コード例 #1
0
// ============================================================================
// could be made static...
void ML_Epetra::MultiLevelPreconditioner::
RandomAndZero(double * tmp_rhs, double * tmp_sol, int size)
{
  // create random numbers between 0.5 and 1.0
  ML_random_vec(tmp_rhs, size, ml_comm_);
  //for( int i=0 ; i<size ; ++i ) tmp_rhs[i] = 0.5+0.25*tmp_rhs[i];
  for( int i=0 ; i<size ; ++i ) tmp_sol[i] = 0.0;
}
コード例 #2
0
ファイル: ml_check.c プロジェクト: 00liujj/trilinos
int ML_Reitzinger_Check_Hierarchy(ML *ml, ML_Operator **Tmat_array, int incr_or_decr)
{
  int i,j;
  int finest_level, coarsest_level;
  ML_Operator *Amat, *Tmat;
  double *randvec, *result, *result1;
  double dnorm;

  finest_level = ml->ML_finest_level;
  coarsest_level = ml->ML_coarsest_level;

  if (incr_or_decr == ML_INCREASING) {
    if (ml->comm->ML_mypid == 0) {
      printf("ML_Reitzinger_Check_Hierarchy: ML_INCREASING is not supported ");
      printf(" at this time.  Not checking hierarchy.\n");
    }
    return 1;
  }

  if ( ML_Get_PrintLevel() > 5 ) {
    printf("ML_Reitzinger_Check_Hierarchy: Checking null space\n");
  }

  for (i=finest_level; i>coarsest_level; i--) {

     Amat = ml->Amat+i;
     Tmat = Tmat_array[i];

     /* normalized random vector */
     randvec = (double *) ML_allocate(Tmat->invec_leng * sizeof(double) );
     ML_random_vec(randvec,Tmat->invec_leng, ml->comm);
     dnorm = sqrt( ML_gdot(Tmat->invec_leng, randvec, randvec, ml->comm) );
     for (j=0; j<Tmat->invec_leng; j++) randvec[j] /=  dnorm;

     result = (double *) ML_allocate(Amat->invec_leng * sizeof(double) );
     result1 = (double *) ML_allocate(Amat->outvec_leng * sizeof(double) );

     ML_Operator_Apply(Tmat, Tmat->invec_leng, randvec,
                       Tmat->outvec_leng, result);
     ML_Operator_Apply(Amat, Amat->invec_leng, result,
                       Amat->outvec_leng, result1);

     dnorm = sqrt( ML_gdot(Amat->outvec_leng, result1, result1, ml->comm) );
     if ( (ML_Get_PrintLevel() > 5) && (ml->comm->ML_mypid == 0) ) {
       printf("Level %d: for random v,  ||S*T*v|| = %15.10e\n",i,dnorm);
     }

     ML_free(randvec);
     ML_free(result);
     ML_free(result1);
  }
  if ( (ML_Get_PrintLevel() > 5) && (ml->comm->ML_mypid == 0) ) printf("\n");

  return 0;

}
コード例 #3
0
int main(int argc, char *argv[])
{
  int    Nnodes=16*16;              /* Total number of nodes in the problem.*/
                                    /* 'Nnodes' must be a perfect square.   */
  int    MaxMgLevels=6;             /* Maximum number of Multigrid Levels   */
  int    Nits_per_presmooth=1;      /* # of pre & post smoothings per level */
  double tolerance = 1.0e-8;        /* At convergence:                      */
                                    /*   ||r_k||_2 < tolerance ||r_0||_2    */
  int smoothPe_flag = ML_YES;       /* ML_YES: smooth tentative prolongator */
                                    /* ML_NO: don't smooth prolongator      */

