Actual source code: aij.c

petsc-3.15.0 2021-04-05
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  1: /*
  2:     Defines the basic matrix operations for the AIJ (compressed row)
  3:   matrix storage format.
  4: */


  7: #include <../src/mat/impls/aij/seq/aij.h>
  8: #include <petscblaslapack.h>
  9: #include <petscbt.h>
 10: #include <petsc/private/kernels/blocktranspose.h>

 12: PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
 13: {
 14:   PetscErrorCode       ierr;
 15:   PetscBool            flg;
 16:   char                 type[256];

 19:   PetscObjectOptionsBegin((PetscObject)A);
 20:   PetscOptionsFList("-mat_seqaij_type","Matrix SeqAIJ type","MatSeqAIJSetType",MatSeqAIJList,"seqaij",type,256,&flg);
 21:   if (flg) {
 22:     MatSeqAIJSetType(A,type);
 23:   }
 24:   PetscOptionsEnd();
 25:   return(0);
 26: }

 28: PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms)
 29: {
 31:   PetscInt       i,m,n;
 32:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

 35:   MatGetSize(A,&m,&n);
 36:   PetscArrayzero(norms,n);
 37:   if (type == NORM_2) {
 38:     for (i=0; i<aij->i[m]; i++) {
 39:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]);
 40:     }
 41:   } else if (type == NORM_1) {
 42:     for (i=0; i<aij->i[m]; i++) {
 43:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]);
 44:     }
 45:   } else if (type == NORM_INFINITY) {
 46:     for (i=0; i<aij->i[m]; i++) {
 47:       norms[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),norms[aij->j[i]]);
 48:     }
 49:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown NormType");

 51:   if (type == NORM_2) {
 52:     for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
 53:   }
 54:   return(0);
 55: }

 57: PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A,IS *is)
 58: {
 59:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
 60:   PetscInt        i,m=A->rmap->n,cnt = 0, bs = A->rmap->bs;
 61:   const PetscInt  *jj = a->j,*ii = a->i;
 62:   PetscInt        *rows;
 63:   PetscErrorCode  ierr;

 66:   for (i=0; i<m; i++) {
 67:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
 68:       cnt++;
 69:     }
 70:   }
 71:   PetscMalloc1(cnt,&rows);
 72:   cnt  = 0;
 73:   for (i=0; i<m; i++) {
 74:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
 75:       rows[cnt] = i;
 76:       cnt++;
 77:     }
 78:   }
 79:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);
 80:   return(0);
 81: }

 83: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows)
 84: {
 85:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
 86:   const MatScalar *aa = a->a;
 87:   PetscInt        i,m=A->rmap->n,cnt = 0;
 88:   const PetscInt  *ii = a->i,*jj = a->j,*diag;
 89:   PetscInt        *rows;
 90:   PetscErrorCode  ierr;

 93:   MatMarkDiagonal_SeqAIJ(A);
 94:   diag = a->diag;
 95:   for (i=0; i<m; i++) {
 96:     if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
 97:       cnt++;
 98:     }
 99:   }
100:   PetscMalloc1(cnt,&rows);
101:   cnt  = 0;
102:   for (i=0; i<m; i++) {
103:     if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
104:       rows[cnt++] = i;
105:     }
106:   }
107:   *nrows = cnt;
108:   *zrows = rows;
109:   return(0);
110: }

112: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
113: {
114:   PetscInt       nrows,*rows;

118:   *zrows = NULL;
119:   MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
120:   ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);
121:   return(0);
122: }

124: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
125: {
126:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
127:   const MatScalar *aa;
128:   PetscInt        m=A->rmap->n,cnt = 0;
129:   const PetscInt  *ii;
130:   PetscInt        n,i,j,*rows;
131:   PetscErrorCode  ierr;

134:   MatSeqAIJGetArrayRead(A,&aa);
135:   *keptrows = NULL;
136:   ii        = a->i;
137:   for (i=0; i<m; i++) {
138:     n = ii[i+1] - ii[i];
139:     if (!n) {
140:       cnt++;
141:       goto ok1;
142:     }
143:     for (j=ii[i]; j<ii[i+1]; j++) {
144:       if (aa[j] != 0.0) goto ok1;
145:     }
146:     cnt++;
147: ok1:;
148:   }
149:   if (!cnt) {
150:     MatSeqAIJRestoreArrayRead(A,&aa);
151:     return(0);
152:   }
153:   PetscMalloc1(A->rmap->n-cnt,&rows);
154:   cnt  = 0;
155:   for (i=0; i<m; i++) {
156:     n = ii[i+1] - ii[i];
157:     if (!n) continue;
158:     for (j=ii[i]; j<ii[i+1]; j++) {
159:       if (aa[j] != 0.0) {
160:         rows[cnt++] = i;
161:         break;
162:       }
163:     }
164:   }
165:   MatSeqAIJRestoreArrayRead(A,&aa);
166:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);
167:   return(0);
168: }

170: PetscErrorCode  MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
171: {
172:   PetscErrorCode    ierr;
173:   Mat_SeqAIJ        *aij = (Mat_SeqAIJ*) Y->data;
174:   PetscInt          i,m = Y->rmap->n;
175:   const PetscInt    *diag;
176:   MatScalar         *aa;
177:   const PetscScalar *v;
178:   PetscBool         missing;
179: #if defined(PETSC_HAVE_DEVICE)
180:   PetscBool         inserted = PETSC_FALSE;
181: #endif

184:   if (Y->assembled) {
185:     MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
186:     if (!missing) {
187:       diag = aij->diag;
188:       VecGetArrayRead(D,&v);
189:       MatSeqAIJGetArray(Y,&aa);
190:       if (is == INSERT_VALUES) {
191: #if defined(PETSC_HAVE_DEVICE)
192:         inserted = PETSC_TRUE;
193: #endif
194:         for (i=0; i<m; i++) {
195:           aa[diag[i]] = v[i];
196:         }
197:       } else {
198:         for (i=0; i<m; i++) {
199: #if defined(PETSC_HAVE_DEVICE)
200:           if (v[i] != 0.0) inserted = PETSC_TRUE;
201: #endif
202:           aa[diag[i]] += v[i];
203:         }
204:       }
205:       MatSeqAIJRestoreArray(Y,&aa);
206: #if defined(PETSC_HAVE_DEVICE)
207:       if (inserted) Y->offloadmask = PETSC_OFFLOAD_CPU;
208: #endif
209:       VecRestoreArrayRead(D,&v);
210:       return(0);
211:     }
212:     MatSeqAIJInvalidateDiagonal(Y);
213:   }
214:   MatDiagonalSet_Default(Y,D,is);
215:   return(0);
216: }

218: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
219: {
220:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
222:   PetscInt       i,ishift;

225:   *m = A->rmap->n;
226:   if (!ia) return(0);
227:   ishift = 0;
228:   if (symmetric && !A->structurally_symmetric) {
229:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);
230:   } else if (oshift == 1) {
231:     PetscInt *tia;
232:     PetscInt nz = a->i[A->rmap->n];
233:     /* malloc space and  add 1 to i and j indices */
234:     PetscMalloc1(A->rmap->n+1,&tia);
235:     for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1;
236:     *ia = tia;
237:     if (ja) {
238:       PetscInt *tja;
239:       PetscMalloc1(nz+1,&tja);
240:       for (i=0; i<nz; i++) tja[i] = a->j[i] + 1;
241:       *ja = tja;
242:     }
243:   } else {
244:     *ia = a->i;
245:     if (ja) *ja = a->j;
246:   }
247:   return(0);
248: }

250: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
251: {

255:   if (!ia) return(0);
256:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
257:     PetscFree(*ia);
258:     if (ja) {PetscFree(*ja);}
259:   }
260:   return(0);
261: }

263: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
264: {
265:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
267:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
268:   PetscInt       nz = a->i[m],row,*jj,mr,col;

271:   *nn = n;
272:   if (!ia) return(0);
273:   if (symmetric) {
274:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,0,oshift,(PetscInt**)ia,(PetscInt**)ja);
275:   } else {
276:     PetscCalloc1(n,&collengths);
277:     PetscMalloc1(n+1,&cia);
278:     PetscMalloc1(nz,&cja);
279:     jj   = a->j;
280:     for (i=0; i<nz; i++) {
281:       collengths[jj[i]]++;
282:     }
283:     cia[0] = oshift;
284:     for (i=0; i<n; i++) {
285:       cia[i+1] = cia[i] + collengths[i];
286:     }
287:     PetscArrayzero(collengths,n);
288:     jj   = a->j;
289:     for (row=0; row<m; row++) {
290:       mr = a->i[row+1] - a->i[row];
291:       for (i=0; i<mr; i++) {
292:         col = *jj++;

294:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
295:       }
296:     }
297:     PetscFree(collengths);
298:     *ia  = cia; *ja = cja;
299:   }
300:   return(0);
301: }

303: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
304: {

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

310:   PetscFree(*ia);
311:   PetscFree(*ja);
312:   return(0);
313: }

315: /*
316:  MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
317:  MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
318:  spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
319: */
320: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
321: {
322:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
324:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
325:   PetscInt       nz = a->i[m],row,mr,col,tmp;
326:   PetscInt       *cspidx;
327:   const PetscInt *jj;

330:   *nn = n;
331:   if (!ia) return(0);

333:   PetscCalloc1(n,&collengths);
334:   PetscMalloc1(n+1,&cia);
335:   PetscMalloc1(nz,&cja);
336:   PetscMalloc1(nz,&cspidx);
337:   jj   = a->j;
338:   for (i=0; i<nz; i++) {
339:     collengths[jj[i]]++;
340:   }
341:   cia[0] = oshift;
342:   for (i=0; i<n; i++) {
343:     cia[i+1] = cia[i] + collengths[i];
344:   }
345:   PetscArrayzero(collengths,n);
346:   jj   = a->j;
347:   for (row=0; row<m; row++) {
348:     mr = a->i[row+1] - a->i[row];
349:     for (i=0; i<mr; i++) {
350:       col         = *jj++;
351:       tmp         = cia[col] + collengths[col]++ - oshift;
352:       cspidx[tmp] = a->i[row] + i; /* index of a->j */
353:       cja[tmp]    = row + oshift;
354:     }
355:   }
356:   PetscFree(collengths);
357:   *ia    = cia;
358:   *ja    = cja;
359:   *spidx = cspidx;
360:   return(0);
361: }

363: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
364: {

368:   MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);
369:   PetscFree(*spidx);
370:   return(0);
371: }

373: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
374: {
375:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
376:   PetscInt       *ai = a->i;

380:   PetscArraycpy(a->a+ai[row],v,ai[row+1]-ai[row]);
381: #if defined(PETSC_HAVE_DEVICE)
382:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && ai[row+1]-ai[row]) A->offloadmask = PETSC_OFFLOAD_CPU;
383: #endif
384:   return(0);
385: }

387: /*
388:     MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions

390:       -   a single row of values is set with each call
391:       -   no row or column indices are negative or (in error) larger than the number of rows or columns
392:       -   the values are always added to the matrix, not set
393:       -   no new locations are introduced in the nonzero structure of the matrix

395:      This does NOT assume the global column indices are sorted

397: */

399: #include <petsc/private/isimpl.h>
400: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
401: {
402:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
403:   PetscInt       low,high,t,row,nrow,i,col,l;
404:   const PetscInt *rp,*ai = a->i,*ailen = a->ilen,*aj = a->j;
405:   PetscInt       lastcol = -1;
406:   MatScalar      *ap,value,*aa = a->a;
407:   const PetscInt *ridx = A->rmap->mapping->indices,*cidx = A->cmap->mapping->indices;

409:   row  = ridx[im[0]];
410:   rp   = aj + ai[row];
411:   ap   = aa + ai[row];
412:   nrow = ailen[row];
413:   low  = 0;
414:   high = nrow;
415:   for (l=0; l<n; l++) { /* loop over added columns */
416:     col = cidx[in[l]];
417:     value = v[l];

419:     if (col <= lastcol) low = 0;
420:     else high = nrow;
421:     lastcol = col;
422:     while (high-low > 5) {
423:       t = (low+high)/2;
424:       if (rp[t] > col) high = t;
425:       else low = t;
426:     }
427:     for (i=low; i<high; i++) {
428:       if (rp[i] == col) {
429:         ap[i] += value;
430:         low = i + 1;
431:         break;
432:       }
433:     }
434:   }
435: #if defined(PETSC_HAVE_DEVICE)
436:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
437: #endif
438:   return 0;
439: }

441: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
442: {
443:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
444:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
445:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
447:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
448:   MatScalar      *ap=NULL,value=0.0,*aa;
449:   PetscBool      ignorezeroentries = a->ignorezeroentries;
450:   PetscBool      roworiented       = a->roworiented;
451: #if defined(PETSC_HAVE_DEVICE)
452:   PetscBool      inserted          = PETSC_FALSE;
453: #endif

456: #if defined(PETSC_HAVE_DEVICE)
457:   if (A->offloadmask == PETSC_OFFLOAD_GPU) {
458:     const PetscScalar *dummy;
459:     MatSeqAIJGetArrayRead(A,&dummy);
460:     MatSeqAIJRestoreArrayRead(A,&dummy);
461:   }
462: #endif
463:   aa = a->a;
464:   for (k=0; k<m; k++) { /* loop over added rows */
465:     row = im[k];
466:     if (row < 0) continue;
467:     if (PetscUnlikelyDebug(row >= A->rmap->n)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
468:     rp   = aj + ai[row];
469:     if (!A->structure_only) ap = aa + ai[row];
470:     rmax = imax[row]; nrow = ailen[row];
471:     low  = 0;
472:     high = nrow;
473:     for (l=0; l<n; l++) { /* loop over added columns */
474:       if (in[l] < 0) continue;
475:       if (PetscUnlikelyDebug(in[l] >= A->cmap->n)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
476:       col = in[l];
477:       if (v && !A->structure_only) value = roworiented ? v[l + k*n] : v[k + l*m];
478:       if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue;

480:       if (col <= lastcol) low = 0;
481:       else high = nrow;
482:       lastcol = col;
483:       while (high-low > 5) {
484:         t = (low+high)/2;
485:         if (rp[t] > col) high = t;
486:         else low = t;
487:       }
488:       for (i=low; i<high; i++) {
489:         if (rp[i] > col) break;
490:         if (rp[i] == col) {
491:           if (!A->structure_only) {
492:             if (is == ADD_VALUES) {
493:               ap[i] += value;
494:               (void)PetscLogFlops(1.0);
495:             }
496:             else ap[i] = value;
497: #if defined(PETSC_HAVE_DEVICE)
498:             inserted = PETSC_TRUE;
499: #endif
500:           }
501:           low = i + 1;
502:           goto noinsert;
503:         }
504:       }
505:       if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
506:       if (nonew == 1) goto noinsert;
507:       if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
508:       if (A->structure_only) {
509:         MatSeqXAIJReallocateAIJ_structure_only(A,A->rmap->n,1,nrow,row,col,rmax,ai,aj,rp,imax,nonew,MatScalar);
510:       } else {
511:         MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
512:       }
513:       N = nrow++ - 1; a->nz++; high++;
514:       /* shift up all the later entries in this row */
515:       PetscArraymove(rp+i+1,rp+i,N-i+1);
516:       rp[i] = col;
517:       if (!A->structure_only){
518:         PetscArraymove(ap+i+1,ap+i,N-i+1);
519:         ap[i] = value;
520:       }
521:       low = i + 1;
522:       A->nonzerostate++;
523: #if defined(PETSC_HAVE_DEVICE)
524:       inserted = PETSC_TRUE;
525: #endif
526: noinsert:;
527:     }
528:     ailen[row] = nrow;
529:   }
530: #if defined(PETSC_HAVE_DEVICE)
531:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && inserted) A->offloadmask = PETSC_OFFLOAD_CPU;
532: #endif
533:   return(0);
534: }


537: PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
538: {
539:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
540:   PetscInt       *rp,k,row;
541:   PetscInt       *ai = a->i;
543:   PetscInt       *aj = a->j;
544:   MatScalar      *aa = a->a,*ap;

547:   if (A->was_assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot call on assembled matrix.");
548:   if (m*n+a->nz > a->maxnz) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Number of entries in matrix will be larger than maximum nonzeros allocated for %D in MatSeqAIJSetTotalPreallocation()",a->maxnz);
549:   for (k=0; k<m; k++) { /* loop over added rows */
550:     row  = im[k];
551:     rp   = aj + ai[row];
552:     ap   = aa + ai[row];

554:     PetscMemcpy(rp,in,n*sizeof(PetscInt));
555:     if (!A->structure_only) {
556:       if (v) {
557:         PetscMemcpy(ap,v,n*sizeof(PetscScalar));
558:         v   += n;
559:       } else {
560:         PetscMemzero(ap,n*sizeof(PetscScalar));
561:       }
562:     }
563:     a->ilen[row] = n;
564:     a->imax[row] = n;
565:     a->i[row+1]  = a->i[row]+n;
566:     a->nz       += n;
567:   }
568: #if defined(PETSC_HAVE_DEVICE)
569:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
570: #endif
571:   return(0);
572: }

574: /*@
575:     MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.

577:   Input Parameters:
578: +  A - the SeqAIJ matrix
579: -  nztotal - bound on the number of nonzeros

581:   Level: advanced

583:   Notes:
584:     This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row.
585:     Simply call MatSetValues() after this call to provide the matrix entries in the usual manner. This matrix may be used
586:     as always with multiple matrix assemblies.

588: .seealso: MatSetOption(), MAT_SORTED_FULL, MatSetValues(), MatSeqAIJSetPreallocation()
589: @*/

591: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A,PetscInt nztotal)
592: {
594:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

597:   PetscLayoutSetUp(A->rmap);
598:   PetscLayoutSetUp(A->cmap);
599:   a->maxnz  = nztotal;
600:   if (!a->imax) {
601:     PetscMalloc1(A->rmap->n,&a->imax);
602:     PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
603:   }
604:   if (!a->ilen) {
605:     PetscMalloc1(A->rmap->n,&a->ilen);
606:     PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
607:   } else {
608:     PetscMemzero(a->ilen,A->rmap->n*sizeof(PetscInt));
609:   }

611:   /* allocate the matrix space */
612:   if (A->structure_only) {
613:     PetscMalloc1(nztotal,&a->j);
614:     PetscMalloc1(A->rmap->n+1,&a->i);
615:     PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*sizeof(PetscInt));
616:   } else {
617:     PetscMalloc3(nztotal,&a->a,nztotal,&a->j,A->rmap->n+1,&a->i);
618:     PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*(sizeof(PetscScalar)+sizeof(PetscInt)));
619:   }
620:   a->i[0] = 0;
621:   if (A->structure_only) {
622:     a->singlemalloc = PETSC_FALSE;
623:     a->free_a       = PETSC_FALSE;
624:   } else {
625:     a->singlemalloc = PETSC_TRUE;
626:     a->free_a       = PETSC_TRUE;
627:   }
628:   a->free_ij         = PETSC_TRUE;
629:   A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
630:   A->preallocated   = PETSC_TRUE;
631:   return(0);
632: }

634: PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
635: {
636:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
637:   PetscInt       *rp,k,row;
638:   PetscInt       *ai = a->i,*ailen = a->ilen;
640:   PetscInt       *aj = a->j;
641:   MatScalar      *aa = a->a,*ap;

644:   for (k=0; k<m; k++) { /* loop over added rows */
645:     row  = im[k];
646:     if (PetscUnlikelyDebug(n > a->imax[row])) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Preallocation for row %D does not match number of columns provided",n);
647:     rp   = aj + ai[row];
648:     ap   = aa + ai[row];
649:     if (!A->was_assembled) {
650:       PetscMemcpy(rp,in,n*sizeof(PetscInt));
651:     }
652:     if (!A->structure_only) {
653:       if (v) {
654:         PetscMemcpy(ap,v,n*sizeof(PetscScalar));
655:         v   += n;
656:       } else {
657:         PetscMemzero(ap,n*sizeof(PetscScalar));
658:       }
659:     }
660:     ailen[row] = n;
661:     a->nz      += n;
662:   }
663: #if defined(PETSC_HAVE_DEVICE)
664:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
665: #endif
666:   return(0);
667: }


670: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
671: {
672:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
673:   PetscInt   *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
674:   PetscInt   *ai = a->i,*ailen = a->ilen;
675:   MatScalar  *ap,*aa = a->a;

678:   for (k=0; k<m; k++) { /* loop over rows */
679:     row = im[k];
680:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
681:     if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
682:     rp   = aj + ai[row]; ap = aa + ai[row];
683:     nrow = ailen[row];
684:     for (l=0; l<n; l++) { /* loop over columns */
685:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
686:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
687:       col  = in[l];
688:       high = nrow; low = 0; /* assume unsorted */
689:       while (high-low > 5) {
690:         t = (low+high)/2;
691:         if (rp[t] > col) high = t;
692:         else low = t;
693:       }
694:       for (i=low; i<high; i++) {
695:         if (rp[i] > col) break;
696:         if (rp[i] == col) {
697:           *v++ = ap[i];
698:           goto finished;
699:         }
700:       }
701:       *v++ = 0.0;
702: finished:;
703:     }
704:   }
705:   return(0);
706: }

708: PetscErrorCode MatView_SeqAIJ_Binary(Mat mat,PetscViewer viewer)
709: {
710:   Mat_SeqAIJ        *A = (Mat_SeqAIJ*)mat->data;
711:   const PetscScalar *av;
712:   PetscInt          header[4],M,N,m,nz,i;
713:   PetscInt          *rowlens;
714:   PetscErrorCode    ierr;

