libdeal.ii-dev 8.4.2-2+b1 / usr / include / deal.II / lac / sparse_vanka.h

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#ifndef dealii__sparse_vanka_h
#define dealii__sparse_vanka_h



#include <deal.II/base/config.h>
#include <deal.II/base/smartpointer.h>
#include <deal.II/base/multithread_info.h>

#include <vector>
#include <map>

DEAL_II_NAMESPACE_OPEN

template <typename number> class FullMatrix;
template <typename number> class SparseMatrix;
template <typename number> class Vector;

template <typename number> class SparseVanka;
template <typename number> class SparseBlockVanka;

/*! @addtogroup Preconditioners
 *@{
 */

/**
 * Point-wise Vanka preconditioning. This class does Vanka preconditioning  on
 * a point-wise base. Vanka preconditioners are used for saddle point problems
 * like Stokes' problem or problems arising in optimization where Lagrange
 * multipliers occur and the Newton method matrix has a zero block. With these
 * matrices the application of Jacobi or Gauss-Seidel methods is impossible,
 * because some diagonal elements are zero in the rows of the Lagrange
 * multiplier. The approach of Vanka is to solve a small (usually indefinite)
 * system of equations for each Langrange multiplier variable (we will also
 * call the pressure in Stokes' equation a Langrange multiplier since it can
 * be interpreted as such).
 *
 * Objects of this class are constructed by passing a vector of indices of the
 * degrees of freedom of the Lagrange multiplier. In the actual
 * preconditioning method, these rows are traversed in the order in which the
 * appear in the matrix. Since this is a Gauß-Seidel like procedure, remember
 * to have a good ordering in advance (for transport dominated problems,
 * Cuthill-McKee algorithms are a good means for this, if points on the inflow
 * boundary are chosen as starting points for the renumbering).
 *
 * For each selected degree of freedom, a local system of equations is built
 * by the degree of freedom itself and all other values coupling immediately,
 * i.e. the set of degrees of freedom considered for the local system of
 * equations for degree of freedom @p i is @p i itself and all @p j such that
 * the element <tt>(i,j)</tt> is a nonzero entry in the sparse matrix under
 * consideration. The elements <tt>(j,i)</tt> are not considered. We now pick
 * all matrix entries from rows and columns out of the set of degrees of
 * freedom just described out of the global matrix and put it into a local
 * matrix, which is subsequently inverted. This system may be of different
 * size for each degree of freedom, depending for example on the local
 * neighborhood of the respective node on a computational grid.
 *
 * The right hand side is built up in the same way, i.e. by copying all
 * entries that coupled with the one under present consideration, but it is
 * augmented by all degrees of freedom coupling with the degrees from the set
 * described above (i.e. the DoFs coupling second order to the present one).
 * The reason for this is, that the local problems to be solved shall have
 * Dirichlet boundary conditions on the second order coupling DoFs, so we have
 * to take them into account but eliminate them before actually solving; this
 * elimination is done by the modification of the right hand side, and in the
 * end these degrees of freedom do not occur in the matrix and solution vector
 * any more at all.
 *
 * This local system is solved and the values are updated into the destination
 * vector.
 *
 * Remark: the Vanka method is a non-symmetric preconditioning method.
 *
 *
 * <h3>Example of Use</h3> This little example is taken from a program doing
 * parameter optimization. The Lagrange multiplier is the third component of
 * the finite element used. The system is solved by the GMRES method.
 * @code
 *    // tag the Lagrange multiplier variable
 *    vector<bool> signature(3);
 *    signature[0] = signature[1] = false;
 *    signature[2] = true;
 *
 *    // tag all dofs belonging to the Lagrange multiplier
 *    vector<bool> selected_dofs (dof.n_dofs(), false);
 *    DoFTools::extract_dofs(dof, signature, p_select);
 *    // create the Vanka object
 *    SparseVanka<double> vanka (global_matrix, selected_dofs);
 *
 *    // create the solver
 *    SolverGMRES<> gmres(control,memory,504);
 *
 *    // solve
 *    gmres.solve (global_matrix, solution, right_hand_side,
 *                 vanka);
 * @endcode
 *
 *
 * <h4>Implementor's remark</h4> At present, the local matrices are built up
 * such that the degree of freedom associated with the local Lagrange
 * multiplier is the first one. Thus, usually the upper left entry in the
 * local matrix is zero. It is not clear to me (W.B.) whether this might pose
 * some problems in the inversion of the local matrices. Maybe someone would
 * like to check this.
 *
 * @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 Guido Kanschat, Wolfgang Bangerth; 1999, 2000; extension for full
 * compatibility with LinearOperator class: Jean-Paul Pelteret, 2015
 */
template<typename number>
class SparseVanka
{
public:
  /**
   * Declare type for container size.
   */
  typedef types::global_dof_index size_type;

