ROL_MoreauYosidaPenaltyStep.hpp

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#ifndef ROL_MOREAUYOSIDAPENALTYSTEP_H
#define ROL_MOREAUYOSIDAPENALTYSTEP_H

#include "ROL_MoreauYosidaPenalty.hpp"
#include "ROL_Types.hpp"
#include "ROL_AugmentedLagrangianStep.hpp"
#include "ROL_CompositeStep.hpp"
#include "ROL_FletcherStep.hpp"
#include "ROL_Algorithm.hpp"
#include "ROL_ParameterList.hpp"

namespace ROL {

template <class Real>
class MoreauYosidaPenaltyStep : public Step<Real> {
private:
  ROL::Ptr<Algorithm<Real> >       algo_;
  ROL::Ptr<Vector<Real> >          x_; 
  ROL::Ptr<Vector<Real> >          g_; 
  ROL::Ptr<Vector<Real> >          l_; 
  ROL::Ptr<BoundConstraint<Real> > bnd_;

  Real compViolation_;
  Real gLnorm_;
  Real tau_;
  bool print_;
  bool updatePenalty_;

  ROL::ParameterList parlist_;
  int subproblemIter_;
  bool hasEquality_;

  EStep stepType_;
  std::string stepname_;

  void updateState(const Vector<Real> &x, const Vector<Real> &l,
                   Objective<Real> &obj,
                   Constraint<Real> &con, BoundConstraint<Real> &bnd,
                   AlgorithmState<Real> &algo_state) {
    MoreauYosidaPenalty<Real> &myPen
      = dynamic_cast<MoreauYosidaPenalty<Real>&>(obj);
    Real zerotol = std::sqrt(ROL_EPSILON<Real>());
    ROL::Ptr<StepState<Real> > state = Step<Real>::getState();
    // Update objective and constraint.
    myPen.update(x,true,algo_state.iter);
    con.update(x,true,algo_state.iter);
    // Compute norm of the gradient of the Lagrangian
    algo_state.value = myPen.value(x, zerotol);
    myPen.gradient(*(state->gradientVec), x, zerotol);
    con.applyAdjointJacobian(*g_,l,x,zerotol);
    state->gradientVec->plus(*g_);
    gLnorm_ = (state->gradientVec)->norm();
    // Compute constraint violation
    con.value(*(state->constraintVec),x, zerotol);
    algo_state.cnorm = (state->constraintVec)->norm();
    compViolation_ = myPen.testComplementarity(x);
    algo_state.gnorm = std::max(gLnorm_,compViolation_);
    // Update state
    algo_state.nfval++;
    algo_state.ngrad++;
    algo_state.ncval++;
  }

  void updateState(const Vector<Real> &x,
                   Objective<Real> &obj,
                   BoundConstraint<Real> &bnd,
                   AlgorithmState<Real> &algo_state) {
    MoreauYosidaPenalty<Real> &myPen
      = dynamic_cast<MoreauYosidaPenalty<Real>&>(obj);
    Real zerotol = std::sqrt(ROL_EPSILON<Real>());
    ROL::Ptr<StepState<Real> > state = Step<Real>::getState();
    // Update objective and constraint.
    myPen.update(x,true,algo_state.iter);
    // Compute norm of the gradient of the Lagrangian
    algo_state.value = myPen.value(x, zerotol);
    myPen.gradient(*(state->gradientVec), x, zerotol);
    gLnorm_ = (state->gradientVec)->norm();
    // Compute constraint violation
    algo_state.cnorm = static_cast<Real>(0);
    compViolation_ = myPen.testComplementarity(x);
    algo_state.gnorm = std::max(gLnorm_,compViolation_);
    // Update state
    algo_state.nfval++;
    algo_state.ngrad++;
  }

public:

  using Step<Real>::initialize;
  using Step<Real>::compute;
  using Step<Real>::update;

