ROL
burgers-control/example_02.cpp
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43 
50 #include "example_02.hpp"
51 
52 typedef double RealT;
53 
54 int main(int argc, char *argv[]) {
55 
56  Teuchos::GlobalMPISession mpiSession(&argc, &argv);
57 
58  // This little trick lets us print to std::cout only if a (dummy) command-line argument is provided.
59  int iprint = argc - 1;
60  Teuchos::RCP<std::ostream> outStream;
61  Teuchos::oblackholestream bhs; // outputs nothing
62  if (iprint > 0)
63  outStream = Teuchos::rcp(&std::cout, false);
64  else
65  outStream = Teuchos::rcp(&bhs, false);
66 
67  int errorFlag = 0;
68 
69  // *** Example body.
70 
71  try {
72  // Initialize full objective function.
73  int nx = 256; // Set spatial discretization.
74  RealT alpha = 1.e-3; // Set penalty parameter.
75  RealT nu = 1e-2; // Viscosity parameter.
76  Objective_BurgersControl<RealT> obj(alpha,nx);
77  // Initialize equality constraints
79  Teuchos::ParameterList list;
80  list.sublist("SimOpt").sublist("Solve").set("Residual Tolerance",1.e2*ROL::ROL_EPSILON);
81  con.setSolveParameters(list);
82  // Initialize iteration vectors.
83  Teuchos::RCP<std::vector<RealT> > z_rcp = Teuchos::rcp( new std::vector<RealT> (nx+2, 1.0) );
84  Teuchos::RCP<std::vector<RealT> > gz_rcp = Teuchos::rcp( new std::vector<RealT> (nx+2, 1.0) );
85  Teuchos::RCP<std::vector<RealT> > yz_rcp = Teuchos::rcp( new std::vector<RealT> (nx+2, 1.0) );
86  for (int i=0; i<nx+2; i++) {
87  (*z_rcp)[i] = (RealT)rand()/(RealT)RAND_MAX;
88  (*yz_rcp)[i] = (RealT)rand()/(RealT)RAND_MAX;
89  }
90  ROL::StdVector<RealT> z(z_rcp);
91  ROL::StdVector<RealT> gz(gz_rcp);
92  ROL::StdVector<RealT> yz(yz_rcp);
93  Teuchos::RCP<ROL::Vector<RealT> > zp = Teuchos::rcp(&z,false);
94  Teuchos::RCP<ROL::Vector<RealT> > gzp = Teuchos::rcp(&z,false);
95  Teuchos::RCP<ROL::Vector<RealT> > yzp = Teuchos::rcp(&yz,false);
96 
97  Teuchos::RCP<std::vector<RealT> > u_rcp = Teuchos::rcp( new std::vector<RealT> (nx, 1.0) );
98  Teuchos::RCP<std::vector<RealT> > gu_rcp = Teuchos::rcp( new std::vector<RealT> (nx, 1.0) );
99  Teuchos::RCP<std::vector<RealT> > yu_rcp = Teuchos::rcp( new std::vector<RealT> (nx, 1.0) );
100  for (int i=0; i<nx; i++) {
101  (*u_rcp)[i] = (RealT)rand()/(RealT)RAND_MAX;
102  (*yu_rcp)[i] = (RealT)rand()/(RealT)RAND_MAX;
103  }
104  ROL::StdVector<RealT> u(u_rcp);
105  ROL::StdVector<RealT> gu(gu_rcp);
106  ROL::StdVector<RealT> yu(yu_rcp);
107  Teuchos::RCP<ROL::Vector<RealT> > up = Teuchos::rcp(&u,false);
108  Teuchos::RCP<ROL::Vector<RealT> > gup = Teuchos::rcp(&u,false);
109  Teuchos::RCP<ROL::Vector<RealT> > yup = Teuchos::rcp(&yu,false);
110 
111  Teuchos::RCP<std::vector<RealT> > c_rcp = Teuchos::rcp( new std::vector<RealT> (nx, 1.0) );
112  Teuchos::RCP<std::vector<RealT> > l_rcp = Teuchos::rcp( new std::vector<RealT> (nx, 1.0) );
113  ROL::StdVector<RealT> c(c_rcp);
114  ROL::StdVector<RealT> l(l_rcp);
115 
116  ROL::Vector_SimOpt<RealT> x(up,zp);
117  ROL::Vector_SimOpt<RealT> g(gup,gzp);
118  ROL::Vector_SimOpt<RealT> y(yup,yzp);
119 
120  // Check derivatives.
121  obj.checkGradient(x,x,y,true,*outStream);
122  obj.checkHessVec(x,x,y,true,*outStream);
123  con.checkApplyJacobian(x,y,c,true,*outStream);
124  con.checkApplyAdjointJacobian(x,yu,c,x,true,*outStream);
125  con.checkApplyAdjointHessian(x,yu,y,x,true,*outStream);
126 
127  // Initialize reduced objective function.
