ROL_Bundle_AS.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_BUNDLE_AS_H
#define ROL_BUNDLE_AS_H

#include "ROL_Bundle.hpp"

namespace ROL {

template<class Real>
class Bundle_AS : public Bundle<Real> {
/***********************************************************************************************/
/***************** BUNDLE STORAGE **************************************************************/
/***********************************************************************************************/
private:

  ROL::Ptr<Vector<Real> > tG_;
  ROL::Ptr<Vector<Real> > eG_;
  ROL::Ptr<Vector<Real> > yG_;
  ROL::Ptr<Vector<Real> > gx_;
  ROL::Ptr<Vector<Real> > ge_;

  std::set<unsigned> workingSet_;
  std::set<unsigned> nworkingSet_;

  bool isInitialized_;
  
/***********************************************************************************************/
/***************** BUNDLE MODIFICATION AND ACCESS ROUTINES *************************************/
/***********************************************************************************************/
public:
  Bundle_AS(const unsigned maxSize = 10,
            const Real coeff = 0.0,
            const Real omega = 2.0,
            const unsigned remSize = 2) 
    : Bundle<Real>(maxSize,coeff,omega,remSize), isInitialized_(false) {}

  void initialize(const Vector<Real> &g) {
    Bundle<Real>::initialize(g);
    if ( !isInitialized_ ) {
      tG_ = g.clone();
      yG_ = g.clone();
      eG_ = g.clone();
      gx_ = g.clone();
      ge_ = g.clone();
      isInitialized_ = true;
    }
  }

  unsigned solveDual(const Real t, const unsigned maxit = 1000, const Real tol = 1.e-8) {
    unsigned iter = 0;
    if (Bundle<Real>::size() == 1) {
      iter = Bundle<Real>::solveDual_dim1(t,maxit,tol);
    }
    else if (Bundle<Real>::size() == 2) {
      iter = Bundle<Real>::solveDual_dim2(t,maxit,tol);
    }
    else {
      iter = solveDual_arbitrary(t,maxit,tol);
    }
    return iter;
  }

/***********************************************************************************************/
/***************** DUAL CUTTING PLANE PROBLEM ROUTINES *****************************************/
/***********************************************************************************************/
private:
  void initializeDualSolver(void) {
    Real sum(0), err(0), tmp(0), y(0), zero(0);
    for (unsigned i = 0; i < Bundle<Real>::size(); ++i) {
      // Compute sum of dualVariables_ using Kahan's compensated sum
      //sum += Bundle<Real>::getDualVariable(i);
      y   = Bundle<Real>::getDualVariable(i) - err;
      tmp = sum + y;
      err = (tmp - sum) - y;
      sum = tmp;
    }
    for (unsigned i = 0; i < Bundle<Real>::size(); ++i) {
      tmp = Bundle<Real>::getDualVariable(i)/sum;
      Bundle<Real>::setDualVariable(i,tmp);
    }
    nworkingSet_.clear();
    workingSet_.clear();
    for (unsigned i = 0; i < Bundle<Real>::size(); ++i) {
      if ( Bundle<Real>::getDualVariable(i) == zero ) {
        workingSet_.insert(i);
      }
      else {
        nworkingSet_.insert(i);
      }
    }
  }

  void computeLagMult(std::vector<Real> &lam, const Real mu, const std::vector<Real> &g) const {
    Real zero(0);
    unsigned n = workingSet_.size();
    if ( n > 0 ) {
      lam.resize(n,zero);
      typename std::set<unsigned>::iterator it = workingSet_.begin();
      for (unsigned i = 0; i < n; ++i) {
        lam[i] = g[*it] - mu; it++;
      }
    }
    else {
      lam.clear();
    }
  }
 
  bool isNonnegative(unsigned &ind, const std::vector<Real> &x) const {
    bool flag = true;
    unsigned n = workingSet_.size(); ind = Bundle<Real>::size();
    if ( n > 0 ) {
      Real min = ROL_OVERFLOW<Real>();
      typename std::set<unsigned>::iterator it = workingSet_.begin();
      for (unsigned i = 0; i < n; ++i) {
        if ( x[i] < min ) {
          ind = *it;
          min = x[i];
        }
        it++;
      }
      flag = ((min < -ROL_EPSILON<Real>()) ? false : true);
    }
    return flag;
  }