  /***************************************************************************/
  /* Select Hiptmair relaxation subsmoothers for the nodal and edge problems */
  /* Choices include                                                         */
  /*   1) ML_Gen_Smoother_SymGaussSeidel: this corresponds to a processor    */
  /*      local version of symmetric Gauss-Seidel/SOR. The number of sweeps  */
  /*      can be set via either 'edge_its' or 'nodal_its'. The damping can   */
  /*      be set via 'edge_omega' or 'nodal_omega'. When set to ML_DDEFAULT, */
  /*      the damping is set to '1' on one processor. On multiple processors */
  /*      a lower damping value is set. This is needed to converge processor */
  /*      local SOR.                                                         */
  /*   2) ML_Gen_Smoother_Cheby: this corresponds to polynomial relaxation.    */
  /*      The degree of the polynomial is set via 'edge_its' or 'nodal_its'. */
  /*      If the degree is '-1', Marian Brezina's MLS polynomial is chosen.  */
  /*      Otherwise, a Chebyshev polynomial is used over high frequencies    */
  /*      [ lambda_max/alpha , lambda_max]. Lambda_max is computed. 'alpha'  */
  /*      is hardwired in this example to correspond to twice the ratio of   */
  /*      unknowns in the fine and coarse meshes.                            */
  /*                                                                         */
  /* Using 'hiptmair_type' (see comments below) it is also possible to choose*/
  /* when edge and nodal problems are relaxed within the Hiptmair smoother.  */
  /***************************************************************************/

  void  *edge_smoother=(void *)     /* Edge relaxation:                     */
               ML_Gen_Smoother_Cheby; /*   ML_Gen_Smoother_Cheby            */
                                    /*     ML_Gen_Smoother_SymGaussSeidel   */
  void *nodal_smoother=(void *)     /* Nodal relaxation                     */
               ML_Gen_Smoother_Cheby;/*     ML_Gen_Smoother_Cheby           */
                                    /*     ML_Gen_Smoother_SymGaussSeidel   */

  int  edge_its = 3;                /* Iterations or polynomial degree for  */
  int  nodal_its = 3;               /* edge/nodal subsmoothers.             */
  double nodal_omega = ML_DDEFAULT, /* SOR damping parameter for noda/edge  */
         edge_omega  = ML_DDEFAULT; /* subsmoothers (see comments above).   */
  int   hiptmair_type=HALF_HIPTMAIR;/* FULL_HIPTMAIR: each invokation       */
                                    /*     smoothes on edges, then nodes,   */
                                    /*     and then once again on edges.    */
                                    /* HALF_HIPTMAIR: each pre-invokation   */
                                    /*     smoothes on edges, then nodes.   */
                                    /*     Each post-invokation smoothes    */
                                    /*     on nodes then edges. .           */


  ML_Operator  *Tmat, *Tmat_trans, **Tmat_array, **Tmat_trans_array;
  ML           *ml_edges, *ml_nodes;
  ML_Aggregate *ag;
  int          Nfine_edge, Ncoarse_edge, Nfine_node, Ncoarse_node, Nlevels;
  int          level, coarsest_level, itmp;
  double       edge_coarsening_rate, node_coarsening_rate, *rhs, *xxx;
  void         **edge_args, **nodal_args;
  struct       user_partition Edge_Partition = {NULL, NULL,0,0}, 
                                Node_Partition = {NULL, NULL,0,0};
  struct Tmat_data Tmat_data;
int i, Ntotal;
 ML_Comm *comm;

  /* See Aztec User's Guide for information on these variables */

#ifdef AZTEC
  AZ_MATRIX    *Ke_mat, *Kn_mat;
  AZ_PRECOND   *Pmat = NULL;
  int          proc_config[AZ_PROC_SIZE], options[AZ_OPTIONS_SIZE];
  double       params[AZ_PARAMS_SIZE], status[AZ_STATUS_SIZE];
#endif


  /* get processor information (proc id & # of procs) and set ML's printlevel. */

#ifdef ML_MPI
  MPI_Init(&argc,&argv);
#endif
#ifdef AZTEC
  AZ_set_proc_config(proc_config, COMMUNICATOR);
#endif
  ML_Set_PrintLevel(10);   /* set ML's output level: 0 gives least output */

  /* Set the # of global nodes/edges and partition both the edges and the */
  /* nodes over the processors. NOTE: I believe we assume that if an edge */
  /* is assigned to a processor at least one of its nodes must be also    */
  /* assigned to that processor.                                          */

