Actual source code: sbaij.c

  1: #define PETSCMAT_DLL

  3: /*
  4:     Defines the basic matrix operations for the SBAIJ (compressed row)
  5:   matrix storage format.
  6: */
 7:  #include src/mat/impls/baij/seq/baij.h
 8:  #include src/inline/spops.h
 9:  #include src/mat/impls/sbaij/seq/sbaij.h

 11: #define CHUNKSIZE  10

 13: /*
 14:      Checks for missing diagonals
 15: */
 18: PetscErrorCode MatMissingDiagonal_SeqSBAIJ(Mat A)
 19: {
 20:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;
 22:   PetscInt       *diag,*jj = a->j,i;

 25:   MatMarkDiagonal_SeqSBAIJ(A);
 26:   diag = a->diag;
 27:   for (i=0; i<a->mbs; i++) {
 28:     if (jj[diag[i]] != i) SETERRQ1(PETSC_ERR_ARG_CORRUPT,"Matrix is missing diagonal number %D",i);
 29:   }
 30:   return(0);
 31: }

 35: PetscErrorCode MatMarkDiagonal_SeqSBAIJ(Mat A)
 36: {
 37:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;
 39:   PetscInt       i;

 42:   if (!a->diag) {
 43:     PetscMalloc(a->mbs*sizeof(PetscInt),&a->diag);
 44:   }
 45:   for (i=0; i<a->mbs; i++) a->diag[i] = a->i[i];
 46:   return(0);
 47: }

 51: static PetscErrorCode MatGetRowIJ_SeqSBAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 52: {
 53:   Mat_SeqSBAIJ *a = (Mat_SeqSBAIJ*)A->data;
 54:   PetscInt     n = a->mbs,i;

 57:   *nn = n;
 58:   if (!ia) return(0);

 60:   if (oshift == 1) {
 61:     /* temporarily add 1 to i and j indices */
 62:     PetscInt nz = a->i[n];
 63:     for (i=0; i<nz; i++) a->j[i]++;
 64:     for (i=0; i<n+1; i++) a->i[i]++;
 65:     *ia = a->i; *ja = a->j;
 66:   } else {
 67:     *ia = a->i; *ja = a->j;
 68:   }
 69:   return(0);
 70: }

 74: static PetscErrorCode MatRestoreRowIJ_SeqSBAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 75: {
 76:   Mat_SeqSBAIJ *a = (Mat_SeqSBAIJ*)A->data;
 77:   PetscInt     i,n = a->mbs;

 80:   if (!ia) return(0);

 82:   if (oshift == 1) {
 83:     PetscInt nz = a->i[n]-1;
 84:     for (i=0; i<nz; i++) a->j[i]--;
 85:     for (i=0; i<n+1; i++) a->i[i]--;
 86:   }
 87:   return(0);
 88: }

 92: PetscErrorCode MatDestroy_SeqSBAIJ(Mat A)
 93: {
 94:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;

 98: #if defined(PETSC_USE_LOG)
 99:   PetscLogObjectState((PetscObject)A,"Rows=%D, NZ=%D",A->rmap.N,a->nz);
100: #endif
101:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
102:   if (a->row) {ISDestroy(a->row);}
103:   if (a->col){ISDestroy(a->col);}
104:   if (a->icol) {ISDestroy(a->icol);}
105:   PetscFree(a->diag);
106:   PetscFree2(a->imax,a->ilen);
107:   PetscFree(a->solve_work);
108:   PetscFree(a->relax_work);
109:   PetscFree(a->solves_work);
110:   PetscFree(a->mult_work);
111:   PetscFree(a->saved_values);
112:   PetscFree(a->xtoy);

114:   PetscFree(a->inew);
115:   PetscFree(a);

117:   PetscObjectChangeTypeName((PetscObject)A,0);
118:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
119:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
120:   PetscObjectComposeFunction((PetscObject)A,"MatSeqSBAIJSetColumnIndices_C","",PETSC_NULL);
121:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqsbaij_seqaij_C","",PETSC_NULL);
122:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqsbaij_seqbaij_C","",PETSC_NULL);
123:   PetscObjectComposeFunction((PetscObject)A,"MatSeqSBAIJSetPreallocation_C","",PETSC_NULL);
124:   return(0);
125: }

129: PetscErrorCode MatSetOption_SeqSBAIJ(Mat A,MatOption op)
130: {
131:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;

135:   switch (op) {
136:   case MAT_ROW_ORIENTED:
137:     a->roworiented = PETSC_TRUE;
138:     break;
139:   case MAT_COLUMN_ORIENTED:
140:     a->roworiented = PETSC_FALSE;
141:     break;
142:   case MAT_COLUMNS_SORTED:
143:     a->sorted = PETSC_TRUE;
144:     break;
145:   case MAT_COLUMNS_UNSORTED:
146:     a->sorted = PETSC_FALSE;
147:     break;
148:   case MAT_KEEP_ZEROED_ROWS:
149:     a->keepzeroedrows = PETSC_TRUE;
150:     break;
151:   case MAT_NO_NEW_NONZERO_LOCATIONS:
152:     a->nonew = 1;
153:     break;
154:   case MAT_NEW_NONZERO_LOCATION_ERR:
155:     a->nonew = -1;
156:     break;
157:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
158:     a->nonew = -2;
159:     break;
160:   case MAT_YES_NEW_NONZERO_LOCATIONS:
161:     a->nonew = 0;
162:     break;
163:   case MAT_ROWS_SORTED:
164:   case MAT_ROWS_UNSORTED:
165:   case MAT_YES_NEW_DIAGONALS:
166:   case MAT_IGNORE_OFF_PROC_ENTRIES:
167:   case MAT_USE_HASH_TABLE:
168:     PetscInfo1(A,"Option %d ignored\n",op);
169:     break;
170:   case MAT_NO_NEW_DIAGONALS:
171:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
172:   case MAT_NOT_SYMMETRIC:
173:   case MAT_NOT_STRUCTURALLY_SYMMETRIC:
174:   case MAT_HERMITIAN:
175:     SETERRQ(PETSC_ERR_SUP,"Matrix must be symmetric");
176:   case MAT_SYMMETRIC:
177:   case MAT_STRUCTURALLY_SYMMETRIC:
178:   case MAT_NOT_HERMITIAN:
179:   case MAT_SYMMETRY_ETERNAL:
180:   case MAT_NOT_SYMMETRY_ETERNAL:
181:   case MAT_IGNORE_LOWER_TRIANGULAR:
182:     a->ignore_ltriangular = PETSC_TRUE;
183:     break;
184:   case MAT_ERROR_LOWER_TRIANGULAR:
185:     a->ignore_ltriangular = PETSC_FALSE;
186:     break;
187:   case MAT_GETROW_UPPERTRIANGULAR:
188:     a->getrow_utriangular = PETSC_TRUE;
189:     break;
190:   default:
191:     SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
192:   }
193:   return(0);
194: }

198: PetscErrorCode MatGetRow_SeqSBAIJ(Mat A,PetscInt row,PetscInt *ncols,PetscInt **cols,PetscScalar **v)
199: {
200:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;
202:   PetscInt       itmp,i,j,k,M,*ai,*aj,bs,bn,bp,*cols_i,bs2;
203:   MatScalar      *aa,*aa_i;
204:   PetscScalar    *v_i;

207:   if (A && !a->getrow_utriangular) SETERRQ(PETSC_ERR_SUP,"MatGetRow is not supported for SBAIJ matrix format. Getting the upper triangular part of row, run with -mat_getrow_uppertriangular, call MatSetOption(mat,MAT_GETROW_UPPERTRIANGULAR) or MatGetRowUpperTriangular()");
208:   /* Get the upper triangular part of the row */
209:   bs  = A->rmap.bs;
210:   ai  = a->i;
211:   aj  = a->j;
212:   aa  = a->a;
213:   bs2 = a->bs2;
214: 
215:   if (row < 0 || row >= A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE, "Row %D out of range", row);
216: 
217:   bn  = row/bs;   /* Block number */
218:   bp  = row % bs; /* Block position */
219:   M   = ai[bn+1] - ai[bn];
220:   *ncols = bs*M;
221: 
222:   if (v) {
223:     *v = 0;
224:     if (*ncols) {
225:       PetscMalloc((*ncols+row)*sizeof(PetscScalar),v);
226:       for (i=0; i<M; i++) { /* for each block in the block row */
227:         v_i  = *v + i*bs;
228:         aa_i = aa + bs2*(ai[bn] + i);
229:         for (j=bp,k=0; j<bs2; j+=bs,k++) {v_i[k] = aa_i[j];}
230:       }
231:     }
232:   }
233: 
234:   if (cols) {
235:     *cols = 0;
236:     if (*ncols) {
237:       PetscMalloc((*ncols+row)*sizeof(PetscInt),cols);
238:       for (i=0; i<M; i++) { /* for each block in the block row */
239:         cols_i = *cols + i*bs;
240:         itmp  = bs*aj[ai[bn] + i];
241:         for (j=0; j<bs; j++) {cols_i[j] = itmp++;}
242:       }
243:     }
244:   }
245: 
246:   /*search column A(0:row-1,row) (=A(row,0:row-1)). Could be expensive! */
247:   /* this segment is currently removed, so only entries in the upper triangle are obtained */
248: #ifdef column_search
249:   v_i    = *v    + M*bs;
250:   cols_i = *cols + M*bs;
251:   for (i=0; i<bn; i++){ /* for each block row */
252:     M = ai[i+1] - ai[i];
253:     for (j=0; j<M; j++){
254:       itmp = aj[ai[i] + j];    /* block column value */
255:       if (itmp == bn){
256:         aa_i   = aa    + bs2*(ai[i] + j) + bs*bp;
257:         for (k=0; k<bs; k++) {
258:           *cols_i++ = i*bs+k;
259:           *v_i++    = aa_i[k];
260:         }
261:         *ncols += bs;
262:         break;
263:       }
264:     }
265:   }
266: #endif
267:   return(0);
268: }

272: PetscErrorCode MatRestoreRow_SeqSBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
273: {
275: 
277:   if (idx) {PetscFree(*idx);}
278:   if (v)   {PetscFree(*v);}
279:   return(0);
280: }

284: PetscErrorCode MatGetRowUpperTriangular_SeqSBAIJ(Mat A)
285: {
286:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;

289:   a->getrow_utriangular = PETSC_TRUE;
290:   return(0);
291: }
294: PetscErrorCode MatRestoreRowUpperTriangular_SeqSBAIJ(Mat A)
295: {
296:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;

299:   a->getrow_utriangular = PETSC_FALSE;
300:   return(0);
301: }

305: PetscErrorCode MatTranspose_SeqSBAIJ(Mat A,Mat *B)
306: {
309:   MatDuplicate(A,MAT_COPY_VALUES,B);
310:   return(0);
311: }