  Real computeStepSize(unsigned &ind, const std::vector<Real> &x, const std::vector<Real> &p) const {
    Real alpha(1), tmp(0), zero(0); ind = Bundle<Real>::size();
    typename std::set<unsigned>::iterator it;
    for (it = nworkingSet_.begin(); it != nworkingSet_.end(); it++) {
      if ( p[*it] < -ROL_EPSILON<Real>() ) {
        tmp = -x[*it]/p[*it];
        if ( alpha >= tmp ) {
          alpha = tmp;
          ind = *it;
        }
      }
    }
    return std::max(zero,alpha);
  }

  unsigned solveEQPsubproblem(std::vector<Real> &s, Real &mu,
                        const std::vector<Real> &g, const Real tol) const {
    // Build reduced QP information
    Real zero(0);
    unsigned n = nworkingSet_.size(), cnt = 0;
    mu = zero;
    s.assign(Bundle<Real>::size(),zero);
    if ( n > 0 ) {
      std::vector<Real> gk(n,zero);
      typename std::set<unsigned>::iterator it = nworkingSet_.begin();
      for (unsigned i = 0; i < n; ++i) {
        gk[i] = g[*it]; it++;
      }
      std::vector<Real> sk(n,zero);
      cnt = projectedCG(sk,mu,gk,tol);
      it  = nworkingSet_.begin();
      for (unsigned i = 0; i < n; ++i) {
        s[*it] = sk[i]; it++;
      }
    }
    return cnt;
  }

  void applyPreconditioner(std::vector<Real> &Px, const std::vector<Real> &x) const {
    Real zero(0);
    int type = 0;
    std::vector<Real> tmp(Px.size(),zero);
    switch (type) {
      case 0: applyPreconditioner_Identity(tmp,x); break;
      case 1: applyPreconditioner_Jacobi(tmp,x);   break;
      case 2: applyPreconditioner_SymGS(tmp,x);    break;
    }
    applyPreconditioner_Identity(Px,tmp);
  }

  void applyG(std::vector<Real> &Gx, const std::vector<Real> &x) const {
    int type = 0;
    switch (type) {
      case 0: applyG_Identity(Gx,x); break;
      case 1: applyG_Jacobi(Gx,x);   break;
      case 2: applyG_SymGS(Gx,x);    break;
    }
  }

  void applyPreconditioner_Identity(std::vector<Real> &Px, const std::vector<Real> &x) const {
    unsigned dim = nworkingSet_.size();
    Real sum(0), err(0), tmp(0), y(0);
    for (unsigned i = 0; i < dim; ++i) {
      // Compute sum of x using Kahan's compensated sum
      //sum += x[i];
      y   = x[i] - err;
      tmp = sum + y;
      err = (tmp - sum) - y;
      sum = tmp;
    }
    sum /= (Real)dim;
    for (unsigned i = 0; i < dim; ++i) {
      Px[i] = x[i] - sum;
    }
  }

  void applyG_Identity(std::vector<Real> &Gx, const std::vector<Real> &x) const {
    Gx.assign(x.begin(),x.end());
  }

  void applyPreconditioner_Jacobi(std::vector<Real> &Px, const std::vector<Real> &x) const {
    unsigned dim = nworkingSet_.size();
    Real eHe(0), sum(0), one(1), zero(0);
    Real errX(0), tmpX(0), yX(0), errE(0), tmpE(0), yE(0);
    std::vector<Real> gg(dim,zero);
    typename std::set<unsigned>::iterator it = nworkingSet_.begin(); 
    for (unsigned i = 0; i < dim; ++i) {
      gg[i] = one/std::abs(Bundle<Real>::GiGj(*it,*it)); it++;
      // Compute sum of inv(D)x using Kahan's aggregated sum
      //sum += x[i]*gg[i];
      yX   = x[i]*gg[i] - errX;
      tmpX = sum + yX;
      errX = (tmpX - sum) - yX;
      sum  = tmpX;
      // Compute sum of inv(D)e using Kahan's aggregated sum
      //eHe += gg[i];
      yE   = gg[i] - errE;
      tmpE = eHe + yE;
      errE = (tmpE - eHe) - yE;
      eHe  = tmpE;
    }
    sum /= eHe;
    for (unsigned i = 0; i < dim; ++i) {
      Px[i] = (x[i]-sum)*gg[i];
    }
  }

