ROL_NonlinearLeastSquaresObjective_Dynamic.hpp

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// @HEADER
// *****************************************************************************
//               Rapid Optimization Library (ROL) Package
//
// Copyright 2014 NTESS and the ROL contributors.
// SPDX-License-Identifier: BSD-3-Clause
// *****************************************************************************
// @HEADER
 
#ifndef ROL_NONLINEARLEASTSQUARESOBJECTIVE_DYNAMIC_H
#define ROL_NONLINEARLEASTSQUARESOBJECTIVE_DYNAMIC_H
 
#include "ROL_Objective.hpp"
#include "ROL_DynamicConstraint.hpp"
#include "ROL_Types.hpp"

 
 
namespace ROL {
 
template <class Real>
class DynamicConstraint;
 
template <class Real>
class NonlinearLeastSquaresObjective_Dynamic : public Objective<Real> {
private:
  const Ptr<DynamicConstraint<Real>> con_;
  const Ptr<const Vector<Real>> uo_;
  const Ptr<const Vector<Real>> z_;
  const Ptr<const TimeStamp<Real>> ts_;
  const bool GaussNewtonHessian_;
 
  Ptr<Vector<Real> > c1_, c2_, cdual_, udual_;
 
public:
  NonlinearLeastSquaresObjective_Dynamic(const Ptr<DynamicConstraint<Real>> &con,
                                         const Vector<Real> &c,
                                         const Ptr<const Vector<Real>> &uo,
                                         const Ptr<const Vector<Real>> &z,
                                         const Ptr<const TimeStamp<Real>> &ts,
                                         const bool GNH = false)
    : con_(con), uo_(uo), z_(z), ts_(ts), GaussNewtonHessian_(GNH) {
    c1_ = c.clone();
    c2_ = c.clone();
    cdual_ = c.dual().clone();
    udual_ = uo->dual().clone();
  }
 
  void update( const Vector<Real> &u, bool flag = true, int iter = -1 ) {
    //con_->update_un(u,*ts_);
    con_->update(*uo_,u,*z_,*ts_);
    con_->value(*c1_,*uo_,u,*z_,*ts_);
    cdual_->set(c1_->dual());
  }
 
  Real value( const Vector<Real> &x, Real &tol ) {
    Real half(0.5);
    return half*(c1_->dot(*cdual_));
  }
 
  void gradient( Vector<Real> &g, const Vector<Real> &u, Real &tol ) {
    con_->applyAdjointJacobian_un(g,*cdual_,*uo_,u,*z_,*ts_);
  }
 
  void hessVec( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, Real &tol ) {
    con_->applyJacobian_un(*c2_,v,*uo_,u,*z_,*ts_);
    con_->applyAdjointJacobian_un(hv,c2_->dual(),*uo_,u,*z_,*ts_);
    if ( !GaussNewtonHessian_ ) {
      con_->applyAdjointHessian_un_un(*udual_,*cdual_,v,*uo_,u,*z_,*ts_);
      hv.plus(*udual_);
    }
  }
 
  void precond( Vector<Real> &pv, const Vector<Real> &v, const Vector<Real> &u, Real &tol ) {
    con_->applyInverseAdjointJacobian_un(*cdual_,v,*uo_,u,*z_,*ts_);
    con_->applyInverseJacobian_un(pv,cdual_->dual(),*uo_,u,*z_,*ts_);
  }
 
// Definitions for parametrized (stochastic) equality constraints
//public:
//  void setParameter(const std::vector<Real> &param) {
//    Objective<Real>::setParameter(param);
//    con_->setParameter(param);
//  }
};
 
} // namespace ROL
 
#endif