  Node_Partition.Nglobal = Nnodes;
  Edge_Partition.Nglobal = Node_Partition.Nglobal*2;
  Node_Partition.type = NODE;
  Edge_Partition.type = EDGE;
#define perxodic
#ifdef periodic
Node_Partition.Nglobal += 2; 
#endif
  partition_edges(&Edge_Partition);
  partition_nodes(&Node_Partition);
xxx = (double *) ML_allocate((Edge_Partition.Nlocal+100)*sizeof(double)); 
rhs = (double *) ML_allocate((Edge_Partition.Nlocal+100)*sizeof(double)); 
 for (i = 0; i < Edge_Partition.Nlocal + 100; i++) xxx[i] = -1.;
 for (i = 0; i < Edge_Partition.Nlocal; i++) xxx[i] = (double) 
        Edge_Partition.my_global_ids[i];

update_ghost_edges(xxx, (void *) &Edge_Partition);


  /* Create an empty multigrid hierarchy and set the 'MaxMGLevels-1'th   */
  /* level discretization within this hierarchy to the ML matrix         */
  /* representing Ke (Maxwell edge discretization).                      */

  ML_Create(&ml_edges, MaxMgLevels);
#ifdef AZTEC
  /* Build Ke as an Aztec matrix. Use built-in function AZ_ML_Set_Amat() */
  /* to convert to an ML matrix and put in hierarchy.                    */

  Ke_mat = user_Ke_build(&Edge_Partition);
  AZ_ML_Set_Amat(ml_edges, MaxMgLevels-1, Edge_Partition.Nlocal,
      		 Edge_Partition.Nlocal, Ke_mat, proc_config);
#else
  /* Build Ke directly as an ML matrix.                                  */

  ML_Init_Amatrix      (ml_edges, MaxMgLevels-1, Edge_Partition.Nlocal,
			Edge_Partition.Nlocal, &Edge_Partition);

  Ntotal = Edge_Partition.Nlocal;
  if (Edge_Partition.nprocs == 2) Ntotal += Edge_Partition.Nghost;
  ML_Set_Amatrix_Getrow(ml_edges, MaxMgLevels-1,  Ke_getrow, update_ghost_edges, Ntotal);
  ML_Set_Amatrix_Matvec(ml_edges, MaxMgLevels-1,  Ke_matvec);

#endif



  /* Build an Aztec matrix representing an auxiliary nodal PDE problem.  */
  /* This should be a variable coefficient Poisson problem (with unknowns*/
  /* at the nodes). The coefficients should be chosen to reflect the     */
  /* conductivity of the original edge problems.                         */
  /* Create an empty multigrid hierarchy. Convert the Aztec matrix to an */
  /* ML matrix and put it in the 'MaxMGLevels-1' level of the hierarchy. */
  /* Note it is possible to multiply T'*T for get this matrix though this*/
  /* will not incorporate material properties.                           */

  ML_Create(&ml_nodes, MaxMgLevels);

#ifdef AZTEC
  Kn_mat = user_Kn_build( &Node_Partition);
  AZ_ML_Set_Amat(ml_nodes, MaxMgLevels-1, Node_Partition.Nlocal, 
		 Node_Partition.Nlocal, Kn_mat, proc_config);
#else
  ML_Init_Amatrix      (ml_nodes, MaxMgLevels-1 , Node_Partition.Nlocal,
			Node_Partition.Nlocal, &Node_Partition);
  Ntotal = Node_Partition.Nlocal;
  if (Node_Partition.nprocs == 2) Ntotal += Node_Partition.Nghost;
  ML_Set_Amatrix_Getrow(ml_nodes, MaxMgLevels-1,  Kn_getrow, update_ghost_nodes, Ntotal);
#endif

  /* Build an ML matrix representing the null space of the PDE problem. */
  /* This should be a discrete gradient (nodes to edges).               */

#ifdef AZTEC
    Tmat = user_T_build (&Edge_Partition, &Node_Partition, 
  		   &(ml_nodes->Amat[MaxMgLevels-1]));
#else
    Tmat = ML_Operator_Create(ml_nodes->comm);
    Tmat_data.edge = &Edge_Partition;
    Tmat_data.node = &Node_Partition;
    Tmat_data.Kn   = &(ml_nodes->Amat[MaxMgLevels-1]);