  ~MoreauYosidaPenaltyStep() {}

  MoreauYosidaPenaltyStep(ROL::ParameterList &parlist)
    : Step<Real>(), algo_(ROL::nullPtr),
      x_(ROL::nullPtr), g_(ROL::nullPtr), l_(ROL::nullPtr),
      tau_(10), print_(false), parlist_(parlist), subproblemIter_(0),
      hasEquality_(false) {
    // Parse parameters
    Real ten(10), oem6(1.e-6), oem8(1.e-8);
    ROL::ParameterList& steplist = parlist.sublist("Step").sublist("Moreau-Yosida Penalty");
    Step<Real>::getState()->searchSize = steplist.get("Initial Penalty Parameter",ten);
    tau_ = steplist.get("Penalty Parameter Growth Factor",ten);
    updatePenalty_ = steplist.get("Update Penalty",true);
    print_ = steplist.sublist("Subproblem").get("Print History",false);
    // Set parameters for step subproblem
    Real gtol = steplist.sublist("Subproblem").get("Optimality Tolerance",oem8);
    Real ctol = steplist.sublist("Subproblem").get("Feasibility Tolerance",oem8);
    Real stol = oem6*std::min(gtol,ctol);
    int maxit = steplist.sublist("Subproblem").get("Iteration Limit",1000);
    parlist_.sublist("Status Test").set("Gradient Tolerance",   gtol);
    parlist_.sublist("Status Test").set("Constraint Tolerance", ctol);
    parlist_.sublist("Status Test").set("Step Tolerance",       stol);
    parlist_.sublist("Status Test").set("Iteration Limit",      maxit);
    // Get step name from parameterlist
    stepname_ = steplist.sublist("Subproblem").get("Step Type","Composite Step");
    stepType_ = StringToEStep(stepname_);
  }

  void initialize( Vector<Real> &x, const Vector<Real> &g, Vector<Real> &l, const Vector<Real> &c,
                   Objective<Real> &obj, Constraint<Real> &con, BoundConstraint<Real> &bnd,
                   AlgorithmState<Real> &algo_state ) {
    hasEquality_ = true;
    // Initialize step state
    ROL::Ptr<StepState<Real> > state = Step<Real>::getState();
    state->descentVec    = x.clone();
    state->gradientVec   = g.clone();
    state->constraintVec = c.clone();
    // Initialize additional storage
    x_ = x.clone();
    g_ = g.clone();
    l_ = l.clone();
    // Project x onto the feasible set
    if ( bnd.isActivated() ) {
      bnd.project(x);
    }
    // Initialize the algorithm state
    algo_state.nfval = 0;
    algo_state.ncval = 0;
    algo_state.ngrad = 0;
    updateState(x,l,obj,con,bnd,algo_state);
  }

  void initialize( Vector<Real> &x, const Vector<Real> &g,
                   Objective<Real> &obj, BoundConstraint<Real> &bnd,
                   AlgorithmState<Real> &algo_state ) {
    // Initialize step state
    ROL::Ptr<StepState<Real> > state = Step<Real>::getState();
    state->descentVec    = x.clone();
    state->gradientVec   = g.clone();
    // Initialize additional storage
    x_ = x.clone();
    g_ = g.clone();
    // Project x onto the feasible set
    if ( bnd.isActivated() ) {
      bnd.project(x);
    }
    // Initialize the algorithm state
    algo_state.nfval = 0;
    algo_state.ncval = 0;
    algo_state.ngrad = 0;
    updateState(x,obj,bnd,algo_state);