315: static PetscErrorCode MatView_SeqSBAIJ_ASCII(Mat A,PetscViewer viewer)
316: {
317:   Mat_SeqSBAIJ      *a = (Mat_SeqSBAIJ*)A->data;
318:   PetscErrorCode    ierr;
319:   PetscInt          i,j,bs = A->rmap.bs,k,l,bs2=a->bs2;
320:   const char        *name;
321:   PetscViewerFormat format;
322: 
324:   PetscObjectGetName((PetscObject)A,&name);
325:   PetscViewerGetFormat(viewer,&format);
326:   if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
327:     PetscViewerASCIIPrintf(viewer,"  block size is %D\n",bs);
328:   } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
329:     SETERRQ(PETSC_ERR_SUP,"Matlab format not supported");
330:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
331:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
332:     for (i=0; i<a->mbs; i++) {
333:       for (j=0; j<bs; j++) {
334:         PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
335:         for (k=a->i[i]; k<a->i[i+1]; k++) {
336:           for (l=0; l<bs; l++) {
337: #if defined(PETSC_USE_COMPLEX)
338:             if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
339:               PetscViewerASCIIPrintf(viewer," (%D, %G + %G i) ",bs*a->j[k]+l,
340:                                             PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
341:             } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
342:               PetscViewerASCIIPrintf(viewer," (%D, %G - %G i) ",bs*a->j[k]+l,
343:                                             PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
344:             } else if (PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
345:               PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
346:             }
347: #else
348:             if (a->a[bs2*k + l*bs + j] != 0.0) {
349:               PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
350:             }
351: #endif
352:           }
353:         }
354:         PetscViewerASCIIPrintf(viewer,"\n");
355:       }
356:     }
357:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
358:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
359:      return(0);
360:   } else {
361:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
362:     for (i=0; i<a->mbs; i++) {
363:       for (j=0; j<bs; j++) {
364:         PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
365:         for (k=a->i[i]; k<a->i[i+1]; k++) {
366:           for (l=0; l<bs; l++) {
367: #if defined(PETSC_USE_COMPLEX)
368:             if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0) {
369:               PetscViewerASCIIPrintf(viewer," (%D, %G + %G i) ",bs*a->j[k]+l,
370:                                             PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
371:             } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0) {
372:               PetscViewerASCIIPrintf(viewer," (%D, %G - %G i) ",bs*a->j[k]+l,
373:                                             PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
374:             } else {
375:               PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
376:             }
377: #else
378:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
379: #endif
380:           }
381:         }
382:         PetscViewerASCIIPrintf(viewer,"\n");
383:       }
384:     }
385:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
386:   }
387:   PetscViewerFlush(viewer);
388:   return(0);
389: }

393: static PetscErrorCode MatView_SeqSBAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
394: {
395:   Mat            A = (Mat) Aa;
396:   Mat_SeqSBAIJ   *a=(Mat_SeqSBAIJ*)A->data;
398:   PetscInt       row,i,j,k,l,mbs=a->mbs,color,bs=A->rmap.bs,bs2=a->bs2;
399:   PetscMPIInt    rank;
400:   PetscReal      xl,yl,xr,yr,x_l,x_r,y_l,y_r;
401:   MatScalar      *aa;
402:   MPI_Comm       comm;
403:   PetscViewer    viewer;
404: 
406:   /*
407:     This is nasty. If this is called from an originally parallel matrix
408:     then all processes call this,but only the first has the matrix so the
409:     rest should return immediately.
410:   */
411:   PetscObjectGetComm((PetscObject)draw,&comm);
412:   MPI_Comm_rank(comm,&rank);
413:   if (rank) return(0);
414: 
415:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
416: 
417:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
418:   PetscDrawString(draw, .3*(xl+xr), .3*(yl+yr), PETSC_DRAW_BLACK, "symmetric");
419: 
420:   /* loop over matrix elements drawing boxes */
421:   color = PETSC_DRAW_BLUE;
422:   for (i=0,row=0; i<mbs; i++,row+=bs) {
423:     for (j=a->i[i]; j<a->i[i+1]; j++) {
424:       y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
425:       x_l = a->j[j]*bs; x_r = x_l + 1.0;
426:       aa = a->a + j*bs2;
427:       for (k=0; k<bs; k++) {
428:         for (l=0; l<bs; l++) {
429:           if (PetscRealPart(*aa++) >=  0.) continue;
430:           PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
431:         }
432:       }
433:     }
434:   }
435:   color = PETSC_DRAW_CYAN;
436:   for (i=0,row=0; i<mbs; i++,row+=bs) {
437:     for (j=a->i[i]; j<a->i[i+1]; j++) {
438:       y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
439:       x_l = a->j[j]*bs; x_r = x_l + 1.0;
440:       aa = a->a + j*bs2;
441:       for (k=0; k<bs; k++) {
442:         for (l=0; l<bs; l++) {
443:           if (PetscRealPart(*aa++) != 0.) continue;
444:           PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
445:         }
446:       }
447:     }
448:   }
449: 
450:   color = PETSC_DRAW_RED;
451:   for (i=0,row=0; i<mbs; i++,row+=bs) {
452:     for (j=a->i[i]; j<a->i[i+1]; j++) {
453:       y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
454:       x_l = a->j[j]*bs; x_r = x_l + 1.0;
455:       aa = a->a + j*bs2;
456:       for (k=0; k<bs; k++) {
457:         for (l=0; l<bs; l++) {
458:           if (PetscRealPart(*aa++) <= 0.) continue;
459:           PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
460:         }
461:       }
462:     }
463:   }
464:   return(0);
465: }

469: static PetscErrorCode MatView_SeqSBAIJ_Draw(Mat A,PetscViewer viewer)
470: {
472:   PetscReal      xl,yl,xr,yr,w,h;
473:   PetscDraw      draw;
474:   PetscTruth     isnull;
475: 
477: 
478:   PetscViewerDrawGetDraw(viewer,0,&draw);
479:   PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
480: 
481:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
482:   xr  = A->rmap.N; yr = A->rmap.N; h = yr/10.0; w = xr/10.0;
483:   xr += w;    yr += h;  xl = -w;     yl = -h;
484:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
485:   PetscDrawZoom(draw,MatView_SeqSBAIJ_Draw_Zoom,A);
486:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
487:   return(0);
488: }

492: PetscErrorCode MatView_SeqSBAIJ(Mat A,PetscViewer viewer)
493: {
495:   PetscTruth     iascii,isdraw;
496: 
498:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
499:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
500:   if (iascii){
501:     MatView_SeqSBAIJ_ASCII(A,viewer);
502:   } else if (isdraw) {
503:     MatView_SeqSBAIJ_Draw(A,viewer);
504:   } else {
505:     Mat B;
506:     MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&B);
507:     MatView(B,viewer);
508:     MatDestroy(B);
509:   }
510:   return(0);
511: }


516: PetscErrorCode MatGetValues_SeqSBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
517: {
518:   Mat_SeqSBAIJ *a = (Mat_SeqSBAIJ*)A->data;
519:   PetscInt     *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
520:   PetscInt     *ai = a->i,*ailen = a->ilen;
521:   PetscInt     brow,bcol,ridx,cidx,bs=A->rmap.bs,bs2=a->bs2;
522:   MatScalar    *ap,*aa = a->a,zero = 0.0;
523: 
525:   for (k=0; k<m; k++) { /* loop over rows */
526:     row  = im[k]; brow = row/bs;
527:     if (row < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row);
528:     if (row >= A->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap.N-1);
529:     rp   = aj + ai[brow] ; ap = aa + bs2*ai[brow] ;
530:     nrow = ailen[brow];
531:     for (l=0; l<n; l++) { /* loop over columns */
532:       if (in[l] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]);
533:       if (in[l] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap.n-1);
534:       col  = in[l] ;
535:       bcol = col/bs;
536:       cidx = col%bs;
537:       ridx = row%bs;
538:       high = nrow;
539:       low  = 0; /* assume unsorted */
540:       while (high-low > 5) {
541:         t = (low+high)/2;
542:         if (rp[t] > bcol) high = t;
543:         else             low  = t;
544:       }
545:       for (i=low; i<high; i++) {
546:         if (rp[i] > bcol) break;
547:         if (rp[i] == bcol) {
548:           *v++ = ap[bs2*i+bs*cidx+ridx];
549:           goto finished;
550:         }
551:       }
552:       *v++ = zero;
553:        finished:;
554:     }
555:   }
556:   return(0);
557: }


562: PetscErrorCode MatSetValuesBlocked_SeqSBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
563: {
564:   Mat_SeqSBAIJ    *a = (Mat_SeqSBAIJ*)A->data;
565:   PetscErrorCode  ierr;
566:   PetscInt        *rp,k,low,high,t,ii,jj,row,nrow,i,col,l,rmax,N,lastcol = -1;
567:   PetscInt        *imax=a->imax,*ai=a->i,*ailen=a->ilen;
568:   PetscInt        *aj=a->j,nonew=a->nonew,bs2=a->bs2,bs=A->rmap.bs,stepval;
569:   PetscTruth      roworiented=a->roworiented;
570:   const MatScalar *value = v;
571:   MatScalar       *ap,*aa = a->a,*bap;
572: 
574:   if (roworiented) {
575:     stepval = (n-1)*bs;
576:   } else {
577:     stepval = (m-1)*bs;
578:   }
579:   for (k=0; k<m; k++) { /* loop over added rows */
580:     row  = im[k];
581:     if (row < 0) continue;
582: #if defined(PETSC_USE_DEBUG)  
583:     if (row >= a->mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,a->mbs-1);
584: #endif
585:     rp   = aj + ai[row];
586:     ap   = aa + bs2*ai[row];
587:     rmax = imax[row];
588:     nrow = ailen[row];
589:     low  = 0;
590:     high = nrow;
591:     for (l=0; l<n; l++) { /* loop over added columns */
592:       if (in[l] < 0) continue;
593:       col = in[l];
594: #if defined(PETSC_USE_DEBUG)  
595:       if (col >= a->nbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",col,a->nbs-1);
596: #endif
597:       if (col < row) continue; /* ignore lower triangular block */
598:       if (roworiented) {
599:         value = v + k*(stepval+bs)*bs + l*bs;
600:       } else {
601:         value = v + l*(stepval+bs)*bs + k*bs;
602:       }
603:       if (col <= lastcol) low = 0; else high = nrow;
604:       lastcol = col;
605:       while (high-low > 7) {
606:         t = (low+high)/2;
607:         if (rp[t] > col) high = t;
608:         else             low  = t;
609:       }
610:       for (i=low; i<high; i++) {
611:         if (rp[i] > col) break;
612:         if (rp[i] == col) {
613:           bap  = ap +  bs2*i;
614:           if (roworiented) {
615:             if (is == ADD_VALUES) {
616:               for (ii=0; ii<bs; ii++,value+=stepval) {
617:                 for (jj=ii; jj<bs2; jj+=bs) {
618:                   bap[jj] += *value++;
619:                 }
620:               }
621:             } else {
622:               for (ii=0; ii<bs; ii++,value+=stepval) {
623:                 for (jj=ii; jj<bs2; jj+=bs) {
624:                   bap[jj] = *value++;
625:                 }
626:                }
627:             }
628:           } else {
629:             if (is == ADD_VALUES) {
630:               for (ii=0; ii<bs; ii++,value+=stepval) {
631:                 for (jj=0; jj<bs; jj++) {
632:                   *bap++ += *value++;
633:                 }
634:               }
635:             } else {
636:               for (ii=0; ii<bs; ii++,value+=stepval) {
637:                 for (jj=0; jj<bs; jj++) {
638:                   *bap++  = *value++;
639:                 }
640:               }
641:             }
642:           }
643:           goto noinsert2;
644:         }
645:       }
646:       if (nonew == 1) goto noinsert2;
647:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
648:       MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew);
649:       N = nrow++ - 1; high++;
650:       /* shift up all the later entries in this row */
651:       for (ii=N; ii>=i; ii--) {
652:         rp[ii+1] = rp[ii];
653:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
654:       }
655:       if (N >= i) {
656:         PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
657:       }
658:       rp[i] = col;
659:       bap   = ap +  bs2*i;
660:       if (roworiented) {
661:         for (ii=0; ii<bs; ii++,value+=stepval) {
662:           for (jj=ii; jj<bs2; jj+=bs) {
663:             bap[jj] = *value++;
664:           }
665:         }
666:       } else {
667:         for (ii=0; ii<bs; ii++,value+=stepval) {
668:           for (jj=0; jj<bs; jj++) {
669:             *bap++  = *value++;
670:           }
671:         }
672:        }
673:     noinsert2:;
674:       low = i;
675:     }
676:     ailen[row] = nrow;
677:   }
678:    return(0);
679: }

