/usr/include/deal.II/lac/trilinos_sparse_matrix.h is in libdeal.ii-dev 8.4.2-2+b1.
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//
// Copyright (C) 2008 - 2016 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__trilinos_sparse_matrix_h
#define dealii__trilinos_sparse_matrix_h
#include <deal.II/base/config.h>
#ifdef DEAL_II_WITH_TRILINOS
# include <deal.II/base/std_cxx11/shared_ptr.h>
# include <deal.II/base/subscriptor.h>
# include <deal.II/base/index_set.h>
# include <deal.II/lac/full_matrix.h>
# include <deal.II/lac/exceptions.h>
# include <deal.II/lac/trilinos_vector.h>
# include <deal.II/lac/vector_view.h>
# include <vector>
# include <cmath>
# include <memory>
# define TrilinosScalar double
DEAL_II_DISABLE_EXTRA_DIAGNOSTICS
# include <Epetra_FECrsMatrix.h>
# include <Epetra_Map.h>
# include <Epetra_CrsGraph.h>
# include <Epetra_MultiVector.h>
# ifdef DEAL_II_WITH_MPI
# include <Epetra_MpiComm.h>
# include "mpi.h"
# else
# include "Epetra_SerialComm.h"
# endif
DEAL_II_ENABLE_EXTRA_DIAGNOSTICS
class Epetra_Export;
DEAL_II_NAMESPACE_OPEN
// forward declarations
template <typename MatrixType> class BlockMatrixBase;
template <typename number> class SparseMatrix;
class SparsityPattern;
class DynamicSparsityPattern;
namespace TrilinosWrappers
{
// forward declarations
class SparseMatrix;
class SparsityPattern;
/**
* Iterators for Trilinos matrices
*/
namespace SparseMatrixIterators
{
// forward declaration
template <bool Constness> class Iterator;
/**
* Exception
*/
DeclException0 (ExcBeyondEndOfMatrix);
/**
* Exception
*/
DeclException3 (ExcAccessToNonlocalRow,
std::size_t, std::size_t, std::size_t,
<< "You tried to access row " << arg1
<< " of a distributed sparsity pattern, "
<< " but only rows " << arg2 << " through " << arg3
<< " are stored locally and can be accessed.");
/**
* Handling of indices for both constant and non constant Accessor objects
*
* For a regular dealii::SparseMatrix, we would use an accessor for the
* sparsity pattern. For Trilinos matrices, this does not seem so simple,
* therefore, we write a little base class here.
*
* @author Guido Kanschat
* @date 2012
*/
class AccessorBase
{
public:
/**
* Declare the type for container size.
*/
typedef dealii::types::global_dof_index size_type;
/**
* Constructor.
*/
AccessorBase (SparseMatrix *matrix,
const size_type row,
const size_type index);
/**
* Row number of the element represented by this object.
*/
size_type row() const;
/**
* Index in row of the element represented by this object.
*/
size_type index() const;
/**
* Column number of the element represented by this object.
*/
size_type column() const;
protected:
/**
* Pointer to the matrix object. This object should be handled as a
* const pointer or non-const by the appropriate derived classes. In
* order to be able to implement both, it is not const here, so handle
* with care!
*/
mutable SparseMatrix *matrix;
/**
* Current row number.
*/
size_type a_row;
/**
* Current index in row.
*/
size_type a_index;
/**
* Discard the old row caches (they may still be used by other
* accessors) and generate new ones for the row pointed to presently by
* this accessor.
*/
void visit_present_row ();
/**
* Cache where we store the column indices of the present row. This is
* necessary, since Trilinos makes access to the elements of its
* matrices rather hard, and it is much more efficient to copy all
* column entries of a row once when we enter it than repeatedly asking
* Trilinos for individual ones. This also makes some sense since it is
* likely that we will access them sequentially anyway.
*
* In order to make copying of iterators/accessor of acceptable
* performance, we keep a shared pointer to these entries so that more
* than one accessor can access this data if necessary.
*/
std_cxx11::shared_ptr<std::vector<size_type> > colnum_cache;
/**
* Cache for the values of this row.
*/
std_cxx11::shared_ptr<std::vector<TrilinosScalar> > value_cache;
};
/**
* General template for sparse matrix accessors. The first template
* argument denotes the underlying numeric type, the second the constness
* of the matrix.
*
* The general template is not implemented, only the specializations for
* the two possible values of the second template argument. Therefore, the
* interface listed here only serves as a template provided since doxygen
* does not link the specializations.
*/
template <bool Constess>
class Accessor : public AccessorBase
{
/**
* Value of this matrix entry.
*/
TrilinosScalar value() const;
/**
* Value of this matrix entry.
*/
TrilinosScalar &value();
};
/**
* The specialization for a const Accessor.
*/
template<>
class Accessor<true> : public AccessorBase
{
public:
/**
* Typedef for the type (including constness) of the matrix to be used
* here.
*/
typedef const SparseMatrix MatrixType;
/**
* Constructor. Since we use accessors only for read access, a const
* matrix pointer is sufficient.
*/
Accessor (MatrixType *matrix,
const size_type row,
const size_type index);
/**
* Copy constructor to get from a const or non-const accessor to a const
* accessor.
*/
template <bool Other>
Accessor (const Accessor<Other> &a);
/**
* Value of this matrix entry.
*/
TrilinosScalar value() const;
private:
/**
* Make iterator class a friend.
*/
template <bool> friend class Iterator;
};
/**
* The specialization for a mutable Accessor.
*/
template<>
class Accessor<false> : public AccessorBase
{
class Reference
{
public:
/**
* Constructor.
*/
Reference (const Accessor<false> &accessor);
/**
* Conversion operator to the data type of the matrix.
*/
operator TrilinosScalar () const;
/**
* Set the element of the matrix we presently point to to @p n.
*/
const Reference &operator = (const TrilinosScalar n) const;
/**
* Add @p n to the element of the matrix we presently point to.
*/
const Reference &operator += (const TrilinosScalar n) const;
/**
* Subtract @p n from the element of the matrix we presently point to.
*/
const Reference &operator -= (const TrilinosScalar n) const;
/**
* Multiply the element of the matrix we presently point to by @p n.
*/
const Reference &operator *= (const TrilinosScalar n) const;
/**
* Divide the element of the matrix we presently point to by @p n.
*/
const Reference &operator /= (const TrilinosScalar n) const;
private:
/**
* Pointer to the accessor that denotes which element we presently
* point to.
*/
Accessor &accessor;
};
public:
/**
* Typedef for the type (including constness) of the matrix to be used
* here.
*/
typedef SparseMatrix MatrixType;
/**
* Constructor. Since we use accessors only for read access, a const
* matrix pointer is sufficient.
*/
Accessor (MatrixType *matrix,
const size_type row,
const size_type index);
/**
* Value of this matrix entry.
*/
Reference value() const;
private:
/**
* Make iterator class a friend.
*/
template <bool> friend class Iterator;
/**
* Make Reference object a friend.
*/
friend class Reference;
};
/**
* This class acts as an iterator walking over the elements of Trilinos
* matrices. The implementation of this class is similar to the one for
* PETSc matrices.
*
* Note that Trilinos stores the elements within each row in ascending
* order. This is opposed to the deal.II sparse matrix style where the
* diagonal element (if it exists) is stored before all other values, and
* the PETSc sparse matrices, where one can't guarantee a certain order of
* the elements.
*
* @ingroup TrilinosWrappers
* @author Martin Kronbichler, Wolfgang Bangerth, 2008
*/
template <bool Constness>
class Iterator
{
public:
/**
* Declare type for container size.
*/
typedef dealii::types::global_dof_index size_type;
/**
* Typedef for the matrix type (including constness) we are to operate
* on.
*/
typedef typename Accessor<Constness>::MatrixType MatrixType;
/**
* Constructor. Create an iterator into the matrix @p matrix for the
* given row and the index within it.
*/
Iterator (MatrixType *matrix,
const size_type row,
const size_type index);
/**
* Copy constructor with optional change of constness.
*/
template <bool Other>
Iterator(const Iterator<Other> &other);
/**
* Prefix increment.
*/
Iterator<Constness> &operator++ ();
/**
* Postfix increment.
*/
Iterator<Constness> operator++ (int);
/**
* Dereferencing operator.
*/
const Accessor<Constness> &operator* () const;
/**
* Dereferencing operator.
*/
const Accessor<Constness> *operator-> () const;
/**
* Comparison. True, if both iterators point to the same matrix
* position.
*/
bool operator == (const Iterator<Constness> &) const;
/**
* Inverse of <tt>==</tt>.
*/
bool operator != (const Iterator<Constness> &) const;
/**
* Comparison operator. Result is true if either the first row number is
* smaller or if the row numbers are equal and the first index is
* smaller.
*/
bool operator < (const Iterator<Constness> &) const;
/**
* Comparison operator. The opposite of the previous operator
*/
bool operator > (const Iterator<Constness> &) const;
/**
* Exception
*/
DeclException2 (ExcInvalidIndexWithinRow,
size_type, size_type,
<< "Attempt to access element " << arg2
<< " of row " << arg1
<< " which doesn't have that many elements.");
private:
/**
* Store an object of the accessor class.
*/
Accessor<Constness> accessor;
template <bool Other> friend class Iterator;
};
}
/**
* This class implements a wrapper to use the Trilinos distributed sparse
* matrix class Epetra_FECrsMatrix. This is precisely the kind of matrix we
* deal with all the time - we most likely get it from some assembly
* process, where also entries not locally owned might need to be written
* and hence need to be forwarded to the owner process. This class is
* designed to be used in a distributed memory architecture with an MPI
* compiler on the bottom, but works equally well also for serial processes.