    ML_Operator_Set_ApplyFuncData( Tmat,	Node_Partition.Nlocal,
				   Edge_Partition.Nlocal, ML_EMPTY, (void *) &Tmat_data, 
				   Edge_Partition.Nlocal, NULL, 0);
    ML_Operator_Set_Getrow( Tmat, ML_INTERNAL, Edge_Partition.Nlocal,Tmat_getrow);
    ML_Operator_Set_ApplyFunc(Tmat, ML_INTERNAL, Tmat_matvec);
  ML_Comm_Create( &comm);

  ML_CommInfoOP_Generate( &(Tmat->getrow->pre_comm), update_ghost_nodes, 
			  &Node_Partition,comm, Tmat->invec_leng, 
			  Node_Partition.Nghost);
#endif


  /********************************************************************/
  /* Set some ML parameters.                                          */
  /*------------------------------------------------------------------*/
	
  ML_Set_ResidualOutputFrequency(ml_edges, 1);
  ML_Set_Tolerance(ml_edges, 1.0e-8);
  ML_Aggregate_Create( &ag );
  ML_Aggregate_Set_CoarsenScheme_Uncoupled(ag);
  ML_Aggregate_Set_DampingFactor(ag, 0.0); /* must use 0 for maxwell */
  ML_Aggregate_Set_MaxCoarseSize(ag, 30);
  ML_Aggregate_Set_Threshold(ag, 0.0);


  /********************************************************************/
  /*                      Set up Tmat_trans                           */
  /*------------------------------------------------------------------*/

  Tmat_trans = ML_Operator_Create(ml_edges->comm);
  ML_Operator_Transpose_byrow(Tmat, Tmat_trans);


  Nlevels=ML_Gen_MGHierarchy_UsingReitzinger(ml_edges, &ml_nodes,MaxMgLevels-1,
					     ML_DECREASING,ag,Tmat,Tmat_trans, 
					     &Tmat_array,&Tmat_trans_array, 
					     smoothPe_flag, 1.5);

  /* Set the Hiptmair subsmoothers */

  if (nodal_smoother == (void *) ML_Gen_Smoother_SymGaussSeidel) {
    nodal_args = ML_Smoother_Arglist_Create(2);
    ML_Smoother_Arglist_Set(nodal_args, 0, &nodal_its);
    ML_Smoother_Arglist_Set(nodal_args, 1, &nodal_omega);
  }
  if (edge_smoother == (void *) ML_Gen_Smoother_SymGaussSeidel) {
    edge_args = ML_Smoother_Arglist_Create(2);
    ML_Smoother_Arglist_Set(edge_args, 0, &edge_its);
    ML_Smoother_Arglist_Set(edge_args, 1, &edge_omega);
  }
  if (nodal_smoother == (void *) ML_Gen_Smoother_Cheby) {
    nodal_args = ML_Smoother_Arglist_Create(2);
    ML_Smoother_Arglist_Set(nodal_args, 0, &nodal_its);
    Nfine_node = Tmat_array[MaxMgLevels-1]->invec_leng;
    Nfine_node = ML_gsum_int(Nfine_node, ml_edges->comm);
  }
  if (edge_smoother == (void *) ML_Gen_Smoother_Cheby) {
    edge_args = ML_Smoother_Arglist_Create(2);
    ML_Smoother_Arglist_Set(edge_args, 0, &edge_its);
    Nfine_edge = Tmat_array[MaxMgLevels-1]->outvec_leng;
    Nfine_edge = ML_gsum_int(Nfine_edge, ml_edges->comm);
  }