717:   PetscViewerSetUp(viewer);

719:   M  = mat->rmap->N;
720:   N  = mat->cmap->N;
721:   m  = mat->rmap->n;
722:   nz = A->nz;

724:   /* write matrix header */
725:   header[0] = MAT_FILE_CLASSID;
726:   header[1] = M; header[2] = N; header[3] = nz;
727:   PetscViewerBinaryWrite(viewer,header,4,PETSC_INT);

729:   /* fill in and store row lengths */
730:   PetscMalloc1(m,&rowlens);
731:   for (i=0; i<m; i++) rowlens[i] = A->i[i+1] - A->i[i];
732:   PetscViewerBinaryWrite(viewer,rowlens,m,PETSC_INT);
733:   PetscFree(rowlens);
734:   /* store column indices */
735:   PetscViewerBinaryWrite(viewer,A->j,nz,PETSC_INT);
736:   /* store nonzero values */
737:   MatSeqAIJGetArrayRead(mat,&av);
738:   PetscViewerBinaryWrite(viewer,av,nz,PETSC_SCALAR);
739:   MatSeqAIJRestoreArrayRead(mat,&av);

741:   /* write block size option to the viewer's .info file */
742:   MatView_Binary_BlockSizes(mat,viewer);
743:   return(0);
744: }

746: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A,PetscViewer viewer)
747: {
749:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
750:   PetscInt       i,k,m=A->rmap->N;

753:   PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
754:   for (i=0; i<m; i++) {
755:     PetscViewerASCIIPrintf(viewer,"row %D:",i);
756:     for (k=a->i[i]; k<a->i[i+1]; k++) {
757:       PetscViewerASCIIPrintf(viewer," (%D) ",a->j[k]);
758:     }
759:     PetscViewerASCIIPrintf(viewer,"\n");
760:   }
761:   PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
762:   return(0);
763: }

765: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

767: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
768: {
769:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
770:   const PetscScalar *av;
771:   PetscErrorCode    ierr;
772:   PetscInt          i,j,m = A->rmap->n;
773:   const char        *name;
774:   PetscViewerFormat format;

777:   if (A->structure_only) {
778:     MatView_SeqAIJ_ASCII_structonly(A,viewer);
779:     return(0);
780:   }

782:   PetscViewerGetFormat(viewer,&format);
783:   if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) return(0);

785:   /* trigger copy to CPU if needed */
786:   MatSeqAIJGetArrayRead(A,&av);
787:   MatSeqAIJRestoreArrayRead(A,&av);
788:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
789:     PetscInt nofinalvalue = 0;
790:     if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) {
791:       /* Need a dummy value to ensure the dimension of the matrix. */
792:       nofinalvalue = 1;
793:     }
794:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
795:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
796:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
797: #if defined(PETSC_USE_COMPLEX)
798:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);
799: #else
800:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
801: #endif
802:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

804:     for (i=0; i<m; i++) {
805:       for (j=a->i[i]; j<a->i[i+1]; j++) {
806: #if defined(PETSC_USE_COMPLEX)
807:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e %18.16e\n",i+1,a->j[j]+1,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
808: #else
809:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);
810: #endif
811:       }
812:     }
813:     if (nofinalvalue) {
814: #if defined(PETSC_USE_COMPLEX)
815:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
816: #else
817:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap->n,0.0);
818: #endif
819:     }
820:     PetscObjectGetName((PetscObject)A,&name);
821:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
822:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
823:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
824:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
825:     for (i=0; i<m; i++) {
826:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
827:       for (j=a->i[i]; j<a->i[i+1]; j++) {
828: #if defined(PETSC_USE_COMPLEX)
829:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
830:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
831:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
832:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
833:         } else if (PetscRealPart(a->a[j]) != 0.0) {
834:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
835:         }
836: #else
837:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);}
838: #endif
839:       }
840:       PetscViewerASCIIPrintf(viewer,"\n");
841:     }
842:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
843:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
844:     PetscInt nzd=0,fshift=1,*sptr;
845:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
846:     PetscMalloc1(m+1,&sptr);
847:     for (i=0; i<m; i++) {
848:       sptr[i] = nzd+1;
849:       for (j=a->i[i]; j<a->i[i+1]; j++) {
850:         if (a->j[j] >= i) {
851: #if defined(PETSC_USE_COMPLEX)
852:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
853: #else
854:           if (a->a[j] != 0.0) nzd++;
855: #endif
856:         }
857:       }
858:     }
859:     sptr[m] = nzd+1;
860:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
861:     for (i=0; i<m+1; i+=6) {
862:       if (i+4<m) {
863:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);
864:       } else if (i+3<m) {
865:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
866:       } else if (i+2<m) {
867:         PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
868:       } else if (i+1<m) {
869:         PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);
870:       } else if (i<m) {
871:         PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);
872:       } else {
873:         PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);
874:       }
875:     }
876:     PetscViewerASCIIPrintf(viewer,"\n");
877:     PetscFree(sptr);
878:     for (i=0; i<m; i++) {
879:       for (j=a->i[i]; j<a->i[i+1]; j++) {
880:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
881:       }
882:       PetscViewerASCIIPrintf(viewer,"\n");
883:     }
884:     PetscViewerASCIIPrintf(viewer,"\n");
885:     for (i=0; i<m; i++) {
886:       for (j=a->i[i]; j<a->i[i+1]; j++) {
887:         if (a->j[j] >= i) {
888: #if defined(PETSC_USE_COMPLEX)
889:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
890:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
891:           }
892: #else
893:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);}
894: #endif
895:         }
896:       }
897:       PetscViewerASCIIPrintf(viewer,"\n");
898:     }
899:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
900:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
901:     PetscInt    cnt = 0,jcnt;
902:     PetscScalar value;
903: #if defined(PETSC_USE_COMPLEX)
904:     PetscBool   realonly = PETSC_TRUE;

906:     for (i=0; i<a->i[m]; i++) {
907:       if (PetscImaginaryPart(a->a[i]) != 0.0) {
908:         realonly = PETSC_FALSE;
909:         break;
910:       }
911:     }
912: #endif

914:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
915:     for (i=0; i<m; i++) {
916:       jcnt = 0;
917:       for (j=0; j<A->cmap->n; j++) {
918:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
919:           value = a->a[cnt++];
920:           jcnt++;
921:         } else {
922:           value = 0.0;
923:         }
924: #if defined(PETSC_USE_COMPLEX)
925:         if (realonly) {
926:           PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
927:         } else {
928:           PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
929:         }
930: #else
931:         PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
932: #endif
933:       }
934:       PetscViewerASCIIPrintf(viewer,"\n");
935:     }
936:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
937:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
938:     PetscInt fshift=1;
939:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
940: #if defined(PETSC_USE_COMPLEX)
941:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
942: #else
943:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
944: #endif
945:     PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
946:     for (i=0; i<m; i++) {
947:       for (j=a->i[i]; j<a->i[i+1]; j++) {
948: #if defined(PETSC_USE_COMPLEX)
949:         PetscViewerASCIIPrintf(viewer,"%D %D %g %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
950: #else
951:         PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);
952: #endif
953:       }
954:     }
955:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
956:   } else {
957:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
958:     if (A->factortype) {
959:       for (i=0; i<m; i++) {
960:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
961:         /* L part */
962:         for (j=a->i[i]; j<a->i[i+1]; j++) {
963: #if defined(PETSC_USE_COMPLEX)
964:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
965:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
966:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
967:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
968:           } else {
969:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
970:           }
971: #else
972:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
973: #endif
974:         }
975:         /* diagonal */
976:         j = a->diag[i];
977: #if defined(PETSC_USE_COMPLEX)
978:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
979:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)PetscImaginaryPart(1.0/a->a[j]));
980:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
981:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)(-PetscImaginaryPart(1.0/a->a[j])));
982:         } else {
983:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));
984:         }
985: #else
986:         PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)(1.0/a->a[j]));
987: #endif

989:         /* U part */
990:         for (j=a->diag[i+1]+1; j<a->diag[i]; j++) {
991: #if defined(PETSC_USE_COMPLEX)
992:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
993:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
994:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
995:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
996:           } else {
997:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
998:           }
999: #else
1000:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
1001: #endif
1002:         }
1003:         PetscViewerASCIIPrintf(viewer,"\n");
1004:       }
1005:     } else {
1006:       for (i=0; i<m; i++) {
1007:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
1008:         for (j=a->i[i]; j<a->i[i+1]; j++) {
1009: #if defined(PETSC_USE_COMPLEX)
1010:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
1011:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
1012:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
1013:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
1014:           } else {
1015:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
1016:           }
1017: #else
1018:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
1019: #endif
1020:         }
1021:         PetscViewerASCIIPrintf(viewer,"\n");
1022:       }
1023:     }
1024:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1025:   }
1026:   PetscViewerFlush(viewer);
1027:   return(0);
1028: }

1030: #include <petscdraw.h>
1031: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
1032: {
1033:   Mat               A  = (Mat) Aa;
1034:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1035:   PetscErrorCode    ierr;
1036:   PetscInt          i,j,m = A->rmap->n;
1037:   int               color;
1038:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r;
1039:   PetscViewer       viewer;
1040:   PetscViewerFormat format;

1043:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
1044:   PetscViewerGetFormat(viewer,&format);
1045:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);

1047:   /* loop over matrix elements drawing boxes */

1049:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
1050:     PetscDrawCollectiveBegin(draw);
1051:     /* Blue for negative, Cyan for zero and  Red for positive */
1052:     color = PETSC_DRAW_BLUE;
1053:     for (i=0; i<m; i++) {
1054:       y_l = m - i - 1.0; y_r = y_l + 1.0;
1055:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1056:         x_l = a->j[j]; x_r = x_l + 1.0;
1057:         if (PetscRealPart(a->a[j]) >=  0.) continue;
1058:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1059:       }
1060:     }
1061:     color = PETSC_DRAW_CYAN;
1062:     for (i=0; i<m; i++) {
1063:       y_l = m - i - 1.0; y_r = y_l + 1.0;
1064:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1065:         x_l = a->j[j]; x_r = x_l + 1.0;
1066:         if (a->a[j] !=  0.) continue;
1067:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1068:       }
1069:     }
1070:     color = PETSC_DRAW_RED;
1071:     for (i=0; i<m; i++) {
1072:       y_l = m - i - 1.0; y_r = y_l + 1.0;
1073:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1074:         x_l = a->j[j]; x_r = x_l + 1.0;
1075:         if (PetscRealPart(a->a[j]) <=  0.) continue;
1076:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1077:       }
1078:     }
1079:     PetscDrawCollectiveEnd(draw);
1080:   } else {
1081:     /* use contour shading to indicate magnitude of values */
1082:     /* first determine max of all nonzero values */
1083:     PetscReal minv = 0.0, maxv = 0.0;
1084:     PetscInt  nz = a->nz, count = 0;
1085:     PetscDraw popup;

1087:     for (i=0; i<nz; i++) {
1088:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
1089:     }
1090:     if (minv >= maxv) maxv = minv + PETSC_SMALL;
1091:     PetscDrawGetPopup(draw,&popup);
1092:     PetscDrawScalePopup(popup,minv,maxv);

1094:     PetscDrawCollectiveBegin(draw);
1095:     for (i=0; i<m; i++) {
1096:       y_l = m - i - 1.0;
1097:       y_r = y_l + 1.0;
1098:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1099:         x_l = a->j[j];
1100:         x_r = x_l + 1.0;
1101:         color = PetscDrawRealToColor(PetscAbsScalar(a->a[count]),minv,maxv);
1102:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1103:         count++;
1104:       }
1105:     }
1106:     PetscDrawCollectiveEnd(draw);
1107:   }
1108:   return(0);
1109: }

1111: #include <petscdraw.h>
1112: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
1113: {
1115:   PetscDraw      draw;
1116:   PetscReal      xr,yr,xl,yl,h,w;
1117:   PetscBool      isnull;

1120:   PetscViewerDrawGetDraw(viewer,0,&draw);
1121:   PetscDrawIsNull(draw,&isnull);
1122:   if (isnull) return(0);

1124:   xr   = A->cmap->n; yr  = A->rmap->n; h = yr/10.0; w = xr/10.0;
1125:   xr  += w;          yr += h;         xl = -w;     yl = -h;
1126:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1127:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1128:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
1129:   PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
1130:   PetscDrawSave(draw);
1131:   return(0);
1132: }

1134: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
1135: {
1137:   PetscBool      iascii,isbinary,isdraw;

1140:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1141:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1142:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1143:   if (iascii) {
1144:     MatView_SeqAIJ_ASCII(A,viewer);
1145:   } else if (isbinary) {
1146:     MatView_SeqAIJ_Binary(A,viewer);
1147:   } else if (isdraw) {
1148:     MatView_SeqAIJ_Draw(A,viewer);
1149:   }
1150:   MatView_SeqAIJ_Inode(A,viewer);
1151:   return(0);
1152: }

1154: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
1155: {
1156:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1158:   PetscInt       fshift = 0,i,*ai = a->i,*aj = a->j,*imax = a->imax;
1159:   PetscInt       m      = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
1160:   MatScalar      *aa    = a->a,*ap;
1161:   PetscReal      ratio  = 0.6;

1164:   if (mode == MAT_FLUSH_ASSEMBLY) return(0);
1165:   MatSeqAIJInvalidateDiagonal(A);
1166:   if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
1167:     /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
1168:     MatAssemblyEnd_SeqAIJ_Inode(A,mode);
1169:     return(0);
1170:   }

1172:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1173:   for (i=1; i<m; i++) {
1174:     /* move each row back by the amount of empty slots (fshift) before it*/
1175:     fshift += imax[i-1] - ailen[i-1];
1176:     rmax    = PetscMax(rmax,ailen[i]);
1177:     if (fshift) {
1178:       ip = aj + ai[i];
1179:       ap = aa + ai[i];
1180:       N  = ailen[i];
1181:       PetscArraymove(ip-fshift,ip,N);
1182:       if (!A->structure_only) {
1183:         PetscArraymove(ap-fshift,ap,N);
1184:       }
1185:     }
1186:     ai[i] = ai[i-1] + ailen[i-1];
1187:   }
1188:   if (m) {
1189:     fshift += imax[m-1] - ailen[m-1];
1190:     ai[m]   = ai[m-1] + ailen[m-1];
1191:   }

1193:   /* reset ilen and imax for each row */
1194:   a->nonzerorowcnt = 0;
1195:   if (A->structure_only) {
1196:     PetscFree(a->imax);
1197:     PetscFree(a->ilen);
1198:   } else { /* !A->structure_only */
1199:     for (i=0; i<m; i++) {
1200:       ailen[i] = imax[i] = ai[i+1] - ai[i];
1201:       a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0);
1202:     }
1203:   }
1204:   a->nz = ai[m];
1205:   if (fshift && a->nounused == -1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D, %D unneeded", m, A->cmap->n, fshift);

1207:   MatMarkDiagonal_SeqAIJ(A);
1208:   PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
1209:   PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
1210:   PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);

1212:   A->info.mallocs    += a->reallocs;
1213:   a->reallocs         = 0;
1214:   A->info.nz_unneeded = (PetscReal)fshift;
1215:   a->rmax             = rmax;

1217:   if (!A->structure_only) {
1218:     MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);
1219:   }
1220:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);
1221:   return(0);
1222: }

1224: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1225: {
1226:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1227:   PetscInt       i,nz = a->nz;
1228:   MatScalar      *aa;

1232:   MatSeqAIJGetArray(A,&aa);
1233:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
1234:   MatSeqAIJRestoreArray(A,&aa);
1235:   MatSeqAIJInvalidateDiagonal(A);
1236: #if defined(PETSC_HAVE_DEVICE)
1237:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1238: #endif
1239:   return(0);
1240: }

1242: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1243: {
1244:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1245:   PetscInt       i,nz = a->nz;
1246:   MatScalar      *aa;

1250:   MatSeqAIJGetArray(A,&aa);
1251:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1252:   MatSeqAIJRestoreArray(A,&aa);
1253:   MatSeqAIJInvalidateDiagonal(A);
1254: #if defined(PETSC_HAVE_DEVICE)
1255:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1256: #endif
1257:   return(0);
1258: }

1260: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1261: {
1262:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1266:   PetscArrayzero(a->a,a->i[A->rmap->n]);
1267:   MatSeqAIJInvalidateDiagonal(A);
1268: #if defined(PETSC_HAVE_DEVICE)
1269:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1270: #endif
1271:   return(0);
1272: }

1274: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1275: {
1276:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1280: #if defined(PETSC_USE_LOG)
1281:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
1282: #endif
1283:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
1284:   ISDestroy(&a->row);
1285:   ISDestroy(&a->col);
1286:   PetscFree(a->diag);
1287:   PetscFree(a->ibdiag);
1288:   PetscFree(a->imax);
1289:   PetscFree(a->ilen);
1290:   PetscFree(a->ipre);
1291:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
1292:   PetscFree(a->solve_work);
1293:   ISDestroy(&a->icol);
1294:   PetscFree(a->saved_values);
1295:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);

1297:   MatDestroy_SeqAIJ_Inode(A);
1298:   PetscFree(A->data);

1300:   /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1301:      That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1302:      that is hard to properly add this data to the MatProduct data. We free it here to avoid
1303:      users reusing the matrix object with different data to incur in obscure segmentation faults
1304:      due to different matrix sizes */
1305:   PetscObjectCompose((PetscObject)A,"__PETSc__ab_dense",NULL);

1307:   PetscObjectChangeTypeName((PetscObject)A,NULL);
1308:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
1309:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
1310:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
1311:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
1312:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
1313:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);
1314: #if defined(PETSC_HAVE_CUDA)
1315:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcusparse_C",NULL);
1316:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",NULL);
1317:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaijcusparse_C",NULL);
1318: #endif
1319: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1320:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijkokkos_C",NULL);
1321: #endif
1322:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcrl_C",NULL);
1323: #if defined(PETSC_HAVE_ELEMENTAL)
1324:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);
1325: #endif
1326: #if defined(PETSC_HAVE_SCALAPACK)
1327:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_scalapack_C",NULL);
1328: #endif
1329: #if defined(PETSC_HAVE_HYPRE)
1330:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_hypre_C",NULL);
1331:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",NULL);
1332: #endif
1333:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);
1334:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsell_C",NULL);
1335:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_is_C",NULL);
1336:   PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
1337:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
1338:   PetscObjectComposeFunction((PetscObject)A,"MatResetPreallocation_C",NULL);
1339:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
1340:   PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
1341:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_is_seqaij_C",NULL);
1342:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqdense_seqaij_C",NULL);
1343:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaij_C",NULL);
1344:   return(0);
1345: }

1347: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
1348: {
1349:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1353:   switch (op) {
1354:   case MAT_ROW_ORIENTED:
1355:     a->roworiented = flg;
1356:     break;
1357:   case MAT_KEEP_NONZERO_PATTERN:
1358:     a->keepnonzeropattern = flg;
1359:     break;
1360:   case MAT_NEW_NONZERO_LOCATIONS:
1361:     a->nonew = (flg ? 0 : 1);
1362:     break;
1363:   case MAT_NEW_NONZERO_LOCATION_ERR:
1364:     a->nonew = (flg ? -1 : 0);
1365:     break;
1366:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1367:     a->nonew = (flg ? -2 : 0);
1368:     break;
1369:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1370:     a->nounused = (flg ? -1 : 0);
1371:     break;
1372:   case MAT_IGNORE_ZERO_ENTRIES:
1373:     a->ignorezeroentries = flg;
1374:     break;
1375:   case MAT_SPD:
1376:   case MAT_SYMMETRIC:
1377:   case MAT_STRUCTURALLY_SYMMETRIC:
1378:   case MAT_HERMITIAN:
1379:   case MAT_SYMMETRY_ETERNAL:
1380:   case MAT_STRUCTURE_ONLY:
1381:     /* These options are handled directly by MatSetOption() */
1382:     break;
1383:   case MAT_FORCE_DIAGONAL_ENTRIES:
1384:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1385:   case MAT_USE_HASH_TABLE:
1386:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1387:     break;
1388:   case MAT_USE_INODES:
1389:     MatSetOption_SeqAIJ_Inode(A,MAT_USE_INODES,flg);
1390:     break;
1391:   case MAT_SUBMAT_SINGLEIS:
1392:     A->submat_singleis = flg;
1393:     break;
1394:   case MAT_SORTED_FULL:
1395:     if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1396:     else     A->ops->setvalues = MatSetValues_SeqAIJ;
1397:     break;
1398:   case MAT_FORM_EXPLICIT_TRANSPOSE:
1399:     A->form_explicit_transpose = flg;
1400:     break;
1401:   default:
1402:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1403:   }
1404:   return(0);
1405: }

1407: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1408: {
1409:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1410:   PetscErrorCode    ierr;
1411:   PetscInt          i,j,n,*ai=a->i,*aj=a->j;
1412:   PetscScalar       *x;
1413:   const PetscScalar *aa;

1416:   VecGetLocalSize(v,&n);
1417:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
1418:   MatSeqAIJGetArrayRead(A,&aa);
1419:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1420:     PetscInt *diag=a->diag;
1421:     VecGetArrayWrite(v,&x);
1422:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1423:     VecRestoreArrayWrite(v,&x);
1424:     MatSeqAIJRestoreArrayRead(A,&aa);
1425:     return(0);
1426:   }

