int ML_Aggregate_CoarsenUser(ML_Aggregate *ml_ag, ML_Operator *Amatrix,
ML_Operator **Pmatrix, ML_Comm *comm)
{
unsigned int nbytes, length;
int i, j, k, Nrows, exp_Nrows;
int diff_level;
int aggr_count, index, mypid, num_PDE_eqns;
int *aggr_index = NULL, nullspace_dim;
int Ncoarse, count;
int *new_ia = NULL, *new_ja = NULL, new_Nrows;
int exp_Ncoarse;
int *aggr_cnt_array = NULL;
int level, index3, max_agg_size;
int **rows_in_aggs = NULL, lwork, info;
double *new_val = NULL, epsilon;
double *nullspace_vect = NULL, *qr_tmp = NULL;
double *tmp_vect = NULL, *work = NULL, *new_null = NULL;
ML_SuperNode *aggr_head = NULL, *aggr_curr, *supernode;
struct ML_CSR_MSRdata *csr_data;
int total_nz = 0;
char str[80];
int * graph_decomposition = NULL;
ML_Aggregate_Viz_Stats * aggr_viz_and_stats;
ML_Aggregate_Viz_Stats * grid_info;
int Nprocs;
char * unamalg_bdry = NULL;
char* label;
int N_dimensions;
double* x_coord = NULL;
double* y_coord = NULL;
double* z_coord = NULL;
/* ------------------- execution begins --------------------------------- */
label = ML_GetUserLabel();
sprintf(str, "%s (level %d) :", label, ml_ag->cur_level);
/* ============================================================= */
/* get the machine information and matrix references */
/* ============================================================= */
mypid = comm->ML_mypid;
Nprocs = comm->ML_nprocs;
epsilon = ml_ag->threshold;
num_PDE_eqns = ml_ag->num_PDE_eqns;
nullspace_dim = ml_ag->nullspace_dim;
nullspace_vect = ml_ag->nullspace_vect;
Nrows = Amatrix->outvec_leng;
if (mypid == 0 && 5 < ML_Get_PrintLevel()) {
printf("%s num PDE eqns = %d\n",
str,
num_PDE_eqns);
}
/* ============================================================= */
/* check the system size versus null dimension size */
/* ============================================================= */
if ( Nrows % num_PDE_eqns != 0 )
{
printf("ML_Aggregate_CoarsenUser ERROR : Nrows must be multiples");
printf(" of num_PDE_eqns.\n");
exit(EXIT_FAILURE);
}
diff_level = ml_ag->max_levels - ml_ag->cur_level - 1;
if ( diff_level > 0 ) num_PDE_eqns = nullspace_dim; /* ## 12/20/99 */
/* ============================================================= */
/* set up the threshold for weight-based coarsening */
/* ============================================================= */
diff_level = ml_ag->begin_level - ml_ag->cur_level;
if (diff_level == 0)
ml_ag->curr_threshold = ml_ag->threshold;
epsilon = ml_ag->curr_threshold;
ml_ag->curr_threshold *= 0.5;
if (mypid == 0 && 7 < ML_Get_PrintLevel())
printf("%s current eps = %e\n", str, epsilon);
epsilon = epsilon * epsilon;
ML_Operator_AmalgamateAndDropWeak(Amatrix, num_PDE_eqns, epsilon);
Nrows /= num_PDE_eqns;
exp_Nrows = Nrows;
/* ********************************************************************** */
/* allocate memory for aggr_index, which will contain the decomposition */
/* ********************************************************************** */
nbytes = (Nrows*num_PDE_eqns) * sizeof(int);
if ( nbytes > 0 ) {
ML_memory_alloc((void**) &aggr_index, nbytes, "ACJ");
if( aggr_index == NULL ) {
fprintf( stderr,
"*ML*ERR* not enough memory for %d bytes\n"
"*ML*ERR* (file %s, line %d)\n",
nbytes,
__FILE__,
__LINE__ );
exit( EXIT_FAILURE );
}
}
else aggr_index = NULL;
for( i=0 ; i<Nrows*num_PDE_eqns ; i++ ) aggr_index[i] = -1;
unamalg_bdry = (char *) ML_allocate( sizeof(char) * (Nrows+1) );
if( unamalg_bdry == NULL ) {
fprintf( stderr,
"*ML*ERR* on proc %d, not enough space for %d bytes\n"
"*ML*ERR* (file %s, line %d)\n",
mypid,
(int)sizeof(char) * Nrows,
__FILE__,
__LINE__ );
exit( EXIT_FAILURE );
}
N_dimensions = ml_ag->N_dimensions;
grid_info = (ML_Aggregate_Viz_Stats*) Amatrix->to->Grid->Grid;
x_coord = grid_info->x;
if (N_dimensions > 1 && x_coord)
y_coord = grid_info->y;
else
y_coord = 0;
if (N_dimensions > 2 && x_coord)
z_coord = grid_info->z;
else
z_coord = 0;
aggr_count = ML_GetUserPartitions(Amatrix,unamalg_bdry,
epsilon,
x_coord,y_coord,z_coord,
aggr_index,&total_nz);
#ifdef ML_MPI
MPI_Allreduce( &Nrows, &i, 1, MPI_INT, MPI_SUM, Amatrix->comm->USR_comm );
MPI_Allreduce( &aggr_count, &j, 1, MPI_INT, MPI_SUM, Amatrix->comm->USR_comm );
#else
i = Nrows;
j = aggr_count;
#endif
if( mypid == 0 && 7 < ML_Get_PrintLevel() ) {
printf("%s Using %d (block) aggregates (globally)\n",
str,
j );
printf("%s # (block) aggre/ # (block) rows = %8.5f %% ( = %d / %d)\n",
str,
100.0*j/i,
j, i);
}
j = ML_gsum_int( aggr_count, comm );
if (mypid == 0 && 7 < ML_Get_PrintLevel()) {
printf("%s %d (block) aggregates (globally)\n",
str, j );
}
/* ********************************************************************** */
/* I allocate room to copy aggr_index and pass this value to the user, */
/* who will be able to analyze and visualize this after the construction */
/* of the levels. This way, the only price we have to pay for stats and */
/* viz is essentially a little bit of memory. */
/* this memory will be cleaned with the object ML_Aggregate ml_ag. */
/* I set the pointers using the ML_Aggregate_Info structure. This is */
/* allocated using ML_Aggregate_Info_Setup(ml,MaxNumLevels) */
/* ********************************************************************** */
if (Amatrix->to->Grid->Grid != NULL) {
graph_decomposition = (int *)ML_allocate(sizeof(int)*(Nrows+1));
if( graph_decomposition == NULL ) {
fprintf( stderr,
"*ML*ERR* Not enough memory for %d bytes\n"
"*ML*ERR* (file %s, line %d)\n",
(int)sizeof(int)*Nrows,
__FILE__,
__LINE__ );
exit( EXIT_FAILURE );
}
for( i=0 ; i<Nrows ; i++ ) graph_decomposition[i] = aggr_index[i];
aggr_viz_and_stats = (ML_Aggregate_Viz_Stats *) (Amatrix->to->Grid->Grid);
aggr_viz_and_stats->graph_decomposition = graph_decomposition;
aggr_viz_and_stats->Nlocal = Nrows;
aggr_viz_and_stats->Naggregates = aggr_count;
aggr_viz_and_stats->local_or_global = ML_LOCAL_INDICES;