683: PetscErrorCode MatAssemblyEnd_SeqSBAIJ(Mat A,MatAssemblyType mode)
684: {
685:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;
687:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
688:   PetscInt       m = A->rmap.N,*ip,N,*ailen = a->ilen;
689:   PetscInt       mbs = a->mbs,bs2 = a->bs2,rmax = 0;
690:   MatScalar      *aa = a->a,*ap;
691: 
693:   if (mode == MAT_FLUSH_ASSEMBLY) return(0);
694: 
695:   if (m) rmax = ailen[0];
696:   for (i=1; i<mbs; i++) {
697:     /* move each row back by the amount of empty slots (fshift) before it*/
698:     fshift += imax[i-1] - ailen[i-1];
699:      rmax   = PetscMax(rmax,ailen[i]);
700:      if (fshift) {
701:        ip = aj + ai[i]; ap = aa + bs2*ai[i];
702:        N = ailen[i];
703:        for (j=0; j<N; j++) {
704:          ip[j-fshift] = ip[j];
705:          PetscMemcpy(ap+(j-fshift)*bs2,ap+j*bs2,bs2*sizeof(MatScalar));
706:        }
707:      }
708:      ai[i] = ai[i-1] + ailen[i-1];
709:   }
710:   if (mbs) {
711:     fshift += imax[mbs-1] - ailen[mbs-1];
712:      ai[mbs] = ai[mbs-1] + ailen[mbs-1];
713:   }
714:   /* reset ilen and imax for each row */
715:   for (i=0; i<mbs; i++) {
716:     ailen[i] = imax[i] = ai[i+1] - ai[i];
717:   }
718:   a->nz = ai[mbs];
719: 
720:   /* diagonals may have moved, reset it */
721:   if (a->diag) {
722:     PetscMemcpy(a->diag,ai,(mbs+1)*sizeof(PetscInt));
723:   }
724:   PetscInfo5(A,"Matrix size: %D X %D, block size %D; storage space: %D unneeded, %D used\n",m,A->rmap.N,A->rmap.bs,fshift*bs2,a->nz*bs2);
725:   PetscInfo1(A,"Number of mallocs during MatSetValues is %D\n",a->reallocs);
726:   PetscInfo1(A,"Most nonzeros blocks in any row is %D\n",rmax);
727:   a->reallocs          = 0;
728:   A->info.nz_unneeded  = (PetscReal)fshift*bs2;
729:   return(0);
730: }

732: /* 
733:    This function returns an array of flags which indicate the locations of contiguous
734:    blocks that should be zeroed. for eg: if bs = 3  and is = [0,1,2,3,5,6,7,8,9]
735:    then the resulting sizes = [3,1,1,3,1] correspondig to sets [(0,1,2),(3),(5),(6,7,8),(9)]
736:    Assume: sizes should be long enough to hold all the values.
737: */
740: PetscErrorCode MatZeroRows_SeqSBAIJ_Check_Blocks(PetscInt idx[],PetscInt n,PetscInt bs,PetscInt sizes[], PetscInt *bs_max)
741: {
742:   PetscInt   i,j,k,row;
743:   PetscTruth flg;
744: 
746:    for (i=0,j=0; i<n; j++) {
747:      row = idx[i];
748:      if (row%bs!=0) { /* Not the begining of a block */
749:        sizes[j] = 1;
750:        i++;
751:      } else if (i+bs > n) { /* Beginning of a block, but complete block doesn't exist (at idx end) */
752:        sizes[j] = 1;         /* Also makes sure atleast 'bs' values exist for next else */
753:        i++;
754:      } else { /* Begining of the block, so check if the complete block exists */
755:        flg = PETSC_TRUE;
756:        for (k=1; k<bs; k++) {
757:          if (row+k != idx[i+k]) { /* break in the block */
758:            flg = PETSC_FALSE;
759:            break;
760:          }
761:        }
762:        if (flg) { /* No break in the bs */
763:          sizes[j] = bs;
764:          i+= bs;
765:        } else {
766:          sizes[j] = 1;
767:          i++;
768:        }
769:      }
770:    }
771:    *bs_max = j;
772:    return(0);
773: }


776: /* Only add/insert a(i,j) with i<=j (blocks). 
777:    Any a(i,j) with i>j input by user is ingored. 
778: */

782: PetscErrorCode MatSetValues_SeqSBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
783: {
784:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;
786:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,lastcol = -1;
787:   PetscInt       *imax=a->imax,*ai=a->i,*ailen=a->ilen,roworiented=a->roworiented;
788:   PetscInt       *aj=a->j,nonew=a->nonew,bs=A->rmap.bs,brow,bcol;
789:   PetscInt       ridx,cidx,bs2=a->bs2;
790:   MatScalar      *ap,value,*aa=a->a,*bap;
791: 
793:   for (k=0; k<m; k++) { /* loop over added rows */
794:     row  = im[k];       /* row number */
795:     brow = row/bs;      /* block row number */
796:     if (row < 0) continue;
797: #if defined(PETSC_USE_DEBUG)  
798:     if (row >= A->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap.N-1);
799: #endif
800:     rp   = aj + ai[brow]; /*ptr to beginning of column value of the row block*/
801:     ap   = aa + bs2*ai[brow]; /*ptr to beginning of element value of the row block*/
802:     rmax = imax[brow];  /* maximum space allocated for this row */
803:     nrow = ailen[brow]; /* actual length of this row */
804:     low  = 0;
805: 
806:     for (l=0; l<n; l++) { /* loop over added columns */
807:       if (in[l] < 0) continue;
808: #if defined(PETSC_USE_DEBUG)  
809:       if (in[l] >= A->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->rmap.N-1);
810: #endif
811:       col = in[l];
812:       bcol = col/bs;              /* block col number */
813: 
814:       if (brow > bcol) {
815:         if (a->ignore_ltriangular){
816:           continue; /* ignore lower triangular values */
817:         } else {
818:           SETERRQ(PETSC_ERR_USER,"Lower triangular value cannot be set for sbaij format. Ignoring these values, run with -mat_ignore_lower_triangular or call MatSetOption(mat,MAT_IGNORE_LOWER_TRIANGULAR)");
819:         }
820:       }
821: 
822:       ridx = row % bs; cidx = col % bs; /*row and col index inside the block */
823:       if ((brow==bcol && ridx<=cidx) || (brow<bcol)){
824:         /* element value a(k,l) */
825:         if (roworiented) {
826:           value = v[l + k*n];
827:         } else {
828:           value = v[k + l*m];
829:         }
830: 
831:         /* move pointer bap to a(k,l) quickly and add/insert value */
832:         if (col <= lastcol) low = 0; high = nrow;
833:         lastcol = col;
834:         while (high-low > 7) {
835:           t = (low+high)/2;
836:           if (rp[t] > bcol) high = t;
837:           else              low  = t;
838:         }
839:         for (i=low; i<high; i++) {
840:           if (rp[i] > bcol) break;
841:           if (rp[i] == bcol) {
842:             bap  = ap +  bs2*i + bs*cidx + ridx;
843:             if (is == ADD_VALUES) *bap += value;
844:             else                  *bap  = value;
845:             /* for diag block, add/insert its symmetric element a(cidx,ridx) */
846:             if (brow == bcol && ridx < cidx){
847:               bap  = ap +  bs2*i + bs*ridx + cidx;
848:               if (is == ADD_VALUES) *bap += value;
849:               else                  *bap  = value;
850:             }
851:             goto noinsert1;
852:           }
853:         }
854: 
855:         if (nonew == 1) goto noinsert1;
856:         if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
857:         MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,imax,nonew);
858: 
859:         N = nrow++ - 1; high++;
860:         /* shift up all the later entries in this row */
861:         for (ii=N; ii>=i; ii--) {
862:           rp[ii+1] = rp[ii];
863:           PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
864:         }
865:         if (N>=i) {
866:           PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
867:         }
868:         rp[i]                      = bcol;
869:         ap[bs2*i + bs*cidx + ridx] = value;
870:       noinsert1:;
871:         low = i;
872:       }
873:     }   /* end of loop over added columns */
874:     ailen[brow] = nrow;
875:   }   /* end of loop over added rows */
876:   return(0);
877: }

879: EXTERN PetscErrorCode MatCholeskyFactorSymbolic_SeqSBAIJ(Mat,IS,MatFactorInfo*,Mat*);
880: EXTERN PetscErrorCode MatCholeskyFactor_SeqSBAIJ(Mat,IS,MatFactorInfo*);
881: EXTERN PetscErrorCode MatIncreaseOverlap_SeqSBAIJ(Mat,PetscInt,IS[],PetscInt);
882: EXTERN PetscErrorCode MatGetSubMatrix_SeqSBAIJ(Mat,IS,IS,PetscInt,MatReuse,Mat*);
883: EXTERN PetscErrorCode MatGetSubMatrices_SeqSBAIJ(Mat,PetscInt,const IS[],const IS[],MatReuse,Mat*[]);
884: EXTERN PetscErrorCode MatScale_SeqSBAIJ(Mat,PetscScalar);
885: EXTERN PetscErrorCode MatNorm_SeqSBAIJ(Mat,NormType,PetscReal *);
886: EXTERN PetscErrorCode MatEqual_SeqSBAIJ(Mat,Mat,PetscTruth*);
887: EXTERN PetscErrorCode MatGetDiagonal_SeqSBAIJ(Mat,Vec);
888: EXTERN PetscErrorCode MatDiagonalScale_SeqSBAIJ(Mat,Vec,Vec);
889: EXTERN PetscErrorCode MatGetInfo_SeqSBAIJ(Mat,MatInfoType,MatInfo *);
890: EXTERN PetscErrorCode MatZeroEntries_SeqSBAIJ(Mat);
891: EXTERN PetscErrorCode MatGetRowMax_SeqSBAIJ(Mat,Vec);

