SmartPtr<const Vector> AugRestoSystemSolver::Rhs_cR(const Vector& rhs_c,
      const SmartPtr<const Vector>& sigma_tilde_n_c_inv, const Vector& rhs_n_c,
      const SmartPtr<const Vector>& sigma_tilde_p_c_inv, const Vector& rhs_p_c)
  {
    DBG_START_METH("AugRestoSystemSolver::Rhs_cR",dbg_verbosity);
    SmartPtr<Vector> retVec;
    std::vector<const TaggedObject*> deps(5);
    std::vector<Number> scalar_deps;
    deps[0] = &rhs_c;
    deps[1] = GetRawPtr(sigma_tilde_n_c_inv);
    deps[2] = &rhs_n_c;
    deps[3] = GetRawPtr(sigma_tilde_p_c_inv);
    deps[4] = &rhs_p_c;
    if (!rhs_cR_cache_.GetCachedResult(retVec, deps, scalar_deps)) {
      DBG_PRINT((1,"Not found in cache\n"));
      retVec = rhs_c.MakeNew();
      retVec->Copy(rhs_c);

      SmartPtr<Vector> tmp = retVec->MakeNew();
      if (IsValid(sigma_tilde_n_c_inv)) {
        tmp->Copy(*sigma_tilde_n_c_inv);
        tmp->ElementWiseMultiply(rhs_n_c);
        retVec->Axpy(-1.0, *tmp);
      }

      if (IsValid(sigma_tilde_p_c_inv)) {
        tmp->Copy(*sigma_tilde_p_c_inv);
        tmp->ElementWiseMultiply(rhs_p_c);
        retVec->Axpy(1.0, *tmp);
      }
      rhs_cR_cache_.AddCachedResult(retVec, deps, scalar_deps);
    }
    return ConstPtr(retVec);
  }
  SmartPtr<const Vector> AugRestoSystemSolver::Rhs_dR(const Vector& rhs_d,
      const SmartPtr<const Vector>& sigma_tilde_n_d_inv, const Vector& rhs_n_d, const Matrix& pd_L,
      const SmartPtr<const Vector>& sigma_tilde_p_d_inv, const Vector& rhs_p_d, const Matrix& neg_pd_U)
  {
    DBG_START_METH("AugRestoSystemSolver::Rhs_dR",dbg_verbosity);
    SmartPtr<Vector> retVec;
    std::vector<const TaggedObject*> deps(7);
    std::vector<Number> scalar_deps;
    deps[0] = &rhs_d;
    deps[1] = GetRawPtr(sigma_tilde_n_d_inv);
    deps[2] = &rhs_n_d;
    deps[3] = &pd_L;
    deps[4] = GetRawPtr(sigma_tilde_p_d_inv);
    deps[5] = &rhs_p_d;
    deps[6] = &neg_pd_U;
    if (!rhs_dR_cache_.GetCachedResult(retVec, deps, scalar_deps)) {
      DBG_PRINT((1,"Not found in cache\n"));
      retVec = rhs_d.MakeNew();
      retVec->Copy(rhs_d);

      if (IsValid(sigma_tilde_n_d_inv)) {
        SmartPtr<Vector> tmpn = sigma_tilde_n_d_inv->MakeNew();
        tmpn->Copy(*sigma_tilde_n_d_inv);
        tmpn->ElementWiseMultiply(rhs_n_d);
        pd_L.MultVector(-1.0, *tmpn, 1.0, *retVec);
      }

      if (IsValid(sigma_tilde_p_d_inv)) {
        SmartPtr<Vector> tmpp = sigma_tilde_p_d_inv->MakeNew();
        tmpp->Copy(*sigma_tilde_p_d_inv);
        tmpp->ElementWiseMultiply(rhs_p_d);
        neg_pd_U.MultVector(-1.0, *tmpp, 1.0, *retVec);
      }

      rhs_dR_cache_.AddCachedResult(retVec, deps, scalar_deps);
    }
    return ConstPtr(retVec);
  }
Example #3
0
  void Vector::AddVectorQuotientImpl(Number a, const Vector& z,
                                     const Vector& s, Number c)
  {
    DBG_ASSERT(Dim() == z.Dim());
    DBG_ASSERT(Dim() == s.Dim());