1428:   VecGetArrayWrite(v,&x);
1429:   for (i=0; i<n; i++) {
1430:     x[i] = 0.0;
1431:     for (j=ai[i]; j<ai[i+1]; j++) {
1432:       if (aj[j] == i) {
1433:         x[i] = aa[j];
1434:         break;
1435:       }
1436:     }
1437:   }
1438:   VecRestoreArrayWrite(v,&x);
1439:   MatSeqAIJRestoreArrayRead(A,&aa);
1440:   return(0);
1441: }

1443: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1444: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1445: {
1446:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1447:   PetscScalar       *y;
1448:   const PetscScalar *x;
1449:   PetscErrorCode    ierr;
1450:   PetscInt          m = A->rmap->n;
1451: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1452:   const MatScalar   *v;
1453:   PetscScalar       alpha;
1454:   PetscInt          n,i,j;
1455:   const PetscInt    *idx,*ii,*ridx=NULL;
1456:   Mat_CompressedRow cprow    = a->compressedrow;
1457:   PetscBool         usecprow = cprow.use;
1458: #endif

1461:   if (zz != yy) {VecCopy(zz,yy);}
1462:   VecGetArrayRead(xx,&x);
1463:   VecGetArray(yy,&y);

1465: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1466:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1467: #else
1468:   if (usecprow) {
1469:     m    = cprow.nrows;
1470:     ii   = cprow.i;
1471:     ridx = cprow.rindex;
1472:   } else {
1473:     ii = a->i;
1474:   }
1475:   for (i=0; i<m; i++) {
1476:     idx = a->j + ii[i];
1477:     v   = a->a + ii[i];
1478:     n   = ii[i+1] - ii[i];
1479:     if (usecprow) {
1480:       alpha = x[ridx[i]];
1481:     } else {
1482:       alpha = x[i];
1483:     }
1484:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1485:   }
1486: #endif
1487:   PetscLogFlops(2.0*a->nz);
1488:   VecRestoreArrayRead(xx,&x);
1489:   VecRestoreArray(yy,&y);
1490:   return(0);
1491: }

1493: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1494: {

1498:   VecSet(yy,0.0);
1499:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1500:   return(0);
1501: }

1503: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>

1505: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1506: {
1507:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1508:   PetscScalar       *y;
1509:   const PetscScalar *x;
1510:   const MatScalar   *aa;
1511:   PetscErrorCode    ierr;
1512:   PetscInt          m=A->rmap->n;
1513:   const PetscInt    *aj,*ii,*ridx=NULL;
1514:   PetscInt          n,i;
1515:   PetscScalar       sum;
1516:   PetscBool         usecprow=a->compressedrow.use;

1518: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1519: #pragma disjoint(*x,*y,*aa)
1520: #endif

1523:   if (a->inode.use && a->inode.checked) {
1524:     MatMult_SeqAIJ_Inode(A,xx,yy);
1525:     return(0);
1526:   }
1527:   VecGetArrayRead(xx,&x);
1528:   VecGetArray(yy,&y);
1529:   ii   = a->i;
1530:   if (usecprow) { /* use compressed row format */
1531:     PetscArrayzero(y,m);
1532:     m    = a->compressedrow.nrows;
1533:     ii   = a->compressedrow.i;
1534:     ridx = a->compressedrow.rindex;
1535:     for (i=0; i<m; i++) {
1536:       n           = ii[i+1] - ii[i];
1537:       aj          = a->j + ii[i];
1538:       aa          = a->a + ii[i];
1539:       sum         = 0.0;
1540:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1541:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1542:       y[*ridx++] = sum;
1543:     }
1544:   } else { /* do not use compressed row format */
1545: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1546:     aj   = a->j;
1547:     aa   = a->a;
1548:     fortranmultaij_(&m,x,ii,aj,aa,y);
1549: #else
1550:     for (i=0; i<m; i++) {
1551:       n           = ii[i+1] - ii[i];
1552:       aj          = a->j + ii[i];
1553:       aa          = a->a + ii[i];
1554:       sum         = 0.0;
1555:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1556:       y[i] = sum;
1557:     }
1558: #endif
1559:   }
1560:   PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
1561:   VecRestoreArrayRead(xx,&x);
1562:   VecRestoreArray(yy,&y);
1563:   return(0);
1564: }

1566: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
1567: {
1568:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1569:   PetscScalar       *y;
1570:   const PetscScalar *x;
1571:   const MatScalar   *aa;
1572:   PetscErrorCode    ierr;
1573:   PetscInt          m=A->rmap->n;
1574:   const PetscInt    *aj,*ii,*ridx=NULL;
1575:   PetscInt          n,i,nonzerorow=0;
1576:   PetscScalar       sum;
1577:   PetscBool         usecprow=a->compressedrow.use;

1579: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1580: #pragma disjoint(*x,*y,*aa)
1581: #endif

1584:   VecGetArrayRead(xx,&x);
1585:   VecGetArray(yy,&y);
1586:   if (usecprow) { /* use compressed row format */
1587:     m    = a->compressedrow.nrows;
1588:     ii   = a->compressedrow.i;
1589:     ridx = a->compressedrow.rindex;
1590:     for (i=0; i<m; i++) {
1591:       n           = ii[i+1] - ii[i];
1592:       aj          = a->j + ii[i];
1593:       aa          = a->a + ii[i];
1594:       sum         = 0.0;
1595:       nonzerorow += (n>0);
1596:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1597:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1598:       y[*ridx++] = sum;
1599:     }
1600:   } else { /* do not use compressed row format */
1601:     ii = a->i;
1602:     for (i=0; i<m; i++) {
1603:       n           = ii[i+1] - ii[i];
1604:       aj          = a->j + ii[i];
1605:       aa          = a->a + ii[i];
1606:       sum         = 0.0;
1607:       nonzerorow += (n>0);
1608:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1609:       y[i] = sum;
1610:     }
1611:   }
1612:   PetscLogFlops(2.0*a->nz - nonzerorow);
1613:   VecRestoreArrayRead(xx,&x);
1614:   VecRestoreArray(yy,&y);
1615:   return(0);
1616: }

1618: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1619: {
1620:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1621:   PetscScalar       *y,*z;
1622:   const PetscScalar *x;
1623:   const MatScalar   *aa;
1624:   PetscErrorCode    ierr;
1625:   PetscInt          m = A->rmap->n,*aj,*ii;
1626:   PetscInt          n,i,*ridx=NULL;
1627:   PetscScalar       sum;
1628:   PetscBool         usecprow=a->compressedrow.use;

1631:   VecGetArrayRead(xx,&x);
1632:   VecGetArrayPair(yy,zz,&y,&z);
1633:   if (usecprow) { /* use compressed row format */
1634:     if (zz != yy) {
1635:       PetscArraycpy(z,y,m);
1636:     }
1637:     m    = a->compressedrow.nrows;
1638:     ii   = a->compressedrow.i;
1639:     ridx = a->compressedrow.rindex;
1640:     for (i=0; i<m; i++) {
1641:       n   = ii[i+1] - ii[i];
1642:       aj  = a->j + ii[i];
1643:       aa  = a->a + ii[i];
1644:       sum = y[*ridx];
1645:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1646:       z[*ridx++] = sum;
1647:     }
1648:   } else { /* do not use compressed row format */
1649:     ii = a->i;
1650:     for (i=0; i<m; i++) {
1651:       n   = ii[i+1] - ii[i];
1652:       aj  = a->j + ii[i];
1653:       aa  = a->a + ii[i];
1654:       sum = y[i];
1655:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1656:       z[i] = sum;
1657:     }
1658:   }
1659:   PetscLogFlops(2.0*a->nz);
1660:   VecRestoreArrayRead(xx,&x);
1661:   VecRestoreArrayPair(yy,zz,&y,&z);
1662:   return(0);
1663: }

1665: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1666: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1667: {
1668:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1669:   PetscScalar       *y,*z;
1670:   const PetscScalar *x;
1671:   const MatScalar   *aa;
1672:   PetscErrorCode    ierr;
1673:   const PetscInt    *aj,*ii,*ridx=NULL;
1674:   PetscInt          m = A->rmap->n,n,i;
1675:   PetscScalar       sum;
1676:   PetscBool         usecprow=a->compressedrow.use;

1679:   if (a->inode.use && a->inode.checked) {
1680:     MatMultAdd_SeqAIJ_Inode(A,xx,yy,zz);
1681:     return(0);
1682:   }
1683:   VecGetArrayRead(xx,&x);
1684:   VecGetArrayPair(yy,zz,&y,&z);
1685:   if (usecprow) { /* use compressed row format */
1686:     if (zz != yy) {
1687:       PetscArraycpy(z,y,m);
1688:     }
1689:     m    = a->compressedrow.nrows;
1690:     ii   = a->compressedrow.i;
1691:     ridx = a->compressedrow.rindex;
1692:     for (i=0; i<m; i++) {
1693:       n   = ii[i+1] - ii[i];
1694:       aj  = a->j + ii[i];
1695:       aa  = a->a + ii[i];
1696:       sum = y[*ridx];
1697:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1698:       z[*ridx++] = sum;
1699:     }
1700:   } else { /* do not use compressed row format */
1701:     ii = a->i;
1702: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1703:     aj = a->j;
1704:     aa = a->a;
1705:     fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1706: #else
1707:     for (i=0; i<m; i++) {
1708:       n   = ii[i+1] - ii[i];
1709:       aj  = a->j + ii[i];
1710:       aa  = a->a + ii[i];
1711:       sum = y[i];
1712:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1713:       z[i] = sum;
1714:     }
1715: #endif
1716:   }
1717:   PetscLogFlops(2.0*a->nz);
1718:   VecRestoreArrayRead(xx,&x);
1719:   VecRestoreArrayPair(yy,zz,&y,&z);
1720:   return(0);
1721: }

1723: /*
1724:      Adds diagonal pointers to sparse matrix structure.
1725: */
1726: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1727: {
1728:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1730:   PetscInt       i,j,m = A->rmap->n;

1733:   if (!a->diag) {
1734:     PetscMalloc1(m,&a->diag);
1735:     PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
1736:   }
1737:   for (i=0; i<A->rmap->n; i++) {
1738:     a->diag[i] = a->i[i+1];
1739:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1740:       if (a->j[j] == i) {
1741:         a->diag[i] = j;
1742:         break;
1743:       }
1744:     }
1745:   }
1746:   return(0);
1747: }

1749: PetscErrorCode MatShift_SeqAIJ(Mat A,PetscScalar v)
1750: {
1751:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1752:   const PetscInt    *diag = (const PetscInt*)a->diag;
1753:   const PetscInt    *ii = (const PetscInt*) a->i;
1754:   PetscInt          i,*mdiag = NULL;
1755:   PetscErrorCode    ierr;
1756:   PetscInt          cnt = 0; /* how many diagonals are missing */

1759:   if (!A->preallocated || !a->nz) {
1760:     MatSeqAIJSetPreallocation(A,1,NULL);
1761:     MatShift_Basic(A,v);
1762:     return(0);
1763:   }

1765:   if (a->diagonaldense) {
1766:     cnt = 0;
1767:   } else {
1768:     PetscCalloc1(A->rmap->n,&mdiag);
1769:     for (i=0; i<A->rmap->n; i++) {
1770:       if (diag[i] >= ii[i+1]) {
1771:         cnt++;
1772:         mdiag[i] = 1;
1773:       }
1774:     }
1775:   }
1776:   if (!cnt) {
1777:     MatShift_Basic(A,v);
1778:   } else {
1779:     PetscScalar *olda = a->a;  /* preserve pointers to current matrix nonzeros structure and values */
1780:     PetscInt    *oldj = a->j, *oldi = a->i;
1781:     PetscBool   singlemalloc = a->singlemalloc,free_a = a->free_a,free_ij = a->free_ij;

1783:     a->a = NULL;
1784:     a->j = NULL;
1785:     a->i = NULL;
1786:     /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1787:     for (i=0; i<A->rmap->n; i++) {
1788:       a->imax[i] += mdiag[i];
1789:       a->imax[i] = PetscMin(a->imax[i],A->cmap->n);
1790:     }
1791:     MatSeqAIJSetPreallocation_SeqAIJ(A,0,a->imax);

1793:     /* copy old values into new matrix data structure */
1794:     for (i=0; i<A->rmap->n; i++) {
1795:       MatSetValues(A,1,&i,a->imax[i] - mdiag[i],&oldj[oldi[i]],&olda[oldi[i]],ADD_VALUES);
1796:       if (i < A->cmap->n) {
1797:         MatSetValue(A,i,i,v,ADD_VALUES);
1798:       }
1799:     }
1800:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1801:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1802:     if (singlemalloc) {
1803:       PetscFree3(olda,oldj,oldi);
1804:     } else {
1805:       if (free_a)  {PetscFree(olda);}
1806:       if (free_ij) {PetscFree(oldj);}
1807:       if (free_ij) {PetscFree(oldi);}
1808:     }
1809:   }
1810:   PetscFree(mdiag);
1811:   a->diagonaldense = PETSC_TRUE;
1812:   return(0);
1813: }

1815: /*
1816:      Checks for missing diagonals
1817: */
1818: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
1819: {
1820:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1821:   PetscInt       *diag,*ii = a->i,i;

1825:   *missing = PETSC_FALSE;
1826:   if (A->rmap->n > 0 && !ii) {
1827:     *missing = PETSC_TRUE;
1828:     if (d) *d = 0;
1829:     PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
1830:   } else {
1831:     PetscInt n;
1832:     n = PetscMin(A->rmap->n, A->cmap->n);
1833:     diag = a->diag;
1834:     for (i=0; i<n; i++) {
1835:       if (diag[i] >= ii[i+1]) {
1836:         *missing = PETSC_TRUE;
1837:         if (d) *d = i;
1838:         PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
1839:         break;
1840:       }
1841:     }
1842:   }
1843:   return(0);
1844: }

1846: #include <petscblaslapack.h>
1847: #include <petsc/private/kernels/blockinvert.h>

1849: /*
1850:     Note that values is allocated externally by the PC and then passed into this routine
1851: */
1852: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A,PetscInt nblocks,const PetscInt *bsizes,PetscScalar *diag)
1853: {
1854:   PetscErrorCode  ierr;
1855:   PetscInt        n = A->rmap->n, i, ncnt = 0, *indx,j,bsizemax = 0,*v_pivots;
1856:   PetscBool       allowzeropivot,zeropivotdetected=PETSC_FALSE;
1857:   const PetscReal shift = 0.0;
1858:   PetscInt        ipvt[5];
1859:   PetscScalar     work[25],*v_work;

1862:   allowzeropivot = PetscNot(A->erroriffailure);
1863:   for (i=0; i<nblocks; i++) ncnt += bsizes[i];
1864:   if (ncnt != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Total blocksizes %D doesn't match number matrix rows %D",ncnt,n);
1865:   for (i=0; i<nblocks; i++) {
1866:     bsizemax = PetscMax(bsizemax,bsizes[i]);
1867:   }
1868:   PetscMalloc1(bsizemax,&indx);
1869:   if (bsizemax > 7) {
1870:     PetscMalloc2(bsizemax,&v_work,bsizemax,&v_pivots);
1871:   }
1872:   ncnt = 0;
1873:   for (i=0; i<nblocks; i++) {
1874:     for (j=0; j<bsizes[i]; j++) indx[j] = ncnt+j;
1875:     MatGetValues(A,bsizes[i],indx,bsizes[i],indx,diag);
1876:     switch (bsizes[i]) {
1877:     case 1:
1878:       *diag = 1.0/(*diag);
1879:       break;
1880:     case 2:
1881:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
1882:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1883:       PetscKernel_A_gets_transpose_A_2(diag);
1884:       break;
1885:     case 3:
1886:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
1887:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1888:       PetscKernel_A_gets_transpose_A_3(diag);
1889:       break;
1890:     case 4:
1891:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
1892:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1893:       PetscKernel_A_gets_transpose_A_4(diag);
1894:       break;
1895:     case 5:
1896:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
1897:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1898:       PetscKernel_A_gets_transpose_A_5(diag);
1899:       break;
1900:     case 6:
1901:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
1902:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1903:       PetscKernel_A_gets_transpose_A_6(diag);
1904:       break;
1905:     case 7:
1906:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
1907:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1908:       PetscKernel_A_gets_transpose_A_7(diag);
1909:       break;
1910:     default:
1911:       PetscKernel_A_gets_inverse_A(bsizes[i],diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
1912:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1913:       PetscKernel_A_gets_transpose_A_N(diag,bsizes[i]);
1914:     }
1915:     ncnt   += bsizes[i];
1916:     diag += bsizes[i]*bsizes[i];
1917:   }
1918:   if (bsizemax > 7) {
1919:     PetscFree2(v_work,v_pivots);
1920:   }
1921:   PetscFree(indx);
1922:   return(0);
1923: }

1925: /*
1926:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1927: */
1928: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1929: {
1930:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*) A->data;
1931:   PetscErrorCode  ierr;
1932:   PetscInt        i,*diag,m = A->rmap->n;
1933:   const MatScalar *v;
1934:   PetscScalar     *idiag,*mdiag;

1937:   if (a->idiagvalid) return(0);
1938:   MatMarkDiagonal_SeqAIJ(A);
1939:   diag = a->diag;
1940:   if (!a->idiag) {
1941:     PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
1942:     PetscLogObjectMemory((PetscObject)A,3*m*sizeof(PetscScalar));
1943:   }

1945:   mdiag = a->mdiag;
1946:   idiag = a->idiag;
1947:   MatSeqAIJGetArrayRead(A,&v);
1948:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1949:     for (i=0; i<m; i++) {
1950:       mdiag[i] = v[diag[i]];
1951:       if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1952:         if (PetscRealPart(fshift)) {
1953:           PetscInfo1(A,"Zero diagonal on row %D\n",i);
1954:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1955:           A->factorerror_zeropivot_value = 0.0;
1956:           A->factorerror_zeropivot_row   = i;
1957:         } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1958:       }
1959:       idiag[i] = 1.0/v[diag[i]];
1960:     }
1961:     PetscLogFlops(m);
1962:   } else {
1963:     for (i=0; i<m; i++) {
1964:       mdiag[i] = v[diag[i]];
1965:       idiag[i] = omega/(fshift + v[diag[i]]);
1966:     }
1967:     PetscLogFlops(2.0*m);
1968:   }
1969:   a->idiagvalid = PETSC_TRUE;
1970:   MatSeqAIJRestoreArrayRead(A,&v);
1971:   return(0);
1972: }

1974: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1975: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1976: {
1977:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1978:   PetscScalar       *x,d,sum,*t,scale;
1979:   const MatScalar   *v,*idiag=NULL,*mdiag,*aa;
1980:   const PetscScalar *b, *bs,*xb, *ts;
1981:   PetscErrorCode    ierr;
1982:   PetscInt          n,m = A->rmap->n,i;
1983:   const PetscInt    *idx,*diag;

1986:   if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1987:     MatSOR_SeqAIJ_Inode(A,bb,omega,flag,fshift,its,lits,xx);
1988:     return(0);
1989:   }
1990:   its = its*lits;

1992:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1993:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1994:   a->fshift = fshift;
1995:   a->omega  = omega;

1997:   diag  = a->diag;
1998:   t     = a->ssor_work;
1999:   idiag = a->idiag;
2000:   mdiag = a->mdiag;

2002:   MatSeqAIJGetArrayRead(A,&aa);
2003:   VecGetArray(xx,&x);
2004:   VecGetArrayRead(bb,&b);
2005:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
2006:   if (flag == SOR_APPLY_UPPER) {
2007:     /* apply (U + D/omega) to the vector */
2008:     bs = b;
2009:     for (i=0; i<m; i++) {
2010:       d   = fshift + mdiag[i];
2011:       n   = a->i[i+1] - diag[i] - 1;
2012:       idx = a->j + diag[i] + 1;
2013:       v   = aa + diag[i] + 1;
2014:       sum = b[i]*d/omega;
2015:       PetscSparseDensePlusDot(sum,bs,v,idx,n);
2016:       x[i] = sum;
2017:     }
2018:     VecRestoreArray(xx,&x);
2019:     VecRestoreArrayRead(bb,&b);
2020:     MatSeqAIJRestoreArrayRead(A,&aa);
2021:     PetscLogFlops(a->nz);
2022:     return(0);
2023:   }

2025:   if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
2026:   else if (flag & SOR_EISENSTAT) {
2027:     /* Let  A = L + U + D; where L is lower triangular,
2028:     U is upper triangular, E = D/omega; This routine applies

2030:             (L + E)^{-1} A (U + E)^{-1}

2032:     to a vector efficiently using Eisenstat's trick.
2033:     */
2034:     scale = (2.0/omega) - 1.0;

2036:     /*  x = (E + U)^{-1} b */
2037:     for (i=m-1; i>=0; i--) {
2038:       n   = a->i[i+1] - diag[i] - 1;
2039:       idx = a->j + diag[i] + 1;
2040:       v   = aa + diag[i] + 1;
2041:       sum = b[i];
2042:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
2043:       x[i] = sum*idiag[i];
2044:     }

2046:     /*  t = b - (2*E - D)x */
2047:     v = aa;
2048:     for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];

