Actual source code: aij.c

petsc-master 2015-07-06
Report Typos and Errors
  2: /*
  3:     Defines the basic matrix operations for the AIJ (compressed row)
  4:   matrix storage format.
  5: */


  8: #include <../src/mat/impls/aij/seq/aij.h>          /*I "petscmat.h" I*/
  9: #include <petscblaslapack.h>
 10: #include <petscbt.h>
 11: #include <petsc/private/kernels/blocktranspose.h>

 15: PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms)
 16: {
 18:   PetscInt       i,m,n;
 19:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

 22:   MatGetSize(A,&m,&n);
 23:   PetscMemzero(norms,n*sizeof(PetscReal));
 24:   if (type == NORM_2) {
 25:     for (i=0; i<aij->i[m]; i++) {
 26:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]);
 27:     }
 28:   } else if (type == NORM_1) {
 29:     for (i=0; i<aij->i[m]; i++) {
 30:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]);
 31:     }
 32:   } else if (type == NORM_INFINITY) {
 33:     for (i=0; i<aij->i[m]; i++) {
 34:       norms[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),norms[aij->j[i]]);
 35:     }
 36:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown NormType");

 38:   if (type == NORM_2) {
 39:     for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
 40:   }
 41:   return(0);
 42: }

 46: PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A,IS *is)
 47: {
 48:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
 49:   PetscInt        i,m=A->rmap->n,cnt = 0, bs = A->rmap->bs;
 50:   const PetscInt  *jj = a->j,*ii = a->i;
 51:   PetscInt        *rows;
 52:   PetscErrorCode  ierr;

 55:   for (i=0; i<m; i++) {
 56:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
 57:       cnt++;
 58:     }
 59:   }
 60:   PetscMalloc1(cnt,&rows);
 61:   cnt  = 0;
 62:   for (i=0; i<m; i++) {
 63:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
 64:       rows[cnt] = i;
 65:       cnt++;
 66:     }
 67:   }
 68:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);
 69:   return(0);
 70: }

 74: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows)
 75: {
 76:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
 77:   const MatScalar *aa = a->a;
 78:   PetscInt        i,m=A->rmap->n,cnt = 0;
 79:   const PetscInt  *jj = a->j,*diag;
 80:   PetscInt        *rows;
 81:   PetscErrorCode  ierr;

 84:   MatMarkDiagonal_SeqAIJ(A);
 85:   diag = a->diag;
 86:   for (i=0; i<m; i++) {
 87:     if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
 88:       cnt++;
 89:     }
 90:   }
 91:   PetscMalloc1(cnt,&rows);
 92:   cnt  = 0;
 93:   for (i=0; i<m; i++) {
 94:     if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
 95:       rows[cnt++] = i;
 96:     }
 97:   }
 98:   *nrows = cnt;
 99:   *zrows = rows;
100:   return(0);
101: }

105: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
106: {
107:   PetscInt       nrows,*rows;

111:   *zrows = NULL;
112:   MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
113:   ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);
114:   return(0);
115: }

119: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
120: {
121:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
122:   const MatScalar *aa;
123:   PetscInt        m=A->rmap->n,cnt = 0;
124:   const PetscInt  *ii;
125:   PetscInt        n,i,j,*rows;
126:   PetscErrorCode  ierr;

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

164: PetscErrorCode  MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
165: {
166:   PetscErrorCode    ierr;
167:   Mat_SeqAIJ        *aij = (Mat_SeqAIJ*) Y->data;
168:   PetscInt          i,m = Y->rmap->n;
169:   const PetscInt    *diag;
170:   MatScalar         *aa = aij->a;
171:   const PetscScalar *v;
172:   PetscBool         missing;

175:   if (Y->assembled) {
176:     MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
177:     if (!missing) {
178:       diag = aij->diag;
179:       VecGetArrayRead(D,&v);
180:       if (is == INSERT_VALUES) {
181:         for (i=0; i<m; i++) {
182:           aa[diag[i]] = v[i];
183:         }
184:       } else {
185:         for (i=0; i<m; i++) {
186:           aa[diag[i]] += v[i];
187:         }
188:       }
189:       VecRestoreArrayRead(D,&v);
190:       return(0);
191:     }
192:     MatSeqAIJInvalidateDiagonal(Y);
193:   }
194:   MatDiagonalSet_Default(Y,D,is);
195:   return(0);
196: }

200: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
201: {
202:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
204:   PetscInt       i,ishift;

207:   *m = A->rmap->n;
208:   if (!ia) return(0);
209:   ishift = 0;
210:   if (symmetric && !A->structurally_symmetric) {
211:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);
212:   } else if (oshift == 1) {
213:     PetscInt *tia;
214:     PetscInt nz = a->i[A->rmap->n];
215:     /* malloc space and  add 1 to i and j indices */
216:     PetscMalloc1(A->rmap->n+1,&tia);
217:     for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1;
218:     *ia = tia;
219:     if (ja) {
220:       PetscInt *tja;
221:       PetscMalloc1(nz+1,&tja);
222:       for (i=0; i<nz; i++) tja[i] = a->j[i] + 1;
223:       *ja = tja;
224:     }
225:   } else {
226:     *ia = a->i;
227:     if (ja) *ja = a->j;
228:   }
229:   return(0);
230: }

234: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
235: {

239:   if (!ia) return(0);
240:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
241:     PetscFree(*ia);
242:     if (ja) {PetscFree(*ja);}
243:   }
244:   return(0);
245: }

249: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
250: {
251:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
253:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
254:   PetscInt       nz = a->i[m],row,*jj,mr,col;

257:   *nn = n;
258:   if (!ia) return(0);
259:   if (symmetric) {
260:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,0,oshift,(PetscInt**)ia,(PetscInt**)ja);
261:   } else {
262:     PetscCalloc1(n+1,&collengths);
263:     PetscMalloc1(n+1,&cia);
264:     PetscMalloc1(nz+1,&cja);
265:     jj   = a->j;
266:     for (i=0; i<nz; i++) {
267:       collengths[jj[i]]++;
268:     }
269:     cia[0] = oshift;
270:     for (i=0; i<n; i++) {
271:       cia[i+1] = cia[i] + collengths[i];
272:     }
273:     PetscMemzero(collengths,n*sizeof(PetscInt));
274:     jj   = a->j;
275:     for (row=0; row<m; row++) {
276:       mr = a->i[row+1] - a->i[row];
277:       for (i=0; i<mr; i++) {
278:         col = *jj++;

280:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
281:       }
282:     }
283:     PetscFree(collengths);
284:     *ia  = cia; *ja = cja;
285:   }
286:   return(0);
287: }

291: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
292: {

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

298:   PetscFree(*ia);
299:   PetscFree(*ja);
300:   return(0);
301: }

303: /*
304:  MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
305:  MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
306:  spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
307: */
310: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
311: {
312:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
314:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
315:   PetscInt       nz = a->i[m],row,*jj,mr,col;
316:   PetscInt       *cspidx;

319:   *nn = n;
320:   if (!ia) return(0);

322:   PetscCalloc1(n+1,&collengths);
323:   PetscMalloc1(n+1,&cia);
324:   PetscMalloc1(nz+1,&cja);
325:   PetscMalloc1(nz+1,&cspidx);
326:   jj   = a->j;
327:   for (i=0; i<nz; i++) {
328:     collengths[jj[i]]++;
329:   }
330:   cia[0] = oshift;
331:   for (i=0; i<n; i++) {
332:     cia[i+1] = cia[i] + collengths[i];
333:   }
334:   PetscMemzero(collengths,n*sizeof(PetscInt));
335:   jj   = a->j;
336:   for (row=0; row<m; row++) {
337:     mr = a->i[row+1] - a->i[row];
338:     for (i=0; i<mr; i++) {
339:       col = *jj++;
340:       cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */
341:       cja[cia[col] + collengths[col]++ - oshift]  = row + oshift;
342:     }
343:   }
344:   PetscFree(collengths);
345:   *ia    = cia; *ja = cja;
346:   *spidx = cspidx;
347:   return(0);
348: }

352: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
353: {

357:   MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);
358:   PetscFree(*spidx);
359:   return(0);
360: }

364: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
365: {
366:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
367:   PetscInt       *ai = a->i;

371:   PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
372:   return(0);
373: }

375: /*
376:     MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions

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

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

385: */

387: #include <petsc/private/isimpl.h>
390: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
391: {
392:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
393:   PetscInt       low,high,t,row,nrow,i,col,l;
394:   const PetscInt *rp,*ai = a->i,*ailen = a->ilen,*aj = a->j;
395:   PetscInt       lastcol = -1;
396:   MatScalar      *ap,value,*aa = a->a;
397:   const PetscInt *ridx = A->rmap->mapping->indices,*cidx = A->cmap->mapping->indices;

399:   row = ridx[im[0]];
400:   rp   = aj + ai[row];
401:   ap = aa + ai[row];
402:   nrow = ailen[row];
403:   low  = 0;
404:   high = nrow;
405:   for (l=0; l<n; l++) { /* loop over added columns */
406:     col = cidx[in[l]];
407:     value = v[l];

409:     if (col <= lastcol) low = 0;
410:     else high = nrow;
411:     lastcol = col;
412:     while (high-low > 5) {
413:       t = (low+high)/2;
414:       if (rp[t] > col) high = t;
415:       else low = t;
416:     }
417:     for (i=low; i<high; i++) {
418:       if (rp[i] == col) {
419:         ap[i] += value;
420:         low = i + 1;
421:         break;
422:       }
423:     }
424:   }
425:   return 0;
426: }

430: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
431: {
432:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
433:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
434:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
436:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
437:   MatScalar      *ap,value,*aa = a->a;
438:   PetscBool      ignorezeroentries = a->ignorezeroentries;
439:   PetscBool      roworiented       = a->roworiented;

442:   for (k=0; k<m; k++) { /* loop over added rows */
443:     row = im[k];
444:     if (row < 0) continue;
445: #if defined(PETSC_USE_DEBUG)
446:     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);
447: #endif
448:     rp   = aj + ai[row]; ap = aa + ai[row];
449:     rmax = imax[row]; nrow = ailen[row];
450:     low  = 0;
451:     high = nrow;
452:     for (l=0; l<n; l++) { /* loop over added columns */
453:       if (in[l] < 0) continue;
454: #if defined(PETSC_USE_DEBUG)
455:       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);
456: #endif
457:       col = in[l];
458:       if (roworiented) {
459:         value = v[l + k*n];
460:       } else {
461:         value = v[k + l*m];
462:       }
463:       if ((value == 0.0 && ignorezeroentries) && (is == ADD_VALUES)) continue;

465:       if (col <= lastcol) low = 0;
466:       else high = nrow;
467:       lastcol = col;
468:       while (high-low > 5) {
469:         t = (low+high)/2;
470:         if (rp[t] > col) high = t;
471:         else low = t;
472:       }
473:       for (i=low; i<high; i++) {
474:         if (rp[i] > col) break;
475:         if (rp[i] == col) {
476:           if (is == ADD_VALUES) ap[i] += value;
477:           else ap[i] = value;
478:           low = i + 1;
479:           goto noinsert;
480:         }
481:       }
482:       if (value == 0.0 && ignorezeroentries) goto noinsert;
483:       if (nonew == 1) goto noinsert;
484:       if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
485:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
486:       N = nrow++ - 1; a->nz++; high++;
487:       /* shift up all the later entries in this row */
488:       for (ii=N; ii>=i; ii--) {
489:         rp[ii+1] = rp[ii];
490:         ap[ii+1] = ap[ii];
491:       }
492:       rp[i] = col;
493:       ap[i] = value;
494:       low   = i + 1;
495:       A->nonzerostate++;
496: noinsert:;
497:     }
498:     ailen[row] = nrow;
499:   }
500:   return(0);
501: }


506: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
507: {
508:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
509:   PetscInt   *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
510:   PetscInt   *ai = a->i,*ailen = a->ilen;
511:   MatScalar  *ap,*aa = a->a;

514:   for (k=0; k<m; k++) { /* loop over rows */
515:     row = im[k];
516:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
517:     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);
518:     rp   = aj + ai[row]; ap = aa + ai[row];
519:     nrow = ailen[row];
520:     for (l=0; l<n; l++) { /* loop over columns */
521:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
522:       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);
523:       col  = in[l];
524:       high = nrow; low = 0; /* assume unsorted */
525:       while (high-low > 5) {
526:         t = (low+high)/2;
527:         if (rp[t] > col) high = t;
528:         else low = t;
529:       }
530:       for (i=low; i<high; i++) {
531:         if (rp[i] > col) break;
532:         if (rp[i] == col) {
533:           *v++ = ap[i];
534:           goto finished;
535:         }
536:       }
537:       *v++ = 0.0;
538: finished:;
539:     }
540:   }
541:   return(0);
542: }


547: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
548: {
549:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
551:   PetscInt       i,*col_lens;
552:   int            fd;
553:   FILE           *file;

556:   PetscViewerBinaryGetDescriptor(viewer,&fd);
557:   PetscMalloc1(4+A->rmap->n,&col_lens);

559:   col_lens[0] = MAT_FILE_CLASSID;
560:   col_lens[1] = A->rmap->n;
561:   col_lens[2] = A->cmap->n;
562:   col_lens[3] = a->nz;

564:   /* store lengths of each row and write (including header) to file */
565:   for (i=0; i<A->rmap->n; i++) {
566:     col_lens[4+i] = a->i[i+1] - a->i[i];
567:   }
568:   PetscBinaryWrite(fd,col_lens,4+A->rmap->n,PETSC_INT,PETSC_TRUE);
569:   PetscFree(col_lens);

571:   /* store column indices (zero start index) */
572:   PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,PETSC_FALSE);

574:   /* store nonzero values */
575:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);

577:   PetscViewerBinaryGetInfoPointer(viewer,&file);
578:   if (file) {
579:     fprintf(file,"-matload_block_size %d\n",(int)PetscAbs(A->rmap->bs));
580:   }
581:   return(0);
582: }

584: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

588: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
589: {
590:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
591:   PetscErrorCode    ierr;
592:   PetscInt          i,j,m = A->rmap->n;
593:   const char        *name;
594:   PetscViewerFormat format;

597:   PetscViewerGetFormat(viewer,&format);
598:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
599:     PetscInt nofinalvalue = 0;
600:     if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) {
601:       /* Need a dummy value to ensure the dimension of the matrix. */
602:       nofinalvalue = 1;
603:     }
604:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
605:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
606:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
607: #if defined(PETSC_USE_COMPLEX)
608:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);
609: #else
610:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
611: #endif
612:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

