Actual source code: aij.c

petsc-master 2017-05-25
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>
  9:  #include <petscblaslapack.h>
 10:  #include <petscbt.h>
 11:  #include <petsc/private/kernels/blocktranspose.h>

 13: PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms)
 14: {
 16:   PetscInt       i,m,n;
 17:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

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

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

 42: PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A,IS *is)
 43: {
 44:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
 45:   PetscInt        i,m=A->rmap->n,cnt = 0, bs = A->rmap->bs;
 46:   const PetscInt  *jj = a->j,*ii = a->i;
 47:   PetscInt        *rows;
 48:   PetscErrorCode  ierr;

 51:   for (i=0; i<m; i++) {
 52:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
 53:       cnt++;
 54:     }
 55:   }
 56:   PetscMalloc1(cnt,&rows);
 57:   cnt  = 0;
 58:   for (i=0; i<m; i++) {
 59:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
 60:       rows[cnt] = i;
 61:       cnt++;
 62:     }
 63:   }
 64:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);
 65:   return(0);
 66: }

 68: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows)
 69: {
 70:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
 71:   const MatScalar *aa = a->a;
 72:   PetscInt        i,m=A->rmap->n,cnt = 0;
 73:   const PetscInt  *ii = a->i,*jj = a->j,*diag;
 74:   PetscInt        *rows;
 75:   PetscErrorCode  ierr;

 78:   MatMarkDiagonal_SeqAIJ(A);
 79:   diag = a->diag;
 80:   for (i=0; i<m; i++) {
 81:     if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
 82:       cnt++;
 83:     }
 84:   }
 85:   PetscMalloc1(cnt,&rows);
 86:   cnt  = 0;
 87:   for (i=0; i<m; i++) {
 88:     if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
 89:       rows[cnt++] = i;
 90:     }
 91:   }
 92:   *nrows = cnt;
 93:   *zrows = rows;
 94:   return(0);
 95: }

 97: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
 98: {
 99:   PetscInt       nrows,*rows;

103:   *zrows = NULL;
104:   MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
105:   ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);
106:   return(0);
107: }

109: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
110: {
111:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
112:   const MatScalar *aa;
113:   PetscInt        m=A->rmap->n,cnt = 0;
114:   const PetscInt  *ii;
115:   PetscInt        n,i,j,*rows;
116:   PetscErrorCode  ierr;

119:   *keptrows = 0;
120:   ii        = a->i;
121:   for (i=0; i<m; i++) {
122:     n = ii[i+1] - ii[i];
123:     if (!n) {
124:       cnt++;
125:       goto ok1;
126:     }
127:     aa = a->a + ii[i];
128:     for (j=0; j<n; j++) {
129:       if (aa[j] != 0.0) goto ok1;
130:     }
131:     cnt++;
132: ok1:;
133:   }
134:   if (!cnt) return(0);
135:   PetscMalloc1(A->rmap->n-cnt,&rows);
136:   cnt  = 0;
137:   for (i=0; i<m; i++) {
138:     n = ii[i+1] - ii[i];
139:     if (!n) continue;
140:     aa = a->a + ii[i];
141:     for (j=0; j<n; j++) {
142:       if (aa[j] != 0.0) {
143:         rows[cnt++] = i;
144:         break;
145:       }
146:     }
147:   }
148:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);
149:   return(0);
150: }

152: PetscErrorCode  MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
153: {
154:   PetscErrorCode    ierr;
155:   Mat_SeqAIJ        *aij = (Mat_SeqAIJ*) Y->data;
156:   PetscInt          i,m = Y->rmap->n;
157:   const PetscInt    *diag;
158:   MatScalar         *aa = aij->a;
159:   const PetscScalar *v;
160:   PetscBool         missing;

163:   if (Y->assembled) {
164:     MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
165:     if (!missing) {
166:       diag = aij->diag;
167:       VecGetArrayRead(D,&v);
168:       if (is == INSERT_VALUES) {
169:         for (i=0; i<m; i++) {
170:           aa[diag[i]] = v[i];
171:         }
172:       } else {
173:         for (i=0; i<m; i++) {
174:           aa[diag[i]] += v[i];
175:         }
176:       }
177:       VecRestoreArrayRead(D,&v);
178:       return(0);
179:     }
180:     MatSeqAIJInvalidateDiagonal(Y);
181:   }
182:   MatDiagonalSet_Default(Y,D,is);
183:   return(0);
184: }