2050:     /*  t = (E + L)^{-1}t */
2051:     ts   = t;
2052:     diag = a->diag;
2053:     for (i=0; i<m; i++) {
2054:       n   = diag[i] - a->i[i];
2055:       idx = a->j + a->i[i];
2056:       v   = aa + a->i[i];
2057:       sum = t[i];
2058:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
2059:       t[i] = sum*idiag[i];
2060:       /*  x = x + t */
2061:       x[i] += t[i];
2062:     }

2064:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
2065:     VecRestoreArray(xx,&x);
2066:     VecRestoreArrayRead(bb,&b);
2067:     return(0);
2068:   }
2069:   if (flag & SOR_ZERO_INITIAL_GUESS) {
2070:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2071:       for (i=0; i<m; i++) {
2072:         n   = diag[i] - a->i[i];
2073:         idx = a->j + a->i[i];
2074:         v   = aa + a->i[i];
2075:         sum = b[i];
2076:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
2077:         t[i] = sum;
2078:         x[i] = sum*idiag[i];
2079:       }
2080:       xb   = t;
2081:       PetscLogFlops(a->nz);
2082:     } else xb = b;
2083:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2084:       for (i=m-1; i>=0; i--) {
2085:         n   = a->i[i+1] - diag[i] - 1;
2086:         idx = a->j + diag[i] + 1;
2087:         v   = aa + diag[i] + 1;
2088:         sum = xb[i];
2089:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
2090:         if (xb == b) {
2091:           x[i] = sum*idiag[i];
2092:         } else {
2093:           x[i] = (1-omega)*x[i] + sum*idiag[i];  /* omega in idiag */
2094:         }
2095:       }
2096:       PetscLogFlops(a->nz); /* assumes 1/2 in upper */
2097:     }
2098:     its--;
2099:   }
2100:   while (its--) {
2101:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2102:       for (i=0; i<m; i++) {
2103:         /* lower */
2104:         n   = diag[i] - a->i[i];
2105:         idx = a->j + a->i[i];
2106:         v   = aa + a->i[i];
2107:         sum = b[i];
2108:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
2109:         t[i] = sum;             /* save application of the lower-triangular part */
2110:         /* upper */
2111:         n   = a->i[i+1] - diag[i] - 1;
2112:         idx = a->j + diag[i] + 1;
2113:         v   = aa + diag[i] + 1;
2114:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
2115:         x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
2116:       }
2117:       xb   = t;
2118:       PetscLogFlops(2.0*a->nz);
2119:     } else xb = b;
2120:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2121:       for (i=m-1; i>=0; i--) {
2122:         sum = xb[i];
2123:         if (xb == b) {
2124:           /* whole matrix (no checkpointing available) */
2125:           n   = a->i[i+1] - a->i[i];
2126:           idx = a->j + a->i[i];
2127:           v   = aa + a->i[i];
2128:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
2129:           x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
2130:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2131:           n   = a->i[i+1] - diag[i] - 1;
2132:           idx = a->j + diag[i] + 1;
2133:           v   = aa + diag[i] + 1;
2134:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
2135:           x[i] = (1. - omega)*x[i] + sum*idiag[i];  /* omega in idiag */
2136:         }
2137:       }
2138:       if (xb == b) {
2139:         PetscLogFlops(2.0*a->nz);
2140:       } else {
2141:         PetscLogFlops(a->nz); /* assumes 1/2 in upper */
2142:       }
2143:     }
2144:   }
2145:   MatSeqAIJRestoreArrayRead(A,&aa);
2146:   VecRestoreArray(xx,&x);
2147:   VecRestoreArrayRead(bb,&b);
2148:   return(0);
2149: }


2152: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
2153: {
2154:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

2157:   info->block_size   = 1.0;
2158:   info->nz_allocated = a->maxnz;
2159:   info->nz_used      = a->nz;
2160:   info->nz_unneeded  = (a->maxnz - a->nz);
2161:   info->assemblies   = A->num_ass;
2162:   info->mallocs      = A->info.mallocs;
2163:   info->memory       = ((PetscObject)A)->mem;
2164:   if (A->factortype) {
2165:     info->fill_ratio_given  = A->info.fill_ratio_given;
2166:     info->fill_ratio_needed = A->info.fill_ratio_needed;
2167:     info->factor_mallocs    = A->info.factor_mallocs;
2168:   } else {
2169:     info->fill_ratio_given  = 0;
2170:     info->fill_ratio_needed = 0;
2171:     info->factor_mallocs    = 0;
2172:   }
2173:   return(0);
2174: }

2176: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
2177: {
2178:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
2179:   PetscInt          i,m = A->rmap->n - 1;
2180:   PetscErrorCode    ierr;
2181:   const PetscScalar *xx;
2182:   PetscScalar       *bb,*aa;
2183:   PetscInt          d = 0;

2186:   if (x && b) {
2187:     VecGetArrayRead(x,&xx);
2188:     VecGetArray(b,&bb);
2189:     for (i=0; i<N; i++) {
2190:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2191:       if (rows[i] >= A->cmap->n) continue;
2192:       bb[rows[i]] = diag*xx[rows[i]];
2193:     }
2194:     VecRestoreArrayRead(x,&xx);
2195:     VecRestoreArray(b,&bb);
2196:   }

2198:   MatSeqAIJGetArray(A,&aa);
2199:   if (a->keepnonzeropattern) {
2200:     for (i=0; i<N; i++) {
2201:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2202:       PetscArrayzero(&aa[a->i[rows[i]]],a->ilen[rows[i]]);
2203:     }
2204:     if (diag != 0.0) {
2205:       for (i=0; i<N; i++) {
2206:         d = rows[i];
2207:         if (rows[i] >= A->cmap->n) continue;
2208:         if (a->diag[d] >= a->i[d+1]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in the zeroed row %D",d);
2209:       }
2210:       for (i=0; i<N; i++) {
2211:         if (rows[i] >= A->cmap->n) continue;
2212:         aa[a->diag[rows[i]]] = diag;
2213:       }
2214:     }
2215:   } else {
2216:     if (diag != 0.0) {
2217:       for (i=0; i<N; i++) {
2218:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2219:         if (a->ilen[rows[i]] > 0) {
2220:           if (rows[i] >= A->cmap->n) {
2221:             a->ilen[rows[i]] = 0;
2222:           } else {
2223:             a->ilen[rows[i]]    = 1;
2224:             aa[a->i[rows[i]]]   = diag;
2225:             a->j[a->i[rows[i]]] = rows[i];
2226:           }
2227:         } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2228:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
2229:         }
2230:       }
2231:     } else {
2232:       for (i=0; i<N; i++) {
2233:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2234:         a->ilen[rows[i]] = 0;
2235:       }
2236:     }
2237:     A->nonzerostate++;
2238:   }
2239:   MatSeqAIJRestoreArray(A,&aa);
2240: #if defined(PETSC_HAVE_DEVICE)
2241:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2242: #endif
2243:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2244:   return(0);
2245: }

2247: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
2248: {
2249:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
2250:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
2251:   PetscErrorCode    ierr;
2252:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
2253:   const PetscScalar *xx;
2254:   PetscScalar       *bb,*aa;

2257:   if (!N) return(0);
2258:   MatSeqAIJGetArray(A,&aa);
2259:   if (x && b) {
2260:     VecGetArrayRead(x,&xx);
2261:     VecGetArray(b,&bb);
2262:     vecs = PETSC_TRUE;
2263:   }
2264:   PetscCalloc1(A->rmap->n,&zeroed);
2265:   for (i=0; i<N; i++) {
2266:     if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2267:     PetscArrayzero(&aa[a->i[rows[i]]],a->ilen[rows[i]]);

2269:     zeroed[rows[i]] = PETSC_TRUE;
2270:   }
2271:   for (i=0; i<A->rmap->n; i++) {
2272:     if (!zeroed[i]) {
2273:       for (j=a->i[i]; j<a->i[i+1]; j++) {
2274:         if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2275:           if (vecs) bb[i] -= aa[j]*xx[a->j[j]];
2276:           aa[j] = 0.0;
2277:         }
2278:       }
2279:     } else if (vecs && i < A->cmap->N) bb[i] = diag*xx[i];
2280:   }
2281:   if (x && b) {
2282:     VecRestoreArrayRead(x,&xx);
2283:     VecRestoreArray(b,&bb);
2284:   }
2285:   PetscFree(zeroed);
2286:   if (diag != 0.0) {
2287:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
2288:     if (missing) {
2289:       for (i=0; i<N; i++) {
2290:         if (rows[i] >= A->cmap->N) continue;
2291:         if (a->nonew && rows[i] >= d) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D (%D)",d,rows[i]);
2292:         MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
2293:       }
2294:     } else {
2295:       for (i=0; i<N; i++) {
2296:         aa[a->diag[rows[i]]] = diag;
2297:       }
2298:     }
2299:   }
2300:   MatSeqAIJRestoreArray(A,&aa);
2301: #if defined(PETSC_HAVE_DEVICE)
2302:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2303: #endif
2304:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2305:   return(0);
2306: }

2308: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2309: {
2310:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
2311:   const PetscScalar *aa = a->a;
2312:   PetscInt          *itmp;
2313: #if defined(PETSC_HAVE_DEVICE)
2314:   PetscErrorCode    ierr;
2315:   PetscBool         rest = PETSC_FALSE;
2316: #endif

2319: #if defined(PETSC_HAVE_DEVICE)
2320:   if (v && A->offloadmask == PETSC_OFFLOAD_GPU) {
2321:     /* triggers copy to CPU */
2322:     rest = PETSC_TRUE;
2323:     MatSeqAIJGetArrayRead(A,&aa);
2324:   } else aa = a->a;
2325: #endif
2326:   *nz = a->i[row+1] - a->i[row];
2327:   if (v) *v = (PetscScalar*)(aa + a->i[row]);
2328:   if (idx) {
2329:     itmp = a->j + a->i[row];
2330:     if (*nz) *idx = itmp;
2331:     else *idx = NULL;
2332:   }
2333: #if defined(PETSC_HAVE_DEVICE)
2334:   if (rest) {
2335:     MatSeqAIJRestoreArrayRead(A,&aa);
2336:   }
2337: #endif
2338:   return(0);
2339: }

2341: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2342: {
2344:   *nz = 0;
2345:   if (idx) *idx = NULL;
2346:   if (v) *v = NULL;
2347:   return(0);
2348: }

2350: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
2351: {
2352:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
2353:   const MatScalar *v;
2354:   PetscReal       sum = 0.0;
2355:   PetscErrorCode  ierr;
2356:   PetscInt        i,j;

2359:   MatSeqAIJGetArrayRead(A,&v);
2360:   if (type == NORM_FROBENIUS) {
2361: #if defined(PETSC_USE_REAL___FP16)
2362:     PetscBLASInt one = 1,nz = a->nz;
2363:     PetscStackCallBLAS("BLASnrm2",*nrm = BLASnrm2_(&nz,v,&one));
2364: #else
2365:     for (i=0; i<a->nz; i++) {
2366:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
2367:     }
2368:     *nrm = PetscSqrtReal(sum);
2369: #endif
2370:     PetscLogFlops(2.0*a->nz);
2371:   } else if (type == NORM_1) {
2372:     PetscReal *tmp;
2373:     PetscInt  *jj = a->j;
2374:     PetscCalloc1(A->cmap->n+1,&tmp);
2375:     *nrm = 0.0;
2376:     for (j=0; j<a->nz; j++) {
2377:       tmp[*jj++] += PetscAbsScalar(*v);  v++;
2378:     }
2379:     for (j=0; j<A->cmap->n; j++) {
2380:       if (tmp[j] > *nrm) *nrm = tmp[j];
2381:     }
2382:     PetscFree(tmp);
2383:     PetscLogFlops(PetscMax(a->nz-1,0));
2384:   } else if (type == NORM_INFINITY) {
2385:     *nrm = 0.0;
2386:     for (j=0; j<A->rmap->n; j++) {
2387:       const PetscScalar *v2 = v + a->i[j];
2388:       sum = 0.0;
2389:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
2390:         sum += PetscAbsScalar(*v2); v2++;
2391:       }
2392:       if (sum > *nrm) *nrm = sum;
2393:     }
2394:     PetscLogFlops(PetscMax(a->nz-1,0));
2395:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
2396:   MatSeqAIJRestoreArrayRead(A,&v);
2397:   return(0);
2398: }

2400: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
2401: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
2402: {
2404:   PetscInt       i,j,anzj;
2405:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b;
2406:   PetscInt       an=A->cmap->N,am=A->rmap->N;
2407:   PetscInt       *ati,*atj,*atfill,*ai=a->i,*aj=a->j;

2410:   /* Allocate space for symbolic transpose info and work array */
2411:   PetscCalloc1(an+1,&ati);
2412:   PetscMalloc1(ai[am],&atj);
2413:   PetscMalloc1(an,&atfill);

2415:   /* Walk through aj and count ## of non-zeros in each row of A^T. */
2416:   /* Note: offset by 1 for fast conversion into csr format. */
2417:   for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
2418:   /* Form ati for csr format of A^T. */
2419:   for (i=0;i<an;i++) ati[i+1] += ati[i];

2421:   /* Copy ati into atfill so we have locations of the next free space in atj */
2422:   PetscArraycpy(atfill,ati,an);

2424:   /* Walk through A row-wise and mark nonzero entries of A^T. */
2425:   for (i=0;i<am;i++) {
2426:     anzj = ai[i+1] - ai[i];
2427:     for (j=0;j<anzj;j++) {
2428:       atj[atfill[*aj]] = i;
2429:       atfill[*aj++]   += 1;
2430:     }
2431:   }

2433:   /* Clean up temporary space and complete requests. */
2434:   PetscFree(atfill);
2435:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
2436:   MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2437:   MatSetType(*B,((PetscObject)A)->type_name);

2439:   b          = (Mat_SeqAIJ*)((*B)->data);
2440:   b->free_a  = PETSC_FALSE;
2441:   b->free_ij = PETSC_TRUE;
2442:   b->nonew   = 0;
2443:   return(0);
2444: }

2446: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
2447: {
2448:   Mat_SeqAIJ      *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2449:   PetscInt        *adx,*bdx,*aii,*bii,*aptr,*bptr;
2450:   const MatScalar *va,*vb;
2451:   PetscErrorCode  ierr;
2452:   PetscInt        ma,na,mb,nb, i;

2455:   MatGetSize(A,&ma,&na);
2456:   MatGetSize(B,&mb,&nb);
2457:   if (ma!=nb || na!=mb) {
2458:     *f = PETSC_FALSE;
2459:     return(0);
2460:   }
2461:   MatSeqAIJGetArrayRead(A,&va);
2462:   MatSeqAIJGetArrayRead(B,&vb);
2463:   aii  = aij->i; bii = bij->i;
2464:   adx  = aij->j; bdx = bij->j;
2465:   PetscMalloc1(ma,&aptr);
2466:   PetscMalloc1(mb,&bptr);
2467:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2468:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2470:   *f = PETSC_TRUE;
2471:   for (i=0; i<ma; i++) {
2472:     while (aptr[i]<aii[i+1]) {
2473:       PetscInt    idc,idr;
2474:       PetscScalar vc,vr;
2475:       /* column/row index/value */
2476:       idc = adx[aptr[i]];
2477:       idr = bdx[bptr[idc]];
2478:       vc  = va[aptr[i]];
2479:       vr  = vb[bptr[idc]];
2480:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
2481:         *f = PETSC_FALSE;
2482:         goto done;
2483:       } else {
2484:         aptr[i]++;
2485:         if (B || i!=idc) bptr[idc]++;
2486:       }
2487:     }
2488:   }
2489: done:
2490:   PetscFree(aptr);
2491:   PetscFree(bptr);
2492:   MatSeqAIJRestoreArrayRead(A,&va);
2493:   MatSeqAIJRestoreArrayRead(B,&vb);
2494:   return(0);
2495: }

2497: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
2498: {
2499:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2500:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
2501:   MatScalar      *va,*vb;
2503:   PetscInt       ma,na,mb,nb, i;

2506:   MatGetSize(A,&ma,&na);
2507:   MatGetSize(B,&mb,&nb);
2508:   if (ma!=nb || na!=mb) {
2509:     *f = PETSC_FALSE;
2510:     return(0);
2511:   }
2512:   aii  = aij->i; bii = bij->i;
2513:   adx  = aij->j; bdx = bij->j;
2514:   va   = aij->a; vb = bij->a;
2515:   PetscMalloc1(ma,&aptr);
2516:   PetscMalloc1(mb,&bptr);
2517:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2518:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2520:   *f = PETSC_TRUE;
2521:   for (i=0; i<ma; i++) {
2522:     while (aptr[i]<aii[i+1]) {
2523:       PetscInt    idc,idr;
2524:       PetscScalar vc,vr;
2525:       /* column/row index/value */
2526:       idc = adx[aptr[i]];
2527:       idr = bdx[bptr[idc]];
2528:       vc  = va[aptr[i]];
2529:       vr  = vb[bptr[idc]];
2530:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2531:         *f = PETSC_FALSE;
2532:         goto done;
2533:       } else {
2534:         aptr[i]++;
2535:         if (B || i!=idc) bptr[idc]++;
2536:       }
2537:     }
2538:   }
2539: done:
2540:   PetscFree(aptr);
2541:   PetscFree(bptr);
2542:   return(0);
2543: }

2545: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2546: {

2550:   MatIsTranspose_SeqAIJ(A,A,tol,f);
2551:   return(0);
2552: }

2554: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2555: {

2559:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2560:   return(0);
2561: }

2563: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
2564: {
2565:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
2566:   const PetscScalar *l,*r;
2567:   PetscScalar       x;
2568:   MatScalar         *v;
2569:   PetscErrorCode    ierr;
2570:   PetscInt          i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz;
2571:   const PetscInt    *jj;

2574:   if (ll) {
2575:     /* The local size is used so that VecMPI can be passed to this routine
2576:        by MatDiagonalScale_MPIAIJ */
2577:     VecGetLocalSize(ll,&m);
2578:     if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
2579:     VecGetArrayRead(ll,&l);
2580:     MatSeqAIJGetArray(A,&v);
2581:     for (i=0; i<m; i++) {
2582:       x = l[i];
2583:       M = a->i[i+1] - a->i[i];
2584:       for (j=0; j<M; j++) (*v++) *= x;
2585:     }
2586:     VecRestoreArrayRead(ll,&l);
2587:     PetscLogFlops(nz);
2588:     MatSeqAIJRestoreArray(A,&v);
2589:   }
2590:   if (rr) {
2591:     VecGetLocalSize(rr,&n);
2592:     if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
2593:     VecGetArrayRead(rr,&r);
2594:     MatSeqAIJGetArray(A,&v);
2595:     jj = a->j;
2596:     for (i=0; i<nz; i++) (*v++) *= r[*jj++];
2597:     MatSeqAIJRestoreArray(A,&v);
2598:     VecRestoreArrayRead(rr,&r);
2599:     PetscLogFlops(nz);
2600:   }
2601:   MatSeqAIJInvalidateDiagonal(A);
2602: #if defined(PETSC_HAVE_DEVICE)
2603:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2604: #endif
2605:   return(0);
2606: }

2608: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2609: {
2610:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data,*c;
2611:   PetscErrorCode    ierr;
2612:   PetscInt          *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2613:   PetscInt          row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2614:   const PetscInt    *irow,*icol;
2615:   const PetscScalar *aa;
2616:   PetscInt          nrows,ncols;
2617:   PetscInt          *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2618:   MatScalar         *a_new,*mat_a;
2619:   Mat               C;
2620:   PetscBool         stride;

2623:   ISGetIndices(isrow,&irow);
2624:   ISGetLocalSize(isrow,&nrows);
2625:   ISGetLocalSize(iscol,&ncols);

2627:   PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2628:   if (stride) {
2629:     ISStrideGetInfo(iscol,&first,&step);
2630:   } else {
2631:     first = 0;
2632:     step  = 0;
2633:   }
2634:   if (stride && step == 1) {
2635:     /* special case of contiguous rows */
2636:     PetscMalloc2(nrows,&lens,nrows,&starts);
2637:     /* loop over new rows determining lens and starting points */
2638:     for (i=0; i<nrows; i++) {
2639:       kstart = ai[irow[i]];
2640:       kend   = kstart + ailen[irow[i]];
2641:       starts[i] = kstart;
2642:       for (k=kstart; k<kend; k++) {
2643:         if (aj[k] >= first) {
2644:           starts[i] = k;
2645:           break;
2646:         }
2647:       }
2648:       sum = 0;
2649:       while (k < kend) {
2650:         if (aj[k++] >= first+ncols) break;
2651:         sum++;
2652:       }
2653:       lens[i] = sum;
2654:     }
2655:     /* create submatrix */
2656:     if (scall == MAT_REUSE_MATRIX) {
2657:       PetscInt n_cols,n_rows;
2658:       MatGetSize(*B,&n_rows,&n_cols);
2659:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2660:       MatZeroEntries(*B);
2661:       C    = *B;
2662:     } else {
2663:       PetscInt rbs,cbs;
2664:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2665:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2666:       ISGetBlockSize(isrow,&rbs);
2667:       ISGetBlockSize(iscol,&cbs);
2668:       MatSetBlockSizes(C,rbs,cbs);
2669:       MatSetType(C,((PetscObject)A)->type_name);
2670:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2671:     }
2672:     c = (Mat_SeqAIJ*)C->data;