614:     for (i=0; i<m; i++) {
615:       for (j=a->i[i]; j<a->i[i+1]; j++) {
616: #if defined(PETSC_USE_COMPLEX)
617:         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]));
618: #else
619:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);
620: #endif
621:       }
622:     }
623:     if (nofinalvalue) {
624: #if defined(PETSC_USE_COMPLEX)
625:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
626: #else
627:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap->n,0.0);
628: #endif
629:     }
630:     PetscObjectGetName((PetscObject)A,&name);
631:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
632:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
633:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
634:     return(0);
635:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
636:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
637:     for (i=0; i<m; i++) {
638:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
639:       for (j=a->i[i]; j<a->i[i+1]; j++) {
640: #if defined(PETSC_USE_COMPLEX)
641:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
642:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
643:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
644:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
645:         } else if (PetscRealPart(a->a[j]) != 0.0) {
646:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
647:         }
648: #else
649:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);}
650: #endif
651:       }
652:       PetscViewerASCIIPrintf(viewer,"\n");
653:     }
654:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
655:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
656:     PetscInt nzd=0,fshift=1,*sptr;
657:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
658:     PetscMalloc1(m+1,&sptr);
659:     for (i=0; i<m; i++) {
660:       sptr[i] = nzd+1;
661:       for (j=a->i[i]; j<a->i[i+1]; j++) {
662:         if (a->j[j] >= i) {
663: #if defined(PETSC_USE_COMPLEX)
664:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
665: #else
666:           if (a->a[j] != 0.0) nzd++;
667: #endif
668:         }
669:       }
670:     }
671:     sptr[m] = nzd+1;
672:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
673:     for (i=0; i<m+1; i+=6) {
674:       if (i+4<m) {
675:         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]);
676:       } else if (i+3<m) {
677:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
678:       } else if (i+2<m) {
679:         PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
680:       } else if (i+1<m) {
681:         PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);
682:       } else if (i<m) {
683:         PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);
684:       } else {
685:         PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);
686:       }
687:     }
688:     PetscViewerASCIIPrintf(viewer,"\n");
689:     PetscFree(sptr);
690:     for (i=0; i<m; i++) {
691:       for (j=a->i[i]; j<a->i[i+1]; j++) {
692:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
693:       }
694:       PetscViewerASCIIPrintf(viewer,"\n");
695:     }
696:     PetscViewerASCIIPrintf(viewer,"\n");
697:     for (i=0; i<m; i++) {
698:       for (j=a->i[i]; j<a->i[i+1]; j++) {
699:         if (a->j[j] >= i) {
700: #if defined(PETSC_USE_COMPLEX)
701:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
702:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
703:           }
704: #else
705:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);}
706: #endif
707:         }
708:       }
709:       PetscViewerASCIIPrintf(viewer,"\n");
710:     }
711:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
712:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
713:     PetscInt    cnt = 0,jcnt;
714:     PetscScalar value;
715: #if defined(PETSC_USE_COMPLEX)
716:     PetscBool   realonly = PETSC_TRUE;

718:     for (i=0; i<a->i[m]; i++) {
719:       if (PetscImaginaryPart(a->a[i]) != 0.0) {
720:         realonly = PETSC_FALSE;
721:         break;
722:       }
723:     }
724: #endif

726:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
727:     for (i=0; i<m; i++) {
728:       jcnt = 0;
729:       for (j=0; j<A->cmap->n; j++) {
730:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
731:           value = a->a[cnt++];
732:           jcnt++;
733:         } else {
734:           value = 0.0;
735:         }
736: #if defined(PETSC_USE_COMPLEX)
737:         if (realonly) {
738:           PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
739:         } else {
740:           PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
741:         }
742: #else
743:         PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
744: #endif
745:       }
746:       PetscViewerASCIIPrintf(viewer,"\n");
747:     }
748:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
749:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
750:     PetscInt fshift=1;
751:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
752: #if defined(PETSC_USE_COMPLEX)
753:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
754: #else
755:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
756: #endif
757:     PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
758:     for (i=0; i<m; i++) {
759:       for (j=a->i[i]; j<a->i[i+1]; j++) {
760: #if defined(PETSC_USE_COMPLEX)
761:         PetscViewerASCIIPrintf(viewer,"%D %D %g %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
762: #else
763:         PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);
764: #endif
765:       }
766:     }
767:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
768:   } else {
769:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
770:     if (A->factortype) {
771:       for (i=0; i<m; i++) {
772:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
773:         /* L part */
774:         for (j=a->i[i]; j<a->i[i+1]; j++) {
775: #if defined(PETSC_USE_COMPLEX)
776:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
777:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
778:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
779:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
780:           } else {
781:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
782:           }
783: #else
784:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
785: #endif
786:         }
787:         /* diagonal */
788:         j = a->diag[i];
789: #if defined(PETSC_USE_COMPLEX)
790:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
791:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)PetscImaginaryPart(1.0/a->a[j]));
792:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
793:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)(-PetscImaginaryPart(1.0/a->a[j])));
794:         } else {
795:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));
796:         }
797: #else
798:         PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)(1.0/a->a[j]));
799: #endif

801:         /* U part */
802:         for (j=a->diag[i+1]+1; j<a->diag[i]; j++) {
803: #if defined(PETSC_USE_COMPLEX)
804:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
805:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
806:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
807:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
808:           } else {
809:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
810:           }
811: #else
812:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
813: #endif
814:         }
815:         PetscViewerASCIIPrintf(viewer,"\n");
816:       }
817:     } else {
818:       for (i=0; i<m; i++) {
819:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
820:         for (j=a->i[i]; j<a->i[i+1]; j++) {
821: #if defined(PETSC_USE_COMPLEX)
822:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
823:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
824:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
825:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
826:           } else {
827:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
828:           }
829: #else
830:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
831: #endif
832:         }
833:         PetscViewerASCIIPrintf(viewer,"\n");
834:       }
835:     }
836:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
837:   }
838:   PetscViewerFlush(viewer);
839:   return(0);
840: }

842: #include <petscdraw.h>
845: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
846: {
847:   Mat               A  = (Mat) Aa;
848:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
849:   PetscErrorCode    ierr;
850:   PetscInt          i,j,m = A->rmap->n,color;
851:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
852:   PetscViewer       viewer;
853:   PetscViewerFormat format;

856:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
857:   PetscViewerGetFormat(viewer,&format);

859:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
860:   /* loop over matrix elements drawing boxes */

862:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
863:     /* Blue for negative, Cyan for zero and  Red for positive */
864:     color = PETSC_DRAW_BLUE;
865:     for (i=0; i<m; i++) {
866:       y_l = m - i - 1.0; y_r = y_l + 1.0;
867:       for (j=a->i[i]; j<a->i[i+1]; j++) {
868:         x_l = a->j[j]; x_r = x_l + 1.0;
869:         if (PetscRealPart(a->a[j]) >=  0.) continue;
870:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
871:       }
872:     }
873:     color = PETSC_DRAW_CYAN;
874:     for (i=0; i<m; i++) {
875:       y_l = m - i - 1.0; y_r = y_l + 1.0;
876:       for (j=a->i[i]; j<a->i[i+1]; j++) {
877:         x_l = a->j[j]; x_r = x_l + 1.0;
878:         if (a->a[j] !=  0.) continue;
879:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
880:       }
881:     }
882:     color = PETSC_DRAW_RED;
883:     for (i=0; i<m; i++) {
884:       y_l = m - i - 1.0; y_r = y_l + 1.0;
885:       for (j=a->i[i]; j<a->i[i+1]; j++) {
886:         x_l = a->j[j]; x_r = x_l + 1.0;
887:         if (PetscRealPart(a->a[j]) <=  0.) continue;
888:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
889:       }
890:     }
891:   } else {
892:     /* use contour shading to indicate magnitude of values */
893:     /* first determine max of all nonzero values */
894:     PetscInt  nz = a->nz,count;
895:     PetscDraw popup;
896:     PetscReal scale;

898:     for (i=0; i<nz; i++) {
899:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
900:     }
901:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
902:     PetscDrawGetPopup(draw,&popup);
903:     if (popup) {
904:       PetscDrawScalePopup(popup,0.0,maxv);
905:     }
906:     count = 0;
907:     for (i=0; i<m; i++) {
908:       y_l = m - i - 1.0; y_r = y_l + 1.0;
909:       for (j=a->i[i]; j<a->i[i+1]; j++) {
910:         x_l   = a->j[j]; x_r = x_l + 1.0;
911:         color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
912:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
913:         count++;
914:       }
915:     }
916:   }
917:   return(0);
918: }

920: #include <petscdraw.h>
923: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
924: {
926:   PetscDraw      draw;
927:   PetscReal      xr,yr,xl,yl,h,w;
928:   PetscBool      isnull;

931:   PetscViewerDrawGetDraw(viewer,0,&draw);
932:   PetscDrawIsNull(draw,&isnull);
933:   if (isnull) return(0);

935:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
936:   xr   = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0;
937:   xr  += w;    yr += h;  xl = -w;     yl = -h;
938:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
939:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
940:   PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
941:   return(0);
942: }

946: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
947: {
949:   PetscBool      iascii,isbinary,isdraw;

952:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
953:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
954:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
955:   if (iascii) {
956:     MatView_SeqAIJ_ASCII(A,viewer);
957:   } else if (isbinary) {
958:     MatView_SeqAIJ_Binary(A,viewer);
959:   } else if (isdraw) {
960:     MatView_SeqAIJ_Draw(A,viewer);
961:   }
962:   MatView_SeqAIJ_Inode(A,viewer);
963:   return(0);
964: }

968: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
969: {
970:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
972:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
973:   PetscInt       m      = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
974:   MatScalar      *aa    = a->a,*ap;
975:   PetscReal      ratio  = 0.6;

978:   if (mode == MAT_FLUSH_ASSEMBLY) return(0);

980:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
981:   for (i=1; i<m; i++) {
982:     /* move each row back by the amount of empty slots (fshift) before it*/
983:     fshift += imax[i-1] - ailen[i-1];
984:     rmax    = PetscMax(rmax,ailen[i]);
985:     if (fshift) {
986:       ip = aj + ai[i];
987:       ap = aa + ai[i];
988:       N  = ailen[i];
989:       for (j=0; j<N; j++) {
990:         ip[j-fshift] = ip[j];
991:         ap[j-fshift] = ap[j];
992:       }
993:     }
994:     ai[i] = ai[i-1] + ailen[i-1];
995:   }
996:   if (m) {
997:     fshift += imax[m-1] - ailen[m-1];
998:     ai[m]   = ai[m-1] + ailen[m-1];
999:   }

1001:   /* reset ilen and imax for each row */
1002:   a->nonzerorowcnt = 0;
1003:   for (i=0; i<m; i++) {
1004:     ailen[i] = imax[i] = ai[i+1] - ai[i];
1005:     a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0);
1006:   }
1007:   a->nz = ai[m];
1008:   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);

1010:   MatMarkDiagonal_SeqAIJ(A);
1011:   PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
1012:   PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
1013:   PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);

1015:   A->info.mallocs    += a->reallocs;
1016:   a->reallocs         = 0;
1017:   A->info.nz_unneeded = (PetscReal)fshift;
1018:   a->rmax             = rmax;

1020:   MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);
1021:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);
1022:   MatSeqAIJInvalidateDiagonal(A);
1023:   return(0);
1024: }

1028: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1029: {
1030:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1031:   PetscInt       i,nz = a->nz;
1032:   MatScalar      *aa = a->a;

1036:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
1037:   MatSeqAIJInvalidateDiagonal(A);
1038:   return(0);
1039: }

1043: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1044: {
1045:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1046:   PetscInt       i,nz = a->nz;
1047:   MatScalar      *aa = a->a;

1051:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1052:   MatSeqAIJInvalidateDiagonal(A);
1053:   return(0);
1054: }

1058: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1059: {
1060:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1064:   PetscMemzero(a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
1065:   MatSeqAIJInvalidateDiagonal(A);
1066:   return(0);
1067: }

1071: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1072: {
1073:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1077: #if defined(PETSC_USE_LOG)
1078:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
1079: #endif
1080:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
1081:   ISDestroy(&a->row);
1082:   ISDestroy(&a->col);
1083:   PetscFree(a->diag);
1084:   PetscFree(a->ibdiag);
1085:   PetscFree2(a->imax,a->ilen);
1086:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
1087:   PetscFree(a->solve_work);
1088:   ISDestroy(&a->icol);
1089:   PetscFree(a->saved_values);
1090:   ISColoringDestroy(&a->coloring);
1091:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);
1092:   PetscFree(a->matmult_abdense);

1094:   MatDestroy_SeqAIJ_Inode(A);
1095:   PetscFree(A->data);

1097:   PetscObjectChangeTypeName((PetscObject)A,0);
1098:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
1099:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
1100:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
1101:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
1102:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
1103:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);
1104: #if defined(PETSC_HAVE_ELEMENTAL)
1105:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);
1106: #endif
1107:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);
1108:   PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
1109:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
1110:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
1111:   PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
1112:   return(0);
1113: }

1117: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
1118: {
1119:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1123:   switch (op) {
1124:   case MAT_ROW_ORIENTED:
1125:     a->roworiented = flg;
1126:     break;
1127:   case MAT_KEEP_NONZERO_PATTERN:
1128:     a->keepnonzeropattern = flg;
1129:     break;
1130:   case MAT_NEW_NONZERO_LOCATIONS:
1131:     a->nonew = (flg ? 0 : 1);
1132:     break;
1133:   case MAT_NEW_NONZERO_LOCATION_ERR:
1134:     a->nonew = (flg ? -1 : 0);
1135:     break;
1136:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1137:     a->nonew = (flg ? -2 : 0);
1138:     break;
1139:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1140:     a->nounused = (flg ? -1 : 0);
1141:     break;
1142:   case MAT_IGNORE_ZERO_ENTRIES:
1143:     a->ignorezeroentries = flg;
1144:     break;
1145:   case MAT_SPD:
1146:   case MAT_SYMMETRIC:
1147:   case MAT_STRUCTURALLY_SYMMETRIC:
1148:   case MAT_HERMITIAN:
1149:   case MAT_SYMMETRY_ETERNAL:
1150:     /* These options are handled directly by MatSetOption() */
1151:     break;
1152:   case MAT_NEW_DIAGONALS:
1153:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1154:   case MAT_USE_HASH_TABLE:
1155:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1156:     break;
1157:   case MAT_USE_INODES:
1158:     /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
1159:     break;
1160:   default:
1161:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1162:   }
1163:   MatSetOption_SeqAIJ_Inode(A,op,flg);
1164:   return(0);
1165: }

1169: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1170: {
1171:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1173:   PetscInt       i,j,n,*ai=a->i,*aj=a->j,nz;
1174:   PetscScalar    *aa=a->a,*x,zero=0.0;

1177:   VecGetLocalSize(v,&n);
1178:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");

1180:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1181:     PetscInt *diag=a->diag;
1182:     VecGetArray(v,&x);
1183:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1184:     VecRestoreArray(v,&x);
1185:     return(0);
1186:   }

1188:   VecSet(v,zero);
1189:   VecGetArray(v,&x);
1190:   for (i=0; i<n; i++) {
1191:     nz = ai[i+1] - ai[i];
1192:     if (!nz) x[i] = 0.0;
1193:     for (j=ai[i]; j<ai[i+1]; j++) {
1194:       if (aj[j] == i) {
1195:         x[i] = aa[j];
1196:         break;
1197:       }
1198:     }
1199:   }
1200:   VecRestoreArray(v,&x);
1201:   return(0);
1202: }

1204: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1207: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1208: {
1209:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1210:   PetscScalar       *y;
1211:   const PetscScalar *x;
1212:   PetscErrorCode    ierr;
1213:   PetscInt          m = A->rmap->n;
1214: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1215:   const MatScalar   *v;
1216:   PetscScalar       alpha;
1217:   PetscInt          n,i,j;
1218:   const PetscInt    *idx,*ii,*ridx=NULL;
1219:   Mat_CompressedRow cprow    = a->compressedrow;
1220:   PetscBool         usecprow = cprow.use;
1221: #endif

1224:   if (zz != yy) {VecCopy(zz,yy);}
1225:   VecGetArrayRead(xx,&x);
1226:   VecGetArray(yy,&y);

1228: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1229:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1230: #else
1231:   if (usecprow) {
1232:     m    = cprow.nrows;
1233:     ii   = cprow.i;
1234:     ridx = cprow.rindex;
1235:   } else {
1236:     ii = a->i;
1237:   }
1238:   for (i=0; i<m; i++) {
1239:     idx = a->j + ii[i];
1240:     v   = a->a + ii[i];
1241:     n   = ii[i+1] - ii[i];
1242:     if (usecprow) {
1243:       alpha = x[ridx[i]];
1244:     } else {
1245:       alpha = x[i];
1246:     }
1247:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1248:   }
1249: #endif
1250:   PetscLogFlops(2.0*a->nz);
1251:   VecRestoreArrayRead(xx,&x);
1252:   VecRestoreArray(yy,&y);
1253:   return(0);
1254: }

1258: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1259: {

1263:   VecSet(yy,0.0);
1264:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1265:   return(0);
1266: }