  void applyG_Jacobi(std::vector<Real> &Gx, const std::vector<Real> &x) const {
    unsigned dim = nworkingSet_.size();
    typename std::set<unsigned>::iterator it = nworkingSet_.begin();
    for (unsigned i = 0; i < dim; ++i) {
      Gx[i] = std::abs(Bundle<Real>::GiGj(*it,*it))*x[i]; it++;
    }
  }

  void applyPreconditioner_SymGS(std::vector<Real> &Px, const std::vector<Real> &x) const {
    int dim = nworkingSet_.size();
    //unsigned cnt = 0;
    gx_->zero(); ge_->zero();
    Real eHx(0), eHe(0), one(1);
    // Forward substitution
    std::vector<Real> x1(dim,0), e1(dim,0),gg(dim,0);
    typename std::set<unsigned>::iterator it, jt;
    it = nworkingSet_.begin(); 
    for (int i = 0; i < dim; ++i) {
      gx_->zero(); ge_->zero(); jt = nworkingSet_.begin();
      for (int j = 0; j < i; ++j) {
        Bundle<Real>::addGi(*jt,x1[j],*gx_);
        Bundle<Real>::addGi(*jt,e1[j],*ge_);
        jt++;
      }
      gg[i] = Bundle<Real>::GiGj(*it,*it);
      x1[i] = (x[i] - Bundle<Real>::dotGi(*it,*gx_))/gg[i];
      e1[i] = (one  - Bundle<Real>::dotGi(*it,*ge_))/gg[i];
      it++;
    }
    // Apply diagonal
    for (int i = 0; i < dim; ++i) {
      x1[i] *= gg[i];
      e1[i] *= gg[i];
    }
    // Back substitution
    std::vector<Real> Hx(dim,0), He(dim,0); it = nworkingSet_.end();
    for (int i = dim-1; i >= 0; --i) {
      it--;
      gx_->zero(); ge_->zero(); jt = nworkingSet_.end();
      for (int j = dim-1; j >= i+1; --j) {
        jt--;
        Bundle<Real>::addGi(*jt,Hx[j],*gx_);
        Bundle<Real>::addGi(*jt,He[j],*ge_);
      }
      Hx[i] = (x1[i] - Bundle<Real>::dotGi(*it,*gx_))/gg[i];
      He[i] = (e1[i] - Bundle<Real>::dotGi(*it,*ge_))/gg[i];
      // Compute sums
      eHx += Hx[i];
      eHe += He[i];
    }
    // Accumulate preconditioned vector
    for (int i = 0; i < dim; ++i) {
      Px[i] = Hx[i] - (eHx/eHe)*He[i];
    }
  }

  void applyG_SymGS(std::vector<Real> &Gx, const std::vector<Real> &x) const {
    unsigned dim = nworkingSet_.size();
    typename std::set<unsigned>::iterator it = nworkingSet_.begin();
    for (unsigned i = 0; i < dim; ++i) {
      Gx[i] = std::abs(Bundle<Real>::GiGj(*it,*it))*x[i]; it++;
    }
  }

  void computeResidualUpdate(std::vector<Real> &r, std::vector<Real> &g) const {
    unsigned n = g.size();
    std::vector<Real> Gg(n,0);
    Real y(0), ytmp(0), yprt(0), yerr(0);
    applyPreconditioner(g,r);
    applyG(Gg,g);
    // Compute multiplier using Kahan's compensated sum
    for (unsigned i = 0; i < n; ++i) {
      //y += (r[i] - Gg[i]);
      yprt = (r[i] - Gg[i]) - yerr;
      ytmp = y + yprt;
      yerr = (ytmp - y) - yprt;
      y    = ytmp;
    }
    y /= (Real)n;
    for (unsigned i = 0; i < n; ++i) {
      r[i] -= y;
    }
    applyPreconditioner(g,r);
  }

