-// Some helper definitions for the
-// local_to_global functions.
+// Some helper definitions for the local_to_global functions.
namespace internals
{
typedef types::global_dof_index size_type;
- // this struct contains all the information
- // we need to store about each of the
- // global entries (global_row): are they
- // obtained directly by some local entry
- // (local_row) or some constraints
- // (constraint_position). This is not
- // directly used in the user code, but
- // accessed via the
- // GlobalRowsFromLocal.
+ // this struct contains all the information we need to store about each of
+ // the global entries (global_row): are they obtained directly by some local
+ // entry (local_row) or some constraints (constraint_position). This is not
+ // directly used in the user code, but accessed via the GlobalRowsFromLocal.
//
- // The actions performed here correspond to
- // reshaping the constraint information
- // from global degrees of freedom to local
- // ones (i.e., cell-related DoFs), and also
- // transforming the constraint information
- // from compressed row storage (each local
- // dof that is constrained has a list of
- // constraint entries associated to it)
- // into compressed column storage based on
- // the cell-related DoFs (we have a list of
- // global degrees of freedom, and to each
- // we have a list of local rows where the
- // entries come from). To increase the
- // speed, we additionally store whether an
- // entry is generated directly from the
- // local degrees of freedom or whether it
- // comes from a constraint.
+ // The actions performed here correspond to reshaping the constraint
+ // information from global degrees of freedom to local ones (i.e.,
+ // cell-related DoFs), and also transforming the constraint information from
+ // compressed row storage (each local dof that is constrained has a list of
+ // constraint entries associated to it) into compressed column storage based
+ // on the cell-related DoFs (we have a list of global degrees of freedom,
+ // and to each we have a list of local rows where the entries come from). To
+ // increase the speed, we additionally store whether an entry is generated
+ // directly from the local degrees of freedom or whether it comes from a
+ // constraint.
struct Distributing
{
Distributing (const size_type global_row = numbers::invalid_size_type,
{
global_row = in.global_row;
local_row = in.local_row;
- // the constraints pointer should not
- // contain any data here.
+ // the constraints pointer should not contain any data here.
Assert (constraint_position == numbers::invalid_size_type,
ExcInternalError());
- // this is a cache for constraints that
- // are encountered on a local level.
+ // this is a cache for constraints that are encountered on a local level.
// The functionality is similar to
- // std::vector<std::vector<std::pair<uint,double>
- // > >, but tuned so that frequent memory
- // allocation for each entry is
- // avoided. This is not directly used in
- // the user code, but accessed via the
- // GlobalRowsFromLocal.
+ // std::vector<std::vector<std::pair<uint,double> > >, but tuned so that
+ // frequent memory allocation for each entry is avoided. This is not
+ // directly used in the user code, but accessed via the GlobalRowsFromLocal.
struct DataCache
{
DataCache ()
- // collects all the global rows from a
- // local contribution (cell) and their
- // origin (direct/constraint). this is
- // basically a vector consisting of
- // "Distributing" structs using access via
- // the DataCache. Provides some
+ // collects all the global rows from a local contribution (cell) and their
+ // origin (direct/constraint). this is basically a vector consisting of
+ // "Distributing" structs using access via the DataCache. Provides some
// specialized sort and insert functions.
//
- // in case there are no constraints, this is
- // basically a list of pairs <uint,unit> with
- // the first index being the global index and
- // the second index the local index. The list
- // is sorted with respect to the global index.
+ // in case there are no constraints, this is basically a list of pairs
+ // <uint,unit> with the first index being the global index and the second
+ // index the local index. The list is sorted with respect to the global
+ // index.
//
- // in case there are constraints, a global dof
- // might get a contribution also because it
- // gets data from a constrained dof. This
- // means that a global dof might also have
- // indirect contributions from a local dof via
- // a constraint, besides the direct ones.
+ // in case there are constraints, a global dof might get a contribution also
+ // because it gets data from a constrained dof. This means that a global dof
+ // might also have indirect contributions from a local dof via a constraint,
+ // besides the direct ones.
//
- // The actions performed here correspond to
- // reshaping the constraint information
- // from global degrees of freedom to local
- // ones (i.e., cell-related DoFs), and also
- // transforming the constraint information
- // from compressed row storage (each local
- // dof that is constrained has a list of
- // constraint entries associated to it)
- // into compressed column storage based on
- // the cell-related DoFs (we have a list of
- // global degrees of freedom, and to each
- // we have a list of local rows where the
- // entries come from). To increase the
- // speed, we additionally store whether an
- // entry is generated directly from the
- // local degrees of freedom or whether it
- // comes from a constraint.