1268: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>

1272: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1273: {
1274:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1275:   PetscScalar       *y;
1276:   const PetscScalar *x;
1277:   const MatScalar   *aa;
1278:   PetscErrorCode    ierr;
1279:   PetscInt          m=A->rmap->n;
1280:   const PetscInt    *aj,*ii,*ridx=NULL;
1281:   PetscInt          n,i;
1282:   PetscScalar       sum;
1283:   PetscBool         usecprow=a->compressedrow.use;

1285: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1286: #pragma disjoint(*x,*y,*aa)
1287: #endif

1290:   VecGetArrayRead(xx,&x);
1291:   VecGetArray(yy,&y);
1292:   aj   = a->j;
1293:   aa   = a->a;
1294:   ii   = a->i;
1295:   if (usecprow) { /* use compressed row format */
1296:     PetscMemzero(y,m*sizeof(PetscScalar));
1297:     m    = a->compressedrow.nrows;
1298:     ii   = a->compressedrow.i;
1299:     ridx = a->compressedrow.rindex;
1300:     for (i=0; i<m; i++) {
1301:       n           = ii[i+1] - ii[i];
1302:       aj          = a->j + ii[i];
1303:       aa          = a->a + ii[i];
1304:       sum         = 0.0;
1305:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1306:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1307:       y[*ridx++] = sum;
1308:     }
1309:   } else { /* do not use compressed row format */
1310: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1311:     fortranmultaij_(&m,x,ii,aj,aa,y);
1312: #else
1313:     for (i=0; i<m; i++) {
1314:       n           = ii[i+1] - ii[i];
1315:       aj          = a->j + ii[i];
1316:       aa          = a->a + ii[i];
1317:       sum         = 0.0;
1318:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1319:       y[i] = sum;
1320:     }
1321: #endif
1322:   }
1323:   PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
1324:   VecRestoreArrayRead(xx,&x);
1325:   VecRestoreArray(yy,&y);
1326:   return(0);
1327: }

1331: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
1332: {
1333:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1334:   PetscScalar       *y;
1335:   const PetscScalar *x;
1336:   const MatScalar   *aa;
1337:   PetscErrorCode    ierr;
1338:   PetscInt          m=A->rmap->n;
1339:   const PetscInt    *aj,*ii,*ridx=NULL;
1340:   PetscInt          n,i,nonzerorow=0;
1341:   PetscScalar       sum;
1342:   PetscBool         usecprow=a->compressedrow.use;

1344: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1345: #pragma disjoint(*x,*y,*aa)
1346: #endif

1349:   VecGetArrayRead(xx,&x);
1350:   VecGetArray(yy,&y);
1351:   aj   = a->j;
1352:   aa   = a->a;
1353:   ii   = a->i;
1354:   if (usecprow) { /* use compressed row format */
1355:     m    = a->compressedrow.nrows;
1356:     ii   = a->compressedrow.i;
1357:     ridx = a->compressedrow.rindex;
1358:     for (i=0; i<m; i++) {
1359:       n           = ii[i+1] - ii[i];
1360:       aj          = a->j + ii[i];
1361:       aa          = a->a + ii[i];
1362:       sum         = 0.0;
1363:       nonzerorow += (n>0);
1364:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1365:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1366:       y[*ridx++] = sum;
1367:     }
1368:   } else { /* do not use compressed row format */
1369:     for (i=0; i<m; i++) {
1370:       n           = ii[i+1] - ii[i];
1371:       aj          = a->j + ii[i];
1372:       aa          = a->a + ii[i];
1373:       sum         = 0.0;
1374:       nonzerorow += (n>0);
1375:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1376:       y[i] = sum;
1377:     }
1378:   }
1379:   PetscLogFlops(2.0*a->nz - nonzerorow);
1380:   VecRestoreArrayRead(xx,&x);
1381:   VecRestoreArray(yy,&y);
1382:   return(0);
1383: }

1387: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1388: {
1389:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1390:   PetscScalar       *y,*z;
1391:   const PetscScalar *x;
1392:   const MatScalar   *aa;
1393:   PetscErrorCode    ierr;
1394:   PetscInt          m = A->rmap->n,*aj,*ii;
1395:   PetscInt          n,i,*ridx=NULL;
1396:   PetscScalar       sum;
1397:   PetscBool         usecprow=a->compressedrow.use;

1400:   VecGetArrayRead(xx,&x);
1401:   VecGetArrayPair(yy,zz,&y,&z);

1403:   aj = a->j;
1404:   aa = a->a;
1405:   ii = a->i;
1406:   if (usecprow) { /* use compressed row format */
1407:     if (zz != yy) {
1408:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
1409:     }
1410:     m    = a->compressedrow.nrows;
1411:     ii   = a->compressedrow.i;
1412:     ridx = a->compressedrow.rindex;
1413:     for (i=0; i<m; i++) {
1414:       n   = ii[i+1] - ii[i];
1415:       aj  = a->j + ii[i];
1416:       aa  = a->a + ii[i];
1417:       sum = y[*ridx];
1418:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1419:       z[*ridx++] = sum;
1420:     }
1421:   } else { /* do not use compressed row format */
1422:     for (i=0; i<m; i++) {
1423:       n   = ii[i+1] - ii[i];
1424:       aj  = a->j + ii[i];
1425:       aa  = a->a + ii[i];
1426:       sum = y[i];
1427:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1428:       z[i] = sum;
1429:     }
1430:   }
1431:   PetscLogFlops(2.0*a->nz);
1432:   VecRestoreArrayRead(xx,&x);
1433:   VecRestoreArrayPair(yy,zz,&y,&z);
1434:   return(0);
1435: }

1437: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1440: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1441: {
1442:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1443:   PetscScalar       *y,*z;
1444:   const PetscScalar *x;
1445:   const MatScalar   *aa;
1446:   PetscErrorCode    ierr;
1447:   const PetscInt    *aj,*ii,*ridx=NULL;
1448:   PetscInt          m = A->rmap->n,n,i;
1449:   PetscScalar       sum;
1450:   PetscBool         usecprow=a->compressedrow.use;

1453:   VecGetArrayRead(xx,&x);
1454:   VecGetArrayPair(yy,zz,&y,&z);

1456:   aj = a->j;
1457:   aa = a->a;
1458:   ii = a->i;
1459:   if (usecprow) { /* use compressed row format */
1460:     if (zz != yy) {
1461:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
1462:     }
1463:     m    = a->compressedrow.nrows;
1464:     ii   = a->compressedrow.i;
1465:     ridx = a->compressedrow.rindex;
1466:     for (i=0; i<m; i++) {
1467:       n   = ii[i+1] - ii[i];
1468:       aj  = a->j + ii[i];
1469:       aa  = a->a + ii[i];
1470:       sum = y[*ridx];
1471:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1472:       z[*ridx++] = sum;
1473:     }
1474:   } else { /* do not use compressed row format */
1475: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1476:     fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1477: #else
1478:     for (i=0; i<m; i++) {
1479:       n   = ii[i+1] - ii[i];
1480:       aj  = a->j + ii[i];
1481:       aa  = a->a + ii[i];
1482:       sum = y[i];
1483:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1484:       z[i] = sum;
1485:     }
1486: #endif
1487:   }
1488:   PetscLogFlops(2.0*a->nz);
1489:   VecRestoreArrayRead(xx,&x);
1490:   VecRestoreArrayPair(yy,zz,&y,&z);
1491: #if defined(PETSC_HAVE_CUSP)
1492:   /*
1493:   VecView(xx,0);
1494:   VecView(zz,0);
1495:   MatView(A,0);
1496:   */
1497: #endif
1498:   return(0);
1499: }

1501: /*
1502:      Adds diagonal pointers to sparse matrix structure.
1503: */
1506: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1507: {
1508:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1510:   PetscInt       i,j,m = A->rmap->n;

1513:   if (!a->diag) {
1514:     PetscMalloc1(m,&a->diag);
1515:     PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
1516:   }
1517:   for (i=0; i<A->rmap->n; i++) {
1518:     a->diag[i] = a->i[i+1];
1519:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1520:       if (a->j[j] == i) {
1521:         a->diag[i] = j;
1522:         break;
1523:       }
1524:     }
1525:   }
1526:   return(0);
1527: }

1529: /*
1530:      Checks for missing diagonals
1531: */
1534: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
1535: {
1536:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1537:   PetscInt   *diag,*ii = a->i,i;

1540:   *missing = PETSC_FALSE;
1541:   if (A->rmap->n > 0 && !ii) {
1542:     *missing = PETSC_TRUE;
1543:     if (d) *d = 0;
1544:     PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
1545:   } else {
1546:     diag = a->diag;
1547:     for (i=0; i<A->rmap->n; i++) {
1548:       if (diag[i] >= ii[i+1]) {
1549:         *missing = PETSC_TRUE;
1550:         if (d) *d = i;
1551:         PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
1552:         break;
1553:       }
1554:     }
1555:   }
1556:   return(0);
1557: }

1561: /*
1562:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1563: */
1564: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1565: {
1566:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
1568:   PetscInt       i,*diag,m = A->rmap->n;
1569:   MatScalar      *v = a->a;
1570:   PetscScalar    *idiag,*mdiag;

1573:   if (a->idiagvalid) return(0);
1574:   MatMarkDiagonal_SeqAIJ(A);
1575:   diag = a->diag;
1576:   if (!a->idiag) {
1577:     PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
1578:     PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));
1579:     v    = a->a;
1580:   }
1581:   mdiag = a->mdiag;
1582:   idiag = a->idiag;

1584:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1585:     for (i=0; i<m; i++) {
1586:       mdiag[i] = v[diag[i]];
1587:       if (!PetscAbsScalar(mdiag[i]) && !PetscRealPart(fshift)) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1588:       idiag[i] = 1.0/v[diag[i]];
1589:     }
1590:     PetscLogFlops(m);
1591:   } else {
1592:     for (i=0; i<m; i++) {
1593:       mdiag[i] = v[diag[i]];
1594:       idiag[i] = omega/(fshift + v[diag[i]]);
1595:     }
1596:     PetscLogFlops(2.0*m);
1597:   }
1598:   a->idiagvalid = PETSC_TRUE;
1599:   return(0);
1600: }

1602: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1605: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1606: {
1607:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1608:   PetscScalar       *x,d,sum,*t,scale;
1609:   const MatScalar   *v = a->a,*idiag=0,*mdiag;
1610:   const PetscScalar *b, *bs,*xb, *ts;
1611:   PetscErrorCode    ierr;
1612:   PetscInt          n = A->cmap->n,m = A->rmap->n,i;
1613:   const PetscInt    *idx,*diag;

1616:   its = its*lits;

1618:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1619:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1620:   a->fshift = fshift;
1621:   a->omega  = omega;

1623:   diag  = a->diag;
1624:   t     = a->ssor_work;
1625:   idiag = a->idiag;
1626:   mdiag = a->mdiag;

1628:   VecGetArray(xx,&x);
1629:   VecGetArrayRead(bb,&b);
1630:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1631:   if (flag == SOR_APPLY_UPPER) {
1632:     /* apply (U + D/omega) to the vector */
1633:     bs = b;
1634:     for (i=0; i<m; i++) {
1635:       d   = fshift + mdiag[i];
1636:       n   = a->i[i+1] - diag[i] - 1;
1637:       idx = a->j + diag[i] + 1;
1638:       v   = a->a + diag[i] + 1;
1639:       sum = b[i]*d/omega;
1640:       PetscSparseDensePlusDot(sum,bs,v,idx,n);
1641:       x[i] = sum;
1642:     }
1643:     VecRestoreArray(xx,&x);
1644:     VecRestoreArrayRead(bb,&b);
1645:     PetscLogFlops(a->nz);
1646:     return(0);
1647:   }

1649:   if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1650:   else if (flag & SOR_EISENSTAT) {
1651:     /* Let  A = L + U + D; where L is lower trianglar,
1652:     U is upper triangular, E = D/omega; This routine applies

1654:             (L + E)^{-1} A (U + E)^{-1}

1656:     to a vector efficiently using Eisenstat's trick.
1657:     */
1658:     scale = (2.0/omega) - 1.0;

1660:     /*  x = (E + U)^{-1} b */
1661:     for (i=m-1; i>=0; i--) {
1662:       n   = a->i[i+1] - diag[i] - 1;
1663:       idx = a->j + diag[i] + 1;
1664:       v   = a->a + diag[i] + 1;
1665:       sum = b[i];
1666:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
1667:       x[i] = sum*idiag[i];
1668:     }

1670:     /*  t = b - (2*E - D)x */
1671:     v = a->a;
1672:     for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];

1674:     /*  t = (E + L)^{-1}t */
1675:     ts   = t;
1676:     diag = a->diag;
1677:     for (i=0; i<m; i++) {
1678:       n   = diag[i] - a->i[i];
1679:       idx = a->j + a->i[i];
1680:       v   = a->a + a->i[i];
1681:       sum = t[i];
1682:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
1683:       t[i] = sum*idiag[i];
1684:       /*  x = x + t */
1685:       x[i] += t[i];
1686:     }

1688:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
1689:     VecRestoreArray(xx,&x);
1690:     VecRestoreArrayRead(bb,&b);
1691:     return(0);
1692:   }
1693:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1694:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1695:       for (i=0; i<m; i++) {
1696:         n   = diag[i] - a->i[i];
1697:         idx = a->j + a->i[i];
1698:         v   = a->a + a->i[i];
1699:         sum = b[i];
1700:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1701:         t[i] = sum;
1702:         x[i] = sum*idiag[i];
1703:       }
1704:       xb   = t;
1705:       PetscLogFlops(a->nz);
1706:     } else xb = b;
1707:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1708:       for (i=m-1; i>=0; i--) {
1709:         n   = a->i[i+1] - diag[i] - 1;
1710:         idx = a->j + diag[i] + 1;
1711:         v   = a->a + diag[i] + 1;
1712:         sum = xb[i];
1713:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1714:         if (xb == b) {
1715:           x[i] = sum*idiag[i];
1716:         } else {
1717:           x[i] = (1-omega)*x[i] + sum*idiag[i];  /* omega in idiag */
1718:         }
1719:       }
1720:       PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1721:     }
1722:     its--;
1723:   }
1724:   while (its--) {
1725:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1726:       for (i=0; i<m; i++) {
1727:         /* lower */
1728:         n   = diag[i] - a->i[i];
1729:         idx = a->j + a->i[i];
1730:         v   = a->a + a->i[i];
1731:         sum = b[i];
1732:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1733:         t[i] = sum;             /* save application of the lower-triangular part */
1734:         /* upper */
1735:         n   = a->i[i+1] - diag[i] - 1;
1736:         idx = a->j + diag[i] + 1;
1737:         v   = a->a + diag[i] + 1;
1738:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1739:         x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1740:       }
1741:       xb   = t;
1742:       PetscLogFlops(2.0*a->nz);
1743:     } else xb = b;
1744:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1745:       for (i=m-1; i>=0; i--) {
1746:         sum = xb[i];
1747:         if (xb == b) {
1748:           /* whole matrix (no checkpointing available) */
1749:           n   = a->i[i+1] - a->i[i];
1750:           idx = a->j + a->i[i];
1751:           v   = a->a + a->i[i];
1752:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
1753:           x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1754:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
1755:           n   = a->i[i+1] - diag[i] - 1;
1756:           idx = a->j + diag[i] + 1;
1757:           v   = a->a + diag[i] + 1;
1758:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
1759:           x[i] = (1. - omega)*x[i] + sum*idiag[i];  /* omega in idiag */
1760:         }
1761:       }
1762:       if (xb == b) {
1763:         PetscLogFlops(2.0*a->nz);
1764:       } else {
1765:         PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1766:       }
1767:     }
1768:   }
1769:   VecRestoreArray(xx,&x);
1770:   VecRestoreArrayRead(bb,&b);
1771:   return(0);
1772: }