  void applyFullMatrix(std::vector<Real> &Hx, const std::vector<Real> &x) const {
    Real one(1);
    gx_->zero(); eG_->zero();
    for (unsigned i = 0; i < Bundle<Real>::size(); ++i) {
      // Compute Gx using Kahan's compensated sum
      //gx_->axpy(x[i],Bundle<Real>::subgradient(i));
      yG_->set(Bundle<Real>::subgradient(i)); yG_->scale(x[i]); yG_->axpy(-one,*eG_);
      tG_->set(*gx_); tG_->plus(*yG_);
      eG_->set(*tG_); eG_->axpy(-one,*gx_); eG_->axpy(-one,*yG_);
      gx_->set(*tG_);
    }
    for (unsigned i = 0; i < Bundle<Real>::size(); ++i) {
      // Compute < g_i, Gx >
      Hx[i] = Bundle<Real>::dotGi(i,*gx_);
    }
  }

  void applyMatrix(std::vector<Real> &Hx, const std::vector<Real> &x) const {
    Real one(1);
    gx_->zero(); eG_->zero();
    unsigned n = nworkingSet_.size();
    typename std::set<unsigned>::iterator it = nworkingSet_.begin(); 
    for (unsigned i = 0; i < n; ++i) {
      // Compute Gx using Kahan's compensated sum
      //gx_->axpy(x[i],Bundle<Real>::subgradient(*it));
      yG_->set(Bundle<Real>::subgradient(*it)); yG_->scale(x[i]); yG_->axpy(-one,*eG_);
      tG_->set(*gx_); tG_->plus(*yG_);
      eG_->set(*tG_); eG_->axpy(-one,*gx_); eG_->axpy(-one,*yG_);
      gx_->set(*tG_);
      it++;
    }
    it = nworkingSet_.begin();
    for (unsigned i = 0; i < n; ++i) {
      // Compute < g_i, Gx >
      Hx[i] = Bundle<Real>::dotGi(*it,*gx_); it++;
    }
  }

  unsigned projectedCG(std::vector<Real> &x, Real &mu, const std::vector<Real> &b, const Real tol) const {
    Real one(1), zero(0);
    unsigned n = nworkingSet_.size();
    std::vector<Real> r(n,0), r0(n,0), g(n,0), d(n,0), Ad(n,0);
    // Compute residual Hx + g = g with x = 0
    x.assign(n,0);
    scale(r,one,b);
    r0.assign(r.begin(),r.end());
    // Precondition residual
    computeResidualUpdate(r,g);
    Real rg = dot(r,g), rg0(0);
    // Get search direction
    scale(d,-one,g);
    Real alpha(0), kappa(0), beta(0), TOL(1.e-2);
    Real CGtol = std::min(tol,TOL*rg);
    unsigned cnt = 0;
    while (rg > CGtol && cnt < 2*n+1) {
      applyMatrix(Ad,d);
      kappa = dot(d,Ad);
      alpha = rg/kappa;
      axpy(alpha,d,x);
      axpy(alpha,Ad,r);
      axpy(alpha,Ad,r0);
      computeResidualUpdate(r,g);
      rg0 = rg;
      rg  = dot(r,g);
      beta = rg/rg0;
      scale(d,beta);
      axpy(-one,g,d);
      cnt++;
    }
    // Compute multiplier for equality constraint using Kahan's compensated sum
    mu = zero;
    Real err(0), tmp(0), y(0);
    for (unsigned i = 0; i < n; ++i) {
      //mu += r0[i];
      y   = r0[i] - err;
      tmp = mu + y;
      err = (tmp - mu) - y;
      mu  = tmp;
    }
    mu /= static_cast<Real>(n);
    // Return iteration count
    return cnt;
  }

  Real dot(const std::vector<Real> &x, const std::vector<Real> &y) const {
    // Compute dot product of two vectors using Kahan's compensated sum
    Real val(0), err(0), tmp(0), y0(0);
    unsigned n = std::min(x.size(),y.size());
    for (unsigned i = 0; i < n; ++i) {
      //val += x[i]*y[i];
      y0  = x[i]*y[i] - err;
      tmp = val + y0;
      err = (tmp - val) - y0;
      val = tmp;
    }
    return val;
  }