aggr_viz_and_stats->is_filled = ML_YES;
aggr_viz_and_stats->Amatrix = Amatrix;
}
/* ********************************************************************** */
/* take the decomposition as created by METIS and form the aggregates */
/* ********************************************************************** */
total_nz = ML_Comm_GsumInt( comm, total_nz);
i = ML_Comm_GsumInt( comm, Nrows);
if ( mypid == 0 && 7 < ML_Get_PrintLevel())
printf("%s Total (block) nnz = %d ( = %5.2f/(block)row)\n",
str,
total_nz,1.0*total_nz/i);
if ( ml_ag->operator_complexity == 0.0 ) {
ml_ag->fine_complexity = total_nz;
ml_ag->operator_complexity = total_nz;
}
else ml_ag->operator_complexity += total_nz;
/* fix aggr_index for num_PDE_eqns > 1 */
for (i = Nrows - 1; i >= 0; i-- ) {
for (j = num_PDE_eqns-1; j >= 0; j--) {
aggr_index[i*num_PDE_eqns+j] = aggr_index[i];
}
}
if ( mypid == 0 && 8 < ML_Get_PrintLevel())
{
printf("Calling ML_Operator_UnAmalgamateAndDropWeak\n");
fflush(stdout);
}
ML_Operator_UnAmalgamateAndDropWeak(Amatrix, num_PDE_eqns, epsilon);
Nrows *= num_PDE_eqns;
exp_Nrows *= num_PDE_eqns;
/* count the size of each aggregate */
aggr_cnt_array = (int *) ML_allocate(sizeof(int)*(aggr_count+1));
for (i = 0; i < aggr_count ; i++) aggr_cnt_array[i] = 0;
for (i = 0; i < exp_Nrows; i++) {
if (aggr_index[i] >= 0) {
if( aggr_index[i] >= aggr_count ) {
fprintf( stderr,
"*ML*WRN* on process %d, something weird happened...\n"
"*ML*WRN* node %d belong to aggregate %d (#aggr = %d)\n"
"*ML*WRN* (file %s, line %d)\n",
comm->ML_mypid,
i,
aggr_index[i],
aggr_count,
__FILE__,
__LINE__ );
} else {
aggr_cnt_array[aggr_index[i]]++;
}
}
}
/* ============================================================= */
/* Form tentative prolongator */
/* ============================================================= */
Ncoarse = aggr_count;
/* ============================================================= */
/* check and copy aggr_index */
/* ------------------------------------------------------------- */
level = ml_ag->cur_level;
nbytes = (Nrows+1) * sizeof( int );
ML_memory_alloc((void**) &(ml_ag->aggr_info[level]), nbytes, "AGl");
count = aggr_count;
for ( i = 0; i < Nrows; i+=num_PDE_eqns )
{
if ( aggr_index[i] >= 0 )
{
for ( j = 0; j < num_PDE_eqns; j++ )
ml_ag->aggr_info[level][i+j] = aggr_index[i];
if (aggr_index[i] >= count) count = aggr_index[i] + 1;
}
/*else
*{
* printf("%d : CoarsenMIS error : aggr_index[%d] < 0\n",
* mypid,i);
* exit(1);
*}*/
}
ml_ag->aggr_count[level] = count; /* for relaxing boundary points */
/* ============================================================= */
/* set up the new operator */
/* ------------------------------------------------------------- */
new_Nrows = Nrows;
exp_Ncoarse = Nrows;
for ( i = 0; i < new_Nrows; i++ )
{
if ( aggr_index[i] >= exp_Ncoarse )
{
printf("*ML*WRN* index out of bound %d = %d(%d)\n",
i, aggr_index[i],
exp_Ncoarse);
}
}
nbytes = ( new_Nrows+1 ) * sizeof(int);
ML_memory_alloc((void**)&(new_ia), nbytes, "AIA");
nbytes = ( new_Nrows+1) * nullspace_dim * sizeof(int);
ML_memory_alloc((void**)&(new_ja), nbytes, "AJA");
nbytes = ( new_Nrows+1) * nullspace_dim * sizeof(double);
ML_memory_alloc((void**)&(new_val), nbytes, "AVA");
for ( i = 0; i < new_Nrows*nullspace_dim; i++ ) new_val[i] = 0.0;
/* ------------------------------------------------------------- */
/* set up the space for storing the new null space */
/* ------------------------------------------------------------- */
nbytes = (Ncoarse+1) * nullspace_dim * nullspace_dim * sizeof(double);
ML_memory_alloc((void**)&(new_null),nbytes,"AGr");
if( new_null == NULL ) {
fprintf( stderr,
"*ML*ERR* on process %d, not enough memory for %d bytes\n"
"*ML*ERR* (file %s, line %d)\n",
mypid,
nbytes,
__FILE__,
__LINE__ );
exit( EXIT_FAILURE );
}
for (i = 0; i < Ncoarse*nullspace_dim*nullspace_dim; i++)
new_null[i] = 0.0;
/* ------------------------------------------------------------- */
/* initialize the row pointer for the CSR prolongation operator */
/* (each row will have at most nullspace_dim nonzero entries) */
/* ------------------------------------------------------------- */
for (i = 0; i <= Nrows; i++) new_ia[i] = i * nullspace_dim;
/* trying this when a Dirichlet row is taken out */
j = 0;
new_ia[0] = 0;
for (i = 0; i < Nrows; i++) {
if (aggr_index[i] != -1) j += nullspace_dim;
new_ia[i+1] = j;
}
/* ------------------------------------------------------------- */
/* generate an array to store which aggregate has which rows.Then*/
/* loop through the rows of A checking which aggregate each row */
/* is in, and adding it to the appropriate spot in rows_in_aggs */
/* ------------------------------------------------------------- */
ML_memory_alloc((void**)&rows_in_aggs,aggr_count*sizeof(int*),"MLs");
for (i = 0; i < aggr_count; i++) {
nbytes = aggr_cnt_array[i]+1;
rows_in_aggs[i] = (int *) ML_allocate(nbytes*sizeof(int));
aggr_cnt_array[i] = 0;
if (rows_in_aggs[i] == NULL) {
printf("*ML*ERR* couldn't allocate memory in CoarsenMETIS\n");
exit(1);
}
}
for (i = 0; i < exp_Nrows; i+=num_PDE_eqns) {
if ( aggr_index[i] >= 0 && aggr_index[i] < aggr_count)
{
for (j = 0; j < num_PDE_eqns; j++)
{
index = aggr_cnt_array[aggr_index[i]]++;
rows_in_aggs[aggr_index[i]][index] = i + j;
}
}
}
/* ------------------------------------------------------------- */
/* allocate work arrays for QR factorization */
/* work and lwork are needed for lapack's QR routine. These */
/* settings seemed easiest since I don't quite understand */
/* what they do, but may want to do something better here later */
/* ------------------------------------------------------------- */
max_agg_size = 0;
for (i = 0; i < aggr_count; i++)
{
if (aggr_cnt_array[i] > max_agg_size) max_agg_size = aggr_cnt_array[i];
}
nbytes = max_agg_size * nullspace_dim * sizeof(double);
ML_memory_alloc((void**)&qr_tmp, nbytes, "AGu");
nbytes = nullspace_dim * sizeof(double);