* The only requirement for this class to work is that Trilinos has been
* installed with the same compiler as is used for generating deal.II.
*
* The interface of this class is modeled after the existing SparseMatrix
* class in deal.II. It has almost the same member functions, and is often
* exchangeable. However, since Trilinos only supports a single scalar type
* (double), it is not templated, and only works with doubles.
*
* Note that Trilinos only guarantees that operations do what you expect if
* the functions @p GlobalAssemble has been called after matrix assembly.
* Therefore, you need to call SparseMatrix::compress() before you actually
* use the matrix. This also calls @p FillComplete that compresses the
* storage format for sparse matrices by discarding unused elements.
* Trilinos allows to continue with assembling the matrix after calls to
* these functions, though.
*
* <h3>Thread safety of Trilinos matrices</h3>
*
* When writing into Trilinos matrices from several threads in shared
* memory, several things must be kept in mind as there is no built-in locks
* in this class to prevent data races. Simultaneous access to the same
* matrix row at the same time can lead to data races and must be explicitly
* avoided by the user. However, it is possible to access <b>different</b>
* rows of the matrix from several threads simultaneously under the
* following three conditions:
* <ul>
* <li> The matrix uses only one MPI process.
* <li> The matrix has been initialized with the reinit() method with a
* DynamicSparsityPattern (that includes the set of locally relevant rows,
* i.e., the rows that an assembly routine will possibly write into).
* <li> The matrix has been initialized from a
* TrilinosWrappers::SparsityPattern object that in turn has been
* initialized with the reinit function specifying three index sets, one for
* the rows, one for the columns and for the larger set of @p
* writeable_rows, and the operation is an addition. At some point in the
* future, Trilinos support might be complete enough such that initializing
* from a TrilinosWrappers::SparsityPattern that has been filled by a
* function similar to DoFTools::make_sparsity_pattern always results in a
* matrix that allows several processes to write into the same matrix row.
* However, Trilinos until version at least 11.12 does not correctly support
* this feature.
* </ul>
*
* Note that all other reinit methods and constructors of
* TrilinosWrappers::SparsityPattern will result in a matrix that needs to
* allocate off-processor entries on demand, which breaks thread-safety. Of
* course, using the respective reinit method for the block Trilinos
* sparsity pattern and block matrix also results in thread-safety.
*
* @ingroup TrilinosWrappers
* @ingroup Matrix1
* @author Martin Kronbichler, Wolfgang Bangerth, 2008, 2009
*/
class SparseMatrix : public Subscriptor
{
public:
/**
* Declare the type for container size.
*/
typedef dealii::types::global_dof_index size_type;
/**
* A structure that describes some of the traits of this class in terms of
* its run-time behavior. Some other classes (such as the block matrix
* classes) that take one or other of the matrix classes as its template
* parameters can tune their behavior based on the variables in this
* class.
*/
struct Traits
{
/**
* It is safe to elide additions of zeros to individual elements of this
* matrix.
*/
static const bool zero_addition_can_be_elided = true;
};
/**
* Declare a typedef for the iterator class.
*/
typedef SparseMatrixIterators::Iterator<false> iterator;
/**
* Declare a typedef for the const iterator class.
*/
typedef SparseMatrixIterators::Iterator<true> const_iterator;
/**
* Declare a typedef in analogy to all the other container classes.
*/
typedef TrilinosScalar value_type;
/**
* @name Constructors and initialization.
*/
//@{
/**
* Default constructor. Generates an empty (zero-size) matrix.
*/
SparseMatrix ();
/**
* Generate a matrix that is completely stored locally, having #m rows and
* #n columns.
*
* The number of columns entries per row is specified as the maximum
* number of entries argument.
*/
SparseMatrix (const size_type m,
const size_type n,
const unsigned int n_max_entries_per_row);
/**
* Generate a matrix that is completely stored locally, having #m rows and
* #n columns.
*
* The vector <tt>n_entries_per_row</tt> specifies the number of entries
* in each row.
*/
SparseMatrix (const size_type m,
const size_type n,
const std::vector<unsigned int> &n_entries_per_row);
/**
* Generate a matrix from a Trilinos sparsity pattern object.
*/
SparseMatrix (const SparsityPattern &InputSparsityPattern);
/**
* Destructor. Made virtual so that one can use pointers to this class.
*/
virtual ~SparseMatrix ();
/**
* This function initializes the Trilinos matrix with a deal.II sparsity
* pattern, i.e. it makes the Trilinos Epetra matrix know the position of
* nonzero entries according to the sparsity pattern. This function is
* meant for use in serial programs, where there is no need to specify how
* the matrix is going to be distributed among different processors. This
* function works in %parallel, too, but it is recommended to manually
* specify the %parallel partitioning of the matrix using an Epetra_Map.
* When run in %parallel, it is currently necessary that each processor
* holds the sparsity_pattern structure because each processor sets its
* rows.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*/
template<typename SparsityPatternType>
void reinit (const SparsityPatternType &sparsity_pattern);
/**
* This function reinitializes the Trilinos sparse matrix from a (possibly
* distributed) Trilinos sparsity pattern.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*
* If you want to write to the matrix from several threads and use MPI,
* you need to use this reinit method with a sparsity pattern that has
* been created with explicitly stating writeable rows. In all other
* cases, you cannot mix MPI with multithreaded writing into the matrix.
*/
void reinit (const SparsityPattern &sparsity_pattern);
/**
* This function copies the layout of @p sparse_matrix to the calling
* matrix. The values are not copied, but you can use copy_from() for
* this.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*/
void reinit (const SparseMatrix &sparse_matrix);
/**
* 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 with modulus larger than the threshold (so zeros in the
* deal.II matrix can be filtered away).
*
* The optional parameter <tt>copy_values</tt> decides whether only the
* sparsity structure of the input matrix should be used or the matrix
* entries should be copied, too.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a deadlock.
*
* @note If a different sparsity pattern is given in the last argument
* (i.e., one that differs from the one used in the sparse matrix given in
* the first argument), then the resulting Trilinos matrix will have the
* sparsity pattern so given. This of course also means that all entries
* in the given matrix that are not part of this separate sparsity pattern
* will in fact be dropped.
*/
template <typename number>
void reinit (const ::dealii::SparseMatrix<number> &dealii_sparse_matrix,
const double drop_tolerance=1e-13,
const bool copy_values=true,
const ::dealii::SparsityPattern *use_this_sparsity=0);
/**
* This reinit function takes as input a Trilinos Epetra_CrsMatrix and
* copies its sparsity pattern. If so requested, even the content (values)
* will be copied.
*/
void reinit (const Epetra_CrsMatrix &input_matrix,
const bool copy_values = true);
//@}
/**
* @name Constructors and initialization using an Epetra_Map description
*/
//@{
/**
* Constructor using an Epetra_Map to describe the %parallel partitioning.
* The parameter @p n_max_entries_per_row sets the number of nonzero
* entries in each row that will be allocated. Note that this number does
* not need to be exact, and it is even allowed that the actual matrix
* structure has more nonzero entries than specified in the constructor.
* However it is still advantageous to provide good estimates here since
* this will considerably increase the performance of the matrix setup.
* However, there is no effect in the performance of matrix-vector
* products, since Trilinos reorganizes the matrix memory prior to use (in
* the compress() step).
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
SparseMatrix (const Epetra_Map ¶llel_partitioning,
const size_type n_max_entries_per_row = 0) DEAL_II_DEPRECATED;
/**
* Same as before, but now set a value of nonzeros for each matrix row.
* Since we know the number of elements in the matrix exactly in this
* case, we can already allocate the right amount of memory, which makes
* the creation process including the insertion of nonzero elements by the
* respective SparseMatrix::reinit call considerably faster.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
SparseMatrix (const Epetra_Map ¶llel_partitioning,
const std::vector<unsigned int> &n_entries_per_row) DEAL_II_DEPRECATED;
/**
* This constructor is similar to the one above, but it now takes two
* different Epetra maps for rows and columns. This interface is meant to
* be used for generating rectangular matrices, where one map describes
* the %parallel partitioning of the dofs associated with the matrix rows
* and the other one the partitioning of dofs in the matrix columns. Note
* that there is no real parallelism along the columns – the
* processor that owns a certain row always owns all the column elements,
* no matter how far they might be spread out. The second Epetra_Map is
* only used to specify the number of columns and for internal
* arrangements when doing matrix-vector products with vectors based on
* that column map.
*
* The integer input @p n_max_entries_per_row defines the number of
* columns entries per row that will be allocated.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
SparseMatrix (const Epetra_Map &row_parallel_partitioning,
const Epetra_Map &col_parallel_partitioning,
const size_type n_max_entries_per_row = 0) DEAL_II_DEPRECATED;
/**
* This constructor is similar to the one above, but it now takes two
* different Epetra maps for rows and columns. This interface is meant to
* be used for generating rectangular matrices, where one map specifies
* the %parallel distribution of degrees of freedom associated with matrix
* rows and the second one specifies the %parallel distribution the dofs
* associated with columns in the matrix. The second map also provides
* information for the internal arrangement in matrix vector products
* (i.e., the distribution of vector this matrix is to be multiplied
* with), but is not used for the distribution of the columns –
* rather, all column elements of a row are stored on the same processor
* in any case. The vector <tt>n_entries_per_row</tt> specifies the number
* of entries in each row of the newly generated matrix.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
SparseMatrix (const Epetra_Map &row_parallel_partitioning,
const Epetra_Map &col_parallel_partitioning,
const std::vector<unsigned int> &n_entries_per_row) DEAL_II_DEPRECATED;
/**
* This function is initializes the Trilinos Epetra matrix according to
* the specified sparsity_pattern, and also reassigns the matrix rows to
* different processes according to a user-supplied Epetra map. In
* programs following the style of the tutorial programs, this function
* (and the respective call for a rectangular matrix) are the natural way
* to initialize the matrix size, its distribution among the MPI processes
* (if run in %parallel) as well as the location of non-zero elements.