1777: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1778: {
1779:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1782:   info->block_size   = 1.0;
1783:   info->nz_allocated = (double)a->maxnz;
1784:   info->nz_used      = (double)a->nz;
1785:   info->nz_unneeded  = (double)(a->maxnz - a->nz);
1786:   info->assemblies   = (double)A->num_ass;
1787:   info->mallocs      = (double)A->info.mallocs;
1788:   info->memory       = ((PetscObject)A)->mem;
1789:   if (A->factortype) {
1790:     info->fill_ratio_given  = A->info.fill_ratio_given;
1791:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1792:     info->factor_mallocs    = A->info.factor_mallocs;
1793:   } else {
1794:     info->fill_ratio_given  = 0;
1795:     info->fill_ratio_needed = 0;
1796:     info->factor_mallocs    = 0;
1797:   }
1798:   return(0);
1799: }

1803: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1804: {
1805:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1806:   PetscInt          i,m = A->rmap->n - 1,d = 0;
1807:   PetscErrorCode    ierr;
1808:   const PetscScalar *xx;
1809:   PetscScalar       *bb;
1810:   PetscBool         missing;

1813:   if (x && b) {
1814:     VecGetArrayRead(x,&xx);
1815:     VecGetArray(b,&bb);
1816:     for (i=0; i<N; i++) {
1817:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1818:       bb[rows[i]] = diag*xx[rows[i]];
1819:     }
1820:     VecRestoreArrayRead(x,&xx);
1821:     VecRestoreArray(b,&bb);
1822:   }

1824:   if (a->keepnonzeropattern) {
1825:     for (i=0; i<N; i++) {
1826:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1827:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1828:     }
1829:     if (diag != 0.0) {
1830:       MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1831:       if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1832:       for (i=0; i<N; i++) {
1833:         a->a[a->diag[rows[i]]] = diag;
1834:       }
1835:     }
1836:   } else {
1837:     if (diag != 0.0) {
1838:       for (i=0; i<N; i++) {
1839:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1840:         if (a->ilen[rows[i]] > 0) {
1841:           a->ilen[rows[i]]    = 1;
1842:           a->a[a->i[rows[i]]] = diag;
1843:           a->j[a->i[rows[i]]] = rows[i];
1844:         } else { /* in case row was completely empty */
1845:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1846:         }
1847:       }
1848:     } else {
1849:       for (i=0; i<N; i++) {
1850:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1851:         a->ilen[rows[i]] = 0;
1852:       }
1853:     }
1854:     A->nonzerostate++;
1855:   }
1856:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1857:   return(0);
1858: }

1862: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1863: {
1864:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1865:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
1866:   PetscErrorCode    ierr;
1867:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
1868:   const PetscScalar *xx;
1869:   PetscScalar       *bb;

1872:   if (x && b) {
1873:     VecGetArrayRead(x,&xx);
1874:     VecGetArray(b,&bb);
1875:     vecs = PETSC_TRUE;
1876:   }
1877:   PetscCalloc1(A->rmap->n,&zeroed);
1878:   for (i=0; i<N; i++) {
1879:     if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1880:     PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));

1882:     zeroed[rows[i]] = PETSC_TRUE;
1883:   }
1884:   for (i=0; i<A->rmap->n; i++) {
1885:     if (!zeroed[i]) {
1886:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1887:         if (zeroed[a->j[j]]) {
1888:           if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
1889:           a->a[j] = 0.0;
1890:         }
1891:       }
1892:     } else if (vecs) bb[i] = diag*xx[i];
1893:   }
1894:   if (x && b) {
1895:     VecRestoreArrayRead(x,&xx);
1896:     VecRestoreArray(b,&bb);
1897:   }
1898:   PetscFree(zeroed);
1899:   if (diag != 0.0) {
1900:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1901:     if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1902:     for (i=0; i<N; i++) {
1903:       a->a[a->diag[rows[i]]] = diag;
1904:     }
1905:   }
1906:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1907:   return(0);
1908: }

1912: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1913: {
1914:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1915:   PetscInt   *itmp;

1918:   if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

1920:   *nz = a->i[row+1] - a->i[row];
1921:   if (v) *v = a->a + a->i[row];
1922:   if (idx) {
1923:     itmp = a->j + a->i[row];
1924:     if (*nz) *idx = itmp;
1925:     else *idx = 0;
1926:   }
1927:   return(0);
1928: }

1930: /* remove this function? */
1933: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1934: {
1936:   return(0);
1937: }

1941: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1942: {
1943:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
1944:   MatScalar      *v  = a->a;
1945:   PetscReal      sum = 0.0;
1947:   PetscInt       i,j;

1950:   if (type == NORM_FROBENIUS) {
1951:     for (i=0; i<a->nz; i++) {
1952:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1953:     }
1954:     *nrm = PetscSqrtReal(sum);
1955:   } else if (type == NORM_1) {
1956:     PetscReal *tmp;
1957:     PetscInt  *jj = a->j;
1958:     PetscCalloc1(A->cmap->n+1,&tmp);
1959:     *nrm = 0.0;
1960:     for (j=0; j<a->nz; j++) {
1961:       tmp[*jj++] += PetscAbsScalar(*v);  v++;
1962:     }
1963:     for (j=0; j<A->cmap->n; j++) {
1964:       if (tmp[j] > *nrm) *nrm = tmp[j];
1965:     }
1966:     PetscFree(tmp);
1967:   } else if (type == NORM_INFINITY) {
1968:     *nrm = 0.0;
1969:     for (j=0; j<A->rmap->n; j++) {
1970:       v   = a->a + a->i[j];
1971:       sum = 0.0;
1972:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1973:         sum += PetscAbsScalar(*v); v++;
1974:       }
1975:       if (sum > *nrm) *nrm = sum;
1976:     }
1977:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
1978:   return(0);
1979: }

1981: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
1984: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
1985: {
1987:   PetscInt       i,j,anzj;
1988:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b;
1989:   PetscInt       an=A->cmap->N,am=A->rmap->N;
1990:   PetscInt       *ati,*atj,*atfill,*ai=a->i,*aj=a->j;

1993:   /* Allocate space for symbolic transpose info and work array */
1994:   PetscCalloc1(an+1,&ati);
1995:   PetscMalloc1(ai[am],&atj);
1996:   PetscMalloc1(an,&atfill);

1998:   /* Walk through aj and count ## of non-zeros in each row of A^T. */
1999:   /* Note: offset by 1 for fast conversion into csr format. */
2000:   for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
2001:   /* Form ati for csr format of A^T. */
2002:   for (i=0;i<an;i++) ati[i+1] += ati[i];

2004:   /* Copy ati into atfill so we have locations of the next free space in atj */
2005:   PetscMemcpy(atfill,ati,an*sizeof(PetscInt));

2007:   /* Walk through A row-wise and mark nonzero entries of A^T. */
2008:   for (i=0;i<am;i++) {
2009:     anzj = ai[i+1] - ai[i];
2010:     for (j=0;j<anzj;j++) {
2011:       atj[atfill[*aj]] = i;
2012:       atfill[*aj++]   += 1;
2013:     }
2014:   }

2016:   /* Clean up temporary space and complete requests. */
2017:   PetscFree(atfill);
2018:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
2019:   MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));

2021:   b          = (Mat_SeqAIJ*)((*B)->data);
2022:   b->free_a  = PETSC_FALSE;
2023:   b->free_ij = PETSC_TRUE;
2024:   b->nonew   = 0;
2025:   return(0);
2026: }

2030: PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B)
2031: {
2032:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2033:   Mat            C;
2035:   PetscInt       i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col;
2036:   MatScalar      *array = a->a;

2039:   if (reuse == MAT_REUSE_MATRIX && A == *B && m != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");

2041:   if (reuse == MAT_INITIAL_MATRIX || *B == A) {
2042:     PetscCalloc1(1+A->cmap->n,&col);

2044:     for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
2045:     MatCreate(PetscObjectComm((PetscObject)A),&C);
2046:     MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);
2047:     MatSetBlockSizes(C,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2048:     MatSetType(C,((PetscObject)A)->type_name);
2049:     MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
2050:     PetscFree(col);
2051:   } else {
2052:     C = *B;
2053:   }

2055:   for (i=0; i<m; i++) {
2056:     len    = ai[i+1]-ai[i];
2057:     MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
2058:     array += len;
2059:     aj    += len;
2060:   }
2061:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2062:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

2064:   if (reuse == MAT_INITIAL_MATRIX || *B != A) {
2065:     *B = C;
2066:   } else {
2067:     MatHeaderMerge(A,C);
2068:   }
2069:   return(0);
2070: }

2074: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
2075: {
2076:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data;
2077:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
2078:   MatScalar      *va,*vb;
2080:   PetscInt       ma,na,mb,nb, i;

2083:   bij = (Mat_SeqAIJ*) B->data;

2085:   MatGetSize(A,&ma,&na);
2086:   MatGetSize(B,&mb,&nb);
2087:   if (ma!=nb || na!=mb) {
2088:     *f = PETSC_FALSE;
2089:     return(0);
2090:   }
2091:   aii  = aij->i; bii = bij->i;
2092:   adx  = aij->j; bdx = bij->j;
2093:   va   = aij->a; vb = bij->a;
2094:   PetscMalloc1(ma,&aptr);
2095:   PetscMalloc1(mb,&bptr);
2096:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2097:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2099:   *f = PETSC_TRUE;
2100:   for (i=0; i<ma; i++) {
2101:     while (aptr[i]<aii[i+1]) {
2102:       PetscInt    idc,idr;
2103:       PetscScalar vc,vr;
2104:       /* column/row index/value */
2105:       idc = adx[aptr[i]];
2106:       idr = bdx[bptr[idc]];
2107:       vc  = va[aptr[i]];
2108:       vr  = vb[bptr[idc]];
2109:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
2110:         *f = PETSC_FALSE;
2111:         goto done;
2112:       } else {
2113:         aptr[i]++;
2114:         if (B || i!=idc) bptr[idc]++;
2115:       }
2116:     }
2117:   }
2118: done:
2119:   PetscFree(aptr);
2120:   PetscFree(bptr);
2121:   return(0);
2122: }

2126: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
2127: {
2128:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data;
2129:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
2130:   MatScalar      *va,*vb;
2132:   PetscInt       ma,na,mb,nb, i;

2135:   bij = (Mat_SeqAIJ*) B->data;

2137:   MatGetSize(A,&ma,&na);
2138:   MatGetSize(B,&mb,&nb);
2139:   if (ma!=nb || na!=mb) {
2140:     *f = PETSC_FALSE;
2141:     return(0);
2142:   }
2143:   aii  = aij->i; bii = bij->i;
2144:   adx  = aij->j; bdx = bij->j;
2145:   va   = aij->a; vb = bij->a;
2146:   PetscMalloc1(ma,&aptr);
2147:   PetscMalloc1(mb,&bptr);
2148:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2149:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2151:   *f = PETSC_TRUE;
2152:   for (i=0; i<ma; i++) {
2153:     while (aptr[i]<aii[i+1]) {
2154:       PetscInt    idc,idr;
2155:       PetscScalar vc,vr;
2156:       /* column/row index/value */
2157:       idc = adx[aptr[i]];
2158:       idr = bdx[bptr[idc]];
2159:       vc  = va[aptr[i]];
2160:       vr  = vb[bptr[idc]];
2161:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2162:         *f = PETSC_FALSE;
2163:         goto done;
2164:       } else {
2165:         aptr[i]++;
2166:         if (B || i!=idc) bptr[idc]++;
2167:       }
2168:     }
2169:   }
2170: done:
2171:   PetscFree(aptr);
2172:   PetscFree(bptr);
2173:   return(0);
2174: }

2178: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2179: {

2183:   MatIsTranspose_SeqAIJ(A,A,tol,f);
2184:   return(0);
2185: }

2189: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2190: {

2194:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2195:   return(0);
2196: }

2200: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
2201: {
2202:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2203:   PetscScalar    *l,*r,x;
2204:   MatScalar      *v;
2206:   PetscInt       i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz,*jj;

2209:   if (ll) {
2210:     /* The local size is used so that VecMPI can be passed to this routine
2211:        by MatDiagonalScale_MPIAIJ */
2212:     VecGetLocalSize(ll,&m);
2213:     if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
2214:     VecGetArray(ll,&l);
2215:     v    = a->a;
2216:     for (i=0; i<m; i++) {
2217:       x = l[i];
2218:       M = a->i[i+1] - a->i[i];
2219:       for (j=0; j<M; j++) (*v++) *= x;
2220:     }
2221:     VecRestoreArray(ll,&l);
2222:     PetscLogFlops(nz);
2223:   }
2224:   if (rr) {
2225:     VecGetLocalSize(rr,&n);
2226:     if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
2227:     VecGetArray(rr,&r);
2228:     v    = a->a; jj = a->j;
2229:     for (i=0; i<nz; i++) (*v++) *= r[*jj++];
2230:     VecRestoreArray(rr,&r);
2231:     PetscLogFlops(nz);
2232:   }
2233:   MatSeqAIJInvalidateDiagonal(A);
2234:   return(0);
2235: }

2239: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2240: {
2241:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
2243:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2244:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2245:   const PetscInt *irow,*icol;
2246:   PetscInt       nrows,ncols;
2247:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2248:   MatScalar      *a_new,*mat_a;
2249:   Mat            C;
2250:   PetscBool      stride;


2254:   ISGetIndices(isrow,&irow);
2255:   ISGetLocalSize(isrow,&nrows);
2256:   ISGetLocalSize(iscol,&ncols);

2258:   PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2259:   if (stride) {
2260:     ISStrideGetInfo(iscol,&first,&step);
2261:   } else {
2262:     first = 0;
2263:     step  = 0;
2264:   }
2265:   if (stride && step == 1) {
2266:     /* special case of contiguous rows */
2267:     PetscMalloc2(nrows,&lens,nrows,&starts);
2268:     /* loop over new rows determining lens and starting points */
2269:     for (i=0; i<nrows; i++) {
2270:       kstart = ai[irow[i]];
2271:       kend   = kstart + ailen[irow[i]];
2272:       starts[i] = kstart;
2273:       for (k=kstart; k<kend; k++) {
2274:         if (aj[k] >= first) {
2275:           starts[i] = k;
2276:           break;
2277:         }
2278:       }
2279:       sum = 0;
2280:       while (k < kend) {
2281:         if (aj[k++] >= first+ncols) break;
2282:         sum++;
2283:       }
2284:       lens[i] = sum;
2285:     }
2286:     /* create submatrix */
2287:     if (scall == MAT_REUSE_MATRIX) {
2288:       PetscInt n_cols,n_rows;
2289:       MatGetSize(*B,&n_rows,&n_cols);
2290:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2291:       MatZeroEntries(*B);
2292:       C    = *B;
2293:     } else {
2294:       PetscInt rbs,cbs;
2295:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2296:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2297:       ISGetBlockSize(isrow,&rbs);
2298:       ISGetBlockSize(iscol,&cbs);
2299:       MatSetBlockSizes(C,rbs,cbs);
2300:       MatSetType(C,((PetscObject)A)->type_name);
2301:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2302:     }
2303:     c = (Mat_SeqAIJ*)C->data;

2305:     /* loop over rows inserting into submatrix */
2306:     a_new = c->a;
2307:     j_new = c->j;
2308:     i_new = c->i;

2310:     for (i=0; i<nrows; i++) {
2311:       ii    = starts[i];
2312:       lensi = lens[i];
2313:       for (k=0; k<lensi; k++) {
2314:         *j_new++ = aj[ii+k] - first;
2315:       }
2316:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
2317:       a_new     += lensi;
2318:       i_new[i+1] = i_new[i] + lensi;
2319:       c->ilen[i] = lensi;
2320:     }
2321:     PetscFree2(lens,starts);
2322:   } else {
2323:     ISGetIndices(iscol,&icol);
2324:     PetscCalloc1(oldcols,&smap);
2325:     PetscMalloc1(1+nrows,&lens);
2326:     for (i=0; i<ncols; i++) {
2327: #if defined(PETSC_USE_DEBUG)
2328:       if (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);
2329: #endif
2330:       smap[icol[i]] = i+1;
2331:     }

