0);
}
int nRows=siP->getNumRows();
for (i=0; i<nRows; i++){
int vectorsize = siP->getMatrixByRow()->vectorSize(i);
assert(vectorsize==2);
}
kccg.cleanUpCutGenerator(complement,xstar);
delete siP;
}
*/
// Testcase /u/rlh/osl2/mps/tp3.mps
// Models has 3 cols, 3 rows
// Row 0 yields a knapsack, others do not.
{
// setup
OsiSolverInterface * siP = baseSiP->clone();
std::string fn(mpsDir+"tp3");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
OsiCuts cs;
CoinPackedVector krow;
double b=0;
int nCols=siP->getNumCols();
int * complement=new int [nCols];
double * xstar=new double [nCols];
CglKnapsackCover kccg;
// solve LP relaxation
// a "must" before calling initialization
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
std::cout<<"Initial LP value: "<<lpRelaxBefore<<std::endl;
assert( eq(siP->getObjValue(), 97.185) );
double mycs[] = {.627, .667558333333, .038};
siP->setColSolution(mycs);
const double *colsol = siP->getColSolution();
int k;
for (k=0; k<nCols; k++){
xstar[k]=colsol[k];
complement[k]=0;
}
// test deriveAKnapsack
int rind = ( siP->getRowSense()[0] == 'N' ) ? 1 : 0;
const CoinShallowPackedVector reqdBySunCC = siP->getMatrixByRow()->getVector(rind) ;
int deriveaknap = kccg.deriveAKnapsack(*siP, cs, krow,b,complement,xstar,rind,reqdBySunCC);
assert(deriveaknap ==1);
assert(complement[0]==0);
assert(complement[1]==1);
assert(complement[2]==1);
int inx[3] = {0,1,2};
double el[3] = {161, 120, 68};
CoinPackedVector r;
r.setVector(3,inx,el);
assert (krow == r);
//assert (b == 183.0); ????? but x1 and x2 at 1 is valid
// test findGreedyCover
CoinPackedVector cover,remainder;
#if 0
int findgreedy = kccg.findGreedyCover( 0, krow, b, xstar, cover, remainder );
assert( findgreedy == 1 );
int coveri = cover.getNumElements();
assert( cover.getNumElements() == 2);
coveri = cover.getIndices()[0];
assert( cover.getIndices()[0] == 0);
assert( cover.getIndices()[1] == 1);
assert( cover.getElements()[0] == 161.0);
assert( cover.getElements()[1] == 120.0);
assert( remainder.getNumElements() == 1);
assert( remainder.getIndices()[0] == 2);
assert( remainder.getElements()[0] == 68.0);
// test liftCoverCut
CoinPackedVector cut;
double * rowupper = ekk_rowupper(model);
double cutRhs = cover.getNumElements() - 1.0;
kccg.liftCoverCut(b, krow.getNumElements(),
cover, remainder,
cut);
assert ( cut.getNumElements() == 3 );
assert ( cut.getIndices()[0] == 0 );
assert ( cut.getIndices()[1] == 1 );
assert ( cut.getIndices()[2] == 2 );
assert( cut.getElements()[0] == 1 );
assert( cut.getElements()[1] == 1 );
assert( eq(cut.getElements()[2], 0.087719) );
// test liftAndUncomplementAndAdd
