IPMeanSquareFunctional.cc

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// Copyright (C) 2012, 2014, 2015, 2016, 2017, 2020, 2022, 2023  David Maxwell
//
// This file is part of PISM.
//
// PISM is free software; you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the Free Software
// Foundation; either version 3 of the License, or (at your option) any later
// version.
//
// PISM is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
// details.
//
// You should have received a copy of the GNU General Public License
// along with PISM; if not, write to the Free Software
// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
 
#include "pism/inverse/functional/IPMeanSquareFunctional.hh"
#include "pism/util/Grid.hh"
#include "pism/util/array/Vector.hh"
#include "pism/util/array/Scalar.hh"
#include "pism/util/pism_utilities.hh"
 
namespace pism {
namespace inverse {
 
//! Implicitly set the normalization constant for the functional.
/*! The normalization constant is selected so that if an input
array::Vector has component vectors all of length \a scale, then the funtional value will be 1. I.e.
\f[
c_N^{-1} = \sum_{i} w_i {\tt scale}^2.
\f]*/
void IPMeanSquareFunctional2V::normalize(double scale) {
 
  // The local value of the weights
  double value = 0;
 
  if (m_weights) {
    array::AccessScope list{m_weights};
 
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      value += (*m_weights)(i, j);
    }
  } else {
    for (auto p = m_grid->points(); p; p.next()) {
      value += 1;
    }
  }
 
  m_normalization = GlobalSum(m_grid->com, value);
  m_normalization *= (scale*scale);
}
 
void IPMeanSquareFunctional2V::valueAt(array::Vector &x, double *OUTPUT)  {
 
  // The value of the objective
  double value = 0;
 
  array::AccessScope list{&x};
 
  if (m_weights) {
    list.add(*m_weights);
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      Vector2d &x_ij = x(i, j);
      value += (x_ij.u*x_ij.u + x_ij.v*x_ij.v)*(*m_weights)(i, j);
    }
  } else {
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      Vector2d &x_ij = x(i, j);
      value += (x_ij.u*x_ij.u + x_ij.v*x_ij.v);
    }
  }
  value /= m_normalization;
 
  GlobalSum( m_grid->com, &value, OUTPUT, 1);
}
 
void IPMeanSquareFunctional2V::dot(array::Vector &a, array::Vector &b, double *OUTPUT)  {
 
  // The value of the objective
  double value = 0;
 
  array::AccessScope list{&a, &b};
 
  if (m_weights) {
    list.add(*m_weights);
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      Vector2d &a_ij = a(i, j);
      Vector2d &b_ij = b(i, j);
      value += (a_ij.u*b_ij.u + a_ij.v*b_ij.v)*(*m_weights)(i, j);
    }
  } else {
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      Vector2d &a_ij = a(i, j);
      Vector2d &b_ij = b(i, j);
      value += (a_ij.u*b_ij.u + a_ij.v*b_ij.v);
    }
  }
  value /= m_normalization;
 
  GlobalSum( m_grid->com, &value, OUTPUT, 1);
}
 
void IPMeanSquareFunctional2V::gradientAt(array::Vector &x, array::Vector &gradient)  {
  gradient.set(0);
 
  array::AccessScope list{&x, &gradient};
 
  if (m_weights) {
    list.add(*m_weights);
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      gradient(i, j).u = 2*x(i, j).u*(*m_weights)(i, j) / m_normalization;
      gradient(i, j).v = 2*x(i, j).v*(*m_weights)(i, j) / m_normalization;
    }
  } else {
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      gradient(i, j).u = 2*x(i, j).u / m_normalization;
      gradient(i, j).v = 2*x(i, j).v / m_normalization;
    }
  }
}
 
//! Implicitly set the normalization constant for the functional.
/*! The normalization constant is selected so that if an input
array::Scalar has entries all equal to \a scale, then the funtional value will be 1. I.e.
\f[
c_N^{-1} = \sum_{i} w_i {\tt scale}^2.
\f]*/
void IPMeanSquareFunctional2S::normalize(double scale) {
 
  // The local value of the weights
  double value = 0;
 
  if (m_weights) {
    array::AccessScope list(*m_weights);
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      value += (*m_weights)(i, j);
    }
  } else {
    for (auto p = m_grid->points(); p; p.next()) {
      value += 1;
    }
  }
 
  m_normalization = GlobalSum(m_grid->com, value);
  m_normalization *= (scale*scale);
}
 
void IPMeanSquareFunctional2S::valueAt(array::Scalar &x, double *OUTPUT)  {
 
  // The value of the objective
  double value = 0;
 
  array::AccessScope list(x);
 
  if (m_weights) {
    list.add(*m_weights);
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      double &x_ij = x(i, j);
      value += x_ij*x_ij*(*m_weights)(i, j);
    }
  } else {
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      double &x_ij = x(i, j);
      value += x_ij*x_ij;
    }
  }
  value /= m_normalization;
 
  GlobalSum(m_grid->com, &value, OUTPUT, 1);
}
 
void IPMeanSquareFunctional2S::dot(array::Scalar &a, array::Scalar &b, double *OUTPUT)  {
 
  // The value of the objective
  double value = 0;
 
  array::AccessScope list{&a, &b};
 
  if (m_weights) {
    list.add(*m_weights);
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      value += (a(i, j)*b(i, j))*(*m_weights)(i, j);
    }
  } else {
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      value += (a(i, j)*b(i, j));
    }
  }
  value /= m_normalization;
 
  GlobalSum(m_grid->com, &value, OUTPUT, 1);
}
 
 
void IPMeanSquareFunctional2S::gradientAt(array::Scalar &x, array::Scalar &gradient)  {
  gradient.set(0);
 
  array::AccessScope list{&x, &gradient};
 
  if (m_weights) {
    list.add(*m_weights);
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      gradient(i, j) = 2*x(i, j)*(*m_weights)(i, j) / m_normalization;
    }
  } else {
    for (auto p = m_grid->points(); p; p.next()) {
      const int i = p.i(), j = p.j();
 
      gradient(i, j) = 2*x(i, j) / m_normalization;
    }
  }
}
 
} // end of namespace inverse
} // end of namespace pism