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// $Id: trilinos_block_sparse_matrix.h 20934 2010-04-02 13:09:20Z bangerth $
// Version: $Name$
//
// Copyright (C) 2008, 2010 by the deal.II authors
//
// This file is subject to QPL and may not be distributed
// without copyright and license information. Please refer
// to the file deal.II/doc/license.html for the text and
// further information on this license.
//
//---------------------------------------------------------------------------
#ifndef __deal2__trilinos_block_sparse_matrix_h
#define __deal2__trilinos_block_sparse_matrix_h
#include <base/config.h>
#ifdef DEAL_II_USE_TRILINOS
# include <base/table.h>
# include <lac/block_matrix_base.h>
# include <lac/trilinos_sparse_matrix.h>
# include <lac/trilinos_block_vector.h>
# include <lac/full_matrix.h>
# include <lac/exceptions.h>
# include <cmath>
# define TrilinosScalar double
DEAL_II_NAMESPACE_OPEN
// forward declarations
class BlockSparsityPattern;
class BlockCompressedSparsityPattern;
class BlockCompressedSetSparsityPattern;
class BlockCompressedSimpleSparsityPattern;
template <typename number> class BlockSparseMatrix;
namespace TrilinosWrappers
{
/*! @addtogroup TrilinosWrappers
*@{
*/
/**
* Blocked sparse matrix based on the TrilinosWrappers::SparseMatrix class. This
* class implements the functions that are specific to the Trilinos SparseMatrix
* base objects for a blocked sparse matrix, and leaves the actual work
* relaying most of the calls to the individual blocks to the functions
* implemented in the base class. See there also for a description of when
* this class is useful.
*
* In contrast to the deal.II-type SparseMatrix class, the Trilinos matrices do
* not have external objects for the sparsity patterns. Thus, one does not
* determine the size of the individual blocks of a block matrix of this type
* by attaching a block sparsity pattern, but by calling reinit() to set the
* number of blocks and then by setting the size of each block separately. In
* order to fix the data structures of the block matrix, it is then necessary
* to let it know that we have changed the sizes of the underlying
* matrices. For this, one has to call the collect_sizes() function, for much
* the same reason as is documented with the BlockSparsityPattern class.
*
* @ingroup Matrix1
* @see @ref GlossBlockLA "Block (linear algebra)"
* @author Martin Kronbichler, Wolfgang Bangerth, 2008
*/
class BlockSparseMatrix : public BlockMatrixBase<SparseMatrix>
{
public:
/**
* Typedef the base class for simpler
* access to its own typedefs.
*/
typedef BlockMatrixBase<SparseMatrix> BaseClass;
/**
* Typedef the type of the underlying
* matrix.
*/
typedef BaseClass::BlockType BlockType;
/**
* Import the typedefs from the base
* class.
*/
typedef BaseClass::value_type value_type;
typedef BaseClass::pointer pointer;
typedef BaseClass::const_pointer const_pointer;
typedef BaseClass::reference reference;
typedef BaseClass::const_reference const_reference;
typedef BaseClass::size_type size_type;
typedef BaseClass::iterator iterator;
typedef BaseClass::const_iterator const_iterator;
/**
* Constructor; initializes the
* matrix to be empty, without
* any structure, i.e. the
* matrix is not usable at
* all. This constructor is
* therefore only useful for
* matrices which are members of
* a class. All other matrices
* should be created at a point
* in the data flow where all
* necessary information is
* available.
*
* You have to initialize the
* matrix before usage with
* reinit(BlockSparsityPattern). The
* number of blocks per row and
* column are then determined by
* that function.
*/
BlockSparseMatrix ();
/**
* Destructor.
*/
~BlockSparseMatrix ();
/**
* Pseudo copy operator only copying
* empty objects. The sizes of the block
* matrices need to be the same.