OsiCuts cuts;
kccg.liftAndUncomplementAndAdd(*siP.getRowUpper()[0],krow,b,complement,0,
cover,remainder,cuts);
int sizerowcuts = cuts.sizeRowCuts();
assert ( sizerowcuts== 1 );
OsiRowCut testRowCut = cuts.rowCut(0);
CoinPackedVector testRowPV = testRowCut.row();
OsiRowCut sampleRowCut;
const int sampleSize = 3;
int sampleCols[sampleSize]={0,1,2};
double sampleElems[sampleSize]={1.0,-1.0,-0.087719};
sampleRowCut.setRow(sampleSize,sampleCols,sampleElems);
sampleRowCut.setLb(-DBL_MAX);
sampleRowCut.setUb(-0.087719);
bool equiv = testRowPV.equivalent(sampleRowCut.row(),CoinRelFltEq(1.0e-05) );
assert ( equiv );
#endif
// test find PseudoJohnAndEllisCover
cover.setVector(0,NULL, NULL);
remainder.setVector(0,NULL,NULL);
rind = ( siP->getRowSense()[0] == 'N' ) ? 1 : 0;
int findPJE = kccg.findPseudoJohnAndEllisCover( rind, krow,
b, xstar, cover, remainder );
assert( findPJE == 1 );
assert ( cover.getIndices()[0] == 0 );
assert ( cover.getIndices()[1] == 2 );
assert ( cover.getElements()[0] == 161 );
assert ( cover.getElements()[1] == 68 );
assert ( remainder.getIndices()[0] == 1 );
assert ( remainder.getElements()[0] == 120 );
OsiCuts cuts;
kccg.liftAndUncomplementAndAdd((*siP).getRowUpper()[rind],krow,b, complement, rind,
cover,remainder,cuts);
assert (cuts.sizeRowCuts() == 1 );
OsiRowCut testRowCut = cuts.rowCut(0);
CoinPackedVector testRowPV = testRowCut.row();
const int sampleSize = 3;
int sampleCols[sampleSize]={0,1,2};
double sampleElems[sampleSize]={1.0, -1.0, -1.0};
OsiRowCut sampleRowCut;
sampleRowCut.setRow(sampleSize,sampleCols,sampleElems);
sampleRowCut.setLb(-COIN_DBL_MAX);
sampleRowCut.setUb(-1.0);
// test for 'close enough'
assert( testRowPV.isEquivalent(sampleRowCut.row(),CoinRelFltEq(1.0e-05) ) );
// Reset complement & test next row
for (i=0; i<nCols; i++){
complement[i]=0;
}
rind++;
const CoinShallowPackedVector reqdBySunCC2 = siP->getMatrixByRow()->getVector(rind) ;
deriveaknap = kccg.deriveAKnapsack(*siP,cuts,krow,b,complement,xstar,rind,reqdBySunCC2);
assert(deriveaknap==0);
// Reset complement & test next row
for (i=0; i<nCols; i++){
complement[i]=0;
}
const CoinShallowPackedVector reqdBySunCC3 = siP->getMatrixByRow()->getVector(2) ;
deriveaknap = kccg.deriveAKnapsack(*siP,cuts,krow,b,complement,xstar,2,
reqdBySunCC3);
assert(deriveaknap == 0);
// Clean up
delete [] complement;
delete [] xstar;
delete siP;
}
#if 0
// Testcase /u/rlh/osl2/mps/tp4.mps
// Models has 6 cols, 1 knapsack row and
// 3 rows explicily bounding variables
// Row 0 yields a knapsack cover cut
// using findGreedyCover which moves the
// LP objective function value.
