Reference documentation for deal.II version Git d2d482dcec 20201026 10:29:03 0400

#include <deal.II/lac/trilinos_sparsity_pattern.h>
Public Types  
using  size_type = ::types::global_dof_index 
using  const_iterator = SparsityPatternIterators::Iterator 
Public Member Functions  
template<class Archive >  
void  serialize (Archive &ar, const unsigned int version) 
Basic constructors and initialization  
SparsityPattern ()  
SparsityPattern (const size_type m, const size_type n, const size_type n_entries_per_row=0)  
SparsityPattern (const size_type m, const size_type n, const std::vector< size_type > &n_entries_per_row)  
SparsityPattern (SparsityPattern &&other) noexcept  
SparsityPattern (const SparsityPattern &input_sparsity_pattern)  
virtual  ~SparsityPattern () override=default 
void  reinit (const size_type m, const size_type n, const size_type n_entries_per_row=0) 
void  reinit (const size_type m, const size_type n, const std::vector< size_type > &n_entries_per_row) 
void  copy_from (const SparsityPattern &input_sparsity_pattern) 
template<typename SparsityPatternType >  
void  copy_from (const SparsityPatternType &nontrilinos_sparsity_pattern) 
SparsityPattern &  operator= (const SparsityPattern &input_sparsity_pattern) 
void  clear () 
void  compress () 
Constructors and initialization using an IndexSet description  
SparsityPattern (const IndexSet ¶llel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0)  
SparsityPattern (const IndexSet ¶llel_partitioning, const MPI_Comm &communicator, const std::vector< size_type > &n_entries_per_row)  
SparsityPattern (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0)  
SparsityPattern (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const MPI_Comm &communicator, const std::vector< size_type > &n_entries_per_row)  
SparsityPattern (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const IndexSet &writable_rows, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0)  
void  reinit (const IndexSet ¶llel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0) 
void  reinit (const IndexSet ¶llel_partitioning, const MPI_Comm &communicator, const std::vector< size_type > &n_entries_per_row) 
void  reinit (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0) 
void  reinit (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const IndexSet &writeable_rows, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0) 
void  reinit (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const MPI_Comm &communicator, const std::vector< size_type > &n_entries_per_row) 
template<typename SparsityPatternType >  
void  reinit (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const SparsityPatternType &nontrilinos_sparsity_pattern, const MPI_Comm &communicator=MPI_COMM_WORLD, const bool exchange_data=false) 
template<typename SparsityPatternType >  
void  reinit (const IndexSet ¶llel_partitioning, const SparsityPatternType &nontrilinos_sparsity_pattern, const MPI_Comm &communicator=MPI_COMM_WORLD, const bool exchange_data=false) 
Information on the sparsity pattern  
bool  is_compressed () const 
unsigned int  max_entries_per_row () const 
size_type  n_rows () const 
size_type  n_cols () const 
unsigned int  local_size () const 
std::pair< size_type, size_type >  local_range () const 
bool  in_local_range (const size_type index) const 
size_type  n_nonzero_elements () const 
size_type  row_length (const size_type row) const 
size_type  bandwidth () const 
bool  empty () const 
bool  exists (const size_type i, const size_type j) const 
bool  row_is_stored_locally (const size_type i) const 