    if (c==0.) {
      AddOneVector(a, z, 0.);
      ElementWiseDivide(s);
    }
    else {
      SmartPtr<Vector> tmp = MakeNew();
      tmp->Copy(z);
      tmp->ElementWiseDivide(s);
      AddOneVector(a, *tmp, c);
    }
  }
 SmartPtr<const Vector>
 AugRestoSystemSolver::Neg_Omega_d_plus_D_d(
   const Matrix& Pd_L,
   const SmartPtr<const Vector>& sigma_tilde_n_d_inv,
   const Matrix& neg_Pd_U,
   const SmartPtr<const Vector>& sigma_tilde_p_d_inv,
   const Vector* D_d,
   const Vector& any_vec_in_d)
 {
   DBG_START_METH("AugRestoSystemSolver::Neg_Omega_d_plus_D_d",dbg_verbosity);
   SmartPtr<Vector> retVec;
   if (IsValid(sigma_tilde_n_d_inv) || IsValid(sigma_tilde_p_d_inv) || D_d) {
     std::vector<const TaggedObject*> deps(5);
     std::vector<Number> scalar_deps;
     deps[0] = &Pd_L;
     deps[1] = GetRawPtr(sigma_tilde_n_d_inv);
     deps[2] = &neg_Pd_U;
     deps[3] = GetRawPtr(sigma_tilde_p_d_inv);
     deps[4] = D_d;
     if (!neg_omega_d_plus_D_d_cache_.
         GetCachedResult(retVec, deps, scalar_deps)) {
       DBG_PRINT((1,"Not found in cache\n"));
       retVec = any_vec_in_d.MakeNew();
       retVec->Set(0.0);
       if (IsValid(sigma_tilde_n_d_inv)) {
         Pd_L.MultVector(-1.0, *sigma_tilde_n_d_inv, 1.0, *retVec);
       }
       if (IsValid(sigma_tilde_p_d_inv)) {
         neg_Pd_U.MultVector(1.0, *sigma_tilde_p_d_inv, 1.0, *retVec);
       }
       if (D_d) {
         retVec->Copy(*D_d);
       }
       neg_omega_d_plus_D_d_cache_.
       AddCachedResult(retVec, deps, scalar_deps);
     }
   }
   return ConstPtr(retVec);
 }
  SmartPtr<const Vector> AugRestoSystemSolver::Sigma_tilde_n_c_inv(
    const SmartPtr<const Vector>& sigma_n_c,
    Number delta_x,
    const Vector& any_vec_in_c)
  {
    DBG_START_METH("AugRestoSystemSolver::Sigma_tilde_n_c_inv",dbg_verbosity);
    SmartPtr<Vector> retVec;
    if (IsValid(sigma_n_c) || delta_x != 0.0) {
      std::vector<const TaggedObject*> deps(1);
      std::vector<Number> scalar_deps(1);
      deps[0] = GetRawPtr(sigma_n_c);
      scalar_deps[0] = delta_x;
      if (!sigma_tilde_n_c_inv_cache_.GetCachedResult(retVec, deps, scalar_deps)) {
        DBG_PRINT((1,"Not found in cache\n"));
        retVec = any_vec_in_c.MakeNew();
        if (IsValid(sigma_n_c)) {
          if (delta_x != 0.) {
            retVec->Copy(*sigma_n_c);
            retVec->AddScalar(delta_x);
            retVec->ElementWiseReciprocal();
          }
          else {
            // Given a "homogenous vector" implementation (such as in
            // DenseVector) the following should be more efficient
            retVec->Set(1.);
            retVec->ElementWiseDivide(*sigma_n_c);
          }
        }
        else {
          retVec->Set(1./delta_x);
        }

        sigma_tilde_n_c_inv_cache_.AddCachedResult(retVec, deps, scalar_deps);
      }
    }

    return ConstPtr(retVec);
  }
  SmartPtr<const Vector> AugRestoSystemSolver::Sigma_tilde_p_d_inv(
    const SmartPtr<const Vector>& sigma_p_d,
    Number delta_x,
    const Vector& any_vec_in_p_d)
  {
    DBG_START_METH("AugRestoSystemSolver::Sigma_tilde_p_d_inv",dbg_verbosity);
    SmartPtr<Vector> retVec;
    if (IsValid(sigma_p_d) || delta_x != 0) {
      std::vector<const TaggedObject*> deps(1);
      std::vector<Number> scalar_deps(1);
      deps[0] = GetRawPtr(sigma_p_d);
      scalar_deps[0] = delta_x;
      if (!sigma_tilde_p_d_inv_cache_.GetCachedResult(retVec, deps, scalar_deps)) {
        DBG_PRINT((1,"Not found in cache\n"));
        retVec = any_vec_in_p_d.MakeNew();

        if (IsValid(sigma_p_d)) {
          if (delta_x != 0.) {
            retVec->Copy(*sigma_p_d);
            retVec->AddScalar(delta_x);
            retVec->ElementWiseReciprocal();
          }
          else {
            retVec->Set(1.);
            retVec->ElementWiseDivide(*sigma_p_d);
          }
        }
        else {
          retVec->Set(1./delta_x);
        }

        sigma_tilde_p_d_inv_cache_.AddCachedResult(retVec, deps, scalar_deps);
      }
    }

    return ConstPtr(retVec);
  }
Example #7
0
  bool
  RestoRestorationPhase::PerformRestoration()
  {
    DBG_START_METH("RestoRestorationPhase::PerformRestoration",
                   dbg_verbosity);
    Jnlst().Printf(J_DETAILED, J_MAIN,
                   "Performing second level restoration phase for current constriant violation %8.2e\n", IpCq().curr_constraint_violation());

    DBG_ASSERT(IpCq().curr_constraint_violation()>0.);

    // Get a grip on the restoration phase NLP and obtain the pointers
    // to the original NLP data
    SmartPtr<RestoIpoptNLP> resto_ip_nlp =
      static_cast<RestoIpoptNLP*> (&IpNLP());
    DBG_ASSERT(dynamic_cast<RestoIpoptNLP*> (&IpNLP()));
    SmartPtr<IpoptNLP> orig_ip_nlp =
      static_cast<IpoptNLP*> (&resto_ip_nlp->OrigIpNLP());
    DBG_ASSERT(dynamic_cast<IpoptNLP*> (&resto_ip_nlp->OrigIpNLP()));