2674:     /* loop over rows inserting into submatrix */
2675:     a_new = c->a;
2676:     j_new = c->j;
2677:     i_new = c->i;
2678:     MatSeqAIJGetArrayRead(A,&aa);
2679:     for (i=0; i<nrows; i++) {
2680:       ii    = starts[i];
2681:       lensi = lens[i];
2682:       for (k=0; k<lensi; k++) {
2683:         *j_new++ = aj[ii+k] - first;
2684:       }
2685:       PetscArraycpy(a_new,aa + starts[i],lensi);
2686:       a_new     += lensi;
2687:       i_new[i+1] = i_new[i] + lensi;
2688:       c->ilen[i] = lensi;
2689:     }
2690:     MatSeqAIJRestoreArrayRead(A,&aa);
2691:     PetscFree2(lens,starts);
2692:   } else {
2693:     ISGetIndices(iscol,&icol);
2694:     PetscCalloc1(oldcols,&smap);
2695:     PetscMalloc1(1+nrows,&lens);
2696:     for (i=0; i<ncols; i++) {
2697:       if (PetscUnlikelyDebug(icol[i] >= oldcols)) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Requesting column beyond largest column icol[%D] %D >= A->cmap->n %D",i,icol[i],oldcols);
2698:       smap[icol[i]] = i+1;
2699:     }

2701:     /* determine lens of each row */
2702:     for (i=0; i<nrows; i++) {
2703:       kstart  = ai[irow[i]];
2704:       kend    = kstart + a->ilen[irow[i]];
2705:       lens[i] = 0;
2706:       for (k=kstart; k<kend; k++) {
2707:         if (smap[aj[k]]) {
2708:           lens[i]++;
2709:         }
2710:       }
2711:     }
2712:     /* Create and fill new matrix */
2713:     if (scall == MAT_REUSE_MATRIX) {
2714:       PetscBool equal;

2716:       c = (Mat_SeqAIJ*)((*B)->data);
2717:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2718:       PetscArraycmp(c->ilen,lens,(*B)->rmap->n,&equal);
2719:       if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2720:       PetscArrayzero(c->ilen,(*B)->rmap->n);
2721:       C    = *B;
2722:     } else {
2723:       PetscInt rbs,cbs;
2724:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2725:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2726:       ISGetBlockSize(isrow,&rbs);
2727:       ISGetBlockSize(iscol,&cbs);
2728:       MatSetBlockSizes(C,rbs,cbs);
2729:       MatSetType(C,((PetscObject)A)->type_name);
2730:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2731:     }
2732:     MatSeqAIJGetArrayRead(A,&aa);
2733:     c = (Mat_SeqAIJ*)(C->data);
2734:     for (i=0; i<nrows; i++) {
2735:       row      = irow[i];
2736:       kstart   = ai[row];
2737:       kend     = kstart + a->ilen[row];
2738:       mat_i    = c->i[i];
2739:       mat_j    = c->j + mat_i;
2740:       mat_a    = c->a + mat_i;
2741:       mat_ilen = c->ilen + i;
2742:       for (k=kstart; k<kend; k++) {
2743:         if ((tcol=smap[a->j[k]])) {
2744:           *mat_j++ = tcol - 1;
2745:           *mat_a++ = aa[k];
2746:           (*mat_ilen)++;

2748:         }
2749:       }
2750:     }
2751:     MatSeqAIJRestoreArrayRead(A,&aa);
2752:     /* Free work space */
2753:     ISRestoreIndices(iscol,&icol);
2754:     PetscFree(smap);
2755:     PetscFree(lens);
2756:     /* sort */
2757:     for (i = 0; i < nrows; i++) {
2758:       PetscInt ilen;

2760:       mat_i = c->i[i];
2761:       mat_j = c->j + mat_i;
2762:       mat_a = c->a + mat_i;
2763:       ilen  = c->ilen[i];
2764:       PetscSortIntWithScalarArray(ilen,mat_j,mat_a);
2765:     }
2766:   }
2767: #if defined(PETSC_HAVE_DEVICE)
2768:   MatBindToCPU(C,A->boundtocpu);
2769: #endif
2770:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2771:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

2773:   ISRestoreIndices(isrow,&irow);
2774:   *B   = C;
2775:   return(0);
2776: }

2778: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
2779: {
2781:   Mat            B;

2784:   if (scall == MAT_INITIAL_MATRIX) {
2785:     MatCreate(subComm,&B);
2786:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2787:     MatSetBlockSizesFromMats(B,mat,mat);
2788:     MatSetType(B,MATSEQAIJ);
2789:     MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2790:     *subMat = B;
2791:   } else {
2792:     MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
2793:   }
2794:   return(0);
2795: }

2797: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2798: {
2799:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
2801:   Mat            outA;
2802:   PetscBool      row_identity,col_identity;

2805:   if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

2807:   ISIdentity(row,&row_identity);
2808:   ISIdentity(col,&col_identity);

2810:   outA             = inA;
2811:   outA->factortype = MAT_FACTOR_LU;
2812:   PetscFree(inA->solvertype);
2813:   PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);

2815:   PetscObjectReference((PetscObject)row);
2816:   ISDestroy(&a->row);

2818:   a->row = row;

2820:   PetscObjectReference((PetscObject)col);
2821:   ISDestroy(&a->col);

2823:   a->col = col;

2825:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2826:   ISDestroy(&a->icol);
2827:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2828:   PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);

2830:   if (!a->solve_work) { /* this matrix may have been factored before */
2831:     PetscMalloc1(inA->rmap->n+1,&a->solve_work);
2832:     PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2833:   }

2835:   MatMarkDiagonal_SeqAIJ(inA);
2836:   if (row_identity && col_identity) {
2837:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2838:   } else {
2839:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2840:   }
2841:   return(0);
2842: }

2844: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2845: {
2846:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
2847:   PetscScalar    *v;
2849:   PetscBLASInt   one = 1,bnz;

2852:   MatSeqAIJGetArray(inA,&v);
2853:   PetscBLASIntCast(a->nz,&bnz);
2854:   PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&alpha,v,&one));
2855:   PetscLogFlops(a->nz);
2856:   MatSeqAIJRestoreArray(inA,&v);
2857:   MatSeqAIJInvalidateDiagonal(inA);
2858:   return(0);
2859: }

2861: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2862: {
2864:   PetscInt       i;

2867:   if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2868:     PetscFree4(submatj->sbuf1,submatj->ptr,submatj->tmp,submatj->ctr);

2870:     for (i=0; i<submatj->nrqr; ++i) {
2871:       PetscFree(submatj->sbuf2[i]);
2872:     }
2873:     PetscFree3(submatj->sbuf2,submatj->req_size,submatj->req_source1);

2875:     if (submatj->rbuf1) {
2876:       PetscFree(submatj->rbuf1[0]);
2877:       PetscFree(submatj->rbuf1);
2878:     }

2880:     for (i=0; i<submatj->nrqs; ++i) {
2881:       PetscFree(submatj->rbuf3[i]);
2882:     }
2883:     PetscFree3(submatj->req_source2,submatj->rbuf2,submatj->rbuf3);
2884:     PetscFree(submatj->pa);
2885:   }

2887: #if defined(PETSC_USE_CTABLE)
2888:   PetscTableDestroy((PetscTable*)&submatj->rmap);
2889:   if (submatj->cmap_loc) {PetscFree(submatj->cmap_loc);}
2890:   PetscFree(submatj->rmap_loc);
2891: #else
2892:   PetscFree(submatj->rmap);
2893: #endif

2895:   if (!submatj->allcolumns) {
2896: #if defined(PETSC_USE_CTABLE)
2897:     PetscTableDestroy((PetscTable*)&submatj->cmap);
2898: #else
2899:     PetscFree(submatj->cmap);
2900: #endif
2901:   }
2902:   PetscFree(submatj->row2proc);

2904:   PetscFree(submatj);
2905:   return(0);
2906: }

2908: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2909: {
2911:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data;
2912:   Mat_SubSppt    *submatj = c->submatis1;

2915:   (*submatj->destroy)(C);
2916:   MatDestroySubMatrix_Private(submatj);
2917:   return(0);
2918: }

2920: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n,Mat *mat[])
2921: {
2923:   PetscInt       i;
2924:   Mat            C;
2925:   Mat_SeqAIJ     *c;
2926:   Mat_SubSppt    *submatj;

2929:   for (i=0; i<n; i++) {
2930:     C       = (*mat)[i];
2931:     c       = (Mat_SeqAIJ*)C->data;
2932:     submatj = c->submatis1;
2933:     if (submatj) {
2934:       if (--((PetscObject)C)->refct <= 0) {
2935:         (*submatj->destroy)(C);
2936:         MatDestroySubMatrix_Private(submatj);
2937:         PetscFree(C->defaultvectype);
2938:         PetscLayoutDestroy(&C->rmap);
2939:         PetscLayoutDestroy(&C->cmap);
2940:         PetscHeaderDestroy(&C);
2941:       }
2942:     } else {
2943:       MatDestroy(&C);
2944:     }
2945:   }

2947:   /* Destroy Dummy submatrices created for reuse */
2948:   MatDestroySubMatrices_Dummy(n,mat);

2950:   PetscFree(*mat);
2951:   return(0);
2952: }

2954: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2955: {
2957:   PetscInt       i;

2960:   if (scall == MAT_INITIAL_MATRIX) {
2961:     PetscCalloc1(n+1,B);
2962:   }

2964:   for (i=0; i<n; i++) {
2965:     MatCreateSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2966:   }
2967:   return(0);
2968: }

2970: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2971: {
2972:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2974:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
2975:   const PetscInt *idx;
2976:   PetscInt       start,end,*ai,*aj;
2977:   PetscBT        table;

2980:   m  = A->rmap->n;
2981:   ai = a->i;
2982:   aj = a->j;

2984:   if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

2986:   PetscMalloc1(m+1,&nidx);
2987:   PetscBTCreate(m,&table);

2989:   for (i=0; i<is_max; i++) {
2990:     /* Initialize the two local arrays */
2991:     isz  = 0;
2992:     PetscBTMemzero(m,table);

2994:     /* Extract the indices, assume there can be duplicate entries */
2995:     ISGetIndices(is[i],&idx);
2996:     ISGetLocalSize(is[i],&n);

2998:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2999:     for (j=0; j<n; ++j) {
3000:       if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
3001:     }
3002:     ISRestoreIndices(is[i],&idx);
3003:     ISDestroy(&is[i]);

3005:     k = 0;
3006:     for (j=0; j<ov; j++) { /* for each overlap */
3007:       n = isz;
3008:       for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
3009:         row   = nidx[k];
3010:         start = ai[row];
3011:         end   = ai[row+1];
3012:         for (l = start; l<end; l++) {
3013:           val = aj[l];
3014:           if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
3015:         }
3016:       }
3017:     }
3018:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
3019:   }
3020:   PetscBTDestroy(&table);
3021:   PetscFree(nidx);
3022:   return(0);
3023: }

3025: /* -------------------------------------------------------------- */
3026: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
3027: {
3028:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
3030:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
3031:   const PetscInt *row,*col;
3032:   PetscInt       *cnew,j,*lens;
3033:   IS             icolp,irowp;
3034:   PetscInt       *cwork = NULL;
3035:   PetscScalar    *vwork = NULL;

3038:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
3039:   ISGetIndices(irowp,&row);
3040:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
3041:   ISGetIndices(icolp,&col);

3043:   /* determine lengths of permuted rows */
3044:   PetscMalloc1(m+1,&lens);
3045:   for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
3046:   MatCreate(PetscObjectComm((PetscObject)A),B);
3047:   MatSetSizes(*B,m,n,m,n);
3048:   MatSetBlockSizesFromMats(*B,A,A);
3049:   MatSetType(*B,((PetscObject)A)->type_name);
3050:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
3051:   PetscFree(lens);

3053:   PetscMalloc1(n,&cnew);
3054:   for (i=0; i<m; i++) {
3055:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
3056:     for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
3057:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
3058:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
3059:   }
3060:   PetscFree(cnew);

3062:   (*B)->assembled = PETSC_FALSE;

3064: #if defined(PETSC_HAVE_DEVICE)
3065:   MatBindToCPU(*B,A->boundtocpu);
3066: #endif
3067:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
3068:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
3069:   ISRestoreIndices(irowp,&row);
3070:   ISRestoreIndices(icolp,&col);
3071:   ISDestroy(&irowp);
3072:   ISDestroy(&icolp);
3073:   if (rowp == colp) {
3074:     MatPropagateSymmetryOptions(A,*B);
3075:   }
3076:   return(0);
3077: }

3079: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
3080: {

3084:   /* If the two matrices have the same copy implementation, use fast copy. */
3085:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
3086:     Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
3087:     Mat_SeqAIJ        *b = (Mat_SeqAIJ*)B->data;
3088:     const PetscScalar *aa;

3090:     MatSeqAIJGetArrayRead(A,&aa);
3091:     if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different %D != %D",a->i[A->rmap->n],b->i[B->rmap->n]);
3092:     PetscArraycpy(b->a,aa,a->i[A->rmap->n]);
3093:     PetscObjectStateIncrease((PetscObject)B);
3094:     MatSeqAIJRestoreArrayRead(A,&aa);
3095:   } else {
3096:     MatCopy_Basic(A,B,str);
3097:   }
3098:   return(0);
3099: }

3101: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
3102: {

3106:   MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,NULL);
3107:   return(0);
3108: }

3110: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
3111: {
3112:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

3115:   *array = a->a;
3116:   return(0);
3117: }

3119: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
3120: {
3122:   *array = NULL;
3123:   return(0);
3124: }

3126: /*
3127:    Computes the number of nonzeros per row needed for preallocation when X and Y
3128:    have different nonzero structure.
3129: */
3130: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
3131: {
3132:   PetscInt       i,j,k,nzx,nzy;

3135:   /* Set the number of nonzeros in the new matrix */
3136:   for (i=0; i<m; i++) {
3137:     const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
3138:     nzx = xi[i+1] - xi[i];
3139:     nzy = yi[i+1] - yi[i];
3140:     nnz[i] = 0;
3141:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
3142:       for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
3143:       if (k<nzy && yjj[k]==xjj[j]) k++;             /* Skip duplicate */
3144:       nnz[i]++;
3145:     }
3146:     for (; k<nzy; k++) nnz[i]++;
3147:   }
3148:   return(0);
3149: }

3151: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
3152: {
3153:   PetscInt       m = Y->rmap->N;
3154:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data;
3155:   Mat_SeqAIJ     *y = (Mat_SeqAIJ*)Y->data;

3159:   /* Set the number of nonzeros in the new matrix */
3160:   MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
3161:   return(0);
3162: }

3164: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
3165: {
3167:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;

3170:   if (str == UNKNOWN_NONZERO_PATTERN && x->nz == y->nz) {
3171:     PetscBool e;
3172:     PetscArraycmp(x->i,y->i,Y->rmap->n+1,&e);
3173:     if (e) {
3174:       PetscArraycmp(x->j,y->j,y->nz,&e);
3175:       if (e) {
3176:         str = SAME_NONZERO_PATTERN;
3177:       }
3178:     }
3179:   }
3180:   if (str == SAME_NONZERO_PATTERN) {
3181:     const PetscScalar *xa;
3182:     PetscScalar       *ya,alpha = a;
3183:     PetscBLASInt      one = 1,bnz;

3185:     PetscBLASIntCast(x->nz,&bnz);
3186:     MatSeqAIJGetArray(Y,&ya);
3187:     MatSeqAIJGetArrayRead(X,&xa);
3188:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,xa,&one,ya,&one));
3189:     MatSeqAIJRestoreArrayRead(X,&xa);
3190:     MatSeqAIJRestoreArray(Y,&ya);
3191:     PetscLogFlops(2.0*bnz);
3192:     MatSeqAIJInvalidateDiagonal(Y);
3193:     PetscObjectStateIncrease((PetscObject)Y);
3194:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3195:     MatAXPY_Basic(Y,a,X,str);
3196:   } else {
3197:     Mat      B;
3198:     PetscInt *nnz;
3199:     PetscMalloc1(Y->rmap->N,&nnz);
3200:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
3201:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
3202:     MatSetLayouts(B,Y->rmap,Y->cmap);
3203:     MatSetType(B,((PetscObject)Y)->type_name);
3204:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
3205:     MatSeqAIJSetPreallocation(B,0,nnz);
3206:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
3207:     MatHeaderReplace(Y,&B);
3208:     PetscFree(nnz);
3209:   }
3210:   return(0);
3211: }

3213: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3214: {
3215: #if defined(PETSC_USE_COMPLEX)
3216:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3217:   PetscInt       i,nz;
3218:   PetscScalar    *a;

3222:   nz = aij->nz;
3223:   MatSeqAIJGetArray(mat,&a);
3224:   for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
3225:   MatSeqAIJRestoreArray(mat,&a);
3226: #else
3228: #endif
3229:   return(0);
3230: }

3232: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3233: {
3234:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3235:   PetscErrorCode  ierr;
3236:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3237:   PetscReal       atmp;
3238:   PetscScalar     *x;
3239:   const MatScalar *aa,*av;

3242:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3243:   MatSeqAIJGetArrayRead(A,&av);
3244:   aa = av;
3245:   ai = a->i;
3246:   aj = a->j;

3248:   VecSet(v,0.0);
3249:   VecGetArrayWrite(v,&x);
3250:   VecGetLocalSize(v,&n);
3251:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3252:   for (i=0; i<m; i++) {
3253:     ncols = ai[1] - ai[0]; ai++;
3254:     for (j=0; j<ncols; j++) {
3255:       atmp = PetscAbsScalar(*aa);
3256:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
3257:       aa++; aj++;
3258:     }
3259:   }
3260:   VecRestoreArrayWrite(v,&x);
3261:   MatSeqAIJRestoreArrayRead(A,&av);
3262:   return(0);
3263: }

3265: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3266: {
3267:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3268:   PetscErrorCode  ierr;
3269:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3270:   PetscScalar     *x;
3271:   const MatScalar *aa,*av;

3274:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3275:   MatSeqAIJGetArrayRead(A,&av);
3276:   aa = av;
3277:   ai = a->i;
3278:   aj = a->j;

3280:   VecSet(v,0.0);
3281:   VecGetArrayWrite(v,&x);
3282:   VecGetLocalSize(v,&n);
3283:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3284:   for (i=0; i<m; i++) {
3285:     ncols = ai[1] - ai[0]; ai++;
3286:     if (ncols == A->cmap->n) { /* row is dense */
3287:       x[i] = *aa; if (idx) idx[i] = 0;
3288:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
3289:       x[i] = 0.0;
3290:       if (idx) {
3291:         for (j=0; j<ncols; j++) { /* find first implicit 0.0 in the row */
3292:           if (aj[j] > j) {
3293:             idx[i] = j;
3294:             break;
3295:           }
3296:         }
3297:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3298:         if (j==ncols && j < A->cmap->n) idx[i] = j;
3299:       }
3300:     }
3301:     for (j=0; j<ncols; j++) {
3302:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3303:       aa++; aj++;
3304:     }
3305:   }
3306:   VecRestoreArrayWrite(v,&x);
3307:   MatSeqAIJRestoreArrayRead(A,&av);
3308:   return(0);
3309: }

3311: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3312: {
3313:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3314:   PetscErrorCode  ierr;
3315:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3316:   PetscScalar     *x;
3317:   const MatScalar *aa,*av;

3320:   MatSeqAIJGetArrayRead(A,&av);
3321:   aa = av;
3322:   ai = a->i;
3323:   aj = a->j;

3325:   VecSet(v,0.0);
3326:   VecGetArrayWrite(v,&x);
3327:   VecGetLocalSize(v,&n);
3328:   if (n != m) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %D vs. %D rows", m, n);
3329:   for (i=0; i<m; i++) {
3330:     ncols = ai[1] - ai[0]; ai++;
3331:     if (ncols == A->cmap->n) { /* row is dense */
3332:       x[i] = *aa; if (idx) idx[i] = 0;
3333:     } else {  /* row is sparse so already KNOW minimum is 0.0 or higher */
3334:       x[i] = 0.0;
3335:       if (idx) {   /* find first implicit 0.0 in the row */
3336:         for (j=0; j<ncols; j++) {
3337:           if (aj[j] > j) {
3338:             idx[i] = j;
3339:             break;
3340:           }
3341:         }
3342:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3343:         if (j==ncols && j < A->cmap->n) idx[i] = j;
3344:       }
3345:     }
3346:     for (j=0; j<ncols; j++) {
3347:       if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3348:       aa++; aj++;
3349:     }
3350:   }
3351:   VecRestoreArrayWrite(v,&x);
3352:   MatSeqAIJRestoreArrayRead(A,&av);
3353:   return(0);
3354: }

3356: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3357: {
3358:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3359:   PetscErrorCode  ierr;
3360:   PetscInt        i,j,m = A->rmap->n,ncols,n;
3361:   const PetscInt  *ai,*aj;
3362:   PetscScalar     *x;
3363:   const MatScalar *aa,*av;

3366:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3367:   MatSeqAIJGetArrayRead(A,&av);
3368:   aa = av;
3369:   ai = a->i;
3370:   aj = a->j;