    bnd_ = ROL::makePtr<BoundConstraint<Real>>();
    bnd_->deactivate();
  }

  void compute( Vector<Real> &s, const Vector<Real> &x, const Vector<Real> &l,
                Objective<Real> &obj, Constraint<Real> &con, 
                BoundConstraint<Real> &bnd, 
                AlgorithmState<Real> &algo_state ) {
    //MoreauYosidaPenalty<Real> &myPen
    //  = dynamic_cast<MoreauYosidaPenalty<Real>&>(obj);
    Real one(1);
    Ptr<Objective<Real>> penObj;
    if (stepType_ == STEP_AUGMENTEDLAGRANGIAN) {
      Ptr<Objective<Real>>  raw_obj = makePtrFromRef(obj);
      Ptr<Constraint<Real>> raw_con = makePtrFromRef(con);
      Ptr<StepState<Real>>  state   = Step<Real>::getState();
      penObj = makePtr<AugmentedLagrangian<Real>>(raw_obj,raw_con,l,one,x,*(state->constraintVec),parlist_);
    }
    else if (stepType_ == STEP_FLETCHER) {
      Ptr<Objective<Real>>  raw_obj = makePtrFromRef(obj);
      Ptr<Constraint<Real>> raw_con = makePtrFromRef(con);
      Ptr<StepState<Real>>  state   = Step<Real>::getState();
      penObj = makePtr<Fletcher<Real>>(raw_obj,raw_con,x,*(state->constraintVec),parlist_);
    }
    else {
      penObj = makePtrFromRef(obj);
      stepname_ = "Composite Step";
      stepType_ = STEP_COMPOSITESTEP;
    }
    algo_ = ROL::makePtr<Algorithm<Real>>(stepname_,parlist_,false);
    x_->set(x); l_->set(l);
    algo_->run(*x_,*l_,*penObj,con,print_);
    s.set(*x_); s.axpy(-one,x);
    subproblemIter_ = (algo_->getState())->iter;
  }

  void compute( Vector<Real> &s, const Vector<Real> &x, Objective<Real> &obj,
                        BoundConstraint<Real> &bnd,
                        AlgorithmState<Real> &algo_state ) {
    Real one(1);
    MoreauYosidaPenalty<Real> &myPen
      = dynamic_cast<MoreauYosidaPenalty<Real>&>(obj);
    algo_ = ROL::makePtr<Algorithm<Real>>("Trust Region",parlist_,false);
    x_->set(x);
    algo_->run(*x_,myPen,*bnd_,print_);
    s.set(*x_); s.axpy(-one,x);
    subproblemIter_ = (algo_->getState())->iter;
  }


  void update( Vector<Real> &x, Vector<Real> &l, const Vector<Real> &s,
               Objective<Real> &obj, Constraint<Real> &con,
               BoundConstraint<Real> &bnd,
               AlgorithmState<Real> &algo_state ) {
    MoreauYosidaPenalty<Real> &myPen
      = dynamic_cast<MoreauYosidaPenalty<Real>&>(obj);
    ROL::Ptr<StepState<Real> > state = Step<Real>::getState();
    state->SPiter = subproblemIter_;
    state->descentVec->set(s);
    // Update iterate and Lagrange multiplier
    x.plus(s);
    l.set(*l_);
    // Update objective and constraint
    algo_state.iter++;
    con.update(x,true,algo_state.iter);
    myPen.update(x,true,algo_state.iter);
    // Update state
    updateState(x,l,obj,con,bnd,algo_state);
    // Update multipliers
    if (updatePenalty_) {
      state->searchSize *= tau_;
    }
    myPen.updateMultipliers(state->searchSize,x);
    algo_state.nfval += myPen.getNumberFunctionEvaluations() + ((algo_->getState())->nfval);
    algo_state.ngrad += myPen.getNumberGradientEvaluations() + ((algo_->getState())->ngrad);
    algo_state.ncval += (algo_->getState())->ncval;
    algo_state.snorm = s.norm();
    algo_state.iterateVec->set(x);
    algo_state.lagmultVec->set(l);
  }