186: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
187: {
188:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
190:   PetscInt       i,ishift;

193:   *m = A->rmap->n;
194:   if (!ia) return(0);
195:   ishift = 0;
196:   if (symmetric && !A->structurally_symmetric) {
197:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);
198:   } else if (oshift == 1) {
199:     PetscInt *tia;
200:     PetscInt nz = a->i[A->rmap->n];
201:     /* malloc space and  add 1 to i and j indices */
202:     PetscMalloc1(A->rmap->n+1,&tia);
203:     for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1;
204:     *ia = tia;
205:     if (ja) {
206:       PetscInt *tja;
207:       PetscMalloc1(nz+1,&tja);
208:       for (i=0; i<nz; i++) tja[i] = a->j[i] + 1;
209:       *ja = tja;
210:     }
211:   } else {
212:     *ia = a->i;
213:     if (ja) *ja = a->j;
214:   }
215:   return(0);
216: }

218: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
219: {

223:   if (!ia) return(0);
224:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
225:     PetscFree(*ia);
226:     if (ja) {PetscFree(*ja);}
227:   }
228:   return(0);
229: }

231: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
232: {
233:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
235:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
236:   PetscInt       nz = a->i[m],row,*jj,mr,col;

239:   *nn = n;
240:   if (!ia) return(0);
241:   if (symmetric) {
242:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,0,oshift,(PetscInt**)ia,(PetscInt**)ja);
243:   } else {
244:     PetscCalloc1(n+1,&collengths);
245:     PetscMalloc1(n+1,&cia);
246:     PetscMalloc1(nz+1,&cja);
247:     jj   = a->j;
248:     for (i=0; i<nz; i++) {
249:       collengths[jj[i]]++;
250:     }
251:     cia[0] = oshift;
252:     for (i=0; i<n; i++) {
253:       cia[i+1] = cia[i] + collengths[i];
254:     }
255:     PetscMemzero(collengths,n*sizeof(PetscInt));
256:     jj   = a->j;
257:     for (row=0; row<m; row++) {
258:       mr = a->i[row+1] - a->i[row];
259:       for (i=0; i<mr; i++) {
260:         col = *jj++;

262:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
263:       }
264:     }
265:     PetscFree(collengths);
266:     *ia  = cia; *ja = cja;
267:   }
268:   return(0);
269: }

271: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
272: {

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

278:   PetscFree(*ia);
279:   PetscFree(*ja);
280:   return(0);
281: }

283: /*
284:  MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
285:  MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
286:  spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
287: */
288: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
289: {
290:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
292:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
293:   PetscInt       nz = a->i[m],row,*jj,mr,col;
294:   PetscInt       *cspidx;

297:   *nn = n;
298:   if (!ia) return(0);

300:   PetscCalloc1(n+1,&collengths);
301:   PetscMalloc1(n+1,&cia);
302:   PetscMalloc1(nz+1,&cja);
303:   PetscMalloc1(nz+1,&cspidx);
304:   jj   = a->j;
305:   for (i=0; i<nz; i++) {
306:     collengths[jj[i]]++;
307:   }
308:   cia[0] = oshift;
309:   for (i=0; i<n; i++) {
310:     cia[i+1] = cia[i] + collengths[i];
311:   }
312:   PetscMemzero(collengths,n*sizeof(PetscInt));
313:   jj   = a->j;
314:   for (row=0; row<m; row++) {
315:     mr = a->i[row+1] - a->i[row];
316:     for (i=0; i<mr; i++) {
317:       col = *jj++;
318:       cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */
319:       cja[cia[col] + collengths[col]++ - oshift]  = row + oshift;
320:     }
321:   }
322:   PetscFree(collengths);
323:   *ia    = cia; *ja = cja;
324:   *spidx = cspidx;
325:   return(0);
326: }

328: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
329: {

333:   MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);
334:   PetscFree(*spidx);
335:   return(0);
336: }

338: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
339: {
340:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
341:   PetscInt       *ai = a->i;

345:   PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
346:   return(0);
347: }