3372:   VecSet(v,0.0);
3373:   VecGetArrayWrite(v,&x);
3374:   VecGetLocalSize(v,&n);
3375:   if (n != m) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3376:   for (i=0; i<m; i++) {
3377:     ncols = ai[1] - ai[0]; ai++;
3378:     if (ncols == A->cmap->n) { /* row is dense */
3379:       x[i] = *aa; if (idx) idx[i] = 0;
3380:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
3381:       x[i] = 0.0;
3382:       if (idx) {   /* find first implicit 0.0 in the row */
3383:         for (j=0; j<ncols; j++) {
3384:           if (aj[j] > j) {
3385:             idx[i] = j;
3386:             break;
3387:           }
3388:         }
3389:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3390:         if (j==ncols && j < A->cmap->n) idx[i] = j;
3391:       }
3392:     }
3393:     for (j=0; j<ncols; j++) {
3394:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3395:       aa++; aj++;
3396:     }
3397:   }
3398:   VecRestoreArrayWrite(v,&x);
3399:   MatSeqAIJRestoreArrayRead(A,&av);
3400:   return(0);
3401: }

3403: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
3404: {
3405:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*) A->data;
3406:   PetscErrorCode  ierr;
3407:   PetscInt        i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
3408:   MatScalar       *diag,work[25],*v_work;
3409:   const PetscReal shift = 0.0;
3410:   PetscBool       allowzeropivot,zeropivotdetected=PETSC_FALSE;

3413:   allowzeropivot = PetscNot(A->erroriffailure);
3414:   if (a->ibdiagvalid) {
3415:     if (values) *values = a->ibdiag;
3416:     return(0);
3417:   }
3418:   MatMarkDiagonal_SeqAIJ(A);
3419:   if (!a->ibdiag) {
3420:     PetscMalloc1(bs2*mbs,&a->ibdiag);
3421:     PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
3422:   }
3423:   diag = a->ibdiag;
3424:   if (values) *values = a->ibdiag;
3425:   /* factor and invert each block */
3426:   switch (bs) {
3427:   case 1:
3428:     for (i=0; i<mbs; i++) {
3429:       MatGetValues(A,1,&i,1,&i,diag+i);
3430:       if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3431:         if (allowzeropivot) {
3432:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3433:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3434:           A->factorerror_zeropivot_row   = i;
3435:           PetscInfo3(A,"Zero pivot, row %D pivot %g tolerance %g\n",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
3436:         } else SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot, row %D pivot %g tolerance %g",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
3437:       }
3438:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3439:     }
3440:     break;
3441:   case 2:
3442:     for (i=0; i<mbs; i++) {
3443:       ij[0] = 2*i; ij[1] = 2*i + 1;
3444:       MatGetValues(A,2,ij,2,ij,diag);
3445:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
3446:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3447:       PetscKernel_A_gets_transpose_A_2(diag);
3448:       diag += 4;
3449:     }
3450:     break;
3451:   case 3:
3452:     for (i=0; i<mbs; i++) {
3453:       ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
3454:       MatGetValues(A,3,ij,3,ij,diag);
3455:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
3456:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3457:       PetscKernel_A_gets_transpose_A_3(diag);
3458:       diag += 9;
3459:     }
3460:     break;
3461:   case 4:
3462:     for (i=0; i<mbs; i++) {
3463:       ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
3464:       MatGetValues(A,4,ij,4,ij,diag);
3465:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
3466:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3467:       PetscKernel_A_gets_transpose_A_4(diag);
3468:       diag += 16;
3469:     }
3470:     break;
3471:   case 5:
3472:     for (i=0; i<mbs; i++) {
3473:       ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
3474:       MatGetValues(A,5,ij,5,ij,diag);
3475:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
3476:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3477:       PetscKernel_A_gets_transpose_A_5(diag);
3478:       diag += 25;
3479:     }
3480:     break;
3481:   case 6:
3482:     for (i=0; i<mbs; i++) {
3483:       ij[0] = 6*i; ij[1] = 6*i + 1; ij[2] = 6*i + 2; ij[3] = 6*i + 3; ij[4] = 6*i + 4; ij[5] = 6*i + 5;
3484:       MatGetValues(A,6,ij,6,ij,diag);
3485:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
3486:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3487:       PetscKernel_A_gets_transpose_A_6(diag);
3488:       diag += 36;
3489:     }
3490:     break;
3491:   case 7:
3492:     for (i=0; i<mbs; i++) {
3493:       ij[0] = 7*i; ij[1] = 7*i + 1; ij[2] = 7*i + 2; ij[3] = 7*i + 3; ij[4] = 7*i + 4; ij[5] = 7*i + 5; ij[5] = 7*i + 6;
3494:       MatGetValues(A,7,ij,7,ij,diag);
3495:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
3496:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3497:       PetscKernel_A_gets_transpose_A_7(diag);
3498:       diag += 49;
3499:     }
3500:     break;
3501:   default:
3502:     PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
3503:     for (i=0; i<mbs; i++) {
3504:       for (j=0; j<bs; j++) {
3505:         IJ[j] = bs*i + j;
3506:       }
3507:       MatGetValues(A,bs,IJ,bs,IJ,diag);
3508:       PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
3509:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3510:       PetscKernel_A_gets_transpose_A_N(diag,bs);
3511:       diag += bs2;
3512:     }
3513:     PetscFree3(v_work,v_pivots,IJ);
3514:   }
3515:   a->ibdiagvalid = PETSC_TRUE;
3516:   return(0);
3517: }

3519: static PetscErrorCode  MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
3520: {
3522:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
3523:   PetscScalar    a;
3524:   PetscInt       m,n,i,j,col;

3527:   if (!x->assembled) {
3528:     MatGetSize(x,&m,&n);
3529:     for (i=0; i<m; i++) {
3530:       for (j=0; j<aij->imax[i]; j++) {
3531:         PetscRandomGetValue(rctx,&a);
3532:         col  = (PetscInt)(n*PetscRealPart(a));
3533:         MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3534:       }
3535:     }
3536:   } else {
3537:     for (i=0; i<aij->nz; i++) {PetscRandomGetValue(rctx,aij->a+i);}
3538:   }
3539: #if defined(PETSC_HAVE_DEVICE)
3540:   if (x->offloadmask != PETSC_OFFLOAD_UNALLOCATED) x->offloadmask = PETSC_OFFLOAD_CPU;
3541: #endif
3542:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3543:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3544:   return(0);
3545: }

3547: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3548: PetscErrorCode  MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x,PetscInt low,PetscInt high,PetscRandom rctx)
3549: {
3551:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
3552:   PetscScalar    a;
3553:   PetscInt       m,n,i,j,col,nskip;

3556:   nskip = high - low;
3557:   MatGetSize(x,&m,&n);
3558:   n    -= nskip; /* shrink number of columns where nonzeros can be set */
3559:   for (i=0; i<m; i++) {
3560:     for (j=0; j<aij->imax[i]; j++) {
3561:       PetscRandomGetValue(rctx,&a);
3562:       col  = (PetscInt)(n*PetscRealPart(a));
3563:       if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3564:       MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3565:     }
3566:   }
3567:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3568:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3569:   return(0);
3570: }


3573: /* -------------------------------------------------------------------*/
3574: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
3575:                                         MatGetRow_SeqAIJ,
3576:                                         MatRestoreRow_SeqAIJ,
3577:                                         MatMult_SeqAIJ,
3578:                                 /*  4*/ MatMultAdd_SeqAIJ,
3579:                                         MatMultTranspose_SeqAIJ,
3580:                                         MatMultTransposeAdd_SeqAIJ,
3581:                                         NULL,
3582:                                         NULL,
3583:                                         NULL,
3584:                                 /* 10*/ NULL,
3585:                                         MatLUFactor_SeqAIJ,
3586:                                         NULL,
3587:                                         MatSOR_SeqAIJ,
3588:                                         MatTranspose_SeqAIJ,
3589:                                 /*1 5*/ MatGetInfo_SeqAIJ,
3590:                                         MatEqual_SeqAIJ,
3591:                                         MatGetDiagonal_SeqAIJ,
3592:                                         MatDiagonalScale_SeqAIJ,
3593:                                         MatNorm_SeqAIJ,
3594:                                 /* 20*/ NULL,
3595:                                         MatAssemblyEnd_SeqAIJ,
3596:                                         MatSetOption_SeqAIJ,
3597:                                         MatZeroEntries_SeqAIJ,
3598:                                 /* 24*/ MatZeroRows_SeqAIJ,
3599:                                         NULL,
3600:                                         NULL,
3601:                                         NULL,
3602:                                         NULL,
3603:                                 /* 29*/ MatSetUp_SeqAIJ,
3604:                                         NULL,
3605:                                         NULL,
3606:                                         NULL,
3607:                                         NULL,
3608:                                 /* 34*/ MatDuplicate_SeqAIJ,
3609:                                         NULL,
3610:                                         NULL,
3611:                                         MatILUFactor_SeqAIJ,
3612:                                         NULL,
3613:                                 /* 39*/ MatAXPY_SeqAIJ,
3614:                                         MatCreateSubMatrices_SeqAIJ,
3615:                                         MatIncreaseOverlap_SeqAIJ,
3616:                                         MatGetValues_SeqAIJ,
3617:                                         MatCopy_SeqAIJ,
3618:                                 /* 44*/ MatGetRowMax_SeqAIJ,
3619:                                         MatScale_SeqAIJ,
3620:                                         MatShift_SeqAIJ,
3621:                                         MatDiagonalSet_SeqAIJ,
3622:                                         MatZeroRowsColumns_SeqAIJ,
3623:                                 /* 49*/ MatSetRandom_SeqAIJ,
3624:                                         MatGetRowIJ_SeqAIJ,
3625:                                         MatRestoreRowIJ_SeqAIJ,
3626:                                         MatGetColumnIJ_SeqAIJ,
3627:                                         MatRestoreColumnIJ_SeqAIJ,
3628:                                 /* 54*/ MatFDColoringCreate_SeqXAIJ,
3629:                                         NULL,
3630:                                         NULL,
3631:                                         MatPermute_SeqAIJ,
3632:                                         NULL,
3633:                                 /* 59*/ NULL,
3634:                                         MatDestroy_SeqAIJ,
3635:                                         MatView_SeqAIJ,
3636:                                         NULL,
3637:                                         NULL,
3638:                                 /* 64*/ NULL,
3639:                                         MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3640:                                         NULL,
3641:                                         NULL,
3642:                                         NULL,
3643:                                 /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3644:                                         MatGetRowMinAbs_SeqAIJ,
3645:                                         NULL,
3646:                                         NULL,
3647:                                         NULL,
3648:                                 /* 74*/ NULL,
3649:                                         MatFDColoringApply_AIJ,
3650:                                         NULL,
3651:                                         NULL,
3652:                                         NULL,
3653:                                 /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3654:                                         NULL,
3655:                                         NULL,
3656:                                         NULL,
3657:                                         MatLoad_SeqAIJ,
3658:                                 /* 84*/ MatIsSymmetric_SeqAIJ,
3659:                                         MatIsHermitian_SeqAIJ,
3660:                                         NULL,
3661:                                         NULL,
3662:                                         NULL,
3663:                                 /* 89*/ NULL,
3664:                                         NULL,
3665:                                         MatMatMultNumeric_SeqAIJ_SeqAIJ,
3666:                                         NULL,
3667:                                         NULL,
3668:                                 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3669:                                         NULL,
3670:                                         NULL,
3671:                                         MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3672:                                         NULL,
3673:                                 /* 99*/ MatProductSetFromOptions_SeqAIJ,
3674:                                         NULL,
3675:                                         NULL,
3676:                                         MatConjugate_SeqAIJ,
3677:                                         NULL,
3678:                                 /*104*/ MatSetValuesRow_SeqAIJ,
3679:                                         MatRealPart_SeqAIJ,
3680:                                         MatImaginaryPart_SeqAIJ,
3681:                                         NULL,
3682:                                         NULL,
3683:                                 /*109*/ MatMatSolve_SeqAIJ,
3684:                                         NULL,
3685:                                         MatGetRowMin_SeqAIJ,
3686:                                         NULL,
3687:                                         MatMissingDiagonal_SeqAIJ,
3688:                                 /*114*/ NULL,
3689:                                         NULL,
3690:                                         NULL,
3691:                                         NULL,
3692:                                         NULL,
3693:                                 /*119*/ NULL,
3694:                                         NULL,
3695:                                         NULL,
3696:                                         NULL,
3697:                                         MatGetMultiProcBlock_SeqAIJ,
3698:                                 /*124*/ MatFindNonzeroRows_SeqAIJ,
3699:                                         MatGetColumnNorms_SeqAIJ,
3700:                                         MatInvertBlockDiagonal_SeqAIJ,
3701:                                         MatInvertVariableBlockDiagonal_SeqAIJ,
3702:                                         NULL,
3703:                                 /*129*/ NULL,
3704:                                         NULL,
3705:                                         NULL,
3706:                                         MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3707:                                         MatTransposeColoringCreate_SeqAIJ,
3708:                                 /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3709:                                         MatTransColoringApplyDenToSp_SeqAIJ,
3710:                                         NULL,
3711:                                         NULL,
3712:                                         MatRARtNumeric_SeqAIJ_SeqAIJ,
3713:                                  /*139*/NULL,
3714:                                         NULL,
3715:                                         NULL,
3716:                                         MatFDColoringSetUp_SeqXAIJ,
3717:                                         MatFindOffBlockDiagonalEntries_SeqAIJ,
3718:                                         MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3719:                                  /*145*/MatDestroySubMatrices_SeqAIJ,
3720:                                         NULL,
3721:                                         NULL
3722: };

3724: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3725: {
3726:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3727:   PetscInt   i,nz,n;

3730:   nz = aij->maxnz;
3731:   n  = mat->rmap->n;
3732:   for (i=0; i<nz; i++) {
3733:     aij->j[i] = indices[i];
3734:   }
3735:   aij->nz = nz;
3736:   for (i=0; i<n; i++) {
3737:     aij->ilen[i] = aij->imax[i];
3738:   }
3739:   return(0);
3740: }

3742: /*
3743:  * When a sparse matrix has many zero columns, we should compact them out to save the space
3744:  * This happens in MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3745:  * */
3746: PetscErrorCode  MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3747: {
3748:   Mat_SeqAIJ         *aij = (Mat_SeqAIJ*)mat->data;
3749:   PetscTable         gid1_lid1;
3750:   PetscTablePosition tpos;
3751:   PetscInt           gid,lid,i,ec,nz = aij->nz;
3752:   PetscInt           *garray,*jj = aij->j;
3753:   PetscErrorCode     ierr;

3758:   /* use a table */
3759:   PetscTableCreate(mat->rmap->n,mat->cmap->N+1,&gid1_lid1);
3760:   ec = 0;
3761:   for (i=0; i<nz; i++) {
3762:     PetscInt data,gid1 = jj[i] + 1;
3763:     PetscTableFind(gid1_lid1,gid1,&data);
3764:     if (!data) {
3765:       /* one based table */
3766:       PetscTableAdd(gid1_lid1,gid1,++ec,INSERT_VALUES);
3767:     }
3768:   }
3769:   /* form array of columns we need */
3770:   PetscMalloc1(ec+1,&garray);
3771:   PetscTableGetHeadPosition(gid1_lid1,&tpos);
3772:   while (tpos) {
3773:     PetscTableGetNext(gid1_lid1,&tpos,&gid,&lid);
3774:     gid--;
3775:     lid--;
3776:     garray[lid] = gid;
3777:   }
3778:   PetscSortInt(ec,garray); /* sort, and rebuild */
3779:   PetscTableRemoveAll(gid1_lid1);
3780:   for (i=0; i<ec; i++) {
3781:     PetscTableAdd(gid1_lid1,garray[i]+1,i+1,INSERT_VALUES);
3782:   }
3783:   /* compact out the extra columns in B */
3784:   for (i=0; i<nz; i++) {
3785:     PetscInt gid1 = jj[i] + 1;
3786:     PetscTableFind(gid1_lid1,gid1,&lid);
3787:     lid--;
3788:     jj[i] = lid;
3789:   }
3790:   PetscLayoutDestroy(&mat->cmap);
3791:   PetscTableDestroy(&gid1_lid1);
3792:   PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat),ec,ec,1,&mat->cmap);
3793:   ISLocalToGlobalMappingCreate(PETSC_COMM_SELF,mat->cmap->bs,mat->cmap->n,garray,PETSC_OWN_POINTER,mapping);
3794:   ISLocalToGlobalMappingSetType(*mapping,ISLOCALTOGLOBALMAPPINGHASH);
3795:   return(0);
3796: }

3798: /*@
3799:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3800:        in the matrix.

3802:   Input Parameters:
3803: +  mat - the SeqAIJ matrix
3804: -  indices - the column indices

3806:   Level: advanced

3808:   Notes:
3809:     This can be called if you have precomputed the nonzero structure of the
3810:   matrix and want to provide it to the matrix object to improve the performance
3811:   of the MatSetValues() operation.

3813:     You MUST have set the correct numbers of nonzeros per row in the call to
3814:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

3816:     MUST be called before any calls to MatSetValues();

3818:     The indices should start with zero, not one.

3820: @*/
3821: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3822: {

3828:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
3829:   return(0);
3830: }

3832: /* ----------------------------------------------------------------------------------------*/

3834: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
3835: {
3836:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3838:   size_t         nz = aij->i[mat->rmap->n];

3841:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

3843:   /* allocate space for values if not already there */
3844:   if (!aij->saved_values) {
3845:     PetscMalloc1(nz+1,&aij->saved_values);
3846:     PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
3847:   }

3849:   /* copy values over */
3850:   PetscArraycpy(aij->saved_values,aij->a,nz);
3851:   return(0);
3852: }

3854: /*@
3855:     MatStoreValues - Stashes a copy of the matrix values; this allows, for
3856:        example, reuse of the linear part of a Jacobian, while recomputing the
3857:        nonlinear portion.

3859:    Collect on Mat

3861:   Input Parameters:
3862: .  mat - the matrix (currently only AIJ matrices support this option)

3864:   Level: advanced

3866:   Common Usage, with SNESSolve():
3867: $    Create Jacobian matrix
3868: $    Set linear terms into matrix
3869: $    Apply boundary conditions to matrix, at this time matrix must have
3870: $      final nonzero structure (i.e. setting the nonlinear terms and applying
3871: $      boundary conditions again will not change the nonzero structure
3872: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3873: $    MatStoreValues(mat);
3874: $    Call SNESSetJacobian() with matrix
3875: $    In your Jacobian routine
3876: $      MatRetrieveValues(mat);
3877: $      Set nonlinear terms in matrix

3879:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3880: $    // build linear portion of Jacobian
3881: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3882: $    MatStoreValues(mat);
3883: $    loop over nonlinear iterations
3884: $       MatRetrieveValues(mat);
3885: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3886: $       // call MatAssemblyBegin/End() on matrix
3887: $       Solve linear system with Jacobian
3888: $    endloop

3890:   Notes:
3891:     Matrix must already be assemblied before calling this routine
3892:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
3893:     calling this routine.

3895:     When this is called multiple times it overwrites the previous set of stored values
3896:     and does not allocated additional space.

3898: .seealso: MatRetrieveValues()

3900: @*/
3901: PetscErrorCode  MatStoreValues(Mat mat)
3902: {

3907:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3908:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3909:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3910:   return(0);
3911: }

3913: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
3914: {
3915:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3917:   PetscInt       nz = aij->i[mat->rmap->n];

3920:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3921:   if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3922:   /* copy values over */
3923:   PetscArraycpy(aij->a,aij->saved_values,nz);
3924:   return(0);
3925: }

3927: /*@
3928:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3929:        example, reuse of the linear part of a Jacobian, while recomputing the
3930:        nonlinear portion.

3932:    Collect on Mat

3934:   Input Parameters:
3935: .  mat - the matrix (currently only AIJ matrices support this option)

3937:   Level: advanced

3939: .seealso: MatStoreValues()

3941: @*/
3942: PetscErrorCode  MatRetrieveValues(Mat mat)
3943: {

3948:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3949:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3950:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3951:   return(0);
3952: }


3955: /* --------------------------------------------------------------------------------*/
3956: /*@C
3957:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3958:    (the default parallel PETSc format).  For good matrix assembly performance
3959:    the user should preallocate the matrix storage by setting the parameter nz
3960:    (or the array nnz).  By setting these parameters accurately, performance
3961:    during matrix assembly can be increased by more than a factor of 50.

3963:    Collective

3965:    Input Parameters:
3966: +  comm - MPI communicator, set to PETSC_COMM_SELF
3967: .  m - number of rows
3968: .  n - number of columns
3969: .  nz - number of nonzeros per row (same for all rows)
3970: -  nnz - array containing the number of nonzeros in the various rows
3971:          (possibly different for each row) or NULL

3973:    Output Parameter:
3974: .  A - the matrix

3976:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3977:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
3978:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

3980:    Notes:
3981:    If nnz is given then nz is ignored

3983:    The AIJ format (also called the Yale sparse matrix format or
3984:    compressed row storage), is fully compatible with standard Fortran 77
3985:    storage.  That is, the stored row and column indices can begin at
3986:    either one (as in Fortran) or zero.  See the users' manual for details.

3988:    Specify the preallocated storage with either nz or nnz (not both).
3989:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3990:    allocation.  For large problems you MUST preallocate memory or you
3991:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3993:    By default, this format uses inodes (identical nodes) when possible, to
3994:    improve numerical efficiency of matrix-vector products and solves. We
3995:    search for consecutive rows with the same nonzero structure, thereby
3996:    reusing matrix information to achieve increased efficiency.