ML_memory_alloc((void**)&tmp_vect, nbytes, "AGv");
lwork = nullspace_dim;
nbytes = nullspace_dim * sizeof(double);
ML_memory_alloc((void**)&work, nbytes, "AGw");
/* ------------------------------------------------------------- */
/* perform block QR decomposition */
/* ------------------------------------------------------------- */
for (i = 0; i < aggr_count; i++)
{
/* ---------------------------------------------------------- */
/* set up the matrix we want to decompose into Q and R: */
/* ---------------------------------------------------------- */
length = aggr_cnt_array[i];
if (nullspace_vect == NULL)
{
for (j = 0; j < (int) length; j++)
{
index = rows_in_aggs[i][j];
for (k = 0; k < nullspace_dim; k++)
{
if ( unamalg_bdry[index/num_PDE_eqns] == 'T')
qr_tmp[k*length+j] = 0.;
else
{
if (index % num_PDE_eqns == k) qr_tmp[k*length+j] = 1.0;
else qr_tmp[k*length+j] = 0.0;
}
}
}
}
else
{
for (k = 0; k < nullspace_dim; k++)
{
for (j = 0; j < (int) length; j++)
{
index = rows_in_aggs[i][j];
if ( unamalg_bdry[index/num_PDE_eqns] == 'T')
qr_tmp[k*length+j] = 0.;
else {
if (index < Nrows) {
qr_tmp[k*length+j] = nullspace_vect[k*Nrows+index];
}
else {
fprintf( stderr,
"*ML*ERR* in QR\n"
"*ML*ERR* (file %s, line %d)\n",
__FILE__,
__LINE__ );
exit( EXIT_FAILURE );
}
}
}
}
}
/* ---------------------------------------------------------- */
/* now calculate QR using an LAPACK routine */
/* ---------------------------------------------------------- */
if (aggr_cnt_array[i] >= nullspace_dim) {
DGEQRF_F77(&(aggr_cnt_array[i]), &nullspace_dim, qr_tmp,
&(aggr_cnt_array[i]), tmp_vect, work, &lwork, &info);
if (info != 0)
pr_error("ErrOr in CoarsenMIS : dgeqrf returned a non-zero %d %d\n",
aggr_cnt_array[i],i);
if (work[0] > lwork)
{
lwork=(int) work[0];
ML_memory_free((void**) &work);
ML_memory_alloc((void**) &work, sizeof(double)*lwork, "AGx");
}
else lwork=(int) work[0];
/* ---------------------------------------------------------- */
/* the upper triangle of qr_tmp is now R, so copy that into */
/* the new nullspace */
/* ---------------------------------------------------------- */
for (j = 0; j < nullspace_dim; j++)
for (k = j; k < nullspace_dim; k++)
new_null[i*nullspace_dim+j+k*Ncoarse*nullspace_dim] =
qr_tmp[j+aggr_cnt_array[i]*k];
/* ---------------------------------------------------------- */
/* to get this block of P, need to run qr_tmp through another */
/* LAPACK function: */
/* ---------------------------------------------------------- */
if ( aggr_cnt_array[i] < nullspace_dim ){
printf("Error in dorgqr on %d row (dims are %d, %d)\n",i,aggr_cnt_array[i],
nullspace_dim);
printf("ERROR : performing QR on a MxN matrix where M<N.\n");
}
DORGQR_F77(&(aggr_cnt_array[i]), &nullspace_dim, &nullspace_dim,
qr_tmp, &(aggr_cnt_array[i]), tmp_vect, work, &lwork, &info);
if (info != 0) {
printf("Error in dorgqr on %d row (dims are %d, %d)\n",i,aggr_cnt_array[i],
nullspace_dim);
pr_error("Error in CoarsenMIS: dorgqr returned a non-zero\n");
}
if (work[0] > lwork)
{
lwork=(int) work[0];
ML_memory_free((void**) &work);
ML_memory_alloc((void**) &work, sizeof(double)*lwork, "AGy");
}
else lwork=(int) work[0];
/* ---------------------------------------------------------- */
/* now copy Q over into the appropriate part of P: */
/* The rows of P get calculated out of order, so I assume the */
/* Q is totally dense and use what I know of how big each Q */
/* will be to determine where in ia, ja, etc each nonzero in */
/* Q belongs. If I did not assume this, I would have to keep */
/* all of P in memory in order to determine where each entry */
/* should go */
/* ---------------------------------------------------------- */
for (j = 0; j < aggr_cnt_array[i]; j++)
{
index = rows_in_aggs[i][j];
if ( index < Nrows )
{
index3 = new_ia[index];
for (k = 0; k < nullspace_dim; k++)
{
new_ja [index3+k] = i * nullspace_dim + k;
new_val[index3+k] = qr_tmp[ k*aggr_cnt_array[i]+j];
}
}
else
{
fprintf( stderr,
"*ML*ERR* in QR: index out of bounds (%d - %d)\n",
index,
Nrows );
}
}
}
else {
/* We have a small aggregate such that the QR factorization can not */
/* be performed. Instead let us copy the null space from the fine */
/* into the coarse grid nullspace and put the identity for the */
/* prolongator???? */
for (j = 0; j < nullspace_dim; j++)
for (k = 0; k < nullspace_dim; k++)
new_null[i*nullspace_dim+j+k*Ncoarse*nullspace_dim] =
qr_tmp[j+aggr_cnt_array[i]*k];
for (j = 0; j < aggr_cnt_array[i]; j++) {
index = rows_in_aggs[i][j];
index3 = new_ia[index];
for (k = 0; k < nullspace_dim; k++) {
new_ja [index3+k] = i * nullspace_dim + k;
if (k == j) new_val[index3+k] = 1.;
else new_val[index3+k] = 0.;
}
}
}
}
ML_Aggregate_Set_NullSpace(ml_ag, num_PDE_eqns, nullspace_dim,
new_null, Ncoarse*nullspace_dim);
ML_memory_free( (void **) &new_null);
/* ------------------------------------------------------------- */
/* set up the csr_data data structure */
/* ------------------------------------------------------------- */
ML_memory_alloc((void**) &csr_data, sizeof(struct ML_CSR_MSRdata),"CSR");
csr_data->rowptr = new_ia;
csr_data->columns = new_ja;
csr_data->values = new_val;
ML_Operator_Set_ApplyFuncData( *Pmatrix, nullspace_dim*Ncoarse, Nrows,
csr_data, Nrows, NULL, 0);
(*Pmatrix)->data_destroy = ML_CSR_MSR_ML_memorydata_Destroy;
(*Pmatrix)->getrow->pre_comm = ML_CommInfoOP_Create();
(*Pmatrix)->max_nz_per_row = 1;
ML_Operator_Set_Getrow((*Pmatrix), Nrows, CSR_getrow);
ML_Operator_Set_ApplyFunc((*Pmatrix), CSR_matvec);
(*Pmatrix)->max_nz_per_row = 1;
/* this must be set so that the hierarchy generation does not abort early
in adaptive SA */
(*Pmatrix)->num_PDEs = nullspace_dim;
/* ------------------------------------------------------------- */
/* clean up */
/* ------------------------------------------------------------- */
ML_free(unamalg_bdry);
