step/test_16.cpp

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// @HEADER
// *****************************************************************************
//               Rapid Optimization Library (ROL) Package
//
// Copyright 2014 NTESS and the ROL contributors.
// SPDX-License-Identifier: BSD-3-Clause
// *****************************************************************************
// @HEADER

#include "ROL_GetTestProblems.hpp"
#include "ROL_OptimizationSolver.hpp"
#include "ROL_Stream.hpp"
#include "Teuchos_GlobalMPISession.hpp"
#include "Teuchos_XMLParameterListHelpers.hpp"

#include <iostream>

typedef double RealT;

int main(int argc, char *argv[]) {

  Teuchos::GlobalMPISession mpiSession(&argc, &argv);

  // This little trick lets us print to std::cout only if a (dummy) command-line argument is provided.
  int iprint     = argc - 1;
  ROL::Ptr<std::ostream> outStream;
  ROL::nullstream bhs; // outputs nothing
  if (iprint > 0)
    outStream = ROL::makePtrFromRef(std::cout);
  else
    outStream = ROL::makePtrFromRef(bhs);

  int errorFlag  = 0;

  // *** Test body.

  try {
    std::string filename = "input.xml";
    auto parlist = ROL::getParametersFromXmlFile( filename );

    // Setup optimization problem
    ROL::Ptr<ROL::Vector<RealT>> x0;
    std::vector<ROL::Ptr<ROL::Vector<RealT>>> z;
    ROL::Ptr<ROL::OptimizationProblem<RealT>> optProblem;
    //ROL::GetTestProblem<RealT>(optProblem,x0,z,ROL::TESTOPTPROBLEM_CANTILEVERBEAM);
    //ROL::GetTestProblem<RealT>(optProblem,x0,z,ROL::TESTOPTPROBLEM_CYLINDERHEAD);
    //ROL::GetTestProblem<RealT>(optProblem,x0,z,ROL::TESTOPTPROBLEM_QUARTIC);
    ROL::GetTestProblem<RealT>(optProblem,x0,z,ROL::TESTOPTPROBLEM_CANTILEVER);

    // Get Dimension of Problem
    int dim = x0->dimension(); 
    parlist->sublist("General").sublist("Krylov").set("Iteration Limit", 2*dim);

    // Check Derivatives
    optProblem->check(*outStream);

    // Error Vector
//    ROL::Ptr<ROL::Vector<RealT>> e = x0->clone();
//    e->zero();

    // Setup optimization solver
    parlist->sublist("Status Test").set("Gradient Tolerance",static_cast<RealT>(1e-6));
    parlist->sublist("Step").set("Type", "Augmented Lagrangian");
    ROL::OptimizationSolver<RealT> optSolver(*optProblem,*parlist);
    optSolver.solve(*outStream);

    // Compute Error
//    RealT err(0);
//    for (int i = 0; i < static_cast<int>(z.size()); ++i) {
//      e->set(*x0);
//      e->axpy(-1.0,*z[i]);
//      if (i == 0) {
//        err = e->norm();
//      }
//      else {
//        err = std::min(err,e->norm());
//      }
//    }
//    *outStream << std::endl << "Norm of Error: " << err << std::endl;
//
//    RealT tol = static_cast<RealT>(1e-3)*std::max(z[0]->norm(),static_cast<RealT>(1));
//    errorFlag += ((err < tol) ? 0 : 1);
  }
  catch (std::logic_error& err) {
    *outStream << err.what() << std::endl;
    errorFlag = -1000;
  }; // end try

  if (errorFlag != 0)
    std::cout << "End Result: TEST FAILED" << std::endl;
  else
    std::cout << "End Result: TEST PASSED" << std::endl;

  return 0;

}