ROL_DogLeg_U.hpp

Go to the documentation of this file.

// @HEADER
// ************************************************************************
//
//               Rapid Optimization Library (ROL) Package
//                 Copyright (2014) Sandia Corporation
//
// Under terms of Contract DE-AC04-94AL85000, there is a non-exclusive
// license for use of this work by or on behalf of the U.S. Government.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// 3. Neither the name of the Corporation nor the names of the
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// Questions? Contact lead developers:
//              Drew Kouri   (dpkouri@sandia.gov) and
//              Denis Ridzal (dridzal@sandia.gov)
//
// ************************************************************************
// @HEADER

#ifndef ROL_DOGLEG_U_H
#define ROL_DOGLEG_U_H

#include "ROL_TrustRegion_U.hpp"
#include "ROL_Types.hpp"

namespace ROL {

template<class Real>
class DogLeg_U : public TrustRegion_U<Real> {
private:

  Ptr<Vector<Real>> primal_, dual_;

public:

  // Constructor
  DogLeg_U() {}

  void initialize(const Vector<Real> &x, const Vector<Real> &g) {
    primal_ = x.clone();
    dual_   = g.clone();
  }

  void solve( Vector<Real>             &s,
              Real                     &snorm,
              Real                     &pRed,
              int                      &iflag,
              int                      &iter,
              const Real                del,
              TrustRegionModel_U<Real> &model ) {
    Real tol = std::sqrt(ROL_EPSILON<Real>());
    const Real zero(0), half(0.5), one(1), two(2);
    iter = 0;
    // Set s to be the gradient
    s.set(model.getGradient()->dual());
    // Compute (quasi-)Newton step
    model.invHessVec(*primal_,*model.getGradient(),s,tol);
    Real sNnorm  = primal_->norm();
    Real gsN     = -primal_->dot(s);
    // Check if (quasi-)Newton step is feasible
    if ( gsN >= zero ) {
      // Use the Cauchy point
      model.hessVec(*dual_,s,s,tol);
      Real gnorm  = s.norm();
      Real gnorm2 = gnorm*gnorm;
      //Real gBg    = dual_->dot(s.dual());
      Real gBg    = dual_->apply(s);
      Real alpha  = gnorm2/gBg;
      if ( alpha*gnorm >= del || gBg <= zero ) {
        alpha = del/gnorm;
      }
      s.scale(-alpha);
      snorm = alpha*gnorm;
      iflag = 2;
      pRed  = alpha*(gnorm2 - half*alpha*gBg);
    }
    else {
      // Approximately solve trust region subproblem using double dogleg curve
      if (sNnorm <= del) { // Use the (quasi-)Newton step
        s.set(*primal_);
        s.scale(-one);
        snorm = sNnorm;
        pRed  = -half*gsN;
        iflag = 0;
      }
      else { // The (quasi-)Newton step is outside of trust region
        model.hessVec(*dual_,s,s,tol);
        Real alpha  = zero;
        Real beta   = zero;
        Real gnorm  = s.norm();
        Real gnorm2 = gnorm*gnorm;
        //Real gBg    = dual_->dot(s.dual());
        Real gBg    = dual_->apply(s);
        Real gamma  = gnorm2/gBg;
        if ( gamma*gnorm >= del || gBg <= zero ) {
          // Use Cauchy point
          alpha = zero;
          beta  = del/gnorm;
          s.scale(-beta);
          snorm = del;
          iflag = 2;
        }
        else {
          // Use a convex combination of Cauchy point and (quasi-)Newton step
          Real a = sNnorm*sNnorm + two*gamma*gsN + gamma*gamma*gnorm2;
          Real b = -gamma*gsN - gamma*gamma*gnorm2;
          Real c = gamma*gamma*gnorm2 - del*del;
          alpha  = (-b + std::sqrt(b*b - a*c))/a;
          beta   = gamma*(one-alpha);
          s.scale(-beta);
          s.axpy(-alpha,*primal_);
          snorm = del;
          iflag = 1;
        }
        pRed  = (alpha*(half*alpha-one)*gsN - half*beta*beta*gBg + beta*(one-alpha)*gnorm2);
      }
    }
  }
};

} // namespace ROL

#endif