ROL_PrimalDualInteriorPointReducedResidual.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_PRIMALDUALINTERIORPOINTREDUCEDRESIDUAL_H
#define ROL_PRIMALDUALINTERIORPOINTREDUCEDRESIDUAL_H
 
#include "ROL_InteriorPointPenalty.hpp"
#include "ROL_Constraint.hpp"
 
#include <iostream>

 
namespace ROL { 
 
template<class Real> 
class PrimalDualInteriorPointResidual : public Constraint<Real> {
 
  typedef ROL::ParameterList     PL;
 
  typedef Vector<Real>               V;
  typedef PartitionedVector<Real>    PV;
  typedef Objective<Real>            OBJ;
  typedef Constraint<Real>   CON;
  typedef BoundConstraint<Real>      BND;
  typedef LinearOperator<Real>       LOP;
  typedef InteriorPointPenalty<Real> PENALTY;
 
  typedef Elementwise::ValueSet<Real>   ValueSet;
 
  typedef typename PV::size_type size_type;
 
private: 
 
  static const size_type OPT   = 0;
  static const size_type EQUAL = 1;
  static const size_type LOWER = 2;
  static const size_type UPPER = 3;
 
  ROL::Ptr<const V>   x_;            // Optimization vector
  ROL::Ptr<const V>   l_;            //  constraint multiplier
  ROL::Ptr<const V>   zl_;           // Lower bound multiplier
  ROL::Ptr<const V>   zu_;           // Upper bound multiplier
 
  ROL::Ptr<const V>   xl_;           // Lower bound
  ROL::Ptr<const V>   xu_;           // Upper bound 
 
  const ROL::Ptr<const V> maskL_;   
  const ROL::Ptr<const V> maskU_;
 
  ROL::Ptr<V> scratch_;
 
  const ROL::Ptr<PENALTY> penalty_;
  const ROL::Ptr<OBJ>     obj_;
  const ROL::Ptr<CON>     con_;
 
 
public:
 
  PrimalDualInteriorPointResidual( const ROL::Ptr<PENALTY> &penalty, 
                                   const ROL::Ptr<CON> &con,
                                   const V &x,
                                         ROL::Ptr<V> &scratch ) :
    penalty_(penalty), con_(con), scratch_(scratch) {
 
    obj_   = penalty_->getObjective();
    maskL_ = penalty_->getLowerMask();
    maskU_ = penalty_->getUpperMask();
 
    ROL::Ptr<BND> bnd = penalty_->getBoundConstraint();
    xl_ = bnd->getLowerBound();
    xu_ = bnd->getUpperBound();
 
 
    // Get access to the four components
    const PV &x_pv = dynamic_cast<const PV&>(x);
    
    x_  = x_pv.get(OPT);
    l_  = x_pv.get(EQUAL);
    zl_ = x_pv.get(LOWER);
    zu_ = x_pv.get(UPPER);   
 
  }
 
 
  void update( const Vector<Real> &x, bool flag = true, int iter = -1  ) {
 
    // Get access to the four components
    const PV &x_pv = dynamic_cast<const PV&>(x);
    
    x_  = x_pv.get(OPT);
    l_  = x_pv.get(EQUAL);
    zl_ = x_pv.get(LOWER);
    zu_ = x_pv.get(UPPER);  
 
    obj_->update(*x_,flag,iter);
    con_->update(*x_,flag,iter);
  }
 
 
  // Evaluate the gradient of the Lagrangian
  void value( V &c, const V &x, Real &tol ) {
    
 
    PV &c_pv = dynamic_cast<PV&>(c);
    const PV &x_pv = dynamic_cast<const PV&>(x); 
 
    x_  = x_pv.get(OPT);
    l_  = x_pv.get(EQUAL);
    zl_ = x_pv.get(LOWER);
    zu_ = x_pv.get(UPPER); 
 
    ROL::Ptr<V> cx  = c_pv.get(OPT);
    ROL::Ptr<V> cl  = c_pv.get(EQUAL);
   
    // TODO: Add check as to whether we really need to recompute these
    penalty_->gradient(*cx,*x_,tol);
    con_->applyAdjointJacobian(*scratch_,*l_,*x_,tol);
   
    // \f$ c_x = \nabla \phi_mu(x) + J^\ast \lambda \f$
    cx_->plus(*scratch_);      
    
    con_->value(*cl_,*x_,tol);
    
  }
 
  void applyJacobian( V &jv, const V &v, const V &x, Real &tol ) {
 
    
 
    PV &jv_pv = dynamic_cast<PV&>(jv);
    const PV &v_pv = dynamic_cast<const PV&>(v);
    const PV &x_pv = dynamic_cast<const PV&>(x); 
 
    // output vector components
    ROL::Ptr<V> jvx  = jv_pv.get(OPT);
    ROL::Ptr<V> jvl  = jv_pv.get(EQUAL);
 
    // input vector components
    ROL::Ptr<const V> vx  = v_pv.get(OPT);
    ROL::Ptr<const V> vl  = v_pv.get(EQUAL); 
 
    x_  = x_pv.get(OPT);
    l_  = x_pv.get(EQUAL);
 
    // \f$ 
    obj_->hessVec(*jvx,*vx,*x_,tol);
    con_->applyAdjointHessian(*scratch_,*l_,*vx,*x_,tol);
    jvx->plus(*scratch_);   
 
    // \f$J^\ast d_\lambda \f$
    con_->applyAdjointJacobian(*scratch_,*vl,*x_,tol);
    jvx->plus(*scratch_);
 
 
    Elementwise::DivideAndInvert<Real> divinv;
    Elementwise::Multiply<Real> mult;
 
    // Note that indices corresponding to infinite bounds should automatically lead to 
    // zero diagonal contributions to the Sigma operators
    scratch_->set(*x_);
    scratch_->axpy(-1.0,*xl_);           // x-l
    scratch_->applyBinary(divinv,*zl_);  // zl/(x-l)
    scratch_->applyBinary(mult,*vx);     // zl*vx/(x-l) = Sigma_l*vx
 
    jvx->plu(*scratch_);
    
    scratch_->set(*xu_);
    scratch_->axpy(-1.0,*x_);           // u-x
    scratch_->applyBinary(divinv,*zu_); // zu/(u-x)
    scratch_->applyBinary(mult,*vx);    // zu*vx/(u-x) = Sigma_u*vx
 
    jvx->plus(*scratch_);  
 
   
     
    
 
 
 
//            \f[ [W+(X-L)^{-1}+(U-X)^{-1}]d_x + J^\ast d_\lambda = 
//                  -g_x + (U-X)^{-1}g_{z_u} - (L-X)^{-1}g_{z_l} \f] 
 
 
 
  }
 
  int getNumberFunctionEvaluations(void) const {
    return nfval_;
  }
 
  int getNumberGradientEvaluations(void) const {
    return ngrad_;
  }
 
  int getNumberConstraintEvaluations(void) const {
    return ncval_;
  }
 
 
}; // class PrimalDualInteriorPointResidual
 
} // namespace ROL
 
#endif // ROL_PRIMALDUALKKTOPERATOR_H