ROL_LogBarrierObjective.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_LOG_BARRIER_OBJECTIVE_H
#define ROL_LOG_BARRIER_OBJECTIVE_H
 
#include "ROL_Objective.hpp"

 
namespace ROL {
 
template <class Real>
class LogBarrierObjective : public Objective<Real> {
public:
 
    
 
  /* \brief Objective value J(x) = \f$-\sum_i \log(x_i) \f$ */
  Real value( const Vector<Real> &x, Real &tol ) {
 
    ROL::Ptr<Vector<Real> > logx = x.clone();
    logx->set(x);
    
    Elementwise::Logarithm<Real> log;
 
    logx->applyUnary(log);
 
    Elementwise::ReductionSum<Real> sum;
 
    Real result = -(logx->reduce(sum));
 
    return result;
  }  
 
  /* \brief gradient g_i = \f$ 1/x_i \f$ */
  void gradient( Vector<Real> &g, const Vector<Real> &x, Real &tol ) {
    
    g.set(x);
     
    Elementwise::Reciprocal<Real> reciprocal;
   
    g.applyUnary(reciprocal);
    g.scale(-1.0);
  }
 
  Real dirDeriv( const Vector<Real> &x, const Vector<Real> &d, Real &tol ) {
 
    ROL::Ptr<Vector<Real> > dbyx = d.clone();
    dbyx->set(x);
 
    struct Division : public Elementwise::BinaryFunction<Real> {
      Real apply( const Real &xc, const Real &dc ) const {
        return dc/xc;    
      }
    } division;
 
    dbyx->applyBinary( division, d );
    
    Elementwise::ReductionSum<Real> sum;
 
    return -dbyx->reduce(sum);
  }
 
  void hessVec( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &x, Real &tol ) {
    hv.set(v);
    
    struct HessianApply : public Elementwise::BinaryFunction<Real> {
      Real apply( const Real &vc, const Real &xc ) const { 
        return vc/(xc*xc);   
      }
    } hessian;
 
    hv.applyBinary(hessian,x);
 
  }
 
};
 
} // namespace ROL
 
#endif // ROL_LOG_BARRIER_OBJECTIVE_H