QuadraticMinimize.C

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//* This file is part of the MOOSE framework
//* https://mooseframework.inl.gov
//*
//* All rights reserved, see COPYRIGHT for full restrictions
//* https://github.com/idaholab/moose/blob/master/COPYRIGHT
//*
//* Licensed under LGPL 2.1, please see LICENSE for details
//* https://www.gnu.org/licenses/lgpl-2.1.html
 
#include "QuadraticMinimize.h"
#include "libmesh/petsc_vector.h"
 
registerMooseObject("OptimizationTestApp", QuadraticMinimize);
 
InputParameters
QuadraticMinimize::validParams()
{
  InputParameters params = GeneralOptimization::validParams();
  params.addClassDescription("Test object that minimizes a quadratic objective function computed "
                             "directly on the main application.");
  params.addRequiredParam<Real>("objective", "Desired value of objective function.");
  params.addRequiredParam<std::vector<Real>>("solution", "Desired solution to optimization.");
  // This object computes the objective itself, so the reporter value declared by
  // GeneralOptimization is never populated by a sub-application transfer.
  params.set<ReporterValueName>("objective_name") = "objective_value";
  params.suppressParameter<ReporterValueName>("objective_name");
  return params;
}
 
QuadraticMinimize::QuadraticMinimize(const InputParameters & parameters)
  : GeneralOptimization(parameters),
    _result(getParam<Real>("objective")),
    _solution(getParam<std::vector<Real>>("solution"))
{
  setICsandBounds();
  if (_solution.size() != _ndof)
    paramError("solution", "Size not equal to number of degrees of freedom (", _ndof, ").");
}
 
Real
QuadraticMinimize::computeObjective()
{
  Real obj = _result;
  unsigned int i = 0;
  for (const auto & param : _parameters)
    for (const auto & val : *param)
    {
      Real tmp = val - _solution[i++];
      obj += tmp * tmp;
    }
 
  return obj;
}
 
void
QuadraticMinimize::computeGradient(libMesh::PetscVector<Number> & gradient) const
{
  unsigned int i = 0;
  for (const auto & param : _parameters)
    for (const auto & val : *param)
    {
      gradient.set(i, 2.0 * (val - _solution[i]));
      i++;
    }
  gradient.close();
}