+ // The actions performed here correspond to reshaping the constraint
+ // information from global degrees of freedom to local ones (i.e.,
+ // cell-related DoFs), and also transforming the constraint information from
+ // compressed row storage (each local dof that is constrained has a list of
+ // constraint entries associated to it) into compressed column storage based
+ // on the cell-related DoFs (we have a list of global degrees of freedom,
+ // and to each we have a list of local rows where the entries come from). To
+ // increase the speed, we additionally store whether an entry is generated
+ // directly from the local degrees of freedom or whether it comes from a
+ // constraint.
class GlobalRowsFromLocal
{
public:
const double constraint_value);
void sort ();
- // Print object for debugging
- // purpose
+ // Print object for debugging purpose
void print(std::ostream &os)
{
os << "Active rows " << n_active_rows << std::endl
}
- // return all kind of information on the
- // constraints
+ // return all kind of information on the constraints
- // returns the number of global indices in the
- // struct
+ // returns the number of global indices in the struct
size_type size () const
{
return n_active_rows;
}
- // returns the global index of the
- // counter_index-th entry in the list
+ // returns the global index of the counter_index-th entry in the list
size_type &global_row (const size_type counter_index)
{
return total_row_indices[counter_index].global_row;
}
- // returns the number of constraints that are
- // associated to the counter_index-th entry in
- // the list
+ // returns the number of constraints that are associated to the
+ // counter_index-th entry in the list
size_type size (const size_type counter_index) const
{
return (total_row_indices[counter_index].constraint_position ==
constraint_position));
}
- // returns the global row associated with the
- // counter_index-th entry in the list
+ // returns the global row associated with the counter_index-th entry in
+ // the list
const size_type &global_row (const size_type counter_index) const
{
return total_row_indices[counter_index].global_row;
}
- // returns the local row in the cell matrix
- // associated with the counter_index-th entry
- // in the list. Returns invalid_size_type
- // for invalid size_types
+ // returns the local row in the cell matrix associated with the
+ // counter_index-th entry in the list. Returns invalid_size_type for
+ // invalid size_types
const size_type &local_row (const size_type counter_index) const
{
return total_row_indices[counter_index].local_row;
return total_row_indices[counter_index].local_row;
}
- // returns the local row in the cell matrix
- // associated with the counter_index-th entry
- // in the list in the index_in_constraint-th
+ // returns the local row in the cell matrix associated with the
+ // counter_index-th entry in the list in the index_in_constraint-th
// position of constraints
size_type local_row (const size_type counter_index,
const size_type index_in_constraint) const
[index_in_constraint]).first;
}
- // returns the value of the constraint in the
- // counter_index-th entry in the list in the
- // index_in_constraint-th position of
- // constraints
+ // returns the value of the constraint in the counter_index-th entry in
+ // the list in the index_in_constraint-th position of constraints
double constraint_value (const size_type counter_index,
const size_type index_in_constraint) const
{
[index_in_constraint]).second;
}
- // returns whether there is one row with
- // indirect contributions (i.e., there has
- // been at least one constraint with
- // non-trivial ConstraintLine)
+ // returns whether there is one row with indirect contributions (i.e.,
+ // there has been at least one constraint with non-trivial ConstraintLine)
bool have_indirect_rows () const
{
return data_cache.element_size;
}
- // append an entry that is
- // constrained. This means that
- // there is one less nontrivial
- // row
+ // append an entry that is constrained. This means that there is one less
+ // nontrivial row
void insert_constraint (const size_type constrained_local_dof)
{
--n_active_rows;
total_row_indices[n_active_rows].local_row = constrained_local_dof;
}
- // returns the number of constrained
- // dofs in the structure. Constrained
- // dofs do not contribute directly to
- // the matrix, but are needed in order
- // to set matrix diagonals and resolve
- // inhomogeneities
+ // returns the number of constrained dofs in the structure. Constrained
+ // dofs do not contribute directly to the matrix, but are needed in order
+ // to set matrix diagonals and resolve inhomogeneities
size_type n_constraints () const
{
return total_row_indices.size()-n_active_rows;
}
- // returns the number of constrained
- // dofs in the structure that have an
+ // returns the number of constrained dofs in the structure that have an
// inhomogeneity
size_type n_inhomogeneities () const
{
return n_inhomogeneous_rows;
}
- // tells the structure that the ith
- // constraint is
- // inhomogeneous. inhomogeneous
- // constraints contribute to right hand
- // sides, so to have fast access to
- // them, put them before homogeneous
+ // tells the structure that the ith constraint is
+ // inhomogeneous. inhomogeneous constraints contribute to right hand
+ // sides, so to have fast access to them, put them before homogeneous
// constraints
void set_ith_constraint_inhomogeneous (const size_type i)
{
n_inhomogeneous_rows++;
}
- // the local row where
- // constraint number i was
- // detected, to find that row
- // easily when the
- // GlobalRowsToLocal has been
- // set up
+ // the local row where constraint number i was detected, to find that row
+ // easily when the GlobalRowsToLocal has been set up
size_type constraint_origin (size_type i) const
{
return total_row_indices[n_active_rows+i].local_row;
}
- // a vector that contains all the
- // global ids and the corresponding
- // local ids as well as a pointer to
- // that data where we store how to
- // resolve constraints.