std::size_t  memory_consumption () const 
Adding entries  
void  add (const size_type i, const size_type j) 
template<typename ForwardIterator >  
void  add_entries (const size_type row, ForwardIterator begin, ForwardIterator end, const bool indices_are_sorted=false) 
Access of underlying Trilinos data  
const Epetra_FECrsGraph &  trilinos_sparsity_pattern () const 
const Epetra_Map &  domain_partitioner () const 
const Epetra_Map &  range_partitioner () const 
MPI_Comm  get_mpi_communicator () const 
Partitioners  
IndexSet  locally_owned_domain_indices () const 
IndexSet  locally_owned_range_indices () const 
Iterators  
const_iterator  begin () const 
const_iterator  end () const 
const_iterator  begin (const size_type r) const 
const_iterator  end (const size_type r) const 
Input/Output  
void  write_ascii () 
void  print (std::ostream &out, const bool write_extended_trilinos_info=false) const 
void  print_gnuplot (std::ostream &out) const 
Subscriptor functionality  
Classes derived from Subscriptor provide a facility to subscribe to this object. This is mostly used by the SmartPointer class.  
void  subscribe (std::atomic< bool > *const validity, const std::string &identifier="") const 
void  unsubscribe (std::atomic< bool > *const validity, const std::string &identifier="") const 
unsigned int  n_subscriptions () const 
template<typename StreamType >  
void  list_subscribers (StreamType &stream) const 
void  list_subscribers () const 
Static Public Member Functions  
static ::ExceptionBase &  ExcTrilinosError (int arg1) 
static ::ExceptionBase &  ExcInvalidIndex (size_type arg1, size_type arg2) 
static ::ExceptionBase &  ExcSourceEqualsDestination () 
static ::ExceptionBase &  ExcAccessToNonLocalElement (size_type arg1, size_type arg2, size_type arg3, size_type arg4) 
static ::ExceptionBase &  ExcAccessToNonPresentElement (size_type arg1, size_type arg2) 
static ::ExceptionBase &  ExcInUse (int arg1, std::string arg2, std::string arg3) 
static ::ExceptionBase &  ExcNoSubscriber (std::string arg1, std::string arg2) 
Private Attributes  
std::unique_ptr< Epetra_Map >  column_space_map 
std::unique_ptr< Epetra_FECrsGraph >  graph 
std::unique_ptr< Epetra_CrsGraph >  nonlocal_graph 
Friends  
class  TrilinosWrappers::SparseMatrix 
class  SparsityPatternIterators::Accessor 
class  SparsityPatternIterators::Iterator 
This class implements a wrapper class to use the Trilinos distributed sparsity pattern class Epetra_FECrsGraph. This class is designed to be used for construction of parallel Trilinos matrices. The functionality of this class is modeled after the existing sparsity pattern classes, with the difference that this class can work fully in parallel according to a partitioning of the sparsity pattern rows.
This class has many similarities to the DynamicSparsityPattern, since it can dynamically add elements to the pattern without any memory being previously reserved for it. However, it also has a method SparsityPattern::compress(), that finalizes the pattern and enables its use with Trilinos sparse matrices.
Definition at line 276 of file trilinos_sparsity_pattern.h.
Declare type for container size.
Definition at line 282 of file trilinos_sparsity_pattern.h.
Declare an alias for the iterator class.
Definition at line 287 of file trilinos_sparsity_pattern.h.
SparsityPattern::SparsityPattern  (  ) 
Default constructor. Generates an empty (zerosize) sparsity pattern.
Definition at line 83 of file trilinos_sparsity_pattern.cc.
SparsityPattern::SparsityPattern  (  const size_type  m, 
const size_type  n,  
const size_type  n_entries_per_row = 0 