ML_memory_free((void**)&aggr_index);
ML_free(aggr_cnt_array);
for (i = 0; i < aggr_count; i++) ML_free(rows_in_aggs[i]);
ML_memory_free((void**)&rows_in_aggs);
ML_memory_free((void**)&qr_tmp);
ML_memory_free((void**)&tmp_vect);
ML_memory_free((void**)&work);
aggr_curr = aggr_head;
while ( aggr_curr != NULL )
{
supernode = aggr_curr;
aggr_curr = aggr_curr->next;
if ( supernode->length > 0 ) ML_free( supernode->list );
ML_free( supernode );
}
return Ncoarse*nullspace_dim;
} /* ML_Aggregate_CoarsenUser */
int main(int argc, char *argv[])
{
int num_PDE_eqns=6, N_levels=4, nsmooth=2;
int leng, level, N_grid_pts, coarsest_level;
/* See Aztec User's Guide for more information on the */
/* variables that follow. */
int proc_config[AZ_PROC_SIZE], options[AZ_OPTIONS_SIZE];
double params[AZ_PARAMS_SIZE], status[AZ_STATUS_SIZE];
/* data structure for matrix corresponding to the fine grid */
double *val = NULL, *xxx, *rhs, solve_time, setup_time, start_time;
AZ_MATRIX *Amat;
AZ_PRECOND *Pmat = NULL;
ML *ml;
FILE *fp;
int i, j, Nrigid, *garbage = NULL;
#ifdef ML_partition
int nblocks;
int *block_list = NULL;
int k;
#endif
struct AZ_SCALING *scaling;
ML_Aggregate *ag;
double *mode, *rigid;
char filename[80];
double alpha;
int allocated = 0;
int old_prec, old_sol;
double old_tol;
/*
double *Amode, beta, biggest;
int big_ind = -1, ii;
*/
ML_Operator *Amatrix;
int *rowi_col = NULL, rowi_N, count2, ccc;
double *rowi_val = NULL;
double max_diag, min_diag, max_sum, sum;
int nBlocks, *blockIndices, Ndof;
#ifdef ML_partition
FILE *fp2;
int count;
if (argc != 2) {
printf("Usage: ml_read_elas num_processors\n");
exit(1);
}
else sscanf(argv[1],"%d",&nblocks);
#endif
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
/* get number of processors and the name of this processor */
AZ_set_proc_config(proc_config, MPI_COMM_WORLD);
#else
AZ_set_proc_config(proc_config, AZ_NOT_MPI);
#endif
/* read in the number of matrix equations */
leng = 0;
if (proc_config[AZ_node] == 0) {
# ifdef binary
fp=fopen(".data","rb");
# else
fp=fopen(".data","r");
# endif
if (fp==NULL) {
printf("couldn't open file .data\n");
exit(1);
}
# ifdef binary
fread(&leng, sizeof(int), 1, fp);
# else
fscanf(fp,"%d",&leng);
# endif
fclose(fp);
}
leng = AZ_gsum_int(leng, proc_config);
N_grid_pts=leng/num_PDE_eqns;
/* initialize the list of global indices. NOTE: the list of global */
/* indices must be in ascending order so that subsequent calls to */
/* AZ_find_index() will function properly. */
if (proc_config[AZ_N_procs] == 1) i = AZ_linear;
else i = AZ_file;
AZ_read_update(&N_update, &update, proc_config, N_grid_pts, num_PDE_eqns,i);
AZ_read_msr_matrix(update, &val, &bindx, N_update, proc_config);
/* This code is to fix things up so that we are sure we have */
/* all block (including the ghost nodes the same size. */
AZ_block_MSR(&bindx, &val, N_update, num_PDE_eqns, update);
AZ_transform_norowreordering(proc_config, &external, bindx, val, update, &update_index,
&extern_index, &data_org, N_update, 0, 0, 0, &cpntr,
AZ_MSR_MATRIX);
Amat = AZ_matrix_create( leng );
AZ_set_MSR(Amat, bindx, val, data_org, 0, NULL, AZ_LOCAL);
Amat->matrix_type = data_org[AZ_matrix_type];
data_org[AZ_N_rows] = data_org[AZ_N_internal] + data_org[AZ_N_border];
#ifdef SCALE_ME
ML_MSR_sym_diagonal_scaling(Amat, proc_config, &scaling_vect);
#endif
start_time = AZ_second();
options[AZ_scaling] = AZ_none;
ML_Create(&ml, N_levels);
ML_Set_PrintLevel(10);
/* set up discretization matrix and matrix vector function */
AZ_ML_Set_Amat(ml, N_levels-1, N_update, N_update, Amat, proc_config);
#ifdef ML_partition
/* this code is meant to partition the matrices so that things can be */
/* run in parallel later. */
/* It is meant to be run on only one processor. */
#ifdef MB_MODIF
fp2 = fopen(".update","w");
#else
fp2 = fopen("partition_file","w");
#endif
ML_Operator_AmalgamateAndDropWeak(&(ml->Amat[N_levels-1]), num_PDE_eqns, 0.0);
ML_Gen_Blocks_Metis(ml, N_levels-1, &nblocks, &block_list);
for (i = 0; i < nblocks; i++) {
count = 0;
for (j = 0; j < ml->Amat[N_levels-1].outvec_leng; j++) {
if (block_list[j] == i) count++;
}
fprintf(fp2," %d\n",count*num_PDE_eqns);
for (j = 0; j < ml->Amat[N_levels-1].outvec_leng; j++) {
if (block_list[j] == i) {
for (k = 0; k < num_PDE_eqns; k++) fprintf(fp2,"%d\n",j*num_PDE_eqns+k);
}
}
}
fclose(fp2);
ML_Operator_UnAmalgamateAndDropWeak(&(ml->Amat[N_levels-1]),num_PDE_eqns,0.0);
#ifdef MB_MODIF
printf(" partition file dumped in .update\n");
#endif
exit(1);
#endif
ML_Aggregate_Create( &ag );
/*
ML_Aggregate_Set_CoarsenScheme_MIS(ag);
*/
#ifdef MB_MODIF
ML_Aggregate_Set_DampingFactor(ag,1.50);
#else
ML_Aggregate_Set_DampingFactor(ag,1.5);
#endif
ML_Aggregate_Set_CoarsenScheme_METIS(ag);
ML_Aggregate_Set_NodesPerAggr( ml, ag, -1, 35);
/*
ML_Aggregate_Set_Phase3AggregateCreationAggressiveness(ag, 10.001);
*/
ML_Aggregate_Set_Threshold(ag, 0.0);
ML_Aggregate_Set_MaxCoarseSize( ag, 300);
/* read in the rigid body modes */
Nrigid = 0;
/* to ensure compatibility with RBM dumping software */
if (proc_config[AZ_node] == 0) {
sprintf(filename,"rigid_body_mode%02d",Nrigid+1);
while( (fp = fopen(filename,"r")) != NULL) {
which_filename = 1;
fclose(fp);
Nrigid++;
sprintf(filename,"rigid_body_mode%02d",Nrigid+1);
}
sprintf(filename,"rigid_body_mode%d",Nrigid+1);
while( (fp = fopen(filename,"r")) != NULL) {
fclose(fp);
Nrigid++;
sprintf(filename,"rigid_body_mode%d",Nrigid+1);
}
}
Nrigid = AZ_gsum_int(Nrigid,proc_config);
if (Nrigid != 0) {
rigid = (double *) ML_allocate( sizeof(double)*Nrigid*(N_update+1) );
if (rigid == NULL) {
printf("Error: Not enough space for rigid body modes\n");
}
}
rhs = (double *) malloc(leng*sizeof(double));
xxx = (double *) malloc(leng*sizeof(double));
for (iii = 0; iii < leng; iii++) xxx[iii] = 0.0;
for (i = 0; i < Nrigid; i++) {
if (which_filename == 1) sprintf(filename,"rigid_body_mode%02d",i+1);