    // Get the current point and create a new vector for the result
    SmartPtr<const CompoundVector> Ccurr_x =
      static_cast<const CompoundVector*> (GetRawPtr(IpData().curr()->x()));
    SmartPtr<Vector> new_x = IpData().curr()->x()->MakeNew();
    SmartPtr<CompoundVector> Cnew_x =
      static_cast<CompoundVector*> (GetRawPtr(new_x));

    // The x values remain unchanged
    SmartPtr<Vector> x = Cnew_x->GetCompNonConst(0);
    x->Copy(*Ccurr_x->GetComp(0));

    // ToDo in free mu mode - what to do here?
    Number mu = IpData().curr_mu();

    // Compute the initial values for the n and p variables for the
    // equality constraints
    Number rho = resto_ip_nlp->Rho();
    SmartPtr<Vector> nc = Cnew_x->GetCompNonConst(1);
    SmartPtr<Vector> pc = Cnew_x->GetCompNonConst(2);
    SmartPtr<const Vector> cvec = orig_ip_nlp->c(*Ccurr_x->GetComp(0));
    SmartPtr<Vector> a = nc->MakeNew();
    SmartPtr<Vector> b = nc->MakeNew();
    a->Set(mu/(2.*rho));
    a->Axpy(-0.5, *cvec);
    b->Copy(*cvec);
    b->Scal(mu/(2.*rho));
    solve_quadratic(*a, *b, *nc);
    pc->Copy(*cvec);
    pc->Axpy(1., *nc);
    DBG_PRINT_VECTOR(2, "nc", *nc);
    DBG_PRINT_VECTOR(2, "pc", *pc);

    // initial values for the n and p variables for the inequality
    // constraints
    SmartPtr<Vector> nd = Cnew_x->GetCompNonConst(3);
    SmartPtr<Vector> pd = Cnew_x->GetCompNonConst(4);
    SmartPtr<Vector> dvec = pd->MakeNew();
    dvec->Copy(*orig_ip_nlp->d(*Ccurr_x->GetComp(0)));
    dvec->Axpy(-1., *IpData().curr()->s());
    a = nd->MakeNew();
    b = nd->MakeNew();
    a->Set(mu/(2.*rho));
    a->Axpy(-0.5, *dvec);
    b->Copy(*dvec);
    b->Scal(mu/(2.*rho));
    solve_quadratic(*a, *b, *nd);
    pd->Copy(*dvec);
    pd->Axpy(1., *nd);
    DBG_PRINT_VECTOR(2, "nd", *nd);
    DBG_PRINT_VECTOR(2, "pd", *pd);

    // Now set the trial point to the solution of the restoration phase
    // s and all multipliers remain unchanged
    SmartPtr<IteratesVector> new_trial = IpData().curr()->MakeNewContainer();
    new_trial->Set_x(*new_x);
    IpData().set_trial(new_trial);

    IpData().Append_info_string("R");

    return true;
  }
  bool RestoIterateInitializer::SetInitialIterates()
  {
    DBG_START_METH("RestoIterateInitializer::SetInitialIterates",
                   dbg_verbosity);

    // Get a grip on the restoration phase NLP and obtain the pointers
    // to the original NLP data
    SmartPtr<RestoIpoptNLP> resto_ip_nlp =
      static_cast<RestoIpoptNLP*> (&IpNLP());
    SmartPtr<IpoptNLP> orig_ip_nlp =
      static_cast<IpoptNLP*> (&resto_ip_nlp->OrigIpNLP());
    SmartPtr<IpoptData> orig_ip_data =
      static_cast<IpoptData*> (&resto_ip_nlp->OrigIpData());
    SmartPtr<IpoptCalculatedQuantities> orig_ip_cq =
      static_cast<IpoptCalculatedQuantities*> (&resto_ip_nlp->OrigIpCq());

    // Set the value of the barrier parameter
    Number resto_mu;
    resto_mu = Max(orig_ip_data->curr_mu(),
                   orig_ip_cq->curr_c()->Amax(),
                   orig_ip_cq->curr_d_minus_s()->Amax());
    IpData().Set_mu(resto_mu);
    Jnlst().Printf(J_DETAILED, J_INITIALIZATION,
                   "Initial barrier parameter resto_mu = %e\n", resto_mu);

    /////////////////////////////////////////////////////////////////////
    //                   Initialize primal varialbes                   //
    /////////////////////////////////////////////////////////////////////

    // initialize the data structures in the restoration phase NLP
    IpData().InitializeDataStructures(IpNLP(), false, false, false,
                                      false, false);

    SmartPtr<Vector> new_x = IpData().curr()->x()->MakeNew();
    SmartPtr<CompoundVector> Cnew_x =
      static_cast<CompoundVector*> (GetRawPtr(new_x));

    // Set the trial x variables from the original NLP
    Cnew_x->GetCompNonConst(0)->Copy(*orig_ip_data->curr()->x());