349: /*
350:     MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions

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

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

359: */

361:  #include <petsc/private/isimpl.h>
362: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
363: {
364:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
365:   PetscInt       low,high,t,row,nrow,i,col,l;
366:   const PetscInt *rp,*ai = a->i,*ailen = a->ilen,*aj = a->j;
367:   PetscInt       lastcol = -1;
368:   MatScalar      *ap,value,*aa = a->a;
369:   const PetscInt *ridx = A->rmap->mapping->indices,*cidx = A->cmap->mapping->indices;

371:   row = ridx[im[0]];
372:   rp   = aj + ai[row];
373:   ap = aa + ai[row];
374:   nrow = ailen[row];
375:   low  = 0;
376:   high = nrow;
377:   for (l=0; l<n; l++) { /* loop over added columns */
378:     col = cidx[in[l]];
379:     value = v[l];

381:     if (col <= lastcol) low = 0;
382:     else high = nrow;
383:     lastcol = col;
384:     while (high-low > 5) {
385:       t = (low+high)/2;
386:       if (rp[t] > col) high = t;
387:       else low = t;
388:     }
389:     for (i=low; i<high; i++) {
390:       if (rp[i] == col) {
391:         ap[i] += value;
392:         low = i + 1;
393:         break;
394:       }
395:     }
396:   }
397:   return 0;
398: }

400: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
401: {
402:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
403:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
404:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
406:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
407:   MatScalar      *ap=NULL,value=0.0,*aa = a->a;
408:   PetscBool      ignorezeroentries = a->ignorezeroentries;
409:   PetscBool      roworiented       = a->roworiented;

412:   for (k=0; k<m; k++) { /* loop over added rows */
413:     row = im[k];
414:     if (row < 0) continue;
415: #if defined(PETSC_USE_DEBUG)
416:     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);
417: #endif
418:     rp   = aj + ai[row];
419:     if (!A->structure_only) ap = aa + ai[row];
420:     rmax = imax[row]; nrow = ailen[row];
421:     low  = 0;
422:     high = nrow;
423:     for (l=0; l<n; l++) { /* loop over added columns */
424:       if (in[l] < 0) continue;
425: #if defined(PETSC_USE_DEBUG)
426:       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);
427: #endif
428:       col = in[l];
429:       if (!A->structure_only) {
430:         if (roworiented) {
431:           value = v[l + k*n];
432:         } else {
433:           value = v[k + l*m];
434:         }
435:       } else { /* A->structure_only */
436:         value = 1; /* avoid 'continue' below?  */
437:       }
438:       if ((value == 0.0 && ignorezeroentries) && (is == ADD_VALUES) && row != col) continue;

440:       if (col <= lastcol) low = 0;
441:       else high = nrow;
442:       lastcol = col;
443:       while (high-low > 5) {
444:         t = (low+high)/2;
445:         if (rp[t] > col) high = t;
446:         else low = t;
447:       }
448:       for (i=low; i<high; i++) {
449:         if (rp[i] > col) break;
450:         if (rp[i] == col) {
451:           if (!A->structure_only) {
452:             if (is == ADD_VALUES) ap[i] += value;
453:             else ap[i] = value;
454:           }
455:           low = i + 1;
456:           goto noinsert;
457:         }
458:       }
459:       if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
460:       if (nonew == 1) goto noinsert;
461:       if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
462:       if (A->structure_only) {
463:         MatSeqXAIJReallocateAIJ_structure_only(A,A->rmap->n,1,nrow,row,col,rmax,ai,aj,rp,imax,nonew,MatScalar);
464:       } else {
465:         MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
466:       }
467:       N = nrow++ - 1; a->nz++; high++;
468:       /* shift up all the later entries in this row */
469:       for (ii=N; ii>=i; ii--) {
470:         rp[ii+1] = rp[ii];
471:         if (!A->structure_only) ap[ii+1] = ap[ii];
472:       }
473:       rp[i] = col;
474:       if (!A->structure_only) ap[i] = value;
475:       low   = i + 1;
476:       A->nonzerostate++;
477: noinsert:;
478:     }
479:     ailen[row] = nrow;
480:   }
481:   return(0);
482: }


485: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
486: {
487:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
488:   PetscInt   *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
489:   PetscInt   *ai = a->i,*ailen = a->ilen;
490:   MatScalar  *ap,*aa = a->a;

