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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
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