{
// Setup
EKKContext * env=ekk_initializeContext();
EKKModel * model = ekk_newModel(env,"");
OsiSolverInterface si(model);
ekk_importModel(model, "tp4.mps");
CglKnapsackCover kccg;
kccg.ekk_validateIntType(si);
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
ekk_allSlackBasis(model);
ekk_crash(model,1);
ekk_primalSimplex(model,1);
double lpRelaxBefore=ekk_getRobjvalue(model);
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
// Determine if lp sol is ip optimal
// Note: no ekk_function to do this
int nCols=ekk_getInumcols(model);
double * optLpSol = ekk_colsol(model);
int ipOpt = 1;
i=0;
while (i++<nCols && ipOpt){
if(optLpSol[i] < 1.0-1.0e-08 && optLpSol[i]> 1.0e-08) ipOpt = 0;
}
if (ipOpt){
#ifdef CGL_DEBUG
printf("Lp solution is within ip optimality tolerance\n");
#endif
}
else {
OsiSolverInterface iModel(model);
OsiCuts cuts;
// Test generateCuts method
kccg.generateCuts(iModel,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = iModel.applyCuts(cuts);
ekk_mergeBlocks(model,1);
ekk_dualSimplex(model);
double lpRelaxAfter=ekk_getRobjvalue(model);
#ifdef CGL_DEBUG
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
// This may need to be updated as other
// minimal cover finders are added
assert( cuts.sizeRowCuts() == 1 );
OsiRowCut testRowCut = cuts.rowCut(0);
CoinPackedVector testRowPV = testRowCut.row();
OsiRowCut sampleRowCut;
const int sampleSize = 6;
int sampleCols[sampleSize]={0,1,2,3,4,5};
double sampleElems[sampleSize]={1.0,1.0,1.0,1.0,0.5, 2.0};
sampleRowCut.setRow(sampleSize,sampleCols,sampleElems);
sampleRowCut.setLb(-DBL_MAX);
sampleRowCut.setUb(3.0);
bool equiv = testRowPV.equivalent(sampleRowCut.row(),CoinRelFltEq(1.0e-05) );
assert( testRowPV.equivalent(sampleRowCut.row(),CoinRelFltEq(1.0e-05) ) );
}
// Exit out of OSL
ekk_deleteModel(model);
ekk_endContext(env);
}
#endif
// Testcase /u/rlh/osl2/mps/tp5.mps
// Models has 6 cols, 1 knapsack row and
// 3 rows explicily bounding variables
// Row 0 yields a knapsack cover cut
// using findGreedyCover which moves the
// LP objective function value.
{
// Setup
OsiSolverInterface * siP = baseSiP->clone();
std::string fn(mpsDir+"tp5");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
CglKnapsackCover kccg;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, -51.66666666667) );
double mycs[] = {.8999999999, .899999999999, .89999999999, 1.110223e-16, .5166666666667, 0};
siP->setColSolution(mycs);
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
// Determine if lp sol is 0/1 optimal
int nCols=siP->getNumCols();
const double * optLpSol = siP->getColSolution();
bool ipOpt = true;
i=0;
while (i++<nCols && ipOpt){
if(optLpSol[i] > kccg.epsilon_ && optLpSol[i] < kccg.onetol_) ipOpt = false;
}
if (ipOpt){
#ifdef CGL_DEBUG
printf("Lp solution is within ip optimality tolerance\n");
#endif
}
else {
// set up
OsiCuts cuts;
CoinPackedVector krow;
double b=0.0;
int * complement=new int[nCols];
double * xstar=new double[nCols];
// initialize cut generator
const double *colsol = siP->getColSolution();
for (i=0; i<nCols; i++){
xstar[i]=colsol[i];
complement[i]=0;
}
int row = ( siP->getRowSense()[0] == 'N' ) ? 1 : 0;
// transform row into canonical knapsack form
const CoinShallowPackedVector reqdBySunCC = siP->getMatrixByRow()->getVector(row) ;