893: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_N(Mat,Vec,Vec);
894: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_1(Mat,Vec,Vec);
895: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_2(Mat,Vec,Vec);
896: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_3(Mat,Vec,Vec);
897: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_4(Mat,Vec,Vec);
898: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_5(Mat,Vec,Vec);
899: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_6(Mat,Vec,Vec);
900: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_7(Mat,Vec,Vec);

902: EXTERN PetscErrorCode MatSolves_SeqSBAIJ_1(Mat,Vecs,Vecs);

904: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_1_NaturalOrdering(Mat,Vec,Vec);
905: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_2_NaturalOrdering(Mat,Vec,Vec);
906: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_3_NaturalOrdering(Mat,Vec,Vec);
907: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_4_NaturalOrdering(Mat,Vec,Vec);
908: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_5_NaturalOrdering(Mat,Vec,Vec);
909: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_6_NaturalOrdering(Mat,Vec,Vec);
910: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_7_NaturalOrdering(Mat,Vec,Vec);
911: EXTERN PetscErrorCode MatSolve_SeqSBAIJ_N_NaturalOrdering(Mat,Vec,Vec);

913: EXTERN PetscErrorCode MatCholeskyFactorNumeric_SeqSBAIJ_N(Mat,MatFactorInfo*,Mat*);
914: EXTERN PetscErrorCode MatCholeskyFactorNumeric_SeqSBAIJ_1(Mat,MatFactorInfo*,Mat*);
915: EXTERN PetscErrorCode MatCholeskyFactorNumeric_SeqSBAIJ_2(Mat,MatFactorInfo*,Mat*);
916: EXTERN PetscErrorCode MatCholeskyFactorNumeric_SeqSBAIJ_3(Mat,MatFactorInfo*,Mat*);
917: EXTERN PetscErrorCode MatCholeskyFactorNumeric_SeqSBAIJ_4(Mat,MatFactorInfo*,Mat*);
918: EXTERN PetscErrorCode MatCholeskyFactorNumeric_SeqSBAIJ_5(Mat,MatFactorInfo*,Mat*);
919: EXTERN PetscErrorCode MatCholeskyFactorNumeric_SeqSBAIJ_6(Mat,MatFactorInfo*,Mat*);
920: EXTERN PetscErrorCode MatCholeskyFactorNumeric_SeqSBAIJ_7(Mat,MatFactorInfo*,Mat*);
921: EXTERN PetscErrorCode MatGetInertia_SeqSBAIJ(Mat,PetscInt*,PetscInt*,PetscInt*);

923: EXTERN PetscErrorCode MatMult_SeqSBAIJ_1(Mat,Vec,Vec);
924: EXTERN PetscErrorCode MatMult_SeqSBAIJ_2(Mat,Vec,Vec);
925: EXTERN PetscErrorCode MatMult_SeqSBAIJ_3(Mat,Vec,Vec);
926: EXTERN PetscErrorCode MatMult_SeqSBAIJ_4(Mat,Vec,Vec);
927: EXTERN PetscErrorCode MatMult_SeqSBAIJ_5(Mat,Vec,Vec);
928: EXTERN PetscErrorCode MatMult_SeqSBAIJ_6(Mat,Vec,Vec);
929: EXTERN PetscErrorCode MatMult_SeqSBAIJ_7(Mat,Vec,Vec);
930: EXTERN PetscErrorCode MatMult_SeqSBAIJ_N(Mat,Vec,Vec);

932: EXTERN PetscErrorCode MatMultAdd_SeqSBAIJ_1(Mat,Vec,Vec,Vec);
933: EXTERN PetscErrorCode MatMultAdd_SeqSBAIJ_2(Mat,Vec,Vec,Vec);
934: EXTERN PetscErrorCode MatMultAdd_SeqSBAIJ_3(Mat,Vec,Vec,Vec);
935: EXTERN PetscErrorCode MatMultAdd_SeqSBAIJ_4(Mat,Vec,Vec,Vec);
936: EXTERN PetscErrorCode MatMultAdd_SeqSBAIJ_5(Mat,Vec,Vec,Vec);
937: EXTERN PetscErrorCode MatMultAdd_SeqSBAIJ_6(Mat,Vec,Vec,Vec);
938: EXTERN PetscErrorCode MatMultAdd_SeqSBAIJ_7(Mat,Vec,Vec,Vec);
939: EXTERN PetscErrorCode MatMultAdd_SeqSBAIJ_N(Mat,Vec,Vec,Vec);

941: #ifdef HAVE_MatICCFactor
942: /* modified from MatILUFactor_SeqSBAIJ, needs further work!  */
945: PetscErrorCode MatICCFactor_SeqSBAIJ(Mat inA,IS row,MatFactorInfo *info)
946: {
947:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)inA->data;
948:   Mat            outA;
950:   PetscTruth     row_identity,col_identity;
951: 
953:   outA          = inA;
954:   inA->factor   = FACTOR_CHOLESKY;
955: 
956:   MatMarkDiagonal_SeqSBAIJ(inA);
957:   /*
958:     Blocksize 2, 3, 4, 5, 6 and 7 have a special faster factorization/solver 
959:     for ILU(0) factorization with natural ordering
960:   */
961:   switch (a->rmap.bs) {
962:   case 1:
963:     inA->ops->solve            = MatSolve_SeqSBAIJ_1_NaturalOrdering;
964:     inA->ops->solvetranspose   = MatSolve_SeqSBAIJ_1_NaturalOrdering;
965:     inA->ops->solves           = MatSolves_SeqSBAIJ_1;
966:     PetscInfo((inA,"Using special in-place natural ordering solvetrans BS=1\n");
967:   case 2:
968:     inA->ops->lufactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_2_NaturalOrdering;
969:     inA->ops->solve           = MatSolve_SeqSBAIJ_2_NaturalOrdering;
970:     inA->ops->solvetranspose  = MatSolve_SeqSBAIJ_2_NaturalOrdering;
971:     PetscInfo(inA,"Using special in-place natural ordering factor and solve BS=2\n");
972:     break;
973:   case 3:
974:      inA->ops->lufactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_3_NaturalOrdering;
975:      inA->ops->solve           = MatSolve_SeqSBAIJ_3_NaturalOrdering;
976:      inA->ops->solvetranspose  = MatSolve_SeqSBAIJ_3_NaturalOrdering;
977:      PetscInfo(inA,"Using special in-place natural ordering factor and solve BS=3\n");
978:      break;
979:   case 4:
980:     inA->ops->lufactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_4_NaturalOrdering;
981:     inA->ops->solve           = MatSolve_SeqSBAIJ_4_NaturalOrdering;
982:     inA->ops->solvetranspose  = MatSolve_SeqSBAIJ_4_NaturalOrdering;
983:     PetscInfo(inA,"Using special in-place natural ordering factor and solve BS=4\n");
984:     break;
985:   case 5:
986:     inA->ops->lufactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_5_NaturalOrdering;
987:     inA->ops->solve           = MatSolve_SeqSBAIJ_5_NaturalOrdering;
988:     inA->ops->solvetranspose  = MatSolve_SeqSBAIJ_5_NaturalOrdering;
989:     PetscInfo(inA,"Using special in-place natural ordering factor and solve BS=5\n");
990:     break;
991:   case 6:
992:     inA->ops->lufactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_6_NaturalOrdering;
993:     inA->ops->solve           = MatSolve_SeqSBAIJ_6_NaturalOrdering;
994:     inA->ops->solvetranspose  = MatSolve_SeqSBAIJ_6_NaturalOrdering;
995:     PetscInfo(inA,"Using special in-place natural ordering factor and solve BS=6\n");
996:     break;
997:   case 7:
998:     inA->ops->lufactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_7_NaturalOrdering;
999:     inA->ops->solvetranspose  = MatSolve_SeqSBAIJ_7_NaturalOrdering;
1000:     inA->ops->solve           = MatSolve_SeqSBAIJ_7_NaturalOrdering;
1001:     PetscInfo(inA,"Using special in-place natural ordering factor and solve BS=7\n");
1002:     break;
1003:   default:
1004:     a->row        = row;
1005:     a->icol       = col;
1006:     PetscObjectReference((PetscObject)row);
1007:     PetscObjectReference((PetscObject)col);
1008: 
1009:     /* Create the invert permutation so that it can be used in MatLUFactorNumeric() */
1010:     ISInvertPermutation(col,PETSC_DECIDE, &(a->icol));
1011:     PetscLogObjectParent(inA,a->icol);
1012: 
1013:     if (!a->solve_work) {
1014:       PetscMalloc((A->rmap.N+a->rmap.bs)*sizeof(PetscScalar),&a->solve_work);
1015:       PetscLogObjectMemory(inA,(A->rmap.N+a->rmap.bs)*sizeof(PetscScalar));
1016:     }
1017:   }
1018: 
1019:   MatCholeskyFactorNumeric(inA,info,&outA);
1020:   return(0);
1021: }
1022: #endif

1027: PetscErrorCode  MatSeqSBAIJSetColumnIndices_SeqSBAIJ(Mat mat,PetscInt *indices)
1028: {
1029:   Mat_SeqSBAIJ *baij = (Mat_SeqSBAIJ *)mat->data;
1030:   PetscInt     i,nz,n;
1031: 
1033:   nz = baij->maxnz;
1034:   n  = mat->cmap.n;
1035:   for (i=0; i<nz; i++) {
1036:     baij->j[i] = indices[i];
1037:   }
1038:    baij->nz = nz;
1039:    for (i=0; i<n; i++) {
1040:      baij->ilen[i] = baij->imax[i];
1041:    }
1042:    return(0);
1043: }

1048: /*@
1049:   MatSeqSBAIJSetColumnIndices - Set the column indices for all the rows
1050:   in the matrix.
1051:   
1052:   Input Parameters:
1053:   +  mat     - the SeqSBAIJ matrix
1054:   -  indices - the column indices
1055:   
1056:   Level: advanced
1057:   
1058:   Notes:
1059:   This can be called if you have precomputed the nonzero structure of the 
1060:   matrix and want to provide it to the matrix object to improve the performance
1061:   of the MatSetValues() operation.
1062:   
1063:   You MUST have set the correct numbers of nonzeros per row in the call to 
1064:   MatCreateSeqSBAIJ(), and the columns indices MUST be sorted.
1065:   
1066:   MUST be called before any calls to MatSetValues()
1067:   
1068:   .seealso: MatCreateSeqSBAIJ
1069: @*/
1070: PetscErrorCode  MatSeqSBAIJSetColumnIndices(Mat mat,PetscInt *indices)
1071: {
1072:   PetscErrorCode ierr,(*f)(Mat,PetscInt *);
1073: 
1077:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqSBAIJSetColumnIndices_C",(void (**)(void))&f);
1078:   if (f) {
1079:     (*f)(mat,indices);
1080:   } else {
1081:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to set column indices");
1082:   }
1083:   return(0);
1084: }