* Trilinos stores the sparsity pattern internally, so it won't be needed
* any more after this call, in contrast to the deal.II own object. The
* optional argument @p exchange_data can be used for reinitialization
* with a sparsity pattern that is not fully constructed. This feature is
* only implemented for input sparsity patterns of type
* DynamicSparsityPattern. If the flag is not set, each processor just
* sets the elements in the sparsity pattern that belong to its rows.
*
* If the sparsity pattern given to this function is of type
* DynamicSparsity pattern, then a matrix will be created that allows
* several threads to write into different rows of the matrix at the same
* also with MPI, as opposed to most other reinit() methods.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
template<typename SparsityPatternType>
void reinit (const Epetra_Map ¶llel_partitioning,
const SparsityPatternType &sparsity_pattern,
const bool exchange_data = false) DEAL_II_DEPRECATED;
/**
* This function is similar to the other initialization function above,
* but now also reassigns the matrix rows and columns according to two
* user-supplied Epetra maps. To be used for rectangular matrices. The
* optional argument @p exchange_data can be used for reinitialization
* with a sparsity pattern that is not fully constructed. This feature is
* only implemented for input sparsity patterns of type
* DynamicSparsityPattern.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
template<typename SparsityPatternType>
void reinit (const Epetra_Map &row_parallel_partitioning,
const Epetra_Map &col_parallel_partitioning,
const SparsityPatternType &sparsity_pattern,
const bool exchange_data = false) DEAL_II_DEPRECATED;
/**
* 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 with modulus larger than the threshold (so zeros in the
* deal.II matrix can be filtered away). In contrast to the other reinit
* function with deal.II sparse matrix argument, this function takes a
* %parallel partitioning specified by the user instead of internally
* generating it.
*
* The optional parameter <tt>copy_values</tt> decides whether only the
* sparsity structure of the input matrix should be used or the matrix
* entries should be copied, too.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
template <typename number>
void reinit (const Epetra_Map ¶llel_partitioning,
const ::dealii::SparseMatrix<number> &dealii_sparse_matrix,
const double drop_tolerance=1e-13,
const bool copy_values=true,
const ::dealii::SparsityPattern *use_this_sparsity=0) DEAL_II_DEPRECATED;
/**
* This function is similar to the other initialization function with
* deal.II sparse matrix input above, but now takes Epetra maps for both
* the rows and the columns of the matrix. Chosen for rectangular
* matrices.
*
* The optional parameter <tt>copy_values</tt> decides whether only the
* sparsity structure of the input matrix should be used or the matrix
* entries should be copied, too.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
template <typename number>
void reinit (const Epetra_Map &row_parallel_partitioning,
const Epetra_Map &col_parallel_partitioning,
const ::dealii::SparseMatrix<number> &dealii_sparse_matrix,
const double drop_tolerance=1e-13,
const bool copy_values=true,
const ::dealii::SparsityPattern *use_this_sparsity=0) DEAL_II_DEPRECATED;
//@}
/**
* @name Constructors and initialization using an IndexSet description
*/
//@{
/**
* Constructor using an IndexSet and an MPI communicator to describe the
* %parallel partitioning. The parameter @p n_max_entries_per_row sets the
* number of nonzero entries in each row that will be allocated. Note that
* this number does not need to be exact, and it is even allowed that the
* actual matrix structure has more nonzero entries than specified in the
* constructor. However it is still advantageous to provide good estimates
* here since this will considerably increase the performance of the
* matrix setup. However, there is no effect in the performance of matrix-
* vector products, since Trilinos reorganizes the matrix memory prior to
* use (in the compress() step).
*/
SparseMatrix (const IndexSet ¶llel_partitioning,
const MPI_Comm &communicator = MPI_COMM_WORLD,
const unsigned int n_max_entries_per_row = 0);
/**
* Same as before, but now set the number of nonzeros in each matrix row
* separately. Since we know the number of elements in the matrix exactly
* in this case, we can already allocate the right amount of memory, which
* makes the creation process including the insertion of nonzero elements
* by the respective SparseMatrix::reinit call considerably faster.
*/
SparseMatrix (const IndexSet ¶llel_partitioning,
const MPI_Comm &communicator,
const std::vector<unsigned int> &n_entries_per_row);
/**
* This constructor is similar to the one above, but it now takes two
* different IndexSet partitions for row and columns. This interface is
* meant to be used for generating rectangular matrices, where the first
* index set describes the %parallel partitioning of the degrees of
* freedom associated with the matrix rows and the second one the
* partitioning of the matrix columns. The second index set specifies the
* partitioning of the vectors this matrix is to be multiplied with, not
* the distribution of the elements that actually appear in the matrix.
*
* The parameter @p n_max_entries_per_row defines how much memory will be
* allocated for each row. This number does not need to be accurate, as
* the structure is reorganized in the compress() call.
*/
SparseMatrix (const IndexSet &row_parallel_partitioning,
const IndexSet &col_parallel_partitioning,
const MPI_Comm &communicator = MPI_COMM_WORLD,
const size_type n_max_entries_per_row = 0);
/**
* This constructor is similar to the one above, but it now takes two
* different Epetra maps for rows and columns. This interface is meant to
* be used for generating rectangular matrices, where one map specifies
* the %parallel distribution of degrees of freedom associated with matrix
* rows and the second one specifies the %parallel distribution the dofs
* associated with columns in the matrix. The second map also provides
* information for the internal arrangement in matrix vector products
* (i.e., the distribution of vector this matrix is to be multiplied
* with), but is not used for the distribution of the columns –
* rather, all column elements of a row are stored on the same processor
* in any case. The vector <tt>n_entries_per_row</tt> specifies the number
* of entries in each row of the newly generated matrix.
*/
SparseMatrix (const IndexSet &row_parallel_partitioning,
const IndexSet &col_parallel_partitioning,
const MPI_Comm &communicator,
const std::vector<unsigned int> &n_entries_per_row);
/**
* This function is initializes the Trilinos Epetra matrix according to
* the specified sparsity_pattern, and also reassigns the matrix rows to
* different processes according to a user-supplied index set and
* %parallel communicator. In programs following the style of the tutorial
* programs, this function (and the respective call for a rectangular
* matrix) are the natural way to initialize the matrix size, its
* distribution among the MPI processes (if run in %parallel) as well as
* the location of non-zero elements. Trilinos stores the sparsity pattern
* internally, so it won't be needed any more after this call, in contrast
* to the deal.II own object. The optional argument @p exchange_data can
* be used for reinitialization with a sparsity pattern that is not fully
* constructed. This feature is only implemented for input sparsity
* patterns of type DynamicSparsityPattern. If the flag is not set, each
* processor just sets the elements in the sparsity pattern that belong to
* its rows.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*/
template<typename SparsityPatternType>
void reinit (const IndexSet ¶llel_partitioning,
const SparsityPatternType &sparsity_pattern,
const MPI_Comm &communicator = MPI_COMM_WORLD,
const bool exchange_data = false);
/**
* This function is similar to the other initialization function above,
* but now also reassigns the matrix rows and columns according to two
* user-supplied index sets. To be used for rectangular matrices. The
* optional argument @p exchange_data can be used for reinitialization
* with a sparsity pattern that is not fully constructed. This feature is
* only implemented for input sparsity patterns of type
* DynamicSparsityPattern.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*/
template<typename SparsityPatternType>
void reinit (const IndexSet &row_parallel_partitioning,
const IndexSet &col_parallel_partitioning,
const SparsityPatternType &sparsity_pattern,
const MPI_Comm &communicator = MPI_COMM_WORLD,
const bool exchange_data = false);
/**
* 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 with modulus larger than the threshold (so zeros in the
* deal.II matrix can be filtered away). In contrast to the other reinit
* function with deal.II sparse matrix argument, this function takes a
* %parallel partitioning specified by the user instead of internally
* generating it.
*
* The optional parameter <tt>copy_values</tt> decides whether only the
* sparsity structure of the input matrix should be used or the matrix
* entries should be copied, too.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*/
template <typename number>
void reinit (const IndexSet ¶llel_partitioning,
const ::dealii::SparseMatrix<number> &dealii_sparse_matrix,
const MPI_Comm &communicator = MPI_COMM_WORLD,
const double drop_tolerance=1e-13,
const bool copy_values=true,
const ::dealii::SparsityPattern *use_this_sparsity=0);
/**
* This function is similar to the other initialization function with
* deal.II sparse matrix input above, but now takes index sets for both
* the rows and the columns of the matrix. Chosen for rectangular
* matrices.
*
* The optional parameter <tt>copy_values</tt> decides whether only the
* sparsity structure of the input matrix should be used or the matrix
* entries should be copied, too.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*/
template <typename number>
void reinit (const IndexSet &row_parallel_partitioning,
const IndexSet &col_parallel_partitioning,
const ::dealii::SparseMatrix<number> &dealii_sparse_matrix,
const MPI_Comm &communicator = MPI_COMM_WORLD,
const double drop_tolerance=1e-13,
const bool copy_values=true,
const ::dealii::SparsityPattern *use_this_sparsity=0);
//@}
/**
* @name Information on the matrix
*/
//@{
/**
* Return the number of rows in this matrix.
