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// $Id: chunk_sparse_matrix.h 19894 2009-10-15 22:19:51Z kanschat $
// Version: $Name: $
//
// Copyright (C) 2008, 2009 by the deal.II authors
//
// This file is subject to QPL and may not be distributed
// without copyright and license information. Please refer
// to the file deal.II/doc/license.html for the text and
// further information on this license.
//
//---------------------------------------------------------------------------
#ifndef __deal2__chunk_sparse_matrix_h
#define __deal2__chunk_sparse_matrix_h
#include <base/config.h>
#include <base/subscriptor.h>
#include <base/smartpointer.h>
#include <lac/chunk_sparsity_pattern.h>
#include <lac/identity_matrix.h>
#include <lac/exceptions.h>
DEAL_II_NAMESPACE_OPEN
template<typename number> class Vector;
template<typename number> class FullMatrix;
/*! @addtogroup Matrix1
*@{
*/
/**
* Sparse matrix. This class implements the function to store values
* in the locations of a sparse matrix denoted by a
* SparsityPattern. The separation of sparsity pattern and values is
* done since one can store data elements of different type in these
* locations without the SparsityPattern having to know this, and more
* importantly one can associate more than one matrix with the same
* sparsity pattern.
*
* @note Instantiations for this template are provided for <tt>@<float@> and
* @<double@></tt>; others can be generated in application programs (see the
* section on @ref Instantiations in the manual).
*
* @author Wolfgang Bangerth, 2008
*/
template <typename number>
class ChunkSparseMatrix : public virtual Subscriptor
{
public:
/**
* Type of matrix entries. In analogy to
* the STL container classes.
*/
typedef number value_type;
/**
* Declare a type that has holds
* real-valued numbers with the
* same precision as the template
* argument to this class. If the
* template argument of this
* class is a real data type,
* then real_type equals the
* template argument. If the
* template argument is a
* std::complex type then
* real_type equals the type
* underlying the complex
* numbers.
*
* This typedef is used to
* represent the return type of
* norms.
*/
typedef typename numbers::NumberTraits<number>::real_type real_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;
};
/**
* @name Constructors and initalization.
*/
//@{
/**
* Constructor; initializes the matrix to
* be empty, without any structure, i.e.
* the matrix is not usable at all. This
* constructor is therefore only useful
* for matrices which are members of a
* class. All other matrices should be
* created at a point in the data flow
* where all necessary information is
* available.
*
* You have to initialize
* the matrix before usage with
* reinit(const ChunkSparsityPattern&).
*/
ChunkSparseMatrix ();
/**
* Copy constructor. This constructor is
* only allowed to be called if the matrix
* to be copied is empty. This is for the
* same reason as for the
* ChunkSparsityPattern, see there for the
* details.
*
* If you really want to copy a whole
* matrix, you can do so by using the
* copy_from() function.
*/
ChunkSparseMatrix (const ChunkSparseMatrix &);
/**
* Constructor. Takes the given
* matrix sparsity structure to
* represent the sparsity pattern
* of this matrix. You can change
* the sparsity pattern later on
* by calling the reinit(const
* ChunkSparsityPattern&) function.
*
* You have to make sure that the
* lifetime of the sparsity
* structure is at least as long
* as that of this matrix or as
* long as reinit(const
* ChunkSparsityPattern&) is not
* called with a new sparsity
* pattern.
*
* The constructor is marked
* explicit so as to disallow
* that someone passes a sparsity
* pattern in place of a sparse
* matrix to some function, where
* an empty matrix would be
* generated then.
*/
explicit ChunkSparseMatrix (const ChunkSparsityPattern &sparsity);
/**
* Copy constructor: initialize
* the matrix with the identity
* matrix. This constructor will
* throw an exception if the
* sizes of the sparsity pattern
* and the identity matrix do not
* coincide, or if the sparsity
* pattern does not provide for
* nonzero entries on the entire
* diagonal.