) 
Generate a sparsity pattern that is completely stored locally, having \(m\) rows and \(n\) columns. The resulting matrix will be completely stored locally, too.
It is possible to specify the number of columns entries per row using the optional n_entries_per_row
argument. However, this value does not need to be accurate or even given at all, since one does usually not have this kind of information before building the sparsity pattern (the usual case when the function DoFTools::make_sparsity_pattern() is called). The entries are allocated dynamically in a similar manner as for the deal.II DynamicSparsityPattern classes. However, a good estimate will reduce the setup time of the sparsity pattern.
Definition at line 98 of file trilinos_sparsity_pattern.cc.
SparsityPattern::SparsityPattern  (  const size_type  m, 
const size_type  n,  
const std::vector< size_type > &  n_entries_per_row  
) 
Generate a sparsity pattern that is completely stored locally, having \(m\) rows and \(n\) columns. The resulting matrix will be completely stored locally, too.
The vector n_entries_per_row
specifies the number of entries in each row (an information usually not available, though).
Definition at line 107 of file trilinos_sparsity_pattern.cc.

noexcept 
Move constructor. Create a new sparse matrix by stealing the internal data.
Definition at line 117 of file trilinos_sparsity_pattern.cc.
SparsityPattern::SparsityPattern  (  const SparsityPattern &  input_sparsity_pattern  ) 
Copy constructor. Sets the calling sparsity pattern to be the same as the input sparsity pattern.
Definition at line 128 of file trilinos_sparsity_pattern.cc.

overridevirtualdefault 
Destructor. Made virtual so that one can use pointers to this class.
SparsityPattern::SparsityPattern  (  const IndexSet &  parallel_partitioning, 
const MPI_Comm &  communicator = MPI_COMM_WORLD , 

const size_type  n_entries_per_row = 0 

) 
Constructor for a square sparsity pattern using an IndexSet and an MPI communicator for the description of the parallel partitioning. Moreover, the number of nonzero entries in the rows of the sparsity pattern can be specified. Note that this number does not need to be exact, and it is even allowed that the actual sparsity structure has more nonzero entries than specified in the constructor. However it is still advantageous to provide good estimates here since a good value will avoid repeated allocation of memory, which considerably increases the performance when creating the sparsity pattern.
Definition at line 144 of file trilinos_sparsity_pattern.cc.
SparsityPattern::SparsityPattern  (  const IndexSet &  parallel_partitioning, 
const MPI_Comm &  communicator,  
const std::vector< size_type > &  n_entries_per_row  
) 
Same as before, but now use the exact number of nonzeros in each m row. Since we know the number of elements in the sparsity pattern exactly in this case, we can already allocate the right amount of memory, which makes the creation process by the respective SparsityPattern::reinit call considerably faster. However, this is a rather unusual situation, since knowing the number of entries in each row is usually connected to knowing the indices of nonzero entries, which the sparsity pattern is designed to describe.
Definition at line 156 of file trilinos_sparsity_pattern.cc.
SparsityPattern::SparsityPattern  (  const IndexSet &  row_parallel_partitioning, 
const IndexSet &  col_parallel_partitioning,  
const MPI_Comm &  communicator = MPI_COMM_WORLD , 

const size_type  n_entries_per_row = 0 

) 
This constructor is similar to the one above, but it now takes two different index sets to describe the parallel partitioning of rows and columns. This interface is meant to be used for generating rectangular sparsity pattern. 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 matrixvector products with vectors based on that column map.
The number of columns entries per row is specified as the maximum number of entries argument.
Definition at line 169 of file trilinos_sparsity_pattern.cc.
SparsityPattern::SparsityPattern  (  const IndexSet &  row_parallel_partitioning, 
const IndexSet &  col_parallel_partitioning,  
const MPI_Comm &  communicator,  
const std::vector< size_type > &  n_entries_per_row  
) 
This constructor is similar to the one above, but it now takes two different index sets for rows and columns. This interface is meant to be used for generating rectangular matrices, where one map specifies the parallel distribution of rows and the second one specifies the distribution of degrees of freedom associated with matrix columns. This second map is however not used for the distribution of the columns themselves – rather, all column elements of a row are stored on the same processor. The vector n_entries_per_row
specifies the number of entries in each row of the newly generated matrix.
Definition at line 182 of file trilinos_sparsity_pattern.cc.
SparsityPattern::SparsityPattern  (  const IndexSet &  row_parallel_partitioning, 
const IndexSet &  col_parallel_partitioning,  
const IndexSet &  writable_rows,  
const MPI_Comm &  communicator = MPI_COMM_WORLD , 

const size_type  n_entries_per_row = 0 

) 
This constructor constructs general sparsity patterns, possible non square ones. Constructing a sparsity pattern this way allows the user to explicitly specify the rows into which we are going to add elements. This set is required to be a superset of the first index set row_parallel_partitioning
that includes also rows that are owned by another processor (ghost rows). Note that elements can only be added to rows specified by writable_rows
.
This method is beneficial when the rows to which a processor is going to write can be determined before actually inserting elements into the matrix. For the typical parallel::distributed::Triangulation class used in deal.II, we know that a processor only will add row elements for what we call the locally relevant dofs (see DoFTools::extract_locally_relevant_dofs). The other constructors methods use general Trilinos facilities that allow to add elements to arbitrary rows (as done by all the other reinit functions). However, this flexibility come at a cost, the most prominent being that adding elements into the same matrix from multiple threads in shared memory is not safe whenever MPI is used. For these settings, the current method is the one to choose: It will store the offprocessor data as an additional sparsity pattern (that is then passed to the Trilinos matrix via the reinit method) which can be organized in such a way that threadsafety can be ensured (as long as the user makes sure to never write into the same matrix row simultaneously, of course).
Definition at line 196 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::reinit  (  const size_type  m, 
const size_type  n,  
const size_type  n_entries_per_row = 0 