*/
size_type m () const;
/**
* Return the number of columns in this matrix.
*/
size_type n () const;
/**
* Return the local dimension of the matrix, i.e. the number of rows
* stored on the present MPI process. For sequential matrices, this number
* is the same as m(), but for %parallel matrices it may be smaller.
*
* To figure out which elements exactly are stored locally, use
* local_range().
*/
unsigned int local_size () const;
/**
* Return a pair of indices indicating which rows of this matrix are
* stored locally. The first number is the index of the first row stored,
* the second the index of the one past the last one that is stored
* locally. If this is a sequential matrix, then the result will be the
* pair (0,m()), otherwise it will be a pair (i,i+n), where
* <tt>n=local_size()</tt>.
*/
std::pair<size_type, size_type>
local_range () const;
/**
* Return whether @p index is in the local range or not, see also
* local_range().
*/
bool in_local_range (const size_type index) const;
/**
* Return the number of nonzero elements of this matrix.
*/
size_type n_nonzero_elements () const;
/**
* Number of entries in a specific row.
*/
unsigned int row_length (const size_type row) const;
/**
* Returns the state of the matrix, i.e., whether compress() needs to be
* called after an operation requiring data exchange. A call to compress()
* is also needed when the method set() has been called (even when working
* in serial).
*/
bool is_compressed () const;
/**
* Determine an estimate for the memory consumption (in bytes) of this
* object. Note that only the memory reserved on the current processor is
* returned in case this is called in an MPI-based program.
*/
size_type memory_consumption () const;
/**
* Return the MPI communicator object in use with this matrix.
*/
MPI_Comm get_mpi_communicator () const;
//@}
/**
* @name Modifying entries
*/
//@{
/**
* 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 keeps the sparsity pattern
* previously used.
*/
SparseMatrix &
operator = (const double d);
/**
* Release all memory and return to a state just like after having called
* the default constructor.
*
* This is a collective operation that needs to be called on all
* processors in order to avoid a dead lock.
*/
void clear ();
/**
* This command does two things:
* <ul>
* <li> If the matrix was initialized without a sparsity pattern, elements
* have been added manually using the set() command. When this process is
* completed, a call to compress() reorganizes the internal data
* structures (sparsity pattern) so that a fast access to data is possible
* in matrix-vector products.
* <li> If the matrix structure has already been fixed (either by
* initialization with a sparsity pattern or by calling compress() during
* the setup phase), this command does the %parallel exchange of data.
* This is necessary when we perform assembly on more than one (MPI)
* process, because then some non-local row data will accumulate on nodes
* that belong to the current's processor element, but are actually held
* by another. This command is usually called after all elements have been
* traversed.
* </ul>
*
* In both cases, this function compresses the data structures and allows
* the resulting matrix to be used in all other operations like matrix-
* vector products. This is a collective operation, i.e., it needs to be
* run on all processors when used in %parallel.
*
* See
* @ref GlossCompress "Compressing distributed objects"
* for more information.
*/
void compress (::dealii::VectorOperation::values operation);
/**
* Set the element (<i>i,j</i>) to @p value.
*
* This function is able to insert new elements into the matrix as long as
* compress() has not been called, so the sparsity pattern will be
* extended. When compress() is called for the first time (or in case the
* matrix is initialized from a sparsity pattern), no new elements can be
* added and an insertion of elements at positions which have not been
* initialized will throw an exception.
*
* For the case that the matrix is constructed without a sparsity pattern
* and new matrix entries are added on demand, please note the following
* behavior imposed by the underlying Epetra_FECrsMatrix data structure:
* If the same matrix entry is inserted more than once, the matrix entries
* will be added upon calling compress() (since Epetra does not track
* values to the same entry before the final compress() is called), even
* if VectorOperation::insert is specified as argument to compress(). In
* the case you cannot make sure that matrix entries are only set once,
* initialize the matrix with a sparsity pattern to fix the matrix
* structure before inserting elements.
*/
void set (const size_type i,
const size_type j,
const TrilinosScalar value);
/**
* Set all elements given in a FullMatrix<double> into the sparse matrix
* locations given by <tt>indices</tt>. In other words, this function
* writes the elements in <tt>full_matrix</tt> into the calling matrix,
* using the local-to-global indexing specified by <tt>indices</tt> for
* both the rows and the columns of the matrix. This function assumes a
* quadratic sparse matrix and a quadratic full_matrix, the usual
* situation in FE calculations.
*
* This function is able to insert new elements into the matrix as long as
* compress() has not been called, so the sparsity pattern will be
* extended. After compress() has been called for the first time or the
* matrix has been initialized from a sparsity pattern, extending the
* sparsity pattern is no longer possible and an insertion of elements at
* positions which have not been initialized will throw an exception.
*
* The optional parameter <tt>elide_zero_values</tt> can be used to
* specify whether zero values should be inserted anyway or they should be
* filtered away. The default value is <tt>false</tt>, i.e., even zero
* values are inserted/replaced.
*
* For the case that the matrix is constructed without a sparsity pattern
* and new matrix entries are added on demand, please note the following
* behavior imposed by the underlying Epetra_FECrsMatrix data structure:
* If the same matrix entry is inserted more than once, the matrix entries
* will be added upon calling compress() (since Epetra does not track
* values to the same entry before the final compress() is called), even
* if VectorOperation::insert is specified as argument to compress(). In
* the case you cannot make sure that matrix entries are only set once,
* initialize the matrix with a sparsity pattern to fix the matrix
* structure before inserting elements.
*/
void set (const std::vector<size_type> &indices,
const FullMatrix<TrilinosScalar> &full_matrix,
const bool elide_zero_values = false);
/**
* Same function as before, but now including the possibility to use
* rectangular full_matrices and different local-to-global indexing on
* rows and columns, respectively.
*/
void set (const std::vector<size_type> &row_indices,
const std::vector<size_type> &col_indices,
const FullMatrix<TrilinosScalar> &full_matrix,
const bool elide_zero_values = false);
/**
* Set several elements in the specified row of the matrix with column
* indices as given by <tt>col_indices</tt> to the respective value.
*
* This function is able to insert new elements into the matrix as long as
* compress() has not been called, so the sparsity pattern will be
* extended. After compress() has been called for the first time or the
* matrix has been initialized from a sparsity pattern, extending the
* sparsity pattern is no longer possible and an insertion of elements at
* positions which have not been initialized will throw an exception.
*
* The optional parameter <tt>elide_zero_values</tt> can be used to
* specify whether zero values should be inserted anyway or they should be
* filtered away. The default value is <tt>false</tt>, i.e., even zero
* values are inserted/replaced.
*
* For the case that the matrix is constructed without a sparsity pattern
* and new matrix entries are added on demand, please note the following
* behavior imposed by the underlying Epetra_FECrsMatrix data structure:
* If the same matrix entry is inserted more than once, the matrix entries
* will be added upon calling compress() (since Epetra does not track
* values to the same entry before the final compress() is called), even
* if VectorOperation::insert is specified as argument to compress(). In
* the case you cannot make sure that matrix entries are only set once,
* initialize the matrix with a sparsity pattern to fix the matrix
* structure before inserting elements.
*/
void set (const size_type row,
const std::vector<size_type> &col_indices,
const std::vector<TrilinosScalar> &values,
const bool elide_zero_values = false);
/**
* Set several elements to values given by <tt>values</tt> in a given row
* in columns given by col_indices into the sparse matrix.
*
* This function is able to insert new elements into the matrix as long as
* compress() has not been called, so the sparsity pattern will be
* extended. After compress() has been called for the first time or the
* matrix has been initialized from a sparsity pattern, extending the
* sparsity pattern is no longer possible and an insertion of elements at
* positions which have not been initialized will throw an exception.
*
* The optional parameter <tt>elide_zero_values</tt> can be used to
* specify whether zero values should be inserted anyway or they should be
* filtered away. The default value is <tt>false</tt>, i.e., even zero
* values are inserted/replaced.
*
* For the case that the matrix is constructed without a sparsity pattern
* and new matrix entries are added on demand, please note the following
* behavior imposed by the underlying Epetra_FECrsMatrix data structure:
* If the same matrix entry is inserted more than once, the matrix entries
* will be added upon calling compress() (since Epetra does not track
* values to the same entry before the final compress() is called), even
* if VectorOperation::insert is specified as argument to compress(). In
* the case you cannot make sure that matrix entries are only set once,
* initialize the matrix with a sparsity pattern to fix the matrix
* structure before inserting elements.
*/
void set (const size_type row,
const size_type n_cols,
const size_type *col_indices,
const TrilinosScalar *values,
const bool elide_zero_values = false);
/**
* Add @p value to the element (<i>i,j</i>).
*
* Just as the respective call in deal.II SparseMatrix<Number> class (but
* in contrast to the situation for PETSc based matrices), this function
* throws an exception if an entry does not exist in the sparsity pattern.
* Moreover, if <tt>value</tt> is not a finite number an exception is
* thrown.
*/
void add (const size_type i,
const size_type j,
const TrilinosScalar value);
/**
* Add all elements given in a FullMatrix<double> into sparse matrix
* locations given by <tt>indices</tt>. In other words, this function adds
* the elements in <tt>full_matrix</tt> to the respective entries in
* calling matrix, using the local-to-global indexing specified by
* <tt>indices</tt> for both the rows and the columns of the matrix. This
* function assumes a quadratic sparse matrix and a quadratic full_matrix,
* the usual situation in FE calculations.