*/
ChunkSparseMatrix (const ChunkSparsityPattern &sparsity,
const IdentityMatrix &id);
/**
* Destructor. Free all memory, but do not
* release the memory of the sparsity
* structure.
*/
virtual ~ChunkSparseMatrix ();
/**
* Copy operator. Since copying
* entire sparse matrices is a
* very expensive operation, we
* disallow doing so except for
* the special case of empty
* matrices of size zero. This
* doesn't seem particularly
* useful, but is exactly what
* one needs if one wanted to
* have a
* <code>std::vector@<ChunkSparseMatrix@<double@>
* @></code>: in that case, one
* can create a vector (which
* needs the ability to copy
* objects) of empty matrices
* that are then later filled
* with something useful.
*/
ChunkSparseMatrix<number>& operator = (const ChunkSparseMatrix<number> &);
/**
* Copy operator: initialize
* the matrix with the identity
* matrix. This operator will
* throw an exception if the
* sizes of the sparsity pattern
* and the identity matrix do not
* coincide, or if the sparsity
* pattern does not provide for
* nonzero entries on the entire
* diagonal.
*/
ChunkSparseMatrix<number> &
operator= (const IdentityMatrix &id);
/**
* This operator assigns a scalar to
* a matrix. Since this does usually
* not make much sense (should we set
* all matrix entries to this value?
* Only the nonzero entries of the
* sparsity pattern?), this operation
* is only allowed if the actual
* value to be assigned is zero. This
* operator only exists to allow for
* the obvious notation
* <tt>matrix=0</tt>, which sets all
* elements of the matrix to zero,
* but keep the sparsity pattern
* previously used.
*/
ChunkSparseMatrix & operator = (const double d);
/**
* Reinitialize the sparse matrix
* with the given sparsity
* pattern. The latter tells the
* matrix how many nonzero
* elements there need to be
* reserved.
*
* Regarding memory allocation,
* the same applies as said
* above.
*
* You have to make sure that the
* lifetime of the sparsity
* structure is at least as long
* as that of this matrix or as
* long as reinit(const
* ChunkSparsityPattern &) is not
* called with a new sparsity
* structure.
*
* The elements of the matrix are
* set to zero by this function.
*/
virtual void reinit (const ChunkSparsityPattern &sparsity);
/**
* Release all memory and return
* to a state just like after
* having called the default
* constructor. It also forgets
* the sparsity pattern it was
* previously tied to.
*/
virtual void clear ();
//@}
/**
* @name Information on the matrix
*/
//@{
/**
* Return whether the object is
* empty. It is empty if either
* both dimensions are zero or no
* ChunkSparsityPattern is
* associated.
*/
bool empty () const;
/**
* Return the dimension of the
* image space. To remember: the
* matrix is of dimension
* $m \times n$.
*/
unsigned int m () const;
/**
* Return the dimension of the
* range space. To remember: the
* matrix is of dimension
* $m \times n$.
*/
unsigned int n () 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.
*/
unsigned int n_nonzero_elements () const;
/**
* Return the number of actually
* nonzero elements of this
* matrix.
*
* Note, that this function does
* (in contrary to
* n_nonzero_elements()) not
* count all entries of the
* sparsity pattern but only the
* ones that are nonzero.
*/
unsigned int n_actually_nonzero_elements () const;
/**
* Return a (constant) reference
* to the underlying sparsity
* pattern of this matrix.
*
* Though the return value is
* declared <tt>const</tt>, you
* should be aware that it may
* change if you call any
* nonconstant function of
* objects which operate on it.
*/
const ChunkSparsityPattern & get_sparsity_pattern () const;
/**
* Determine an estimate for the
* memory consumption (in bytes)
* of this object. See
* MemoryConsumption.