if (kccg.deriveAKnapsack(*siP, cuts, krow, b, complement, xstar, row,reqdBySunCC)){
CoinPackedVector cover, remainder;
// apply greedy logic to detect violated minimal cover inequalities
if (kccg.findGreedyCover(row, krow, b, xstar, cover, remainder) == 1){
// lift, uncomplements, and add cut to cut set
kccg.liftAndUncomplementAndAdd((*siP).getRowUpper()[row],krow, b, complement, row, cover, remainder, cuts);
}
// reset optimal column solution (xstar) information in OSL
const double * rowupper = siP->getRowUpper();
int k;
if (fabs(b-rowupper[row]) > 1.0e-05) {
for(k=0; k<krow.getNumElements(); k++) {
if (complement[krow.getIndices()[k]]){
xstar[krow.getIndices()[k]]= 1.0-xstar[krow.getIndices()[k]];
complement[krow.getIndices()[k]]=0;
}
}
}
// clean up
delete [] complement;
delete [] xstar;
}
// apply the cuts
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
assert( eq(lpRelaxAfter, -30.0) );
#ifdef CGL_DEBUG
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
// test that expected cut was detected
assert( lpRelaxBefore < lpRelaxAfter );
assert( cuts.sizeRowCuts() == 1 );
OsiRowCut testRowCut = cuts.rowCut(0);
CoinPackedVector testRowPV = testRowCut.row();
OsiRowCut sampleRowCut;
const int sampleSize = 6;
int sampleCols[sampleSize]={0,1,2,3,4,5};
double sampleElems[sampleSize]={1.0,1.0,1.0,0.25,1.0,2.0};
sampleRowCut.setRow(sampleSize,sampleCols,sampleElems);
sampleRowCut.setLb(-COIN_DBL_MAX);
sampleRowCut.setUb(3.0);
assert(testRowPV.isEquivalent(sampleRowCut.row(),CoinRelFltEq(1.0e-05)));
}
delete siP;
}
// Testcase /u/rlh/osl2/mps/p0033
// Miplib3 problem p0033
// Test that no cuts chop off the optimal solution
{
// Setup
OsiSolverInterface * siP = baseSiP->clone();
std::string fn(mpsDir+"p0033");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
int nCols=siP->getNumCols();
CglKnapsackCover kccg;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 2520.5717391304347) );
double mycs[] = {0, 1, 0, 0, -2.0837010502455788e-19, 1, 0, 0, 1,
0.021739130434782594, 0.35652173913043478,
-6.7220534694101275e-18, 5.3125906451789717e-18,
1, 0, 1.9298798670241979e-17, 0, 0, 0,
7.8875708048320448e-18, 0.5, 0,
0.85999999999999999, 1, 1, 0.57999999999999996,
1, 0, 1, 0, 0.25, 0, 0.67500000000000004};
siP->setColSolution(mycs);
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
kccg.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
assert( eq(lpRelaxAfter, 2829.0597826086955) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
// the CoinPackedVector p0033 is the optimal
// IP solution to the miplib problem p0033
int objIndices[14] = {
0, 6, 7, 9, 13, 17, 18,
22, 24, 25, 26, 27, 28, 29 };
CoinPackedVector p0033(14,objIndices,1.0);
// Sanity check
const double * objective=siP->getObjCoefficients();
double ofv =0 ;
int r;
for (r=0; r<nCols; r++){
ofv=ofv + p0033[r]*objective[r];
}
CoinRelFltEq eq;
assert( eq(ofv,3089.0) );
int nRowCuts = cuts.sizeRowCuts();
OsiRowCut rcut;
CoinPackedVector rpv;
for (i=0; i<nRowCuts; i++){
rcut = cuts.rowCut(i);
rpv = rcut.row();
double p0033Sum = (rpv*p0033).sum();
assert (p0033Sum <= rcut.ub() );
}
delete siP;
}
// if a debug file is there then look at it
{
FILE * fp = fopen("knapsack.debug","r");
if (fp) {
int ncol,nel;
double up;