2333:     /* determine lens of each row */
2334:     for (i=0; i<nrows; i++) {
2335:       kstart  = ai[irow[i]];
2336:       kend    = kstart + a->ilen[irow[i]];
2337:       lens[i] = 0;
2338:       for (k=kstart; k<kend; k++) {
2339:         if (smap[aj[k]]) {
2340:           lens[i]++;
2341:         }
2342:       }
2343:     }
2344:     /* Create and fill new matrix */
2345:     if (scall == MAT_REUSE_MATRIX) {
2346:       PetscBool equal;

2348:       c = (Mat_SeqAIJ*)((*B)->data);
2349:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2350:       PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);
2351:       if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2352:       PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));
2353:       C    = *B;
2354:     } else {
2355:       PetscInt rbs,cbs;
2356:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2357:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2358:       ISGetBlockSize(isrow,&rbs);
2359:       ISGetBlockSize(iscol,&cbs);
2360:       MatSetBlockSizes(C,rbs,cbs);
2361:       MatSetType(C,((PetscObject)A)->type_name);
2362:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2363:     }
2364:     c = (Mat_SeqAIJ*)(C->data);
2365:     for (i=0; i<nrows; i++) {
2366:       row      = irow[i];
2367:       kstart   = ai[row];
2368:       kend     = kstart + a->ilen[row];
2369:       mat_i    = c->i[i];
2370:       mat_j    = c->j + mat_i;
2371:       mat_a    = c->a + mat_i;
2372:       mat_ilen = c->ilen + i;
2373:       for (k=kstart; k<kend; k++) {
2374:         if ((tcol=smap[a->j[k]])) {
2375:           *mat_j++ = tcol - 1;
2376:           *mat_a++ = a->a[k];
2377:           (*mat_ilen)++;

2379:         }
2380:       }
2381:     }
2382:     /* Free work space */
2383:     ISRestoreIndices(iscol,&icol);
2384:     PetscFree(smap);
2385:     PetscFree(lens);
2386:     /* sort */
2387:     for (i = 0; i < nrows; i++) {
2388:       PetscInt ilen;

2390:       mat_i = c->i[i];
2391:       mat_j = c->j + mat_i;
2392:       mat_a = c->a + mat_i;
2393:       ilen  = c->ilen[i];
2394:       PetscSortIntWithMatScalarArray(ilen,mat_j,mat_a);
2395:     }
2396:   }
2397:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2398:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

2400:   ISRestoreIndices(isrow,&irow);
2401:   *B   = C;
2402:   return(0);
2403: }

2407: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
2408: {
2410:   Mat            B;

2413:   if (scall == MAT_INITIAL_MATRIX) {
2414:     MatCreate(subComm,&B);
2415:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2416:     MatSetBlockSizesFromMats(B,mat,mat);
2417:     MatSetType(B,MATSEQAIJ);
2418:     MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2419:     *subMat = B;
2420:   } else {
2421:     MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
2422:   }
2423:   return(0);
2424: }

2428: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2429: {
2430:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
2432:   Mat            outA;
2433:   PetscBool      row_identity,col_identity;

2436:   if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

2438:   ISIdentity(row,&row_identity);
2439:   ISIdentity(col,&col_identity);

2441:   outA             = inA;
2442:   outA->factortype = MAT_FACTOR_LU;

2444:   PetscObjectReference((PetscObject)row);
2445:   ISDestroy(&a->row);

2447:   a->row = row;

2449:   PetscObjectReference((PetscObject)col);
2450:   ISDestroy(&a->col);

2452:   a->col = col;

2454:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2455:   ISDestroy(&a->icol);
2456:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2457:   PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);

2459:   if (!a->solve_work) { /* this matrix may have been factored before */
2460:     PetscMalloc1(inA->rmap->n+1,&a->solve_work);
2461:     PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2462:   }

2464:   MatMarkDiagonal_SeqAIJ(inA);
2465:   if (row_identity && col_identity) {
2466:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2467:   } else {
2468:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2469:   }
2470:   return(0);
2471: }

2475: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2476: {
2477:   Mat_SeqAIJ     *a     = (Mat_SeqAIJ*)inA->data;
2478:   PetscScalar    oalpha = alpha;
2480:   PetscBLASInt   one = 1,bnz;

2483:   PetscBLASIntCast(a->nz,&bnz);
2484:   PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one));
2485:   PetscLogFlops(a->nz);
2486:   MatSeqAIJInvalidateDiagonal(inA);
2487:   return(0);
2488: }

2492: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2493: {
2495:   PetscInt       i;

2498:   if (scall == MAT_INITIAL_MATRIX) {
2499:     PetscMalloc1(n+1,B);
2500:   }

2502:   for (i=0; i<n; i++) {
2503:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2504:   }
2505:   return(0);
2506: }

2510: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2511: {
2512:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2514:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
2515:   const PetscInt *idx;
2516:   PetscInt       start,end,*ai,*aj;
2517:   PetscBT        table;

2520:   m  = A->rmap->n;
2521:   ai = a->i;
2522:   aj = a->j;

2524:   if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

2526:   PetscMalloc1(m+1,&nidx);
2527:   PetscBTCreate(m,&table);

2529:   for (i=0; i<is_max; i++) {
2530:     /* Initialize the two local arrays */
2531:     isz  = 0;
2532:     PetscBTMemzero(m,table);

2534:     /* Extract the indices, assume there can be duplicate entries */
2535:     ISGetIndices(is[i],&idx);
2536:     ISGetLocalSize(is[i],&n);

2538:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2539:     for (j=0; j<n; ++j) {
2540:       if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
2541:     }
2542:     ISRestoreIndices(is[i],&idx);
2543:     ISDestroy(&is[i]);

2545:     k = 0;
2546:     for (j=0; j<ov; j++) { /* for each overlap */
2547:       n = isz;
2548:       for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
2549:         row   = nidx[k];
2550:         start = ai[row];
2551:         end   = ai[row+1];
2552:         for (l = start; l<end; l++) {
2553:           val = aj[l];
2554:           if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
2555:         }
2556:       }
2557:     }
2558:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
2559:   }
2560:   PetscBTDestroy(&table);
2561:   PetscFree(nidx);
2562:   return(0);
2563: }

2565: /* -------------------------------------------------------------- */
2568: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2569: {
2570:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2572:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2573:   const PetscInt *row,*col;
2574:   PetscInt       *cnew,j,*lens;
2575:   IS             icolp,irowp;
2576:   PetscInt       *cwork = NULL;
2577:   PetscScalar    *vwork = NULL;

2580:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2581:   ISGetIndices(irowp,&row);
2582:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2583:   ISGetIndices(icolp,&col);

2585:   /* determine lengths of permuted rows */
2586:   PetscMalloc1(m+1,&lens);
2587:   for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
2588:   MatCreate(PetscObjectComm((PetscObject)A),B);
2589:   MatSetSizes(*B,m,n,m,n);
2590:   MatSetBlockSizesFromMats(*B,A,A);
2591:   MatSetType(*B,((PetscObject)A)->type_name);
2592:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2593:   PetscFree(lens);

2595:   PetscMalloc1(n,&cnew);
2596:   for (i=0; i<m; i++) {
2597:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2598:     for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
2599:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2600:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2601:   }
2602:   PetscFree(cnew);

2604:   (*B)->assembled = PETSC_FALSE;

2606:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2607:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2608:   ISRestoreIndices(irowp,&row);
2609:   ISRestoreIndices(icolp,&col);
2610:   ISDestroy(&irowp);
2611:   ISDestroy(&icolp);
2612:   return(0);
2613: }

2617: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
2618: {

2622:   /* If the two matrices have the same copy implementation, use fast copy. */
2623:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2624:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2625:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

2627:     if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
2628:     PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
2629:   } else {
2630:     MatCopy_Basic(A,B,str);
2631:   }
2632:   return(0);
2633: }

2637: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
2638: {

2642:    MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2643:   return(0);
2644: }

2648: PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2649: {
2650:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

2653:   *array = a->a;
2654:   return(0);
2655: }

2659: PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2660: {
2662:   return(0);
2663: }

2665: /*
2666:    Computes the number of nonzeros per row needed for preallocation when X and Y
2667:    have different nonzero structure.
2668: */
2671: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
2672: {
2673:   PetscInt       i,j,k,nzx,nzy;

2676:   /* Set the number of nonzeros in the new matrix */
2677:   for (i=0; i<m; i++) {
2678:     const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
2679:     nzx = xi[i+1] - xi[i];
2680:     nzy = yi[i+1] - yi[i];
2681:     nnz[i] = 0;
2682:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
2683:       for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
2684:       if (k<nzy && yjj[k]==xjj[j]) k++;             /* Skip duplicate */
2685:       nnz[i]++;
2686:     }
2687:     for (; k<nzy; k++) nnz[i]++;
2688:   }
2689:   return(0);
2690: }

2694: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
2695: {
2696:   PetscInt       m = Y->rmap->N;
2697:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data;
2698:   Mat_SeqAIJ     *y = (Mat_SeqAIJ*)Y->data;

2702:   /* Set the number of nonzeros in the new matrix */
2703:   MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
2704:   return(0);
2705: }

2709: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2710: {
2712:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;
2713:   PetscBLASInt   one=1,bnz;

2716:   PetscBLASIntCast(x->nz,&bnz);
2717:   if (str == SAME_NONZERO_PATTERN) {
2718:     PetscScalar alpha = a;
2719:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2720:     MatSeqAIJInvalidateDiagonal(Y);
2721:     PetscObjectStateIncrease((PetscObject)Y);
2722:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2723:     MatAXPY_Basic(Y,a,X,str);
2724:   } else {
2725:     Mat      B;
2726:     PetscInt *nnz;
2727:     PetscMalloc1(Y->rmap->N,&nnz);
2728:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
2729:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2730:     MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2731:     MatSetBlockSizesFromMats(B,Y,Y);
2732:     MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
2733:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
2734:     MatSeqAIJSetPreallocation(B,0,nnz);
2735:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2736:     MatHeaderReplace(Y,B);
2737:     PetscFree(nnz);
2738:   }
2739:   return(0);
2740: }

2744: PetscErrorCode  MatConjugate_SeqAIJ(Mat mat)
2745: {
2746: #if defined(PETSC_USE_COMPLEX)
2747:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ*)mat->data;
2748:   PetscInt    i,nz;
2749:   PetscScalar *a;

2752:   nz = aij->nz;
2753:   a  = aij->a;
2754:   for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
2755: #else
2757: #endif
2758:   return(0);
2759: }

2763: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2764: {
2765:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2767:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2768:   PetscReal      atmp;
2769:   PetscScalar    *x;
2770:   MatScalar      *aa;

2773:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2774:   aa = a->a;
2775:   ai = a->i;
2776:   aj = a->j;

2778:   VecSet(v,0.0);
2779:   VecGetArray(v,&x);
2780:   VecGetLocalSize(v,&n);
2781:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2782:   for (i=0; i<m; i++) {
2783:     ncols = ai[1] - ai[0]; ai++;
2784:     x[i]  = 0.0;
2785:     for (j=0; j<ncols; j++) {
2786:       atmp = PetscAbsScalar(*aa);
2787:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2788:       aa++; aj++;
2789:     }
2790:   }
2791:   VecRestoreArray(v,&x);
2792:   return(0);
2793: }

2797: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2798: {
2799:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2801:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2802:   PetscScalar    *x;
2803:   MatScalar      *aa;

2806:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2807:   aa = a->a;
2808:   ai = a->i;
2809:   aj = a->j;

2811:   VecSet(v,0.0);
2812:   VecGetArray(v,&x);
2813:   VecGetLocalSize(v,&n);
2814:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2815:   for (i=0; i<m; i++) {
2816:     ncols = ai[1] - ai[0]; ai++;
2817:     if (ncols == A->cmap->n) { /* row is dense */
2818:       x[i] = *aa; if (idx) idx[i] = 0;
2819:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
2820:       x[i] = 0.0;
2821:       if (idx) {
2822:         idx[i] = 0; /* in case ncols is zero */
2823:         for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
2824:           if (aj[j] > j) {
2825:             idx[i] = j;
2826:             break;
2827:           }
2828:         }
2829:       }
2830:     }
2831:     for (j=0; j<ncols; j++) {
2832:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2833:       aa++; aj++;
2834:     }
2835:   }
2836:   VecRestoreArray(v,&x);
2837:   return(0);
2838: }

2842: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2843: {
2844:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2846:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2847:   PetscReal      atmp;
2848:   PetscScalar    *x;
2849:   MatScalar      *aa;

2852:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2853:   aa = a->a;
2854:   ai = a->i;
2855:   aj = a->j;

2857:   VecSet(v,0.0);
2858:   VecGetArray(v,&x);
2859:   VecGetLocalSize(v,&n);
2860:   if (n != A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %D vs. %D rows", A->rmap->n, n);
2861:   for (i=0; i<m; i++) {
2862:     ncols = ai[1] - ai[0]; ai++;
2863:     if (ncols) {
2864:       /* Get first nonzero */
2865:       for (j = 0; j < ncols; j++) {
2866:         atmp = PetscAbsScalar(aa[j]);
2867:         if (atmp > 1.0e-12) {
2868:           x[i] = atmp;
2869:           if (idx) idx[i] = aj[j];
2870:           break;
2871:         }
2872:       }
2873:       if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;}
2874:     } else {
2875:       x[i] = 0.0; if (idx) idx[i] = 0;
2876:     }
2877:     for (j = 0; j < ncols; j++) {
2878:       atmp = PetscAbsScalar(*aa);
2879:       if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2880:       aa++; aj++;
2881:     }
2882:   }
2883:   VecRestoreArray(v,&x);
2884:   return(0);
2885: }

2889: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2890: {
2891:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
2892:   PetscErrorCode  ierr;
2893:   PetscInt        i,j,m = A->rmap->n,ncols,n;
2894:   const PetscInt  *ai,*aj;
2895:   PetscScalar     *x;
2896:   const MatScalar *aa;

2899:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2900:   aa = a->a;
2901:   ai = a->i;
2902:   aj = a->j;

2904:   VecSet(v,0.0);
2905:   VecGetArray(v,&x);
2906:   VecGetLocalSize(v,&n);
2907:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2908:   for (i=0; i<m; i++) {
2909:     ncols = ai[1] - ai[0]; ai++;
2910:     if (ncols == A->cmap->n) { /* row is dense */
2911:       x[i] = *aa; if (idx) idx[i] = 0;
2912:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
2913:       x[i] = 0.0;
2914:       if (idx) {   /* find first implicit 0.0 in the row */
2915:         idx[i] = 0; /* in case ncols is zero */
2916:         for (j=0; j<ncols; j++) {
2917:           if (aj[j] > j) {
2918:             idx[i] = j;
2919:             break;
2920:           }
2921:         }
2922:       }
2923:     }
2924:     for (j=0; j<ncols; j++) {
2925:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2926:       aa++; aj++;
2927:     }
2928:   }
2929:   VecRestoreArray(v,&x);
2930:   return(0);
2931: }

2933: #include <petscblaslapack.h>
2934: #include <petsc/private/kernels/blockinvert.h>

2938: PetscErrorCode  MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
2939: {
2940:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
2942:   PetscInt       i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
2943:   MatScalar      *diag,work[25],*v_work;
2944:   PetscReal      shift = 0.0;