*/
unsigned int memory_consumption () const;
//@}
/**
* @name Modifying entries
*/
//@{
/**
* Set the element (<i>i,j</i>)
* to <tt>value</tt>. Throws an
* error if the entry does not
* exist or if <tt>value</tt> is
* not a finite number. Still, it
* is allowed to store zero
* values in non-existent fields.
*/
void set (const unsigned int i,
const unsigned int j,
const number value);
/**
* Add <tt>value</tt> to the
* element (<i>i,j</i>). Throws
* an error if the entry does not
* exist or if <tt>value</tt> is
* not a finite number. Still, it
* is allowed to store zero
* values in non-existent fields.
*/
void add (const unsigned int i,
const unsigned int j,
const number value);
/**
* Multiply the entire matrix by a
* fixed factor.
*/
ChunkSparseMatrix & operator *= (const number factor);
/**
* Divide the entire matrix by a
* fixed factor.
*/
ChunkSparseMatrix & operator /= (const number factor);
/**
* Symmetrize the matrix by
* forming the mean value between
* the existing matrix and its
* transpose, $A = \frac 12(A+A^T)$.
*
* This operation assumes that
* the underlying sparsity
* pattern represents a symmetric
* object. If this is not the
* case, then the result of this
* operation will not be a
* symmetric matrix, since it
* only explicitly symmetrizes
* by looping over the lower left
* triangular part for efficiency
* reasons; if there are entries
* in the upper right triangle,
* then these elements are missed
* in the
* symmetrization. Symmetrization
* of the sparsity pattern can be
* obtain by
* ChunkSparsityPattern::symmetrize().
*/
void symmetrize ();
/**
* Copy the given matrix to this
* one. The operation throws an
* error if the sparsity patterns
* of the two involved matrices
* do not point to the same
* object, since in this case the
* copy operation is
* cheaper. Since this operation
* is notheless not for free, we
* do not make it available
* through <tt>operator =</tt>,
* since this may lead to
* unwanted usage, e.g. in copy
* arguments to functions, which
* should really be arguments by
* reference.
*
* The source matrix may be a matrix
* of arbitrary type, as long as its
* data type is convertible to the
* data type of this matrix.
*
* The function returns a reference to
* <tt>*this</tt>.
*/
template <typename somenumber>
ChunkSparseMatrix<number> &
copy_from (const ChunkSparseMatrix<somenumber> &source);
/**
* This function is complete
* analogous to the
* ChunkSparsityPattern::copy_from()
* function in that it allows to
* initialize a whole matrix in
* one step. See there for more
* information on argument types
* and their meaning. You can
* also find a small example on
* how to use this function
* there.
*
* The only difference to the
* cited function is that the
* objects which the inner
* iterator points to need to be
* of type <tt>std::pair<unsigned
* int, value</tt>, where
* <tt>value</tt> needs to be
* convertible to the element
* type of this class, as
* specified by the
* <tt>number</tt> template
* argument.
*
* Previous content of the matrix
* is overwritten. Note that the
* entries specified by the input
* parameters need not
* necessarily cover all elements
* of the matrix. Elements not
* covered remain untouched.
*/
template <typename ForwardIterator>
void copy_from (const ForwardIterator begin,
const ForwardIterator end);
/**
* Copy the nonzero entries of a
* full matrix into this
* object. Previous content is
* deleted. Note that the
* underlying sparsity pattern
* must be appropriate to hold
* the nonzero entries of the
* full matrix.
*/
template <typename somenumber>
void copy_from (const FullMatrix<somenumber> &matrix);
/**
* 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>. This
* function throws an error if the
* sparsity patterns of the two involved
* matrices do not point to the same
* object, since in this case the
* operation is cheaper.
*
* The source matrix may be a sparse
* matrix over an arbitrary underlying
* scalar type, as long as its data type
* is convertible to the data type of
* this matrix.
*/
template <typename somenumber>
void add (const number factor,
const ChunkSparseMatrix<somenumber> &matrix);
//@}
/**
* @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. In
* order to avoid abuse, this
* function throws an exception
* if the required element does
* not exist in the matrix.