    // Compute the initial values for the n and p variables for the
    // equality constraints
    Number rho = resto_ip_nlp->Rho();
    DBG_PRINT((1,"rho = %e\n", rho));
    SmartPtr<Vector> nc = Cnew_x->GetCompNonConst(1);
    SmartPtr<Vector> pc = Cnew_x->GetCompNonConst(2);
    SmartPtr<const Vector> cvec = orig_ip_cq->curr_c();
    DBG_PRINT_VECTOR(2, "cvec", *cvec);
    SmartPtr<Vector> a = nc->MakeNew();
    SmartPtr<Vector> b = nc->MakeNew();
    a->Set(resto_mu/(2.*rho));
    a->Axpy(-0.5, *cvec);
    b->Copy(*cvec);
    b->Scal(resto_mu/(2.*rho));
    DBG_PRINT_VECTOR(2, "a", *a);
    DBG_PRINT_VECTOR(2, "b", *b);
    solve_quadratic(*a, *b, *nc);
    pc->Copy(*cvec);
    pc->Axpy(1., *nc);
    DBG_PRINT_VECTOR(2, "nc", *nc);
    DBG_PRINT_VECTOR(2, "pc", *pc);

    // initial values for the n and p variables for the inequality
    // constraints
    SmartPtr<Vector> nd = Cnew_x->GetCompNonConst(3);
    SmartPtr<Vector> pd = Cnew_x->GetCompNonConst(4);
    cvec = orig_ip_cq->curr_d_minus_s();
    a = nd->MakeNew();
    b = nd->MakeNew();
    a->Set(resto_mu/(2.*rho));
    a->Axpy(-0.5, *cvec);
    b->Copy(*cvec);
    b->Scal(resto_mu/(2.*rho));
    solve_quadratic(*a, *b, *nd);
    pd->Copy(*cvec);
    pd->Axpy(1., *nd);
    DBG_PRINT_VECTOR(2, "nd", *nd);
    DBG_PRINT_VECTOR(2, "pd", *pd);

    // Leave the slacks unchanged
    SmartPtr<const Vector> new_s = orig_ip_data->curr()->s();

    // Now set the primal trial variables
    DBG_PRINT_VECTOR(2,"new_s",*new_s);
    DBG_PRINT_VECTOR(2,"new_x",*new_x);
    SmartPtr<IteratesVector> trial = IpData().curr()->MakeNewContainer();
    trial->Set_primal(*new_x, *new_s);
    IpData().set_trial(trial);

    DBG_PRINT_VECTOR(2, "resto_c", *IpCq().trial_c());
    DBG_PRINT_VECTOR(2, "resto_d_minus_s", *IpCq().trial_d_minus_s());

    /////////////////////////////////////////////////////////////////////
    //                   Initialize bound multipliers                  //
    /////////////////////////////////////////////////////////////////////

    SmartPtr<Vector> new_z_L = IpData().curr()->z_L()->MakeNew();
    SmartPtr<CompoundVector> Cnew_z_L =
      static_cast<CompoundVector*> (GetRawPtr(new_z_L));
    DBG_ASSERT(IsValid(Cnew_z_L));
    SmartPtr<Vector> new_z_U = IpData().curr()->z_U()->MakeNew();
    SmartPtr<Vector> new_v_L = IpData().curr()->v_L()->MakeNew();
    SmartPtr<Vector> new_v_U = IpData().curr()->v_U()->MakeNew();

    // multipliers for the original bounds are
    SmartPtr<const Vector> orig_z_L = orig_ip_data->curr()->z_L();
    SmartPtr<const Vector> orig_z_U = orig_ip_data->curr()->z_U();
    SmartPtr<const Vector> orig_v_L = orig_ip_data->curr()->v_L();
    SmartPtr<const Vector> orig_v_U = orig_ip_data->curr()->v_U();

    // Set the new multipliers to the min of the penalty parameter Rho
    // and their current value
    SmartPtr<Vector> Cnew_z_L0 = Cnew_z_L->GetCompNonConst(0);
    Cnew_z_L0->Set(rho);
    Cnew_z_L0->ElementWiseMin(*orig_z_L);
    new_z_U->Set(rho);
    new_z_U->ElementWiseMin(*orig_z_U);
    new_v_L->Set(rho);
    new_v_L->ElementWiseMin(*orig_v_L);
    new_v_U->Set(rho);
    new_v_U->ElementWiseMin(*orig_v_U);

    // Set the multipliers for the p and n bounds to the "primal" multipliers
    SmartPtr<Vector> Cnew_z_L1 = Cnew_z_L->GetCompNonConst(1);
    Cnew_z_L1->Set(resto_mu);
    Cnew_z_L1->ElementWiseDivide(*nc);
    SmartPtr<Vector> Cnew_z_L2 = Cnew_z_L->GetCompNonConst(2);
    Cnew_z_L2->Set(resto_mu);
    Cnew_z_L2->ElementWiseDivide(*pc);
    SmartPtr<Vector> Cnew_z_L3 = Cnew_z_L->GetCompNonConst(3);
    Cnew_z_L3->Set(resto_mu);
    Cnew_z_L3->ElementWiseDivide(*nd);
    SmartPtr<Vector> Cnew_z_L4 = Cnew_z_L->GetCompNonConst(4);
    Cnew_z_L4->Set(resto_mu);
    Cnew_z_L4->ElementWiseDivide(*pd);