else sprintf(filename,"rigid_body_mode%d",i+1);
AZ_input_msr_matrix(filename,update,&mode,&garbage,N_update,proc_config);
AZ_reorder_vec(mode, data_org, update_index, NULL);
/* here is something to stick a rigid body mode as the initial */
/* The idea is to solve A x = 0 without smoothing with a two */
/* level method. If everything is done properly, we should */
/* converge in 2 iterations. */
/* Note: we must also zero out components of the rigid body */
/* mode that correspond to Dirichlet bcs. */
if (i == -4) {
for (iii = 0; iii < leng; iii++) xxx[iii] = mode[iii];
ccc = 0;
Amatrix = &(ml->Amat[N_levels-1]);
for (iii = 0; iii < Amatrix->outvec_leng; iii++) {
ML_get_matrix_row(Amatrix,1,&iii,&allocated,&rowi_col,&rowi_val,
&rowi_N, 0);
count2 = 0;
for (j = 0; j < rowi_N; j++) if (rowi_val[j] != 0.) count2++;
if (count2 <= 1) { xxx[iii] = 0.; ccc++; }
}
free(rowi_col); free(rowi_val);
allocated = 0; rowi_col = NULL; rowi_val = NULL;
}
/*
* Rescale matrix/rigid body modes and checking
*
AZ_sym_rescale_sl(mode, Amat->data_org, options, proc_config, scaling);
Amat->matvec(mode, rigid, Amat, proc_config);
for (j = 0; j < N_update; j++) printf("this is %d %e\n",j,rigid[j]);
*/
/* Here is some code to check that the rigid body modes are */
/* really rigid body modes. The idea is to multiply by A and */
/* then to zero out things that we "think" are boundaries. */
/* In this hardwired example, things near boundaries */
/* correspond to matrix rows that do not have 81 nonzeros. */
/*
Amode = (double *) malloc(leng*sizeof(double));
Amat->matvec(mode, Amode, Amat, proc_config);
j = 0;
biggest = 0.0;
for (ii = 0; ii < N_update; ii++) {
if ( Amat->bindx[ii+1] - Amat->bindx[ii] != 80) {
Amode[ii] = 0.; j++;
}
else {
if ( fabs(Amode[ii]) > biggest) {
biggest=fabs(Amode[ii]); big_ind = ii;
}
}
}
printf("%d entries zeroed out of %d elements\n",j,N_update);
alpha = AZ_gdot(N_update, Amode, Amode, proc_config);
beta = AZ_gdot(N_update, mode, mode, proc_config);
printf("||A r||^2 =%e, ||r||^2 = %e, ratio = %e\n",
alpha,beta,alpha/beta);
printf("the biggest is %e at row %d\n",biggest,big_ind);
free(Amode);
*/
/* orthogonalize mode with respect to previous modes. */
for (j = 0; j < i; j++) {
alpha = -AZ_gdot(N_update, mode, &(rigid[j*N_update]), proc_config)/
AZ_gdot(N_update, &(rigid[j*N_update]),
&(rigid[j*N_update]), proc_config);
/* daxpy_(&N_update,&alpha,&(rigid[j*N_update]), &one, mode, &one); */
}
#ifndef MB_MODIF
printf(" after mb %e %e %e\n",mode[0],mode[1],mode[2]);
#endif
for (j = 0; j < N_update; j++) rigid[i*N_update+j] = mode[j];
free(mode);
free(garbage); garbage = NULL;
}
if (Nrigid != 0) {
ML_Aggregate_Set_BlockDiagScaling(ag);
ML_Aggregate_Set_NullSpace(ag, num_PDE_eqns, Nrigid, rigid, N_update);
free(rigid);
}
#ifdef SCALE_ME
ML_Aggregate_Scale_NullSpace(ag, scaling_vect, N_update);
#endif
coarsest_level = ML_Gen_MGHierarchy_UsingAggregation(ml, N_levels-1,
ML_DECREASING, ag);
AZ_defaults(options, params);
coarsest_level = N_levels - coarsest_level;
if ( proc_config[AZ_node] == 0 )
printf("Coarse level = %d \n", coarsest_level);
/* set up smoothers */
for (level = N_levels-1; level > coarsest_level; level--) {
/*
ML_Gen_Smoother_BlockGaussSeidel(ml, level,ML_BOTH, 1, 1., num_PDE_eqns);
*/
/* Sparse approximate inverse smoother that acutally does both */
/* pre and post smoothing. */
/*
ML_Gen_Smoother_ParaSails(ml , level, ML_PRESMOOTHER, nsmooth,
parasails_sym, parasails_thresh,
parasails_nlevels, parasails_filter,
parasails_loadbal, parasails_factorized);
*/
/* This is the symmetric Gauss-Seidel smoothing that we usually use. */
/* In parallel, it is not a true Gauss-Seidel in that each processor */
/* does a Gauss-Seidel on its local submatrix independent of the */
/* other processors. */
/* ML_Gen_Smoother_Cheby(ml, level, ML_BOTH, 30., nsmooth); */
Ndof = ml->Amat[level].invec_leng;
ML_Gen_Blocks_Aggregates(ag, level, &nBlocks, &blockIndices);
ML_Gen_Smoother_BlockDiagScaledCheby(ml, level, ML_BOTH, 30.,nsmooth,
nBlocks, blockIndices);
/*
ML_Gen_Smoother_SymGaussSeidel(ml , level, ML_BOTH, nsmooth,1.);
*/
/* This is a true Gauss Seidel in parallel. This seems to work for */
/* elasticity problems. However, I don't believe that this is very */
/* efficient in parallel. */
/*
nblocks = ml->Amat[level].invec_leng/num_PDE_eqns;
blocks = (int *) ML_allocate(sizeof(int)*N_update);
for (i =0; i < ml->Amat[level].invec_leng; i++)
blocks[i] = i/num_PDE_eqns;
ML_Gen_Smoother_VBlockSymGaussSeidelSequential(ml , level, ML_PRESMOOTHER,
nsmooth, 1., nblocks, blocks);
ML_Gen_Smoother_VBlockSymGaussSeidelSequential(ml, level, ML_POSTSMOOTHER,
nsmooth, 1., nblocks, blocks);
free(blocks);
*/
/* Block Jacobi Smoothing */
/*
nblocks = ml->Amat[level].invec_leng/num_PDE_eqns;
blocks = (int *) ML_allocate(sizeof(int)*N_update);
for (i =0; i < ml->Amat[level].invec_leng; i++)
blocks[i] = i/num_PDE_eqns;
ML_Gen_Smoother_VBlockJacobi(ml , level, ML_BOTH, nsmooth,
ML_ONE_STEP_CG, nblocks, blocks);
free(blocks);
*/
/* Jacobi Smoothing */
/*
ML_Gen_Smoother_Jacobi(ml , level, ML_PRESMOOTHER, nsmooth, ML_ONE_STEP_CG);
ML_Gen_Smoother_Jacobi(ml , level, ML_POSTSMOOTHER, nsmooth,ML_ONE_STEP_CG);
*/
/* This does a block Gauss-Seidel (not true GS in parallel) */
/* where each processor has 'nblocks' blocks. */
/*
nblocks = 250;
ML_Gen_Blocks_Metis(ml, level, &nblocks, &blocks);
ML_Gen_Smoother_VBlockJacobi(ml , level, ML_BOTH, nsmooth,ML_ONE_STEP_CG,
nblocks, blocks);
free(blocks);
*/
num_PDE_eqns = 6;
}
/* Choose coarse grid solver: mls, superlu, symGS, or Aztec */
/*
ML_Gen_Smoother_Cheby(ml, coarsest_level, ML_BOTH, 30., nsmooth);
ML_Gen_CoarseSolverSuperLU( ml, coarsest_level);
*/
/*
ML_Gen_Smoother_SymGaussSeidel(ml , coarsest_level, ML_BOTH, nsmooth,1.);
*/
old_prec = options[AZ_precond];
old_sol = options[AZ_solver];
old_tol = params[AZ_tol];
params[AZ_tol] = 1.0e-9;
