コード例 #1
0
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);
   }
  }
コード例 #2
0
/** Get an inner-approximation constraint obtained by drawing a chord linking the two given points x and x2. 
 * This only applies to nonlinear constraints featuring univariate functions (f(x) <= y).**/
bool
HeuristicInnerApproximation::getMyInnerApproximation(OsiTMINLPInterface &si, OsiCuts &cs, int ind,
    const double * x, const double * x2) {

  int n, m, nnz_jac_g, nnz_h_lag;
  Ipopt::TNLP::IndexStyleEnum index_style;
        TMINLP2TNLP * problem = si.problem(); 
  problem->get_nlp_info(n, m, nnz_jac_g, nnz_h_lag, index_style);


  CoinPackedVector cut;
  double lb = 0;
  double ub = 0;

  double infty = si.getInfinity();

  lb = -infty; // we only compute <= constraints

  double g = 0;
  double g2 = 0;
  double diff = 0;
  double a = 0;
  problem->eval_gi(n, x, 1, ind, g);
  problem->eval_gi(n, x2, 1, ind, g2);
  vector<int> jCol(n);
  int nnz;
  problem->eval_grad_gi(n, x2, 0, ind, nnz, jCol(), NULL);
  vector<double> jValues(nnz);
  problem->eval_grad_gi(n, x2, 0, ind, nnz, NULL, jValues());
  bool add = false;
  for (int i = 0; i < nnz; i++) {
    const int &colIdx = jCol[i];
    if(index_style == Ipopt::TNLP::FORTRAN_STYLE) jCol[i]--;
    diff = x[colIdx] - x2[colIdx];

    if (fabs(diff) >= 1e-8) {
                   a = (g - g2) / diff;
                   cut.insert(colIdx, a);
                   ub = a * x[colIdx] - g;
                   add = true;
    } else
                  cut.insert(colIdx, jValues[i]);
  }

  if (add) {

    OsiRowCut newCut;
    newCut.setGloballyValidAsInteger(1);
    newCut.setLb(lb);
    
      //********* Perspective Extension ********//
    int* ids = problem->get_const_xtra_id(); // vector of indices corresponding to the binary variable activating the corresponding constraint
    int binary_id = (ids == NULL) ? -1 : ids[ind]; // Get the index of the corresponding indicator binary variable
    if(binary_id>0) {// If this hyperplane is a linearization of a disjunctive constraint, we link its righthand side to the corresponding indicator binary variable
        cut.insert(binary_id, -ub); // ∂x ≤ ub => ∂x - ub*z ≤ 0
        newCut.setUb(0);
    }
    else
        newCut.setUb(ub);
    //********* Perspective Extension ********//

    newCut.setRow(cut);
    cs.insert(newCut);
    return true;
  }
  return false;
}