ROL_NonlinearLeastSquaresObjective.hpp

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#ifndef ROL_NONLINEARLEASTSQUARESOBJECTIVE_H
#define ROL_NONLINEARLEASTSQUARESOBJECTIVE_H

#include "ROL_Objective.hpp"
#include "ROL_Constraint.hpp"
#include "ROL_Types.hpp"

namespace ROL {

template <class Real>
class NonlinearLeastSquaresObjective : public Objective<Real> {
private:
  const ROL::Ptr<Constraint<Real> > con_;
  const bool GaussNewtonHessian_;

  ROL::Ptr<Vector<Real> > c1_, c2_, c1dual_, x_;

public:
  NonlinearLeastSquaresObjective(const ROL::Ptr<Constraint<Real> > &con,
                                 const Vector<Real> &optvec,
                                 const Vector<Real> &convec,
                                 const bool GNH = false)
    : con_(con), GaussNewtonHessian_(GNH) {
    c1_ = convec.clone(); c1dual_ = c1_->dual().clone();
    c2_ = convec.clone();
    x_  = optvec.dual().clone();
  }

  void update( const Vector<Real> &x, bool flag = true, int iter = -1 ) {
    Real tol = std::sqrt(ROL_EPSILON<Real>());
    con_->update(x,flag,iter);
    con_->value(*c1_,x,tol);
    c1dual_->set(c1_->dual());
  }

  Real value( const Vector<Real> &x, Real &tol ) {
    Real half(0.5);
    return half*(c1_->dot(*c1dual_));
  }

  void gradient( Vector<Real> &g, const Vector<Real> &x, Real &tol ) {
    con_->applyAdjointJacobian(g,*c1dual_,x,tol);
  }

  void hessVec( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &x, Real &tol ) {
    con_->applyJacobian(*c2_,v,x,tol);
    con_->applyAdjointJacobian(hv,c2_->dual(),x,tol);
    if ( !GaussNewtonHessian_ ) {
      con_->applyAdjointHessian(*x_,*c1dual_,v,x,tol);
      hv.plus(*x_);
    }
  }

// Definitions for parametrized (stochastic) equality constraints
public:
  void setParameter(const std::vector<Real> &param) {
    Objective<Real>::setParameter(param);
    con_->setParameter(param);
  }
};

} // namespace ROL

#endif