1088: PetscErrorCode MatCopy_SeqSBAIJ(Mat A,Mat B,MatStructure str)
1089: {

1093:   /* If the two matrices have the same copy implementation, use fast copy. */
1094:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1095:     Mat_SeqSBAIJ *a = (Mat_SeqSBAIJ*)A->data;
1096:     Mat_SeqSBAIJ *b = (Mat_SeqSBAIJ*)B->data;

1098:     if (a->i[A->rmap.N] != b->i[B->rmap.N]) {
1099:       SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
1100:     }
1101:     PetscMemcpy(b->a,a->a,(a->i[A->rmap.N])*sizeof(PetscScalar));
1102:   } else {
1103:     MatGetRowUpperTriangular(A);
1104:     MatCopy_Basic(A,B,str);
1105:     MatRestoreRowUpperTriangular(A);
1106:   }
1107:   return(0);
1108: }

1112: PetscErrorCode MatSetUpPreallocation_SeqSBAIJ(Mat A)
1113: {
1115: 
1117:    MatSeqSBAIJSetPreallocation_SeqSBAIJ(A,PetscMax(A->rmap.bs,1),PETSC_DEFAULT,0);
1118:   return(0);
1119: }

1123: PetscErrorCode MatGetArray_SeqSBAIJ(Mat A,PetscScalar *array[])
1124: {
1125:   Mat_SeqSBAIJ *a = (Mat_SeqSBAIJ*)A->data;
1127:   *array = a->a;
1128:   return(0);
1129: }

1133: PetscErrorCode MatRestoreArray_SeqSBAIJ(Mat A,PetscScalar *array[])
1134: {
1136:   return(0);
1137:  }

1139:  #include petscblaslapack.h
1142: PetscErrorCode MatAXPY_SeqSBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1143: {
1144:   Mat_SeqSBAIJ   *x=(Mat_SeqSBAIJ *)X->data, *y=(Mat_SeqSBAIJ *)Y->data;
1146:   PetscInt       i,bs=Y->rmap.bs,bs2,j;
1147:   PetscBLASInt   bnz = (PetscBLASInt)x->nz,one = 1;
1148: 
1150:   if (str == SAME_NONZERO_PATTERN) {
1151:     PetscScalar alpha = a;
1152:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1153:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
1154:     if (y->xtoy && y->XtoY != X) {
1155:       PetscFree(y->xtoy);
1156:       MatDestroy(y->XtoY);
1157:     }
1158:     if (!y->xtoy) { /* get xtoy */
1159:       MatAXPYGetxtoy_Private(x->mbs,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
1160:       y->XtoY = X;
1161:     }
1162:     bs2 = bs*bs;
1163:     for (i=0; i<x->nz; i++) {
1164:       j = 0;
1165:       while (j < bs2){
1166:         y->a[bs2*y->xtoy[i]+j] += a*(x->a[bs2*i+j]);
1167:         j++;
1168:       }
1169:     }
1170:     PetscInfo3(0,"ratio of nnz_s(X)/nnz_s(Y): %D/%D = %G\n",bs2*x->nz,bs2*y->nz,(PetscReal)(bs2*x->nz)/(bs2*y->nz));
1171:   } else {
1172:     MatGetRowUpperTriangular(X);
1173:     MatAXPY_Basic(Y,a,X,str);
1174:     MatRestoreRowUpperTriangular(X);
1175:   }
1176:   return(0);
1177: }

1181: PetscErrorCode MatIsSymmetric_SeqSBAIJ(Mat A,PetscReal tol,PetscTruth *flg)
1182: {
1184:   *flg = PETSC_TRUE;
1185:   return(0);
1186: }

1190: PetscErrorCode MatIsStructurallySymmetric_SeqSBAIJ(Mat A,PetscTruth *flg)
1191: {
1193:    *flg = PETSC_TRUE;
1194:    return(0);
1195: }

1199: PetscErrorCode MatIsHermitian_SeqSBAIJ(Mat A,PetscTruth *flg)
1200:  {
1202:    *flg = PETSC_FALSE;
1203:    return(0);
1204:  }

1208: PetscErrorCode MatRealPart_SeqSBAIJ(Mat A)
1209: {
1210:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;
1211:   PetscInt       i,nz = a->bs2*a->i[a->mbs];
1212:   PetscScalar    *aa = a->a;

1215:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
1216:   return(0);
1217: }

1221: PetscErrorCode MatImaginaryPart_SeqSBAIJ(Mat A)
1222: {
1223:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;
1224:   PetscInt       i,nz = a->bs2*a->i[a->mbs];
1225:   PetscScalar    *aa = a->a;

1228:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1229:   return(0);
1230: }

1232: /* -------------------------------------------------------------------*/
1233: static struct _MatOps MatOps_Values = {MatSetValues_SeqSBAIJ,
1234:        MatGetRow_SeqSBAIJ,
1235:        MatRestoreRow_SeqSBAIJ,
1236:        MatMult_SeqSBAIJ_N,
1237: /* 4*/ MatMultAdd_SeqSBAIJ_N,
1238:        MatMult_SeqSBAIJ_N,       /* transpose versions are same as non-transpose versions */
1239:        MatMultAdd_SeqSBAIJ_N,
1240:        MatSolve_SeqSBAIJ_N,
1241:        0,
1242:        0,
1243: /*10*/ 0,
1244:        0,
1245:        MatCholeskyFactor_SeqSBAIJ,
1246:        MatRelax_SeqSBAIJ,
1247:        MatTranspose_SeqSBAIJ,
1248: /*15*/ MatGetInfo_SeqSBAIJ,
1249:        MatEqual_SeqSBAIJ,
1250:        MatGetDiagonal_SeqSBAIJ,
1251:        MatDiagonalScale_SeqSBAIJ,
1252:        MatNorm_SeqSBAIJ,
1253: /*20*/ 0,
1254:        MatAssemblyEnd_SeqSBAIJ,
1255:        0,
1256:        MatSetOption_SeqSBAIJ,
1257:        MatZeroEntries_SeqSBAIJ,
1258: /*25*/ 0,
1259:        0,
1260:        0,
1261:        MatCholeskyFactorSymbolic_SeqSBAIJ,
1262:        MatCholeskyFactorNumeric_SeqSBAIJ_N,
1263: /*30*/ MatSetUpPreallocation_SeqSBAIJ,
1264:        0,
1265:        MatICCFactorSymbolic_SeqSBAIJ,
1266:        MatGetArray_SeqSBAIJ,
1267:        MatRestoreArray_SeqSBAIJ,
1268: /*35*/ MatDuplicate_SeqSBAIJ,
1269:        0,
1270:        0,
1271:        0,
1272:        0,
1273: /*40*/ MatAXPY_SeqSBAIJ,
1274:        MatGetSubMatrices_SeqSBAIJ,
1275:        MatIncreaseOverlap_SeqSBAIJ,
1276:        MatGetValues_SeqSBAIJ,
1277:        MatCopy_SeqSBAIJ,
1278: /*45*/ 0,
1279:        MatScale_SeqSBAIJ,
1280:        0,
1281:        0,
1282:        0,
1283: /*50*/ 0,
1284:        MatGetRowIJ_SeqSBAIJ,
1285:        MatRestoreRowIJ_SeqSBAIJ,
1286:        0,
1287:        0,
1288: /*55*/ 0,
1289:        0,
1290:        0,
1291:        0,
1292:        MatSetValuesBlocked_SeqSBAIJ,
1293: /*60*/ MatGetSubMatrix_SeqSBAIJ,
1294:        0,
1295:        0,
1296:        0,
1297:        0,
1298: /*65*/ 0,
1299:        0,
1300:        0,
1301:        0,
1302:        0,
1303: /*70*/ MatGetRowMax_SeqSBAIJ,
1304:        0,
1305:        0,
1306:        0,
1307:        0,
1308: /*75*/ 0,
1309:        0,
1310:        0,
1311:        0,
1312:        0,
1313: /*80*/ 0,
1314:        0,
1315:        0,
1316: #if !defined(PETSC_USE_COMPLEX)
1317:        MatGetInertia_SeqSBAIJ,
1318: #else
1319:        0,
1320: #endif
1321:        MatLoad_SeqSBAIJ,
1322: /*85*/ MatIsSymmetric_SeqSBAIJ,
1323:        MatIsHermitian_SeqSBAIJ,
1324:        MatIsStructurallySymmetric_SeqSBAIJ,
1325:        0,
1326:        0,
1327: /*90*/ 0,
1328:        0,
1329:        0,
1330:        0,
1331:        0,
1332: /*95*/ 0,
1333:        0,
1334:        0,
1335:        0,
1336:        0,
1337: /*100*/0,
1338:        0,
1339:        0,
1340:        0,
1341:        0,
1342: /*105*/0,
1343:        MatRealPart_SeqSBAIJ,
1344:        MatImaginaryPart_SeqSBAIJ,
1345:        MatGetRowUpperTriangular_SeqSBAIJ,
1346:        MatRestoreRowUpperTriangular_SeqSBAIJ
1347: };

1352: PetscErrorCode  MatStoreValues_SeqSBAIJ(Mat mat)
1353: {
1354:   Mat_SeqSBAIJ   *aij = (Mat_SeqSBAIJ *)mat->data;
1355:   PetscInt       nz = aij->i[mat->rmap.N]*mat->rmap.bs*aij->bs2;
1357: 
1359:   if (aij->nonew != 1) {
1360:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
1361:   }
1362: 
1363:   /* allocate space for values if not already there */
1364:   if (!aij->saved_values) {
1365:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
1366:   }
1367: 
1368:   /* copy values over */
1369:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
1370:   return(0);
1371: }

1377: PetscErrorCode  MatRetrieveValues_SeqSBAIJ(Mat mat)
1378: {
1379:   Mat_SeqSBAIJ   *aij = (Mat_SeqSBAIJ *)mat->data;
1381:   PetscInt       nz = aij->i[mat->rmap.N]*mat->rmap.bs*aij->bs2;
1382: 
1384:   if (aij->nonew != 1) {
1385:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
1386:   }
1387:   if (!aij->saved_values) {
1388:     SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
1389:   }
1390: 
1391:   /* copy values over */
1392:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
1393:   return(0);
1394: }

1400: PetscErrorCode  MatSeqSBAIJSetPreallocation_SeqSBAIJ(Mat B,PetscInt bs,PetscInt nz,PetscInt *nnz)
1401: {
1402:   Mat_SeqSBAIJ   *b = (Mat_SeqSBAIJ*)B->data;
1404:   PetscInt       i,mbs,bs2;
1405:   PetscTruth     skipallocation = PETSC_FALSE,flg;
1406: 
1408:   B->preallocated = PETSC_TRUE;
1409:   PetscOptionsGetInt(B->prefix,"-mat_block_size",&bs,PETSC_NULL);
1410:   B->rmap.bs = B->cmap.bs = bs;
1411:   PetscMapInitialize(B->comm,&B->rmap);
1412:   PetscMapInitialize(B->comm,&B->cmap);