  void update( Vector<Real> &x, const Vector<Real> &s,
               Objective<Real> &obj, BoundConstraint<Real> &bnd,
               AlgorithmState<Real> &algo_state ) {
    MoreauYosidaPenalty<Real> &myPen
      = dynamic_cast<MoreauYosidaPenalty<Real>&>(obj);
    ROL::Ptr<StepState<Real> > state = Step<Real>::getState();
    state->descentVec->set(s);
    // Update iterate and Lagrange multiplier
    x.plus(s);
    // Update objective and constraint
    algo_state.iter++;
    myPen.update(x,true,algo_state.iter);
    // Update state
    updateState(x,obj,bnd,algo_state);
    // Update multipliers
    if (updatePenalty_) {
      state->searchSize *= tau_;
    }
    myPen.updateMultipliers(state->searchSize,x);
    algo_state.nfval += myPen.getNumberFunctionEvaluations() + ((algo_->getState())->nfval);
    algo_state.ngrad += myPen.getNumberGradientEvaluations() + ((algo_->getState())->ngrad);
    algo_state.snorm = s.norm();
    algo_state.iterateVec->set(x);
  }

  std::string printHeader( void ) const {
    std::stringstream hist;
    hist << "  ";
    hist << std::setw(6)  << std::left << "iter";
    hist << std::setw(15) << std::left << "fval";
    if (hasEquality_) {
      hist << std::setw(15) << std::left << "cnorm";
    }
    hist << std::setw(15) << std::left << "gnorm";
    hist << std::setw(15) << std::left << "ifeas";
    hist << std::setw(15) << std::left << "snorm";
    hist << std::setw(10) << std::left << "penalty";
    hist << std::setw(8) << std::left << "#fval";
    hist << std::setw(8) << std::left << "#grad";
    if (hasEquality_) {
      hist << std::setw(8) << std::left << "#cval";
    }
    hist << std::setw(8) << std::left << "subIter";
    hist << "\n";
    return hist.str();
  }

  std::string printName( void ) const {
    std::stringstream hist;
    hist << "\n" << " Moreau-Yosida Penalty solver";
    hist << "\n";
    return hist.str();
  }

  std::string print( AlgorithmState<Real> &algo_state, bool pHeader = false ) const {
    std::stringstream hist;
    hist << std::scientific << std::setprecision(6);
    if ( algo_state.iter == 0 ) {
      hist << printName();
    }
    if ( pHeader ) {
      hist << printHeader();
    }
    if ( algo_state.iter == 0 ) {
      hist << "  ";
      hist << std::setw(6)  << std::left << algo_state.iter;
      hist << std::setw(15) << std::left << algo_state.value;
      if (hasEquality_) {
        hist << std::setw(15) << std::left << algo_state.cnorm;
      }
      hist << std::setw(15) << std::left << gLnorm_;
      hist << std::setw(15) << std::left << compViolation_;
      hist << std::setw(15) << std::left << " ";
      hist << std::scientific << std::setprecision(2);
      hist << std::setw(10) << std::left << Step<Real>::getStepState()->searchSize;
      hist << "\n";
    }
    else {
      hist << "  ";
      hist << std::setw(6)  << std::left << algo_state.iter;
      hist << std::setw(15) << std::left << algo_state.value;
      if (hasEquality_) {
        hist << std::setw(15) << std::left << algo_state.cnorm;
      }
      hist << std::setw(15) << std::left << gLnorm_;
      hist << std::setw(15) << std::left << compViolation_;
      hist << std::setw(15) << std::left << algo_state.snorm;
      hist << std::scientific << std::setprecision(2);
      hist << std::setw(10) << std::left << Step<Real>::getStepState()->searchSize;
      hist << std::scientific << std::setprecision(6);
      hist << std::setw(8) << std::left << algo_state.nfval;
      hist << std::setw(8) << std::left << algo_state.ngrad;
      if (hasEquality_) {
        hist << std::setw(8) << std::left << algo_state.ncval;
      }
      hist << std::setw(8) << std::left << subproblemIter_;
      hist << "\n";
    }
    return hist.str();
  }

}; // class MoreauYosidaPenaltyStep

} // namespace ROL

#endif