params[AZ_tol] = 1.0e-5;
options[AZ_precond] = AZ_Jacobi;
options[AZ_solver] = AZ_cg;
options[AZ_poly_ord] = 1;
options[AZ_conv] = AZ_r0;
options[AZ_orth_kvecs] = AZ_TRUE;
j = AZ_gsum_int(ml->Amat[coarsest_level].outvec_leng, proc_config);
options[AZ_keep_kvecs] = j - 6;
options[AZ_max_iter] = options[AZ_keep_kvecs];
ML_Gen_SmootherAztec(ml, coarsest_level, options, params,
proc_config, status, options[AZ_keep_kvecs], ML_PRESMOOTHER, NULL);
options[AZ_conv] = AZ_noscaled;
options[AZ_keep_kvecs] = 0;
options[AZ_orth_kvecs] = 0;
options[AZ_precond] = old_prec;
options[AZ_solver] = old_sol;
params[AZ_tol] = old_tol;
/* */
#ifdef RST_MODIF
ML_Gen_Solver(ml, ML_MGV, N_levels-1, coarsest_level);
#else
#ifdef MB_MODIF
ML_Gen_Solver(ml, ML_SAAMG, N_levels-1, coarsest_level);
#else
ML_Gen_Solver(ml, ML_MGFULLV, N_levels-1, coarsest_level);
#endif
#endif
options[AZ_solver] = AZ_GMRESR;
options[AZ_solver] = AZ_cg;
options[AZ_scaling] = AZ_none;
options[AZ_precond] = AZ_user_precond;
options[AZ_conv] = AZ_r0;
options[AZ_conv] = AZ_noscaled;
options[AZ_output] = 1;
options[AZ_max_iter] = 500;
options[AZ_poly_ord] = 5;
options[AZ_kspace] = 40;
params[AZ_tol] = 4.8e-6;
AZ_set_ML_preconditioner(&Pmat, Amat, ml, options);
setup_time = AZ_second() - start_time;
/* Set rhs */
fp = fopen("AZ_capture_rhs.dat","r");
if (fp == NULL) {
AZ_random_vector(rhs, data_org, proc_config);
if (proc_config[AZ_node] == 0) printf("taking random vector for rhs\n");
for (i = 0; i < -N_update; i++) {
rhs[i] = (double) update[i]; rhs[i] = 7.;
}
}
else {
if (proc_config[AZ_node]== 0) printf("reading rhs guess from file\n");
AZ_input_msr_matrix("AZ_capture_rhs.dat", update, &rhs, &garbage,
N_update, proc_config);
free(garbage);
}
AZ_reorder_vec(rhs, data_org, update_index, NULL);
printf("changing rhs by multiplying with A\n");
Amat->matvec(rhs, xxx, Amat, proc_config);
for (i = 0; i < N_update; i++) rhs[i] = xxx[i];
fp = fopen("AZ_capture_init_guess.dat","r");
if (fp != NULL) {
fclose(fp);
if (proc_config[AZ_node]== 0) printf("reading initial guess from file\n");
AZ_input_msr_matrix("AZ_capture_init_guess.dat", update, &xxx, &garbage,
N_update, proc_config);
free(garbage);
xxx = (double *) realloc(xxx, sizeof(double)*(
Amat->data_org[AZ_N_internal]+
Amat->data_org[AZ_N_border] +
Amat->data_org[AZ_N_external]));
}
AZ_reorder_vec(xxx, data_org, update_index, NULL);
/* if Dirichlet BC ... put the answer in */
/*
for (i = 0; i < data_org[AZ_N_internal]+data_org[AZ_N_border]; i++) {
if ( (val[i] > .99999999) && (val[i] < 1.0000001))
xxx[i] = rhs[i];
}
*/
fp = fopen("AZ_no_multilevel.dat","r");
scaling = AZ_scaling_create();
start_time = AZ_second();
if (fp != NULL) {
fclose(fp);
options[AZ_precond] = AZ_none;
options[AZ_scaling] = AZ_sym_diag;
options[AZ_ignore_scaling] = AZ_TRUE;
options[AZ_keep_info] = 1;
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
/*
options[AZ_pre_calc] = AZ_reuse;
options[AZ_conv] = AZ_expected_values;
if (proc_config[AZ_node] == 0)
printf("\n-------- Second solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
if (proc_config[AZ_node] == 0)
printf("\n-------- Third solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
*/
}
else {
options[AZ_keep_info] = 1;
options[AZ_conv] = AZ_noscaled;
options[AZ_conv] = AZ_r0;
params[AZ_tol] = 1.0e-7;
/* ML_Iterate(ml, xxx, rhs); */
alpha = sqrt(AZ_gdot(N_update, xxx, xxx, proc_config));
printf("init guess = %e\n",alpha);
alpha = sqrt(AZ_gdot(N_update, rhs, rhs, proc_config));
printf("rhs = %e\n",alpha);
#ifdef SCALE_ME
ML_MSR_scalerhs(rhs, scaling_vect, data_org[AZ_N_internal] +
data_org[AZ_N_border]);
ML_MSR_scalesol(xxx, scaling_vect, data_org[AZ_N_internal] +
data_org[AZ_N_border]);
#endif
max_diag = 0.;
min_diag = 1.e30;
max_sum = 0.;
for (i = 0; i < N_update; i++) {
if (Amat->val[i] < 0.) printf("woops negative diagonal A(%d,%d) = %e\n",
i,i,Amat->val[i]);
if (Amat->val[i] > max_diag) max_diag = Amat->val[i];
if (Amat->val[i] < min_diag) min_diag = Amat->val[i];
sum = fabs(Amat->val[i]);
for (j = Amat->bindx[i]; j < Amat->bindx[i+1]; j++) {
sum += fabs(Amat->val[j]);
}
if (sum > max_sum) max_sum = sum;
}
printf("Largest diagonal = %e, min diag = %e large abs row sum = %e\n",
max_diag, min_diag, max_sum);
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
options[AZ_pre_calc] = AZ_reuse;
options[AZ_conv] = AZ_expected_values;
/*
if (proc_config[AZ_node] == 0)
printf("\n-------- Second solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
if (proc_config[AZ_node] == 0)
printf("\n-------- Third solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
*/
}
solve_time = AZ_second() - start_time;
if (proc_config[AZ_node] == 0)
printf("Solve time = %e, MG Setup time = %e\n", solve_time, setup_time);
if (proc_config[AZ_node] == 0)
printf("Printing out a few entries of the solution ...\n");
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 7) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 23) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 47) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 101) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 171) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
ML_Aggregate_Destroy(&ag);
ML_Destroy(&ml);
AZ_free((void *) Amat->data_org);
AZ_free((void *) Amat->val);
AZ_free((void *) Amat->bindx);
AZ_free((void *) update);
AZ_free((void *) external);
AZ_free((void *) extern_index);
AZ_free((void *) update_index);
AZ_scaling_destroy(&scaling);
if (Amat != NULL) AZ_matrix_destroy(&Amat);
if (Pmat != NULL) AZ_precond_destroy(&Pmat);
free(xxx);
free(rhs);
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return 0;
}
// ================================================ ====== ==== ==== == =
// the tentative null space is in input because the user
// has to remember to allocate and fill it, and then to delete
// it after calling this method.