1414:   mbs  = B->rmap.N/bs;
1415:   bs2  = bs*bs;
1416: 
1417:   if (mbs*bs != B->rmap.N) {
1418:     SETERRQ(PETSC_ERR_ARG_SIZ,"Number rows, cols must be divisible by blocksize");
1419:   }
1420: 
1421:   if (nz == MAT_SKIP_ALLOCATION) {
1422:     skipallocation = PETSC_TRUE;
1423:     nz             = 0;
1424:   }

1426:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 3;
1427:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
1428:   if (nnz) {
1429:     for (i=0; i<mbs; i++) {
1430:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
1431:       if (nnz[i] > mbs) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than block row length: local row %D value %D rowlength %D",i,nnz[i],mbs);
1432:     }
1433:   }
1434: 
1435:   PetscOptionsHasName(B->prefix,"-mat_no_unroll",&flg);
1436:   if (!flg) {
1437:     switch (bs) {
1438:     case 1:
1439:       B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_1;
1440:       B->ops->solve            = MatSolve_SeqSBAIJ_1;
1441:       B->ops->solves           = MatSolves_SeqSBAIJ_1;
1442:       B->ops->solvetranspose   = MatSolve_SeqSBAIJ_1;
1443:       B->ops->mult             = MatMult_SeqSBAIJ_1;
1444:       B->ops->multadd          = MatMultAdd_SeqSBAIJ_1;
1445:       B->ops->multtranspose    = MatMult_SeqSBAIJ_1;
1446:       B->ops->multtransposeadd = MatMultAdd_SeqSBAIJ_1;
1447:       break;
1448:     case 2:
1449:       B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_2;
1450:       B->ops->solve            = MatSolve_SeqSBAIJ_2;
1451:       B->ops->solvetranspose   = MatSolve_SeqSBAIJ_2;
1452:       B->ops->mult             = MatMult_SeqSBAIJ_2;
1453:       B->ops->multadd          = MatMultAdd_SeqSBAIJ_2;
1454:       B->ops->multtranspose    = MatMult_SeqSBAIJ_2;
1455:       B->ops->multtransposeadd = MatMultAdd_SeqSBAIJ_2;
1456:       break;
1457:     case 3:
1458:       B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_3;
1459:       B->ops->solve            = MatSolve_SeqSBAIJ_3;
1460:       B->ops->solvetranspose   = MatSolve_SeqSBAIJ_3;
1461:       B->ops->mult             = MatMult_SeqSBAIJ_3;
1462:       B->ops->multadd          = MatMultAdd_SeqSBAIJ_3;
1463:       B->ops->multtranspose    = MatMult_SeqSBAIJ_3;
1464:       B->ops->multtransposeadd = MatMultAdd_SeqSBAIJ_3;
1465:       break;
1466:     case 4:
1467:       B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_4;
1468:       B->ops->solve            = MatSolve_SeqSBAIJ_4;
1469:       B->ops->solvetranspose   = MatSolve_SeqSBAIJ_4;
1470:       B->ops->mult             = MatMult_SeqSBAIJ_4;
1471:       B->ops->multadd          = MatMultAdd_SeqSBAIJ_4;
1472:       B->ops->multtranspose    = MatMult_SeqSBAIJ_4;
1473:       B->ops->multtransposeadd = MatMultAdd_SeqSBAIJ_4;
1474:       break;
1475:     case 5:
1476:       B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_5;
1477:       B->ops->solve            = MatSolve_SeqSBAIJ_5;
1478:       B->ops->solvetranspose   = MatSolve_SeqSBAIJ_5;
1479:       B->ops->mult             = MatMult_SeqSBAIJ_5;
1480:       B->ops->multadd          = MatMultAdd_SeqSBAIJ_5;
1481:       B->ops->multtranspose    = MatMult_SeqSBAIJ_5;
1482:       B->ops->multtransposeadd = MatMultAdd_SeqSBAIJ_5;
1483:       break;
1484:     case 6:
1485:       B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_6;
1486:       B->ops->solve            = MatSolve_SeqSBAIJ_6;
1487:       B->ops->solvetranspose   = MatSolve_SeqSBAIJ_6;
1488:       B->ops->mult             = MatMult_SeqSBAIJ_6;
1489:       B->ops->multadd          = MatMultAdd_SeqSBAIJ_6;
1490:       B->ops->multtranspose    = MatMult_SeqSBAIJ_6;
1491:       B->ops->multtransposeadd = MatMultAdd_SeqSBAIJ_6;
1492:       break;
1493:     case 7:
1494:       B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqSBAIJ_7;
1495:       B->ops->solve            = MatSolve_SeqSBAIJ_7;
1496:       B->ops->solvetranspose   = MatSolve_SeqSBAIJ_7;
1497:       B->ops->mult             = MatMult_SeqSBAIJ_7;
1498:       B->ops->multadd          = MatMultAdd_SeqSBAIJ_7;
1499:       B->ops->multtranspose    = MatMult_SeqSBAIJ_7;
1500:       B->ops->multtransposeadd = MatMultAdd_SeqSBAIJ_7;
1501:       break;
1502:     }
1503:   }
1504: 
1505:   b->mbs = mbs;
1506:   b->nbs = mbs;
1507:   if (!skipallocation) {
1508:     /* b->ilen will count nonzeros in each block row so far. */
1509:     PetscMalloc2(mbs,PetscInt,&b->imax,mbs,PetscInt,&b->ilen);
1510:     for (i=0; i<mbs; i++) { b->ilen[i] = 0;}
1511:     if (!nnz) {
1512:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
1513:       else if (nz <= 0)        nz = 1;
1514:       for (i=0; i<mbs; i++) {
1515:         b->imax[i] = nz;
1516:       }
1517:       nz = nz*mbs; /* total nz */
1518:     } else {
1519:       nz = 0;
1520:       for (i=0; i<mbs; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
1521:     }
1522:     /* nz=(nz+mbs)/2; */ /* total diagonal and superdiagonal nonzero blocks */
1523: 
1524:     /* allocate the matrix space */
1525:     PetscMalloc3(bs2*nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap.N+1,PetscInt,&b->i);
1526:     PetscMemzero(b->a,nz*bs2*sizeof(MatScalar));
1527:     PetscMemzero(b->j,nz*sizeof(PetscInt));
1528:     b->singlemalloc = PETSC_TRUE;
1529: 
1530:     /* pointer to beginning of each row */
1531:     b->i[0] = 0;
1532:     for (i=1; i<mbs+1; i++) {
1533:       b->i[i] = b->i[i-1] + b->imax[i-1];
1534:     }
1535:     b->free_a     = PETSC_TRUE;
1536:     b->free_ij    = PETSC_TRUE;
1537:   } else {
1538:     b->free_a     = PETSC_FALSE;
1539:     b->free_ij    = PETSC_FALSE;
1540:   }
1541: 
1542:   B->rmap.bs               = bs;
1543:   b->bs2              = bs2;
1544:   b->nz             = 0;
1545:   b->maxnz          = nz*bs2;
1546: 
1547:   b->inew             = 0;
1548:   b->jnew             = 0;
1549:   b->anew             = 0;
1550:   b->a2anew           = 0;
1551:   b->permute          = PETSC_FALSE;
1552:   return(0);
1553: }

1557: EXTERN PetscErrorCode  MatConvert_SeqSBAIJ_SeqAIJ(Mat, MatType,MatReuse,Mat*);
1558: EXTERN PetscErrorCode  MatConvert_SeqSBAIJ_SeqBAIJ(Mat, MatType,MatReuse,Mat*);

1561: /*MC
1562:   MATSEQSBAIJ - MATSEQSBAIJ = "seqsbaij" - A matrix type to be used for sequential symmetric block sparse matrices, 
1563:   based on block compressed sparse row format.  Only the upper triangular portion of the matrix is stored.
1564:   
1565:   Options Database Keys:
1566:   . -mat_type seqsbaij - sets the matrix type to "seqsbaij" during a call to MatSetFromOptions()
1567:   
1568:   Level: beginner
1569:   
1570:   .seealso: MatCreateSeqSBAIJ
1571: M*/

1576: PetscErrorCode  MatCreate_SeqSBAIJ(Mat B)
1577: {
1578:   Mat_SeqSBAIJ   *b;
1580:   PetscMPIInt    size;
1581:   PetscTruth     flg;
1582: 
1584:   MPI_Comm_size(B->comm,&size);
1585:   if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"Comm must be of size 1");
1586: 
1587:   PetscNew(Mat_SeqSBAIJ,&b);
1588:   B->data = (void*)b;
1589:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
1590:   B->ops->destroy     = MatDestroy_SeqSBAIJ;
1591:   B->ops->view        = MatView_SeqSBAIJ;
1592:   B->factor           = 0;
1593:   B->mapping          = 0;
1594:   b->row              = 0;
1595:   b->icol             = 0;
1596:   b->reallocs         = 0;
1597:   b->saved_values     = 0;
1598: 
1599: 
1600:   b->sorted           = PETSC_FALSE;
1601:   b->roworiented      = PETSC_TRUE;
1602:   b->nonew            = 0;
1603:   b->diag             = 0;
1604:   b->solve_work       = 0;
1605:   b->mult_work        = 0;
1606:   B->spptr            = 0;
1607:   b->keepzeroedrows   = PETSC_FALSE;
1608:   b->xtoy             = 0;
1609:   b->XtoY             = 0;
1610: 
1611:   b->inew             = 0;
1612:   b->jnew             = 0;
1613:   b->anew             = 0;
1614:   b->a2anew           = 0;
1615:   b->permute          = PETSC_FALSE;

1617:   b->ignore_ltriangular = PETSC_FALSE;
1618:   PetscOptionsHasName(PETSC_NULL,"-mat_ignore_lower_triangular",&flg);
1619:   if (flg) b->ignore_ltriangular = PETSC_TRUE;

1621:   b->getrow_utriangular = PETSC_FALSE;
1622:   PetscOptionsHasName(PETSC_NULL,"-mat_getrow_uppertriangular",&flg);
1623:   if (flg) b->getrow_utriangular = PETSC_TRUE;

1625:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
1626:                                      "MatStoreValues_SeqSBAIJ",
1627:                                      MatStoreValues_SeqSBAIJ);
1628:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
1629:                                      "MatRetrieveValues_SeqSBAIJ",
1630:                                      (void*)MatRetrieveValues_SeqSBAIJ);
1631:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqSBAIJSetColumnIndices_C",
1632:                                      "MatSeqSBAIJSetColumnIndices_SeqSBAIJ",
1633:                                      MatSeqSBAIJSetColumnIndices_SeqSBAIJ);
1634:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqsbaij_seqaij_C",
1635:                                      "MatConvert_SeqSBAIJ_SeqAIJ",
1636:                                       MatConvert_SeqSBAIJ_SeqAIJ);
1637:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqsbaij_seqbaij_C",
1638:                                      "MatConvert_SeqSBAIJ_SeqBAIJ",
1639:                                       MatConvert_SeqSBAIJ_SeqBAIJ);
1640:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqSBAIJSetPreallocation_C",
1641:                                      "MatSeqSBAIJSetPreallocation_SeqSBAIJ",
1642:                                      MatSeqSBAIJSetPreallocation_SeqSBAIJ);

1644:   B->symmetric                  = PETSC_TRUE;
1645:   B->structurally_symmetric     = PETSC_TRUE;
1646:   B->symmetric_set              = PETSC_TRUE;
1647:   B->structurally_symmetric_set = PETSC_TRUE;
1648:   PetscObjectChangeTypeName((PetscObject)B,MATSEQSBAIJ);
1649:   return(0);
1650: }

1655: /*@C
1656:    MatSeqSBAIJSetPreallocation - Creates a sparse symmetric matrix in block AIJ (block
1657:    compressed row) format.  For good matrix assembly performance the
1658:    user should preallocate the matrix storage by setting the parameter nz
1659:    (or the array nnz).  By setting these parameters accurately, performance
1660:    during matrix assembly can be increased by more than a factor of 50.