*
* Just as the respective call in deal.II SparseMatrix<Number> class (but
* in contrast to the situation for PETSc based matrices), this function
* throws an exception if an entry does not exist in the sparsity pattern.
*
* The optional parameter <tt>elide_zero_values</tt> can be used to
* specify whether zero values should be added anyway or these should be
* filtered away and only non-zero data is added. The default value is
* <tt>true</tt>, i.e., zero values won't be added into the matrix.
*/
void add (const std::vector<size_type> &indices,
const FullMatrix<TrilinosScalar> &full_matrix,
const bool elide_zero_values = true);
/**
* Same function as before, but now including the possibility to use
* rectangular full_matrices and different local-to-global indexing on
* rows and columns, respectively.
*/
void add (const std::vector<size_type> &row_indices,
const std::vector<size_type> &col_indices,
const FullMatrix<TrilinosScalar> &full_matrix,
const bool elide_zero_values = true);
/**
* Set several elements in the specified row of the matrix with column
* indices as given by <tt>col_indices</tt> to the respective value.
*
* Just as the respective call in deal.II SparseMatrix<Number> class (but
* in contrast to the situation for PETSc based matrices), this function
* throws an exception if an entry does not exist in the sparsity pattern.
*
* The optional parameter <tt>elide_zero_values</tt> can be used to
* specify whether zero values should be added anyway or these should be
* filtered away and only non-zero data is added. The default value is
* <tt>true</tt>, i.e., zero values won't be added into the matrix.
*/
void add (const size_type row,
const std::vector<size_type> &col_indices,
const std::vector<TrilinosScalar> &values,
const bool elide_zero_values = true);
/**
* Add an array of values given by <tt>values</tt> in the given global
* matrix row at columns specified by col_indices in the sparse matrix.
*
* Just as the respective call in deal.II SparseMatrix<Number> class (but
* in contrast to the situation for PETSc based matrices), this function
* throws an exception if an entry does not exist in the sparsity pattern.
*
* The optional parameter <tt>elide_zero_values</tt> can be used to
* specify whether zero values should be added anyway or these should be
* filtered away and only non-zero data is added. The default value is
* <tt>true</tt>, i.e., zero values won't be added into the matrix.
*/
void add (const size_type row,
const size_type n_cols,
const size_type *col_indices,
const TrilinosScalar *values,
const bool elide_zero_values = true,
const bool col_indices_are_sorted = false);
/**
* Multiply the entire matrix by a fixed factor.
*/
SparseMatrix &operator *= (const TrilinosScalar factor);
/**
* Divide the entire matrix by a fixed factor.
*/
SparseMatrix &operator /= (const TrilinosScalar factor);
/**
* Copy the given (Trilinos) matrix (sparsity pattern and entries).
*/
void copy_from (const SparseMatrix &source);
/**
* Add <tt>matrix</tt> scaled by <tt>factor</tt> to this matrix, i.e. the
* matrix <tt>factor*matrix</tt> is added to <tt>this</tt>. If the
* sparsity pattern of the calling matrix does not contain all the
* elements in the sparsity pattern of the input matrix, this function
* will throw an exception.
*/
void add (const TrilinosScalar factor,
const SparseMatrix &matrix);
/**
* Remove all elements from this <tt>row</tt> by setting them to zero. The
* function does not modify the number of allocated nonzero entries, it
* only sets the entries to zero.
*
* This operation is used in eliminating constraints (e.g. due to hanging
* nodes) and makes sure that we can write this modification to the matrix
* without having to read entries (such as the locations of non-zero
* elements) from it — without this operation, removing constraints
* on %parallel matrices is a rather complicated procedure.
*
* The second parameter can be used to set the diagonal entry of this row
* to a value different from zero. The default is to set it to zero.
*
* @note If the matrix is stored in parallel across multiple processors
* using MPI, this function only touches rows that are locally stored and
* simply ignores all other row indices. Further, in the context of
* parallel computations, you will get into trouble if you clear a row
* while other processors still have pending writes or additions into the
* same row. In other words, if another processor still wants to add
* something to an element of a row and you call this function to zero out
* the row, then the next time you call compress() may add the remote
* value to the zero you just created. Consequently, you will want to call
* compress() after you made the last modifications to a matrix and before
* starting to clear rows.
*/
void clear_row (const size_type row,
const TrilinosScalar new_diag_value = 0);
/**
* Same as clear_row(), except that it works on a number of rows at once.
*
* The second parameter can be used to set the diagonal entries of all
* cleared rows to something different from zero. Note that all of these
* diagonal entries get the same value -- if you want different values for
* the diagonal entries, you have to set them by hand.
*
* @note If the matrix is stored in parallel across multiple processors
* using MPI, this function only touches rows that are locally stored and
* simply ignores all other row indices. Further, in the context of
* parallel computations, you will get into trouble if you clear a row
* while other processors still have pending writes or additions into the
* same row. In other words, if another processor still wants to add
* something to an element of a row and you call this function to zero out
* the row, then the next time you call compress() may add the remote
* value to the zero you just created. Consequently, you will want to call
* compress() after you made the last modifications to a matrix and before
* starting to clear rows.
*/
void clear_rows (const std::vector<size_type> &rows,
const TrilinosScalar new_diag_value = 0);
/**
* Sets an internal flag so that all operations performed by the matrix,
* i.e., multiplications, are done in transposed order. However, this does
* not reshape the matrix to transposed form directly, so care should be
* taken when using this flag.
*/
void transpose ();
//@}
/**
* @name Entry Access
*/
//@{
/**
* Return the value of the entry (<i>i,j</i>). This may be an expensive
* operation and you should always take care where to call this function.
* As in the deal.II sparse matrix class, we throw an exception if the
* respective entry doesn't exist in the sparsity pattern of this class,
* which is requested from Trilinos. Moreover, an exception will be thrown
* when the requested element is not saved on the calling process.
*/
TrilinosScalar operator () (const size_type i,
const size_type j) const;
/**
* Return the value of the matrix entry (<i>i,j</i>). If this entry does
* not exist in the sparsity pattern, then zero is returned. While this
* may be convenient in some cases, note that it is simple to write
* algorithms that are slow compared to an optimal solution, since the
* sparsity of the matrix is not used. On the other hand, if you want to
* be sure the entry exists, you should use operator() instead.
*
* The lack of error checking in this function can also yield surprising
* results if you have a parallel matrix. In that case, just because you
* get a zero result from this function does not mean that either the
* entry does not exist in the sparsity pattern or that it does but has a
* value of zero. Rather, it could also be that it simply isn't stored on
* the current processor; in that case, it may be stored on a different
* processor, and possibly so with a nonzero value.
*/
TrilinosScalar el (const size_type i,
const size_type j) const;
/**
* Return the main diagonal element in the <i>i</i>th row. This function
* throws an error if the matrix is not quadratic and it also throws an
* error if <i>(i,i)</i> is not element of the local matrix. See also the
* comment in trilinos_sparse_matrix.cc.
*/
TrilinosScalar diag_element (const size_type i) const;
//@}
/**
* @name Multiplications
*/
//@{
/**
* Matrix-vector multiplication: let <i>dst = M*src</i> with <i>M</i>
* being this matrix.
*
* Source and destination must not be the same vector.
*
* This function can be called with several different vector objects,
* namely TrilinosWrappers::Vector, TrilinosWrappers::MPI::Vector as well
* as deal.II's own vector classes Vector<double> and
* parallel::distributed::Vector<double>.
*
* 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::VectorBase class (or one of the two derived classes
* Vector and MPI::Vector).
*
* In case of a localized Vector, this function will only work when
* running on one processor, since the matrix object is inherently
* distributed. Otherwise, and exception will be thrown.
*/
template<typename VectorType>
void vmult (VectorType &dst,
const VectorType &src) const;
/**
* Matrix-vector multiplication: let <i>dst = M<sup>T</sup>*src</i> with
* <i>M</i> being this matrix. This function does the same as vmult() but
* takes the transposed matrix.
*
* Source and destination must not be the same vector.
*
* This function can be called with several different vector objects,
* namely TrilinosWrappers::Vector, TrilinosWrappers::MPI::Vector as well
* as deal.II's own vector classes Vector<double> and
* parallel::distributed::Vector<double>.
*
* 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::VectorBase class (or one of the two derived classes
* Vector and MPI::Vector).
*
* In case of a localized Vector, this function will only work when
* running on one processor, since the matrix object is inherently
* distributed. Otherwise, and exception will be thrown.
*/
template <typename VectorType>
void Tvmult (VectorType &dst,
const VectorType &src) const;
/**
* Adding matrix-vector multiplication. Add <i>M*src</i> on <i>dst</i>
* with <i>M</i> being this matrix.
*
* Source and destination must not be the same vector.
*
* This function can be called with several different vector objects,
* namely TrilinosWrappers::Vector, TrilinosWrappers::MPI::Vector as well
* as deal.II's own vector classes Vector<double> and
* parallel::distributed::Vector<double>.
*
* When using a vector of type TrilinosWrappers::MPI::Vector, both vectors
* have to be distributed vectors generated using the same Map as was used
* for the matrix rows and columns in case you work on a distributed
* memory architecture, using the interface in the
* TrilinosWrappers::VectorBase class.
*
* In case of a localized Vector (i.e., TrilinosWrappers::Vector or
* Vector<double>), this function will only work when running on one
* processor, since the matrix object is inherently distributed.