    // Set those initial values to be the trial values in Data
    trial = IpData().trial()->MakeNewContainer();
    trial->Set_bound_mult(*new_z_L, *new_z_U, *new_v_L, *new_v_U);
    IpData().set_trial(trial);

    /////////////////////////////////////////////////////////////////////
    //           Initialize equality constraint multipliers            //
    /////////////////////////////////////////////////////////////////////

    DefaultIterateInitializer::least_square_mults(
      Jnlst(), IpNLP(), IpData(), IpCq(),
      resto_eq_mult_calculator_, constr_mult_init_max_);

    // upgrade the trial to the current point
    IpData().AcceptTrialPoint();

    DBG_PRINT_VECTOR(2, "y_c", *IpData().curr()->y_c());
    DBG_PRINT_VECTOR(2, "y_d", *IpData().curr()->y_d());

    DBG_PRINT_VECTOR(2, "z_L", *IpData().curr()->z_L());
    DBG_PRINT_VECTOR(2, "z_U", *IpData().curr()->z_U());
    DBG_PRINT_VECTOR(2, "v_L", *IpData().curr()->v_L());
    DBG_PRINT_VECTOR(2, "v_U", *IpData().curr()->v_U());

    return true;
  }
 /** Use this method to create a new iterates vector with a copy of
  *  all the data.
  */
 SmartPtr<IteratesVector> MakeNewIteratesVectorCopy() const
 {
   SmartPtr<IteratesVector> ret = MakeNewIteratesVector(true);
   ret->Copy(*this);
   return ret;
 }
Example #10
0
bool RestoIpoptNLP::InitializeStructures(
   SmartPtr<Vector>& x,
   bool              init_x,
   SmartPtr<Vector>& y_c,
   bool              init_y_c,
   SmartPtr<Vector>& y_d,
   bool              init_y_d,
   SmartPtr<Vector>& z_L,
   bool              init_z_L,
   SmartPtr<Vector>& z_U,
   bool              init_z_U,
   SmartPtr<Vector>& v_L,
   SmartPtr<Vector>& v_U)
{
   DBG_START_METH("RestoIpoptNLP::InitializeStructures", 0); DBG_ASSERT(initialized_);
   ///////////////////////////////////////////////////////////
   // Get the vector/matrix spaces for the original problem //
   ///////////////////////////////////////////////////////////

   SmartPtr<const VectorSpace> orig_x_space;
   SmartPtr<const VectorSpace> orig_c_space;
   SmartPtr<const VectorSpace> orig_d_space;
   SmartPtr<const VectorSpace> orig_x_l_space;
   SmartPtr<const MatrixSpace> orig_px_l_space;
   SmartPtr<const VectorSpace> orig_x_u_space;
   SmartPtr<const MatrixSpace> orig_px_u_space;
   SmartPtr<const VectorSpace> orig_d_l_space;
   SmartPtr<const MatrixSpace> orig_pd_l_space;
   SmartPtr<const VectorSpace> orig_d_u_space;
   SmartPtr<const MatrixSpace> orig_pd_u_space;
   SmartPtr<const MatrixSpace> orig_jac_c_space;
   SmartPtr<const MatrixSpace> orig_jac_d_space;
   SmartPtr<const SymMatrixSpace> orig_h_space;

   orig_ip_nlp_->GetSpaces(orig_x_space, orig_c_space, orig_d_space, orig_x_l_space, orig_px_l_space, orig_x_u_space,
      orig_px_u_space, orig_d_l_space, orig_pd_l_space, orig_d_u_space, orig_pd_u_space, orig_jac_c_space,
      orig_jac_d_space, orig_h_space);

   // Create the restoration phase problem vector/matrix spaces, based
   // on the original spaces (pretty inconvenient with all the
   // matrix spaces, isn't it?!?)
   DBG_PRINT((1, "Creating the x_space_\n"));
   // vector x
   Index total_dim = orig_x_space->Dim() + 2 * orig_c_space->Dim() + 2 * orig_d_space->Dim();
   x_space_ = new CompoundVectorSpace(5, total_dim);
   x_space_->SetCompSpace(0, *orig_x_space);
   x_space_->SetCompSpace(1, *orig_c_space); // n_c
   x_space_->SetCompSpace(2, *orig_c_space); // p_c
   x_space_->SetCompSpace(3, *orig_d_space); // n_d
   x_space_->SetCompSpace(4, *orig_d_space); // p_d

   DBG_PRINT((1, "Setting the c_space_\n"));
   // vector c
   //c_space_ = orig_c_space;
   c_space_ = new CompoundVectorSpace(1, orig_c_space->Dim());
   c_space_->SetCompSpace(0, *orig_c_space);

   DBG_PRINT((1, "Setting the d_space_\n"));
   // vector d
   //d_space_ = orig_d_space;
   d_space_ = new CompoundVectorSpace(1, orig_d_space->Dim());
   d_space_->SetCompSpace(0, *orig_d_space);

