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//
// Copyright (C) 2008 - 2015 by the deal.II authors
//
// This file is part of the deal.II library.
//
// The deal.II library is free software; you can use it, redistribute
// it, and/or modify it under the terms of the GNU Lesser General
// Public License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
// The full text of the license can be found in the file LICENSE at
// the top level of the deal.II distribution.
//
// ---------------------------------------------------------------------
#ifndef dealii__chunk_sparsity_pattern_h
#define dealii__chunk_sparsity_pattern_h
#include <deal.II/base/config.h>
#include <deal.II/base/exceptions.h>
#include <deal.II/base/subscriptor.h>
#include <deal.II/base/vector_slice.h>
#include <deal.II/lac/sparsity_pattern.h>
#include <vector>
#include <iostream>
DEAL_II_NAMESPACE_OPEN
template <typename> class ChunkSparseMatrix;
/*! @addtogroup Sparsity
*@{
*/
/**
* Iterators on sparsity patterns
*/
namespace ChunkSparsityPatternIterators
{
// forward declaration
class Iterator;
/**
* Accessor class for iterators into sparsity patterns. This class is also
* the base class for both const and non-const accessor classes into sparse
* matrices.
*
* Note that this class only allows read access to elements, providing their
* row and column number. It does not allow modifying the sparsity pattern
* itself.
*
* @author Martin Kronbichler
* @date 2013
*/
class Accessor
{
public:
/**
* Constructor.
*/
Accessor (const ChunkSparsityPattern *matrix,
const unsigned int row);
/**
* Constructor. Construct the end accessor for the given sparsity pattern.
*/
Accessor (const ChunkSparsityPattern *matrix);
/**
* Row number of the element represented by this object. This function can
* only be called for entries for which is_valid_entry() is true.
*/
unsigned int row () const;
/**
* Returns the global index from the reduced sparsity pattern.
*/
std::size_t reduced_index() const;
/**
* Column number of the element represented by this object. This function
* can only be called for entries for which is_valid_entry() is true.
*/
unsigned int column () const;
/**
* Return whether the sparsity pattern entry pointed to by this iterator
* is valid or not. Note that after compressing the sparsity pattern, all
* entries are valid. However, before compression, the sparsity pattern
* allocated some memory to be used while still adding new nonzero
* entries; if you create iterators in this phase of the sparsity
* pattern's lifetime, you will iterate over elements that are not valid.
* If this is so, then this function will return false.
*/
bool is_valid_entry () const;
/**
* Comparison. True, if both iterators point to the same matrix position.
*/
bool operator == (const Accessor &) 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.
*
* This function is only valid if both iterators point into the same
* sparsity pattern.
*/
bool operator < (const Accessor &) const;
protected:
/**
* The sparsity pattern we operate on accessed.
*/
const ChunkSparsityPattern *sparsity_pattern;
/**
* The accessor of the (reduced) sparsity pattern.
*/
SparsityPatternIterators::Accessor reduced_accessor;
/**
* Current chunk row number.
*/
unsigned int chunk_row;
/**
* Current chunk col number.
*/
unsigned int chunk_col;
/**
* Move the accessor to the next nonzero entry in the matrix.
*/
void advance ();
/**
* Grant access to iterator class.
*/
friend class Iterator;
};
/**
* Iterator that walks over the elements of a sparsity pattern.
*/
class Iterator
{
public:
/**
* Constructor. Create an iterator into the sparsity pattern @p sp for the
* given row and the index within it.
*/
Iterator (const ChunkSparsityPattern *sp,
const unsigned int row);
/**
* Prefix increment.
*/
Iterator &operator++ ();
/**
* Postfix increment.
*/
Iterator operator++ (int);
/**
* Dereferencing operator.
*/
const Accessor &operator* () const;
/**
* Dereferencing operator.
*/
const Accessor *operator-> () const;
/**
* Comparison. True, if both iterators point to the same matrix position.
*/
bool operator == (const Iterator &) const;
/**
* Inverse of <tt>==</tt>.
*/
bool operator != (const Iterator &) 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.
*
* This function is only valid if both iterators point into the same
* matrix.
*/
bool operator < (const Iterator &) const;
private:
/**
* Store an object of the accessor class.
*/
Accessor accessor;
};
}
/**
* Structure representing the sparsity pattern of a sparse matrix. This class
* is an example of the "static" type of
* @ref Sparsity.
* It uses the compressed row storage (CSR) format to store data.