int x = fscanf(fp,"%d %d %lg",&ncol,&nel,&up);
if (x<=0)
throw("bad fscanf");
printf("%d columns, %d elements, upper %g\n",ncol,nel,up);
double * sol1 = new double[nel];
double * el1 = new double[nel];
int * col1 = new int[nel];
CoinBigIndex * start = new CoinBigIndex [ncol+1];
memset(start,0,ncol*sizeof(CoinBigIndex ));
int * row = new int[nel];
int i;
for (i=0;i<nel;i++) {
x=fscanf(fp,"%d %lg %lg",col1+i,el1+i,sol1+i);
if (x<=0)
throw("bad fscanf");
printf("[%d, e=%g, v=%g] ",col1[i],el1[i],sol1[i]);
start[col1[i]]=1;
row[i]=0;
}
printf("\n");
// Setup
OsiSolverInterface * siP = baseSiP->clone();
double lo=-1.0e30;
double * upper = new double[ncol];
start[ncol]=nel;
int last=0;
for (i=0;i<ncol;i++) {
upper[i]=1.0;
int marked=start[i];
start[i]=last;
if (marked)
last++;
}
siP->loadProblem(ncol,1,start,row,el1,NULL,upper,NULL,&lo,&up);
// use upper for solution
memset(upper,0,ncol*sizeof(double));
for (i=0;i<nel;i++) {
int icol=col1[i];
upper[icol]=sol1[i];
siP->setInteger(icol);
}
siP->setColSolution(upper);
delete [] sol1;
delete [] el1;
delete [] col1;
delete [] start;
delete [] row;
delete [] upper;
CglKnapsackCover kccg;
OsiCuts cuts;
// Test generateCuts method
kccg.generateCuts(*siP,cuts);
// print out and compare to known cuts
int numberCuts = cuts.sizeRowCuts();
if (numberCuts) {
for (i=0;i<numberCuts;i++) {
OsiRowCut * thisCut = cuts.rowCutPtr(i);
int n=thisCut->row().getNumElements();
printf("Cut %d has %d entries, rhs %g %g =>",i,n,thisCut->lb(),
thisCut->ub());
int j;
const int * index = thisCut->row().getIndices();
const double * element = thisCut->row().getElements();
for (j=0;j<n;j++) {
printf(" (%d,%g)",index[j],element[j]);
}
printf("\n");
}
}
fclose(fp);
}
}
// Testcase /u/rlh/osl2/mps/p0201
// Miplib3 problem p0282
// Test that no cuts chop off the optimal ip solution
{
// Setup
OsiSolverInterface * siP = baseSiP->clone();
std::string fn(mpsDir+"p0201");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
const int nCols=siP->getNumCols();
CglKnapsackCover kccg;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparisn
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 6875.) );
double mycs[] =
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5,
0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0,
0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1};
siP->setColSolution(mycs);
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
kccg.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
assert( eq(lpRelaxAfter, 7125) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
// Optimal IP solution to p0201
int objIndices[22] = { 8, 10, 21, 38, 39, 56,
60, 74, 79, 92, 94, 110, 111, 128, 132, 146,
151,164, 166, 182,183, 200 };
CoinPackedVector p0201(22,objIndices,1.0);
// Sanity check
const double * objective=siP->getObjCoefficients();
double ofv =0 ;
int r;
for (r=0; r<nCols; r++){
ofv=ofv + p0201[r]*objective[r];
}
CoinRelFltEq eq;
assert( eq(ofv,7615.0) );
//printf("p0201 optimal ofv = %g\n",ofv);
int nRowCuts = cuts.sizeRowCuts();
OsiRowCut rcut;
CoinPackedVector rpv;
for (i=0; i<nRowCuts; i++){
rcut = cuts.rowCut(i);
rpv = rcut.row();
double p0201Sum = (rpv*p0201).sum();
assert (p0201Sum <= rcut.ub() );
}
delete siP;
}
// see if I get the same covers that N&W get
{