   DBG_PRINT((1, "Creating the x_l_space_\n"));
   // vector x_L
   total_dim = orig_x_l_space->Dim() + 2 * orig_c_space->Dim() + 2 * orig_d_space->Dim();
   x_l_space_ = new CompoundVectorSpace(5, total_dim);
   x_l_space_->SetCompSpace(0, *orig_x_l_space);
   x_l_space_->SetCompSpace(1, *orig_c_space); // n_c >=0
   x_l_space_->SetCompSpace(2, *orig_c_space); // p_c >=0
   x_l_space_->SetCompSpace(3, *orig_d_space); // n_d >=0
   x_l_space_->SetCompSpace(4, *orig_d_space); // p_d >=0

   DBG_PRINT((1, "Setting the x_u_space_\n"));
   // vector x_U
   x_u_space_ = new CompoundVectorSpace(1, orig_x_u_space->Dim());
   x_u_space_->SetCompSpace(0, *orig_x_u_space);

   DBG_PRINT((1, "Creating the px_l_space_\n"));
   // matrix px_l
   Index total_rows = orig_x_space->Dim() + 2 * orig_c_space->Dim() + 2 * orig_d_space->Dim();
   Index total_cols = orig_x_l_space->Dim() + 2 * orig_c_space->Dim() + 2 * orig_d_space->Dim();
   px_l_space_ = new CompoundMatrixSpace(5, 5, total_rows, total_cols);
   px_l_space_->SetBlockRows(0, orig_x_space->Dim());
   px_l_space_->SetBlockRows(1, orig_c_space->Dim());
   px_l_space_->SetBlockRows(2, orig_c_space->Dim());
   px_l_space_->SetBlockRows(3, orig_d_space->Dim());
   px_l_space_->SetBlockRows(4, orig_d_space->Dim());
   px_l_space_->SetBlockCols(0, orig_x_l_space->Dim());
   px_l_space_->SetBlockCols(1, orig_c_space->Dim());
   px_l_space_->SetBlockCols(2, orig_c_space->Dim());
   px_l_space_->SetBlockCols(3, orig_d_space->Dim());
   px_l_space_->SetBlockCols(4, orig_d_space->Dim());

   px_l_space_->SetCompSpace(0, 0, *orig_px_l_space);
   // now setup the identity matrix
   // This could be changed to be something like...
   // px_l_space_->SetBlockToIdentity(1,1,1.0);
   // px_l_space_->SetBlockToIdentity(2,2,other_factor);
   // ... etc with some simple changes to the CompoundMatrixSpace
   // to allow this (space should auto create the matrices)
   //
   // for now, we use the new feature and set the true flag for this block
   // to say that the matrices should be auto_allocated
   SmartPtr<const MatrixSpace> identity_mat_space_nc = new IdentityMatrixSpace(orig_c_space->Dim());
   px_l_space_->SetCompSpace(1, 1, *identity_mat_space_nc, true);
   px_l_space_->SetCompSpace(2, 2, *identity_mat_space_nc, true);
   SmartPtr<const MatrixSpace> identity_mat_space_nd = new IdentityMatrixSpace(orig_d_space->Dim());
   px_l_space_->SetCompSpace(3, 3, *identity_mat_space_nd, true);
   px_l_space_->SetCompSpace(4, 4, *identity_mat_space_nd, true);

   DBG_PRINT((1, "Creating the px_u_space_\n"));
   // matrix px_u
   total_rows = orig_x_space->Dim() + 2 * orig_c_space->Dim() + 2 * orig_d_space->Dim();
   total_cols = orig_x_u_space->Dim();
   DBG_PRINT((1, "total_rows = %d, total_cols = %d\n", total_rows, total_cols));
   px_u_space_ = new CompoundMatrixSpace(5, 1, total_rows, total_cols);
   px_u_space_->SetBlockRows(0, orig_x_space->Dim());
   px_u_space_->SetBlockRows(1, orig_c_space->Dim());
   px_u_space_->SetBlockRows(2, orig_c_space->Dim());
   px_u_space_->SetBlockRows(3, orig_d_space->Dim());
   px_u_space_->SetBlockRows(4, orig_d_space->Dim());
   px_u_space_->SetBlockCols(0, orig_x_u_space->Dim());

   px_u_space_->SetCompSpace(0, 0, *orig_px_u_space);
   // other matrices are zero'ed out

   // vector d_L
   //d_l_space_ = orig_d_l_space;
   d_l_space_ = new CompoundVectorSpace(1, orig_d_l_space->Dim());
   d_l_space_->SetCompSpace(0, *orig_d_l_space);

   // vector d_U
   //d_u_space_ = orig_d_u_space;
   d_u_space_ = new CompoundVectorSpace(1, orig_d_u_space->Dim());
   d_u_space_->SetCompSpace(0, *orig_d_u_space);

   // matrix pd_L
   //pd_l_space_ = orig_pd_l_space;
   pd_l_space_ = new CompoundMatrixSpace(1, 1, orig_pd_l_space->NRows(), orig_pd_l_space->NCols());
   pd_l_space_->SetBlockRows(0, orig_pd_l_space->NRows());
   pd_l_space_->SetBlockCols(0, orig_pd_l_space->NCols());
   pd_l_space_->SetCompSpace(0, 0, *orig_pd_l_space);

