ipopt_adapter.cc

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#include <ifopt/ipopt_adapter.h>

namespace Ipopt {

IpoptAdapter::IpoptAdapter(Problem& nlp, bool finite_diff)
{
  nlp_ = &nlp;
  finite_diff_ = finite_diff;
}

bool IpoptAdapter::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g,
                                Index& nnz_h_lag, IndexStyleEnum& index_style)
{
  n = nlp_->GetNumberOfOptimizationVariables();
  m = nlp_->GetNumberOfConstraints();

  if (finite_diff_)
    nnz_jac_g = m*n;
  else
    nnz_jac_g = nlp_->GetJacobianOfConstraints().nonZeros();

  nnz_h_lag = n*n;

  // start index at 0 for row/col entries
  index_style = C_STYLE;

  return true;
}

bool IpoptAdapter::get_bounds_info(Index n, double* x_lower, double* x_upper,
                                   Index m, double* g_l, double* g_u)
{
  auto bounds_x = nlp_->GetBoundsOnOptimizationVariables();
  for (uint c=0; c<bounds_x.size(); ++c) {
    x_lower[c] = bounds_x.at(c).lower_;
    x_upper[c] = bounds_x.at(c).upper_;
  }

  // specific bounds depending on equality and inequality constraints
  auto bounds_g = nlp_->GetBoundsOnConstraints();
  for (uint c=0; c<bounds_g.size(); ++c) {
    g_l[c] = bounds_g.at(c).lower_;
    g_u[c] = bounds_g.at(c).upper_;
  }

  return true;
}

bool IpoptAdapter::get_starting_point(Index n, bool init_x, double* x,
                                      bool init_z, double* z_L, double* z_U,
                                      Index m, bool init_lambda,
                                      double* lambda)
{
  // Here, we assume we only have starting values for x
  assert(init_x == true);
  assert(init_z == false);
  assert(init_lambda == false);

  VectorXd x_all = nlp_->GetVariableValues();
  Eigen::Map<VectorXd>(&x[0], x_all.rows()) = x_all;

  return true;
}

bool IpoptAdapter::eval_f(Index n, const double* x, bool new_x, double& obj_value)
{
  obj_value = nlp_->EvaluateCostFunction(x);
  return true;
}

bool IpoptAdapter::eval_grad_f(Index n, const double* x, bool new_x, double* grad_f)
{
  Eigen::VectorXd grad = nlp_->EvaluateCostFunctionGradient(x);
  Eigen::Map<Eigen::MatrixXd>(grad_f,n,1) = grad;
  return true;
}

bool IpoptAdapter::eval_g(Index n, const double* x, bool new_x, Index m, double* g)
{
  VectorXd g_eig = nlp_->EvaluateConstraints(x);
  Eigen::Map<VectorXd>(g,m) = g_eig;
  return true;
}

bool IpoptAdapter::eval_jac_g(Index n, const double* x, bool new_x,
                              Index m, Index nele_jac, Index* iRow, Index *jCol,
                              double* values)
{
  // defines the positions of the nonzero elements of the jacobian
  if (values == NULL) {
        // If "jacobian_approximation" option is set as "finite-difference-values", the Jacobian is dense!
        Index nele=0;
        if (finite_diff_) { // dense jacobian
          for (Index row = 0; row < m; row++) {
            for (Index col = 0; col < n; col++) {
              iRow[nele] = row;
              jCol[nele] = col;
              nele++;
            }
          }
        }
        else {  // sparse jacobian
          auto jac = nlp_->GetJacobianOfConstraints();
          for (int k=0; k<jac.outerSize(); ++k) {
            for (Jacobian::InnerIterator it(jac,k); it; ++it) {
              iRow[nele] = it.row();
              jCol[nele] = it.col();
              nele++;
            }
          }
        }

    assert(nele == nele_jac); // initial sparsity structure is never allowed to change
  }
  else {
    // only gets used if "jacobian_approximation finite-difference-values" is not set
    nlp_->EvalNonzerosOfJacobian(x, values);
  }

  return true;
}

bool IpoptAdapter::intermediate_callback(AlgorithmMode mode,
                                         Index iter, double obj_value,
                                         double inf_pr, double inf_du,
                                         double mu, double d_norm,
                                         double regularization_size,
                                         double alpha_du, double alpha_pr,
                                         Index ls_trials,
                                         const IpoptData* ip_data,
                                         IpoptCalculatedQuantities* ip_cq)
{
  nlp_->SaveCurrent();
  return true;
}

void IpoptAdapter::finalize_solution(SolverReturn status,
                                     Index n, const double* x, const double* z_L, const double* z_U,
                                     Index m, const double* g, const double* lambda,
                                     double obj_value,
                                     const IpoptData* ip_data,
                                     IpoptCalculatedQuantities* ip_cq)
{
  nlp_->SetVariables(x);
  nlp_->SaveCurrent();
}

} // namespace Ipopt