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"
54#include "ROL_StatusTest.hpp"
55#include "ROL_Stream.hpp"
56#include "Teuchos_GlobalMPISession.hpp"
57
58#include <iostream>
59
60typedef double RealT;
61
62int 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::Ptr<ROL::Step<RealT>>
93 step = ROL::makePtr<ROL::LineSearchStep<RealT>>(parlist);
94 ROL::Ptr<ROL::StatusTest<RealT>>
95 status = ROL::makePtr<ROL::StatusTest<RealT>>(parlist);
96 ROL::Algorithm<RealT> algo(step,status,false);
97
98 // Iteration Vector
99 ROL::Ptr<std::vector<RealT> > x_ptr = ROL::makePtr<std::vector<RealT>>(dim, 0.0);
100 // Set Initial Guess
101 for (int i=0; i<dim/2; i++) {
102 (*x_ptr)[2*i] = -1.2;
103 (*x_ptr)[2*i+1] = 1.0;
104 }
105 ROL::StdVector<RealT> x(x_ptr);
106
107 // Run Algorithm
108 algo.run(x, obj, true, *outStream);
109
110 // Get True Solution
111 ROL::Ptr<std::vector<RealT> > xtrue_ptr = ROL::makePtr<std::vector<RealT>>(dim, 1.0);
112 ROL::StdVector<RealT> xtrue(xtrue_ptr);
113
114 // Compute Error
115 x.axpy(-1.0, xtrue);
116 RealT abserr = x.norm();
117 RealT relerr = abserr/xtrue.norm();
118 *outStream << std::scientific << "\n Absolute Error: " << abserr;
119 *outStream << std::scientific << "\n Relative Error: " << relerr << "\n";
120 if ( relerr > sqrt(ROL::ROL_EPSILON<RealT>()) ) {
121 errorFlag += 1;
122 }
123 }
124 catch (std::logic_error& err) {
125 *outStream << err.what() << "\n";
126 errorFlag = -1000;
127 }; // end try
128
129 if (errorFlag != 0)
130 std::cout << "End Result: TEST FAILED\n";
131 else
132 std::cout << "End Result: TEST PASSED\n";
133
134 return 0;
135
136}
137
Contains definitions for Rosenbrock's function.
Defines a no-output stream class ROL::NullStream and a function makeStreamPtr which either wraps a re...
Provides an interface to run optimization algorithms.
Provides the ROL::Vector interface for scalar values, to be used, for example, with scalar constraint...
void axpy(const Real alpha, const Vector< Real > &x)
Compute where .
Real norm() const
Returns where .
int main(int argc, char *argv[])
double RealT
constexpr auto dim