int ML_Epetra::MultiLevelPreconditioner::
ComputeAdaptivePreconditioner(int TentativeNullSpaceSize,
double* TentativeNullSpace)
{
if ((TentativeNullSpaceSize == 0) || (TentativeNullSpace == 0))
ML_CHK_ERR(-1);
// ================================== //
// get parameters from the input list //
// ================================== //
// maximum number of relaxation sweeps
int MaxSweeps = List_.get("adaptive: max sweeps", 10);
// number of std::vector to be added to the tentative null space
int NumAdaptiveVectors = List_.get("adaptive: num vectors", 1);
if (verbose_) {
std::cout << PrintMsg_ << "*** Adaptive Smoother Aggregation setup ***" << std::endl;
std::cout << PrintMsg_ << " Maximum relaxation sweeps = " << MaxSweeps << std::endl;
std::cout << PrintMsg_ << " Additional vectors to compute = " << NumAdaptiveVectors << std::endl;
}
// ==================================================== //
// compute the preconditioner, set null space from user //
// (who will have to delete std::vector TentativeNullSpace) //
// ==================================================== //
double* NewNullSpace = 0;
double* OldNullSpace = TentativeNullSpace;
int OldNullSpaceSize = TentativeNullSpaceSize;
// need some work otherwise matvec() with Epetra_Vbr fails.
// Also, don't differentiate between range and domain here
// as ML will not work if range != domain
const Epetra_VbrMatrix* VbrA = NULL;
VbrA = dynamic_cast<const Epetra_VbrMatrix*>(RowMatrix_);
Epetra_Vector* LHS = 0;
Epetra_Vector* RHS = 0;
if (VbrA != 0) {
LHS = new Epetra_Vector(VbrA->DomainMap());
RHS = new Epetra_Vector(VbrA->DomainMap());
} else {
LHS = new Epetra_Vector(RowMatrix_->OperatorDomainMap());
RHS = new Epetra_Vector(RowMatrix_->OperatorDomainMap());
}
// destroy what we may already have
if (IsComputePreconditionerOK_ == true) {
DestroyPreconditioner();
}
// build the preconditioner for the first time
List_.set("null space: type", "pre-computed");
List_.set("null space: dimension", OldNullSpaceSize);
List_.set("null space: vectors", OldNullSpace);
ComputePreconditioner();
// ====================== //
// add one std::vector at time //
// ====================== //
for (int istep = 0 ; istep < NumAdaptiveVectors ; ++istep) {
if (verbose_) {
std::cout << PrintMsg_ << "\tAdaptation step " << istep << std::endl;
std::cout << PrintMsg_ << "\t---------------" << std::endl;
}
// ==================== //
// look for "bad" modes //
// ==================== //
// note: should an error occur, ML_CHK_ERR will return,
// and LHS and RHS will *not* be delete'd (--> memory leak).
// Anyway, this means that something wrong happened in the code
// and should be fixed by the user.
LHS->Random();
double Norm2;
for (int i = 0 ; i < MaxSweeps ; ++i) {
// RHS = (I - ML^{-1} A) LHS
ML_CHK_ERR(RowMatrix_->Multiply(false,*LHS,*RHS));
// FIXME: can do something slightly better here
ML_CHK_ERR(ApplyInverse(*RHS,*RHS));
ML_CHK_ERR(LHS->Update(-1.0,*RHS,1.0));
LHS->Norm2(&Norm2);
if (verbose_) {
std::cout << PrintMsg_ << "\titer " << i << ", ||x||_2 = ";
std::cout << Norm2 << std::endl;
}
}
// scaling vectors
double NormInf;
LHS->NormInf(&NormInf);
LHS->Scale(1.0 / NormInf);
// ========================================================= //
// copy tentative and computed null space into NewNullSpace, //
// which now becomes the standard null space //
// ========================================================= //
int NewNullSpaceSize = OldNullSpaceSize + 1;
NewNullSpace = new double[NumMyRows() * NewNullSpaceSize];
assert (NewNullSpace != 0);
int itmp = OldNullSpaceSize * NumMyRows();
for (int i = 0 ; i < itmp ; ++i) {
NewNullSpace[i] = OldNullSpace[i];
}
for (int j = 0 ; j < NumMyRows() ; ++j) {
NewNullSpace[itmp + j] = (*LHS)[j];
}
// =============== //
// visualize modes //
// =============== //
if (List_.get("adaptive: visualize", false)) {
double* x_coord = List_.get("viz: x-coordinates", (double*)0);
double* y_coord = List_.get("viz: y-coordinates", (double*)0);
double* z_coord = List_.get("viz: z-coordinates", (double*)0);
assert (x_coord != 0);
std::vector<double> plot_me(NumMyRows()/NumPDEEqns_);
ML_Aggregate_Viz_Stats info;
info.Amatrix = &(ml_->Amat[LevelID_[0]]);
info.x = x_coord;
info.y = y_coord;
info.z = z_coord;
info.Nlocal = NumMyRows() / NumPDEEqns_;
info.Naggregates = 1;
ML_Operator_AmalgamateAndDropWeak(&(ml_->Amat[LevelID_[0]]),
NumPDEEqns_, 0.0);
for (int ieqn = 0 ; ieqn < NumPDEEqns_ ; ++ieqn) {
for (int j = 0 ; j < NumMyRows() ; j+=NumPDEEqns_) {
plot_me[j / NumPDEEqns_] = (*LHS)[j + ieqn];
}
char FileName[80];
sprintf(FileName,"nullspace-mode%d-eq%d.xyz", istep, ieqn);
if (verbose_)
std::cout << PrintMsg_ << "writing file " << FileName << "..." << std::endl;
ML_Aggregate_VisualizeXYZ(info,FileName,
ml_->comm,&plot_me[0]);
}