1662:    Collective on Mat

1664:    Input Parameters:
1665: +  A - the symmetric matrix 
1666: .  bs - size of block
1667: .  nz - number of block nonzeros per block row (same for all rows)
1668: -  nnz - array containing the number of block nonzeros in the upper triangular plus
1669:          diagonal portion of each block (possibly different for each block row) or PETSC_NULL

1671:    Options Database Keys:
1672: .   -mat_no_unroll - uses code that does not unroll the loops in the 
1673:                      block calculations (much slower)
1674: .    -mat_block_size - size of the blocks to use

1676:    Level: intermediate

1678:    Notes:
1679:    Specify the preallocated storage with either nz or nnz (not both).
1680:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
1681:    allocation.  For additional details, see the users manual chapter on
1682:    matrices.

1684:    If the nnz parameter is given then the nz parameter is ignored


1687: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPISBAIJ()
1688: @*/
1689: PetscErrorCode  MatSeqSBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt nz,const PetscInt nnz[])
1690: {
1691:   PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[]);

1694:   PetscObjectQueryFunction((PetscObject)B,"MatSeqSBAIJSetPreallocation_C",(void (**)(void))&f);
1695:   if (f) {
1696:     (*f)(B,bs,nz,nnz);
1697:   }
1698:   return(0);
1699: }

1703: /*@C
1704:    MatCreateSeqSBAIJ - Creates a sparse symmetric matrix in block AIJ (block
1705:    compressed row) format.  For good matrix assembly performance the
1706:    user should preallocate the matrix storage by setting the parameter nz
1707:    (or the array nnz).  By setting these parameters accurately, performance
1708:    during matrix assembly can be increased by more than a factor of 50.

1710:    Collective on MPI_Comm

1712:    Input Parameters:
1713: +  comm - MPI communicator, set to PETSC_COMM_SELF
1714: .  bs - size of block
1715: .  m - number of rows, or number of columns
1716: .  nz - number of block nonzeros per block row (same for all rows)
1717: -  nnz - array containing the number of block nonzeros in the upper triangular plus
1718:          diagonal portion of each block (possibly different for each block row) or PETSC_NULL

1720:    Output Parameter:
1721: .  A - the symmetric matrix 

1723:    Options Database Keys:
1724: .   -mat_no_unroll - uses code that does not unroll the loops in the 
1725:                      block calculations (much slower)
1726: .    -mat_block_size - size of the blocks to use

1728:    Level: intermediate

1730:    Notes:

1732:    Specify the preallocated storage with either nz or nnz (not both).
1733:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
1734:    allocation.  For additional details, see the users manual chapter on
1735:    matrices.

1737:    If the nnz parameter is given then the nz parameter is ignored

1739: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPISBAIJ()
1740: @*/
1741: PetscErrorCode  MatCreateSeqSBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
1742: {
1744: 
1746:   MatCreate(comm,A);
1747:   MatSetSizes(*A,m,n,m,n);
1748:   MatSetType(*A,MATSEQSBAIJ);
1749:   MatSeqSBAIJSetPreallocation_SeqSBAIJ(*A,bs,nz,(PetscInt*)nnz);
1750:   return(0);
1751: }

1755: PetscErrorCode MatDuplicate_SeqSBAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
1756: {
1757:   Mat            C;
1758:   Mat_SeqSBAIJ   *c,*a = (Mat_SeqSBAIJ*)A->data;
1760:   PetscInt       i,mbs = a->mbs,nz = a->nz,bs2 =a->bs2;

1763:   if (a->i[mbs] != nz) SETERRQ(PETSC_ERR_PLIB,"Corrupt matrix");

1765:   *B = 0;
1766:   MatCreate(A->comm,&C);
1767:   MatSetSizes(C,A->rmap.N,A->cmap.n,A->rmap.N,A->cmap.n);
1768:   MatSetType(C,A->type_name);
1769:   PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));
1770:   c    = (Mat_SeqSBAIJ*)C->data;

1772:   C->preallocated   = PETSC_TRUE;
1773:   C->factor         = A->factor;
1774:   c->row            = 0;
1775:   c->icol           = 0;
1776:   c->saved_values   = 0;
1777:   c->keepzeroedrows = a->keepzeroedrows;
1778:   C->assembled      = PETSC_TRUE;

1780:   PetscMapCopy(A->comm,&A->rmap,&C->rmap);
1781:   PetscMapCopy(A->comm,&A->cmap,&C->cmap);
1782:   c->bs2  = a->bs2;
1783:   c->mbs  = a->mbs;
1784:   c->nbs  = a->nbs;

1786:   PetscMalloc2((mbs+1),PetscInt,&c->imax,(mbs+1),PetscInt,&c->ilen);
1787:   for (i=0; i<mbs; i++) {
1788:     c->imax[i] = a->imax[i];
1789:     c->ilen[i] = a->ilen[i];
1790:   }

1792:   /* allocate the matrix space */
1793:   PetscMalloc3(bs2*nz,MatScalar,&c->a,nz,PetscInt,&c->j,mbs+1,PetscInt,&c->i);
1794:   c->singlemalloc = PETSC_TRUE;
1795:   PetscMemcpy(c->i,a->i,(mbs+1)*sizeof(PetscInt));
1796:   PetscLogObjectMemory(C,(mbs+1)*sizeof(PetscInt) + nz*(bs2*sizeof(MatScalar) + sizeof(PetscInt)));
1797:   if (mbs > 0) {
1798:     PetscMemcpy(c->j,a->j,nz*sizeof(PetscInt));
1799:     if (cpvalues == MAT_COPY_VALUES) {
1800:       PetscMemcpy(c->a,a->a,bs2*nz*sizeof(MatScalar));
1801:     } else {
1802:       PetscMemzero(c->a,bs2*nz*sizeof(MatScalar));
1803:     }
1804:   }

1806:   c->sorted      = a->sorted;
1807:   c->roworiented = a->roworiented;
1808:   c->nonew       = a->nonew;

1810:   if (a->diag) {
1811:     PetscMalloc((mbs+1)*sizeof(PetscInt),&c->diag);
1812:     PetscLogObjectMemory(C,(mbs+1)*sizeof(PetscInt));
1813:     for (i=0; i<mbs; i++) {
1814:       c->diag[i] = a->diag[i];
1815:     }
1816:   } else c->diag  = 0;
1817:   c->nz           = a->nz;
1818:   c->maxnz        = a->maxnz;
1819:   c->solve_work   = 0;
1820:   c->mult_work    = 0;
1821:   c->free_a       = PETSC_TRUE;
1822:   c->free_ij      = PETSC_TRUE;
1823:   *B = C;
1824:   PetscFListDuplicate(A->qlist,&C->qlist);
1825:   return(0);
1826: }

1830: PetscErrorCode MatLoad_SeqSBAIJ(PetscViewer viewer, MatType type,Mat *A)
1831: {
1832:   Mat_SeqSBAIJ   *a;
1833:   Mat            B;
1835:   int            fd;
1836:   PetscMPIInt    size;
1837:   PetscInt       i,nz,header[4],*rowlengths=0,M,N,bs=1;
1838:   PetscInt       *mask,mbs,*jj,j,rowcount,nzcount,k,*s_browlengths,maskcount;
1839:   PetscInt       kmax,jcount,block,idx,point,nzcountb,extra_rows;
1840:   PetscInt       *masked,nmask,tmp,bs2,ishift;
1841:   PetscScalar    *aa;
1842:   MPI_Comm       comm = ((PetscObject)viewer)->comm;

1845:   PetscOptionsGetInt(PETSC_NULL,"-matload_block_size",&bs,PETSC_NULL);
1846:   bs2  = bs*bs;

1848:   MPI_Comm_size(comm,&size);
1849:   if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"view must have one processor");
1850:   PetscViewerBinaryGetDescriptor(viewer,&fd);
1851:   PetscBinaryRead(fd,header,4,PETSC_INT);
1852:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not Mat object");
1853:   M = header[1]; N = header[2]; nz = header[3];

1855:   if (header[3] < 0) {
1856:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as SeqSBAIJ");
1857:   }

1859:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

1861:   /* 
1862:      This code adds extra rows to make sure the number of rows is 
1863:     divisible by the blocksize
1864:   */
1865:   mbs        = M/bs;
1866:   extra_rows = bs - M + bs*(mbs);
1867:   if (extra_rows == bs) extra_rows = 0;
1868:   else                  mbs++;
1869:   if (extra_rows) {
1870:     PetscInfo(0,"Padding loaded matrix to match blocksize\n");
1871:   }

1873:   /* read in row lengths */
1874:   PetscMalloc((M+extra_rows)*sizeof(PetscInt),&rowlengths);
1875:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
1876:   for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;

1878:   /* read in column indices */
1879:   PetscMalloc((nz+extra_rows)*sizeof(PetscInt),&jj);
1880:   PetscBinaryRead(fd,jj,nz,PETSC_INT);
1881:   for (i=0; i<extra_rows; i++) jj[nz+i] = M+i;

1883:   /* loop over row lengths determining block row lengths */
1884:   PetscMalloc(mbs*sizeof(PetscInt),&s_browlengths);
1885:   PetscMemzero(s_browlengths,mbs*sizeof(PetscInt));
1886:   PetscMalloc(2*mbs*sizeof(PetscInt),&mask);
1887:   PetscMemzero(mask,mbs*sizeof(PetscInt));
1888:   masked   = mask + mbs;
1889:   rowcount = 0; nzcount = 0;
1890:   for (i=0; i<mbs; i++) {
1891:     nmask = 0;
1892:     for (j=0; j<bs; j++) {
1893:       kmax = rowlengths[rowcount];
1894:       for (k=0; k<kmax; k++) {
1895:         tmp = jj[nzcount++]/bs;   /* block col. index */
1896:         if (!mask[tmp] && tmp >= i) {masked[nmask++] = tmp; mask[tmp] = 1;}
1897:       }
1898:       rowcount++;
1899:     }
1900:     s_browlengths[i] += nmask;
1901: 
1902:     /* zero out the mask elements we set */
1903:     for (j=0; j<nmask; j++) mask[masked[j]] = 0;
1904:   }

