ROL
rosenbrock/example_01.cpp
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43 
49 #define USE_HESSVEC 1
50 
51 #include "ROL_Rosenbrock.hpp"
52 #include "ROL_Algorithm.hpp"
53 #include "ROL_LineSearchStep.hpp"
54 #include "ROL_StatusTest.hpp"
55 #include "ROL_Stream.hpp"
56 #include "Teuchos_GlobalMPISession.hpp"
57 
58 #include <iostream>
59 
60 typedef double RealT;
61 
62 int main(int argc, char *argv[]) {
63 
64  Teuchos::GlobalMPISession mpiSession(&argc, &argv);
65 
66  // This little trick lets us print to std::cout only if a (dummy) command-line argument is provided.
67  int iprint = argc - 1;
68  ROL::Ptr<std::ostream> outStream;
69  ROL::nullstream bhs; // outputs nothing
70  if (iprint > 0)
71  outStream = ROL::makePtrFromRef(std::cout);
72  else
73  outStream = ROL::makePtrFromRef(bhs);
74 
75  int errorFlag = 0;
76 
77  // *** Example body.
78 
79  try {
80 
82  int dim = 100; // Set problem dimension. Must be even.
83 
84  // Set parameters.
85  ROL::ParameterList parlist;
86  parlist.sublist("Step").sublist("Line Search").sublist("Descent Method").set("Type", "Newton-Krylov");
87  parlist.sublist("Status Test").set("Gradient Tolerance",1.e-12);
88  parlist.sublist("Status Test").set("Step Tolerance",1.e-14);
89  parlist.sublist("Status Test").set("Iteration Limit",100);
90 
91  // Define algorithm.
92  ROL::Algorithm<RealT> algo("Line Search",parlist);
93 
94  // Iteration Vector
95  ROL::Ptr<std::vector<RealT> > x_ptr = ROL::makePtr<std::vector<RealT>>(dim, 0.0);
96  // Set Initial Guess
97  for (int i=0; i<dim/2; i++) {
98  (*x_ptr)[2*i] = -1.2;
99  (*x_ptr)[2*i+1] = 1.0;
100  }
101  ROL::StdVector<RealT> x(x_ptr);
102 
103  // Run Algorithm
104  algo.run(x, obj, true, *outStream);
105 
106  // Get True Solution
107  ROL::Ptr<std::vector<RealT> > xtrue_ptr = ROL::makePtr<std::vector<RealT>>(dim, 1.0);
108  ROL::StdVector<RealT> xtrue(xtrue_ptr);
109 
110  // Compute Error
111  x.axpy(-1.0, xtrue);
112  RealT abserr = x.norm();
113  RealT relerr = abserr/xtrue.norm();
114  *outStream << std::scientific << "\n Absolute Error: " << abserr;
115  *outStream << std::scientific << "\n Relative Error: " << relerr << "\n";
116  if ( relerr > sqrt(ROL::ROL_EPSILON<RealT>()) ) {
117  errorFlag += 1;
118  }
119  }
120  catch (std::logic_error err) {
121  *outStream << err.what() << "\n";
122  errorFlag = -1000;
123  }; // end try
124 
125  if (errorFlag != 0)
126  std::cout << "End Result: TEST FAILED\n";
127  else
128  std::cout << "End Result: TEST PASSED\n";
129 
130  return 0;
131 
132 }
133 
void axpy(const Real alpha, const Vector< Real > &x)
Compute where .
Rosenbrock&#39;s function.
virtual std::vector< std::string > run(Vector< Real > &x, Objective< Real > &obj, bool print=false, std::ostream &outStream=std::cout, bool printVectors=false, std::ostream &vectorStream=std::cout)
Run algorithm on unconstrained problems (Type-U). This is the primary Type-U interface.
Contains definitions for Rosenbrock&#39;s function.
Defines a no-output stream class ROL::NullStream and a function makeStreamPtr which either wraps a re...
Real norm() const
Returns where .
Provides the ROL::Vector interface for scalar values, to be used, for example, with scalar constraint...
Provides an interface to run optimization algorithms.
basic_nullstream< char, char_traits< char >> nullstream
Definition: ROL_Stream.hpp:72
int main(int argc, char *argv[])