* Otherwise, and exception will be thrown.
*
*/
template<typename VectorType>
void vmult_add (VectorType &dst,
const VectorType &src) const;
/**
* Adding matrix-vector multiplication. Add <i>M<sup>T</sup>*src</i> to
* <i>dst</i> with <i>M</i> being this matrix. This function does the same
* as vmult_add() but takes the transposed matrix.
*
* Source and destination must not be the same vector.
*
* This function can be called with several different vector objects,
* namely TrilinosWrappers::Vector, TrilinosWrappers::MPI::Vector as well
* as deal.II's own vector classes Vector<double> and
* parallel::distributed::Vector<double>.
*
* When using a vector of type TrilinosWrappers::MPI::Vector, both vectors
* have to be distributed vectors generated using the same Map as was used
* for the matrix rows and columns in case you work on a distributed
* memory architecture, using the interface in the
* TrilinosWrappers::VectorBase class.
*
* In case of a localized Vector (i.e., TrilinosWrappers::Vector or
* Vector<double>), this function will only work when running on one
* processor, since the matrix object is inherently distributed.
* Otherwise, and exception will be thrown.
*/
template <typename VectorType>
void Tvmult_add (VectorType &dst,
const VectorType &src) const;
/**
* Return the square of the norm of the vector $v$ with respect to the
* norm induced by this matrix, i.e., $\left(v,Mv\right)$. This is useful,
* e.g. in the finite element context, where the $L_2$ norm of a function
* equals the matrix norm with respect to the mass matrix of the vector
* representing the nodal values of the finite element function.
*
* Obviously, the matrix needs to be quadratic for this operation.
*
* The implementation of this function is not as efficient as the one in
* the @p SparseMatrix class used in deal.II (i.e. the original one, not
* the Trilinos wrapper class) since Trilinos doesn't support this
* operation and needs a temporary 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::VectorBase class (or one of the two derived classes
* Vector and MPI::Vector).
*
* In case of a localized Vector, this function will only work when
* running on one processor, since the matrix object is inherently
* distributed. Otherwise, and exception will be thrown.
*/
TrilinosScalar matrix_norm_square (const VectorBase &v) const;
/**
* Compute the matrix scalar product $\left(u,Mv\right)$.
*
* The implementation of this function is not as efficient as the one in
* the @p SparseMatrix class used in deal.II (i.e. the original one, not
* the Trilinos wrapper class) since Trilinos doesn't support this
* operation and needs a temporary 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::VectorBase class (or one of the two derived classes
* Vector and MPI::Vector).
*
* In case of a localized Vector, this function will only work when
* running on one processor, since the matrix object is inherently
* distributed. Otherwise, and exception will be thrown.
*/
TrilinosScalar matrix_scalar_product (const VectorBase &u,
const VectorBase &v) 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::VectorBase class (or one of the two derived classes
* Vector and MPI::Vector).
*
* In case of a localized Vector, this function will only work when
* running on one processor, since the matrix object is inherently
* distributed. Otherwise, and exception will be thrown.
*/
TrilinosScalar residual (VectorBase &dst,
const VectorBase &x,
const VectorBase &b) const;
/**
* Perform the matrix-matrix multiplication <tt>C = A * B</tt>, or, if an
* optional vector argument is given, <tt>C = A * diag(V) * B</tt>, where
* <tt>diag(V)</tt> defines a diagonal matrix with the vector entries.
*
* This function assumes that the calling matrix <tt>A</tt> and <tt>B</tt>
* have compatible sizes. The size of <tt>C</tt> will be set within this
* function.
*
* The content as well as the sparsity pattern of the matrix C will be
* changed by this function, so make sure that the sparsity pattern is not
* used somewhere else in your program. This is an expensive operation, so
* think twice before you use this function.
*/
void mmult (SparseMatrix &C,
const SparseMatrix &B,
const VectorBase &V = VectorBase()) const;
/**
* Perform the matrix-matrix multiplication with the transpose of
* <tt>this</tt>, i.e., <tt>C = A<sup>T</sup> * B</tt>, or, if an optional
* vector argument is given, <tt>C = A<sup>T</sup> * diag(V) * B</tt>,
* where <tt>diag(V)</tt> defines a diagonal matrix with the vector
* entries.
*
* This function assumes that the calling matrix <tt>A</tt> and <tt>B</tt>
* have compatible sizes. The size of <tt>C</tt> will be set within this
* function.
*
* The content as well as the sparsity pattern of the matrix C will be
* changed by this function, so make sure that the sparsity pattern is not
* used somewhere else in your program. This is an expensive operation, so
* think twice before you use this function.
*/
void Tmmult (SparseMatrix &C,
const SparseMatrix &B,
const VectorBase &V = VectorBase()) const;
//@}
/**
* @name Matrix norms
*/
//@{
/**
* Return the <i>l</i><sub>1</sub>-norm of the matrix, that is $|M|_1=
* \max_{\mathrm{all\ columns\ } j} \sum_{\mathrm{all\ rows\ } i}
* |M_{ij}|$, (max. sum of columns). This is the natural matrix norm that
* is compatible to the l1-norm for vectors, i.e. $|Mv|_1 \leq |M|_1
* |v|_1$. (cf. Haemmerlin-Hoffmann: Numerische Mathematik)
*/
TrilinosScalar l1_norm () const;
/**
* Return the linfty-norm of the matrix, that is
* $|M|_\infty=\max_{\mathrm{all\ rows\ } i}\sum_{\mathrm{all\ columns\ }
* j} |M_{ij}|$, (max. sum of rows). This is the natural matrix norm that
* is compatible to the linfty-norm of vectors, i.e. $|Mv|_\infty \leq
* |M|_\infty |v|_\infty$. (cf. Haemmerlin-Hoffmann: Numerische
* Mathematik)
*/
TrilinosScalar linfty_norm () const;
/**
* Return the frobenius norm of the matrix, i.e. the square root of the
* sum of squares of all entries in the matrix.
*/
TrilinosScalar frobenius_norm () const;
//@}
/**
* @name Access to underlying Trilinos data
*/
//@{
/**
* Return a const reference to the underlying Trilinos Epetra_CrsMatrix
* data.
*/
const Epetra_CrsMatrix &trilinos_matrix () const;
/**
* Return a const reference to the underlying Trilinos Epetra_CrsGraph
* data that stores the sparsity pattern of the matrix.
*/
const Epetra_CrsGraph &trilinos_sparsity_pattern () const;
/**
* Return a const reference to the underlying Trilinos Epetra_Map that
* sets the partitioning of the domain space of this matrix, i.e., the
* partitioning of the vectors this matrix has to be multiplied with.
*
* @deprecated Use locally_owned_domain_indices() instead.
*/
const Epetra_Map &domain_partitioner () const DEAL_II_DEPRECATED;
/**
* Return a const reference to the underlying Trilinos Epetra_Map that
* sets the partitioning of the range space of this matrix, i.e., the
* partitioning of the vectors that are result from matrix-vector
* products.
*
* @deprecated Use locally_owned_range_indices() instead.
*/
const Epetra_Map &range_partitioner () const DEAL_II_DEPRECATED;
/**
* Return a const reference to the underlying Trilinos Epetra_Map that
* sets the partitioning of the matrix rows. Equal to the partitioning of
* the range.
*
* @deprecated Use locally_owned_range_indices() instead.
*/
const Epetra_Map &row_partitioner () const DEAL_II_DEPRECATED;
/**
* Return a const reference to the underlying Trilinos Epetra_Map that
* sets the partitioning of the matrix columns. This is in general not
* equal to the partitioner Epetra_Map for the domain because of overlap
* in the matrix.
*
* @deprecated Usually not necessary. If desired, access it via the
* Epetra_CrsMatrix.
*/
const Epetra_Map &col_partitioner () const DEAL_II_DEPRECATED;
//@}
/**
* @name Partitioners
*/
//@{
/**
* Return the partitioning of the domain space of this matrix, i.e., the
* partitioning of the vectors this matrix has to be multiplied with.
*/
IndexSet locally_owned_domain_indices() const;
/**
* Return the partitioning of the range space of this matrix, i.e., the
* partitioning of the vectors that are result from matrix-vector
* products.
*/
IndexSet locally_owned_range_indices() const;
//@}
/**
* @name Iterators
*/
//@{
/**
* Return an iterator pointing to the first element of the matrix.
*
* The elements accessed by iterators within each row are ordered in the
* way in which Trilinos stores them, though the implementation guarantees
* that all elements of one row are accessed before the elements of the
* next row. If your algorithm relies on visiting elements within one row,
* you will need to consult with the Trilinos documentation on the order
* in which it stores data. It is, however, generally not a good and long-
* term stable idea to rely on the order in which receive elements if you
* iterate over them.
*
* When you iterate over the elements of a parallel matrix, you will only
* be able to access the locally owned rows. (You can access the other
* rows as well, but they will look empty.) In that case, you probably
* want to call the begin() function that takes the row as an argument to
* limit the range of elements to loop over.
*/
const_iterator begin () const;
/**
* Like the function above, but for non-const matrices.
*/
iterator begin ();
/**
* Return an iterator pointing the element past the last one of this
* matrix.
*/
const_iterator end () const;
/**
* Like the function above, but for non-const matrices.
*/
iterator end ();
/**
* Return an iterator pointing to the first element of row @p r.
*
* Note that if the given row is empty, i.e. does not contain any nonzero
* entries, then the iterator returned by this function equals
* <tt>end(r)</tt>. The returned iterator may not be dereferencable in
* that case if neither row @p r nor any of the following rows contain any
* nonzero entries.
