C++ OsiSolverInterface::passInMessageHandler方法代码示例

/** Returns a feasible solution to the MINLP 
 * The heuristic constructs a MIP based approximating all univariate functions appearing in nonlinear constraints 
 * The linear approximation is obtained by adding inner chords linking pairs of points until covering the range of each variable **/
int
HeuristicInnerApproximation::solution(double &solutionValue, double *betterSolution)
{
if(model_->getNodeCount() || model_->getCurrentPassNumber() > 1) return 0;
if ((model_->getNodeCount()%howOften_)!=0||model_->getCurrentPassNumber()>1)
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);

int numberColumns;
int numberRows;
int nnz_jac_g;
int nnz_h_lag;
Ipopt::TNLP::IndexStyleEnum index_style;
minlp->get_nlp_info(numberColumns, numberRows, nnz_jac_g,
    nnz_h_lag, index_style);

const Bonmin::TMINLP::VariableType* variableType = minlp->var_types();

const double* x_sol = minlp->x_sol();

double* newSolution = new double [numberColumns];
memcpy(newSolution,x_sol,numberColumns*sizeof(double));
double* new_g_sol = new double [numberRows];

bool feasible = true;
// load the problem to OSI
#ifdef DEBUG_BON_HEURISTIC
cout << "Loading the problem to OSI\n";
#endif
OsiSolverInterface *si = mip_->solver(); // the MIP solver

bool delete_si = false;
if(si == NULL) {
  si = new OsiClpSolverInterface;
  mip_->setLpSolver(si);
  delete_si = true;
}
CoinMessageHandler * handler = model_->messageHandler()->clone();
si->passInMessageHandler(handler);
si->messageHandler()->setLogLevel(2);
#ifdef DEBUG_BON_HEURISTIC
cout << "Loading problem into si\n";
#endif
extractInnerApproximation(*nlp, *si, newSolution, true); // Call the function construncting the inner approximation description 
#ifdef DEBUG_BON_HEURISTIC
cout << "problem loaded\n";
cout << "**** Running optimization ****\n";
#endif
mip_->optimize(DBL_MAX, 2, 180); // Optimize the MIP
#ifdef DEBUG_BON_HEURISTIC
cout << "Optimization finished\n";
#endif
if(mip_->getLastSolution()) { // if the MIP solver returns a feasible solution
  const double* solution = mip_->getLastSolution();
  for (size_t iLCol=0;iLCol<numberColumns;iLCol++) {
    newSolution[iLCol] = solution[iLCol];
  }
}
else
feasible = false;

if(delete_si) {
  delete si;
}
delete handler;

const double* x_l = minlp->x_l();
const double* x_u = minlp->x_u();
const double* g_l = minlp->g_l();
const double* g_u = minlp->g_u();
double primalTolerance = 1.0e-6;
#if 1
if(feasible ) {

  std::vector<double> memLow(numberColumns);
  std::vector<double> memUpp(numberColumns);
  std::copy(minlp->x_l(), minlp->x_l() + numberColumns, memLow.begin());
  std::copy(minlp->x_u(), minlp->x_u() + numberColumns, memUpp.begin());
  // fix the integer variables and solve the NLP
  for (int iColumn=0;iColumn<numberColumns;iColumn++) {
        if (variableType[iColumn] != Bonmin::TMINLP::CONTINUOUS) {
          double value=floor(newSolution[iColumn]+0.5);
          minlp->SetVariableUpperBound(iColumn, value);
          minlp->SetVariableLowerBound(iColumn, value);
        }
  }
//.........这里部分代码省略.........