*/
BlockSparseMatrix &
operator = (const BlockSparseMatrix &);
/**
* This operator assigns a scalar to a
* matrix. Since this does usually not
* make much sense (should we set all
* matrix entries to this value? Only
* the nonzero entries of the sparsity
* pattern?), this operation is only
* allowed if the actual value to be
* assigned is zero. This operator only
* exists to allow for the obvious
* notation <tt>matrix=0</tt>, which
* sets all elements of the matrix to
* zero, but keep the sparsity pattern
* previously used.
*/
BlockSparseMatrix &
operator = (const double d);
/**
* Resize the matrix, by setting
* the number of block rows and
* columns. This deletes all
* blocks and replaces them by
* unitialized ones, i.e. ones
* for which also the sizes are
* not yet set. You have to do
* that by calling the @p reinit
* functions of the blocks
* themselves. Do not forget to
* call collect_sizes() after
* that on this object.
*
* The reason that you have to
* set sizes of the blocks
* yourself is that the sizes may
* be varying, the maximum number
* of elements per row may be
* varying, etc. It is simpler
* not to reproduce the interface
* of the @p SparsityPattern
* class here but rather let the
* user call whatever function
* she desires.
*/
void reinit (const unsigned int n_block_rows,
const unsigned int n_block_columns);
/**
* Resize the matrix, by using an
* array of Epetra maps to determine
* the %parallel distribution of the
* individual matrices. This function
* assumes that a quadratic block
* matrix is generated.
*/
template <typename BlockSparsityType>
void reinit (const std::vector<Epetra_Map> &input_maps,
const BlockSparsityType &block_sparsity_pattern);
/**
* Resize the matrix, by using an
* array of index sets to determine
* the %parallel distribution of the
* individual matrices. This function
* assumes that a quadratic block
* matrix is generated.
*/
template <typename BlockSparsityType>
void reinit (const std::vector<IndexSet> &input_maps,
const BlockSparsityType &block_sparsity_pattern,
const MPI_Comm &communicator = MPI_COMM_WORLD);
/**
* Resize the matrix and initialize it
* by the given sparsity pattern. Since
* no distribution map is given, the
* result is a block matrix for which
* all elements are stored locally.
*/
template <typename BlockSparsityType>
void reinit (const BlockSparsityType &block_sparsity_pattern);
/**
* This function initializes the
* Trilinos matrix using the deal.II
* sparse matrix and the entries stored
* therein. It uses a threshold
* to copy only elements whose
* modulus is larger than the
* threshold (so zeros in the
* deal.II matrix can be filtered
* away).
*/
void reinit (const std::vector<Epetra_Map> &input_maps,
const ::dealii::BlockSparseMatrix<double> &deal_ii_sparse_matrix,
const double drop_tolerance=1e-13);
/**
* This function initializes
* the Trilinos matrix using
* the deal.II sparse matrix
* and the entries stored
* therein. It uses a threshold
* to copy only elements whose
* modulus is larger than the
* threshold (so zeros in the
* deal.II matrix can be
* filtered away). Since no
* Epetra_Map is given, all the
* elements will be locally
* stored.
*/
void reinit (const ::dealii::BlockSparseMatrix<double> &deal_ii_sparse_matrix,
const double drop_tolerance=1e-13);
/**
* This function calls the compress()
* command of all matrices after
* the assembly is
* completed. Note that all MPI
* processes need to call this
* command (whereas the individual
* assembly routines will most probably
* only be called on each processor
* individually) before any
* can complete it.
*/
void compress ();
/**
* Returns the state of the
* matrix, i.e., whether
* compress() needs to be called
* after an operation requiring
* data exchange. Does only
* return non-true values when
* used in <tt>debug</tt> mode,
* since it is quite expensive to
* keep track of all operations
* that lead to the need for
* compress().
