ROL
algorithm/TypeU/trustregion/other/ROL_SemismoothNewtonDualModel.hpp
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43
44
45#pragma once
46#ifndef ROL_SEMISMOOTHNEWTONDUALMODEL_HPP
47#define ROL_SEMISMOOTHNEWTONDUALMODEL_HPP
48
53
67namespace ROL {
68
69
70template<class Real>
71class SemismoothNewtonDualModel : public TrustRegionModel<Real> {
72
73 using V = Vector<Real>;
74 using VPrim = InactiveSet_PrimalVector<Real>;
75 using VDual = InactiveSet_DualVector<Real>;
76
77 using Obj = Objective<Real>;
78 using Sec = Secant<Real>;
79 using Bnd = BoundConstraint<Real>;
80
81private:
82
83 class ProjectedObjective : public Objective<Real> {
84 private:
85 Obj& objPrimal_;
86 Bnd& bnd_;
87 Ptr<V> primalVec_;
88
89 public:
90 ProjectedObjective( Obj& objPrimal, Bnd& bnd, const Ptr<V>& primalVec ) :
91 objPrimal_(objPrimal), bnd_(bnd), primalVec_( primalVec ) {}
92
93 Real value( const V& p, Real& tol ) override {
94 primalVec_->set(p);
95 bnd_.project(*primalVec_);
96 return objPrimal_->value(*primalVec_, tol);
97 }
98
99 void gradient( V& g, const V& p, Real& tol ) override {
100 primalVec_->set(p);
101 bnd_.project(*primalVec_);
102 objPrimal_->gradient(g,*primalVec_, tol);
103 }
104
105 void hessVec( V& hv, const V& v, const V& p, Real& tol ) override {
106 primalVec_->set(p);
107 bnd_.project(*primalVec_);
108 objPrimal_->hessVec(hv,v,*primalVec_, tol);
109 }
110
111 }; // ProjectedObjective
112
113 ProjectedObjective projObj_;
114 Bnd bnd_;
115 Sec secant_;
116 Ptr<V> p_, g_, x_;
117 Ptr<V> ones_;
118 Ptr<VPrim> s_;
119 Real alpha_;
120
122
123
124public:
125
126 SemismoothNewtonDualModel( Obj& obj, Bnd& bnd, const V& p, const V& g, const Real alpha ) :
127 TrustRegionModel( obj, p, g, false ), bnd_( bnd ),
128 p_( p.clone() ), g_( p.dual().clone() ), x_( p.clone() ), ones_( p.clone() ),
129 s_( p.clone(), ones_, p_, bnd_ ), projObj_( obj, bnd, p_ ), alpha_(alpha) {
130
131 ones_->setScalar( Real(1.0) );
132 }
133
134
135 Real value( const V& s, Real& tol ) {
136
137 auto hs = workspace_.clone(*g_);
138
139 gradient(*g_,s,tol);
140 hessVec(*hs,s,s,tol);
141 hs->scale( 0.5 );
142 hs->plus(*g_);
143 s_->set(s);
144 return s_->dot(*hs);
145 }
146
147 void gradient( V& g, const V& s, Real& tol ) {
148 projObj_->gradient(g,*p_,tol);
149 g.axpy(alpha_,*p_);
150 }
151
152 void hessVec( V& hv, const V& v, const V& s, Real& tol ) {
153 auto vprune_ = workspace_.copy(v);
154 bnd_->pruneActive( *vprune_, *p_ );
155 projObj_->hessVec( hv, *vprune_, *p_, tol );
156 hv.axpy(alpha_,v);
157 }
158
159 void update( const V& p, bool flag = true, int iter = -1 ) {
160 p_->set(p);
161 auto x = this->getIterate();
162 }
163
164} // namespace ROL
165
Vector< Real > V
Objective_TimeSimOpt< Real > Obj
VectorWorkspace< Real > workspace_
Implements the dual variable model function for a semismooth Newton step.
Provides a "smart" cloning manager to be used a member variable in a class and called in the member f...
void value(ROL::Vector< Real > &c, const ROL::Vector< Real > &sol, const Real &mu)