128  Teuchos::RCP<std::vector<RealT> > p_rcp = Teuchos::rcp( new std::vector<RealT> (nx, 1.0) );
129  ROL::StdVector<RealT> p(p_rcp);
130  Teuchos::RCP<ROL::Vector<RealT> > pp = Teuchos::rcp(&p,false);
131  Teuchos::RCP<ROL::Objective_SimOpt<RealT> > pobj = Teuchos::rcp(&obj,false);
132  Teuchos::RCP<ROL::EqualityConstraint_SimOpt<RealT> > pcon = Teuchos::rcp(&con,false);
133  ROL::Reduced_Objective_SimOpt<RealT> robj(pobj,pcon,up,pp);
134  // Check derivatives.
135  robj.checkGradient(z,z,yz,true,*outStream);
136  robj.checkHessVec(z,z,yz,true,*outStream);
137 
138  // Get parameter list.
139  std::string filename = "input.xml";
140  Teuchos::RCP<Teuchos::ParameterList> parlist = Teuchos::rcp( new Teuchos::ParameterList() );
141  Teuchos::updateParametersFromXmlFile( filename, parlist.ptr() );
142  parlist->sublist("Status Test").set("Gradient Tolerance",1.e-14);
143  parlist->sublist("Status Test").set("Constraint Tolerance",1.e-14);
144  parlist->sublist("Status Test").set("Step Tolerance",1.e-16);
145  parlist->sublist("Status Test").set("Iteration Limit",1000);
146  // Declare ROL algorithm pointer.
147  Teuchos::RCP<ROL::Algorithm<RealT> > algo;
148 
149  // Run optimization with Composite Step.
150  algo = Teuchos::rcp(new ROL::Algorithm<RealT>("Composite Step",*parlist,false));
151  RealT zerotol = std::sqrt(ROL::ROL_EPSILON);
152  z.zero();
153  con.solve(c,u,z,zerotol);
154  c.zero(); l.zero();
155  algo->run(x, g, l, c, obj, con, true, *outStream);
156  Teuchos::RCP<ROL::Vector<RealT> > zCS = z.clone();
157  zCS->set(z);
158 
159  // Run Optimization with Trust-Region algorithm.
160  algo = Teuchos::rcp(new ROL::Algorithm<RealT>("Trust Region",*parlist,false));
161  z.zero();
162  algo->run(z,robj,true,*outStream);
163 
164  // Check solutions.
165  Teuchos::RCP<ROL::Vector<RealT> > err = z.clone();
166  err->set(*zCS); err->axpy(-1.,z);
167  errorFlag += ((err->norm()) > 1.e-8) ? 1 : 0;
168  }
169  catch (std::logic_error err) {
170  *outStream << err.what() << "\n";
171  errorFlag = -1000;
172  }; // end try
173 
174  if (errorFlag != 0)
175  std::cout << "End Result: TEST FAILED\n";
176  else
177  std::cout << "End Result: TEST PASSED\n";
178 
179  return 0;
180 
181 }
182 
virtual void setSolveParameters(Teuchos::ParameterList &parlist)
Set solve parameters.
Defines the linear algebra or vector space interface for simulation-based optimization.
virtual std::vector< std::vector< Real > > checkApplyAdjointJacobian(const Vector< Real > &x, const Vector< Real > &v, const Vector< Real > &c, const Vector< Real > &ajv, const bool printToStream=true, std::ostream &outStream=std::cout, const int numSteps=ROL_NUM_CHECKDERIV_STEPS)
Finite-difference check for the application of the adjoint of constraint Jacobian.
void solve(ROL::Vector< Real > &c, ROL::Vector< Real > &u, const ROL::Vector< Real > &z, Real &tol)
Given , solve for .
Definition: example_03.hpp:482
virtual void zero()
Set to zero vector.
Definition: ROL_Vector.hpp:157
virtual std::vector< std::vector< Real > > checkGradient(const Vector< Real > &x, const Vector< Real > &d, const bool printToStream=true, std::ostream &outStream=std::cout, const int numSteps=ROL_NUM_CHECKDERIV_STEPS, const int order=1)
Finite-difference gradient check.
virtual std::vector< std::vector< Real > > checkApplyJacobian(const Vector< Real > &x, const Vector< Real > &v, const Vector< Real > &jv, const std::vector< Real > &steps, const bool printToStream=true, std::ostream &outStream=std::cout, const int order=1)
Finite-difference check for the constraint Jacobian application.
Provides the interface to evaluate simulation-based reduced objective functions.
Provides the std::vector implementation of the ROL::Vector interface.
Provides an interface to run optimization algorithms.
virtual Teuchos::RCP< Vector< Real > > clone() const
Clone to make a new (uninitialized) vector.
int main(int argc, char *argv[])
virtual std::vector< std::vector< Real > > checkHessVec(const Vector< Real > &x, const Vector< Real > &v, const bool printToStream=true, std::ostream &outStream=std::cout, const int numSteps=ROL_NUM_CHECKDERIV_STEPS, const int order=1)
Finite-difference Hessian-applied-to-vector check.
double RealT
virtual std::vector< std::vector< Real > > checkApplyAdjointHessian(const Vector< Real > &x, const Vector< Real > &u, const Vector< Real > &v, const Vector< Real > &hv, const std::vector< Real > &step, const bool printToScreen=true, std::ostream &outStream=std::cout, const int order=1)
Finite-difference check for the application of the adjoint of constraint Hessian. ...
static const double ROL_EPSILON
Platform-dependent machine epsilon.
Definition: ROL_Types.hpp:118