*/
bool is_compressed () const;
/**
* This function collects the
* sizes of the sub-objects and
* stores them in internal
* arrays, in order to be able to
* relay global indices into the
* matrix to indices into the
* subobjects. You *must* call
* this function each time after
* you have changed the size of
* the sub-objects. Note that
* this is a collective
* operation, i.e., it needs to
* be called on all MPI
* processes. This command
* internally calls the method
* <tt>compress()</tt>, so you
* don't need to call that
* function in case you use
* <tt>collect_sizes()</tt>.
*/
void collect_sizes ();
/**
* Return the number of nonzero
* elements of this
* matrix.
*/
unsigned int n_nonzero_elements () const;
/**
* Matrix-vector multiplication:
* let $dst = M*src$ with $M$
* being this matrix.
*/
void vmult (MPI::BlockVector &dst,
const MPI::BlockVector &src) const;
/**
* Matrix-vector multiplication:
* let $dst = M*src$ with $M$
* being this matrix, now applied
* to localized block vectors
* (works only when run on one
* processor).
*/
void vmult (BlockVector &dst,
const BlockVector &src) const;
/**
* Matrix-vector
* multiplication. Just like the
* previous function, but only
* applicable if the matrix has
* only one block column.
*/
void vmult (MPI::BlockVector &dst,
const MPI::Vector &src) const;
/**
* Matrix-vector
* multiplication. Just like the
* previous function, but only
* applicable if the matrix has
* only one block column, now
* applied to localized vectors
* (works only when run on one
* processor).
*/
void vmult (BlockVector &dst,
const Vector &src) const;
/**
* Matrix-vector
* multiplication. Just like the
* previous function, but only
* applicable if the matrix has
* only one block row.
*/
void vmult (MPI::Vector &dst,
const MPI::BlockVector &src) const;
/**
* Matrix-vector
* multiplication. Just like the
* previous function, but only
* applicable if the matrix has
* only one block row, now
* applied to localized vectors
* (works only when run on one
* processor).
*/
void vmult (Vector &dst,
const BlockVector &src) const;
/**
* Matrix-vector
* multiplication. Just like the
* previous function, but only
* applicable if the matrix has
* only one block.
*/
void vmult (VectorBase &dst,
const VectorBase &src) const;
/**
* Matrix-vector multiplication:
* let $dst = M^T*src$ with $M$
* being this matrix. This
* function does the same as
* vmult() but takes the
* transposed matrix.
*/
void Tvmult (MPI::BlockVector &dst,
const MPI::BlockVector &src) const;
/**
* Matrix-vector multiplication:
* let $dst = M^T*src$ with $M$
* being this matrix. This
* function does the same as
* vmult() but takes the
* transposed matrix, now applied
* to localized Trilinos vectors
* (works only when run on one
* processor).
*/
void Tvmult (BlockVector &dst,
const BlockVector &src) const;
/**
* Matrix-vector
* multiplication. Just like the
* previous function, but only
* applicable if the matrix has
* only one block row.
*/
void Tvmult (MPI::BlockVector &dst,
const MPI::Vector &src) const;
/**
* Matrix-vector
* multiplication. Just like the
* previous function, but only
* applicable if the matrix has
* only one block row, now
* applied to localized Trilinos
* vectors (works only when run
* on one processor).
*/
void Tvmult (BlockVector &dst,
const Vector &src) const;
/**
* Matrix-vector
* multiplication. Just like the
* previous function, but only
* applicable if the matrix has
* only one block column.
*/
void Tvmult (MPI::Vector &dst,
const MPI::BlockVector &src) const;
/**
* Matrix-vector
* multiplication. Just like the
* previous function, but only
* applicable if the matrix has
* only one block column, now
* applied to localized Trilinos
* vectors (works only when run
* on one processor).
*/
void Tvmult (Vector &dst,
const BlockVector &src) const;
/**
* Matrix-vector
* multiplication. Just like the
* previous function, but only
* applicable if the matrix has
* only one block.
*/
void Tvmult (VectorBase &dst,
const VectorBase &src) const;
/**
* Compute the residual of an
* equation <i>Mx=b</i>, where
* the residual is defined to
* be <i>r=b-Mx</i>. Write the
* residual into @p dst. The
* <i>l<sub>2</sub></i> norm of
* the residual vector is
* returned.