3998:    Options Database Keys:
3999: +  -mat_no_inode  - Do not use inodes
4000: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

4002:    Level: intermediate

4004: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

4006: @*/
4007: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
4008: {

4012:   MatCreate(comm,A);
4013:   MatSetSizes(*A,m,n,m,n);
4014:   MatSetType(*A,MATSEQAIJ);
4015:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
4016:   return(0);
4017: }

4019: /*@C
4020:    MatSeqAIJSetPreallocation - For good matrix assembly performance
4021:    the user should preallocate the matrix storage by setting the parameter nz
4022:    (or the array nnz).  By setting these parameters accurately, performance
4023:    during matrix assembly can be increased by more than a factor of 50.

4025:    Collective

4027:    Input Parameters:
4028: +  B - The matrix
4029: .  nz - number of nonzeros per row (same for all rows)
4030: -  nnz - array containing the number of nonzeros in the various rows
4031:          (possibly different for each row) or NULL

4033:    Notes:
4034:      If nnz is given then nz is ignored

4036:     The AIJ format (also called the Yale sparse matrix format or
4037:    compressed row storage), is fully compatible with standard Fortran 77
4038:    storage.  That is, the stored row and column indices can begin at
4039:    either one (as in Fortran) or zero.  See the users' manual for details.

4041:    Specify the preallocated storage with either nz or nnz (not both).
4042:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
4043:    allocation.  For large problems you MUST preallocate memory or you
4044:    will get TERRIBLE performance, see the users' manual chapter on matrices.

4046:    You can call MatGetInfo() to get information on how effective the preallocation was;
4047:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4048:    You can also run with the option -info and look for messages with the string
4049:    malloc in them to see if additional memory allocation was needed.

4051:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
4052:    entries or columns indices

4054:    By default, this format uses inodes (identical nodes) when possible, to
4055:    improve numerical efficiency of matrix-vector products and solves. We
4056:    search for consecutive rows with the same nonzero structure, thereby
4057:    reusing matrix information to achieve increased efficiency.

4059:    Options Database Keys:
4060: +  -mat_no_inode  - Do not use inodes
4061: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

4063:    Level: intermediate

4065: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo(),
4066:           MatSeqAIJSetTotalPreallocation()

4068: @*/
4069: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
4070: {

4076:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
4077:   return(0);
4078: }

4080: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
4081: {
4082:   Mat_SeqAIJ     *b;
4083:   PetscBool      skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
4085:   PetscInt       i;

4088:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
4089:   if (nz == MAT_SKIP_ALLOCATION) {
4090:     skipallocation = PETSC_TRUE;
4091:     nz             = 0;
4092:   }
4093:   PetscLayoutSetUp(B->rmap);
4094:   PetscLayoutSetUp(B->cmap);

4096:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
4097:   if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
4098:   if (PetscUnlikelyDebug(nnz)) {
4099:     for (i=0; i<B->rmap->n; i++) {
4100:       if (nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
4101:       if (nnz[i] > B->cmap->n) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %D value %d rowlength %D",i,nnz[i],B->cmap->n);
4102:     }
4103:   }

4105:   B->preallocated = PETSC_TRUE;

4107:   b = (Mat_SeqAIJ*)B->data;

4109:   if (!skipallocation) {
4110:     if (!b->imax) {
4111:       PetscMalloc1(B->rmap->n,&b->imax);
4112:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
4113:     }
4114:     if (!b->ilen) {
4115:       /* b->ilen will count nonzeros in each row so far. */
4116:       PetscCalloc1(B->rmap->n,&b->ilen);
4117:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
4118:     } else {
4119:       PetscMemzero(b->ilen,B->rmap->n*sizeof(PetscInt));
4120:     }
4121:     if (!b->ipre) {
4122:       PetscMalloc1(B->rmap->n,&b->ipre);
4123:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
4124:     }
4125:     if (!nnz) {
4126:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
4127:       else if (nz < 0) nz = 1;
4128:       nz = PetscMin(nz,B->cmap->n);
4129:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
4130:       nz = nz*B->rmap->n;
4131:     } else {
4132:       PetscInt64 nz64 = 0;
4133:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz64 += nnz[i];}
4134:       PetscIntCast(nz64,&nz);
4135:     }

4137:     /* allocate the matrix space */
4138:     /* FIXME: should B's old memory be unlogged? */
4139:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
4140:     if (B->structure_only) {
4141:       PetscMalloc1(nz,&b->j);
4142:       PetscMalloc1(B->rmap->n+1,&b->i);
4143:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
4144:     } else {
4145:       PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
4146:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
4147:     }
4148:     b->i[0] = 0;
4149:     for (i=1; i<B->rmap->n+1; i++) {
4150:       b->i[i] = b->i[i-1] + b->imax[i-1];
4151:     }
4152:     if (B->structure_only) {
4153:       b->singlemalloc = PETSC_FALSE;
4154:       b->free_a       = PETSC_FALSE;
4155:     } else {
4156:       b->singlemalloc = PETSC_TRUE;
4157:       b->free_a       = PETSC_TRUE;
4158:     }
4159:     b->free_ij      = PETSC_TRUE;
4160:   } else {
4161:     b->free_a  = PETSC_FALSE;
4162:     b->free_ij = PETSC_FALSE;
4163:   }

4165:   if (b->ipre && nnz != b->ipre  && b->imax) {
4166:     /* reserve user-requested sparsity */
4167:     PetscArraycpy(b->ipre,b->imax,B->rmap->n);
4168:   }


4171:   b->nz               = 0;
4172:   b->maxnz            = nz;
4173:   B->info.nz_unneeded = (double)b->maxnz;
4174:   if (realalloc) {
4175:     MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
4176:   }
4177:   B->was_assembled = PETSC_FALSE;
4178:   B->assembled     = PETSC_FALSE;
4179:   return(0);
4180: }


4183: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4184: {
4185:   Mat_SeqAIJ     *a;
4186:   PetscInt       i;


4192:   /* Check local size. If zero, then return */
4193:   if (!A->rmap->n) return(0);

4195:   a = (Mat_SeqAIJ*)A->data;
4196:   /* if no saved info, we error out */
4197:   if (!a->ipre) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"No saved preallocation info \n");

4199:   if (!a->i || !a->j || !a->a || !a->imax || !a->ilen) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"Memory info is incomplete, and can not reset preallocation \n");

4201:   PetscArraycpy(a->imax,a->ipre,A->rmap->n);
4202:   PetscArrayzero(a->ilen,A->rmap->n);
4203:   a->i[0] = 0;
4204:   for (i=1; i<A->rmap->n+1; i++) {
4205:     a->i[i] = a->i[i-1] + a->imax[i-1];
4206:   }
4207:   A->preallocated     = PETSC_TRUE;
4208:   a->nz               = 0;
4209:   a->maxnz            = a->i[A->rmap->n];
4210:   A->info.nz_unneeded = (double)a->maxnz;
4211:   A->was_assembled    = PETSC_FALSE;
4212:   A->assembled        = PETSC_FALSE;
4213:   return(0);
4214: }

4216: /*@
4217:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.

4219:    Input Parameters:
4220: +  B - the matrix
4221: .  i - the indices into j for the start of each row (starts with zero)
4222: .  j - the column indices for each row (starts with zero) these must be sorted for each row
4223: -  v - optional values in the matrix

4225:    Level: developer

4227:    Notes:
4228:       The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

4230:       This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4231:       structure will be the union of all the previous nonzero structures.

4233:     Developer Notes:
4234:       An optimization could be added to the implementation where it checks if the i, and j are identical to the current i and j and
4235:       then just copies the v values directly with PetscMemcpy().

4237:       This routine could also take a PetscCopyMode argument to allow sharing the values instead of always copying them.

4239: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), MATSEQAIJ, MatResetPreallocation()
4240: @*/
4241: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
4242: {

4248:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
4249:   return(0);
4250: }

4252: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
4253: {
4254:   PetscInt       i;
4255:   PetscInt       m,n;
4256:   PetscInt       nz;
4257:   PetscInt       *nnz;

4261:   if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);

4263:   PetscLayoutSetUp(B->rmap);
4264:   PetscLayoutSetUp(B->cmap);

4266:   MatGetSize(B, &m, &n);
4267:   PetscMalloc1(m+1, &nnz);
4268:   for (i = 0; i < m; i++) {
4269:     nz     = Ii[i+1]- Ii[i];
4270:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
4271:     nnz[i] = nz;
4272:   }
4273:   MatSeqAIJSetPreallocation(B, 0, nnz);
4274:   PetscFree(nnz);

4276:   for (i = 0; i < m; i++) {
4277:     MatSetValues_SeqAIJ(B, 1, &i, Ii[i+1] - Ii[i], J+Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES);
4278:   }

4280:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
4281:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

4283:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
4284:   return(0);
4285: }

4287: #include <../src/mat/impls/dense/seq/dense.h>
4288: #include <petsc/private/kernels/petscaxpy.h>

4290: /*
4291:     Computes (B'*A')' since computing B*A directly is untenable

4293:                n                       p                          p
4294:         [             ]       [             ]         [                 ]
4295:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
4296:         [             ]       [             ]         [                 ]

4298: */
4299: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
4300: {
4301:   PetscErrorCode    ierr;
4302:   Mat_SeqDense      *sub_a = (Mat_SeqDense*)A->data;
4303:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ*)B->data;
4304:   Mat_SeqDense      *sub_c = (Mat_SeqDense*)C->data;
4305:   PetscInt          i,j,n,m,q,p;
4306:   const PetscInt    *ii,*idx;
4307:   const PetscScalar *b,*a,*a_q;
4308:   PetscScalar       *c,*c_q;
4309:   PetscInt          clda = sub_c->lda;
4310:   PetscInt          alda = sub_a->lda;

4313:   m    = A->rmap->n;
4314:   n    = A->cmap->n;
4315:   p    = B->cmap->n;
4316:   a    = sub_a->v;
4317:   b    = sub_b->a;
4318:   c    = sub_c->v;
4319:   if (clda == m) {
4320:     PetscArrayzero(c,m*p);
4321:   } else {
4322:     for (j=0;j<p;j++)
4323:       for (i=0;i<m;i++)
4324:         c[j*clda + i] = 0.0;
4325:   }
4326:   ii  = sub_b->i;
4327:   idx = sub_b->j;
4328:   for (i=0; i<n; i++) {
4329:     q = ii[i+1] - ii[i];
4330:     while (q-->0) {
4331:       c_q = c + clda*(*idx);
4332:       a_q = a + alda*i;
4333:       PetscKernelAXPY(c_q,*b,a_q,m);
4334:       idx++;
4335:       b++;
4336:     }
4337:   }
4338:   return(0);
4339: }

4341: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)
4342: {
4344:   PetscInt       m=A->rmap->n,n=B->cmap->n;
4345:   PetscBool      cisdense;

4348:   if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"A->cmap->n %D != B->rmap->n %D\n",A->cmap->n,B->rmap->n);
4349:   MatSetSizes(C,m,n,m,n);
4350:   MatSetBlockSizesFromMats(C,A,B);
4351:   PetscObjectTypeCompareAny((PetscObject)C,&cisdense,MATSEQDENSE,MATSEQDENSECUDA,"");
4352:   if (!cisdense) {
4353:     MatSetType(C,MATDENSE);
4354:   }
4355:   MatSetUp(C);

4357:   C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4358:   return(0);
4359: }

4361: /* ----------------------------------------------------------------*/
4362: /*MC
4363:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4364:    based on compressed sparse row format.

4366:    Options Database Keys:
4367: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

4369:    Level: beginner

4371:    Notes:
4372:     MatSetValues() may be called for this matrix type with a NULL argument for the numerical values,
4373:     in this case the values associated with the rows and columns one passes in are set to zero
4374:     in the matrix

4376:     MatSetOptions(,MAT_STRUCTURE_ONLY,PETSC_TRUE) may be called for this matrix type. In this no
4377:     space is allocated for the nonzero entries and any entries passed with MatSetValues() are ignored

4379:   Developer Notes:
4380:     It would be nice if all matrix formats supported passing NULL in for the numerical values

4382: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
4383: M*/

4385: /*MC
4386:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

4388:    This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
4389:    and MATMPIAIJ otherwise.  As a result, for single process communicators,
4390:   MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation() is supported
4391:   for communicators controlling multiple processes.  It is recommended that you call both of
4392:   the above preallocation routines for simplicity.

4394:    Options Database Keys:
4395: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()

4397:   Developer Notes:
4398:     Subclasses include MATAIJCUSPARSE, MATAIJPERM, MATAIJSELL, MATAIJMKL, MATAIJCRL, and also automatically switches over to use inodes when
4399:    enough exist.

4401:   Level: beginner

4403: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
4404: M*/

4406: /*MC
4407:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

4409:    This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
4410:    and MATMPIAIJCRL otherwise.  As a result, for single process communicators,
4411:    MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
4412:   for communicators controlling multiple processes.  It is recommended that you call both of
4413:   the above preallocation routines for simplicity.

4415:    Options Database Keys:
4416: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()

4418:   Level: beginner

4420: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
4421: M*/

4423: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
4424: #if defined(PETSC_HAVE_ELEMENTAL)
4425: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
4426: #endif
4427: #if defined(PETSC_HAVE_SCALAPACK)
4428: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat,MatType,MatReuse,Mat*);
4429: #endif
4430: #if defined(PETSC_HAVE_HYPRE)
4431: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A,MatType,MatReuse,Mat*);
4432: #endif

4434: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat,MatType,MatReuse,Mat*);
4435: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat,MatType,MatReuse,Mat*);
4436: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

4438: /*@C
4439:    MatSeqAIJGetArray - gives read/write access to the array where the data for a MATSEQAIJ matrix is stored

4441:    Not Collective

4443:    Input Parameter:
4444: .  mat - a MATSEQAIJ matrix

4446:    Output Parameter:
4447: .   array - pointer to the data

4449:    Level: intermediate

4451: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4452: @*/
4453: PetscErrorCode  MatSeqAIJGetArray(Mat A,PetscScalar **array)
4454: {

4458:   PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
4459: #if defined(PETSC_HAVE_DEVICE)
4460:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
4461: #endif
4462:   return(0);
4463: }

4465: /*@C
4466:    MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a MATSEQAIJ matrix is stored

4468:    Not Collective

4470:    Input Parameter:
4471: .  mat - a MATSEQAIJ matrix

4473:    Output Parameter:
4474: .   array - pointer to the data

4476:    Level: intermediate

4478: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayRead()
4479: @*/
4480: PetscErrorCode  MatSeqAIJGetArrayRead(Mat A,const PetscScalar **array)
4481: {
4482: #if defined(PETSC_HAVE_DEVICE)
4483:   PetscOffloadMask oval;
4484: #endif

4488: #if defined(PETSC_HAVE_DEVICE)
4489:   oval = A->offloadmask;
4490: #endif
4491:   MatSeqAIJGetArray(A,(PetscScalar**)array);
4492: #if defined(PETSC_HAVE_DEVICE)
4493:   if (oval == PETSC_OFFLOAD_GPU || oval == PETSC_OFFLOAD_BOTH) A->offloadmask = PETSC_OFFLOAD_BOTH;
4494: #endif
4495:   return(0);
4496: }

4498: /*@C
4499:    MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from MatSeqAIJGetArrayRead

4501:    Not Collective

4503:    Input Parameter:
4504: .  mat - a MATSEQAIJ matrix

4506:    Output Parameter:
4507: .   array - pointer to the data

4509:    Level: intermediate

4511: .seealso: MatSeqAIJGetArray(), MatSeqAIJGetArrayRead()
4512: @*/
4513: PetscErrorCode  MatSeqAIJRestoreArrayRead(Mat A,const PetscScalar **array)
4514: {
4515: #if defined(PETSC_HAVE_DEVICE)
4516:   PetscOffloadMask oval;
4517: #endif

4521: #if defined(PETSC_HAVE_DEVICE)
4522:   oval = A->offloadmask;
4523: #endif
4524:   MatSeqAIJRestoreArray(A,(PetscScalar**)array);
4525: #if defined(PETSC_HAVE_DEVICE)
4526:   A->offloadmask = oval;
4527: #endif
4528:   return(0);
4529: }

4531: /*@C
4532:    MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

4534:    Not Collective

4536:    Input Parameter:
4537: .  mat - a MATSEQAIJ matrix

4539:    Output Parameter:
4540: .   nz - the maximum number of nonzeros in any row

4542:    Level: intermediate

4544: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4545: @*/
4546: PetscErrorCode  MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
4547: {
4548:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

4551:   *nz = aij->rmax;
4552:   return(0);
4553: }

4555: /*@C
4556:    MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()

4558:    Not Collective

4560:    Input Parameters:
4561: +  mat - a MATSEQAIJ matrix
4562: -  array - pointer to the data

4564:    Level: intermediate

4566: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
4567: @*/
4568: PetscErrorCode  MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
4569: {

4573:   PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
4574:   return(0);
4575: }

4577: #if defined(PETSC_HAVE_CUDA)
4578: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat);
4579: #endif
4580: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4581: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat);
4582: #endif

4584: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4585: {
4586:   Mat_SeqAIJ     *b;
4588:   PetscMPIInt    size;

4591:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
4592:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

4594:   PetscNewLog(B,&b);

4596:   B->data = (void*)b;

4598:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
4599:   if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;

4601:   b->row                = NULL;
4602:   b->col                = NULL;
4603:   b->icol               = NULL;
4604:   b->reallocs           = 0;
4605:   b->ignorezeroentries  = PETSC_FALSE;
4606:   b->roworiented        = PETSC_TRUE;
4607:   b->nonew              = 0;
4608:   b->diag               = NULL;
4609:   b->solve_work         = NULL;
4610:   B->spptr              = NULL;
4611:   b->saved_values       = NULL;
4612:   b->idiag              = NULL;
4613:   b->mdiag              = NULL;
4614:   b->ssor_work          = NULL;
4615:   b->omega              = 1.0;
4616:   b->fshift             = 0.0;
4617:   b->idiagvalid         = PETSC_FALSE;
4618:   b->ibdiagvalid        = PETSC_FALSE;
4619:   b->keepnonzeropattern = PETSC_FALSE;

4621:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4622:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
4623:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);

4625: #if defined(PETSC_HAVE_MATLAB_ENGINE)
4626:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
4627:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
4628: #endif

4630:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
4631:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
4632:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
4633:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
4634:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
4635:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
4636:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijsell_C",MatConvert_SeqAIJ_SeqAIJSELL);
4637: #if defined(PETSC_HAVE_MKL_SPARSE)
4638:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijmkl_C",MatConvert_SeqAIJ_SeqAIJMKL);
4639: #endif
4640: #if defined(PETSC_HAVE_CUDA)
4641:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcusparse_C",MatConvert_SeqAIJ_SeqAIJCUSPARSE);
4642:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",MatProductSetFromOptions_SeqAIJ);
4643:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaijcusparse_C",MatProductSetFromOptions_SeqAIJ);
4644: #endif
4645: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4646:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijkokkos_C",MatConvert_SeqAIJ_SeqAIJKokkos);
4647: #endif
4648:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
4649: #if defined(PETSC_HAVE_ELEMENTAL)
4650:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
4651: #endif
4652: #if defined(PETSC_HAVE_SCALAPACK)
4653:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_scalapack_C",MatConvert_AIJ_ScaLAPACK);
4654: #endif
4655: #if defined(PETSC_HAVE_HYPRE)
4656:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_hypre_C",MatConvert_AIJ_HYPRE);
4657:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",MatProductSetFromOptions_Transpose_AIJ_AIJ);
4658: #endif
4659:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
4660:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsell_C",MatConvert_SeqAIJ_SeqSELL);
4661:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_is_C",MatConvert_XAIJ_IS);
4662:   PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
4663:   PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
4664:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
4665:   PetscObjectComposeFunction((PetscObject)B,"MatResetPreallocation_C",MatResetPreallocation_SeqAIJ);
4666:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
4667:   PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
4668:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_is_seqaij_C",MatProductSetFromOptions_IS_XAIJ);
4669:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqdense_seqaij_C",MatProductSetFromOptions_SeqDense_SeqAIJ);
4670:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaij_C",MatProductSetFromOptions_SeqAIJ);
4671:   MatCreate_SeqAIJ_Inode(B);
4672:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4673:   MatSeqAIJSetTypeFromOptions(B);  /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4674:   return(0);
4675: }

4677: /*
4678:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
4679: */
4680: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
4681: {
4682:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data,*a = (Mat_SeqAIJ*)A->data;
4684:   PetscInt       m = A->rmap->n,i;

4687:   if (!A->assembled && cpvalues!=MAT_DO_NOT_COPY_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot duplicate unassembled matrix");

4689:   C->factortype = A->factortype;
4690:   c->row        = NULL;
4691:   c->col        = NULL;
4692:   c->icol       = NULL;
4693:   c->reallocs   = 0;

4695:   C->assembled = PETSC_TRUE;

4697:   PetscLayoutReference(A->rmap,&C->rmap);
4698:   PetscLayoutReference(A->cmap,&C->cmap);

4700:   PetscMalloc1(m,&c->imax);
4701:   PetscMemcpy(c->imax,a->imax,m*sizeof(PetscInt));
4702:   PetscMalloc1(m,&c->ilen);
4703:   PetscMemcpy(c->ilen,a->ilen,m*sizeof(PetscInt));
4704:   PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));

4706:   /* allocate the matrix space */
4707:   if (mallocmatspace) {
4708:     PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
4709:     PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));

4711:     c->singlemalloc = PETSC_TRUE;

4713:     PetscArraycpy(c->i,a->i,m+1);
4714:     if (m > 0) {
4715:       PetscArraycpy(c->j,a->j,a->i[m]);
4716:       if (cpvalues == MAT_COPY_VALUES) {
4717:         const PetscScalar *aa;