1906:   /* create our matrix */
1907:   MatCreate(comm,&B);
1908:   MatSetSizes(B,M+extra_rows,N+extra_rows,M+extra_rows,N+extra_rows);
1909:   MatSetType(B,type);
1910:   MatSeqSBAIJSetPreallocation_SeqSBAIJ(B,bs,0,s_browlengths);
1911:   a = (Mat_SeqSBAIJ*)B->data;

1913:   /* set matrix "i" values */
1914:   a->i[0] = 0;
1915:   for (i=1; i<= mbs; i++) {
1916:     a->i[i]      = a->i[i-1] + s_browlengths[i-1];
1917:     a->ilen[i-1] = s_browlengths[i-1];
1918:   }
1919:   a->nz = a->i[mbs];

1921:   /* read in nonzero values */
1922:   PetscMalloc((nz+extra_rows)*sizeof(PetscScalar),&aa);
1923:   PetscBinaryRead(fd,aa,nz,PETSC_SCALAR);
1924:   for (i=0; i<extra_rows; i++) aa[nz+i] = 1.0;

1926:   /* set "a" and "j" values into matrix */
1927:   nzcount = 0; jcount = 0;
1928:   for (i=0; i<mbs; i++) {
1929:     nzcountb = nzcount;
1930:     nmask    = 0;
1931:     for (j=0; j<bs; j++) {
1932:       kmax = rowlengths[i*bs+j];
1933:       for (k=0; k<kmax; k++) {
1934:         tmp = jj[nzcount++]/bs; /* block col. index */
1935:         if (!mask[tmp] && tmp >= i) { masked[nmask++] = tmp; mask[tmp] = 1;}
1936:       }
1937:     }
1938:     /* sort the masked values */
1939:     PetscSortInt(nmask,masked);

1941:     /* set "j" values into matrix */
1942:     maskcount = 1;
1943:     for (j=0; j<nmask; j++) {
1944:       a->j[jcount++]  = masked[j];
1945:       mask[masked[j]] = maskcount++;
1946:     }

1948:     /* set "a" values into matrix */
1949:     ishift = bs2*a->i[i];
1950:     for (j=0; j<bs; j++) {
1951:       kmax = rowlengths[i*bs+j];
1952:       for (k=0; k<kmax; k++) {
1953:         tmp       = jj[nzcountb]/bs ; /* block col. index */
1954:         if (tmp >= i){
1955:           block     = mask[tmp] - 1;
1956:           point     = jj[nzcountb] - bs*tmp;
1957:           idx       = ishift + bs2*block + j + bs*point;
1958:           a->a[idx] = aa[nzcountb];
1959:         }
1960:         nzcountb++;
1961:       }
1962:     }
1963:     /* zero out the mask elements we set */
1964:     for (j=0; j<nmask; j++) mask[masked[j]] = 0;
1965:   }
1966:   if (jcount != a->nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Bad binary matrix");

1968:   PetscFree(rowlengths);
1969:   PetscFree(s_browlengths);
1970:   PetscFree(aa);
1971:   PetscFree(jj);
1972:   PetscFree(mask);

1974:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1975:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1976:   MatView_Private(B);
1977:   *A = B;
1978:   return(0);
1979: }

1983: PetscErrorCode MatRelax_SeqSBAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1984: {
1985:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)A->data;
1986:   MatScalar      *aa=a->a,*v,*v1;
1987:   PetscScalar    *x,*b,*t,sum,d;
1989:   PetscInt       m=a->mbs,bs=A->rmap.bs,*ai=a->i,*aj=a->j;
1990:   PetscInt       nz,nz1,*vj,*vj1,i;

1993:   its = its*lits;
1994:   if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);

1996:   if (bs > 1)
1997:     SETERRQ(PETSC_ERR_SUP,"SSOR for block size > 1 is not yet implemented");

1999:   VecGetArray(xx,&x);
2000:   if (xx != bb) {
2001:     VecGetArray(bb,&b);
2002:   } else {
2003:     b = x;
2004:   }

2006:   if (!a->relax_work) {
2007:     PetscMalloc(m*sizeof(PetscScalar),&a->relax_work);
2008:   }
2009:   t = a->relax_work;
2010: 
2011:   if (flag & SOR_ZERO_INITIAL_GUESS) {
2012:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
2013:       for (i=0; i<m; i++)
2014:         t[i] = b[i];

2016:       for (i=0; i<m; i++){
2017:         d  = *(aa + ai[i]);  /* diag[i] */
2018:         v  = aa + ai[i] + 1;
2019:         vj = aj + ai[i] + 1;
2020:         nz = ai[i+1] - ai[i] - 1;
2021:         PetscLogFlops(2*nz-1);
2022:         x[i] = omega*t[i]/d;
2023:         while (nz--) t[*vj++] -= x[i]*(*v++); /* update rhs */
2024:       }
2025:     }

2027:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
2028:       if (!(flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP)){
2029:         t = b;
2030:       }
2031: 
2032:       for (i=m-1; i>=0; i--){
2033:         d  = *(aa + ai[i]);
2034:         v  = aa + ai[i] + 1;
2035:         vj = aj + ai[i] + 1;
2036:         nz = ai[i+1] - ai[i] - 1;
2037:         PetscLogFlops(2*nz-1);
2038:         sum = t[i];
2039:         while (nz--) sum -= x[*vj++]*(*v++);
2040:         x[i] =   (1-omega)*x[i] + omega*sum/d;
2041:       }
2042:       t = a->relax_work;
2043:     }
2044:     its--;
2045:   }

2047:   while (its--) {
2048:     /* 
2049:        forward sweep:
2050:        for i=0,...,m-1:
2051:          sum[i] = (b[i] - U(i,:)x )/d[i];
2052:          x[i]   = (1-omega)x[i] + omega*sum[i];
2053:          b      = b - x[i]*U^T(i,:);
2054:          
2055:     */
2056:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
2057:       for (i=0; i<m; i++)
2058:         t[i] = b[i];

2060:       for (i=0; i<m; i++){
2061:         d  = *(aa + ai[i]);  /* diag[i] */
2062:         v  = aa + ai[i] + 1; v1=v;
2063:         vj = aj + ai[i] + 1; vj1=vj;
2064:         nz = ai[i+1] - ai[i] - 1; nz1=nz;
2065:         sum = t[i];
2066:         PetscLogFlops(4*nz-2);
2067:         while (nz1--) sum -= (*v1++)*x[*vj1++];
2068:         x[i] = (1-omega)*x[i] + omega*sum/d;
2069:         while (nz--) t[*vj++] -= x[i]*(*v++);
2070:       }
2071:     }
2072: 
2073:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
2074:       /* 
2075:        backward sweep:
2076:        b = b - x[i]*U^T(i,:), i=0,...,n-2
2077:        for i=m-1,...,0:
2078:          sum[i] = (b[i] - U(i,:)x )/d[i];
2079:          x[i]   = (1-omega)x[i] + omega*sum[i];
2080:       */
2081:       /* if there was a forward sweep done above then I thing the next two for loops are not needed */
2082:       for (i=0; i<m; i++)
2083:         t[i] = b[i];
2084: 
2085:       for (i=0; i<m-1; i++){  /* update rhs */
2086:         v  = aa + ai[i] + 1;
2087:         vj = aj + ai[i] + 1;
2088:         nz = ai[i+1] - ai[i] - 1;
2089:         PetscLogFlops(2*nz-1);
2090:         while (nz--) t[*vj++] -= x[i]*(*v++);
2091:       }
2092:       for (i=m-1; i>=0; i--){
2093:         d  = *(aa + ai[i]);
2094:         v  = aa + ai[i] + 1;
2095:         vj = aj + ai[i] + 1;
2096:         nz = ai[i+1] - ai[i] - 1;
2097:         PetscLogFlops(2*nz-1);
2098:         sum = t[i];
2099:         while (nz--) sum -= x[*vj++]*(*v++);
2100:         x[i] =   (1-omega)*x[i] + omega*sum/d;
2101:       }
2102:     }
2103:   }

2105:   VecRestoreArray(xx,&x);
2106:   if (bb != xx) {
2107:     VecRestoreArray(bb,&b);
2108:   }
2109:   return(0);
2110: }

2114: /*@
2115:      MatCreateSeqSBAIJWithArrays - Creates an sequential SBAIJ matrix using matrix elements 
2116:               (upper triangular entries in CSR format) provided by the user.

2118:      Collective on MPI_Comm

2120:    Input Parameters:
2121: +  comm - must be an MPI communicator of size 1
2122: .  bs - size of block
2123: .  m - number of rows
2124: .  n - number of columns
2125: .  i - row indices
2126: .  j - column indices
2127: -  a - matrix values

2129:    Output Parameter:
2130: .  mat - the matrix

2132:    Level: intermediate

2134:    Notes:
2135:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
2136:     once the matrix is destroyed

2138:        You cannot set new nonzero locations into this matrix, that will generate an error.

2140:        The i and j indices are 0 based

2142: .seealso: MatCreate(), MatCreateMPISBAIJ(), MatCreateSeqSBAIJ()

2144: @*/
2145: PetscErrorCode  MatCreateSeqSBAIJWithArrays(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
2146: {
2148:   PetscInt       ii;
2149:   Mat_SeqSBAIJ   *sbaij;

2152:   if (bs != 1) SETERRQ1(PETSC_ERR_SUP,"block size %D > 1 is not supported yet",bs);
2153:   if (i[0]) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
2154: 
2155:   MatCreate(comm,mat);
2156:   MatSetSizes(*mat,m,n,m,n);
2157:   MatSetType(*mat,MATSEQSBAIJ);
2158:   MatSeqSBAIJSetPreallocation_SeqSBAIJ(*mat,bs,MAT_SKIP_ALLOCATION,0);
2159:   sbaij = (Mat_SeqSBAIJ*)(*mat)->data;
2160:   PetscMalloc2(m,PetscInt,&sbaij->imax,m,PetscInt,&sbaij->ilen);

2162:   sbaij->i = i;
2163:   sbaij->j = j;
2164:   sbaij->a = a;
2165:   sbaij->singlemalloc = PETSC_FALSE;
2166:   sbaij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
2167:   sbaij->free_a       = PETSC_FALSE;
2168:   sbaij->free_ij      = PETSC_FALSE;

2170:   for (ii=0; ii<m; ii++) {
2171:     sbaij->ilen[ii] = sbaij->imax[ii] = i[ii+1] - i[ii];
2172: #if defined(PETSC_USE_DEBUG)
2173:     if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
2174: #endif    
2175:   }
2176: #if defined(PETSC_USE_DEBUG)
2177:   for (ii=0; ii<sbaij->i[m]; ii++) {
2178:     if (j[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
2179:     if (j[ii] > n - 1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
2180:   }
2181: #endif    

2183:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
2184:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
2185:   return(0);
2186: }