2947:   if (a->ibdiagvalid) {
2948:     if (values) *values = a->ibdiag;
2949:     return(0);
2950:   }
2951:   MatMarkDiagonal_SeqAIJ(A);
2952:   if (!a->ibdiag) {
2953:     PetscMalloc1(bs2*mbs,&a->ibdiag);
2954:     PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
2955:   }
2956:   diag = a->ibdiag;
2957:   if (values) *values = a->ibdiag;
2958:   /* factor and invert each block */
2959:   switch (bs) {
2960:   case 1:
2961:     for (i=0; i<mbs; i++) {
2962:       MatGetValues(A,1,&i,1,&i,diag+i);
2963:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
2964:     }
2965:     break;
2966:   case 2:
2967:     for (i=0; i<mbs; i++) {
2968:       ij[0] = 2*i; ij[1] = 2*i + 1;
2969:       MatGetValues(A,2,ij,2,ij,diag);
2970:       PetscKernel_A_gets_inverse_A_2(diag,shift);
2971:       PetscKernel_A_gets_transpose_A_2(diag);
2972:       diag += 4;
2973:     }
2974:     break;
2975:   case 3:
2976:     for (i=0; i<mbs; i++) {
2977:       ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
2978:       MatGetValues(A,3,ij,3,ij,diag);
2979:       PetscKernel_A_gets_inverse_A_3(diag,shift);
2980:       PetscKernel_A_gets_transpose_A_3(diag);
2981:       diag += 9;
2982:     }
2983:     break;
2984:   case 4:
2985:     for (i=0; i<mbs; i++) {
2986:       ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
2987:       MatGetValues(A,4,ij,4,ij,diag);
2988:       PetscKernel_A_gets_inverse_A_4(diag,shift);
2989:       PetscKernel_A_gets_transpose_A_4(diag);
2990:       diag += 16;
2991:     }
2992:     break;
2993:   case 5:
2994:     for (i=0; i<mbs; i++) {
2995:       ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
2996:       MatGetValues(A,5,ij,5,ij,diag);
2997:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift);
2998:       PetscKernel_A_gets_transpose_A_5(diag);
2999:       diag += 25;
3000:     }
3001:     break;
3002:   case 6:
3003:     for (i=0; i<mbs; i++) {
3004:       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;
3005:       MatGetValues(A,6,ij,6,ij,diag);
3006:       PetscKernel_A_gets_inverse_A_6(diag,shift);
3007:       PetscKernel_A_gets_transpose_A_6(diag);
3008:       diag += 36;
3009:     }
3010:     break;
3011:   case 7:
3012:     for (i=0; i<mbs; i++) {
3013:       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;
3014:       MatGetValues(A,7,ij,7,ij,diag);
3015:       PetscKernel_A_gets_inverse_A_7(diag,shift);
3016:       PetscKernel_A_gets_transpose_A_7(diag);
3017:       diag += 49;
3018:     }
3019:     break;
3020:   default:
3021:     PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
3022:     for (i=0; i<mbs; i++) {
3023:       for (j=0; j<bs; j++) {
3024:         IJ[j] = bs*i + j;
3025:       }
3026:       MatGetValues(A,bs,IJ,bs,IJ,diag);
3027:       PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work);
3028:       PetscKernel_A_gets_transpose_A_N(diag,bs);
3029:       diag += bs2;
3030:     }
3031:     PetscFree3(v_work,v_pivots,IJ);
3032:   }
3033:   a->ibdiagvalid = PETSC_TRUE;
3034:   return(0);
3035: }

3039: static PetscErrorCode  MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
3040: {
3042:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
3043:   PetscScalar    a;
3044:   PetscInt       m,n,i,j,col;

3047:   if (!x->assembled) {
3048:     MatGetSize(x,&m,&n);
3049:     for (i=0; i<m; i++) {
3050:       for (j=0; j<aij->imax[i]; j++) {
3051:         PetscRandomGetValue(rctx,&a);
3052:         col  = (PetscInt)(n*PetscRealPart(a));
3053:         MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3054:       }
3055:     }
3056:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not yet coded");
3057:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3058:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3059:   return(0);
3060: }

3064: PetscErrorCode MatShift_SeqAIJ(Mat Y,PetscScalar a)
3065: {
3067:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)Y->data;

3070:   if (!aij->nz) {
3071:     MatSeqAIJSetPreallocation(Y,1,NULL);
3072:   }
3073:   MatShift_Basic(Y,a);
3074:   return(0);
3075: }

3077: /* -------------------------------------------------------------------*/
3078: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
3079:                                         MatGetRow_SeqAIJ,
3080:                                         MatRestoreRow_SeqAIJ,
3081:                                         MatMult_SeqAIJ,
3082:                                 /*  4*/ MatMultAdd_SeqAIJ,
3083:                                         MatMultTranspose_SeqAIJ,
3084:                                         MatMultTransposeAdd_SeqAIJ,
3085:                                         0,
3086:                                         0,
3087:                                         0,
3088:                                 /* 10*/ 0,
3089:                                         MatLUFactor_SeqAIJ,
3090:                                         0,
3091:                                         MatSOR_SeqAIJ,
3092:                                         MatTranspose_SeqAIJ,
3093:                                 /*1 5*/ MatGetInfo_SeqAIJ,
3094:                                         MatEqual_SeqAIJ,
3095:                                         MatGetDiagonal_SeqAIJ,
3096:                                         MatDiagonalScale_SeqAIJ,
3097:                                         MatNorm_SeqAIJ,
3098:                                 /* 20*/ 0,
3099:                                         MatAssemblyEnd_SeqAIJ,
3100:                                         MatSetOption_SeqAIJ,
3101:                                         MatZeroEntries_SeqAIJ,
3102:                                 /* 24*/ MatZeroRows_SeqAIJ,
3103:                                         0,
3104:                                         0,
3105:                                         0,
3106:                                         0,
3107:                                 /* 29*/ MatSetUp_SeqAIJ,
3108:                                         0,
3109:                                         0,
3110:                                         0,
3111:                                         0,
3112:                                 /* 34*/ MatDuplicate_SeqAIJ,
3113:                                         0,
3114:                                         0,
3115:                                         MatILUFactor_SeqAIJ,
3116:                                         0,
3117:                                 /* 39*/ MatAXPY_SeqAIJ,
3118:                                         MatGetSubMatrices_SeqAIJ,
3119:                                         MatIncreaseOverlap_SeqAIJ,
3120:                                         MatGetValues_SeqAIJ,
3121:                                         MatCopy_SeqAIJ,
3122:                                 /* 44*/ MatGetRowMax_SeqAIJ,
3123:                                         MatScale_SeqAIJ,
3124:                                         MatShift_SeqAIJ,
3125:                                         MatDiagonalSet_SeqAIJ,
3126:                                         MatZeroRowsColumns_SeqAIJ,
3127:                                 /* 49*/ MatSetRandom_SeqAIJ,
3128:                                         MatGetRowIJ_SeqAIJ,
3129:                                         MatRestoreRowIJ_SeqAIJ,
3130:                                         MatGetColumnIJ_SeqAIJ,
3131:                                         MatRestoreColumnIJ_SeqAIJ,
3132:                                 /* 54*/ MatFDColoringCreate_SeqXAIJ,
3133:                                         0,
3134:                                         0,
3135:                                         MatPermute_SeqAIJ,
3136:                                         0,
3137:                                 /* 59*/ 0,
3138:                                         MatDestroy_SeqAIJ,
3139:                                         MatView_SeqAIJ,
3140:                                         0,
3141:                                         MatMatMatMult_SeqAIJ_SeqAIJ_SeqAIJ,
3142:                                 /* 64*/ MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ,
3143:                                         MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3144:                                         0,
3145:                                         0,
3146:                                         0,
3147:                                 /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3148:                                         MatGetRowMinAbs_SeqAIJ,
3149:                                         0,
3150:                                         MatSetColoring_SeqAIJ,
3151:                                         0,
3152:                                 /* 74*/ MatSetValuesAdifor_SeqAIJ,
3153:                                         MatFDColoringApply_AIJ,
3154:                                         0,
3155:                                         0,
3156:                                         0,
3157:                                 /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3158:                                         0,
3159:                                         0,
3160:                                         0,
3161:                                         MatLoad_SeqAIJ,
3162:                                 /* 84*/ MatIsSymmetric_SeqAIJ,
3163:                                         MatIsHermitian_SeqAIJ,
3164:                                         0,
3165:                                         0,
3166:                                         0,
3167:                                 /* 89*/ MatMatMult_SeqAIJ_SeqAIJ,
3168:                                         MatMatMultSymbolic_SeqAIJ_SeqAIJ,
3169:                                         MatMatMultNumeric_SeqAIJ_SeqAIJ,
3170:                                         MatPtAP_SeqAIJ_SeqAIJ,
3171:                                         MatPtAPSymbolic_SeqAIJ_SeqAIJ_DenseAxpy,
3172:                                 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ,
3173:                                         MatMatTransposeMult_SeqAIJ_SeqAIJ,
3174:                                         MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ,
3175:                                         MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3176:                                         0,
3177:                                 /* 99*/ 0,
3178:                                         0,
3179:                                         0,
3180:                                         MatConjugate_SeqAIJ,
3181:                                         0,
3182:                                 /*104*/ MatSetValuesRow_SeqAIJ,
3183:                                         MatRealPart_SeqAIJ,
3184:                                         MatImaginaryPart_SeqAIJ,
3185:                                         0,
3186:                                         0,
3187:                                 /*109*/ MatMatSolve_SeqAIJ,
3188:                                         0,
3189:                                         MatGetRowMin_SeqAIJ,
3190:                                         0,
3191:                                         MatMissingDiagonal_SeqAIJ,
3192:                                 /*114*/ 0,
3193:                                         0,
3194:                                         0,
3195:                                         0,
3196:                                         0,
3197:                                 /*119*/ 0,
3198:                                         0,
3199:                                         0,
3200:                                         0,
3201:                                         MatGetMultiProcBlock_SeqAIJ,
3202:                                 /*124*/ MatFindNonzeroRows_SeqAIJ,
3203:                                         MatGetColumnNorms_SeqAIJ,
3204:                                         MatInvertBlockDiagonal_SeqAIJ,
3205:                                         0,
3206:                                         0,
3207:                                 /*129*/ 0,
3208:                                         MatTransposeMatMult_SeqAIJ_SeqAIJ,
3209:                                         MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ,
3210:                                         MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3211:                                         MatTransposeColoringCreate_SeqAIJ,
3212:                                 /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3213:                                         MatTransColoringApplyDenToSp_SeqAIJ,
3214:                                         MatRARt_SeqAIJ_SeqAIJ,
3215:                                         MatRARtSymbolic_SeqAIJ_SeqAIJ,
3216:                                         MatRARtNumeric_SeqAIJ_SeqAIJ,
3217:                                  /*139*/0,
3218:                                         0,
3219:                                         0,
3220:                                         MatFDColoringSetUp_SeqXAIJ,
3221:                                         MatFindOffBlockDiagonalEntries_SeqAIJ,
3222:                                  /*144*/MatCreateMPIMatConcatenateSeqMat_SeqAIJ
3223: };

3227: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3228: {
3229:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3230:   PetscInt   i,nz,n;

3233:   nz = aij->maxnz;
3234:   n  = mat->rmap->n;
3235:   for (i=0; i<nz; i++) {
3236:     aij->j[i] = indices[i];
3237:   }
3238:   aij->nz = nz;
3239:   for (i=0; i<n; i++) {
3240:     aij->ilen[i] = aij->imax[i];
3241:   }
3242:   return(0);
3243: }

3247: /*@
3248:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3249:        in the matrix.

3251:   Input Parameters:
3252: +  mat - the SeqAIJ matrix
3253: -  indices - the column indices

3255:   Level: advanced

3257:   Notes:
3258:     This can be called if you have precomputed the nonzero structure of the
3259:   matrix and want to provide it to the matrix object to improve the performance
3260:   of the MatSetValues() operation.

3262:     You MUST have set the correct numbers of nonzeros per row in the call to
3263:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

3265:     MUST be called before any calls to MatSetValues();

3267:     The indices should start with zero, not one.

3269: @*/
3270: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3271: {

3277:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
3278:   return(0);
3279: }

3281: /* ----------------------------------------------------------------------------------------*/

3285: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
3286: {
3287:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3289:   size_t         nz = aij->i[mat->rmap->n];

3292:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

3294:   /* allocate space for values if not already there */
3295:   if (!aij->saved_values) {
3296:     PetscMalloc1(nz+1,&aij->saved_values);
3297:     PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
3298:   }

3300:   /* copy values over */
3301:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
3302:   return(0);
3303: }

3307: /*@
3308:     MatStoreValues - Stashes a copy of the matrix values; this allows, for
3309:        example, reuse of the linear part of a Jacobian, while recomputing the
3310:        nonlinear portion.

3312:    Collect on Mat

3314:   Input Parameters:
3315: .  mat - the matrix (currently only AIJ matrices support this option)

3317:   Level: advanced

3319:   Common Usage, with SNESSolve():
3320: $    Create Jacobian matrix
3321: $    Set linear terms into matrix
3322: $    Apply boundary conditions to matrix, at this time matrix must have
3323: $      final nonzero structure (i.e. setting the nonlinear terms and applying
3324: $      boundary conditions again will not change the nonzero structure
3325: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3326: $    MatStoreValues(mat);
3327: $    Call SNESSetJacobian() with matrix
3328: $    In your Jacobian routine
3329: $      MatRetrieveValues(mat);
3330: $      Set nonlinear terms in matrix

3332:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3333: $    // build linear portion of Jacobian
3334: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3335: $    MatStoreValues(mat);
3336: $    loop over nonlinear iterations
3337: $       MatRetrieveValues(mat);
3338: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3339: $       // call MatAssemblyBegin/End() on matrix
3340: $       Solve linear system with Jacobian
3341: $    endloop

3343:   Notes:
3344:     Matrix must already be assemblied before calling this routine
3345:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
3346:     calling this routine.

3348:     When this is called multiple times it overwrites the previous set of stored values
3349:     and does not allocated additional space.

3351: .seealso: MatRetrieveValues()

3353: @*/
3354: PetscErrorCode  MatStoreValues(Mat mat)
3355: {

3360:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3361:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3362:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3363:   return(0);
3364: }

3368: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
3369: {
3370:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3372:   PetscInt       nz = aij->i[mat->rmap->n];

3375:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3376:   if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3377:   /* copy values over */
3378:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
3379:   return(0);
3380: }

3384: /*@
3385:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3386:        example, reuse of the linear part of a Jacobian, while recomputing the
3387:        nonlinear portion.

3389:    Collect on Mat

3391:   Input Parameters:
3392: .  mat - the matrix (currently on AIJ matrices support this option)

3394:   Level: advanced

3396: .seealso: MatStoreValues()

3398: @*/
3399: PetscErrorCode  MatRetrieveValues(Mat mat)
3400: {

3405:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3406:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3407:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3408:   return(0);
3409: }


3412: /* --------------------------------------------------------------------------------*/
3415: /*@C
3416:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3417:    (the default parallel PETSc format).  For good matrix assembly performance
3418:    the user should preallocate the matrix storage by setting the parameter nz
3419:    (or the array nnz).  By setting these parameters accurately, performance
3420:    during matrix assembly can be increased by more than a factor of 50.

3422:    Collective on MPI_Comm

3424:    Input Parameters:
3425: +  comm - MPI communicator, set to PETSC_COMM_SELF
3426: .  m - number of rows
3427: .  n - number of columns
3428: .  nz - number of nonzeros per row (same for all rows)
3429: -  nnz - array containing the number of nonzeros in the various rows
3430:          (possibly different for each row) or NULL

3432:    Output Parameter:
3433: .  A - the matrix

3435:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3436:    MatXXXXSetPreallocation() paradgm instead of this routine directly.
3437:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

3439:    Notes:
3440:    If nnz is given then nz is ignored

3442:    The AIJ format (also called the Yale sparse matrix format or
3443:    compressed row storage), is fully compatible with standard Fortran 77
3444:    storage.  That is, the stored row and column indices can begin at
3445:    either one (as in Fortran) or zero.  See the users' manual for details.

3447:    Specify the preallocated storage with either nz or nnz (not both).
3448:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3449:    allocation.  For large problems you MUST preallocate memory or you
3450:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3452:    By default, this format uses inodes (identical nodes) when possible, to
3453:    improve numerical efficiency of matrix-vector products and solves. We
3454:    search for consecutive rows with the same nonzero structure, thereby
3455:    reusing matrix information to achieve increased efficiency.

