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// ---------------------------------------------------------------------
//
// Copyright (C) 2005 - 2015 by the deal.II authors
//
// This file is part of the deal.II library.
//
// The deal.II library is free software; you can use it, redistribute
// it, and/or modify it under the terms of the GNU Lesser General
// Public License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
// The full text of the license can be found in the file LICENSE at
// the top level of the deal.II distribution.
//
// ---------------------------------------------------------------------

#ifndef dealii__householder_h
#define dealii__householder_h


#include <cmath>
#include <deal.II/base/config.h>
#include <deal.II/lac/full_matrix.h>
#include <deal.II/lac/vector_memory.h>

#include <vector>

DEAL_II_NAMESPACE_OPEN


// forward declarations
template<typename number> class Vector;


/*! @addtogroup Matrix2
 *@{
 */


/**
 * QR-decomposition of a full matrix.
 *
 * Whenever an object of this class is created, it copies the matrix given and
 * computes its QR-decomposition by Householder algorithm. Then, the function
 * least_squares() can be used to compute the vector <i>x</i> minimizing
 * <i>||Ax-b||</i> for a given vector <i>b</i>.
 *
 * @note Instantiations for this template are provided for <tt>@<float@> and
 * @<double@></tt>; others can be generated in application programs (see the
 * section on
 * @ref Instantiations
 * in the manual).
 *
 * @author Guido Kanschat, 2005
 */
template<typename number>
class Householder : private FullMatrix<number>
{
public:
  /**
   * Declare type of container size.
   */
  typedef types::global_dof_index size_type;

  /**
   * Create an empty object.
   */
  Householder ();

  /**
   * Create an object holding the QR-decomposition of a matrix.
   */
  template<typename number2>
  Householder (const FullMatrix<number2> &);

  /**
   * Compute the QR-decomposition of another matrix.
   */
  template<typename number2>
  void
  initialize (const FullMatrix<number2> &);

  /**
   * Solve the least-squares problem for the right hand side <tt>src</tt>. The
   * return value is the Euclidean norm of the approximation error.
   *
   * @arg @c dst contains the solution of the least squares problem on return.
   *
   * @arg @c src contains the right hand side <i>b</i> of the least squares
   * problem. It will be changed during the algorithm and is unusable on
   * return.
   */
  template<typename number2>
  double least_squares (Vector<number2> &dst,
                        const Vector<number2> &src) const;

  /**
   * This function does the same as the one for BlockVectors.
   */
  template<typename number2>
  double least_squares (BlockVector<number2> &dst,
                        const BlockVector<number2> &src) const;

  /**
   * A wrapper to least_squares(), implementing the standard MatrixType
   * interface.
   */
  template<class VectorType>
  void vmult (VectorType &dst, const VectorType &src) const;

  template<class VectorType>
  void Tvmult (VectorType &dst, const VectorType &src) const;


private:
  /**
   * Storage for the diagonal elements of the orthogonal transformation.
   */
  std::vector<number> diagonal;
};

/*@}*/

#ifndef DOXYGEN
/*-------------------------Inline functions -------------------------------*/

// QR-transformation cf. Stoer 1 4.8.2 (p. 191)

template <typename number>
Householder<number>::Householder()
{}



template <typename number>
template <typename number2>
void
Householder<number>::initialize(const FullMatrix<number2> &M)
{
  const size_type m = M.n_rows(), n = M.n_cols();
  this->reinit(m, n);
  this->fill(M);
  Assert (!this->empty(), typename FullMatrix<number2>::ExcEmptyMatrix());
  diagonal.resize(m);

  // m > n, src.n() = m
  Assert (this->n_cols() <= this->n_rows(),
          ExcDimensionMismatch(this->n_cols(), this->n_rows()));

  for (size_type j=0 ; j<n ; ++j)
    {
      number2 sigma = 0;
      size_type i;
      // sigma = ||v||^2
      for (i=j ; i<m ; ++i)
        sigma += this->el(i,j)*this->el(i,j);
      // We are ready if the column is
      // empty. Are we?
      if (std::fabs(sigma) < 1.e-15) return;

      number2 s = (this->el(j,j) < 0) ? std::sqrt(sigma) : -std::sqrt(sigma);
      //
      number2 beta = std::sqrt(1./(sigma-s*this->el(j,j)));

      // Make column j the Householder
      // vector, store first entry in
      // diagonal
      diagonal[j] = beta*(this->el(j,j) - s);
      this->el(j,j) = s;

      for (i=j+1 ; i<m ; ++i)
        this->el(i,j) *= beta;