4719:         MatSeqAIJGetArrayRead(A,&aa);
4720:         PetscArraycpy(c->a,aa,a->i[m]);
4721:         MatSeqAIJGetArrayRead(A,&aa);
4722:       } else {
4723:         PetscArrayzero(c->a,a->i[m]);
4724:       }
4725:     }
4726:   }

4728:   c->ignorezeroentries = a->ignorezeroentries;
4729:   c->roworiented       = a->roworiented;
4730:   c->nonew             = a->nonew;
4731:   if (a->diag) {
4732:     PetscMalloc1(m+1,&c->diag);
4733:     PetscMemcpy(c->diag,a->diag,m*sizeof(PetscInt));
4734:     PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
4735:   } else c->diag = NULL;

4737:   c->solve_work         = NULL;
4738:   c->saved_values       = NULL;
4739:   c->idiag              = NULL;
4740:   c->ssor_work          = NULL;
4741:   c->keepnonzeropattern = a->keepnonzeropattern;
4742:   c->free_a             = PETSC_TRUE;
4743:   c->free_ij            = PETSC_TRUE;

4745:   c->rmax         = a->rmax;
4746:   c->nz           = a->nz;
4747:   c->maxnz        = a->nz;       /* Since we allocate exactly the right amount */
4748:   C->preallocated = PETSC_TRUE;

4750:   c->compressedrow.use   = a->compressedrow.use;
4751:   c->compressedrow.nrows = a->compressedrow.nrows;
4752:   if (a->compressedrow.use) {
4753:     i    = a->compressedrow.nrows;
4754:     PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
4755:     PetscArraycpy(c->compressedrow.i,a->compressedrow.i,i+1);
4756:     PetscArraycpy(c->compressedrow.rindex,a->compressedrow.rindex,i);
4757:   } else {
4758:     c->compressedrow.use    = PETSC_FALSE;
4759:     c->compressedrow.i      = NULL;
4760:     c->compressedrow.rindex = NULL;
4761:   }
4762:   c->nonzerorowcnt = a->nonzerorowcnt;
4763:   C->nonzerostate  = A->nonzerostate;

4765:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
4766:   PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
4767:   return(0);
4768: }

4770: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
4771: {

4775:   MatCreate(PetscObjectComm((PetscObject)A),B);
4776:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
4777:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
4778:     MatSetBlockSizesFromMats(*B,A,A);
4779:   }
4780:   MatSetType(*B,((PetscObject)A)->type_name);
4781:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4782:   return(0);
4783: }

4785: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4786: {
4787:   PetscBool      isbinary, ishdf5;

4793:   /* force binary viewer to load .info file if it has not yet done so */
4794:   PetscViewerSetUp(viewer);
4795:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
4796:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERHDF5,  &ishdf5);
4797:   if (isbinary) {
4798:     MatLoad_SeqAIJ_Binary(newMat,viewer);
4799:   } else if (ishdf5) {
4800: #if defined(PETSC_HAVE_HDF5)
4801:     MatLoad_AIJ_HDF5(newMat,viewer);
4802: #else
4803:     SETERRQ(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
4804: #endif
4805:   } else {
4806:     SETERRQ2(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"Viewer type %s not yet supported for reading %s matrices",((PetscObject)viewer)->type_name,((PetscObject)newMat)->type_name);
4807:   }
4808:   return(0);
4809: }

4811: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
4812: {
4813:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)mat->data;
4815:   PetscInt       header[4],*rowlens,M,N,nz,sum,rows,cols,i;

4818:   PetscViewerSetUp(viewer);

4820:   /* read in matrix header */
4821:   PetscViewerBinaryRead(viewer,header,4,NULL,PETSC_INT);
4822:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Not a matrix object in file");
4823:   M = header[1]; N = header[2]; nz = header[3];
4824:   if (M < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix row size (%D) in file is negative",M);
4825:   if (N < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix column size (%D) in file is negative",N);
4826:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk, cannot load as SeqAIJ");

4828:   /* set block sizes from the viewer's .info file */
4829:   MatLoad_Binary_BlockSizes(mat,viewer);
4830:   /* set local and global sizes if not set already */
4831:   if (mat->rmap->n < 0) mat->rmap->n = M;
4832:   if (mat->cmap->n < 0) mat->cmap->n = N;
4833:   if (mat->rmap->N < 0) mat->rmap->N = M;
4834:   if (mat->cmap->N < 0) mat->cmap->N = N;
4835:   PetscLayoutSetUp(mat->rmap);
4836:   PetscLayoutSetUp(mat->cmap);

4838:   /* check if the matrix sizes are correct */
4839:   MatGetSize(mat,&rows,&cols);
4840:   if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols);

4842:   /* read in row lengths */
4843:   PetscMalloc1(M,&rowlens);
4844:   PetscViewerBinaryRead(viewer,rowlens,M,NULL,PETSC_INT);
4845:   /* check if sum(rowlens) is same as nz */
4846:   sum = 0; for (i=0; i<M; i++) sum += rowlens[i];
4847:   if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Inconsistent matrix data in file: nonzeros = %D, sum-row-lengths = %D\n",nz,sum);
4848:   /* preallocate and check sizes */
4849:   MatSeqAIJSetPreallocation_SeqAIJ(mat,0,rowlens);
4850:   MatGetSize(mat,&rows,&cols);
4851:   if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols);
4852:   /* store row lengths */
4853:   PetscArraycpy(a->ilen,rowlens,M);
4854:   PetscFree(rowlens);

4856:   /* fill in "i" row pointers */
4857:   a->i[0] = 0; for (i=0; i<M; i++) a->i[i+1] = a->i[i] + a->ilen[i];
4858:   /* read in "j" column indices */
4859:   PetscViewerBinaryRead(viewer,a->j,nz,NULL,PETSC_INT);
4860:   /* read in "a" nonzero values */
4861:   PetscViewerBinaryRead(viewer,a->a,nz,NULL,PETSC_SCALAR);

4863:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
4864:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
4865:   return(0);
4866: }

4868: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4869: {
4870:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
4872: #if defined(PETSC_USE_COMPLEX)
4873:   PetscInt k;
4874: #endif

4877:   /* If the  matrix dimensions are not equal,or no of nonzeros */
4878:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4879:     *flg = PETSC_FALSE;
4880:     return(0);
4881:   }

4883:   /* if the a->i are the same */
4884:   PetscArraycmp(a->i,b->i,A->rmap->n+1,flg);
4885:   if (!*flg) return(0);

4887:   /* if a->j are the same */
4888:   PetscArraycmp(a->j,b->j,a->nz,flg);
4889:   if (!*flg) return(0);

4891:   /* if a->a are the same */
4892: #if defined(PETSC_USE_COMPLEX)
4893:   for (k=0; k<a->nz; k++) {
4894:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
4895:       *flg = PETSC_FALSE;
4896:       return(0);
4897:     }
4898:   }
4899: #else
4900:   PetscArraycmp(a->a,b->a,a->nz,flg);
4901: #endif
4902:   return(0);
4903: }

4905: /*@
4906:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4907:               provided by the user.

4909:       Collective

4911:    Input Parameters:
4912: +   comm - must be an MPI communicator of size 1
4913: .   m - number of rows
4914: .   n - number of columns
4915: .   i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4916: .   j - column indices
4917: -   a - matrix values

4919:    Output Parameter:
4920: .   mat - the matrix

4922:    Level: intermediate

4924:    Notes:
4925:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
4926:     once the matrix is destroyed and not before

4928:        You cannot set new nonzero locations into this matrix, that will generate an error.

4930:        The i and j indices are 0 based

4932:        The format which is used for the sparse matrix input, is equivalent to a
4933:     row-major ordering.. i.e for the following matrix, the input data expected is
4934:     as shown

4936: $        1 0 0
4937: $        2 0 3
4938: $        4 5 6
4939: $
4940: $        i =  {0,1,3,6}  [size = nrow+1  = 3+1]
4941: $        j =  {0,0,2,0,1,2}  [size = 6]; values must be sorted for each row
4942: $        v =  {1,2,3,4,5,6}  [size = 6]


4945: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4947: @*/
4948: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
4949: {
4951:   PetscInt       ii;
4952:   Mat_SeqAIJ     *aij;
4953:   PetscInt jj;

4956:   if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4957:   MatCreate(comm,mat);
4958:   MatSetSizes(*mat,m,n,m,n);
4959:   /* MatSetBlockSizes(*mat,,); */
4960:   MatSetType(*mat,MATSEQAIJ);
4961:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,NULL);
4962:   aij  = (Mat_SeqAIJ*)(*mat)->data;
4963:   PetscMalloc1(m,&aij->imax);
4964:   PetscMalloc1(m,&aij->ilen);

4966:   aij->i            = i;
4967:   aij->j            = j;
4968:   aij->a            = a;
4969:   aij->singlemalloc = PETSC_FALSE;
4970:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4971:   aij->free_a       = PETSC_FALSE;
4972:   aij->free_ij      = PETSC_FALSE;

4974:   for (ii=0; ii<m; ii++) {
4975:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4976:     if (PetscDefined(USE_DEBUG)) {
4977:       if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %D length = %D",ii,i[ii+1] - i[ii]);
4978:       for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4979:         if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual column %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
4980:         if (j[jj] == j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual column %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
4981:       }
4982:     }
4983:   }
4984:   if (PetscDefined(USE_DEBUG)) {
4985:     for (ii=0; ii<aij->i[m]; ii++) {
4986:       if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
4987:       if (j[ii] > n - 1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %D index = %D",ii,j[ii]);
4988:     }
4989:   }

4991:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4992:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4993:   return(0);
4994: }
4995: /*@C
4996:      MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4997:               provided by the user.

4999:       Collective

5001:    Input Parameters:
5002: +   comm - must be an MPI communicator of size 1
5003: .   m   - number of rows
5004: .   n   - number of columns
5005: .   i   - row indices
5006: .   j   - column indices
5007: .   a   - matrix values
5008: .   nz  - number of nonzeros
5009: -   idx - 0 or 1 based

5011:    Output Parameter:
5012: .   mat - the matrix

5014:    Level: intermediate

5016:    Notes:
5017:        The i and j indices are 0 based

5019:        The format which is used for the sparse matrix input, is equivalent to a
5020:     row-major ordering.. i.e for the following matrix, the input data expected is
5021:     as shown:

5023:         1 0 0
5024:         2 0 3
5025:         4 5 6

5027:         i =  {0,1,1,2,2,2}
5028:         j =  {0,0,2,0,1,2}
5029:         v =  {1,2,3,4,5,6}


5032: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

5034: @*/
5035: PetscErrorCode  MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat,PetscInt nz,PetscBool idx)
5036: {
5038:   PetscInt       ii, *nnz, one = 1,row,col;


5042:   PetscCalloc1(m,&nnz);
5043:   for (ii = 0; ii < nz; ii++) {
5044:     nnz[i[ii] - !!idx] += 1;
5045:   }
5046:   MatCreate(comm,mat);
5047:   MatSetSizes(*mat,m,n,m,n);
5048:   MatSetType(*mat,MATSEQAIJ);
5049:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
5050:   for (ii = 0; ii < nz; ii++) {
5051:     if (idx) {
5052:       row = i[ii] - 1;
5053:       col = j[ii] - 1;
5054:     } else {
5055:       row = i[ii];
5056:       col = j[ii];
5057:     }
5058:     MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
5059:   }
5060:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
5061:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
5062:   PetscFree(nnz);
5063:   return(0);
5064: }

5066: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5067: {
5068:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data;

5072:   a->idiagvalid  = PETSC_FALSE;
5073:   a->ibdiagvalid = PETSC_FALSE;

5075:   MatSeqAIJInvalidateDiagonal_Inode(A);
5076:   return(0);
5077: }

5079: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
5080: {
5082:   PetscMPIInt    size;

5085:   MPI_Comm_size(comm,&size);
5086:   if (size == 1) {
5087:     if (scall == MAT_INITIAL_MATRIX) {
5088:       MatDuplicate(inmat,MAT_COPY_VALUES,outmat);
5089:     } else {
5090:       MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
5091:     }
5092:   } else {
5093:     MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
5094:   }
5095:   return(0);
5096: }

5098: /*
5099:  Permute A into C's *local* index space using rowemb,colemb.
5100:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5101:  of [0,m), colemb is in [0,n).
5102:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5103:  */
5104: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
5105: {
5106:   /* If making this function public, change the error returned in this function away from _PLIB. */
5108:   Mat_SeqAIJ     *Baij;
5109:   PetscBool      seqaij;
5110:   PetscInt       m,n,*nz,i,j,count;
5111:   PetscScalar    v;
5112:   const PetscInt *rowindices,*colindices;

5115:   if (!B) return(0);
5116:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5117:   PetscObjectBaseTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
5118:   if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
5119:   if (rowemb) {
5120:     ISGetLocalSize(rowemb,&m);
5121:     if (m != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Row IS of size %D is incompatible with matrix row size %D",m,B->rmap->n);
5122:   } else {
5123:     if (C->rmap->n != B->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
5124:   }
5125:   if (colemb) {
5126:     ISGetLocalSize(colemb,&n);
5127:     if (n != B->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Diag col IS of size %D is incompatible with input matrix col size %D",n,B->cmap->n);
5128:   } else {
5129:     if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
5130:   }

5132:   Baij = (Mat_SeqAIJ*)(B->data);
5133:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
5134:     PetscMalloc1(B->rmap->n,&nz);
5135:     for (i=0; i<B->rmap->n; i++) {
5136:       nz[i] = Baij->i[i+1] - Baij->i[i];
5137:     }
5138:     MatSeqAIJSetPreallocation(C,0,nz);
5139:     PetscFree(nz);
5140:   }
5141:   if (pattern == SUBSET_NONZERO_PATTERN) {
5142:     MatZeroEntries(C);
5143:   }
5144:   count = 0;
5145:   rowindices = NULL;
5146:   colindices = NULL;
5147:   if (rowemb) {
5148:     ISGetIndices(rowemb,&rowindices);
5149:   }
5150:   if (colemb) {
5151:     ISGetIndices(colemb,&colindices);
5152:   }
5153:   for (i=0; i<B->rmap->n; i++) {
5154:     PetscInt row;
5155:     row = i;
5156:     if (rowindices) row = rowindices[i];
5157:     for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
5158:       PetscInt col;
5159:       col  = Baij->j[count];
5160:       if (colindices) col = colindices[col];
5161:       v    = Baij->a[count];
5162:       MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
5163:       ++count;
5164:     }
5165:   }
5166:   /* FIXME: set C's nonzerostate correctly. */
5167:   /* Assembly for C is necessary. */
5168:   C->preallocated = PETSC_TRUE;
5169:   C->assembled     = PETSC_TRUE;
5170:   C->was_assembled = PETSC_FALSE;
5171:   return(0);
5172: }

5174: PetscFunctionList MatSeqAIJList = NULL;

5176: /*@C
5177:    MatSeqAIJSetType - Converts a MATSEQAIJ matrix to a subtype

5179:    Collective on Mat

5181:    Input Parameters:
5182: +  mat      - the matrix object
5183: -  matype   - matrix type

5185:    Options Database Key:
5186: .  -mat_seqai_type  <method> - for example seqaijcrl


5189:   Level: intermediate

5191: .seealso: PCSetType(), VecSetType(), MatCreate(), MatType, Mat
5192: @*/
5193: PetscErrorCode  MatSeqAIJSetType(Mat mat, MatType matype)
5194: {
5195:   PetscErrorCode ierr,(*r)(Mat,MatType,MatReuse,Mat*);
5196:   PetscBool      sametype;

5200:   PetscObjectTypeCompare((PetscObject)mat,matype,&sametype);
5201:   if (sametype) return(0);

5203:    PetscFunctionListFind(MatSeqAIJList,matype,&r);
5204:   if (!r) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unknown Mat type given: %s",matype);
5205:   (*r)(mat,matype,MAT_INPLACE_MATRIX,&mat);
5206:   return(0);
5207: }


5210: /*@C
5211:   MatSeqAIJRegister -  - Adds a new sub-matrix type for sequential AIJ matrices

5213:    Not Collective

5215:    Input Parameters:
5216: +  name - name of a new user-defined matrix type, for example MATSEQAIJCRL
5217: -  function - routine to convert to subtype

5219:    Notes:
5220:    MatSeqAIJRegister() may be called multiple times to add several user-defined solvers.


5223:    Then, your matrix can be chosen with the procedural interface at runtime via the option
5224: $     -mat_seqaij_type my_mat

5226:    Level: advanced

5228: .seealso: MatSeqAIJRegisterAll()


5231:   Level: advanced
5232: @*/
5233: PetscErrorCode  MatSeqAIJRegister(const char sname[],PetscErrorCode (*function)(Mat,MatType,MatReuse,Mat *))
5234: {

5238:   MatInitializePackage();
5239:   PetscFunctionListAdd(&MatSeqAIJList,sname,function);
5240:   return(0);
5241: }

5243: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;

5245: /*@C
5246:   MatSeqAIJRegisterAll - Registers all of the matrix subtypes of SeqAIJ

5248:   Not Collective

5250:   Level: advanced

5252:   Developers Note: CUSPARSE does not yet support the MatConvert_SeqAIJ..() paradigm and thus cannot be registered here

5254: .seealso:  MatRegisterAll(), MatSeqAIJRegister()
5255: @*/
5256: PetscErrorCode  MatSeqAIJRegisterAll(void)
5257: {

5261:   if (MatSeqAIJRegisterAllCalled) return(0);
5262:   MatSeqAIJRegisterAllCalled = PETSC_TRUE;

5264:   MatSeqAIJRegister(MATSEQAIJCRL,      MatConvert_SeqAIJ_SeqAIJCRL);
5265:   MatSeqAIJRegister(MATSEQAIJPERM,     MatConvert_SeqAIJ_SeqAIJPERM);
5266:   MatSeqAIJRegister(MATSEQAIJSELL,     MatConvert_SeqAIJ_SeqAIJSELL);
5267: #if defined(PETSC_HAVE_MKL_SPARSE)
5268:   MatSeqAIJRegister(MATSEQAIJMKL,      MatConvert_SeqAIJ_SeqAIJMKL);
5269: #endif
5270: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5271:   MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL);
5272: #endif
5273:   return(0);
5274: }

5276: /*
5277:     Special version for direct calls from Fortran
5278: */
5279: #include <petsc/private/fortranimpl.h>
5280: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5281: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5282: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5283: #define matsetvaluesseqaij_ matsetvaluesseqaij
5284: #endif

5286: /* Change these macros so can be used in void function */
5287: #undef CHKERRQ
5288: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
5289: #undef SETERRQ2
5290: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
5291: #undef SETERRQ3
5292: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)

5294: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
5295: {
5296:   Mat            A  = *AA;
5297:   PetscInt       m  = *mm, n = *nn;
5298:   InsertMode     is = *isis;
5299:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
5300:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
5301:   PetscInt       *imax,*ai,*ailen;
5303:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
5304:   MatScalar      *ap,value,*aa;
5305:   PetscBool      ignorezeroentries = a->ignorezeroentries;
5306:   PetscBool      roworiented       = a->roworiented;

5309:   MatCheckPreallocated(A,1);
5310:   imax  = a->imax;
5311:   ai    = a->i;
5312:   ailen = a->ilen;
5313:   aj    = a->j;
5314:   aa    = a->a;

5316:   for (k=0; k<m; k++) { /* loop over added rows */
5317:     row = im[k];
5318:     if (row < 0) continue;
5319:     if (PetscUnlikelyDebug(row >= A->rmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
5320:     rp   = aj + ai[row]; ap = aa + ai[row];
5321:     rmax = imax[row]; nrow = ailen[row];
5322:     low  = 0;
5323:     high = nrow;
5324:     for (l=0; l<n; l++) { /* loop over added columns */
5325:       if (in[l] < 0) continue;
5326:       if (PetscUnlikelyDebug(in[l] >= A->cmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
5327:       col = in[l];
5328:       if (roworiented) value = v[l + k*n];
5329:       else value = v[k + l*m];

5331:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

5333:       if (col <= lastcol) low = 0;
5334:       else high = nrow;
5335:       lastcol = col;
5336:       while (high-low > 5) {
5337:         t = (low+high)/2;
5338:         if (rp[t] > col) high = t;
5339:         else             low  = t;
5340:       }
5341:       for (i=low; i<high; i++) {
5342:         if (rp[i] > col) break;
5343:         if (rp[i] == col) {
5344:           if (is == ADD_VALUES) ap[i] += value;
5345:           else                  ap[i] = value;
5346:           goto noinsert;
5347:         }
5348:       }
5349:       if (value == 0.0 && ignorezeroentries) goto noinsert;
5350:       if (nonew == 1) goto noinsert;
5351:       if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
5352:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
5353:       N = nrow++ - 1; a->nz++; high++;
5354:       /* shift up all the later entries in this row */
5355:       for (ii=N; ii>=i; ii--) {
5356:         rp[ii+1] = rp[ii];
5357:         ap[ii+1] = ap[ii];
5358:       }
5359:       rp[i] = col;
5360:       ap[i] = value;
5361:       A->nonzerostate++;
5362: noinsert:;
5363:       low = i + 1;
5364:     }
5365:     ailen[row] = nrow;
5366:   }
5367:   PetscFunctionReturnVoid();
5368: }