OsiSolverInterface * siP=baseSiP->clone();
std::string fn(mpsDir+"nw460");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
CglKnapsackCover kccg;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, -225.68951787852194) );
double mycs[] = {0.7099213482046447, 0, 0.34185802225477174, 1, 1, 0, 1, 1, 0};
siP->setColSolution(mycs);
OsiCuts cuts;
// Test generateCuts method
kccg.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
assert( eq(lpRelaxAfter, -176) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
#ifdef MJS
assert( lpRelaxBefore < lpRelaxAfter );
#endif
int nRowCuts = cuts.sizeRowCuts();
OsiRowCut rcut;
CoinPackedVector rpv;
for (i=0; i<nRowCuts; i++){
rcut = cuts.rowCut(i);
rpv = rcut.row();
int j;
printf("Row cut number %i has rhs = %g\n",i,rcut.ub());
for (j=0; j<rpv.getNumElements(); j++){
printf("index %i, element %g\n", rpv.getIndices()[j], rpv.getElements()[j]);
}
printf("\n");
}
delete siP;
}
// Debugging: try "exmip1.mps"
{
// Setup
OsiSolverInterface * siP = baseSiP->clone();
std::string fn(mpsDir+"exmip1");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
CglKnapsackCover kccg;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 3.2368421052631575) );
double mycs[] = {2.5, 0, 0, 0.6428571428571429, 0.5, 4, 0, 0.26315789473684253};
siP->setColSolution(mycs);
// Test generateCuts method
OsiCuts cuts;
kccg.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
assert( eq(lpRelaxAfter, 3.2368421052631575) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore <= lpRelaxAfter );
delete siP;
}
#ifdef CGL_DEBUG
// See what findLPMostViolatedMinCover for knapsack with 2 elements does
{
int nCols = 2;
int row = 1;
CoinPackedVector krow;
double e[2] = {5,10};
int ii[2] = {0,1};
krow.setVector(nCols,ii,e);
double b=11;
double xstar[2] = {.2,.9};
CoinPackedVector cover;
CoinPackedVector remainder;
CglKnapsackCover kccg;
kccg.findLPMostViolatedMinCover(nCols, row, krow, b, xstar, cover, remainder);
printf("num in cover = %i\n",cover.getNumElements());
int j;
for (j=0; j<cover.getNumElements(); j++){
printf(" index %i element % g\n", cover.getIndices()[j], cover.getElements()[j]);
}
}
#endif
#ifdef CGL_DEBUG
// see what findLPMostViolatedMinCover does
{
int nCols = 5;
int row = 1;
CoinPackedVector krow;
double e[5] = {1,1,1,1,10};
int ii[5] = {0,1,2,3,4};
krow.setVector(nCols,ii,e);
double b=11;
double xstar[5] = {.9,.9,1,1,.1};
CoinPackedVector cover;
CoinPackedVector remainder;
CglKnapsackCover kccg;
kccg.findLPMostViolatedMinCover(nCols, row, krow, b, xstar, cover, remainder);
printf("num in cover = %i\n",cover.getNumElements());
int j;
for (j=0; j<cover.getNumElements(); j++){
printf(" index %i element % g\n", cover.getIndices()[j], cover.getElements()[j]);
}
}
#endif
}
int
MilpRounding::solution(double &solutionValue, double *betterSolution)
{
if(model_->getCurrentPassNumber() > 1) return 0;
if (model_->currentDepth() > 2 && (model_->getNodeCount()%howOften_)!=0)
return 0;
int returnCode = 0; // 0 means it didn't find a feasible solution
OsiTMINLPInterface * nlp = NULL;
if(setup_->getAlgorithm() == B_BB)
nlp = dynamic_cast<OsiTMINLPInterface *>(model_->solver()->clone());
else
nlp = dynamic_cast<OsiTMINLPInterface *>(setup_->nonlinearSolver()->clone());
TMINLP2TNLP* minlp = nlp->problem();