  /**
   * Constructor. Does nothing.
   *
   * Call the initialize() function before using this object as preconditioner
   * (vmult()).
   */
  SparseVanka ();

  /**
   * Constructor. Gets the matrix for preconditioning and a bit vector with
   * entries @p true for all rows to be updated. A reference to this vector
   * will be stored, so it must persist longer than the Vanka object. The same
   * is true for the matrix.
   *
   * The matrix @p M which is passed here may or may not be the same matrix
   * for which this object shall act as preconditioner. In particular, it is
   * conceivable that the preconditioner is build up for one matrix once, but
   * is used for subsequent steps in a nonlinear process as well, where the
   * matrix changes in each step slightly.
   *
   * If @p conserve_mem is @p false, then the inverses of the local systems
   * are computed now; if the flag is @p true, then they are computed every
   * time the preconditioner is applied. This saves some memory, but makes
   * preconditioning very slow. Note also, that if the flag is @p false, then
   * the contents of the matrix @p M at the time of calling this constructor
   * are used, while if the flag is @p true, then the values in @p M at the
   * time of preconditioning are used. This may lead to different results,
   * obviously, of @p M changes.
   *
   * The parameter @p n_threads determines how many threads shall be used in
   * parallel when building the inverses of the diagonal blocks. This
   * parameter is ignored if not in multithreaded mode.
   */
  SparseVanka(const SparseMatrix<number> &M,
              const std::vector<bool>    &selected,
              const bool                  conserve_memory = false,
              const unsigned int          n_threads       = MultithreadInfo::n_threads());

  /**
   * Destructor. Delete all allocated matrices.
   */
  ~SparseVanka();

  /**
   * Parameters for SparseVanka.
   */
  class AdditionalData
  {
  public:
    /**
     * Constructor. For the parameters' description, see below.
     */
    AdditionalData (const std::vector<bool> &selected,
                    const bool               conserve_memory = false,
                    const unsigned int       n_threads       = MultithreadInfo::n_threads());

    /**
     * Indices of those degrees of freedom that we shall work on.
     */
    const std::vector<bool> &selected;

    /**
     * Conserve memory flag.
     */
    const bool conserve_mem;

    /**
     * Number of threads to be used when building the inverses. Only relevant
     * in multithreaded mode.
     */
    const unsigned int n_threads;
  };


  /**
   * If the default constructor is used then this function needs to be called
   * before an object of this class is used as preconditioner.
   *
   * For more detail about possible parameters, see the class documentation
   * and the documentation of the SparseVanka::AdditionalData class.
   *
   * After this function is called the preconditioner is ready to be used
   * (using the <code>vmult</code> function of derived classes).
   */
  void initialize (const SparseMatrix<number> &M,
                   const AdditionalData       &additional_data);

  /**
   * Do the preconditioning. This function takes the residual in @p src and
   * returns the resulting update vector in @p dst.
   */
  template<typename number2>
  void vmult (Vector<number2>       &dst,
              const Vector<number2> &src) const;

  /**
   * Apply transpose preconditioner. This function takes the residual in @p
   * src  and returns the resulting update vector in @p dst.
   */
  template<typename number2>
  void Tvmult (Vector<number2>       &dst,
               const Vector<number2> &src) const;

  /**
   * Return the dimension of the codomain (or range) space. To remember: the
   * matrix is of dimension $m \times n$.
   *
   * @note This function should only be called if the preconditioner has been
   * initialized.
   */
  size_type m () const;