*
* The use of this class is demonstrated in step-51.
*
* @author Wolfgang Bangerth, 2008
*/
class ChunkSparsityPattern : public Subscriptor
{
public:
/**
* Declare the type for container size.
*/
typedef types::global_dof_index size_type;
/**
* Typedef an iterator class that allows to walk over all nonzero elements
* of a sparsity pattern.
*/
typedef ChunkSparsityPatternIterators::Iterator const_iterator;
/**
* Typedef an iterator class that allows to walk over all nonzero elements
* of a sparsity pattern.
*
* Since the iterator does not allow to modify the sparsity pattern, this
* type is the same as that for @p const_iterator.
*/
typedef ChunkSparsityPatternIterators::Iterator iterator;
/**
* Define a value which is used to indicate that a certain value in the
* colnums array is unused, i.e. does not represent a certain column number
* index.
*
* Indices with this invalid value are used to insert new entries to the
* sparsity pattern using the add() member function, and are removed when
* calling compress().
*
* You should not assume that the variable declared here has a certain
* value. The initialization is given here only to enable the compiler to
* perform some optimizations, but the actual value of the variable may
* change over time.
*/
static const size_type invalid_entry = SparsityPattern::invalid_entry;
/**
* Initialize the matrix empty, that is with no memory allocated. This is
* useful if you want such objects as member variables in other classes. You
* can make the structure usable by calling the reinit() function.
*/
ChunkSparsityPattern ();
/**
* Copy constructor. This constructor is only allowed to be called if the
* matrix structure to be copied is empty. This is so in order to prevent
* involuntary copies of objects for temporaries, which can use large
* amounts of computing time. However, copy constructors are needed if one
* wants to place a ChunkSparsityPattern in a container, e.g., to write such
* statements like <tt>v.push_back (ChunkSparsityPattern());</tt>, with
* <tt>v</tt> a vector of ChunkSparsityPattern objects.
*
* Usually, it is sufficient to use the explicit keyword to disallow
* unwanted temporaries, but this does not work for <tt>std::vector</tt>.
* Since copying a structure like this is not useful anyway because multiple
* matrices can use the same sparsity structure, copies are only allowed for
* empty objects, as described above.
*/
ChunkSparsityPattern (const ChunkSparsityPattern &);
/**
* Initialize a rectangular matrix.
*
* @arg m number of rows @arg n number of columns @arg max_per_row maximum
* number of nonzero entries per row
*/
ChunkSparsityPattern (const size_type m,
const size_type n,
const size_type max_chunks_per_row,
const size_type chunk_size);
/**
* Initialize a rectangular matrix.
*
* @arg m number of rows @arg n number of columns @arg row_lengths possible
* number of nonzero entries for each row. This vector must have one entry
* for each row.
*/
ChunkSparsityPattern (const size_type m,
const size_type n,
const std::vector<size_type> &row_lengths,
const size_type chunk_size);
/**
* Initialize a quadratic matrix of dimension <tt>n</tt> with at most
* <tt>max_per_row</tt> nonzero entries per row.
*
* This constructor automatically enables optimized storage of diagonal
* elements. To avoid this, use the constructor taking row and column
* numbers separately.
*/
ChunkSparsityPattern (const size_type n,
const size_type max_per_row,
const size_type chunk_size);
/**
* Initialize a quadratic matrix.
*
* @arg m number of rows and columns @arg row_lengths possible number of
* nonzero entries for each row. This vector must have one entry for each
* row.
*/
ChunkSparsityPattern (const size_type m,
const std::vector<size_type> &row_lengths,
const size_type chunk_size);
/**
* Destructor.
*/
~ChunkSparsityPattern ();
/**
* Copy operator. For this the same holds as for the copy constructor: it is
* declared, defined and fine to be called, but the latter only for empty
* objects.
*/
ChunkSparsityPattern &operator = (const ChunkSparsityPattern &);
/**
* Reallocate memory and set up data structures for a new matrix with <tt>m
* </tt>rows and <tt>n</tt> columns, with at most <tt>max_per_row</tt>
* nonzero entries per row.
*
* This function simply maps its operations to the other <tt>reinit</tt>
* function.
*/
void reinit (const size_type m,
const size_type n,
const size_type max_per_row,
const size_type chunk_size);
/**
* Reallocate memory for a matrix of size <tt>m x n</tt>. The number of
* entries for each row is taken from the array <tt>row_lengths</tt> which
* has to give this number of each row <tt>i=1...m</tt>.
