ROL_ProgressiveHedging.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_PROGRESSIVEHEDGING_H
#define ROL_PROGRESSIVEHEDGING_H
 
#include "ROL_OptimizationSolver.hpp"
#include "ROL_Solver.hpp"
#include "ROL_PH_Objective.hpp"
#include "ROL_PH_StatusTest.hpp"
#include "ROL_RiskVector.hpp"
#include "ROL_RiskBoundConstraint.hpp"
#include "ROL_RiskLessConstraint.hpp"

 
 
namespace ROL {
 
template <class Real>
class ProgressiveHedging {
private:
  const Ptr<Problem<Real>> input_;
  const Ptr<SampleGenerator<Real>> sampler_;
  ParameterList parlist_;
  bool usePresolve_;
  bool useInexact_;
  Real penaltyParam_;
  Real maxPen_;
  Real update_;
  int  freq_;
  Real ztol_;
  int maxit_;
  bool print_;
 
  bool hasStat_;
  Ptr<PH_Objective<Real>>           ph_objective_;
  Ptr<Vector<Real>>                 ph_vector_;
  Ptr<BoundConstraint<Real>>        ph_bound_;
  Ptr<Constraint<Real>>             ph_constraint_;
  Ptr<Problem<Real>>                ph_problem_;
  Ptr<Solver<Real>>                 ph_solver_;
  Ptr<PH_StatusTest<Real>>          ph_status_;
  Ptr<Vector<Real>> z_psum_, z_gsum_;
  std::vector<Ptr<Vector<Real>>> wvec_;
 
  void presolve(void) {
    Solver<Real> solver(input_,parlist_);
    for (int j = 0; j < sampler_->numMySamples(); ++j) {
      input_->getObjective()->setParameter(sampler_->getMyPoint(j));
      if (input_->getConstraint() != nullPtr) {
        input_->getConstraint()->setParameter(sampler_->getMyPoint(j));
      }
      solver.solve();
      z_psum_->axpy(sampler_->getMyWeight(j),*input_->getPrimalOptimizationVector());
      solver.reset();
    }
    // Aggregation
    z_gsum_->zero();
    sampler_->sumAll(*z_psum_,*z_gsum_);
  }
 