+ // a vector that contains all the global ids and the corresponding local
+ // ids as well as a pointer to that data where we store how to resolve
+ // constraints.
std::vector<Distributing> total_row_indices;
private:
- // holds the actual data from
- // the constraints
+ // holds the actual data from the constraints
DataCache data_cache;
- // how many rows there are,
- // constraints disregarded
+ // how many rows there are, constraints disregarded
size_type n_active_rows;
- // the number of rows with
- // inhomogeneous constraints
+ // the number of rows with inhomogeneous constraints
size_type n_inhomogeneous_rows;
};
- // a function that appends an additional
- // row to the list of values, or appends a
- // value to an already existing
- // row. Similar functionality as for
- // std::map<size_type,Distributing>, but
- // here done for a
- // std::vector<Distributing>, much faster
- // for short lists as we have them here
+ // a function that appends an additional row to the list of values, or
+ // appends a value to an already existing row. Similar functionality as for
+ // std::map<size_type,Distributing>, but here done for a
+ // std::vector<Distributing>, much faster for short lists as we have them
+ // here
inline
void
GlobalRowsFromLocal::insert_index (const size_type global_row,
Distributing row_value (global_row);
std::pair<size_type,double> constraint (local_row, constraint_value);
- // check whether the list was really
- // sorted before entering here
+ // check whether the list was really sorted before entering here
for (size_type i=1; i<n_active_rows; ++i)
Assert (total_row_indices[i-1] < total_row_indices[i], ExcInternalError());
data_cache.append_index (pos1->constraint_position, constraint);
}
- // this sort algorithm sorts
- // std::vector<Distributing>, but does not
- // take the constraints into account. this
- // means that in case that constraints are
- // already inserted, this function does not
- // work as expected. Use shellsort, which
- // is very fast in case the indices are
- // already sorted (which is the usual case
- // with DG elements), and not too slow in
- // other cases
+ // this sort algorithm sorts std::vector<Distributing>, but does not take
+ // the constraints into account. this means that in case that constraints
+ // are already inserted, this function does not work as expected. Use
+ // shellsort, which is very fast in case the indices are already sorted
+ // (which is the usual case with DG elements), and not too slow in other
+ // cases
inline
void
GlobalRowsFromLocal::sort ()
size_type i, j, j2, temp, templ, istep;
size_type step;
- // check whether the
- // constraints are really empty.
+ // check whether the constraints are really empty.
const size_type length = size();
- // make sure that we are in the
- // range of the vector
+ // make sure that we are in the range of the vector
AssertIndexRange (length, total_row_indices.size()+1);
for (size_type i=0; i<length; ++i)
Assert (total_row_indices[i].constraint_position ==
}
}
- // function for block matrices: Find out
- // where in the list of local dofs (sorted
- // according to global ids) the individual
- // blocks start. Transform the global
- // indices to block-local indices in order
- // to be able to use functions like
- // vector.block(1)(block_local_id), instead
- // of vector(global_id). This avoids
- // transforming indices one-by-one later
- // on.
+ // function for block matrices: Find out where in the list of local dofs
+ // (sorted according to global ids) the individual blocks start. Transform
+ // the global indices to block-local indices in order to be able to use
+ // functions like vector.block(1)(block_local_id), instead of
+ // vector(global_id). This avoids transforming indices one-by-one later on.
template <class BlockType>
inline
void
block_indices = first_block;
}
- // transform row indices to block-local
- // index space
+ // transform row indices to block-local index space
for (size_type i=block_starts[1]; i<n_active_rows; ++i)
global_rows.global_row(i) = block_object.get_row_indices().
global_to_local(global_rows.global_row(i)).second;
- // same as before, but for
- // std::vector<uint> instead of
- // GlobalRowsFromLocal. Used in functions
- // for sparsity patterns.
+ // same as before, but for std::vector<uint> instead of
+ // GlobalRowsFromLocal. Used in functions for sparsity patterns.
template <class BlockType>
inline
void
col_indices = first_block;
}
- // transform row indices to local index
- // space
+ // transform row indices to local index space
for (size_type i=block_starts[1]; i<row_indices.size(); ++i)
row_indices[i] = block_object.get_row_indices().
global_to_local(row_indices[i]).second;
- // resolves constraints of one column at
- // the innermost loop. goes through the
- // origin of each global entry and finds
- // out which data we need to collect.