// set tolerances
double integerTolerance = model_->getDblParam(CbcModel::CbcIntegerTolerance);
//double primalTolerance = 1.0e-6;
int n;
int m;
int nnz_jac_g;
int nnz_h_lag;
Ipopt::TNLP::IndexStyleEnum index_style;
minlp->get_nlp_info(n, m, nnz_jac_g,
nnz_h_lag, index_style);
const Bonmin::TMINLP::VariableType* variableType = minlp->var_types();
const double* x_sol = minlp->x_sol();
const double* g_l = minlp->g_l();
const double* g_u = minlp->g_u();
const double * colsol = model_->solver()->getColSolution();
// Get information about the linear and nonlinear part of the instance
TMINLP* tminlp = nlp->model();
vector<Ipopt::TNLP::LinearityType> c_lin(m);
tminlp->get_constraints_linearity(m, c_lin());
vector<int> c_idx(m);
int n_lin = 0;
for (int i=0;i<m;i++) {
if (c_lin[i]==Ipopt::TNLP::LINEAR)
c_idx[i] = n_lin++;
else
c_idx[i] = -1;
}
// Get the structure of the jacobian
vector<int> indexRow(nnz_jac_g);
vector<int> indexCol(nnz_jac_g);
minlp->eval_jac_g(n, x_sol, false,
m, nnz_jac_g,
indexRow(), indexCol(), 0);
// get the jacobian values
vector<double> jac_g(nnz_jac_g);
minlp->eval_jac_g(n, x_sol, false,
m, nnz_jac_g,
NULL, NULL, jac_g());
// Sort the matrix to column ordered
vector<int> sortedIndex(nnz_jac_g);
CoinIotaN(sortedIndex(), nnz_jac_g, 0);
MatComp c;
c.iRow = indexRow();
c.jCol = indexCol();
std::sort(sortedIndex.begin(), sortedIndex.end(), c);
vector<int> row (nnz_jac_g);
vector<double> value (nnz_jac_g);
vector<int> columnStart(n,0);
vector<int> columnLength(n,0);
int indexCorrection = (index_style == Ipopt::TNLP::C_STYLE) ? 0 : 1;
int iniCol = -1;
int nnz = 0;
for(int i=0; i<nnz_jac_g; i++) {
int thisIndexCol = indexCol[sortedIndex[i]]-indexCorrection;
int thisIndexRow = c_idx[indexRow[sortedIndex[i]] - indexCorrection];
if(thisIndexCol != iniCol) {
iniCol = thisIndexCol;
columnStart[thisIndexCol] = nnz;
columnLength[thisIndexCol] = 0;
}
if(thisIndexRow == -1) continue;
columnLength[thisIndexCol]++;
row[nnz] = thisIndexRow;
value[nnz] = jac_g[i];
nnz++;
}
// Build the row lower and upper bounds
vector<double> newRowLower(n_lin);
vector<double> newRowUpper(n_lin);
for(int i = 0 ; i < m ; i++){
if(c_idx[i] == -1) continue;
newRowLower[c_idx[i]] = g_l[i];
newRowUpper[c_idx[i]] = g_u[i];
}
// Get solution array for heuristic solution
vector<double> newSolution(n);
std::copy(x_sol, x_sol + n, newSolution.begin());
// Define the constraint matrix for MILP
CoinPackedMatrix matrix(true,n_lin,n, nnz, value(), row(), columnStart(), columnLength());
// create objective function and columns lower and upper bounds for MILP
// and create columns for matrix in MILP
//double alpha = 0;
double beta = 1;
vector<double> objective(n);
vector<int> idxIntegers;
idxIntegers.reserve(n);
for(int i = 0 ; i < n ; i++){
if(variableType[i] != Bonmin::TMINLP::CONTINUOUS){
idxIntegers.push_back(i);
objective[i] = beta*(1 - 2*colsol[i]);
}
}
#if 0
// Get dual multipliers and build gradient of the lagrangean
const double * duals = nlp->getRowPrice() + 2 *n;
vector<double> grad(n, 0);
vector<int> indices(n, 0);
tminlp->eval_grad_f(n, x_sol, false, grad());
for(int i = 0 ; i < m ; i++){
if(c_lin[i] == Ipopt::TNLP::LINEAR) continue;
int nnz;
tminlp->eval_grad_gi(n, x_sol, false, i, nnz, indices(), NULL);