*
* Source <i>x</i> and
* destination <i>dst</i> must
* not be the same vector.
*
* Note that both vectors have
* to be distributed vectors
* generated using the same Map
* as was used for the matrix
* in case you work on a
* distributed memory
* architecture, using the
* interface in the
* TrilinosWrappers::MPI::BlockVector
* class.
*/
TrilinosScalar residual (MPI::BlockVector &dst,
const MPI::BlockVector &x,
const MPI::BlockVector &b) const;
/**
* Compute the residual of an
* equation <i>Mx=b</i>, where
* the residual is defined to
* be <i>r=b-Mx</i>. Write the
* residual into @p dst. The
* <i>l<sub>2</sub></i> norm of
* the residual vector is
* returned.
*
* Source <i>x</i> and
* destination <i>dst</i> must
* not be the same vector.
*
* Note that both vectors have
* to be distributed vectors
* generated using the same Map
* as was used for the matrix
* in case you work on a
* distributed memory
* architecture, using the
* interface in the
* TrilinosWrappers::BlockVector
* class. Since the block
* matrix is in general
* distributed among processes,
* this function only works
* when running the program on
* one processor.
*/
TrilinosScalar residual (BlockVector &dst,
const BlockVector &x,
const BlockVector &b) const;
/**
* Compute the residual of an
* equation <i>Mx=b</i>, where
* the residual is defined to
* be <i>r=b-Mx</i>. Write the
* residual into @p dst. The
* <i>l<sub>2</sub></i> norm of
* the residual vector is
* returned. Just like the
* previous function, but only
* applicable if the matrix
* only has one block row.
*/
TrilinosScalar residual (MPI::BlockVector &dst,
const MPI::Vector &x,
const MPI::BlockVector &b) const;
/**
* Compute the residual of an
* equation <i>Mx=b</i>, where
* the residual is defined to
* be <i>r=b-Mx</i>. Write the
* residual into @p dst. The
* <i>l<sub>2</sub></i> norm of
* the residual vector is
* returned. Just like the
* previous function, but only
* applicable if the matrix
* only has one block row.
*/
TrilinosScalar residual (BlockVector &dst,
const Vector &x,
const BlockVector &b) const;
/**
* Compute the residual of an
* equation <i>Mx=b</i>, where
* the residual is defined to
* be <i>r=b-Mx</i>. Write the
* residual into @p dst. The
* <i>l<sub>2</sub></i> norm of
* the residual vector is
* returned. Just like the
* previous function, but only
* applicable if the matrix
* only has one block column.
*/
TrilinosScalar residual (MPI::Vector &dst,
const MPI::BlockVector &x,
const MPI::Vector &b) const;
/**
* Compute the residual of an
* equation <i>Mx=b</i>, where
* the residual is defined to
* be <i>r=b-Mx</i>. Write the
* residual into @p dst. The
* <i>l<sub>2</sub></i> norm of
* the residual vector is
* returned. Just like the
* previous function, but only
* applicable if the matrix
* only has one block column.
*/
TrilinosScalar residual (Vector &dst,
const BlockVector &x,
const Vector &b) const;
/**
* Compute the residual of an
* equation <i>Mx=b</i>, where
* the residual is defined to
* be <i>r=b-Mx</i>. Write the
* residual into @p dst. The
* <i>l<sub>2</sub></i> norm of
* the residual vector is
* returned. Just like the
* previous function, but only
* applicable if the matrix
* only has one block.
*/
TrilinosScalar residual (VectorBase &dst,
const VectorBase &x,
const VectorBase &b) const;
/**
* Make the clear() function in the
* base class visible, though it is
* protected.