) 
Initialize a sparsity pattern that is completely stored locally, having \(m\) rows and \(n\) columns. The resulting matrix will be completely stored locally.
The number of columns entries per row is specified as the maximum number of entries argument. This does not need to be an accurate number since the entries are allocated dynamically in a similar manner as for the deal.II DynamicSparsityPattern classes, but a good estimate will reduce the setup time of the sparsity pattern.
Definition at line 212 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::reinit  (  const size_type  m, 
const size_type  n,  
const std::vector< size_type > &  n_entries_per_row  
) 
Initialize a sparsity pattern that is completely stored locally, having \(m\) rows and \(n\) columns. The resulting matrix will be completely stored locally.
The vector n_entries_per_row
specifies the number of entries in each row.
Definition at line 225 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::copy_from  (  const SparsityPattern &  input_sparsity_pattern  ) 
Copy function. Sets the calling sparsity pattern to be the same as the input sparsity pattern.
Definition at line 607 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::copy_from  (  const SparsityPatternType &  nontrilinos_sparsity_pattern  ) 
Copy function from one of the deal.II sparsity patterns. If used in parallel, this function uses an adhoc partitioning of the rows and columns.
Definition at line 622 of file trilinos_sparsity_pattern.cc.
SparsityPattern & SparsityPattern::operator=  (  const SparsityPattern &  input_sparsity_pattern  ) 
Copy operator. This operation is only allowed for empty objects, to avoid potentially very costly operations automatically synthesized by the compiler. Use copy_from() instead if you know that you really want to copy a sparsity pattern with nontrivial content.
Definition at line 598 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::clear  (  ) 
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.
Definition at line 638 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::compress  (  ) 
In analogy to our own SparsityPattern class, this function compresses the sparsity pattern and allows the resulting pattern to be used for actually generating a (Trilinosbased) matrix. This function also exchanges nonlocal data that might have accumulated during the addition of new elements. This function must therefore be called once the structure is fixed. This is a collective operation, i.e., it needs to be run on all processors when used in parallel.
Definition at line 659 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::reinit  (  const IndexSet &  parallel_partitioning, 
const MPI_Comm &  communicator = MPI_COMM_WORLD , 

const size_type  n_entries_per_row = 0 

) 
Reinitialization function for generating a square sparsity pattern using an IndexSet and an MPI communicator for the description of the parallel partitioning and the number of nonzero entries in the rows of the sparsity pattern. Note that this number does not need to be exact, and it is even allowed that the actual sparsity 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 when creating the sparsity pattern.
This function does not create any entries by itself, but provides the correct data structures that can be used by the respective add() function.
Definition at line 443 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::reinit  (  const IndexSet &  parallel_partitioning, 
const MPI_Comm &  communicator,  
const std::vector< size_type > &  n_entries_per_row  
) 
Same as before, but now use the exact number of nonzeros in each m row. Since we know the number of elements in the sparsity pattern exactly in this case, we can already allocate the right amount of memory, which makes process of adding entries to the sparsity pattern considerably faster. However, this is a rather unusual situation, since knowing the number of entries in each row is usually connected to knowing the indices of nonzero entries, which the sparsity pattern is designed to describe.
Definition at line 456 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::reinit  (  const IndexSet &  row_parallel_partitioning, 
const IndexSet &  col_parallel_partitioning,  
const MPI_Comm &  communicator = MPI_COMM_WORLD , 