*
* If <tt>m*n==0</tt> all memory is freed, resulting in a total
* reinitialization of the object. If it is nonzero, new memory is only
* allocated if the new size extends the old one. This is done to save time
* and to avoid fragmentation of the heap.
*
* If the number of rows equals the number of columns then diagonal elements
* are stored first in each row to allow optimized access in relaxation
* methods of SparseMatrix.
*/
void reinit (const size_type m,
const size_type n,
const std::vector<size_type> &row_lengths,
const size_type chunk_size);
/**
* Same as above, but with a VectorSlice argument instead.
*/
void reinit (const size_type m,
const size_type n,
const VectorSlice<const std::vector<size_type> > &row_lengths,
const size_type chunk_size);
/**
* This function compresses the sparsity structure that this object
* represents. It does so by eliminating unused entries and sorting the
* remaining ones to allow faster access by usage of binary search
* algorithms. A special sorting scheme is used for the diagonal entry of
* quadratic matrices, which is always the first entry of each row.
*
* The memory which is no more needed is released.
*
* SparseMatrix objects require the ChunkSparsityPattern objects they are
* initialized with to be compressed, to reduce memory requirements.
*/
void compress ();
/**
* This function can be used as a replacement for reinit(), subsequent calls
* to add() and a final call to close() if you know exactly in advance the
* entries that will form the matrix sparsity pattern.
*
* The first two parameters determine the size of the matrix. For the two
* last ones, note that a sparse matrix can be described by a sequence of
* rows, each of which is represented by a sequence of pairs of column
* indices and values. In the present context, the begin() and end()
* parameters designate iterators (of forward iterator type) into a
* container, one representing one row. The distance between begin() and
* end() should therefore be equal to n_rows(). These iterators may be
* iterators of <tt>std::vector</tt>, <tt>std::list</tt>, pointers into a
* C-style array, or any other iterator satisfying the requirements of a
* forward iterator. The objects pointed to by these iterators (i.e. what we
* get after applying <tt>operator*</tt> or <tt>operator-></tt> to one of
* these iterators) must be a container itself that provides functions
* <tt>begin</tt> and <tt>end</tt> designating a range of iterators that
* describe the contents of one line. Dereferencing these inner iterators
* must either yield a pair of an unsigned integer as column index and a
* value of arbitrary type (such a type would be used if we wanted to
* describe a sparse matrix with one such object), or simply an unsigned
* integer (of we only wanted to describe a sparsity pattern). The function
* is able to determine itself whether an unsigned integer or a pair is what
* we get after dereferencing the inner iterators, through some template
* magic.
*
* While the order of the outer iterators denotes the different rows of the
* matrix, the order of the inner iterator denoting the columns does not
* matter, as they are sorted internal to this function anyway.
*
* Since that all sounds very complicated, consider the following example
* code, which may be used to fill a sparsity pattern:
* @code
* std::vector<std::vector<size_type> > column_indices (n_rows);
* for (size_type row=0; row<n_rows; ++row)
* // generate necessary columns in this row
* fill_row (column_indices[row]);
*
* sparsity.copy_from (n_rows, n_cols,
* column_indices.begin(),
* column_indices.end());
* @endcode
*
* Note that this example works since the iterators dereferenced yield
* containers with functions <tt>begin</tt> and <tt>end</tt> (namely
* <tt>std::vector</tt>s), and the inner iterators dereferenced yield
* unsigned integers as column indices. Note that we could have replaced
* each of the two <tt>std::vector</tt> occurrences by <tt>std::list</tt>,
* and the inner one by <tt>std::set</tt> as well.
*
* Another example would be as follows, where we initialize a whole matrix,
* not only a sparsity pattern:
* @code
* std::vector<std::map<size_type,double> > entries (n_rows);
* for (size_type row=0; row<n_rows; ++row)
* // generate necessary pairs of columns
* // and corresponding values in this row
* fill_row (entries[row]);
*
* sparsity.copy_from (n_rows, n_cols,
* column_indices.begin(),
* column_indices.end());
* matrix.reinit (sparsity);
* matrix.copy_from (column_indices.begin(),
* column_indices.end());
* @endcode
*
* This example works because dereferencing iterators of the inner type
* yields a pair of unsigned integers and a value, the first of which we
* take as column index. As previously, the outer <tt>std::vector</tt> could
* be replaced by <tt>std::list</tt>, and the inner <tt>std::map<unsigned
* int,double></tt> could be replaced by <tt>std::vector<std::pair<unsigned
* int,double> ></tt>, or a list or set of such pairs, as they all return
* iterators that point to such pairs.