+ // resolves constraints of one column at the innermost loop. goes through
+ // the origin of each global entry and finds out which data we need to
+ // collect.
static inline
double resolve_matrix_entry (const GlobalRowsFromLocal &global_rows,
const GlobalRowsFromLocal &global_cols,
const size_type loc_col = global_cols.local_row(j);
double col_val;
- // case 1: row has direct contribution in
- // local matrix. decide whether col has a
- // direct contribution. if not,
- // set the value to zero.
+ // case 1: row has direct contribution in local matrix. decide whether col
+ // has a direct contribution. if not, set the value to zero.
if (loc_row != numbers::invalid_size_type)
{
col_val = ((loc_col != numbers::invalid_size_type) ?
local_matrix(loc_row, loc_col) : 0);
- // account for indirect contributions by
- // constraints in column
+ // account for indirect contributions by constraints in column
for (size_type p=0; p<global_cols.size(j); ++p)
col_val += (local_matrix(loc_row, global_cols.local_row(j,p)) *
global_cols.constraint_value(j,p));
}
- // case 2: row has no direct contribution in
- // local matrix
+ // case 2: row has no direct contribution in local matrix
else
col_val = 0;
- // account for indirect contributions by
- // constraints in row, going trough the
- // direct and indirect references in the
- // given column.
+ // account for indirect contributions by constraints in row, going trough
+ // the direct and indirect references in the given column.
for (size_type q=0; q<global_rows.size(i); ++q)
{
double add_this = (loc_col != numbers::invalid_size_type)
- // computes all entries that need to be
- // written into global_rows[i]. Lists the
- // resulting values in val_ptr, and the
- // corresponding column indices in col_ptr.
+ // computes all entries that need to be written into global_rows[i]. Lists
+ // the resulting values in val_ptr, and the corresponding column indices in
+ // col_ptr.
template <typename number>
inline
void
AssertIndexRange (column_end-1, global_cols.size());
const size_type loc_row = global_rows.local_row(i);
- // fast function if there are no indirect
- // references to any of the local rows at
- // all on this set of dofs (saves a lot
- // of checks). the only check we actually
- // need to perform is whether the matrix
- // element is zero.
+ // fast function if there are no indirect references to any of the local
+ // rows at all on this set of dofs (saves a lot of checks). the only check
+ // we actually need to perform is whether the matrix element is zero.
if (global_rows.have_indirect_rows() == false &&
global_cols.have_indirect_rows() == false)
{
}
}
- // more difficult part when there are
- // indirect references and when we need
+ // more difficult part when there are indirect references and when we need
// to do some more checks.
else
{
double col_val = resolve_matrix_entry (global_rows, global_cols, i, j,
loc_row, local_matrix);
- // if we got some nontrivial value,
- // append it to the array of values.
+ // if we got some nontrivial value, append it to the array of
+ // values.
if (col_val != 0.)
{
*val_ptr++ = static_cast<number> (col_val);
- // specialized function that can write into
- // the row of a SparseMatrix<number>.
+ // specialized function that can write into the row of a
+ // SparseMatrix<number>.
namespace dealiiSparseMatrix
{
template <typename SparseMatrixIterator>
}
- // similar as before, now with shortcut for
- // deal.II sparse matrices. this lets us
- // avoid using extra arrays, and does all the
- // operations just in place, i.e., in the
- // respective matrix row
+ // similar as before, now with shortcut for deal.II sparse matrices. this
+ // lets us avoid using extra arrays, and does all the operations just in
+ // place, i.e., in the respective matrix row
template <typename number>
inline
void
matrix_values = sparse_matrix->begin(row);
const bool optimize_diagonal = sparsity.n_rows() == sparsity.n_cols();
- // distinguish three cases about what can
- // happen for checking whether the diagonal is
- // the first element of the row. this avoids
- // if statements at the innermost loop
- // positions
+ // distinguish three cases about what can happen for checking whether the
+ // diagonal is the first element of the row. this avoids if statements at
+ // the innermost loop positions
if (!optimize_diagonal) // case 1: no diagonal optimization in matrix
{
}
}
}
- // case 3: can't say - need to check inside
- // the loop
+ // case 3: can't say - need to check inside the loop
else if (global_rows.have_indirect_rows() == false)
{
++matrix_values; // jump over diagonal element
- // Same function to resolve all entries that
- // will be added to the given global row
- // global_rows[i] as before, now for sparsity
- // pattern
+ // Same function to resolve all entries that will be added to the given
+ // global row global_rows[i] as before, now for sparsity pattern
inline
void
resolve_matrix_row (const GlobalRowsFromLocal &global_rows,
const size_type loc_row = global_rows.local_row(i);
- // fast function if there are no indirect
- // references to any of the local rows at
- // all on this set of dofs
+ // fast function if there are no indirect references to any of the local
+ // rows at all on this set of dofs
if (global_rows.have_indirect_rows() == false)
{
Assert(loc_row < dof_mask.n_rows(),
}
}
- // slower functions when there are
- // indirect references and when we need
- // to do some more checks.