const size_type  n_entries_per_row = 0 

) 
This reinit function is similar to the one above, but it now takes two different index sets for rows and columns. This interface is meant to be used for generating rectangular sparsity pattern, where one index set describes the parallel partitioning of the dofs associated with the sparsity pattern rows and the other one of the sparsity pattern 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 IndexSet is only used to specify the number of columns and for internal arrangements when doing matrixvector products with vectors based on an EpetraMap based on that IndexSet.
The number of columns entries per row is specified by the argument n_entries_per_row
.
Definition at line 469 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::reinit  (  const IndexSet &  row_parallel_partitioning, 
const IndexSet &  col_parallel_partitioning,  
const IndexSet &  writeable_rows,  
const MPI_Comm &  communicator = MPI_COMM_WORLD , 

const size_type  n_entries_per_row = 0 

) 
This reinit function is used to specify general matrices, possibly non square ones. In addition to the arguments of the other reinit method above, it allows the user to explicitly specify the rows into which we are going to add elements. This set is a superset of the first index set row_parallel_partitioning
that includes also rows that are owned by another processor (ghost rows).
This method is beneficial when the rows to which a processor is going to write can be determined before actually inserting elements into the matrix. For the typical parallel::distributed::Triangulation class used in deal.II, we know that a processor only will add row elements for what we call the locally relevant dofs (see DoFTools::extract_locally_relevant_dofs). Trilinos matrices allow to add elements to arbitrary rows (as done by all the other reinit functions) and this is what all the other reinit methods do, too. However, this flexibility come at a cost, the most prominent being that adding elements into the same matrix from multiple threads in shared memory is not safe whenever MPI is used. For these settings, the current method is the one to choose: It will store the offprocessor data as an additional sparsity pattern (that is then passed to the Trilinos matrix via the reinit method) which can be organized in such a way that threadsafety can be ensured (as long as the user makes sure to never write into the same matrix row simultaneously, of course).
Definition at line 509 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::reinit  (  const IndexSet &  row_parallel_partitioning, 
const IndexSet &  col_parallel_partitioning,  
const MPI_Comm &  communicator,  
const std::vector< size_type > &  n_entries_per_row  
) 
Same as before, but now using a vector n_entries_per_row
for specifying the number of entries in each row of the sparsity pattern.
Definition at line 489 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::reinit  (  const IndexSet &  row_parallel_partitioning, 
const IndexSet &  col_parallel_partitioning,  
const SparsityPatternType &  nontrilinos_sparsity_pattern,  
const MPI_Comm &  communicator = MPI_COMM_WORLD , 

const bool  exchange_data = false 

) 
Reinit function. Takes one of the deal.II sparsity patterns and the parallel partitioning of the rows and columns specified by two index sets and a parallel communicator for initializing the current Trilinos sparsity pattern. The optional argument 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.
Definition at line 554 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::reinit  (  const IndexSet &  parallel_partitioning, 
const SparsityPatternType &  nontrilinos_sparsity_pattern,  
const MPI_Comm &  communicator = MPI_COMM_WORLD , 