*/
using BlockMatrixBase<SparseMatrix>::clear;
/** @addtogroup Exceptions
* @{
*/
/**
* Exception
*/
DeclException4 (ExcIncompatibleRowNumbers,
int, int, int, int,
<< "The blocks [" << arg1 << ',' << arg2 << "] and ["
<< arg3 << ',' << arg4 << "] have differing row numbers.");
/**
* Exception
*/
DeclException4 (ExcIncompatibleColNumbers,
int, int, int, int,
<< "The blocks [" << arg1 << ',' << arg2 << "] and ["
<< arg3 << ',' << arg4 << "] have differing column numbers.");
///@}
};
/*@}*/
// ------------- inline and template functions -----------------
inline
BlockSparseMatrix &
BlockSparseMatrix::operator = (const double d)
{
Assert (d==0, ExcScalarAssignmentOnlyForZeroValue());
for (unsigned int r=0; r<this->n_block_rows(); ++r)
for (unsigned int c=0; c<this->n_block_cols(); ++c)
this->block(r,c) = d;
return *this;
}
inline
bool
BlockSparseMatrix::is_compressed () const
{
bool compressed = true;
for (unsigned int row=0; row<n_block_rows(); ++row)
for (unsigned int col=0; col<n_block_cols(); ++col)
if (block(row, col).is_compressed() == false)
{
compressed = false;
break;
}
return compressed;
}
inline
void
BlockSparseMatrix::vmult (MPI::BlockVector &dst,
const MPI::BlockVector &src) const
{
BaseClass::vmult_block_block (dst, src);
}
inline
void
BlockSparseMatrix::vmult (BlockVector &dst,
const BlockVector &src) const
{
BaseClass::vmult_block_block (dst, src);
}
inline
void
BlockSparseMatrix::vmult (MPI::BlockVector &dst,
const MPI::Vector &src) const
{
BaseClass::vmult_block_nonblock (dst, src);
}
inline
void
BlockSparseMatrix::vmult (BlockVector &dst,
const Vector &src) const
{
BaseClass::vmult_block_nonblock (dst, src);
}
inline
void
BlockSparseMatrix::vmult (MPI::Vector &dst,
const MPI::BlockVector &src) const
{
BaseClass::vmult_nonblock_block (dst, src);
}
inline
void
BlockSparseMatrix::vmult (Vector &dst,
const BlockVector &src) const
{
BaseClass::vmult_nonblock_block (dst, src);
}
inline
void
BlockSparseMatrix::vmult (VectorBase &dst,
const VectorBase &src) const
{
BaseClass::vmult_nonblock_nonblock (dst, src);
}
inline
void
BlockSparseMatrix::Tvmult (MPI::BlockVector &dst,
const MPI::BlockVector &src) const
{
BaseClass::Tvmult_block_block (dst, src);
}
inline
void
BlockSparseMatrix::Tvmult (BlockVector &dst,
const BlockVector &src) const
{
BaseClass::Tvmult_block_block (dst, src);
}
inline
void
BlockSparseMatrix::Tvmult (MPI::BlockVector &dst,
const MPI::Vector &src) const
{
BaseClass::Tvmult_block_nonblock (dst, src);
}
inline
void
BlockSparseMatrix::Tvmult (BlockVector &dst,
const Vector &src) const
{
BaseClass::Tvmult_block_nonblock (dst, src);
}
inline
void
BlockSparseMatrix::Tvmult (MPI::Vector &dst,
const MPI::BlockVector &src) const
{
BaseClass::Tvmult_nonblock_block (dst, src);
}
inline
void
BlockSparseMatrix::Tvmult (Vector &dst,
const BlockVector &src) const
{
BaseClass::Tvmult_nonblock_block (dst, src);
}
inline
void
BlockSparseMatrix::Tvmult (VectorBase &dst,
const VectorBase &src) const
{
BaseClass::Tvmult_nonblock_nonblock (dst, src);
}
}
DEAL_II_NAMESPACE_CLOSE
#endif // DEAL_II_USE_TRILINOS
#endif // __deal2__trilinos_block_sparse_matrix_h
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