  /**
   * Return the dimension of the domain space. To remember: the matrix is of
   * dimension $m \times n$.
   *
   * @note This function should only be called if the preconditioner has been
   * initialized.
   */
  size_type n () const;

protected:
  /**
   * Apply the inverses corresponding to those degrees of freedom that have a
   * @p true value in @p dof_mask, to the @p src vector and move the result
   * into @p dst. Actually, only values for allowed indices are written to @p
   * dst, so the application of this function only does what is announced in
   * the general documentation if the given mask sets all values to zero
   *
   * The reason for providing the mask anyway is that in derived classes we
   * may want to apply the preconditioner to parts of the matrix only, in
   * order to parallelize the application. Then, it is important to only write
   * to some slices of @p dst, in order to eliminate the dependencies of
   * threads of each other.
   *
   * If a null pointer is passed instead of a pointer to the @p dof_mask (as
   * is the default value), then it is assumed that we shall work on all
   * degrees of freedom. This is then equivalent to calling the function with
   * a <tt>vector<bool>(n_dofs,true)</tt>.
   *
   * The @p vmult of this class of course calls this function with a null
   * pointer
   */
  template<typename number2>
  void apply_preconditioner (Vector<number2>         &dst,
                             const Vector<number2>   &src,
                             const std::vector<bool> *const dof_mask = 0) const;

  /**
   * Determine an estimate for the memory consumption (in bytes) of this
   * object.
   */
  std::size_t memory_consumption () const;

private:
  /**
   * Pointer to the matrix.
   */
  SmartPointer<const SparseMatrix<number>,SparseVanka<number> > matrix;

  /**
   * Conserve memory flag.
   */
  bool conserve_mem;

  /**
   * Indices of those degrees of freedom that we shall work on.
   */
  const std::vector<bool> *selected;

  /**
   * Number of threads to be used when building the inverses. Only relevant in
   * multithreaded mode.
   */
  unsigned int n_threads;

  /**
   * Array of inverse matrices, one for each degree of freedom. Only those
   * elements will be used that are tagged in @p selected.
   */
  mutable std::vector<SmartPointer<FullMatrix<float>,SparseVanka<number> > > inverses;

  /**
   * The dimension of the range space.
   */
  size_type _m;

  /**
   * The dimension of the domain space.
   */
  size_type _n;

  /**
   * Compute the inverses of all selected diagonal elements.
   */
  void compute_inverses ();

  /**
   * Compute the inverses at positions in the range <tt>[begin,end)</tt>. In
   * non-multithreaded mode, <tt>compute_inverses()</tt> calls this function
   * with the whole range, but in multithreaded mode, several copies of this
   * function are spawned.
   */
  void compute_inverses (const size_type begin,
                         const size_type end);

  /**
   * Compute the inverse of the block located at position @p row. Since the
   * vector is used quite often, it is generated only once in the caller of
   * this function and passed to this function which first clears it. Reusing
   * the vector makes the process significantly faster than in the case where
   * this function re-creates it each time.
   */
  void compute_inverse (const size_type         row,
                        std::vector<size_type> &local_indices);

  /**
   * Make the derived class a friend. This seems silly, but is actually
   * necessary, since derived classes can only access non-public members
   * through their @p this pointer, but not access these members as member
   * functions of other objects of the type of this base class (i.e. like
   * <tt>x.f()</tt>, where @p x is an object of the base class, and @p f one
   * of it's non-public member functions).
   *
   * Now, in the case of the @p SparseBlockVanka class, we would like to take
   * the address of a function of the base class in order to call it through
   * the multithreading framework, so the derived class has to be a friend.
   */
  template <typename T> friend class SparseBlockVanka;
};