493:   for (k=0; k<m; k++) { /* loop over rows */
494:     row = im[k];
495:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
496:     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);
497:     rp   = aj + ai[row]; ap = aa + ai[row];
498:     nrow = ailen[row];
499:     for (l=0; l<n; l++) { /* loop over columns */
500:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
501:       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);
502:       col  = in[l];
503:       high = nrow; low = 0; /* assume unsorted */
504:       while (high-low > 5) {
505:         t = (low+high)/2;
506:         if (rp[t] > col) high = t;
507:         else low = t;
508:       }
509:       for (i=low; i<high; i++) {
510:         if (rp[i] > col) break;
511:         if (rp[i] == col) {
512:           *v++ = ap[i];
513:           goto finished;
514:         }
515:       }
516:       *v++ = 0.0;
517: finished:;
518:     }
519:   }
520:   return(0);
521: }


524: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
525: {
526:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
528:   PetscInt       i,*col_lens;
529:   int            fd;
530:   FILE           *file;

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

536:   col_lens[0] = MAT_FILE_CLASSID;
537:   col_lens[1] = A->rmap->n;
538:   col_lens[2] = A->cmap->n;
539:   col_lens[3] = a->nz;

541:   /* store lengths of each row and write (including header) to file */
542:   for (i=0; i<A->rmap->n; i++) {
543:     col_lens[4+i] = a->i[i+1] - a->i[i];
544:   }
545:   PetscBinaryWrite(fd,col_lens,4+A->rmap->n,PETSC_INT,PETSC_TRUE);
546:   PetscFree(col_lens);

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

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

554:   PetscViewerBinaryGetInfoPointer(viewer,&file);
555:   if (file) {
556:     fprintf(file,"-matload_block_size %d\n",(int)PetscAbs(A->rmap->bs));
557:   }
558:   return(0);
559: }

561: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A,PetscViewer viewer)
562: {
564:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
565:   PetscInt       i,k,m=A->rmap->N;

568:   PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
569:   for (i=0; i<m; i++) {
570:     PetscViewerASCIIPrintf(viewer,"row %D:",i);
571:     for (k=a->i[i]; k<a->i[i+1]; k++) {
572:       PetscViewerASCIIPrintf(viewer," (%D) ",a->j[k]);
573:     }
574:     PetscViewerASCIIPrintf(viewer,"\n");
575:   }
576:   PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
577:   return(0);
578: }

580: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

582: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
583: {
584:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
585:   PetscErrorCode    ierr;
586:   PetscInt          i,j,m = A->rmap->n;
587:   const char        *name;
588:   PetscViewerFormat format;

591:   if (A->structure_only) {
592:     MatView_SeqAIJ_ASCII_structonly(A,viewer);
593:     return(0);
594:   }

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

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

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

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

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

841:  #include <petscdraw.h>
842: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
843: {
844:   Mat               A  = (Mat) Aa;
845:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
846:   PetscErrorCode    ierr;
847:   PetscInt          i,j,m = A->rmap->n;
848:   int               color;
849:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r;
850:   PetscViewer       viewer;
851:   PetscViewerFormat format;

854:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
855:   PetscViewerGetFormat(viewer,&format);
856:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);

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

860:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
861:     PetscDrawCollectiveBegin(draw);
862:     /* Blue for negative, Cyan for zero and  Red for positive */
863:     color = PETSC_DRAW_BLUE;
864:     for (i=0; i<m; i++) {
865:       y_l = m - i - 1.0; y_r = y_l + 1.0;
866:       for (j=a->i[i]; j<a->i[i+1]; j++) {
867:         x_l = a->j[j]; x_r = x_l + 1.0;
868:         if (PetscRealPart(a->a[j]) >=  0.) continue;
869:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
870:       }
871:     }
872:     color = PETSC_DRAW_CYAN;
873:     for (i=0; i<m; i++) {
874:       y_l = m - i - 1.0; y_r = y_l + 1.0;
875:       for (j=a->i[i]; j<a->i[i+1]; j++) {
876:         x_l = a->j[j]; x_r = x_l + 1.0;
877:         if (a->a[j] !=  0.) continue;
878:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
879:       }
880:     }
881:     color = PETSC_DRAW_RED;
882:     for (i=0; i<m; i++) {
883:       y_l = m - i - 1.0; y_r = y_l + 1.0;
884:       for (j=a->i[i]; j<a->i[i+1]; j++) {
885:         x_l = a->j[j]; x_r = x_l + 1.0;
886:         if (PetscRealPart(a->a[j]) <=  0.) continue;
887:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
888:       }
889:     }
890:     PetscDrawCollectiveEnd(draw);
891:   } else {
892:     /* use contour shading to indicate magnitude of values */
893:     /* first determine max of all nonzero values */
894:     PetscReal minv = 0.0, maxv = 0.0;
895:     PetscInt  nz = a->nz, count = 0;
896:     PetscDraw popup;