+ // slower functions when there are indirect references and when we need to
+ // do some more checks.
else
{
for (size_type j=column_start; j<column_end; ++j)
}
continue;
- // if we got some nontrivial value,
- // append it to the array of values.
+ // if we got some nontrivial value, append it to the array of
+ // values.
add_this_index:
*col_ptr++ = global_rows.global_row(j);
}
- // to make sure that the global matrix
- // remains invertible, we need to do
- // something with the diagonal
- // elements. add the absolute value of
- // the local matrix, so the resulting
- // entry will always be positive and
- // furthermore be in the same order of
- // magnitude as the other elements of the
- // matrix
+ // to make sure that the global matrix remains invertible, we need to do
+ // something with the diagonal elements. add the absolute value of the local
+ // matrix, so the resulting entry will always be positive and furthermore be
+ // in the same order of magnitude as the other elements of the matrix
//
- // note that this also captures the
- // special case that a dof is both
- // constrained and fixed (this can
- // happen for hanging nodes in 3d that
- // also happen to be on the
- // boundary). in that case, following
- // the program flow in
- // distribute_local_to_global, it is
- // realized that when distributing the
- // row and column no elements of the
- // matrix are actually touched if all
- // the degrees of freedom to which this
- // dof is constrained are also
- // constrained (the usual case with
- // hanging nodes in 3d). however, in
- // the line below, we do actually do
- // something with this dof
+ // note that this also captures the special case that a dof is both
+ // constrained and fixed (this can happen for hanging nodes in 3d that also
+ // happen to be on the boundary). in that case, following the program flow
+ // in distribute_local_to_global, it is realized that when distributing the
+ // row and column no elements of the matrix are actually touched if all the
+ // degrees of freedom to which this dof is constrained are also constrained
+ // (the usual case with hanging nodes in 3d). however, in the line below, we
+ // do actually do something with this dof
template <typename MatrixType, typename VectorType>
inline void
set_matrix_diagonals (const internals::GlobalRowsFromLocal &global_rows,
std::fabs(local_matrix(local_row,local_row)) : average_diagonal);
global_matrix.add(global_row, global_row, new_diagonal);
- // if the use_inhomogeneities_for_rhs flag is
- // set to true, the inhomogeneities are used
- // to create the global vector. instead of
- // fill in a zero in the ith components with an
- // inhomogeneity, we set those to:
- // inhomogeneity(i)*global_matrix (i,i).
+ // if the use_inhomogeneities_for_rhs flag is set to true, the
+ // inhomogeneities are used to create the global vector. instead
+ // of fill in a zero in the ith components with an inhomogeneity,
+ // we set those to: inhomogeneity(i)*global_matrix (i,i).
if (use_inhomogeneities_for_rhs == true)
global_vector(global_row) += constraints.get_inhomogeneity(global_row) * new_diagonal;
}
- // similar function as the one above for
- // setting matrix diagonals, but now doing
- // that for sparsity patterns when setting
- // them up using
- // add_entries_local_to_global. In case we
- // keep constrained entries, add all the rows
- // and columns related to the constrained dof,
- // otherwise just add the diagonal
+ // similar function as the one above for setting matrix diagonals, but now
+ // doing that for sparsity patterns when setting them up using
+ // add_entries_local_to_global. In case we keep constrained entries, add all
+ // the rows and columns related to the constrained dof, otherwise just add
+ // the diagonal
template <typename SparsityType>
inline void
set_sparsity_diagonals (const internals::GlobalRowsFromLocal &global_rows,
const bool keep_constrained_entries,
SparsityType &sparsity_pattern)
{
- // if we got constraints, need to add
- // the diagonal element and, if the
- // user requested so, also the rest of
- // the entries in rows and columns
+ // if we got constraints, need to add the diagonal element and, if the
+ // user requested so, also the rest of the entries in rows and columns
// that have been left out above
if (global_rows.n_constraints() > 0)
{
}
}
else
- // don't keep constrained entries - just
- // add the diagonal.
+ // don't keep constrained entries - just add the diagonal.
sparsity_pattern.add(global_row,global_row);
}
}
const size_type n_local_dofs = local_dof_indices.size();
AssertDimension (n_local_dofs, global_rows.size());
- // when distributing the local data to
- // the global matrix, we can quite
- // cheaply sort the indices (obviously,
- // this introduces the need for
- // allocating some memory on the way, but
- // we need to do this only for rows,
- // whereas the distribution process
- // itself goes over rows and
- // columns). This has the advantage that
- // when writing into the global matrix,
- // we can make use of the sortedness.