      // For all subsequent columns do
      // the Householder reflection
      for (size_type k=j+1 ; k<n ; ++k)
        {
          number2 sum = diagonal[j]*this->el(j,k);
          for (i=j+1 ; i<m ; ++i)
            sum += this->el(i,j)*this->el(i,k);

          this->el(j,k) -= sum*this->diagonal[j];
          for (i=j+1 ; i<m ; ++i)
            this->el(i,k) -= sum*this->el(i,j);
        }
    }
}


template <typename number>
template <typename number2>
Householder<number>::Householder(const FullMatrix<number2> &M)
{
  initialize(M);
}


template <typename number>
template <typename number2>
double
Householder<number>::least_squares (Vector<number2> &dst,
                                    const Vector<number2> &src) const
{
  Assert (!this->empty(), typename FullMatrix<number2>::ExcEmptyMatrix());
  AssertDimension(dst.size(), this->n());
  AssertDimension(src.size(), this->m());

  const size_type m = this->m(), n = this->n();

  GrowingVectorMemory<Vector<number2> > mem;
  Vector<number2> *aux = mem.alloc();
  aux->reinit(src, true);
  *aux = src;
  // m > n, m = src.n, n = dst.n

  // Multiply Q_n ... Q_2 Q_1 src
  // Where Q_i = I-v_i v_i^T
  for (size_type j=0; j<n; ++j)
    {
      // sum = v_i^T dst
      number2 sum = diagonal[j]* (*aux)(j);
      for (size_type i=j+1 ; i<m ; ++i)
        sum += static_cast<number2>(this->el(i,j))*(*aux)(i);
      // dst -= v * sum
      (*aux)(j) -= sum*diagonal[j];
      for (size_type i=j+1 ; i<m ; ++i)
        (*aux)(i) -= sum*static_cast<number2>(this->el(i,j));
    }
  // Compute norm of residual
  number2 sum = 0.;
  for (size_type i=n ; i<m ; ++i)
    sum += (*aux)(i) * (*aux)(i);
  AssertIsFinite(sum);

  // Compute solution
  this->backward(dst, *aux);

  mem.free(aux);

  return std::sqrt(sum);
}

template <typename number>
template <typename number2>
double
Householder<number>::least_squares (BlockVector<number2> &dst,
                                    const BlockVector<number2> &src) const
{
  Assert (!this->empty(), typename FullMatrix<number2>::ExcEmptyMatrix());
  AssertDimension(dst.size(), this->n());
  AssertDimension(src.size(), this->m());

  const size_type m = this->m(), n = this->n();

  GrowingVectorMemory<BlockVector<number2> > mem;
  BlockVector<number2> *aux = mem.alloc();
  aux->reinit(src, true);
  *aux = src;
  // m > n, m = src.n, n = dst.n

  // Multiply Q_n ... Q_2 Q_1 src
  // Where Q_i = I-v_i v_i^T
  for (size_type j=0; j<n; ++j)
    {
      // sum = v_i^T dst
      number2 sum = diagonal[j]* (*aux)(j);
      for (size_type i=j+1 ; i<m ; ++i)
        sum += this->el(i,j)*(*aux)(i);
      // dst -= v * sum
      (*aux)(j) -= sum*diagonal[j];
      for (size_type i=j+1 ; i<m ; ++i)
        (*aux)(i) -= sum*this->el(i,j);
    }
  // Compute norm of residual
  number2 sum = 0.;
  for (size_type i=n ; i<m ; ++i)
    sum += (*aux)(i) * (*aux)(i);
  AssertIsFinite(sum);

  //backward works for
  //Vectors only, so copy
  //them before
  Vector<number2> v_dst, v_aux;
  v_dst = dst;
  v_aux = *aux;
  // Compute solution
  this->backward(v_dst, v_aux);
  //copy the result back
  //to the BlockVector
  dst = v_dst;

  mem.free(aux);

  return std::sqrt(sum);
}


template <typename number>
template <class VectorType>
void
Householder<number>::vmult (VectorType &dst, const VectorType &src) const
{
  least_squares (dst, src);
}


template <typename number>
template <class VectorType>
void
Householder<number>::Tvmult (VectorType &, const VectorType &) const
{
  Assert(false, ExcNotImplemented());
}



#endif // DOXYGEN

DEAL_II_NAMESPACE_CLOSE

#endif