3457:    Options Database Keys:
3458: +  -mat_no_inode  - Do not use inodes
3459: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3461:    Level: intermediate

3463: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

3465: @*/
3466: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3467: {

3471:   MatCreate(comm,A);
3472:   MatSetSizes(*A,m,n,m,n);
3473:   MatSetType(*A,MATSEQAIJ);
3474:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
3475:   return(0);
3476: }

3480: /*@C
3481:    MatSeqAIJSetPreallocation - For good matrix assembly performance
3482:    the user should preallocate the matrix storage by setting the parameter nz
3483:    (or the array nnz).  By setting these parameters accurately, performance
3484:    during matrix assembly can be increased by more than a factor of 50.

3486:    Collective on MPI_Comm

3488:    Input Parameters:
3489: +  B - The matrix
3490: .  nz - number of nonzeros per row (same for all rows)
3491: -  nnz - array containing the number of nonzeros in the various rows
3492:          (possibly different for each row) or NULL

3494:    Notes:
3495:      If nnz is given then nz is ignored

3497:     The AIJ format (also called the Yale sparse matrix format or
3498:    compressed row storage), is fully compatible with standard Fortran 77
3499:    storage.  That is, the stored row and column indices can begin at
3500:    either one (as in Fortran) or zero.  See the users' manual for details.

3502:    Specify the preallocated storage with either nz or nnz (not both).
3503:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3504:    allocation.  For large problems you MUST preallocate memory or you
3505:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3507:    You can call MatGetInfo() to get information on how effective the preallocation was;
3508:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3509:    You can also run with the option -info and look for messages with the string
3510:    malloc in them to see if additional memory allocation was needed.

3512:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
3513:    entries or columns indices

3515:    By default, this format uses inodes (identical nodes) when possible, to
3516:    improve numerical efficiency of matrix-vector products and solves. We
3517:    search for consecutive rows with the same nonzero structure, thereby
3518:    reusing matrix information to achieve increased efficiency.

3520:    Options Database Keys:
3521: +  -mat_no_inode  - Do not use inodes
3522: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3523: -  -mat_aij_oneindex - Internally use indexing starting at 1
3524:         rather than 0.  Note that when calling MatSetValues(),
3525:         the user still MUST index entries starting at 0!

3527:    Level: intermediate

3529: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()

3531: @*/
3532: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
3533: {

3539:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
3540:   return(0);
3541: }

3545: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
3546: {
3547:   Mat_SeqAIJ     *b;
3548:   PetscBool      skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
3550:   PetscInt       i;

3553:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3554:   if (nz == MAT_SKIP_ALLOCATION) {
3555:     skipallocation = PETSC_TRUE;
3556:     nz             = 0;
3557:   }

3559:   PetscLayoutSetUp(B->rmap);
3560:   PetscLayoutSetUp(B->cmap);

3562:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3563:   if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
3564:   if (nnz) {
3565:     for (i=0; i<B->rmap->n; i++) {
3566:       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]);
3567:       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);
3568:     }
3569:   }

3571:   B->preallocated = PETSC_TRUE;

3573:   b = (Mat_SeqAIJ*)B->data;

3575:   if (!skipallocation) {
3576:     if (!b->imax) {
3577:       PetscMalloc2(B->rmap->n,&b->imax,B->rmap->n,&b->ilen);
3578:       PetscLogObjectMemory((PetscObject)B,2*B->rmap->n*sizeof(PetscInt));
3579:     }
3580:     if (!nnz) {
3581:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3582:       else if (nz < 0) nz = 1;
3583:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
3584:       nz = nz*B->rmap->n;
3585:     } else {
3586:       nz = 0;
3587:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
3588:     }
3589:     /* b->ilen will count nonzeros in each row so far. */
3590:     for (i=0; i<B->rmap->n; i++) b->ilen[i] = 0;

3592:     /* allocate the matrix space */
3593:     /* FIXME: should B's old memory be unlogged? */
3594:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3595:     PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
3596:     PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
3597:     b->i[0] = 0;
3598:     for (i=1; i<B->rmap->n+1; i++) {
3599:       b->i[i] = b->i[i-1] + b->imax[i-1];
3600:     }
3601:     b->singlemalloc = PETSC_TRUE;
3602:     b->free_a       = PETSC_TRUE;
3603:     b->free_ij      = PETSC_TRUE;
3604:   } else {
3605:     b->free_a  = PETSC_FALSE;
3606:     b->free_ij = PETSC_FALSE;
3607:   }

3609:   b->nz               = 0;
3610:   b->maxnz            = nz;
3611:   B->info.nz_unneeded = (double)b->maxnz;
3612:   if (realalloc) {
3613:     MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
3614:   }
3615:   return(0);
3616: }

3618: #undef  __FUNCT__
3620: /*@
3621:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.

3623:    Input Parameters:
3624: +  B - the matrix
3625: .  i - the indices into j for the start of each row (starts with zero)
3626: .  j - the column indices for each row (starts with zero) these must be sorted for each row
3627: -  v - optional values in the matrix

3629:    Level: developer

3631:    The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

3633: .keywords: matrix, aij, compressed row, sparse, sequential

3635: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ
3636: @*/
3637: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
3638: {

3644:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
3645:   return(0);
3646: }

3648: #undef  __FUNCT__
3650: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3651: {
3652:   PetscInt       i;
3653:   PetscInt       m,n;
3654:   PetscInt       nz;
3655:   PetscInt       *nnz, nz_max = 0;
3656:   PetscScalar    *values;

3660:   if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);

3662:   PetscLayoutSetUp(B->rmap);
3663:   PetscLayoutSetUp(B->cmap);

3665:   MatGetSize(B, &m, &n);
3666:   PetscMalloc1(m+1, &nnz);
3667:   for (i = 0; i < m; i++) {
3668:     nz     = Ii[i+1]- Ii[i];
3669:     nz_max = PetscMax(nz_max, nz);
3670:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
3671:     nnz[i] = nz;
3672:   }
3673:   MatSeqAIJSetPreallocation(B, 0, nnz);
3674:   PetscFree(nnz);

3676:   if (v) {
3677:     values = (PetscScalar*) v;
3678:   } else {
3679:     PetscCalloc1(nz_max, &values);
3680:   }

3682:   for (i = 0; i < m; i++) {
3683:     nz   = Ii[i+1] - Ii[i];
3684:     MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
3685:   }

3687:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3688:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

3690:   if (!v) {
3691:     PetscFree(values);
3692:   }
3693:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3694:   return(0);
3695: }

3697: #include <../src/mat/impls/dense/seq/dense.h>
3698: #include <petsc/private/kernels/petscaxpy.h>

3702: /*
3703:     Computes (B'*A')' since computing B*A directly is untenable

3705:                n                       p                          p
3706:         (              )       (              )         (                  )
3707:       m (      A       )  *  n (       B      )   =   m (         C        )
3708:         (              )       (              )         (                  )

3710: */
3711: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
3712: {
3713:   PetscErrorCode    ierr;
3714:   Mat_SeqDense      *sub_a = (Mat_SeqDense*)A->data;
3715:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ*)B->data;
3716:   Mat_SeqDense      *sub_c = (Mat_SeqDense*)C->data;
3717:   PetscInt          i,n,m,q,p;
3718:   const PetscInt    *ii,*idx;
3719:   const PetscScalar *b,*a,*a_q;
3720:   PetscScalar       *c,*c_q;

3723:   m    = A->rmap->n;
3724:   n    = A->cmap->n;
3725:   p    = B->cmap->n;
3726:   a    = sub_a->v;
3727:   b    = sub_b->a;
3728:   c    = sub_c->v;
3729:   PetscMemzero(c,m*p*sizeof(PetscScalar));

3731:   ii  = sub_b->i;
3732:   idx = sub_b->j;
3733:   for (i=0; i<n; i++) {
3734:     q = ii[i+1] - ii[i];
3735:     while (q-->0) {
3736:       c_q = c + m*(*idx);
3737:       a_q = a + m*i;
3738:       PetscKernelAXPY(c_q,*b,a_q,m);
3739:       idx++;
3740:       b++;
3741:     }
3742:   }
3743:   return(0);
3744: }

3748: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
3749: {
3751:   PetscInt       m=A->rmap->n,n=B->cmap->n;
3752:   Mat            Cmat;

3755:   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);
3756:   MatCreate(PetscObjectComm((PetscObject)A),&Cmat);
3757:   MatSetSizes(Cmat,m,n,m,n);
3758:   MatSetBlockSizesFromMats(Cmat,A,B);
3759:   MatSetType(Cmat,MATSEQDENSE);
3760:   MatSeqDenseSetPreallocation(Cmat,NULL);

3762:   Cmat->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;

3764:   *C = Cmat;
3765:   return(0);
3766: }

3768: /* ----------------------------------------------------------------*/
3771: PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
3772: {

3776:   if (scall == MAT_INITIAL_MATRIX) {
3777:     PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
3778:     MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);
3779:     PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
3780:   }
3781:   PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
3782:   MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);
3783:   PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
3784:   return(0);
3785: }


3788: /*MC
3789:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
3790:    based on compressed sparse row format.

3792:    Options Database Keys:
3793: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

3795:   Level: beginner

3797: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
3798: M*/

3800: /*MC
3801:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

3803:    This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
3804:    and MATMPIAIJ otherwise.  As a result, for single process communicators,
3805:   MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
3806:   for communicators controlling multiple processes.  It is recommended that you call both of
3807:   the above preallocation routines for simplicity.

3809:    Options Database Keys:
3810: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()

3812:   Developer Notes: Subclasses include MATAIJCUSP, MATAIJPERM, MATAIJCRL, and also automatically switches over to use inodes when
3813:    enough exist.

3815:   Level: beginner

3817: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
3818: M*/

3820: /*MC
3821:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

3823:    This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
3824:    and MATMPIAIJCRL otherwise.  As a result, for single process communicators,
3825:    MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
3826:   for communicators controlling multiple processes.  It is recommended that you call both of
3827:   the above preallocation routines for simplicity.

3829:    Options Database Keys:
3830: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()

3832:   Level: beginner

3834: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
3835: M*/

3837: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
3838: #if defined(PETSC_HAVE_ELEMENTAL)
3839: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
3840: #endif
3841: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat,MatType,MatReuse,Mat*);

3843: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3844: PETSC_EXTERN PetscErrorCode  MatlabEnginePut_SeqAIJ(PetscObject,void*);
3845: PETSC_EXTERN PetscErrorCode  MatlabEngineGet_SeqAIJ(PetscObject,void*);
3846: #endif


3851: /*@C
3852:    MatSeqAIJGetArray - gives access to the array where the data for a SeqSeqAIJ matrix is stored

3854:    Not Collective

3856:    Input Parameter:
3857: .  mat - a MATSEQAIJ matrix

3859:    Output Parameter:
3860: .   array - pointer to the data

3862:    Level: intermediate

3864: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
3865: @*/
3866: PetscErrorCode  MatSeqAIJGetArray(Mat A,PetscScalar **array)
3867: {

3871:   PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
3872:   return(0);
3873: }

3877: /*@C
3878:    MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

3880:    Not Collective

3882:    Input Parameter:
3883: .  mat - a MATSEQAIJ matrix

3885:    Output Parameter:
3886: .   nz - the maximum number of nonzeros in any row

3888:    Level: intermediate

3890: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
3891: @*/
3892: PetscErrorCode  MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
3893: {
3894:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

3897:   *nz = aij->rmax;
3898:   return(0);
3899: }

3903: /*@C
3904:    MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()

3906:    Not Collective

3908:    Input Parameters:
3909: .  mat - a MATSEQAIJ matrix
3910: .  array - pointer to the data

3912:    Level: intermediate

3914: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
3915: @*/
3916: PetscErrorCode  MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
3917: {

3921:   PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
3922:   return(0);
3923: }

3927: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
3928: {
3929:   Mat_SeqAIJ     *b;
3931:   PetscMPIInt    size;

3934:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
3935:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

3937:   PetscNewLog(B,&b);

3939:   B->data = (void*)b;

3941:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));

3943:   b->row                = 0;
3944:   b->col                = 0;
3945:   b->icol               = 0;
3946:   b->reallocs           = 0;
3947:   b->ignorezeroentries  = PETSC_FALSE;
3948:   b->roworiented        = PETSC_TRUE;
3949:   b->nonew              = 0;
3950:   b->diag               = 0;
3951:   b->solve_work         = 0;
3952:   B->spptr              = 0;
3953:   b->saved_values       = 0;
3954:   b->idiag              = 0;
3955:   b->mdiag              = 0;
3956:   b->ssor_work          = 0;
3957:   b->omega              = 1.0;
3958:   b->fshift             = 0.0;
3959:   b->idiagvalid         = PETSC_FALSE;
3960:   b->ibdiagvalid        = PETSC_FALSE;
3961:   b->keepnonzeropattern = PETSC_FALSE;

3963:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3964:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
3965:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);

3967: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3968:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
3969:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
3970: #endif

3972:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
3973:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
3974:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
3975:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
3976:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
3977:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
3978:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
3979: #if defined(PETSC_HAVE_ELEMENTAL)
3980:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
3981: #endif
3982:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
3983:   PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
3984:   PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
3985:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
3986:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
3987:   PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
3988:   PetscObjectComposeFunction((PetscObject)B,"MatMatMult_seqdense_seqaij_C",MatMatMult_SeqDense_SeqAIJ);
3989:   PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C",MatMatMultSymbolic_SeqDense_SeqAIJ);
3990:   PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C",MatMatMultNumeric_SeqDense_SeqAIJ);
3991:   MatCreate_SeqAIJ_Inode(B);
3992:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3993:   return(0);
3994: }

3998: /*
3999:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
4000: */
4001: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
4002: {
4003:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
4005:   PetscInt       i,m = A->rmap->n;

4008:   c = (Mat_SeqAIJ*)C->data;

4010:   C->factortype = A->factortype;
4011:   c->row        = 0;
4012:   c->col        = 0;
4013:   c->icol       = 0;
4014:   c->reallocs   = 0;

4016:   C->assembled = PETSC_TRUE;

4018:   PetscLayoutReference(A->rmap,&C->rmap);
4019:   PetscLayoutReference(A->cmap,&C->cmap);

4021:   PetscMalloc2(m,&c->imax,m,&c->ilen);
4022:   PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));
4023:   for (i=0; i<m; i++) {
4024:     c->imax[i] = a->imax[i];
4025:     c->ilen[i] = a->ilen[i];
4026:   }

4028:   /* allocate the matrix space */
4029:   if (mallocmatspace) {
4030:     PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
4031:     PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));

4033:     c->singlemalloc = PETSC_TRUE;

4035:     PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
4036:     if (m > 0) {
4037:       PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
4038:       if (cpvalues == MAT_COPY_VALUES) {
4039:         PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
4040:       } else {
4041:         PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
4042:       }
4043:     }
4044:   }

4046:   c->ignorezeroentries = a->ignorezeroentries;
4047:   c->roworiented       = a->roworiented;
4048:   c->nonew             = a->nonew;
4049:   if (a->diag) {
4050:     PetscMalloc1(m+1,&c->diag);
4051:     PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
4052:     for (i=0; i<m; i++) {
4053:       c->diag[i] = a->diag[i];
4054:     }
4055:   } else c->diag = 0;

4057:   c->solve_work         = 0;
4058:   c->saved_values       = 0;
4059:   c->idiag              = 0;
4060:   c->ssor_work          = 0;
4061:   c->keepnonzeropattern = a->keepnonzeropattern;
4062:   c->free_a             = PETSC_TRUE;
4063:   c->free_ij            = PETSC_TRUE;

4065:   c->rmax         = a->rmax;
4066:   c->nz           = a->nz;
4067:   c->maxnz        = a->nz;       /* Since we allocate exactly the right amount */
4068:   C->preallocated = PETSC_TRUE;