-
- // so the first step is to create a
- // sorted list of all row values that are
- // possible. these values are either the
- // rows from unconstrained dofs, or some
- // indices introduced by dofs constrained
- // to a combination of some other
- // dofs. regarding the data type, choose
- // an STL vector of a pair of unsigned
- // ints (for global columns) and internal
- // data (containing local columns +
- // possible jumps from
- // constraints). Choosing an STL map or
- // anything else M.K. knows of would be
- // much more expensive here!
-
- // cache whether we have to resolve any
- // indirect rows generated from resolving
- // constrained dofs.
+ // when distributing the local data to the global matrix, we can quite
+ // cheaply sort the indices (obviously, this introduces the need for
+ // allocating some memory on the way, but we need to do this only for rows,
+ // whereas the distribution process itself goes over rows and columns). This
+ // has the advantage that when writing into the global matrix, we can make
+ // use of the sortedness.
+
+ // so the first step is to create a sorted list of all row values that are
+ // possible. these values are either the rows from unconstrained dofs, or
+ // some indices introduced by dofs constrained to a combination of some
+ // other dofs. regarding the data type, choose an STL vector of a pair of
+ // unsigned ints (for global columns) and internal data (containing local
+ // columns + possible jumps from constraints). Choosing an STL map or
+ // anything else M.K. knows of would be much more expensive here!
+
+ // cache whether we have to resolve any indirect rows generated from
+ // resolving constrained dofs.
size_type added_rows = 0;
- // first add the indices in an unsorted
- // way and only keep track of the
- // constraints that appear. They are
- // resolved in a second step.
+ // first add the indices in an unsorted way and only keep track of the
+ // constraints that appear. They are resolved in a second step.
for (size_type i = 0; i<n_local_dofs; ++i)
{
if (is_constrained(local_dof_indices[i]) == false)
-// Same function as before, but now do
-// only extract the global indices
-// that come from the local ones
-// without storing their origin. Used
-// for sparsity pattern generation.
+// Same function as before, but now do only extract the global indices that
+// come from the local ones without storing their origin. Used for sparsity
+// pattern generation.
inline
void
ConstraintMatrix::
{
const size_type local_row = active_dofs.back();
- // remove constrained entry since we
- // are going to resolve it in place
+ // remove constrained entry since we are going to resolve it in place
active_dofs.pop_back();
const size_type global_row = local_dof_indices[local_row];
const ConstraintLine &position =
if (active_dofs[active_dofs.size()-i] < new_index)
active_dofs.insert(active_dofs.end()-i+1,new_index);
- // make binary search to find where to
- // put the new index in order to keep
- // the list sorted
+ // make binary search to find where to put the new index in order to
+ // keep the list sorted
else
{
std::vector<size_type>::iterator it =
-// Resolve the constraints from the vector and
-// apply inhomogeneities.
+// Resolve the constraints from the vector and apply inhomogeneities.
inline
double
ConstraintMatrix::
const size_type loc_row = global_rows.local_row(i);
const size_type n_inhomogeneous_rows = global_rows.n_inhomogeneities();
double val = 0;
- // has a direct contribution from some local
- // entry. If we have inhomogeneous
- // constraints, compute the contribution of
- // the inhomogeneity in the current row.
+ // has a direct contribution from some local entry. If we have inhomogeneous
+ // constraints, compute the contribution of the inhomogeneity in the current
+ // row.
if (loc_row != numbers::invalid_size_type)
{
val = local_vector(loc_row);
}
-// internal implementation for
-// distribute_local_to_global for
-// standard (non-block) matrices
+// internal implementation for distribute_local_to_global for standard
+// (non-block) matrices
template <typename MatrixType, typename VectorType>
void
ConstraintMatrix::distribute_local_to_global (
bool use_inhomogeneities_for_rhs,
internal::bool2type<false>) const
{
- // check whether we work on real vectors
- // or we just used a dummy when calling
- // the other function above.
+ // check whether we work on real vectors or we just used a dummy when
+ // calling the other function above.
const bool use_vectors = (local_vector.size() == 0 &&
global_vector.size() == 0) ? false : true;
typedef typename MatrixType::value_type number;
const size_type n_actual_dofs = global_rows.size();
- // create arrays for the column data
- // (indices and values) that will then be
- // written into the matrix. Shortcut for
- // deal.II sparse matrix
+ // create arrays for the column data (indices and values) that will then be
+ // written into the matrix. Shortcut for deal.II sparse matrix
std::vector<size_type> cols;
std::vector<number> vals;
SparseMatrix<number> *sparse_matrix
else
Assert (sparse_matrix != 0, ExcInternalError());
- // now do the actual job. go through all
- // the global rows that we will touch and
- // call resolve_matrix_row for each of
- // those.