*
* In case you want a function
* that returns zero instead (for
* entries that are not in the
* sparsity pattern of the
* matrix), use the el()
* function.
*
* If you are looping over all elements,
* consider using one of the iterator
* classes instead, since they are
* tailored better to a sparse matrix
* structure.
*/
number operator () (const unsigned int i,
const unsigned int j) const;
/**
* This function is mostly like
* operator()() in that it
* returns the value of the
* matrix entry (<i>i,j</i>). The
* only difference is that if
* this entry does not exist in
* the sparsity pattern, then
* instead of raising an
* exception, 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.
*
* If you are looping over all elements,
* consider using one of the iterator
* classes instead, since they are
* tailored better to a sparse matrix
* structure.
*/
number el (const unsigned int i,
const unsigned int 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 (see
* ChunkSparsityPattern::optimize_diagonal()).
*
* This function is considerably
* faster than the operator()(),
* since for quadratic matrices, the
* diagonal entry may be the
* first to be stored in each row
* and access therefore does not
* involve searching for the
* right column number.
*/
number diag_element (const unsigned int i) const;
/**
* Same as above, but return a
* writeable reference. You're
* sure you know what you do?
*/
number & diag_element (const unsigned int i);
//@}
/**
* @name Matrix vector multiplications
*/
//@{
/**
* Matrix-vector multiplication:
* let <i>dst = M*src</i> with
* <i>M</i> being this matrix.
*
* Note that while this function can
* operate on all vectors that offer
* iterator classes, it is only really
* effective for objects of type @ref
* Vector. For all classes for which
* iterating over elements, or random
* member access is expensive, this
* function is not efficient. In
* particular, if you want to multiply
* with BlockVector objects, you should
* consider using a BlockChunkSparseMatrix as
* well.
*
* Source and destination must
* not be the same vector.
*/
template <class OutVector, class InVector>
void vmult (OutVector& dst,
const InVector& 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.
*
* Note that while this function can
* operate on all vectors that offer
* iterator classes, it is only really
* effective for objects of type @ref
* Vector. For all classes for which
* iterating over elements, or random
* member access is expensive, this
* function is not efficient. In
* particular, if you want to multiply
* with BlockVector objects, you should
* consider using a BlockChunkSparseMatrix as
* well.
*
* Source and destination must
* not be the same vector.
*/
template <class OutVector, class InVector>
void Tvmult (OutVector& dst,
const InVector& src) const;
/**
* Adding Matrix-vector
* multiplication. Add
* <i>M*src</i> on <i>dst</i>
* with <i>M</i> being this
* matrix.
*
* Note that while this function can
* operate on all vectors that offer
* iterator classes, it is only really
* effective for objects of type @ref
* Vector. For all classes for which
* iterating over elements, or random
* member access is expensive, this
* function is not efficient. In
* particular, if you want to multiply
* with BlockVector objects, you should
* consider using a BlockChunkSparseMatrix as
* well.
*
* Source and destination must
* not be the same vector.
*/
template <class OutVector, class InVector>
void vmult_add (OutVector& dst,
const InVector& 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.
*
* Note that while this function can
* operate on all vectors that offer
* iterator classes, it is only really
* effective for objects of type @ref
* Vector. For all classes for which
* iterating over elements, or random
* member access is expensive, this
* function is not efficient. In
* particular, if you want to multiply
* with BlockVector objects, you should
* consider using a BlockChunkSparseMatrix as
* well.
*
* Source and destination must
* not be the same vector.
*/
template <class OutVector, class InVector>
void Tvmult_add (OutVector& dst,
const InVector& 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, and for
* the result to actually be a norm it
* also needs to be either real symmetric
* or complex hermitian.
*
* The underlying template types of both
* this matrix and the given vector
* should either both be real or
* complex-valued, but not mixed, for
* this function to make sense.