/**
 * Block version of the sparse Vanka preconditioner. This class divides the
 * matrix into blocks and works on the diagonal blocks only, which of course
 * reduces the efficiency as preconditioner, but is perfectly parallelizable.
 * The constructor takes a parameter into how many blocks the matrix shall be
 * subdivided and then lets the underlying class do the work. Division of the
 * matrix is done in several ways which are described in detail below.
 *
 * This class is probably useless if you don't have a multiprocessor system,
 * since then the amount of work per preconditioning step is the same as for
 * the @p SparseVanka class, but preconditioning properties are worse. On the
 * other hand, if you have a multiprocessor system, the worse preconditioning
 * quality (leading to more iterations of the linear solver) usually is well
 * balanced by the increased speed of application due to the parallelization,
 * leading to an overall decrease in elapsed wall-time for solving your linear
 * system. It should be noted that the quality as preconditioner reduces with
 * growing number of blocks, so there may be an optimal value (in terms of
 * wall-time per linear solve) for the number of blocks.
 *
 * To facilitate writing portable code, if the number of blocks into which the
 * matrix is to be subdivided, is set to one, then this class acts just like
 * the @p SparseVanka class. You may therefore want to set the number of
 * blocks equal to the number of processors you have.
 *
 * Note that the parallelization is done if <tt>deal.II</tt> was configured
 * for multithread use and that the number of threads which is spawned equals
 * the number of blocks. This is reasonable since you will not want to set the
 * number of blocks unnecessarily large, since, as mentioned, this reduces the
 * preconditioning properties.
 *
 *
 * <h3>Splitting the matrix into blocks</h3>
 *
 * Splitting the matrix into blocks is always done in a way such that the
 * blocks are not necessarily of equal size, but such that the number of
 * selected degrees of freedom for which a local system is to be solved is
 * equal between blocks. The reason for this strategy to subdivision is load-
 * balancing for multithreading. There are several possibilities to actually
 * split the matrix into blocks, which are selected by the flag @p
 * blocking_strategy that is passed to the constructor. By a block, we will in
 * the sequel denote a list of indices of degrees of freedom; the algorithm
 * will work on each block separately, i.e. the solutions of the local systems
 * corresponding to a degree of freedom of one block will only be used to
 * update the degrees of freedom belonging to the same block, but never to
 * update degrees of freedoms of other blocks. A block can be a consecutive
 * list of indices, as in the first alternative below, or a nonconsecutive
 * list of indices. Of course, we assume that the intersection of each two
 * blocks is empty and that the union of all blocks equals the interval
 * <tt>[0,N)</tt>, where @p N is the number of degrees of freedom of the
 * system of equations.
 *
 * <ul>
 * <li> @p index_intervals: Here, we chose the blocks to be intervals
 * <tt>[a_i,a_{i+1</tt>)}, i.e. consecutive degrees of freedom are usually
 * also within the same block. This is a reasonable strategy, if the degrees
 * of freedom have, for example, be re-numbered using the Cuthill-McKee
 * algorithm, in which spatially neighboring degrees of freedom have
 * neighboring indices. In that case, coupling in the matrix is usually
 * restricted to the vicinity of the diagonal as well, and we can simply cut
 * the matrix into blocks.
 *
 * The bounds of the intervals, i.e. the @p a_i above, are chosen such that
 * the number of degrees of freedom on which we shall work (i.e. usually the
 * degrees of freedom corresponding to Lagrange multipliers) is about the same
 * in each block; this does not mean, however, that the sizes of the blocks
 * are equal, since the blocks also comprise the other degrees of freedom for
 * which no local system is solved. In the extreme case, consider that all
 * Lagrange multipliers are sorted to the end of the range of DoF indices,
 * then the first block would be very large, since it comprises all other DoFs
 * and some Lagrange multipliers, while all other blocks are rather small and
 * comprise only Langrange multipliers. This strategy therefore does not only
 * depend on the order in which the Lagrange DoFs are sorted, but also on the
 * order in which the other DoFs are sorted. It is therefore necessary to note
 * that this almost renders the capability as preconditioner useless if the
 * degrees of freedom are numbered by component, i.e. all Lagrange multipliers
 * en bloc.
 *
 * <li> @p adaptive: This strategy is a bit more clever in cases where the
 * Langrange DoFs are clustered, as in the example above. It works as follows:
 * it first groups the Lagrange DoFs into blocks, using the same strategy as
 * above. However, instead of grouping the other DoFs into the blocks of
 * Lagrange DoFs with nearest DoF index, it decides for each non-Lagrange DoF
 * to put it into the block of Lagrange DoFs which write to this non-Lagrange
 * DoF most often. This makes it possible to even sort the Lagrange DoFs to
 * the end and still associate spatially neighboring non-Lagrange DoFs to the
 * same blocks where the respective Lagrange DoFs are, since they couple to
 * each other while spatially distant DoFs don't couple.
 *
 * The additional computational effort to sorting the non-Lagrange DoFs is not
 * very large compared with the inversion of the local systems and applying
 * the preconditioner, so this strategy might be reasonable if you want to
 * sort your degrees of freedom by component. If the degrees of freedom are
 * not sorted by component, the results of the both strategies outlined above
 * does not differ much. However, unlike the first strategy, the performance
 * of the second strategy does not deteriorate if the DoFs are renumbered by
 * component.
 * </ul>
 *
 *
 * <h3>Typical results</h3>
 *
 * As a prototypical test case, we use a nonlinear problem from optimization,
 * which leads to a series of saddle point problems, each of which is solved
 * using GMRES with Vanka as preconditioner. The equation had approx. 850
 * degrees of freedom. With the non-blocked version @p SparseVanka (or @p
 * SparseBlockVanka with <tt>n_blocks==1</tt>), the following numbers of
 * iterations is needed to solver the linear system in each nonlinear step:
 * @verbatim
 *   101 68 64 53 35 21
 * @endverbatim
 *
 * With four blocks, we need the following numbers of iterations
 * @verbatim
 *   124 88 83 66 44 28
 * @endverbatim
 * As can be seen, more iterations are needed. However, in terms of computing
 * time, the first version needs 72 seconds wall time (and 79 seconds CPU
 * time, which is more than wall time since some other parts of the program
 * were parallelized as well), while the second version needed 53 second wall
 * time (and 110 seconds CPU time) on a four processor machine. The total time
 * is in both cases dominated by the linear solvers. In this case, it is
 * therefore worth while using the blocked version of the preconditioner if
 * wall time is more important than CPU time.
 *
 * The results with the block version above were obtained with the first
 * blocking strategy and the degrees of freedom were not numbered by
 * component. Using the second strategy does not much change the numbers of
 * iterations (at most by one in each step) and they also do not change when
 * the degrees of freedom are sorted by component, while the first strategy
 * significantly deteriorated.
 *
 * @author Wolfgang Bangerth, 2000
 */
template<typename number>
class SparseBlockVanka : public SparseVanka<number>
{
public:
  /**
   * Declare type for container size.
   */
  typedef types::global_dof_index size_type;