public:
  ProgressiveHedging(const Ptr<Problem<Real>> &input,
                     const Ptr<SampleGenerator<Real>> &sampler,
                     ParameterList &parlist)
    : input_(input), sampler_(sampler), parlist_(parlist), hasStat_(false) {
    // Get algorithmic parameters
    usePresolve_  = parlist.sublist("SOL").sublist("Progressive Hedging").get("Use Presolve",true);
    useInexact_   = parlist.sublist("SOL").sublist("Progressive Hedging").get("Use Inexact Solve",true);
    penaltyParam_ = parlist.sublist("SOL").sublist("Progressive Hedging").get("Initial Penalty Parameter",10.0);
    maxPen_       = parlist.sublist("SOL").sublist("Progressive Hedging").get("Maximum Penalty Parameter",-1.0);
    update_       = parlist.sublist("SOL").sublist("Progressive Hedging").get("Penalty Update Scale",10.0);
    freq_         = parlist.sublist("SOL").sublist("Progressive Hedging").get("Penalty Update Frequency",0);
    ztol_         = parlist.sublist("SOL").sublist("Progressive Hedging").get("Nonanticipativity Constraint Tolerance",1e-4);
    maxit_        = parlist.sublist("SOL").sublist("Progressive Hedging").get("Iteration Limit",100);
    print_        = parlist.sublist("SOL").sublist("Progressive Hedging").get("Print Subproblem Solve History",false);
    maxPen_       = (maxPen_ <= static_cast<Real>(0) ? ROL_INF<Real>() : maxPen_);
    penaltyParam_ = std::min(penaltyParam_,maxPen_);
    // Create progressive hedging vector
    ParameterList olist; olist.sublist("SOL") = parlist.sublist("SOL").sublist("Objective");
    std::string type = olist.sublist("SOL").get("Type","Risk Neutral");
    std::string prob = olist.sublist("SOL").sublist("Probability").get("Name","bPOE");
    hasStat_ = ((type=="Risk Averse") ||
                (type=="Deviation")   ||
                (type=="Probability" && prob=="bPOE"));
    Ptr<ParameterList> parlistptr = makePtrFromRef<ParameterList>(olist);
    if (hasStat_) {
      ph_vector_  = makePtr<RiskVector<Real>>(parlistptr,
                                              input_->getPrimalOptimizationVector());
    }
    else {
      ph_vector_  = input_->getPrimalOptimizationVector();
    }
    // Create progressive hedging objective function
    ph_objective_ = makePtr<PH_Objective<Real>>(input_->getObjective(),
                                                ph_vector_,
                                                penaltyParam_,
                                                olist);
    // Create progressive hedging bound constraint
    if (hasStat_) {
      ph_bound_   = makePtr<RiskBoundConstraint<Real>>(parlistptr,
                                                       input_->getBoundConstraint());
    }
    else {
      ph_bound_   = input_->getBoundConstraint();
    }
    // Create progressive hedging constraint
    ph_constraint_ = nullPtr;
    if (input_->getConstraint() != nullPtr) {
      if (hasStat_) {
        ph_constraint_ = makePtr<RiskLessConstraint<Real>>(input_->getConstraint());
      }
      else {
        ph_constraint_ = input_->getConstraint();
      }
    }
    // Build progressive hedging subproblems
    ph_problem_ = makePtr<Problem<Real>>(ph_objective_, ph_vector_);
    if (ph_bound_ != nullPtr) {
      if (ph_bound_->isActivated()) {
        ph_problem_->addBoundConstraint(ph_bound_);
      }
    }
    if (ph_constraint_ != nullPtr) {
      ph_problem_->addConstraint("PH Constraint",ph_constraint_,
                                 input_->getMultiplierVector());
    }
    // Build progressive hedging subproblem solver
    ph_solver_    = makePtr<Solver<Real>>(ph_problem_, parlist);
    // Build progressive hedging status test for inexact solves
    if (useInexact_) {
      ph_status_  = makePtr<PH_StatusTest<Real>>(parlist,
                                                 *ph_vector_);
    }
    else {
      ph_status_  = nullPtr;
    }
    // Initialize vector storage
    z_psum_       = ph_problem_->getPrimalOptimizationVector()->clone();
    z_gsum_       = ph_problem_->getPrimalOptimizationVector()->clone();
    z_gsum_->set(*ph_problem_->getPrimalOptimizationVector());
    wvec_.resize(sampler_->numMySamples());
    for (int i = 0; i < sampler_->numMySamples(); ++i) {
      wvec_[i] = z_psum_->clone(); wvec_[i]->zero();
    }
    if (usePresolve_) {
      presolve();
    }
  }
 
  void check(std::ostream &outStream = std::cout, const int numSamples = 1) {
    int nsamp = std::min(sampler_->numMySamples(),numSamples);
    for (int i = 0; i < nsamp; ++i) {
      ph_objective_->setParameter(sampler_->getMyPoint(i));
      ph_objective_->setData(z_gsum_,wvec_[i],penaltyParam_);
      if (ph_constraint_ != nullPtr) {
        ph_constraint_->setParameter(sampler_->getMyPoint(i));
      }
      ph_problem_->check(true,outStream);
    }
  }
 
