//#############################################################################
void
MibSHeuristic::greedyHeuristic()
{
MibSModel * model = MibSModel_;
//OsiSolverInterface * oSolver = model->getSolver();
OsiSolverInterface * oSolver = model->solver();
double uObjSense(oSolver->getObjSense());
double lObjSense(model->getLowerObjSense());
int lCols(model->getLowerDim());
int uCols(model->getUpperDim());
int * uColIndices = model->getUpperColInd();
int * lColIndices = model->getLowerColInd();
double * lObjCoeffs = model->getLowerObjCoeffs();
double * intCost = model->getInterdictCost();
double intBudget = model->getInterdictBudget();
int tCols(uCols + lCols);
assert(tCols == oSolver->getNumCols());
int i(0), ind_min_wt(0);
double usedBudget(0.0);
double * fixedVars = new double[lCols];
double * testsol = new double[tCols];
CoinZeroN(fixedVars, lCols);
CoinZeroN(testsol, tCols);
std::multimap<double, int> lObjCoeffsOrd;
for(i = 0; i < lCols; i++)
lObjCoeffsOrd.insert(std::pair<double, int>(lObjCoeffs[i] * lObjSense, i));
if(!bestSol_)
bestSol_ = new double[tCols];
//initialize the best solution information
//bestObjVal_ = model->getSolver()->getInfinity() * uObjSense;
//CoinZeroN(bestSol_, tCols);
std::multimap<double, int>::iterator iter;
//std::multimap<double, int>::iterator first;
//std::multimap<double, int>::iterator last;
//int dist = std::distance(first, last);
srandom((unsigned) time(NULL));
int randchoice(0);
if(0)
std::cout << "randchoice " << randchoice << std::endl;
double cost(0.0);
//starting from the largest, fix corr upper-level variables
//then, with these fixed, solve the lower-level problem
//this yields a feasible solution
iter = lObjCoeffsOrd.begin();
while((usedBudget < intBudget) && (iter != lObjCoeffsOrd.end())){
ind_min_wt = iter->second;
cost = intCost[ind_min_wt];
testsol[uColIndices[ind_min_wt]] = 1.0;
double min_wt = iter->first;
if(0){
std::cout << "upper: " << ind_min_wt << " "
<< uColIndices[ind_min_wt] << " "
<< oSolver->getColUpper()[uColIndices[ind_min_wt]] << " "
<< oSolver->getColLower()[uColIndices[ind_min_wt]] << std::endl;
std::cout << "lower: " << ind_min_wt << " "
<< lColIndices[ind_min_wt] << " "
<< oSolver->getColUpper()[lColIndices[ind_min_wt]] << std::endl;
}
//if((oSolver->getColUpper()[uColIndices[ind_min_wt]] == 1.0)
//&& (oSolver->getColUpper()[lColIndices[ind_min_wt]] > 0)){
if(oSolver->getColUpper()[uColIndices[ind_min_wt]] > etol_){
//if(((usedBudget + cost) <= intBudget)
// && checkLowerFeasibility(oSolver, testsol)){
if((usedBudget + cost) <= intBudget){
//FIXME: SHOULD BE CHECKING FOR CURRENT BOUNDS HERE
//fix the corresponding upper-level variable to 1
randchoice = random() % 2;
if(0)
std::cout << "randchoice " << random << std::endl;
if(randchoice){
fixedVars[ind_min_wt] = 1.0;
usedBudget += intCost[ind_min_wt];
}
}
}
else{
testsol[uColIndices[ind_min_wt]] = 0;
//break;
}
iter++;
}
/*
now we find a feasible solution by fixing upper-level vars
and solving the lower-level problem
*/
double * incumbentSol = new double[tCols];
double * colsol = new double[tCols];
CoinZeroN(colsol, tCols);
for(i = 0; i < uCols; i++){
colsol[uColIndices[i]] = fixedVars[i];
if(fixedVars[i] == 1.0)
if(0)
std::cout << "fixed " << i << std::endl;
}
bfSol * sol = getBilevelSolution(colsol, lObjSense * oSolver->getInfinity());
if(sol){
double incumbentObjVal = sol->getObjVal();
CoinCopyN(sol->getColumnSol(), tCols, incumbentSol);
MibSSolution * mibSol = new MibSSolution(tCols,
incumbentSol,
incumbentObjVal,
model);
model->storeSolution(BlisSolutionTypeHeuristic, mibSol);
}
//bestObjVal_ = incumbentObjVal;
//CoinCopyN(incumbentSol, tCols, bestSol_);
delete [] incumbentSol;
delete [] testsol;
//delete [] colsol;
//delete [] fixedVars;
//delete sol;
}