*/
template <typename ForwardIterator>
void copy_from (const size_type n_rows,
const size_type n_cols,
const ForwardIterator begin,
const ForwardIterator end,
const size_type chunk_size);
/**
* Copy data from an object of type DynamicSparsityPattern. Previous content
* of this object is lost, and the sparsity pattern is in compressed mode
* afterwards.
*/
template <typename SparsityPatternType>
void copy_from (const SparsityPatternType &dsp,
const size_type chunk_size);
/**
* Take a full matrix and use its nonzero entries to generate a sparse
* matrix entry pattern for this object.
*
* Previous content of this object is lost, and the sparsity pattern is in
* compressed mode afterwards.
*/
template <typename number>
void copy_from (const FullMatrix<number> &matrix,
const size_type chunk_size);
/**
* Set the sparsity pattern of the chunk sparsity pattern to be given by
* <tt>chunk_size*chunksize</tt> blocks of the sparsity pattern for chunks
* specified. Note that the final number of rows <tt>m</tt> of the sparsity
* pattern will be approximately <tt>sparsity_pattern_for_chunks.n_rows() *
* chunk_size</tt> (modulo padding elements in the last chunk) and similarly
* for the number of columns <tt>n</tt>.
*
* This is a special initialization option in case you can tell the position
* of the chunk already from the beginning without generating the sparsity
* pattern using <tt>make_sparsity_pattern</tt> calls. This bypasses the
* search for chunks but of course needs to be handled with care in order to
* give a correct sparsity pattern.
*
* Previous content of this object is lost, and the sparsity pattern is in
* compressed mode afterwards.
*/
template <typename Sparsity>
void create_from (const unsigned int m,
const unsigned int n,
const Sparsity &sparsity_pattern_for_chunks,
const unsigned int chunk_size,
const bool optimize_diagonal = true);
/**
* Return whether the object is empty. It is empty if no memory is
* allocated, which is the same as that both dimensions are zero.
*/
bool empty () const;
/**
* Return the chunk size given as argument when constructing this object.
*/
size_type get_chunk_size () const;
/**
* Return the maximum number of entries per row. Before compression, this
* equals the number given to the constructor, while after compression, it
* equals the maximum number of entries actually allocated by the user.
*/
size_type max_entries_per_row () const;
/**
* Add a nonzero entry to the matrix. This function may only be called for
* non-compressed sparsity patterns.
*
* If the entry already exists, nothing bad happens.
*/
void add (const size_type i,
const size_type j);
/**
* Make the sparsity pattern symmetric by adding the sparsity pattern of the
* transpose object.
*
* This function throws an exception if the sparsity pattern does not
* represent a quadratic matrix.
*/
void symmetrize ();
/**
* Return number of rows of this matrix, which equals the dimension of the
* image space.
*/
inline size_type n_rows () const;
/**
* Return number of columns of this matrix, which equals the dimension of
* the range space.
*/
inline size_type n_cols () const;
/**
* Check if a value at a certain position may be non-zero.
*/
bool exists (const size_type i,
const size_type j) const;
/**
* Number of entries in a specific row.
*/
size_type row_length (const size_type row) const;
/**
* Compute the bandwidth of the matrix represented by this structure. The
* bandwidth is the maximum of $|i-j|$ 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 $\max\{n-1,m-1\}$.
*/
size_type bandwidth () const;
/**
* Return the number of nonzero elements of this matrix. Actually, it
* returns the number of entries in the sparsity pattern; if any of the
* entries should happen to be zero, it is counted anyway.
*
* This function may only be called if the matrix struct is compressed. It
* does not make too much sense otherwise anyway.
*/
size_type n_nonzero_elements () const;
/**
* Return whether the structure is compressed or not.
*/
bool is_compressed () const;
/**
* Return whether this object stores only those entries that have been added
* explicitly, or if the sparsity pattern contains elements that have been
* added through other means (implicitly) while building it. For the current
* class, the result is true if and only if it is square because it then
* unconditionally stores the diagonal entries whether they have been added
* explicitly or not.
*
* This function mainly serves the purpose of describing the current class
* in cases where several kinds of sparsity patterns can be passed as
* template arguments.