  void run(std::ostream &outStream = std::cout) {
    const Real zero(0);
    std::vector<Real> vec_p(2), vec_g(2);
    Real znorm(ROL_INF<Real>()), zdotz(0);
    int iter(0), tspiter(0), flag = 1;
    bool converged = true;
    // Output
    outStream << std::scientific << std::setprecision(6);
    outStream << std::endl << "Progressive Hedging"
              << std::endl << "  "
              << std::setw(8)  << std::left << "iter"
              << std::setw(15) << std::left << "||z-Ez||"
              << std::setw(15) << std::left << "penalty"
              << std::setw(10) << std::left << "subiter"
              << std::setw(8)  << std::left << "success"
              << std::endl;
    for (iter = 0; iter < maxit_; ++iter) {
      z_psum_->zero(); vec_p[0] = zero; vec_p[1] = zero;
      ph_problem_->getPrimalOptimizationVector()->set(*z_gsum_);
      // Solve concurrent optimization problems
      for (int j = 0; j < sampler_->numMySamples(); ++j) {
        ph_objective_->setData(z_gsum_,wvec_[j],penaltyParam_);
        ph_problem_->getObjective()->setParameter(sampler_->getMyPoint(j));
        if (useInexact_) {
          ph_status_->setData(iter,z_gsum_);
        }
        if (ph_problem_->getConstraint() != nullPtr) {
          ph_problem_->getConstraint()->setParameter(sampler_->getMyPoint(j));
        }
        if (print_) {
          ph_solver_->solve(outStream,ph_status_,true);
        }
        else {
          ph_solver_->solve(ph_status_,true);
        }
        wvec_[j]->axpy(penaltyParam_,*ph_problem_->getPrimalOptimizationVector());
        vec_p[0] += sampler_->getMyWeight(j)
                   *ph_problem_->getPrimalOptimizationVector()->dot(
                   *ph_problem_->getPrimalOptimizationVector());
        vec_p[1] += static_cast<Real>(ph_solver_->getAlgorithmState()->iter);
        z_psum_->axpy(sampler_->getMyWeight(j),*ph_problem_->getPrimalOptimizationVector());
        converged = (ph_solver_->getAlgorithmState()->statusFlag == EXITSTATUS_CONVERGED
                   ||ph_solver_->getAlgorithmState()->statusFlag == EXITSTATUS_USERDEFINED
                    ? converged : false);
        ph_solver_->reset();
      }
      // Aggregation
      z_gsum_->zero(); vec_g[0] = zero; vec_g[1] = zero;
      sampler_->sumAll(*z_psum_,*z_gsum_);
      sampler_->sumAll(&vec_p[0],&vec_g[0],2);
      // Multiplier Update
      for (int j = 0; j < sampler_->numMySamples(); ++j) {
        wvec_[j]->axpy(-penaltyParam_,*z_gsum_);
      }
      zdotz  = z_gsum_->dot(*z_gsum_);
      znorm  = std::sqrt(std::abs(vec_g[0] - zdotz));
      tspiter += static_cast<int>(vec_g[1]);
      // Output
      outStream << "  "
                << std::setw(8)  << std::left << iter
                << std::setw(15) << std::left << znorm
                << std::setw(15) << std::left << penaltyParam_
                << std::setw(10) << std::left << static_cast<int>(vec_g[1])
                << std::setw(8)  << std::left << converged
                << std::endl;
      // Check termination criterion
      if (znorm <= ztol_ && converged) {
        flag = 0;
        outStream << "Converged: Nonanticipativity constraint tolerance satisfied!" << std::endl;
        break;
      }
      converged = true;
      // Update penalty parameter
      if (freq_ > 0 && iter%freq_ == 0) {
        penaltyParam_ *= update_;
      }
      penaltyParam_ = std::min(penaltyParam_,maxPen_);
    }
    if (hasStat_) {
      input_->getPrimalOptimizationVector()->set(*dynamicPtrCast<RiskVector<Real>>(z_gsum_)->getVector());
    }
    else {
      input_->getPrimalOptimizationVector()->set(*z_gsum_);
    }
    // Output reason for termination
    if (iter >= maxit_ && flag != 0) {
      outStream << "Maximum number of iterations exceeded" << std::endl;
    }
    outStream << "Total number of subproblem iterations per sample: "
              << tspiter << " / " << sampler_->numGlobalSamples()
              << " ~ " << static_cast<int>(std::ceil(static_cast<Real>(tspiter)/static_cast<Real>(sampler_->numGlobalSamples())))
              << std::endl;
  }
 
}; // class ProgressiveHedging
 
} // namespace ROL
 
#endif