4070:   c->compressedrow.use   = a->compressedrow.use;
4071:   c->compressedrow.nrows = a->compressedrow.nrows;
4072:   if (a->compressedrow.use) {
4073:     i    = a->compressedrow.nrows;
4074:     PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
4075:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
4076:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
4077:   } else {
4078:     c->compressedrow.use    = PETSC_FALSE;
4079:     c->compressedrow.i      = NULL;
4080:     c->compressedrow.rindex = NULL;
4081:   }
4082:   c->nonzerorowcnt = a->nonzerorowcnt;
4083:   C->nonzerostate  = A->nonzerostate;

4085:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
4086:   PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
4087:   return(0);
4088: }

4092: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
4093: {

4097:   MatCreate(PetscObjectComm((PetscObject)A),B);
4098:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
4099:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
4100:     MatSetBlockSizesFromMats(*B,A,A);
4101:   }
4102:   MatSetType(*B,((PetscObject)A)->type_name);
4103:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4104:   return(0);
4105: }

4109: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4110: {
4111:   Mat_SeqAIJ     *a;
4113:   PetscInt       i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols;
4114:   int            fd;
4115:   PetscMPIInt    size;
4116:   MPI_Comm       comm;
4117:   PetscInt       bs = newMat->rmap->bs;

4120:   /* force binary viewer to load .info file if it has not yet done so */
4121:   PetscViewerSetUp(viewer);
4122:   PetscObjectGetComm((PetscObject)viewer,&comm);
4123:   MPI_Comm_size(comm,&size);
4124:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor");

4126:   PetscOptionsBegin(comm,NULL,"Options for loading SEQAIJ matrix","Mat");
4127:   PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
4128:   PetscOptionsEnd();
4129:   if (bs < 0) bs = 1;
4130:   MatSetBlockSize(newMat,bs);

4132:   PetscViewerBinaryGetDescriptor(viewer,&fd);
4133:   PetscBinaryRead(fd,header,4,PETSC_INT);
4134:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
4135:   M = header[1]; N = header[2]; nz = header[3];

4137:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");

4139:   /* read in row lengths */
4140:   PetscMalloc1(M,&rowlengths);
4141:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

4143:   /* check if sum of rowlengths is same as nz */
4144:   for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
4145:   if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %dD, sum-row-lengths = %D\n",nz,sum);

4147:   /* set global size if not set already*/
4148:   if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) {
4149:     MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);
4150:   } else {
4151:     /* if sizes and type are already set, check if the matrix  global sizes are correct */
4152:     MatGetSize(newMat,&rows,&cols);
4153:     if (rows < 0 && cols < 0) { /* user might provide local size instead of global size */
4154:       MatGetLocalSize(newMat,&rows,&cols);
4155:     }
4156:     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);
4157:   }
4158:   MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);
4159:   a    = (Mat_SeqAIJ*)newMat->data;

4161:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

4163:   /* read in nonzero values */
4164:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

4166:   /* set matrix "i" values */
4167:   a->i[0] = 0;
4168:   for (i=1; i<= M; i++) {
4169:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
4170:     a->ilen[i-1] = rowlengths[i-1];
4171:   }
4172:   PetscFree(rowlengths);

4174:   MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
4175:   MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
4176:   return(0);
4177: }

4181: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4182: {
4183:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
4185: #if defined(PETSC_USE_COMPLEX)
4186:   PetscInt k;
4187: #endif

4190:   /* If the  matrix dimensions are not equal,or no of nonzeros */
4191:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4192:     *flg = PETSC_FALSE;
4193:     return(0);
4194:   }

4196:   /* if the a->i are the same */
4197:   PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);
4198:   if (!*flg) return(0);

4200:   /* if a->j are the same */
4201:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
4202:   if (!*flg) return(0);

4204:   /* if a->a are the same */
4205: #if defined(PETSC_USE_COMPLEX)
4206:   for (k=0; k<a->nz; k++) {
4207:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
4208:       *flg = PETSC_FALSE;
4209:       return(0);
4210:     }
4211:   }
4212: #else
4213:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
4214: #endif
4215:   return(0);
4216: }

4220: /*@
4221:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4222:               provided by the user.

4224:       Collective on MPI_Comm

4226:    Input Parameters:
4227: +   comm - must be an MPI communicator of size 1
4228: .   m - number of rows
4229: .   n - number of columns
4230: .   i - row indices
4231: .   j - column indices
4232: -   a - matrix values

4234:    Output Parameter:
4235: .   mat - the matrix

4237:    Level: intermediate

4239:    Notes:
4240:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
4241:     once the matrix is destroyed and not before

4243:        You cannot set new nonzero locations into this matrix, that will generate an error.

4245:        The i and j indices are 0 based

4247:        The format which is used for the sparse matrix input, is equivalent to a
4248:     row-major ordering.. i.e for the following matrix, the input data expected is
4249:     as shown:

4251:         1 0 0
4252:         2 0 3
4253:         4 5 6

4255:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
4256:         j =  {0,0,2,0,1,2}  [size = nz = 6]; values must be sorted for each row
4257:         v =  {1,2,3,4,5,6}  [size = nz = 6]


4260: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4262: @*/
4263: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat)
4264: {
4266:   PetscInt       ii;
4267:   Mat_SeqAIJ     *aij;
4268: #if defined(PETSC_USE_DEBUG)
4269:   PetscInt jj;
4270: #endif

4273:   if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4274:   MatCreate(comm,mat);
4275:   MatSetSizes(*mat,m,n,m,n);
4276:   /* MatSetBlockSizes(*mat,,); */
4277:   MatSetType(*mat,MATSEQAIJ);
4278:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
4279:   aij  = (Mat_SeqAIJ*)(*mat)->data;
4280:   PetscMalloc2(m,&aij->imax,m,&aij->ilen);

4282:   aij->i            = i;
4283:   aij->j            = j;
4284:   aij->a            = a;
4285:   aij->singlemalloc = PETSC_FALSE;
4286:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4287:   aij->free_a       = PETSC_FALSE;
4288:   aij->free_ij      = PETSC_FALSE;

4290:   for (ii=0; ii<m; ii++) {
4291:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4292: #if defined(PETSC_USE_DEBUG)
4293:     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]);
4294:     for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4295:       if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
4296:       if (j[jj] == j[jj]-1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
4297:     }
4298: #endif
4299:   }
4300: #if defined(PETSC_USE_DEBUG)
4301:   for (ii=0; ii<aij->i[m]; ii++) {
4302:     if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
4303:     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]);
4304:   }
4305: #endif

4307:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4308:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4309:   return(0);
4310: }
4313: /*@C
4314:      MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4315:               provided by the user.

4317:       Collective on MPI_Comm

4319:    Input Parameters:
4320: +   comm - must be an MPI communicator of size 1
4321: .   m   - number of rows
4322: .   n   - number of columns
4323: .   i   - row indices
4324: .   j   - column indices
4325: .   a   - matrix values
4326: .   nz  - number of nonzeros
4327: -   idx - 0 or 1 based

4329:    Output Parameter:
4330: .   mat - the matrix

4332:    Level: intermediate

4334:    Notes:
4335:        The i and j indices are 0 based

4337:        The format which is used for the sparse matrix input, is equivalent to a
4338:     row-major ordering.. i.e for the following matrix, the input data expected is
4339:     as shown:

4341:         1 0 0
4342:         2 0 3
4343:         4 5 6

4345:         i =  {0,1,1,2,2,2}
4346:         j =  {0,0,2,0,1,2}
4347:         v =  {1,2,3,4,5,6}


4350: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4352: @*/
4353: PetscErrorCode  MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat,PetscInt nz,PetscBool idx)
4354: {
4356:   PetscInt       ii, *nnz, one = 1,row,col;


4360:   PetscCalloc1(m,&nnz);
4361:   for (ii = 0; ii < nz; ii++) {
4362:     nnz[i[ii] - !!idx] += 1;
4363:   }
4364:   MatCreate(comm,mat);
4365:   MatSetSizes(*mat,m,n,m,n);
4366:   MatSetType(*mat,MATSEQAIJ);
4367:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
4368:   for (ii = 0; ii < nz; ii++) {
4369:     if (idx) {
4370:       row = i[ii] - 1;
4371:       col = j[ii] - 1;
4372:     } else {
4373:       row = i[ii];
4374:       col = j[ii];
4375:     }
4376:     MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
4377:   }
4378:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4379:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4380:   PetscFree(nnz);
4381:   return(0);
4382: }

4386: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
4387: {
4389:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

4392:   if (coloring->ctype == IS_COLORING_GLOBAL) {
4393:     ISColoringReference(coloring);
4394:     a->coloring = coloring;
4395:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
4396:     PetscInt        i,*larray;
4397:     ISColoring      ocoloring;
4398:     ISColoringValue *colors;

4400:     /* set coloring for diagonal portion */
4401:     PetscMalloc1(A->cmap->n,&larray);
4402:     for (i=0; i<A->cmap->n; i++) larray[i] = i;
4403:     ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,A->cmap->n,larray,NULL,larray);
4404:     PetscMalloc1(A->cmap->n,&colors);
4405:     for (i=0; i<A->cmap->n; i++) colors[i] = coloring->colors[larray[i]];
4406:     PetscFree(larray);
4407:     ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap->n,colors,PETSC_OWN_POINTER,&ocoloring);
4408:     a->coloring = ocoloring;
4409:   }
4410:   return(0);
4411: }

4415: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
4416: {
4417:   Mat_SeqAIJ      *a      = (Mat_SeqAIJ*)A->data;
4418:   PetscInt        m       = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j;
4419:   MatScalar       *v      = a->a;
4420:   PetscScalar     *values = (PetscScalar*)advalues;
4421:   ISColoringValue *color;

4424:   if (!a->coloring) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
4425:   color = a->coloring->colors;
4426:   /* loop over rows */
4427:   for (i=0; i<m; i++) {
4428:     nz = ii[i+1] - ii[i];
4429:     /* loop over columns putting computed value into matrix */
4430:     for (j=0; j<nz; j++) *v++ = values[color[*jj++]];
4431:     values += nl; /* jump to next row of derivatives */
4432:   }
4433:   return(0);
4434: }

4438: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
4439: {
4440:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data;

4444:   a->idiagvalid  = PETSC_FALSE;
4445:   a->ibdiagvalid = PETSC_FALSE;

4447:   MatSeqAIJInvalidateDiagonal_Inode(A);
4448:   return(0);
4449: }

4453: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
4454: {

4458:   MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
4459:   return(0);
4460: }

4462: /*
4463:  Permute A into C's *local* index space using rowemb,colemb.
4464:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
4465:  of [0,m), colemb is in [0,n).
4466:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
4467:  */
4470: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
4471: {
4472:   /* If making this function public, change the error returned in this function away from _PLIB. */
4474:   Mat_SeqAIJ     *Baij;
4475:   PetscBool      seqaij;
4476:   PetscInt       m,n,*nz,i,j,count;
4477:   PetscScalar    v;
4478:   const PetscInt *rowindices,*colindices;

4481:   if (!B) return(0);
4482:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
4483:   PetscObjectTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
4484:   if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
4485:   if (rowemb) {
4486:     ISGetLocalSize(rowemb,&m);
4487:     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);
4488:   } else {
4489:     if (C->rmap->n != B->rmap->n) {
4490:       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
4491:     }
4492:   }
4493:   if (colemb) {
4494:     ISGetLocalSize(colemb,&n);
4495:     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);
4496:   } else {
4497:     if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
4498:   }

4500:   Baij = (Mat_SeqAIJ*)(B->data);
4501:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
4502:     PetscMalloc1(B->rmap->n,&nz);
4503:     for (i=0; i<B->rmap->n; i++) {
4504:       nz[i] = Baij->i[i+1] - Baij->i[i];
4505:     }
4506:     MatSeqAIJSetPreallocation(C,0,nz);
4507:     PetscFree(nz);
4508:   }
4509:   if (pattern == SUBSET_NONZERO_PATTERN) {
4510:     MatZeroEntries(C);
4511:   }
4512:   count = 0;
4513:   rowindices = NULL;
4514:   colindices = NULL;
4515:   if (rowemb) {
4516:     ISGetIndices(rowemb,&rowindices);
4517:   }
4518:   if (colemb) {
4519:     ISGetIndices(colemb,&colindices);
4520:   }
4521:   for (i=0; i<B->rmap->n; i++) {
4522:     PetscInt row;
4523:     row = i;
4524:     if (rowindices) row = rowindices[i];
4525:     for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
4526:       PetscInt col;
4527:       col  = Baij->j[count];
4528:       if (colindices) col = colindices[col];
4529:       v    = Baij->a[count];
4530:       MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
4531:       ++count;
4532:     }
4533:   }
4534:   /* FIXME: set C's nonzerostate correctly. */
4535:   /* Assembly for C is necessary. */
4536:   C->preallocated = PETSC_TRUE;
4537:   C->assembled     = PETSC_TRUE;
4538:   C->was_assembled = PETSC_FALSE;
4539:   return(0);
4540: }


4543: /*
4544:     Special version for direct calls from Fortran
4545: */
4546: #include <petsc/private/fortranimpl.h>
4547: #if defined(PETSC_HAVE_FORTRAN_CAPS)
4548: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
4549: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
4550: #define matsetvaluesseqaij_ matsetvaluesseqaij
4551: #endif

4553: /* Change these macros so can be used in void function */
4554: #undef CHKERRQ
4555: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
4556: #undef SETERRQ2
4557: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
4558: #undef SETERRQ3
4559: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)

4563: PETSC_EXTERN void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
4564: {
4565:   Mat            A  = *AA;
4566:   PetscInt       m  = *mm, n = *nn;
4567:   InsertMode     is = *isis;
4568:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
4569:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
4570:   PetscInt       *imax,*ai,*ailen;
4572:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
4573:   MatScalar      *ap,value,*aa;
4574:   PetscBool      ignorezeroentries = a->ignorezeroentries;
4575:   PetscBool      roworiented       = a->roworiented;

4578:   MatCheckPreallocated(A,1);
4579:   imax  = a->imax;
4580:   ai    = a->i;
4581:   ailen = a->ilen;
4582:   aj    = a->j;
4583:   aa    = a->a;

4585:   for (k=0; k<m; k++) { /* loop over added rows */
4586:     row = im[k];
4587:     if (row < 0) continue;
4588: #if defined(PETSC_USE_DEBUG)
4589:     if (row >= A->rmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
4590: #endif
4591:     rp   = aj + ai[row]; ap = aa + ai[row];
4592:     rmax = imax[row]; nrow = ailen[row];
4593:     low  = 0;
4594:     high = nrow;
4595:     for (l=0; l<n; l++) { /* loop over added columns */
4596:       if (in[l] < 0) continue;
4597: #if defined(PETSC_USE_DEBUG)
4598:       if (in[l] >= A->cmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
4599: #endif
4600:       col = in[l];
4601:       if (roworiented) value = v[l + k*n];
4602:       else value = v[k + l*m];

4604:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

4606:       if (col <= lastcol) low = 0;
4607:       else high = nrow;
4608:       lastcol = col;
4609:       while (high-low > 5) {
4610:         t = (low+high)/2;
4611:         if (rp[t] > col) high = t;
4612:         else             low  = t;
4613:       }
4614:       for (i=low; i<high; i++) {
4615:         if (rp[i] > col) break;
4616:         if (rp[i] == col) {
4617:           if (is == ADD_VALUES) ap[i] += value;
4618:           else                  ap[i] = value;
4619:           goto noinsert;
4620:         }
4621:       }
4622:       if (value == 0.0 && ignorezeroentries) goto noinsert;
4623:       if (nonew == 1) goto noinsert;
4624:       if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
4625:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
4626:       N = nrow++ - 1; a->nz++; high++;
4627:       /* shift up all the later entries in this row */
4628:       for (ii=N; ii>=i; ii--) {
4629:         rp[ii+1] = rp[ii];
4630:         ap[ii+1] = ap[ii];
4631:       }
4632:       rp[i] = col;
4633:       ap[i] = value;
4634:       A->nonzerostate++;
4635: noinsert:;
4636:       low = i + 1;
4637:     }
4638:     ailen[row] = nrow;
4639:   }
4640:   PetscFunctionReturnVoid();
4641: }