*/
bool stores_only_added_elements () const;
/**
* Iterator starting at the first entry of the matrix. The resulting
* iterator can be used to walk over all nonzero entries of the sparsity
* pattern.
*/
iterator begin () const;
/**
* Final iterator.
*/
iterator end () const;
/**
* Iterator starting at the first entry of row <tt>r</tt>.
*
* 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>. Note also that the iterator may not be dereferencable in
* that case.
*/
iterator begin (const unsigned int r) const;
/**
* Final iterator of row <tt>r</tt>. It points to the first element past the
* end of line @p r, or past the end of the entire sparsity pattern.
*
* 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.
*/
iterator end (const unsigned int r) const;
/**
* Write the data of this object en bloc to a file. This is done in a binary
* mode, so the output is neither readable by humans nor (probably) by other
* computers using a different operating system of number format.
*
* The purpose of this function is that you can swap out matrices and
* sparsity pattern if you are short of memory, want to communicate between
* different programs, or allow objects to be persistent across different
* runs of the program.
*/
void block_write (std::ostream &out) const;
/**
* Read data that has previously been written by block_write() from a file.
* This is done using the inverse operations to the above function, so it is
* reasonably fast because the bitstream is not interpreted except for a few
* numbers up front.
*
* The object is resized on this operation, and all previous contents are
* lost.
*
* A primitive form of error checking is performed which will recognize the
* bluntest attempts to interpret some data as a vector stored bitwise to a
* file, but not more.
*/
void block_read (std::istream &in);
/**
* Print the sparsity of the matrix. The output consists of one line per row
* of the format <tt>[i,j1,j2,j3,...]</tt>. <i>i</i> is the row number and
* <i>jn</i> are the allocated columns in this row.
*/
void print (std::ostream &out) const;
/**
* Print the sparsity of the matrix in a format that <tt>gnuplot</tt>
* understands and which can be used to plot the sparsity pattern in a
* graphical way. The format consists of pairs <tt>i j</tt> 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 <tt>(0,0)</tt>
* 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 <tt>plot</tt> command.
*/
void print_gnuplot (std::ostream &out) const;
/**
* Determine an estimate for the memory consumption (in bytes) of this
* object. See MemoryConsumption.
*/
std::size_t memory_consumption () const;
/**
* @addtogroup Exceptions
* @{
*/
/**
* Exception
*/
DeclException1 (ExcInvalidNumber,
int,
<< "The provided number is invalid here: " << arg1);
/**
* Exception
*/
DeclException2 (ExcInvalidIndex,
int, int,
<< "The given index " << arg1
<< " should be less than " << arg2 << ".");
/**
* Exception
*/
DeclException2 (ExcNotEnoughSpace,
int, int,
<< "Upon entering a new entry to row " << arg1
<< ": there was no free entry any more. " << std::endl
<< "(Maximum number of entries for this row: "
<< arg2 << "; maybe the matrix is already compressed?)");
/**
* Exception
*/
DeclException0 (ExcNotCompressed);
/**
* Exception
*/
DeclException0 (ExcMatrixIsCompressed);
/**
* Exception
*/
DeclException0 (ExcEmptyObject);
/**
* Exception
*/
DeclException0 (ExcInvalidConstructorCall);
/**
* Exception
*/
DeclException2 (ExcIteratorRange,
int, int,
<< "The iterators denote a range of " << arg1
<< " elements, but the given number of rows was " << arg2);
/**
* Exception
*/
DeclException0 (ExcMETISNotInstalled);
/**
* Exception
*/
DeclException1 (ExcInvalidNumberOfPartitions,
int,
<< "The number of partitions you gave is " << arg1
<< ", but must be greater than zero.");
/**
* Exception
*/
DeclException2 (ExcInvalidArraySize,
int, int,
<< "The array has size " << arg1 << " but should have size "
<< arg2);
//@}
private:
/**
* Number of rows that this sparsity structure shall represent.
*/
size_type rows;
/**
* Number of columns that this sparsity structure shall represent.
*/
size_type cols;
/**
* The size of chunks.
*/
size_type chunk_size;
/**
* The reduced sparsity pattern. We store only which chunks exist, with each
* chunk a block in the matrix of size chunk_size by chunk_size.
*/
SparsityPattern sparsity_pattern;
/**
* Make all the chunk sparse matrix kinds friends.