*/
template <typename somenumber>
somenumber matrix_norm_square (const Vector<somenumber> &v) const;
/**
* Compute the matrix scalar
* product $\left(u,Mv\right)$.
*/
template <typename somenumber>
somenumber matrix_scalar_product (const Vector<somenumber> &u,
const Vector<somenumber> &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
* <tt>dst</tt>. 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.
*/
template <typename somenumber>
somenumber residual (Vector<somenumber> &dst,
const Vector<somenumber> &x,
const Vector<somenumber> &b) const;
//@}
/**
* @name Matrix norms
*/
//@{
/**
* Return the l1-norm of the matrix, that is
* $|M|_1=max_{all columns j}\sum_{all
* rows i} |M_ij|$,
* (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)
*/
real_type l1_norm () const;
/**
* Return the linfty-norm of the
* matrix, that is
* $|M|_infty=max_{all rows i}\sum_{all
* columns j} |M_ij|$,
* (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)
*/
real_type 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.
*/
real_type frobenius_norm () const;
//@}
/**
* @name Preconditioning methods
*/
//@{
/**
* Apply the Jacobi
* preconditioner, which
* multiplies every element of
* the <tt>src</tt> vector by the
* inverse of the respective
* diagonal element and
* multiplies the result with the
* relaxation factor <tt>omega</tt>.
*/
template <typename somenumber>
void precondition_Jacobi (Vector<somenumber> &dst,
const Vector<somenumber> &src,
const number omega = 1.) const;
/**
* Apply SSOR preconditioning to
* <tt>src</tt>.
*/
template <typename somenumber>
void precondition_SSOR (Vector<somenumber> &dst,
const Vector<somenumber> &src,
const number om = 1.) const;
/**
* Apply SOR preconditioning
* matrix to <tt>src</tt>.
*/
template <typename somenumber>
void precondition_SOR (Vector<somenumber> &dst,
const Vector<somenumber> &src,
const number om = 1.) const;
/**
* Apply transpose SOR
* preconditioning matrix to
* <tt>src</tt>.
*/
template <typename somenumber>
void precondition_TSOR (Vector<somenumber> &dst,
const Vector<somenumber> &src,
const number om = 1.) const;
/**
* Perform SSOR preconditioning
* in-place. Apply the
* preconditioner matrix without
* copying to a second vector.
* <tt>omega</tt> is the relaxation
* parameter.
*/
template <typename somenumber>
void SSOR (Vector<somenumber> &v,
const number omega = 1.) const;
/**
* Perform an SOR preconditioning
* in-place. <tt>omega</tt> is
* the relaxation parameter.
*/
template <typename somenumber>
void SOR (Vector<somenumber> &v,
const number om = 1.) const;
/**
* Perform a transpose SOR
* preconditioning in-place.
* <tt>omega</tt> is the
* relaxation parameter.
*/
template <typename somenumber>
void TSOR (Vector<somenumber> &v,
const number om = 1.) const;
/**
* Perform a permuted SOR
* preconditioning in-place.
*
* The standard SOR method is
* applied in the order
* prescribed by <tt>permutation</tt>,
* that is, first the row
* <tt>permutation[0]</tt>, then
* <tt>permutation[1]</tt> and so
* on. For efficiency reasons,
* the permutation as well as its
* inverse are required.
*
* <tt>omega</tt> is the
* relaxation parameter.
*/
template <typename somenumber>
void PSOR (Vector<somenumber> &v,
const std::vector<unsigned int>& permutation,
const std::vector<unsigned int>& inverse_permutation,
const number om = 1.) const;
/**
* Perform a transposed permuted SOR
* preconditioning in-place.
*
* The transposed SOR method is
* applied in the order
* prescribed by
* <tt>permutation</tt>, that is,
* first the row
* <tt>permutation[m()-1]</tt>,
* then
* <tt>permutation[m()-2]</tt>
* and so on. For efficiency
* reasons, the permutation as
* well as its inverse are
* required.