898:     for (i=0; i<nz; i++) {
899:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
900:     }
901:     if (minv >= maxv) maxv = minv + PETSC_SMALL;
902:     PetscDrawGetPopup(draw,&popup);
903:     PetscDrawScalePopup(popup,minv,maxv);

905:     PetscDrawCollectiveBegin(draw);
906:     for (i=0; i<m; i++) {
907:       y_l = m - i - 1.0;
908:       y_r = y_l + 1.0;
909:       for (j=a->i[i]; j<a->i[i+1]; j++) {
910:         x_l = a->j[j];
911:         x_r = x_l + 1.0;
912:         color = PetscDrawRealToColor(PetscAbsScalar(a->a[count]),minv,maxv);
913:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
914:         count++;
915:       }
916:     }
917:     PetscDrawCollectiveEnd(draw);
918:   }
919:   return(0);
920: }

922:  #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:   xr   = A->cmap->n; yr  = A->rmap->n; h = yr/10.0; w = xr/10.0;
936:   xr  += w;          yr += h;         xl = -w;     yl = -h;
937:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
938:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
939:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
940:   PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
941:   PetscDrawSave(draw);
942:   return(0);
943: }

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

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

965: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
966: {
967:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
969:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
970:   PetscInt       m      = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
971:   MatScalar      *aa    = a->a,*ap;
972:   PetscReal      ratio  = 0.6;

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

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

998:   /* reset ilen and imax for each row */
999:   a->nonzerorowcnt = 0;
1000:   if (A->structure_only) {
1001:     PetscFree2(a->imax,a->ilen);
1002:   } else { /* !A->structure_only */
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:   }
1008:   a->nz = ai[m];
1009:   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);

1011:   MatMarkDiagonal_SeqAIJ(A);
1012:   PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
1013:   PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
1014:   PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);

1016:   A->info.mallocs    += a->reallocs;
1017:   a->reallocs         = 0;
1018:   A->info.nz_unneeded = (PetscReal)fshift;
1019:   a->rmax             = rmax;

1021:   if (!A->structure_only) {
1022:     MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);
1023:   }
1024:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);
1025:   MatSeqAIJInvalidateDiagonal(A);
1026:   return(0);
1027: }

1029: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1030: {
1031:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1032:   PetscInt       i,nz = a->nz;
1033:   MatScalar      *aa = a->a;

1037:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
1038:   MatSeqAIJInvalidateDiagonal(A);
1039:   return(0);
1040: }

1042: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1043: {
1044:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1045:   PetscInt       i,nz = a->nz;
1046:   MatScalar      *aa = a->a;

1050:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1051:   MatSeqAIJInvalidateDiagonal(A);
1052:   return(0);
1053: }

1055: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1056: {
1057:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1061:   PetscMemzero(a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
1062:   MatSeqAIJInvalidateDiagonal(A);
1063:   return(0);
1064: }

1066: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1067: {
1068:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1072: #if defined(PETSC_USE_LOG)
1073:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
1074: #endif
1075:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
1076:   ISDestroy(&a->row);
1077:   ISDestroy(&a->col);
1078:   PetscFree(a->diag);
1079:   PetscFree(a->ibdiag);
1080:   PetscFree2(a->imax,a->ilen);
1081:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
1082:   PetscFree(a->solve_work);
1083:   ISDestroy(&a->icol);
1084:   PetscFree(a->saved_values);
1085:   ISColoringDestroy(&a->coloring);
1086:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);
1087:   PetscFree(a->matmult_abdense);

1089:   MatDestroy_SeqAIJ_Inode(A);
1090:   PetscFree(A->data);