*/
template <typename> friend class ChunkSparseMatrix;
/**
* Make the accessor class a friend.
*/
friend class ChunkSparsityPatternIterators::Accessor;
};
/*@}*/
/*---------------------- Inline functions -----------------------------------*/
#ifndef DOXYGEN
namespace ChunkSparsityPatternIterators
{
inline
Accessor::
Accessor (const ChunkSparsityPattern *sparsity_pattern,
const unsigned int row)
:
sparsity_pattern(sparsity_pattern),
reduced_accessor(row==sparsity_pattern->n_rows() ?
*sparsity_pattern->sparsity_pattern.end() :
*sparsity_pattern->sparsity_pattern.
begin(row/sparsity_pattern->get_chunk_size())),
chunk_row (row==sparsity_pattern->n_rows() ? 0 :
row%sparsity_pattern->get_chunk_size()),
chunk_col (0)
{}
inline
Accessor::
Accessor (const ChunkSparsityPattern *sparsity_pattern)
:
sparsity_pattern(sparsity_pattern),
reduced_accessor(*sparsity_pattern->sparsity_pattern.end()),
chunk_row (0),
chunk_col (0)
{}
inline
bool
Accessor::is_valid_entry () const
{
return reduced_accessor.is_valid_entry()
&&
sparsity_pattern->get_chunk_size()*reduced_accessor.row()+chunk_row <
sparsity_pattern->n_rows()
&&
sparsity_pattern->get_chunk_size()*reduced_accessor.column()+chunk_col <
sparsity_pattern->n_cols();
}
inline
unsigned int
Accessor::row() const
{
Assert (is_valid_entry() == true, ExcInvalidIterator());
return sparsity_pattern->get_chunk_size()*reduced_accessor.row() +
chunk_row;
}
inline
unsigned int
Accessor::column() const
{
Assert (is_valid_entry() == true, ExcInvalidIterator());
return sparsity_pattern->get_chunk_size()*reduced_accessor.column() +
chunk_col;
}
inline
std::size_t
Accessor::reduced_index() const
{
Assert (is_valid_entry() == true, ExcInvalidIterator());
return reduced_accessor.index_within_sparsity;
}
inline
bool
Accessor::operator == (const Accessor &other) const
{
// no need to check for equality of sparsity patterns as this is done in
// the reduced case already and every ChunkSparsityPattern has its own
// reduced sparsity pattern
return (reduced_accessor == other.reduced_accessor &&
chunk_row == other.chunk_row &&
chunk_col == other.chunk_col);
}
inline
bool
Accessor::operator < (const Accessor &other) const
{
Assert (sparsity_pattern == other.sparsity_pattern,
ExcInternalError());
if (chunk_row != other.chunk_row)
{
if (reduced_accessor.index_within_sparsity ==
reduced_accessor.sparsity_pattern->n_nonzero_elements())
return false;
if (other.reduced_accessor.index_within_sparsity ==
reduced_accessor.sparsity_pattern->n_nonzero_elements())
return true;
const unsigned int
global_row = sparsity_pattern->get_chunk_size()*reduced_accessor.row()
+chunk_row,
other_global_row = sparsity_pattern->get_chunk_size()*
other.reduced_accessor.row()+other.chunk_row;
if (global_row < other_global_row)
return true;
else if (global_row > other_global_row)
return false;
}
return (reduced_accessor.index_within_sparsity <
other.reduced_accessor.index_within_sparsity ||
(reduced_accessor.index_within_sparsity ==
other.reduced_accessor.index_within_sparsity &&
chunk_col < other.chunk_col));
}
inline
void
Accessor::advance ()
{
const unsigned int chunk_size = sparsity_pattern->get_chunk_size();
Assert (chunk_row < chunk_size && chunk_col < chunk_size,
ExcIteratorPastEnd());
Assert (reduced_accessor.row() * chunk_size + chunk_row <
sparsity_pattern->n_rows()
&&
reduced_accessor.column() * chunk_size + chunk_col <
sparsity_pattern->n_cols(),
ExcIteratorPastEnd());
if (chunk_size == 1)
{
reduced_accessor.advance();
return;
}
++chunk_col;
// end of chunk
if (chunk_col == chunk_size
||
reduced_accessor.column() * chunk_size + chunk_col ==
sparsity_pattern->n_cols())
{
const unsigned int reduced_row = reduced_accessor.row();
// end of row
if (reduced_accessor.index_within_sparsity + 1 ==
reduced_accessor.sparsity_pattern->rowstart[reduced_row+1])
{