   // matrix pd_U
   //pd_u_space_ = orig_pd_u_space;
   pd_u_space_ = new CompoundMatrixSpace(1, 1, orig_pd_u_space->NRows(), orig_pd_u_space->NCols());
   pd_u_space_->SetBlockRows(0, orig_pd_u_space->NRows());
   pd_u_space_->SetBlockCols(0, orig_pd_u_space->NCols());
   pd_u_space_->SetCompSpace(0, 0, *orig_pd_u_space);

   DBG_PRINT((1, "Creating the jac_c_space_\n"));
   // matrix jac_c
   total_rows = orig_c_space->Dim();
   total_cols = orig_x_space->Dim() + 2 * orig_c_space->Dim() + 2 * orig_d_space->Dim();
   jac_c_space_ = new CompoundMatrixSpace(1, 5, total_rows, total_cols);
   jac_c_space_->SetBlockRows(0, orig_c_space->Dim());
   jac_c_space_->SetBlockCols(0, orig_x_space->Dim());
   jac_c_space_->SetBlockCols(1, orig_c_space->Dim());
   jac_c_space_->SetBlockCols(2, orig_c_space->Dim());
   jac_c_space_->SetBlockCols(3, orig_d_space->Dim());
   jac_c_space_->SetBlockCols(4, orig_d_space->Dim());

   jac_c_space_->SetCompSpace(0, 0, *orig_jac_c_space);
   // **NOTE: By placing "flat" identity matrices here, we are creating
   //         potential issues for linalg operations that arise when the original
   //         NLP has a "compound" c_space. To avoid problems like this,
   //         we place all unmodified component spaces in trivial (size 1)
   //         "compound" spaces.
   jac_c_space_->SetCompSpace(0, 1, *identity_mat_space_nc, true);
   jac_c_space_->SetCompSpace(0, 2, *identity_mat_space_nc, true);
   // remaining blocks are zero'ed

   DBG_PRINT((1, "Creating the jac_d_space_\n"));
   // matrix jac_d
   total_rows = orig_d_space->Dim();
   total_cols = orig_x_space->Dim() + 2 * orig_c_space->Dim() + 2 * orig_d_space->Dim();
   jac_d_space_ = new CompoundMatrixSpace(1, 5, total_rows, total_cols);
   jac_d_space_->SetBlockRows(0, orig_d_space->Dim());
   jac_d_space_->SetBlockCols(0, orig_x_space->Dim());
   jac_d_space_->SetBlockCols(1, orig_c_space->Dim());
   jac_d_space_->SetBlockCols(2, orig_c_space->Dim());
   jac_d_space_->SetBlockCols(3, orig_d_space->Dim());
   jac_d_space_->SetBlockCols(4, orig_d_space->Dim());

   jac_d_space_->SetCompSpace(0, 0, *orig_jac_d_space);
   DBG_PRINT((1, "orig_jac_d_space = %x\n", GetRawPtr(orig_jac_d_space)))
   // Blocks (0,1) and (0,2) are zero'ed out
   // **NOTE: By placing "flat" identity matrices here, we are creating
   //         potential issues for linalg operations that arise when the original
   //         NLP has a "compound" d_space. To avoid problems like this,
   //         we place all unmodified component spaces in trivial (size 1)
   //         "compound" spaces.
   jac_d_space_->SetCompSpace(0, 3, *identity_mat_space_nd, true);
   jac_d_space_->SetCompSpace(0, 4, *identity_mat_space_nd, true);

   DBG_PRINT((1, "Creating the h_space_\n"));
   // matrix h
   total_dim = orig_x_space->Dim() + 2 * orig_c_space->Dim() + 2 * orig_d_space->Dim();
   h_space_ = new CompoundSymMatrixSpace(5, total_dim);
   h_space_->SetBlockDim(0, orig_x_space->Dim());
   h_space_->SetBlockDim(1, orig_c_space->Dim());
   h_space_->SetBlockDim(2, orig_c_space->Dim());
   h_space_->SetBlockDim(3, orig_d_space->Dim());
   h_space_->SetBlockDim(4, orig_d_space->Dim());

   SmartPtr<DiagMatrixSpace> DR_x_space = new DiagMatrixSpace(orig_x_space->Dim());
   if( hessian_approximation_ == LIMITED_MEMORY )
   {
      const LowRankUpdateSymMatrixSpace* LR_h_space = static_cast<const LowRankUpdateSymMatrixSpace*>(GetRawPtr(
         orig_h_space));
      DBG_ASSERT(LR_h_space);
      SmartPtr<LowRankUpdateSymMatrixSpace> new_orig_h_space = new LowRankUpdateSymMatrixSpace(LR_h_space->Dim(),
      NULL, orig_x_space, false);
      h_space_->SetCompSpace(0, 0, *new_orig_h_space, true);
   }
   else
   {
      SmartPtr<SumSymMatrixSpace> sumsym_mat_space = new SumSymMatrixSpace(orig_x_space->Dim(), 2);
      sumsym_mat_space->SetTermSpace(0, *orig_h_space);
      sumsym_mat_space->SetTermSpace(1, *DR_x_space);
      h_space_->SetCompSpace(0, 0, *sumsym_mat_space, true);
      // All remaining blocks are zero'ed out
   }

   ///////////////////////////
   // Create the bound data //
   ///////////////////////////