tminlp->eval_grad_gi(n, x_sol, false, i, nnz, NULL, grad());
for(int k = 0 ; k < nnz ; k++){
objective[indices[k]] += alpha *duals[i] * grad[k];
}
}
for(int i = 0 ; i < n ; i++){
if(variableType[i] != Bonmin::TMINLP::CONTINUOUS)
objective[i] += alpha * grad[i];
//if(fabs(objective[i]) < 1e-4) objective[i] = 0;
else objective[i] = 0;
}
std::copy(objective.begin(), objective.end(), std::ostream_iterator<double>(std::cout, " "));
std::cout<<std::endl;
#endif
// load the problem to OSI
OsiSolverInterface *si = mip_->solver();
assert(si != NULL);
CoinMessageHandler * handler = model_->messageHandler()->clone();
si->passInMessageHandler(handler);
si->messageHandler()->setLogLevel(1);
si->loadProblem(matrix, model_->solver()->getColLower(), model_->solver()->getColUpper(), objective(),
newRowLower(), newRowUpper());
si->setInteger(idxIntegers(), static_cast<int>(idxIntegers.size()));
si->applyCuts(noGoods);
bool hasFractionnal = true;
while(hasFractionnal){
mip_->optimize(DBL_MAX, 0, 60);
hasFractionnal = false;
#if 0
bool feasible = false;
if(mip_->getLastSolution()) {
const double* solution = mip_->getLastSolution();
std::copy(solution, solution + n, newSolution.begin());
feasible = true;
}
if(feasible) {
// fix the integer variables and solve the NLP
// also add no good cut
CoinPackedVector v;
double lb = 1;
for (int iColumn=0;iColumn<n;iColumn++) {
if (variableType[iColumn] != Bonmin::TMINLP::CONTINUOUS) {
double value=newSolution[iColumn];
if (fabs(floor(value+0.5)-value)>integerTolerance) {
#ifdef DEBUG_BON_HEURISTIC_DIVE_MIP
cout<<"It should be infeasible because: "<<endl;
cout<<"variable "<<iColumn<<" is not integer"<<endl;
#endif
feasible = false;
break;
}
else {
value=floor(newSolution[iColumn]+0.5);
if(value > 0.5){
v.insert(iColumn, -1);
lb -= value;
}
minlp->SetVariableUpperBound(iColumn, value);
minlp->SetVariableLowerBound(iColumn, value);
}
}
}
}
#endif
}
bool feasible = false;
if(mip_->getLastSolution()) {
const double* solution = mip_->getLastSolution();
std::copy(solution, solution + n, newSolution.begin());
feasible = true;
delete handler;
}
if(feasible) {
// fix the integer variables and solve the NLP
// also add no good cut
CoinPackedVector v;
double lb = 1;
for (int iColumn=0;iColumn<n;iColumn++) {
if (variableType[iColumn] != Bonmin::TMINLP::CONTINUOUS) {
double value=newSolution[iColumn];
if (fabs(floor(value+0.5)-value)>integerTolerance) {
feasible = false;
break;
}
else {
value=floor(newSolution[iColumn]+0.5);
if(value > 0.5){
v.insert(iColumn, -1);
lb -= value;
}
minlp->SetVariableUpperBound(iColumn, value);
minlp->SetVariableLowerBound(iColumn, value);
}
}
}
OsiRowCut c;
c.setRow(v);
c.setLb(lb);
c.setUb(DBL_MAX);
noGoods.insert(c);
if(feasible) {
nlp->initialSolve();
if(minlp->optimization_status() != Ipopt::SUCCESS) {
feasible = false;
}
std::copy(x_sol,x_sol+n, newSolution.begin());
}
}
if(feasible) {
double newSolutionValue;
minlp->eval_f(n, newSolution(), true, newSolutionValue);
if(newSolutionValue < solutionValue) {
std::copy(newSolution.begin(), newSolution.end(), betterSolution);
solutionValue = newSolutionValue;
returnCode = 1;
}
}
delete nlp;
#ifdef DEBUG_BON_HEURISTIC_DIVE_MIP
std::cout<<"DiveMIP returnCode = "<<returnCode<<std::endl;
#endif
return returnCode;
}