++chunk_row;
chunk_col = 0;
// end of chunk rows or end of matrix
if (chunk_row == chunk_size ||
(reduced_row * chunk_size + chunk_row ==
sparsity_pattern->n_rows()))
{
chunk_row = 0;
reduced_accessor.advance();
}
// go back to the beginning of the same reduced row but with
// chunk_row increased by one
else
reduced_accessor.index_within_sparsity =
reduced_accessor.sparsity_pattern->rowstart[reduced_row];
}
// advance within chunk
else
{
reduced_accessor.advance();
chunk_col = 0;
}
}
}
inline
Iterator::Iterator (const ChunkSparsityPattern *sparsity_pattern,
const unsigned int row)
:
accessor(sparsity_pattern, row)
{}
inline
Iterator &
Iterator::operator++ ()
{
accessor.advance ();
return *this;
}
inline
Iterator
Iterator::operator++ (int)
{
const Iterator iter = *this;
accessor.advance ();
return iter;
}
inline
const Accessor &
Iterator::operator* () const
{
return accessor;
}
inline
const Accessor *
Iterator::operator-> () const
{
return &accessor;
}
inline
bool
Iterator::operator == (const Iterator &other) const
{
return (accessor == other.accessor);
}
inline
bool
Iterator::operator != (const Iterator &other) const
{
return ! (accessor == other.accessor);
}
inline
bool
Iterator::operator < (const Iterator &other) const
{
return accessor < other.accessor;
}
}
inline
ChunkSparsityPattern::iterator
ChunkSparsityPattern::begin () const
{
return iterator(this, 0);
}
inline
ChunkSparsityPattern::iterator
ChunkSparsityPattern::end () const
{
return iterator(this, n_rows());
}
inline
ChunkSparsityPattern::iterator
ChunkSparsityPattern::begin (const unsigned int r) const
{
Assert (r<n_rows(), ExcIndexRange(r,0,n_rows()));
return iterator(this, r);
}
inline
ChunkSparsityPattern::iterator
ChunkSparsityPattern::end (const unsigned int r) const
{
Assert (r<n_rows(), ExcIndexRange(r,0,n_rows()))
return iterator(this, r+1);
}
inline
ChunkSparsityPattern::size_type
ChunkSparsityPattern::n_rows () const
{
return rows;
}
inline
ChunkSparsityPattern::size_type
ChunkSparsityPattern::n_cols () const
{
return cols;
}
inline
ChunkSparsityPattern::size_type
ChunkSparsityPattern::get_chunk_size () const
{
return chunk_size;
}
inline
bool
ChunkSparsityPattern::is_compressed () const
{
return sparsity_pattern.compressed;
}
template <typename ForwardIterator>
void
ChunkSparsityPattern::copy_from (const size_type n_rows,
const size_type n_cols,
const ForwardIterator begin,
const ForwardIterator end,
const size_type chunk_size)
{
Assert (static_cast<size_type>(std::distance (begin, end)) == n_rows,
ExcIteratorRange (std::distance (begin, end), n_rows));
// first determine row lengths for each row. if the matrix is quadratic,
// then we might have to add an additional entry for the diagonal, if that
// is not yet present. as we have to call compress anyway later on, don't
// bother to check whether that diagonal entry is in a certain row or not
const bool is_square = (n_rows == n_cols);
std::vector<size_type> row_lengths;
row_lengths.reserve(n_rows);
for (ForwardIterator i=begin; i!=end; ++i)
row_lengths.push_back (std::distance (i->begin(), i->end())
+
(is_square ? 1 : 0));
reinit (n_rows, n_cols, row_lengths, chunk_size);
// now enter all the elements into the matrix
size_type row = 0;
typedef typename std::iterator_traits<ForwardIterator>::value_type::const_iterator inner_iterator;
for (ForwardIterator i=begin; i!=end; ++i, ++row)
{
const inner_iterator end_of_row = i->end();
for (inner_iterator j=i->begin(); j!=end_of_row; ++j)
{
const size_type col
= internal::SparsityPatternTools::get_column_index_from_iterator(*j);
Assert (col < n_cols, ExcInvalidIndex(col,n_cols));
add (row, col);
}
}
// finally compress everything. this also sorts the entries within each row
compress ();
}
#endif // DOXYGEN
DEAL_II_NAMESPACE_CLOSE
#endif
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