ROL
ROL_Rosenbrock.hpp
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43 
49 // Whether or not to use the exact Hessian-times-a-vector
50 #ifndef USE_HESSVEC
51 #define USE_HESSVEC 1
52 #endif
53 
54 #ifndef ROL_ROSENBROCK_HPP
55 #define ROL_ROSENBROCK_HPP
56 
57 #include "ROL_StdVector.hpp"
58 #include "ROL_TestProblem.hpp"
59 
60 namespace ROL {
61 namespace ZOO {
62 
65 template< class Real, class XPrim=StdVector<Real>, class XDual=StdVector<Real> >
66 class Objective_Rosenbrock : public Objective<Real> {
67 
68  typedef std::vector<Real> vector;
69  typedef Vector<Real> V;
70 
71  typedef typename vector::size_type uint;
72 
73 private:
74  Real alpha_;
75 
76  Real const1_;
77  Real const2_;
78 
79  template<class VectorType>
80  ROL::Ptr<const vector> getVector( const V& x ) {
81  return dynamic_cast<const VectorType&>((x)).getVector();
82  }
83 
84  template<class VectorType>
85  ROL::Ptr<vector> getVector( V& x ) {
86  return dynamic_cast<VectorType&>(x).getVector();
87  }
88 
89 public:
90  Objective_Rosenbrock(Real alpha = 100.0) : alpha_(alpha), const1_(100.0), const2_(20.0) {}
91 
92  Real value( const Vector<Real> &x, Real &tol ) {
93 
94 
95  ROL::Ptr<const vector> xp = getVector<XPrim>(x);
96 
97  uint n = xp->size();
98  Real val = 0;
99  for( uint i=0; i<n/2; i++ ) {
100  val += alpha_ * pow(pow((*xp)[2*i],2) - (*xp)[2*i+1], 2);
101  val += pow((*xp)[2*i] - 1.0, 2);
102  }
103 
105  //Real error = tol*(2.0*((Real)rand())/((Real)RAND_MAX)-1.0);
106  //val += this->const1_*error;
107 
108  return val;
109  }
110 
111  void gradient( Vector<Real> &g, const Vector<Real> &x, Real &tol ) {
112 
113 
114  ROL::Ptr<const vector> xp = getVector<XPrim>(x);
115  ROL::Ptr<vector> gp = getVector<XDual>(g);
116 
117  uint n = xp->size();
118  for( uint i=0; i<n/2; i++ ) {
119  (*gp)[2*i] = 4.0*alpha_*(pow((*xp)[2*i],2) - (*xp)[2*i+1])*(*xp)[2*i] + 2.0*((*xp)[2*i]-1.0);
120  (*gp)[2*i+1] = -2.0*alpha_*(pow((*xp)[2*i],2) - (*xp)[2*i+1]);
121 
123  //Real error0 = tol*(2.0*((Real)rand())/((Real)RAND_MAX)-1.0);
124  //Real error1 = tol*(2.0*((Real)rand())/((Real)RAND_MAX)-1.0);
125  //(*gp)[2*i] += this->const2_*error0/std::sqrt(n);
126  //(*gp)[2*i+1] += this->const2_*error1/std::sqrt(n);
127  }
128  }
129 #if USE_HESSVEC
130  void hessVec( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &x, Real &tol ) {
131 
132 
133  ROL::Ptr<const vector> xp = getVector<XPrim>(x);
134  ROL::Ptr<const vector> vp = getVector<XPrim>(v);
135  ROL::Ptr<vector> hvp = getVector<XDual>(hv);
136 
137  uint n = xp->size();
138  for( uint i=0; i<n/2; i++ ) {
139  Real h11 = 4.0*alpha_*(3.0*pow((*xp)[2*i],2)-(*xp)[2*i+1]) + 2.0;
140  Real h12 = -4.0*alpha_*(*xp)[2*i];
141  Real h22 = 2.0*alpha_;
142 
143  (*hvp)[2*i] = h11*(*vp)[2*i] + h12*(*vp)[2*i+1];
144  (*hvp)[2*i+1] = h12*(*vp)[2*i] + h22*(*vp)[2*i+1];
145  }
146  }
147 #endif
148  void invHessVec( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &x, Real &tol ) {
149 
150 
151 
152  ROL::Ptr<const vector> xp = getVector<XPrim>(x);
153  ROL::Ptr<const vector> vp = getVector<XDual>(v);
154  ROL::Ptr<vector> hvp = getVector<XPrim>(hv);
155 
156  uint n = xp->size();
157  for( uint i=0; i<n/2; i++ ) {
158  Real h11 = 4.0*alpha_*(3.0*pow((*xp)[2*i],2)-(*xp)[2*i+1]) + 2.0;
159  Real h12 = -4.0*alpha_*(*xp)[2*i];
160  Real h22 = 2.0*alpha_;
161 
162  (*hvp)[2*i] = (1.0/(h11*h22-h12*h12))*( h22*(*vp)[2*i] - h12*(*vp)[2*i+1]);
163  (*hvp)[2*i+1] = (1.0/(h11*h22-h12*h12))*(-h12*(*vp)[2*i] + h11*(*vp)[2*i+1]);
164  }
165  }
166 };
167 
168 template<class Real>
169 class getRosenbrock : public TestProblem<Real> {
170 public:
171  getRosenbrock(void) {}
172 
173  Ptr<Objective<Real>> getObjective(void) const {
174  // Instantiate Objective Function
175  return ROL::makePtr<Objective_Rosenbrock<Real>>();
176  }
177 
178  Ptr<Vector<Real>> getInitialGuess(void) const {
179  // Problem dimension
180  int n = 100;
181  // Get Initial Guess
182  ROL::Ptr<std::vector<Real> > x0p = ROL::makePtr<std::vector<Real>>(n,0.0);
183  for ( int i = 0; i < n/2; i++ ) {
184  (*x0p)[2*i] = -1.2;
185  (*x0p)[2*i+1] = 1.0;
186  }
187  return ROL::makePtr<StdVector<Real>>(x0p);
188  }
189 
190  Ptr<Vector<Real>> getSolution(const int i = 0) const {
191  // Problem dimension
192  int n = 100;
193  // Get Solution
194  ROL::Ptr<std::vector<Real> > xp = ROL::makePtr<std::vector<Real>>(n,0.0);
195  for ( int i = 0; i < n; i++ ) {
196  (*xp)[i] = 1.0;
197  }
198  return ROL::makePtr<StdVector<Real>>(xp);
199  }
200 };
201 
202 }// End ZOO Namespace
203 }// End ROL Namespace
204 
205 #endif
Provides the interface to evaluate objective functions.
typename PV< Real >::size_type size_type
Ptr< Vector< Real > > getSolution(const int i=0) const
Rosenbrock&#39;s function.
ROL::Ptr< const vector > getVector(const V &x)
virtual void hessVec(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
Apply Hessian approximation to vector.
void invHessVec(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
Apply inverse Hessian approximation to vector.
ROL::Ptr< vector > getVector(V &x)
Defines the linear algebra or vector space interface.
Definition: ROL_Vector.hpp:80
void gradient(Vector< Real > &g, const Vector< Real > &x, Real &tol)
Compute gradient.
Ptr< Objective< Real > > getObjective(void) const
Real value(const Vector< Real > &x, Real &tol)
Compute value.
Contains definitions of test objective functions.
Objective_Rosenbrock(Real alpha=100.0)
Ptr< Vector< Real > > getInitialGuess(void) const