  Real norm(const std::vector<Real> &x) const {
    return std::sqrt(dot(x,x));
  }

  void axpy(const Real a, const std::vector<Real> &x, std::vector<Real> &y) const {
    unsigned n = std::min(y.size(),x.size());
    for (unsigned i = 0; i < n; ++i) {
      y[i] += a*x[i];
    }
  }

  void scale(std::vector<Real> &x, const Real a) const {
    for (unsigned i = 0; i < x.size(); ++i) {
      x[i] *= a;
    }
  }

  void scale(std::vector<Real> &x, const Real a, const std::vector<Real> &y) const {
    unsigned n = std::min(x.size(),y.size());
    for (unsigned i = 0; i < n; ++i) {
      x[i] = a*y[i];
    }
  }

  unsigned solveDual_arbitrary(const Real t, const unsigned maxit = 1000, const Real tol = 1.e-8) {
    initializeDualSolver();
    bool nonneg = false;
    unsigned ind = 0, i = 0, CGiter = 0;
    Real snorm(0), alpha(0), mu(0), one(1), zero(0);
    std::vector<Real> s(Bundle<Real>::size(),0), Hs(Bundle<Real>::size(),0);
    std::vector<Real> g(Bundle<Real>::size(),0), lam(Bundle<Real>::size()+1,0);
    std::vector<Real> dualVariables(Bundle<Real>::size(),0);
    for (unsigned j = 0; j < Bundle<Real>::size(); ++j) {
      dualVariables[j] = Bundle<Real>::getDualVariable(j);
    }
    //Real val = Bundle<Real>::evaluateObjective(g,dualVariables,t);
    Bundle<Real>::evaluateObjective(g,dualVariables,t);
    for (i = 0; i < maxit; ++i) {
      CGiter += solveEQPsubproblem(s,mu,g,tol);
      snorm = norm(s);
      if ( snorm < ROL_EPSILON<Real>() ) {
        computeLagMult(lam,mu,g);
        nonneg = isNonnegative(ind,lam);
        if ( nonneg ) {
          break;
        }
        else {
          alpha = one;
          if ( ind < Bundle<Real>::size() ) {
            workingSet_.erase(ind);
            nworkingSet_.insert(ind);
          }
        }
      }
      else {
        alpha = computeStepSize(ind,dualVariables,s);
        if ( alpha > zero ) {
          axpy(alpha,s,dualVariables);
          applyFullMatrix(Hs,s);
          axpy(alpha,Hs,g);
        }
        if (ind < Bundle<Real>::size()) {
          workingSet_.insert(ind);
          nworkingSet_.erase(ind);
        }
      }
      //std::cout << "iter = " << i << "  snorm = " << snorm << "  alpha = " << alpha << "\n";
    }
    //Real crit = computeCriticality(g,dualVariables);
    //std::cout << "Criticality Measure: " << crit << "\n";
    //std::cout << "dim = " << Bundle<Real>::size() << "  iter = " << i << "   CGiter = " << CGiter << "  CONVERGED!\n";
    for (unsigned j = 0; j < Bundle<Real>::size(); ++j) {
      Bundle<Real>::setDualVariable(j,dualVariables[j]);
    }
    return i;
  }

  /************************************************************************/
  /********************** PROJECTION ONTO FEASIBLE SET ********************/
  /************************************************************************/
  void project(std::vector<Real> &x, const std::vector<Real> &v) const {
    std::vector<Real> vsort(Bundle<Real>::size(),0);
    vsort.assign(v.begin(),v.end());
    std::sort(vsort.begin(),vsort.end());
    Real sum(-1), lam(0), zero(0), one(1);
    for (unsigned i = Bundle<Real>::size()-1; i > 0; i--) {
      sum += vsort[i];
      if ( sum >= static_cast<Real>(Bundle<Real>::size()-i)*vsort[i-1] ) {
        lam = sum/static_cast<Real>(Bundle<Real>::size()-i);
        break;
      }
    }
    if (lam == zero) {
      lam = (sum+vsort[0])/static_cast<Real>(Bundle<Real>::size());
    }
    for (unsigned i = 0; i < Bundle<Real>::size(); ++i) {
      x[i] = std::max(zero,v[i] - lam);
    }
  }

  Real computeCriticality(const std::vector<Real> &g, const std::vector<Real> &sol) {
    Real zero(0), one(1);
    std::vector<Real> x(Bundle<Real>::size(),0), Px(Bundle<Real>::size(),0);
    axpy(one,sol,x);
    axpy(-one,g,x);
    project(Px,x);
    scale(x,zero);
    axpy(one,sol,x);
    axpy(-one,Px,x);
    return norm(x);
  }
}; // class Bundle_AS

} // namespace ROL

#endif