*
* The elements accessed by iterators within each row are ordered in the
* way in which Trilinos stores them, though the implementation guarantees
* that all elements of one row are accessed before the elements of the
* next row. If your algorithm relies on visiting elements within one row,
* you will need to consult with the Trilinos documentation on the order
* in which it stores data. It is, however, generally not a good and long-
* term stable idea to rely on the order in which receive elements if you
* iterate over them.
*
* @note When you access the elements of a parallel matrix, you can only
* access the elements of rows that are actually stored locally. (You can
* access the other rows as well, but they will look empty.) Even then, if
* another processor has since written into, or added to, an element of
* the matrix that is stored on the current processor, then you will still
* see the old value of this entry unless you have called compress()
* between modifying the matrix element on the remote processor and
* accessing it on the current processor. See the documentation of the
* compress() function for more information.
*/
const_iterator begin (const size_type r) const;
/**
* Like the function above, but for non-const matrices.
*/
iterator begin (const size_type r);
/**
* Return an iterator pointing the element past the last one of row @p r ,
* or past the end of the entire sparsity pattern if none of the rows
* after @p r contain any entries at all.
*
* Note that the end iterator is not necessarily dereferencable. This is
* in particular the case if it is the end iterator for the last row of a
* matrix.
*/
const_iterator end (const size_type r) const;
/**
* Like the function above, but for non-const matrices.
*/
iterator end (const size_type r);
//@}
/**
* @name Input/Output
*/
//@{
/**
* Abstract Trilinos object that helps view in ASCII other Trilinos
* objects. Currently this function is not implemented. TODO: Not
* implemented.
*/
void write_ascii ();
/**
* Print the matrix to the given stream, using the format <tt>(line,col)
* value</tt>, i.e. one nonzero entry of the matrix per line. The optional
* flag outputs the sparsity pattern in Trilinos style, where the data is
* sorted according to the processor number when printed to the stream, as
* well as a summary of the matrix like the global size.
*/
void print (std::ostream &out,
const bool write_extended_trilinos_info = false) const;
//@}
/**
* @addtogroup Exceptions
*
*/
//@{
/**
* Exception
*/
DeclException1 (ExcTrilinosError,
int,
<< "An error with error number " << arg1
<< " occurred while calling a Trilinos function");
/**
* Exception
*/
DeclException2 (ExcInvalidIndex,
size_type, size_type,
<< "The entry with index <" << arg1 << ',' << arg2
<< "> does not exist.");
/**
* Exception
*/
DeclException0 (ExcSourceEqualsDestination);
/**
* Exception
*/
DeclException0 (ExcMatrixNotCompressed);
/**
* Exception
*/
DeclException4 (ExcAccessToNonLocalElement,
size_type, size_type, size_type, size_type,
<< "You tried to access element (" << arg1
<< "/" << arg2 << ")"
<< " of a distributed matrix, but only rows "
<< arg3 << " through " << arg4
<< " are stored locally and can be accessed.");
/**
* Exception
*/
DeclException2 (ExcAccessToNonPresentElement,
size_type, size_type,
<< "You tried to access element (" << arg1
<< "/" << arg2 << ")"
<< " of a sparse matrix, but it appears to not"
<< " exist in the Trilinos sparsity pattern.");
//@}
protected:
/**
* For some matrix storage formats, in particular for the PETSc
* distributed blockmatrices, set and add operations on individual
* elements can not be freely mixed. Rather, one has to synchronize
* operations when one wants to switch from setting elements to adding to
* elements. BlockMatrixBase automatically synchronizes the access by
* calling this helper function for each block. This function ensures
* that the matrix is in a state that allows adding elements; if it
* previously already was in this state, the function does nothing.
*/
void prepare_add();
/**
* Same as prepare_add() but prepare the matrix for setting elements if
* the representation of elements in this class requires such an
* operation.
*/
void prepare_set();
private:
/**
* Copy constructor is disabled.
*/
SparseMatrix (const SparseMatrix &);
/**
* operator= is disabled.
*/
SparseMatrix &operator = (const SparseMatrix &);
/**
* Pointer to the user-supplied Epetra Trilinos mapping of the matrix
* columns that assigns parts of the matrix to the individual processes.
*/
std_cxx11::shared_ptr<Epetra_Map> column_space_map;
/**
* A sparse matrix object in Trilinos to be used for finite element based
* problems which allows for assembling into non-local elements. The
* actual type, a sparse matrix, is set in the constructor.
*/
std_cxx11::shared_ptr<Epetra_FECrsMatrix> matrix;
/**
* A sparse matrix object in Trilinos to be used for collecting the non-
* local elements if the matrix was constructed from a Trilinos sparsity
* pattern with the respective option.
*/
std_cxx11::shared_ptr<Epetra_CrsMatrix> nonlocal_matrix;
/**
* An export object used to communicate the nonlocal matrix.
*/
std_cxx11::shared_ptr<Epetra_Export> nonlocal_matrix_exporter;
/**
* Trilinos doesn't allow to mix additions to matrix entries and
* overwriting them (to make synchronisation of %parallel computations
* simpler). The way we do it is to, for each access operation, store
* whether it is an insertion or an addition. If the previous one was of
* different type, then we first have to flush the Trilinos buffers;
* otherwise, we can simply go on. Luckily, Trilinos has an object for
* this which does already all the %parallel communications in such a
* case, so we simply use their model, which stores whether the last
* operation was an addition or an insertion.
*/
Epetra_CombineMode last_action;
/**
* A boolean variable to hold information on whether the vector is
* compressed or not.
*/
bool compressed;
/**
* To allow calling protected prepare_add() and prepare_set().
*/
friend class BlockMatrixBase<SparseMatrix>;
};
// -------------------------- inline and template functions ----------------------
#ifndef DOXYGEN
namespace SparseMatrixIterators
{
inline
AccessorBase::AccessorBase(SparseMatrix *matrix, size_type row, size_type index)
:
matrix(matrix),
a_row(row),
a_index(index)
{
visit_present_row ();
}
inline
AccessorBase::size_type
AccessorBase::row() const
{
Assert (a_row < matrix->m(), ExcBeyondEndOfMatrix());
return a_row;
}
inline
AccessorBase::size_type
AccessorBase::column() const
{
Assert (a_row < matrix->m(), ExcBeyondEndOfMatrix());
return (*colnum_cache)[a_index];
}
inline
AccessorBase::size_type
AccessorBase::index() const
{
Assert (a_row < matrix->m(), ExcBeyondEndOfMatrix());
return a_index;
}
inline
Accessor<true>::Accessor (MatrixType *matrix,
const size_type row,
const size_type index)
:
AccessorBase(const_cast<SparseMatrix *>(matrix), row, index)
{}
template <bool Other>
inline
Accessor<true>::Accessor(const Accessor<Other> &other)
:
AccessorBase(other)
{}
inline
TrilinosScalar
Accessor<true>::value() const
{
Assert (a_row < matrix->m(), ExcBeyondEndOfMatrix());
return (*value_cache)[a_index];
}
inline
Accessor<false>::Reference::Reference (
const Accessor<false> &acc)
:
accessor(const_cast<Accessor<false>&>(acc))
{}
inline
Accessor<false>::Reference::operator TrilinosScalar () const
{
return (*accessor.value_cache)[accessor.a_index];
}
inline
const Accessor<false>::Reference &
Accessor<false>::Reference::operator = (const TrilinosScalar n) const
{
(*accessor.value_cache)[accessor.a_index] = n;
accessor.matrix->set(accessor.row(), accessor.column(),
static_cast<TrilinosScalar>(*this));
return *this;
}
inline
const Accessor<false>::Reference &
Accessor<false>::Reference::operator += (const TrilinosScalar n) const
{
(*accessor.value_cache)[accessor.a_index] += n;
accessor.matrix->set(accessor.row(), accessor.column(),
static_cast<TrilinosScalar>(*this));
return *this;
}
inline
const Accessor<false>::Reference &
Accessor<false>::Reference::operator -= (const TrilinosScalar n) const
{
(*accessor.value_cache)[accessor.a_index] -= n;
accessor.matrix->set(accessor.row(), accessor.column(),
static_cast<TrilinosScalar>(*this));
return *this;
}
inline
const Accessor<false>::Reference &
Accessor<false>::Reference::operator *= (const TrilinosScalar n) const
{
(*accessor.value_cache)[accessor.a_index] *= n;
accessor.matrix->set(accessor.row(), accessor.column(),
static_cast<TrilinosScalar>(*this));
return *this;
}
inline
const Accessor<false>::Reference &
Accessor<false>::Reference::operator /= (const TrilinosScalar n) const
{
(*accessor.value_cache)[accessor.a_index] /= n;
accessor.matrix->set(accessor.row(), accessor.column(),
static_cast<TrilinosScalar>(*this));
return *this;
}
inline
Accessor<false>::Accessor (MatrixType *matrix,
const size_type row,
const size_type index)
:
AccessorBase(matrix, row, index)
{}
inline
Accessor<false>::Reference
Accessor<false>::value() const
{
Assert (a_row < matrix->m(), ExcBeyondEndOfMatrix());
return Reference(*this);
}
template <bool Constness>
inline
Iterator<Constness>::Iterator(MatrixType *matrix,
const size_type row,
const size_type index)
:
accessor(matrix, row, index)
{}
template <bool Constness>
template <bool Other>
inline
Iterator<Constness>::Iterator(const Iterator<Other> &other)
:
accessor(other.accessor)
{}
template <bool Constness>
inline
Iterator<Constness> &
Iterator<Constness>::operator++ ()
{
Assert (accessor.a_row < accessor.matrix->m(), ExcIteratorPastEnd());
++accessor.a_index;
// If at end of line: do one
// step, then cycle until we
// find a row with a nonzero
// number of entries.