ML_Operator_UnAmalgamateAndDropWeak(&(ml_->Amat[LevelID_[0]]),
NumPDEEqns_, 0.0);
}
// Destroy the old preconditioner
DestroyPreconditioner();
// ==================================================== //
// build the new preconditioner with the new null space //
// ==================================================== //
List_.set("null space: type", "pre-computed");
List_.set("null space: dimension", NewNullSpaceSize);
List_.set("null space: vectors", NewNullSpace);
ML_CHK_ERR(ComputePreconditioner());
if (istep && (istep != NumAdaptiveVectors))
delete OldNullSpace;
OldNullSpace = NewNullSpace;
OldNullSpaceSize = NewNullSpaceSize;
}
// keep trace of this pointer, it will be delete'd later
NullSpaceToFree_ = NewNullSpace;
delete LHS;
delete RHS;
return(0);
}
// ================================================ ====== ==== ==== == =
// Copied from ml_agg_genP.c
static void ML_Init_Aux(ML_Operator* A, Teuchos::ParameterList &List) {
int i, j, n, count, num_PDEs, BlockRow, BlockCol;
double threshold;
int* columns;
double* values;
int allocated, entries = 0;
int N_dimensions;
int DiagID;
double DiagValue;
int** filter;
double dist;
double *LaplacianDiag;
int Nghost;
// Boundary exchange the coords
double *x_coord=0, *y_coord=0, *z_coord=0;
RefMaxwell_SetupCoordinates(A,List,x_coord,y_coord,z_coord);
int dim=(x_coord!=0) + (y_coord!=0) + (z_coord!=0);
/* Sanity Checks */
if(dim == 0 || ((!x_coord && (y_coord || z_coord)) || (x_coord && !y_coord && z_coord))){
std::cerr<<"Error: Coordinates not defined. This is necessary for aux aggregation (found "<<dim<<" coordinates).\n";
exit(-1);
}
num_PDEs = A->num_PDEs;
N_dimensions = dim;
threshold = A->aux_data->threshold;
ML_Operator_AmalgamateAndDropWeak(A, num_PDEs, 0.0);
n = A->invec_leng;
Nghost = ML_CommInfoOP_Compute_TotalRcvLength(A->getrow->pre_comm);
LaplacianDiag = (double *) ML_allocate((A->getrow->Nrows+Nghost+1)*
sizeof(double));
filter = (int**) ML_allocate(sizeof(int*) * n);
allocated = 128;
columns = (int *) ML_allocate(allocated * sizeof(int));
values = (double *) ML_allocate(allocated * sizeof(double));
for (i = 0 ; i < n ; ++i) {
BlockRow = i;
DiagID = -1;
DiagValue = 0.0;
ML_get_matrix_row(A,1,&i,&allocated,&columns,&values, &entries,0);
for (j = 0; j < entries; j++) {
BlockCol = columns[j];
if (BlockRow != BlockCol) {
dist = 0.0;
switch (N_dimensions) {
case 3:
dist += (z_coord[BlockRow] - z_coord[BlockCol]) * (z_coord[BlockRow] - z_coord[BlockCol]);
case 2:
dist += (y_coord[BlockRow] - y_coord[BlockCol]) * (y_coord[BlockRow] - y_coord[BlockCol]);
case 1:
dist += (x_coord[BlockRow] - x_coord[BlockCol]) * (x_coord[BlockRow] - x_coord[BlockCol]);
}
if (dist == 0.0) {
printf("node %d = %e ", i, x_coord[BlockRow]);
if (N_dimensions > 1) printf(" %e ", y_coord[BlockRow]);
if (N_dimensions > 2) printf(" %e ", z_coord[BlockRow]);
printf("\n");
printf("node %d = %e ", j, x_coord[BlockCol]);
if (N_dimensions > 1) printf(" %e ", y_coord[BlockCol]);
if (N_dimensions > 2) printf(" %e ", z_coord[BlockCol]);
printf("\n");
printf("Operator has inlen = %d and outlen = %d\n",
A->invec_leng, A->outvec_leng);
}
dist = 1.0 / dist;
DiagValue += dist;
}
else if (columns[j] == i) {
DiagID = j;
}
}
if (DiagID == -1) {
fprintf(stderr, "ERROR: matrix has no diagonal!\n"
"ERROR: (file %s, line %d)\n",
__FILE__, __LINE__);
exit(EXIT_FAILURE);
}
LaplacianDiag[BlockRow] = DiagValue;
}
if ( A->getrow->pre_comm != NULL )
ML_exchange_bdry(LaplacianDiag,A->getrow->pre_comm,A->getrow->Nrows,
A->comm, ML_OVERWRITE,NULL);
for (i = 0 ; i < n ; ++i) {
BlockRow = i;
ML_get_matrix_row(A,1,&i,&allocated,&columns,&values, &entries,0);
for (j = 0; j < entries; j++) {
BlockCol = columns[j];
if (BlockRow != BlockCol) {
dist = 0.0;
switch (N_dimensions) {
case 3:
dist += (z_coord[BlockRow] - z_coord[BlockCol]) * (z_coord[BlockRow] - z_coord[BlockCol]);
case 2:
dist += (y_coord[BlockRow] - y_coord[BlockCol]) * (y_coord[BlockRow] - y_coord[BlockCol]);
case 1:
dist += (x_coord[BlockRow] - x_coord[BlockCol]) * (x_coord[BlockRow] - x_coord[BlockCol]);
}
dist = 1.0 / dist;
values[j] = dist;
}
}
count = 0;
for (j = 0 ; j < entries ; ++j) {
if ( (i != columns[j]) &&
(values[j]*values[j] <
LaplacianDiag[BlockRow]*LaplacianDiag[columns[j]]*threshold*threshold)){
columns[count++] = columns[j];
}
}
/* insert the rows */
filter[BlockRow] = (int*) ML_allocate(sizeof(int) * (count + 1));
filter[BlockRow][0] = count;
for (j = 0 ; j < count ; ++j)
filter[BlockRow][j + 1] = columns[j];
}
ML_free(columns);
ML_free(values);
ML_free(LaplacianDiag);
ML_Operator_UnAmalgamateAndDropWeak(A, num_PDEs, 0.0);
A->aux_data->aux_func_ptr = A->getrow->func_ptr;
A->getrow->func_ptr = ML_Aux_Getrow;
A->aux_data->filter = filter;
A->aux_data->filter_size = n;
// Cleanup
ML_free(x_coord);
ML_free(y_coord);
ML_free(z_coord);
}
