ROL_MoreauYosidaPenalty.hpp

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#ifndef ROL_MOREAUYOSIDAPENALTY_H
#define ROL_MOREAUYOSIDAPENALTY_H

#include "ROL_Objective.hpp"
#include "ROL_BoundConstraint.hpp"
#include "ROL_Vector.hpp"
#include "ROL_Types.hpp"
#include "Teuchos_RCP.hpp"
#include <iostream>

namespace ROL {

template <class Real>
class MoreauYosidaPenalty : public Objective<Real> {
private:
  const Teuchos::RCP<Objective<Real> > obj_;
  const Teuchos::RCP<BoundConstraint<Real> > con_;

  Teuchos::RCP<Vector<Real> > g_;
  Teuchos::RCP<Vector<Real> > l_;
  Teuchos::RCP<Vector<Real> > u_;
  Teuchos::RCP<Vector<Real> > l1_;
  Teuchos::RCP<Vector<Real> > u1_;
  Teuchos::RCP<Vector<Real> > dl1_;
  Teuchos::RCP<Vector<Real> > du1_;
  Teuchos::RCP<Vector<Real> > xlam_;
  Teuchos::RCP<Vector<Real> > v_;
  Teuchos::RCP<Vector<Real> > dv_;
  Teuchos::RCP<Vector<Real> > dv2_;
  Teuchos::RCP<Vector<Real> > lam_;
  Teuchos::RCP<Vector<Real> > tmp_;

  Real mu_;
  Real fval_;
  bool isConEvaluated_;
  int nfval_;
  int ngval_;

  void computePenalty(const Vector<Real> &x) {
    if ( con_->isActivated() ) {
      Real one = 1.0;
      if ( !isConEvaluated_ ) {
        xlam_->set(x);
        xlam_->axpy(one/mu_,*lam_);

        if ( con_->isFeasible(*xlam_) ) {
          l1_->zero(); dl1_->zero();
          u1_->zero(); du1_->zero();
        }
        else {
          // Compute lower penalty component
          l1_->set(*l_);
          con_->pruneLowerInactive(*l1_,*xlam_);
          tmp_->set(*xlam_);
          con_->pruneLowerInactive(*tmp_,*xlam_);
          l1_->axpy(-one,*tmp_);

          // Compute upper penalty component
          u1_->set(*xlam_);
          con_->pruneUpperInactive(*u1_,*xlam_);
          tmp_->set(*u_);
          con_->pruneUpperInactive(*tmp_,*xlam_);
          u1_->axpy(-one,*tmp_);

          // Compute derivative of lower penalty component
          dl1_->set(l1_->dual());
          con_->pruneLowerInactive(*dl1_,*xlam_);

          // Compute derivative of upper penalty component
          du1_->set(u1_->dual());
          con_->pruneUpperInactive(*du1_,*xlam_);
        }

        isConEvaluated_ = true;
      }
    }
  }

public:
  ~MoreauYosidaPenalty() {}

  MoreauYosidaPenalty(const Teuchos::RCP<Objective<Real> > &obj,
                      const Teuchos::RCP<BoundConstraint<Real> > &con, 
                      const ROL::Vector<Real> &x, const Real mu = 1.0)
    : obj_(obj), con_(con), mu_(mu),
      fval_(0), isConEvaluated_(false), nfval_(0), ngval_(0) {

    g_    = x.dual().clone();
    l_    = x.clone();
    l1_   = x.clone();
    dl1_  = x.dual().clone();
    u_    = x.clone();
    u1_   = x.clone();
    du1_  = x.dual().clone();
    xlam_ = x.clone();
    v_    = x.clone();
    dv_   = x.dual().clone();
    dv2_  = x.dual().clone();
    lam_  = x.clone();
    tmp_  = x.clone();

    con_->setVectorToLowerBound(*l_);
    con_->setVectorToUpperBound(*u_);

    lam_->zero();
    //lam_->set(*u_);
    //lam_->plus(*l_);
    //lam_->scale(0.5);
  }

  void updateMultipliers(Real mu, const ROL::Vector<Real> &x) {
    if ( con_->isActivated() ) {
      Real one = 1.0;
      computePenalty(x);

      lam_->set(*u1_);
      lam_->axpy(-one,*l1_);
      lam_->scale(mu_);

      mu_ = mu;
    }

    nfval_ = 0; ngval_ = 0;
    isConEvaluated_ = false;
  }

  Real getObjectiveValue(void) const {
    return fval_;
  }

  Teuchos::RCP<Vector<Real> > getGradient(void) const {
    return g_;
  }

  int getNumberFunctionEvaluations(void) {
    return nfval_;
  }

  int getNumberGradientEvaluations(void) {
    return ngval_;
  }

  void update( const Vector<Real> &x, bool flag = true, int iter = -1 ) {
    obj_->update(x,flag,iter);
    con_->update(x,flag,iter);
    isConEvaluated_ = false;
  }

  Real value( const Vector<Real> &x, Real &tol ) {
    Real half = 0.5;
    // Compute objective function value
    fval_ = obj_->value(x,tol);
    nfval_++;
    // Add value of the Moreau-Yosida penalty
    Real fval = fval_;
    if ( con_->isActivated() ) {
      computePenalty(x);
      fval += half*mu_*(l1_->dot(*l1_) + u1_->dot(*u1_));
    }
    return fval;
  }

  void gradient( Vector<Real> &g, const Vector<Real> &x, Real &tol ) {
    // Compute gradient of objective function
    obj_->gradient(*g_,x,tol);
    ngval_++;
    g.set(*g_);
    // Add gradient of the Moreau-Yosida penalty
    if ( con_->isActivated() ) {
      computePenalty(x);
      g.axpy(-mu_,*dl1_);
      g.axpy(mu_,*du1_);
    }
  }

  void hessVec( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &x, Real &tol ) {
    // Apply objective Hessian to a vector
    obj_->hessVec(hv,v,x,tol);
    // Add Hessian of the Moreau-Yosida penalty
    if ( con_->isActivated() ) {
      Real one = 1.0;
      computePenalty(x);

      v_->set(v);
      con_->pruneLowerActive(*v_,*xlam_);
      v_->scale(-one);
      v_->plus(v);
      dv_->set(v_->dual());
      dv2_->set(*dv_);
      con_->pruneLowerActive(*dv_,*xlam_);
      dv_->scale(-one);
      dv_->plus(*dv2_);
      hv.axpy(mu_,*dv_);

      v_->set(v);
      con_->pruneUpperActive(*v_,*xlam_);
      v_->scale(-one);
      v_->plus(v);
      dv_->set(v_->dual());
      dv2_->set(*dv_);
      con_->pruneUpperActive(*dv_,*xlam_);
      dv_->scale(-one);
      dv_->plus(*dv2_);
      hv.axpy(mu_,*dv_);
    }
  }

// Definitions for parametrized (stochastic) objective functions
public:
  void setParameter(const std::vector<Real> &param) {
    Objective<Real>::setParameter(param);
    obj_->setParameter(param);
  }
}; // class MoreauYosidaPenalty

} // namespace ROL

#endif