  /****************************************************
  * Set up smoothers for all levels but the coarsest. *
  ****************************************************/
  coarsest_level = MaxMgLevels - Nlevels;

  for (level = MaxMgLevels-1; level > coarsest_level; level--)
    {
      if (edge_smoother == (void *) ML_Gen_Smoother_Cheby) {
	Ncoarse_edge = Tmat_array[level-1]->outvec_leng;
	Ncoarse_edge = ML_gsum_int(Ncoarse_edge, ml_edges->comm);
	edge_coarsening_rate =  2.*((double) Nfine_edge)/ ((double) Ncoarse_edge);
	ML_Smoother_Arglist_Set(edge_args, 1, &edge_coarsening_rate);
	Nfine_edge = Ncoarse_edge;
      }
      if (nodal_smoother == (void *) ML_Gen_Smoother_Cheby) {
	Ncoarse_node = Tmat_array[level-1]->invec_leng;
	Ncoarse_node = ML_gsum_int(Ncoarse_node, ml_edges->comm);
	node_coarsening_rate =  2.*((double) Nfine_node)/ ((double) Ncoarse_node);
	ML_Smoother_Arglist_Set(nodal_args, 1, &node_coarsening_rate);
	Nfine_node = Ncoarse_node;
      }
      ML_Gen_Smoother_Hiptmair(ml_edges, level, ML_BOTH, Nits_per_presmooth,
			       Tmat_array, Tmat_trans_array, NULL, edge_smoother,
			       edge_args, nodal_smoother,nodal_args, hiptmair_type);
    }

  /*******************************************
  * Set up coarsest level smoother
  *******************************************/

  if (edge_smoother == (void *) ML_Gen_Smoother_Cheby) {
    edge_coarsening_rate = (double) Nfine_edge;
    ML_Smoother_Arglist_Set(edge_args, 1, &edge_coarsening_rate);
  }
  if (nodal_smoother == (void *) ML_Gen_Smoother_Cheby) {
    node_coarsening_rate = (double) Nfine_node;
    ML_Smoother_Arglist_Set(nodal_args,1,&node_coarsening_rate);
  }
  ML_Gen_CoarseSolverSuperLU( ml_edges, coarsest_level);
  

  /* Must be called before invoking the preconditioner */
  ML_Gen_Solver(ml_edges, ML_MGV, MaxMgLevels-1, coarsest_level); 



  /* Set the initial guess and the right hand side. Invoke solver */	

  xxx = (double *) ML_allocate(Edge_Partition.Nlocal*sizeof(double)); 
  ML_random_vec(xxx, Edge_Partition.Nlocal, ml_edges->comm);
  rhs = (double *) ML_allocate(Edge_Partition.Nlocal*sizeof(double)); 
  ML_random_vec(rhs, Edge_Partition.Nlocal, ml_edges->comm);

#ifdef AZTEC
  /* Choose the Aztec solver and criteria. Also tell Aztec that */
  /* ML will be supplying the preconditioner.                   */

  AZ_defaults(options, params);
  options[AZ_solver]   = AZ_fixed_pt;
  options[AZ_solver]   = AZ_gmres;
  options[AZ_kspace]   = 80;
  params[AZ_tol]       = tolerance;
  AZ_set_ML_preconditioner(&Pmat, Ke_mat, ml_edges, options); 
  options[AZ_conv] = AZ_noscaled;
  AZ_iterate(xxx, rhs, options, params, status, proc_config, Ke_mat, Pmat, NULL);
#else
  ML_Iterate(ml_edges, xxx, rhs);
#endif


  /* clean up. */

  ML_Smoother_Arglist_Delete(&nodal_args);
  ML_Smoother_Arglist_Delete(&edge_args);
  ML_Aggregate_Destroy(&ag);
  ML_Destroy(&ml_edges);
  ML_Destroy(&ml_nodes);
#ifdef AZTEC
  AZ_free((void *) Ke_mat->data_org);
  AZ_free((void *) Ke_mat->val);
  AZ_free((void *) Ke_mat->bindx);
  if (Ke_mat  != NULL) AZ_matrix_destroy(&Ke_mat);
  if (Pmat  != NULL) AZ_precond_destroy(&Pmat);
  if (Kn_mat != NULL) AZ_matrix_destroy(&Kn_mat);
#endif
  free(xxx);
  free(rhs);
  ML_Operator_Destroy(&Tmat);
  ML_Operator_Destroy(&Tmat_trans);
  ML_MGHierarchy_ReitzingerDestroy(MaxMgLevels-2, &Tmat_array, &Tmat_trans_array);

#ifdef ML_MPI
  MPI_Finalize();
#endif
		
  return 0;
		
}