*
* <tt>omega</tt> is the
* relaxation parameter.
*/
template <typename somenumber>
void TPSOR (Vector<somenumber> &v,
const std::vector<unsigned int>& permutation,
const std::vector<unsigned int>& inverse_permutation,
const number om = 1.) const;
/**
* Do one SOR step on <tt>v</tt>.
* Performs a direct SOR step
* with right hand side
* <tt>b</tt>.
*/
template <typename somenumber>
void SOR_step (Vector<somenumber> &v,
const Vector<somenumber> &b,
const number om = 1.) const;
/**
* Do one adjoint SOR step on
* <tt>v</tt>. Performs a direct
* TSOR step with right hand side
* <tt>b</tt>.
*/
template <typename somenumber>
void TSOR_step (Vector<somenumber> &v,
const Vector<somenumber> &b,
const number om = 1.) const;
/**
* Do one SSOR step on
* <tt>v</tt>. Performs a direct
* SSOR step with right hand side
* <tt>b</tt> by performing TSOR
* after SOR.
*/
template <typename somenumber>
void SSOR_step (Vector<somenumber> &v,
const Vector<somenumber> &b,
const number om = 1.) const;
//@}
/**
* @name Input/Output
*/
//@{
/**
* 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.
*/
void print (std::ostream &out) const;
/**
* Print the matrix in the usual
* format, i.e. as a matrix and
* not as a list of nonzero
* elements. For better
* readability, elements not in
* the matrix are displayed as
* empty space, while matrix
* elements which are explicitly
* set to zero are displayed as
* such.
*
* The parameters allow for a
* flexible setting of the output
* format: <tt>precision</tt> and
* <tt>scientific</tt> are used
* to determine the number
* format, where <tt>scientific =
* false</tt> means fixed point
* notation. A zero entry for
* <tt>width</tt> makes the
* function compute a width, but
* it may be changed to a
* positive value, if output is
* crude.
*
* Additionally, a character for
* an empty value may be
* specified.
*
* Finally, the whole matrix can
* be multiplied with a common
* denominator to produce more
* readable output, even
* integers.
*
* @attention This function may
* produce <b>large</b> amounts
* of output if applied to a
* large matrix!
*/
void print_formatted (std::ostream &out,
const unsigned int precision = 3,
const bool scientific = true,
const unsigned int width = 0,
const char *zero_string = " ",
const double denominator = 1.) const;
/**
* Print the actual pattern of
* the matrix. For each entry
* with an absolute value larger
* than threshold, a '*' is
* printed, a ':' for every value
* smaller and a '.' for every
* entry not allocated.
*/
void print_pattern(std::ostream& out,
const double threshold = 0.) 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. Note,
* however, that no checks are
* performed whether new data and
* the underlying ChunkSparsityPattern
* object fit together. It is
* your responsibility to make
* sure that the sparsity pattern
* and the data to be read match.
*
* A primitive form of error
* checking is performed which
* will recognize the bluntest
* attempts to interpret some
* data as a matrix stored
* bitwise to a file that wasn't
* actually created that way, but
* not more.
*/
void block_read (std::istream &in);
//@}
/** @addtogroup Exceptions
* @{ */
/**
* Exception
*/
DeclException2 (ExcInvalidIndex,
int, int,
<< "The entry with index <" << arg1 << ',' << arg2
<< "> does not exist.");
/**
* Exception
*/
DeclException1 (ExcInvalidIndex1,
int,
<< "The index " << arg1 << " is not in the allowed range.");
/**
* Exception
*/
DeclException0 (ExcDifferentChunkSparsityPatterns);
/**
* Exception
*/
DeclException2 (ExcIteratorRange,
int, int,
<< "The iterators denote a range of " << arg1
<< " elements, but the given number of rows was " << arg2);
/**
* Exception
*/
DeclException0 (ExcSourceEqualsDestination);
//@}
private:
/**
* Pointer to the sparsity
* pattern used for this
* matrix. In order to guarantee
* that it is not deleted while
* still in use, we subscribe to
* it using the SmartPointer
* class.