+ // now do the actual job. go through all the global rows that we will touch
+ // and call resolve_matrix_row for each of those.
for (size_type i=0; i<n_actual_dofs; ++i)
{
const size_type row = global_rows.global_row(i);
- // calculate all the data that will be
- // written into the matrix row.
+ // calculate all the data that will be written into the matrix row.
if (use_dealii_matrix == false)
{
size_type *col_ptr = &cols[0];
internals::resolve_matrix_row (global_rows, i, 0, n_actual_dofs,
local_matrix, sparse_matrix);
- // now to the vectors. besides doing the
- // same job as we did above (i.e.,
- // distribute the content of the local
- // vector into the global one), need to
- // account for inhomogeneities here: thie
- // corresponds to eliminating the
- // respective column in the local matrix
- // with value on the right hand side.
+ // now to the vectors. besides doing the same job as we did above (i.e.,
+ // distribute the content of the local vector into the global one), need
+ // to account for inhomogeneities here: thie corresponds to eliminating
+ // the respective column in the local matrix with value on the right
+ // hand side.
if (use_vectors == true)
{
const double val = resolve_vector_entry (i, global_rows,
const size_type n_actual_row_dofs = global_rows.size();
const size_type n_actual_col_dofs = global_cols.size();
- // create arrays for the column data
- // (indices and values) that will then be
- // written into the matrix. Shortcut for
- // deal.II sparse matrix
+ // create arrays for the column data (indices and values) that will then be
+ // written into the matrix. Shortcut for deal.II sparse matrix
std::vector<size_type> cols (n_actual_col_dofs);
std::vector<number> vals (n_actual_col_dofs);
{
const size_type row = global_rows.global_row(i);
- // calculate all the data that will be
- // written into the matrix row.
+ // calculate all the data that will be written into the matrix row.
size_type *col_ptr = &cols[0];
number *val_ptr = &vals[0];
internals::resolve_matrix_row (global_rows, global_cols, i, 0,
}
-// similar function as above, but now
-// specialized for block matrices. See
-// the other function for additional
-// comments.
+// similar function as above, but now specialized for block matrices. See the
+// other function for additional comments.
template <typename MatrixType, typename VectorType>
void
ConstraintMatrix::
global_indices[i] = global_rows.global_row(i);
}
- // additional construct that also takes
- // care of block indices.
+ // additional construct that also takes care of block indices.
const size_type num_blocks = global_matrix.n_block_rows();
std::vector<size_type> block_starts(num_blocks+1, n_actual_dofs);
internals::make_block_starts (global_matrix, global_rows, block_starts);
vals.resize (n_actual_dofs);
}
- // the basic difference to the non-block
- // variant from now onwards is that we go
- // through the blocks of the matrix
- // separately, which allows us to set the
- // block entries individually
+ // the basic difference to the non-block variant from now onwards is that we
+ // go through the blocks of the matrix separately, which allows us to set
+ // the block entries individually
for (size_type block=0; block<num_blocks; ++block)
{
const size_type next_block = block_starts[block+1];
AssertDimension (dof_mask.n_cols(), n_local_dofs);
}
- // if the dof mask is not active, all we
- // have to do is to add some indices in a
- // matrix format. To do this, we first
- // create an array of all the indices
- // that are to be added. these indices
- // are the local dof indices plus some
- // indices that come from constraints.
+ // if the dof mask is not active, all we have to do is to add some indices
+ // in a matrix format. To do this, we first create an array of all the
+ // indices that are to be added. these indices are the local dof indices
+ // plus some indices that come from constraints.
if (dof_mask_is_active == false)
{
std::vector<size_type> actual_dof_indices (n_local_dofs);
make_sorted_row_list (local_dof_indices, actual_dof_indices);
const size_type n_actual_dofs = actual_dof_indices.size();
- // now add the indices we collected above
- // to the sparsity pattern. Very easy
- // here - just add the same array to all
- // the rows...
+ // now add the indices we collected above to the sparsity pattern. Very
+ // easy here - just add the same array to all the rows...
for (size_type i=0; i<n_actual_dofs; ++i)
sparsity_pattern.add_entries(actual_dof_indices[i],
actual_dof_indices.begin(),
actual_dof_indices.end(),
true);
- // need to add the whole row and column
- // structure in case we keep constrained
- // entries. Unfortunately, we can't use
- // the nice matrix structure we use
- // elsewhere, so manually add those
- // indices one by one.