const bool  exchange_data = false 

) 
Reinit function. Takes one of the deal.II sparsity patterns and a parallel partitioning of the rows and columns for initializing the current Trilinos sparsity pattern. The optional argument 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.
Definition at line 578 of file trilinos_sparsity_pattern.cc.
bool TrilinosWrappers::SparsityPattern::is_compressed  (  )  const 
Return the state of the sparsity pattern, i.e., whether compress() needs to be called after an operation requiring data exchange.
unsigned int SparsityPattern::max_entries_per_row  (  )  const 
Return the maximum number of entries per row on the current processor.
Definition at line 891 of file trilinos_sparsity_pattern.cc.
SparsityPattern::size_type SparsityPattern::n_rows  (  )  const 
Return the number of rows in this sparsity pattern.
Definition at line 836 of file trilinos_sparsity_pattern.cc.
SparsityPattern::size_type SparsityPattern::n_cols  (  )  const 
Return the number of columns in this sparsity pattern.
Definition at line 845 of file trilinos_sparsity_pattern.cc.
unsigned int SparsityPattern::local_size  (  )  const 
Return the local dimension of the sparsity pattern, i.e. the number of rows stored on the present MPI process. In the sequential case, this number is the same as n_rows(), but for parallel matrices it may be smaller.
To figure out which elements exactly are stored locally, use local_range().
Definition at line 859 of file trilinos_sparsity_pattern.cc.
std::pair< SparsityPattern::size_type, SparsityPattern::size_type > SparsityPattern::local_range  (  )  const 
Return a pair of indices indicating which rows of this sparsity pattern 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,n_rows()), otherwise it will be a pair (i,i+n), where n=local_size()
.
Definition at line 869 of file trilinos_sparsity_pattern.cc.
Return whether index
is in the local range or not, see also local_range().
SparsityPattern::size_type SparsityPattern::n_nonzero_elements  (  )  const 
Return the number of nonzero elements of this sparsity pattern.
Definition at line 881 of file trilinos_sparsity_pattern.cc.
SparsityPattern::size_type SparsityPattern::row_length  (  const size_type  row  )  const 
Return the number of entries in the given row.
In a parallel context, the row in question may of course not be stored on the current processor, and in that case it is not possible to query the number of entries in it. In that case, the returned value is static_cast<size_type>(1)
.
Definition at line 901 of file trilinos_sparsity_pattern.cc.
SparsityPattern::size_type SparsityPattern::bandwidth  (  )  const 
Compute the bandwidth of the matrix represented by this structure. The bandwidth is the maximum of \(ij\) for which the index pair \((i,j)\) represents a nonzero entry of the matrix. Consequently, the maximum bandwidth a \(n\times m\) matrix can have is \(\max\{n1,m1\}\).
Definition at line 810 of file trilinos_sparsity_pattern.cc.
bool TrilinosWrappers::SparsityPattern::empty  (  )  const 
Return whether the object is empty. It is empty if no memory is allocated, which is the same as when both dimensions are zero.
Return whether the index (i,j) exists in the sparsity pattern (i.e., it may be nonzero) or not.
Definition at line 729 of file trilinos_sparsity_pattern.cc.
Return whether a given row
is stored in the current object on this process.
Definition at line 720 of file trilinos_sparsity_pattern.cc.
std::size_t SparsityPattern::memory_consumption  (  )  const 
Determine an estimate for the memory consumption (in bytes) of this object. Currently not implemented for this class.
Definition at line 1030 of file trilinos_sparsity_pattern.cc.
Add the element (i,j) to the sparsity pattern.
void TrilinosWrappers::SparsityPattern::add_entries  (  const size_type  row, 
ForwardIterator  begin,  
ForwardIterator  end,  
const bool  indices_are_sorted = false 

) 
Add several elements in one row to the sparsity pattern.
const Epetra_FECrsGraph& TrilinosWrappers::SparsityPattern::trilinos_sparsity_pattern  (  )  const 
Return a const reference to the underlying Trilinos Epetra_CrsGraph data that stores the sparsity pattern.
const Epetra_Map & SparsityPattern::domain_partitioner  (  )  const 
Return a const reference to the underlying Trilinos Epetra_Map that sets the parallel partitioning of the domain space of this sparsity pattern, i.e., the partitioning of the vectors matrices based on this sparsity pattern are multiplied with.
Definition at line 921 of file trilinos_sparsity_pattern.cc.
const Epetra_Map & SparsityPattern::range_partitioner  (  )  const 
Return a const reference to the underlying Trilinos Epetra_Map that sets the partitioning of the range space of this sparsity pattern, i.e., the partitioning of the vectors that are result from matrix vector products.
Definition at line 932 of file trilinos_sparsity_pattern.cc.
MPI_Comm SparsityPattern::get_mpi_communicator  (  )  const 
Return the MPI communicator object in use with this matrix.
Definition at line 943 of file trilinos_sparsity_pattern.cc.
IndexSet TrilinosWrappers::SparsityPattern::locally_owned_domain_indices  (  )  const 
Return the partitioning of the domain space of this pattern, i.e., the partitioning of the vectors a matrix based on this sparsity pattern has to be multiplied with.
IndexSet TrilinosWrappers::SparsityPattern::locally_owned_range_indices  (  )  const 
Return the partitioning of the range space of this pattern, i.e., the partitioning of the vectors that are the result from matrixvector products from a matrix based on this pattern.
const_iterator TrilinosWrappers::SparsityPattern::begin  (  )  const 
Iterator starting at the first entry.
const_iterator TrilinosWrappers::SparsityPattern::end  (  )  const 
Final iterator.
const_iterator TrilinosWrappers::SparsityPattern::begin  (  const size_type  r  )  const 
Iterator starting at the first entry of row 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 end(r)
. Note also that the iterator may not be dereferenceable in that case.
const_iterator TrilinosWrappers::SparsityPattern::end  (  const size_type  r  )  const 
Final iterator of row r
. It points to the first element past the end of line r
, or past the end of the entire sparsity pattern.
Note that the end iterator is not necessarily dereferenceable. This is in particular the case if it is the end iterator for the last row of a matrix.
void SparsityPattern::write_ascii  (  ) 
Abstract Trilinos object that helps view in ASCII other Trilinos objects. Currently this function is not implemented. TODO: Not implemented.
Definition at line 961 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::print  (  std::ostream &  out, 
const bool  write_extended_trilinos_info = false 