/** Returns a feasible solution to the MINLP 
 * The heuristic constructs a MIP based approximating all univariate functions appearing in nonlinear constraints 
 * The linear approximation is obtained by adding inner chords linking pairs of points until covering the range of each variable **/
int
HeuristicInnerApproximation::solution(double &solutionValue, double *betterSolution)
{
if(model_->getNodeCount() || model_->getCurrentPassNumber() > 1) return 0;
if ((model_->getNodeCount()%howOften_)!=0||model_->getCurrentPassNumber()>1)
return 0;

int returnCode = 0; // 0 means it didn't find a feasible solution

Bonmin::OsiTMINLPInterface * nlp = NULL;
if(setup_->getAlgorithm() == Bonmin::B_BB)
nlp = dynamic_cast<Bonmin::OsiTMINLPInterface *>(model_->solver()->clone());
else
nlp = dynamic_cast<Bonmin::OsiTMINLPInterface *>(setup_->nonlinearSolver()->clone());

Bonmin::TMINLP2TNLP* minlp = nlp->problem();
// set tolerances

//double integerTolerance = model_->getDblParam(CbcModel::CbcIntegerTolerance);

int numberColumns;
int numberRows;
int nnz_jac_g;
int nnz_h_lag;
Ipopt::TNLP::IndexStyleEnum index_style;
minlp->get_nlp_info(numberColumns, numberRows, nnz_jac_g,
    nnz_h_lag, index_style);

//const Bonmin::TMINLP::VariableType* variableType = minlp->var_types();

const double* x_sol = minlp->x_sol();

double* newSolution = new double [numberColumns];
memcpy(newSolution,x_sol,numberColumns*sizeof(double));
double* new_g_sol = new double [numberRows];

bool feasible = true;
// load the problem to OSI
#ifdef DEBUG_BON_HEURISTIC
std::cout << "Loading the problem to OSI\n";
#endif
OsiSolverInterface *si = mip_->solver(); // the MIP solver

bool delete_si = false;
if(si == NULL) {
  si = new OsiClpSolverInterface;
  mip_->setLpSolver(si);
  delete_si = true;
}
CoinMessageHandler * handler = model_->messageHandler()->clone();
si->passInMessageHandler(handler);
si->messageHandler()->setLogLevel(2);
#ifdef DEBUG_BON_HEURISTIC
std::cout << "Loading problem into si\n";
#endif
extractInnerApproximation(*nlp, *si, newSolution, true); // Call the function construncting the inner approximation description 
#ifdef DEBUG_BON_HEURISTIC
std::cout << "problem loaded\n";
std::cout << "**** Running optimization ****\n";
#endif
mip_->optimize(DBL_MAX, 2, time_limit_); // Optimize the MIP
#ifdef DEBUG_BON_HEURISTIC
std::cout << "Optimization finished\n";
#endif
if(mip_->getLastSolution()) { // if the MIP solver returns a feasible solution
  const double* solution = mip_->getLastSolution();
  std::copy(solution, solution + numberColumns, newSolution);
  }
  else
  feasible = false;

if(delete_si) {
  delete si;
}
delete handler;


#if 0 // Set to 1 if you need to test the feasibility of the returned solution
const double* x_l = minlp->x_l();
const double* x_u = minlp->x_u();
const double* g_l = minlp->g_l();
const double* g_u = minlp->g_u();
double primalTolerance = 1.0e-6;

Bonmin::vector<Ipopt::TNLP::LinearityType>  constTypes(numberRows);
minlp->get_constraints_linearity(numberRows, constTypes());
feasible = true;
for (int iColumn=0;iColumn<numberColumns;iColumn++) {
  double value=newSolution[iColumn];
  if(value - x_l[iColumn] < -1e-8|| value - x_u[iColumn] > 1e-8) {
    std::cout<<"Solution found infeasible because: "<<std::endl;
    std::cout<<"x_l["<<iColumn<<"]= "<<x_l[iColumn]<<" "
    <<"x_sol["<<iColumn<<"]= "<<value<<" "
    <<"x_u["<<iColumn<<"]= "<<x_u[iColumn]<<std::endl;
    feasible = false;
    break;
  }
//.........这里部分代码省略.........

//.........这里部分代码省略.........
            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;