if (accessor.a_index >= accessor.colnum_cache->size())
{
accessor.a_index = 0;
++accessor.a_row;
while ((accessor.a_row < accessor.matrix->m())
&&
((accessor.matrix->in_local_range (accessor.a_row) == false)
||
(accessor.matrix->row_length(accessor.a_row) == 0)))
++accessor.a_row;
accessor.visit_present_row();
}
return *this;
}
template <bool Constness>
inline
Iterator<Constness>
Iterator<Constness>::operator++ (int)
{
const Iterator<Constness> old_state = *this;
++(*this);
return old_state;
}
template <bool Constness>
inline
const Accessor<Constness> &
Iterator<Constness>::operator* () const
{
return accessor;
}
template <bool Constness>
inline
const Accessor<Constness> *
Iterator<Constness>::operator-> () const
{
return &accessor;
}
template <bool Constness>
inline
bool
Iterator<Constness>::operator == (const Iterator<Constness> &other) const
{
return (accessor.a_row == other.accessor.a_row &&
accessor.a_index == other.accessor.a_index);
}
template <bool Constness>
inline
bool
Iterator<Constness>::operator != (const Iterator<Constness> &other) const
{
return ! (*this == other);
}
template <bool Constness>
inline
bool
Iterator<Constness>::operator < (const Iterator<Constness> &other) const
{
return (accessor.row() < other.accessor.row() ||
(accessor.row() == other.accessor.row() &&
accessor.index() < other.accessor.index()));
}
template <bool Constness>
inline
bool
Iterator<Constness>::operator > (const Iterator<Constness> &other) const
{
return (other < *this);
}
}
inline
SparseMatrix::const_iterator
SparseMatrix::begin() const
{
return begin(0);
}
inline
SparseMatrix::const_iterator
SparseMatrix::end() const
{
return const_iterator(this, m(), 0);
}
inline
SparseMatrix::const_iterator
SparseMatrix::begin(const size_type r) const
{
Assert (r < m(), ExcIndexRange(r, 0, m()));
if (in_local_range (r)
&&
(row_length(r) > 0))
return const_iterator(this, r, 0);
else
return end (r);
}
inline
SparseMatrix::const_iterator
SparseMatrix::end(const size_type r) const
{
Assert (r < m(), ExcIndexRange(r, 0, m()));
// place the iterator on the first entry
// past this line, or at the end of the
// matrix
for (size_type i=r+1; i<m(); ++i)
if (in_local_range (i)
&&
(row_length(i) > 0))
return const_iterator(this, i, 0);
// if there is no such line, then take the
// end iterator of the matrix
return end();
}
inline
SparseMatrix::iterator
SparseMatrix::begin()
{
return begin(0);
}
inline
SparseMatrix::iterator
SparseMatrix::end()
{
return iterator(this, m(), 0);
}
inline
SparseMatrix::iterator
SparseMatrix::begin(const size_type r)
{
Assert (r < m(), ExcIndexRange(r, 0, m()));
if (in_local_range (r)
&&
(row_length(r) > 0))
return iterator(this, r, 0);
else
return end (r);
}
inline
SparseMatrix::iterator
SparseMatrix::end(const size_type r)
{
Assert (r < m(), ExcIndexRange(r, 0, m()));
// place the iterator on the first entry
// past this line, or at the end of the
// matrix
for (size_type i=r+1; i<m(); ++i)
if (in_local_range (i)
&&
(row_length(i) > 0))
return iterator(this, i, 0);
// if there is no such line, then take the
// end iterator of the matrix
return end();
}
inline
bool
SparseMatrix::in_local_range (const size_type index) const
{
TrilinosWrappers::types::int_type begin, end;
#ifndef DEAL_II_WITH_64BIT_INDICES
begin = matrix->RowMap().MinMyGID();
end = matrix->RowMap().MaxMyGID()+1;
#else
begin = matrix->RowMap().MinMyGID64();
end = matrix->RowMap().MaxMyGID64()+1;
#endif
return ((index >= static_cast<size_type>(begin)) &&
(index < static_cast<size_type>(end)));
}
inline
bool
SparseMatrix::is_compressed () const
{
return compressed;
}
// Inline the set() and add() functions, since they will be called
// frequently, and the compiler can optimize away some unnecessary loops
// when the sizes are given at compile time.
inline
void
SparseMatrix::set (const size_type i,
const size_type j,
const TrilinosScalar value)
{
AssertIsFinite(value);
set (i, 1, &j, &value, false);
}
inline
void
SparseMatrix::set (const std::vector<size_type> &indices,
const FullMatrix<TrilinosScalar> &values,
const bool elide_zero_values)
{
Assert (indices.size() == values.m(),
ExcDimensionMismatch(indices.size(), values.m()));
Assert (values.m() == values.n(), ExcNotQuadratic());
for (size_type i=0; i<indices.size(); ++i)
set (indices[i], indices.size(), &indices[0], &values(i,0),
elide_zero_values);
}
inline
void
SparseMatrix::add (const size_type i,
const size_type j,
const TrilinosScalar value)
{
AssertIsFinite(value);
if (value == 0)
{
// we have to check after Insert/Add in any case to be consistent
// with the MPI communication model (see the comments in the
// documentation of TrilinosWrappers::Vector), but we can save some
// work if the addend is zero. However, these actions are done in case
// we pass on to the other function.
// TODO: fix this (do not run compress here, but fail)
if (last_action == Insert)
{
int ierr;
ierr = matrix->GlobalAssemble(*column_space_map,
matrix->RowMap(), false);
Assert (ierr == 0, ExcTrilinosError(ierr));
(void)ierr; // removes -Wunused-but-set-variable in optimized mode
}
last_action = Add;
return;
}
else
add (i, 1, &j, &value, false);
}
// inline "simple" functions that are called frequently and do only involve
// a call to some Trilinos function.
inline
SparseMatrix::size_type
SparseMatrix::m () const
{
#ifndef DEAL_II_WITH_64BIT_INDICES
return matrix->NumGlobalRows();
#else
return matrix->NumGlobalRows64();
#endif
}
inline
SparseMatrix::size_type
SparseMatrix::n () const
{
// If the matrix structure has not been fixed (i.e., we did not have a
// sparsity pattern), it does not know about the number of columns so we
// must always take this from the additional column space map
Assert(column_space_map.get() != 0, ExcInternalError());
#ifndef DEAL_II_WITH_64BIT_INDICES
return column_space_map->NumGlobalElements();
#else
return column_space_map->NumGlobalElements64();
#endif
}
inline
unsigned int
SparseMatrix::local_size () const
{
return matrix -> NumMyRows();
}
inline
std::pair<SparseMatrix::size_type, SparseMatrix::size_type>
SparseMatrix::local_range () const
{
size_type begin, end;
#ifndef DEAL_II_WITH_64BIT_INDICES
begin = matrix->RowMap().MinMyGID();
end = matrix->RowMap().MaxMyGID()+1;
#else
begin = matrix->RowMap().MinMyGID64();
end = matrix->RowMap().MaxMyGID64()+1;
#endif
return std::make_pair (begin, end);
}
inline
SparseMatrix::size_type
SparseMatrix::n_nonzero_elements () const
{
#ifndef DEAL_II_WITH_64BIT_INDICES
return matrix->NumGlobalNonzeros();
#else
return matrix->NumGlobalNonzeros64();
#endif
}
template <typename SparsityPatternType>
inline
void SparseMatrix::reinit (const IndexSet ¶llel_partitioning,
const SparsityPatternType &sparsity_pattern,
const MPI_Comm &communicator,
const bool exchange_data)
{
reinit (parallel_partitioning, parallel_partitioning,
sparsity_pattern, communicator, exchange_data);
}
template <typename number>
inline
void SparseMatrix::reinit (const IndexSet ¶llel_partitioning,
const ::dealii::SparseMatrix<number> &sparse_matrix,
const MPI_Comm &communicator,
const double drop_tolerance,
const bool copy_values,
const ::dealii::SparsityPattern *use_this_sparsity)
{
Epetra_Map map = parallel_partitioning.make_trilinos_map (communicator, false);
reinit (parallel_partitioning, parallel_partitioning, sparse_matrix,
drop_tolerance, copy_values, use_this_sparsity);
}
inline
const Epetra_CrsMatrix &
SparseMatrix::trilinos_matrix () const
{
return static_cast<const Epetra_CrsMatrix &>(*matrix);
}
inline
const Epetra_CrsGraph &
SparseMatrix::trilinos_sparsity_pattern () const
{
return matrix->Graph();
}
inline
IndexSet
SparseMatrix::locally_owned_domain_indices () const
{
return IndexSet(matrix->DomainMap());
}
inline
IndexSet
SparseMatrix::locally_owned_range_indices () const
{
return IndexSet(matrix->RangeMap());
}
inline
void
SparseMatrix::prepare_add()
{
//nothing to do here
}
inline
void
SparseMatrix::prepare_set()
{
//nothing to do here
}
#endif // DOXYGEN
}
DEAL_II_NAMESPACE_CLOSE
#endif // DEAL_II_WITH_TRILINOS
/*----------------------- trilinos_sparse_matrix.h --------------------*/
#endif
/*----------------------- trilinos_sparse_matrix.h --------------------*/
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