*/
SmartPointer<const ChunkSparsityPattern,ChunkSparseMatrix<number> > cols;
/**
* Array of values for all the
* nonzero entries. The position
* within the matrix, i.e. the
* row and column number for a
* given entry can only be
* deduced using the sparsity
* pattern. The same holds for
* the more common operation of
* finding an entry by its
* coordinates.
*/
number *val;
/**
* Allocated size of #val. This
* can be larger than the
* actually used part if the size
* of the matrix was reduced
* somewhen in the past by
* associating a sparsity pattern
* with a smaller size to this
* object, using the reinit()
* function.
*/
unsigned int max_len;
/**
* Return the location of entry
* $(i,j)$ within the val array.
*/
unsigned int compute_location (const unsigned int i,
const unsigned int j) const;
// make all other sparse matrices
// friends
template <typename somenumber> friend class ChunkSparseMatrix;
};
/*@}*/
#ifndef DOXYGEN
/*---------------------- Inline functions -----------------------------------*/
template <typename number>
inline
unsigned int ChunkSparseMatrix<number>::m () const
{
Assert (cols != 0, ExcNotInitialized());
return cols->rows;
}
template <typename number>
inline
unsigned int ChunkSparseMatrix<number>::n () const
{
Assert (cols != 0, ExcNotInitialized());
return cols->cols;
}
template <typename number>
inline
unsigned int
ChunkSparseMatrix<number>::compute_location (const unsigned int i,
const unsigned int j) const
{
const unsigned int chunk_size = cols->get_chunk_size();
const unsigned int chunk_index
= cols->sparsity_pattern(i/chunk_size, j/chunk_size);
if (chunk_index == ChunkSparsityPattern::invalid_entry)
return ChunkSparsityPattern::invalid_entry;
else
{
return (chunk_index * chunk_size * chunk_size
+
(i % chunk_size) * chunk_size
+
(j % chunk_size));
}
}
template <typename number>
inline
void ChunkSparseMatrix<number>::set (const unsigned int i,
const unsigned int j,
const number value)
{
Assert (numbers::is_finite(value),
ExcMessage("The given value is not finite but either infinite or Not A Number (NaN)"));
Assert (cols != 0, ExcNotInitialized());
// it is allowed to set elements of
// the matrix that are not part of
// the sparsity pattern, if the
// value to which we set it is zero
const unsigned int index = compute_location(i,j);
Assert ((index != SparsityPattern::invalid_entry) ||
(value == 0.),
ExcInvalidIndex(i,j));
if (index != SparsityPattern::invalid_entry)
val[index] = value;
}
template <typename number>
inline
void ChunkSparseMatrix<number>::add (const unsigned int i,
const unsigned int j,
const number value)
{
Assert (numbers::is_finite(value),
ExcMessage("The given value is not finite but either infinite or Not A Number (NaN)"));
Assert (cols != 0, ExcNotInitialized());
const unsigned int index = compute_location(i,j);
Assert ((index != ChunkSparsityPattern::invalid_entry) ||
(value == 0.),
ExcInvalidIndex(i,j));
if (value != 0.)