)  const 
Print (the locally owned part of) the sparsity pattern to the given stream, using the format (line,col)
. The optional flag outputs the sparsity pattern in Trilinos style, where even the according processor number is printed to the stream, as well as a summary before actually writing the entries.
Definition at line 972 of file trilinos_sparsity_pattern.cc.
void SparsityPattern::print_gnuplot  (  std::ostream &  out  )  const 
Print the sparsity of the matrix in a format that gnuplot
understands and which can be used to plot the sparsity pattern in a graphical way. The format consists of pairs i j
of nonzero elements, each representing one entry of this matrix, one per line of the output file. Indices are counted from zero on, as usual. Since sparsity patterns are printed in the same way as matrices are displayed, we print the negative of the column index, which means that the (0,0)
element is in the top left rather than in the bottom left corner.
Print the sparsity pattern in gnuplot by setting the data style to dots or points and use the plot
command.
Definition at line 999 of file trilinos_sparsity_pattern.cc.

inherited 
Subscribes a user of the object by storing the pointer validity
. The subscriber may be identified by text supplied as identifier
.
Definition at line 136 of file subscriptor.cc.

inherited 
Unsubscribes a user from the object.
identifier
and the validity
pointer must be the same as the one supplied to subscribe(). Definition at line 156 of file subscriptor.cc.

inlineinherited 
Return the present number of subscriptions to this object. This allows to use this class for reference counted lifetime determination where the last one to unsubscribe also deletes the object.
Definition at line 300 of file subscriptor.h.

inlineinherited 
List the subscribers to the input stream
.
Definition at line 317 of file subscriptor.h.

inherited 
List the subscribers to deallog
.
Definition at line 204 of file subscriptor.cc.

inlineinherited 
Read or write the data of this object to or from a stream for the purpose of serialization.
This function does not actually serialize any of the member variables of this class. The reason is that what this class stores is only who subscribes to this object, but who does so at the time of storing the contents of this object does not necessarily have anything to do with who subscribes to the object when it is restored. Consequently, we do not want to overwrite the subscribers at the time of restoring, and then there is no reason to write the subscribers out in the first place.
Definition at line 309 of file subscriptor.h.

friend 
Definition at line 1009 of file trilinos_sparsity_pattern.h.

friend 
Definition at line 1010 of file trilinos_sparsity_pattern.h.

friend 
Definition at line 1011 of file trilinos_sparsity_pattern.h.

private 
Pointer to the usersupplied Epetra Trilinos mapping of the matrix columns that assigns parts of the matrix to the individual processes.
Definition at line 992 of file trilinos_sparsity_pattern.h.

private 
A sparsity pattern object in Trilinos to be used for finite element based problems which allows for adding nonlocal elements to the pattern.
Definition at line 999 of file trilinos_sparsity_pattern.h.

private 
A sparsity pattern object for the nonlocal part of the sparsity pattern that is going to be sent to the owning processor. Only used when the particular constructor or reinit method with writable_rows argument is set
Definition at line 1007 of file trilinos_sparsity_pattern.h.