ROL_NonlinearLeastSquaresObjective_SimOpt.hpp

Go to the documentation of this file.

// @HEADER
// ************************************************************************
//
//               Rapid Optimization Library (ROL) Package
//                 Copyright (2014) Sandia Corporation
//
// Under terms of Contract DE-AC04-94AL85000, there is a non-exclusive
// license for use of this work by or on behalf of the U.S. Government.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// 3. Neither the name of the Corporation nor the names of the
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// Questions? Contact lead developers:
//              Drew Kouri   (dpkouri@sandia.gov) and
//              Denis Ridzal (dridzal@sandia.gov)
//
// ************************************************************************
// @HEADER

#ifndef ROL_NONLINEARLEASTSQUARESOBJECTIVE_SIMOPT_H
#define ROL_NONLINEARLEASTSQUARESOBJECTIVE_SIMOPT_H

#include "ROL_Objective.hpp"
#include "ROL_Constraint_SimOpt.hpp"
#include "ROL_Types.hpp"

namespace ROL {

template <class Real>
class Constraint_SimOpt;

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

  ROL::Ptr<Vector<Real> > c1_, c2_, cdual_, udual_, z_;

public:
  NonlinearLeastSquaresObjective_SimOpt(const ROL::Ptr<Constraint_SimOpt<Real> > &con,
                                        const Vector<Real> &uvec,
                                        const Vector<Real> &zvec,
                                        const Vector<Real> &cvec,
                                        const bool GNH = false)
    : con_(con), GaussNewtonHessian_(GNH) {
    c1_ = cvec.clone();
    c2_ = cvec.clone();
    z_  = zvec.clone(); z_->set(zvec);
    cdual_ = cvec.dual().clone();
    udual_ = uvec.dual().clone();
  }

  void update( const Vector<Real> &u, bool flag = true, int iter = -1 ) {
    Real tol = std::sqrt(ROL_EPSILON<Real>());
    con_->update_1(u,flag,iter);
    con_->value(*c1_,u,*z_,tol);
    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_1(g,*cdual_,u,*z_,tol);
  }

  void hessVec( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, Real &tol ) {
    con_->applyJacobian_1(*c2_,v,u,*z_,tol);
    con_->applyAdjointJacobian_1(hv,c2_->dual(),u,*z_,tol);
    if ( !GaussNewtonHessian_ ) {
      con_->applyAdjointHessian_11(*udual_,*cdual_,v,u,*z_,tol);
      hv.plus(*udual_);
    }
  }

  void precond( Vector<Real> &pv, const Vector<Real> &v, const Vector<Real> &u, Real &tol ) {
    con_->applyInverseAdjointJacobian_1(*cdual_,v,u,*z_,tol);
    con_->applyInverseJacobian_1(pv,cdual_->dual(),u,*z_,tol);
  }

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

} // namespace ROL

#endif