   // x_L
   x_L_ = x_l_space_->MakeNewCompoundVector();
   x_L_->SetComp(0, *orig_ip_nlp_->x_L()); // x >= x_L
   x_L_->GetCompNonConst(1)->Set(0.0); // n_c >= 0
   x_L_->GetCompNonConst(2)->Set(0.0); // p_c >= 0
   x_L_->GetCompNonConst(3)->Set(0.0); // n_d >= 0
   x_L_->GetCompNonConst(4)->Set(0.0); // p_d >= 0
   DBG_PRINT_VECTOR(2, "resto_x_L", *x_L_);

   // x_U
   x_U_ = x_u_space_->MakeNewCompoundVector();
   x_U_->SetComp(0, *orig_ip_nlp_->x_U());

   // d_L
   d_L_ = d_l_space_->MakeNewCompoundVector();
   d_L_->SetComp(0, *orig_ip_nlp_->d_L());

   // d_U
   d_U_ = d_u_space_->MakeNewCompoundVector();
   d_U_->SetComp(0, *orig_ip_nlp_->d_U());

   // Px_L
   Px_L_ = px_l_space_->MakeNewCompoundMatrix();
   Px_L_->SetComp(0, 0, *orig_ip_nlp_->Px_L());
   // Identities are auto-created (true flag passed into SetCompSpace)

   // Px_U
   Px_U_ = px_u_space_->MakeNewCompoundMatrix();
   Px_U_->SetComp(0, 0, *orig_ip_nlp_->Px_U());
   // Remaining matrices will be zero'ed out

   // Pd_L
   //Pd_L_ = orig_ip_nlp_->Pd_L();
   Pd_L_ = pd_l_space_->MakeNewCompoundMatrix();
   Pd_L_->SetComp(0, 0, *orig_ip_nlp_->Pd_L());

   // Pd_U
   //Pd_U_ = orig_ip_nlp_->Pd_U();
   Pd_U_ = pd_u_space_->MakeNewCompoundMatrix();
   Pd_U_->SetComp(0, 0, *orig_ip_nlp_->Pd_U());

   // Getting the NLP scaling

   SmartPtr<const MatrixSpace> scaled_jac_c_space;
   SmartPtr<const MatrixSpace> scaled_jac_d_space;
   SmartPtr<const SymMatrixSpace> scaled_h_space;
   NLP_scaling()->DetermineScaling(GetRawPtr(x_space_), c_space_, d_space_, GetRawPtr(jac_c_space_),
      GetRawPtr(jac_d_space_), GetRawPtr(h_space_), scaled_jac_c_space, scaled_jac_d_space, scaled_h_space, *Px_L_,
      *x_L_, *Px_U_, *x_U_);
   // For now we assume that no scaling is done inside the NLP_Scaling
   DBG_ASSERT(scaled_jac_c_space == jac_c_space_); DBG_ASSERT(scaled_jac_d_space == jac_d_space_); DBG_ASSERT(scaled_h_space == h_space_);

   /////////////////////////////////////////////////////////////////////////
   // Create and initialize the vectors for the restoration phase problem //
   /////////////////////////////////////////////////////////////////////////

   // Vector x
   SmartPtr<CompoundVector> comp_x = x_space_->MakeNewCompoundVector();
   if( init_x )
   {
      comp_x->GetCompNonConst(0)->Copy(*orig_ip_data_->curr()->x());
      comp_x->GetCompNonConst(1)->Set(1.0);
      comp_x->GetCompNonConst(2)->Set(1.0);
      comp_x->GetCompNonConst(3)->Set(1.0);
      comp_x->GetCompNonConst(4)->Set(1.0);
   }
   x = GetRawPtr(comp_x);

   // Vector y_c
   y_c = c_space_->MakeNew();
   if( init_y_c )
   {
      y_c->Set(0.0);  // ToDo
   }

   // Vector y_d
   y_d = d_space_->MakeNew();
   if( init_y_d )
   {
      y_d->Set(0.0);
   }

   // Vector z_L
   z_L = x_l_space_->MakeNew();
   if( init_z_L )
   {
      z_L->Set(1.0);
   }

   // Vector z_U
   z_U = x_u_space_->MakeNew();
   if( init_z_U )
   {
      z_U->Set(1.0);
   }

   // Vector v_L
   v_L = d_l_space_->MakeNew();

   // Vector v_U
   v_U = d_u_space_->MakeNew();

   // Initialize other data needed by the restoration nlp.  x_ref is
   // the point to reference to which we based the regularization
   // term
   x_ref_ = orig_x_space->MakeNew();
   x_ref_->Copy(*orig_ip_data_->curr()->x());

   dr_x_ = orig_x_space->MakeNew();
   dr_x_->Set(1.0);
   SmartPtr<Vector> tmp = dr_x_->MakeNew();
   tmp->Copy(*x_ref_);
   dr_x_->ElementWiseMax(*tmp);
   tmp->Scal(-1.);
   dr_x_->ElementWiseMax(*tmp);
   dr_x_->ElementWiseReciprocal();
   DBG_PRINT_VECTOR(2, "dr_x_", *dr_x_);
   DR_x_ = DR_x_space->MakeNewDiagMatrix();
   DR_x_->SetDiag(*dr_x_);

   return true;
}