val[index] += value;
}
template <typename number>
inline
ChunkSparseMatrix<number> &
ChunkSparseMatrix<number>::operator *= (const number factor)
{
Assert (cols != 0, ExcNotInitialized());
Assert (val != 0, ExcNotInitialized());
const unsigned int chunk_size = cols->get_chunk_size();
// multiply all elements of the matrix with
// the given factor. this includes the
// padding elements in chunks that overlap
// the boundaries of the actual matrix --
// but since multiplication with a number
// does not violate the invariant of
// keeping these elements at zero nothing
// can happen
number *val_ptr = val;
const number *const end_ptr = val +
cols->sparsity_pattern.n_nonzero_elements()
*
chunk_size * chunk_size;
while (val_ptr != end_ptr)
*val_ptr++ *= factor;
return *this;
}
template <typename number>
inline
ChunkSparseMatrix<number> &
ChunkSparseMatrix<number>::operator /= (const number factor)
{
Assert (cols != 0, ExcNotInitialized());
Assert (val != 0, ExcNotInitialized());
Assert (factor !=0, ExcDivideByZero());
const number factor_inv = 1. / factor;
const unsigned int chunk_size = cols->get_chunk_size();
// multiply all elements of the matrix with
// the given factor. this includes the
// padding elements in chunks that overlap
// the boundaries of the actual matrix --
// but since multiplication with a number
// does not violate the invariant of
// keeping these elements at zero nothing
// can happen
number *val_ptr = val;
const number *const end_ptr = val +
cols->sparsity_pattern.n_nonzero_elements()
*
chunk_size * chunk_size;
while (val_ptr != end_ptr)
*val_ptr++ *= factor_inv;
return *this;
}
template <typename number>
inline
number ChunkSparseMatrix<number>::operator () (const unsigned int i,
const unsigned int j) const
{
Assert (cols != 0, ExcNotInitialized());
AssertThrow (compute_location(i,j) != SparsityPattern::invalid_entry,
ExcInvalidIndex(i,j));
return val[compute_location(i,j)];
}
template <typename number>
inline
number ChunkSparseMatrix<number>::el (const unsigned int i,
const unsigned int j) const
{
Assert (cols != 0, ExcNotInitialized());
const unsigned int index = compute_location(i,j);
if (index != ChunkSparsityPattern::invalid_entry)
return val[index];
else
return 0;
}
template <typename number>
inline
number ChunkSparseMatrix<number>::diag_element (const unsigned int i) const
{
Assert (cols != 0, ExcNotInitialized());
Assert (cols->optimize_diagonal(), ExcNotQuadratic());
Assert (i<m(), ExcInvalidIndex1(i));
// Use that the first element in each row
// of a quadratic matrix is the main
// diagonal of the chunk sparsity pattern
const unsigned int chunk_size = cols->get_chunk_size();
return val[cols->sparsity_pattern.rowstart[i/chunk_size]
*
chunk_size * chunk_size
+
(i % chunk_size) * chunk_size
+
(i % chunk_size)];
}
template <typename number>
inline
number & ChunkSparseMatrix<number>::diag_element (const unsigned int i)
{
Assert (cols != 0, ExcNotInitialized());
Assert (cols->optimize_diagonal(), ExcNotQuadratic());
Assert (i<m(), ExcInvalidIndex1(i));
// Use that the first element in each row
// of a quadratic matrix is the main
// diagonal of the chunk sparsity pattern
const unsigned int chunk_size = cols->get_chunk_size();
return val[cols->sparsity_pattern.rowstart[i/chunk_size]
*
chunk_size * chunk_size
+
(i % chunk_size) * chunk_size
+
(i % chunk_size)];
}
template <typename number>
template <typename ForwardIterator>
void
ChunkSparseMatrix<number>::copy_from (const ForwardIterator begin,
const ForwardIterator end)
{
Assert (static_cast<unsigned int>(std::distance (begin, end)) == m(),
ExcIteratorRange (std::distance (begin, end), m()));
// for use in the inner loop, we
// define a typedef to the type of
// the inner iterators
typedef typename std::iterator_traits<ForwardIterator>::value_type::const_iterator inner_iterator;
unsigned int row=0;
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)
// write entries
set (row, j->first, j->second);
}
}
#endif // DOXYGEN
/*---------------------------- chunk_sparse_matrix.h ---------------------------*/
DEAL_II_NAMESPACE_CLOSE
#endif
/*---------------------------- chunk_sparse_matrix.h ---------------------------*/
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