  /**
   * Enumeration of the different methods by which the DoFs are distributed to
   * the blocks on which we are to work.
   */
  enum BlockingStrategy
  {
    index_intervals, adaptive
  };

  /**
   * Constructor. Pass all arguments except for @p n_blocks to the base class.
   */
  SparseBlockVanka (const SparseMatrix<number> &M,
                    const std::vector<bool>    &selected,
                    const unsigned int          n_blocks,
                    const BlockingStrategy      blocking_strategy,
                    const bool                  conserve_memory = false,
                    const unsigned int          n_threads       = MultithreadInfo::n_threads());

  /**
   * Apply the preconditioner.
   */
  template<typename number2>
  void vmult (Vector<number2>       &dst,
              const Vector<number2> &src) const;

  /**
   * Determine an estimate for the memory consumption (in bytes) of this
   * object.
   */
  std::size_t memory_consumption () const;

private:
  /**
   * Store the number of blocks.
   */
  const unsigned int n_blocks;

  /**
   * In this field, we precompute for each block which degrees of freedom
   * belong to it. Thus, if <tt>dof_masks[i][j]==true</tt>, then DoF @p j
   * belongs to block @p i. Of course, no other <tt>dof_masks[l][j]</tt> may
   * be @p true for <tt>l!=i</tt>. This computation is done in the
   * constructor, to avoid recomputing each time the preconditioner is called.
   */
  std::vector<std::vector<bool> > dof_masks;

  /**
   * Compute the contents of the field @p dof_masks. This function is called
   * from the constructor.
   */
  void compute_dof_masks (const SparseMatrix<number> &M,
                          const std::vector<bool>    &selected,
                          const BlockingStrategy      blocking_strategy);
};

/*@}*/
/* ---------------------------------- Inline functions ------------------- */

#ifndef DOXYGEN

template<typename number>
inline typename SparseVanka<number>::size_type
SparseVanka<number>::m () const
{
  Assert(_m != 0, ExcNotInitialized());
  return _m;
}

template<typename number>
inline typename SparseVanka<number>::size_type
SparseVanka<number>::n () const
{
  Assert(_n != 0, ExcNotInitialized());
  return _n;
}

template<typename number>
template<typename number2>
inline void
SparseVanka<number>::Tvmult (Vector<number2>       &dst,
                             const Vector<number2> &src) const
{
  AssertThrow(false, ExcNotImplemented());
}

#endif // DOXYGEN

DEAL_II_NAMESPACE_CLOSE

#endif