+ // need to add the whole row and column structure in case we keep
+ // constrained entries. Unfortunately, we can't use the nice matrix
+ // structure we use elsewhere, so manually add those indices one by one.
for (size_type i=0; i<n_local_dofs; i++)
if (is_constrained(local_dof_indices[i]))
{
}
- // complicated case: we need to filter
- // out some indices. then the function
- // gets similar to the function for
- // distributing matrix entries, see there
+ // complicated case: we need to filter out some indices. then the function
+ // gets similar to the function for distributing matrix entries, see there
// for additional comments.
internals::GlobalRowsFromLocal global_rows (n_local_dofs);
make_sorted_row_list (local_dof_indices, global_rows);
const size_type n_actual_dofs = global_rows.size();
- // create arrays for the column indices
- // that will then be written into the
+ // create arrays for the column indices that will then be written into the
// sparsity pattern.
std::vector<size_type> cols (n_actual_dofs);
internals::resolve_matrix_row (global_rows, i, 0, n_actual_dofs,
dof_mask, col_ptr);
- // finally, write all the information
- // that accumulated under the given
- // process into the global matrix row and
- // into the vector
+ // finally, write all the information that accumulated under the given
+ // process into the global matrix row and into the vector
if (col_ptr != cols.begin())
sparsity_pattern.add_entries(row, cols.begin(), col_ptr,
true);
if (dof_mask.n_rows() == n_local_rows && dof_mask.n_cols() == n_local_cols)
dof_mask_is_active = true;
- // if the dof mask is not active, all we
- // have to do is to add some indices in a
- // matrix format. To do this, we first
- // create an array of all the indices
- // that are to be added. these indices
- // are the local dof indices plus some
- // indices that come from constraints.
+ // if the dof mask is not active, all we have to do is to add some indices
+ // in a matrix format. To do this, we first create an array of all the
+ // indices that are to be added. these indices are the local dof indices
+ // plus some indices that come from constraints.
if (dof_mask_is_active == false)
{
std::vector<size_type> actual_row_indices (n_local_rows);
make_sorted_row_list (col_indices, actual_col_indices);
const size_type n_actual_rows = actual_row_indices.size();
- // now add the indices we collected above
- // to the sparsity pattern. Very easy
- // here - just add the same array to all
- // the rows...
+ // now add the indices we collected above to the sparsity pattern. Very
+ // easy here - just add the same array to all the rows...
for (size_type i=0; i<n_actual_rows; ++i)
sparsity_pattern.add_entries(actual_row_indices[i],
actual_col_indices.begin(),
return;
}
- // if constrained entries should be
- // kept, need to add rows and columns
- // of those to the sparsity pattern
+ // if constrained entries should be kept, need to add rows and columns of
+ // those to the sparsity pattern
if (keep_constrained_entries == true)
{
for (size_type i=0; i<row_indices.size(); i++)
const Table<2,bool> &dof_mask,
internal::bool2type<true> ) const
{
- // just as the other
- // add_entries_local_to_global function,
- // but now specialized for block
- // matrices.
+ // just as the other add_entries_local_to_global function, but now
+ // specialized for block matrices.
Assert (sparsity_pattern.n_rows() == sparsity_pattern.n_cols(), ExcNotQuadratic());
Assert (sparsity_pattern.n_block_rows() == sparsity_pattern.n_block_cols(),
ExcNotQuadratic());
make_sorted_row_list (local_dof_indices, actual_dof_indices);
const size_type n_actual_dofs = actual_dof_indices.size();
- // additional construct that also takes
- // care of block indices.
+ // additional construct that also takes care of block indices.
std::vector<size_type> block_starts(num_blocks+1, n_actual_dofs);
internals::make_block_starts (sparsity_pattern, actual_dof_indices,
block_starts);
return;
}
- // difficult case with dof_mask, similar
- // to the distribute_local_to_global
+ // difficult case with dof_mask, similar to the distribute_local_to_global
// function for block matrices
internals::GlobalRowsFromLocal global_rows (n_local_dofs);
make_sorted_row_list (local_dof_indices, global_rows);
const size_type n_actual_dofs = global_rows.size();
- // additional construct that also takes
- // care of block indices.
+ // additional construct that also takes care of block indices.
std::vector<size_type> block_starts(num_blocks+1, n_actual_dofs);
internals::make_block_starts(sparsity_pattern, global_rows,
block_starts);
std::vector<size_type> cols (n_actual_dofs);
- // the basic difference to the
- // non-block variant from now onwards
- // is that we go through the blocks
- // of the matrix separately.
+ // the basic difference to the non-block variant from now onwards is that we
+ // go through the blocks of the matrix separately.
for (size_type block=0; block<num_blocks; ++block)
{
const size_type next_block = block_starts[block+1];
projects / dealii.git / commitdiff