1092:   PetscObjectChangeTypeName((PetscObject)A,0);
1093:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
1094:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
1095:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
1096:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
1097:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
1098:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);
1099: #if defined(PETSC_HAVE_ELEMENTAL)
1100:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);
1101: #endif
1102: #if defined(PETSC_HAVE_HYPRE)
1103:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_hypre_C",NULL);
1104:   PetscObjectComposeFunction((PetscObject)A,"MatMatMatMult_transpose_seqaij_seqaij_C",NULL);
1105: #endif
1106:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);
1107:   PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
1108:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
1109:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
1110:   PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
1111:   return(0);
1112: }

1114: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
1115: {
1116:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1120:   switch (op) {
1121:   case MAT_ROW_ORIENTED:
1122:     a->roworiented = flg;
1123:     break;
1124:   case MAT_KEEP_NONZERO_PATTERN:
1125:     a->keepnonzeropattern = flg;
1126:     break;
1127:   case MAT_NEW_NONZERO_LOCATIONS:
1128:     a->nonew = (flg ? 0 : 1);
1129:     break;
1130:   case MAT_NEW_NONZERO_LOCATION_ERR:
1131:     a->nonew = (flg ? -1 : 0);
1132:     break;
1133:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1134:     a->nonew = (flg ? -2 : 0);
1135:     break;
1136:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1137:     a->nounused = (flg ? -1 : 0);
1138:     break;
1139:   case MAT_IGNORE_ZERO_ENTRIES:
1140:     a->ignorezeroentries = flg;
1141:     break;
1142:   case MAT_SPD:
1143:   case MAT_SYMMETRIC:
1144:   case MAT_STRUCTURALLY_SYMMETRIC:
1145:   case MAT_HERMITIAN:
1146:   case MAT_SYMMETRY_ETERNAL:
1147:   case MAT_STRUCTURE_ONLY:
1148:     /* These options are handled directly by MatSetOption() */
1149:     break;
1150:   case MAT_NEW_DIAGONALS:
1151:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1152:   case MAT_USE_HASH_TABLE:
1153:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1154:     break;
1155:   case MAT_USE_INODES:
1156:     /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
1157:     break;
1158:   case MAT_SUBMAT_SINGLEIS:
1159:     A->submat_singleis = flg;
1160:     break;
1161:   default:
1162:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1163:   }
1164:   MatSetOption_SeqAIJ_Inode(A,op,flg);
1165:   return(0);
1166: }

1168: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1169: {
1170:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1172:   PetscInt       i,j,n,*ai=a->i,*aj=a->j,nz;
1173:   PetscScalar    *aa=a->a,*x,zero=0.0;

1176:   VecGetLocalSize(v,&n);
1177:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");

1179:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1180:     PetscInt *diag=a->diag;
1181:     VecGetArray(v,&x);
1182:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1183:     VecRestoreArray(v,&x);
1184:     return(0);
1185:   }

1187:   VecSet(v,zero);
1188:   VecGetArray(v,&x);
1189:   for (i=0; i<n; i++) {
1190:     nz = ai[i+1] - ai[i];
1191:     if (!nz) x[i] = 0.0;
1192:     for (j=ai[i]; j<ai[i+1]; j++) {
1193:       if (aj[j] == i) {
1194:         x[i] = aa[j];
1195:         break;
1196:       }
1197:     }
1198:   }
1199:   VecRestoreArray(v,&x);
1200:   return(0);
1201: }

1203: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1204: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1205: {
1206:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1207:   PetscScalar       *y;
1208:   const PetscScalar *x;
1209:   PetscErrorCode    ierr;
1210:   PetscInt          m = A->rmap->n;
1211: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1212:   const MatScalar   *v;
1213:   PetscScalar       alpha;
1214:   PetscInt          n,i,j;
1215:   const PetscInt    *idx,*ii,*ridx=NULL;
1216:   Mat_CompressedRow cprow    = a->compressedrow;
1217:   PetscBool         usecprow = cprow.use;
1218: #endif

1221:   if (zz != yy) {VecCopy(zz,yy);}
1222:   VecGetArrayRead(xx,&x);
1223:   VecGetArray(yy,&y);

1225: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1226:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1227: #else
1228:   if (usecprow) {
1229:     m    = cprow.nrows;
1230:     ii   = cprow.i;
1231:     ridx = cprow.rindex;
1232:   } else {
1233:     ii = a->i;
1234:   }
1235:   for (i=0; i<m; i++) {
1236:     idx = a->j + ii[i];
1237:     v   = a->a + ii[i];
1238:     n   = ii[i+1] - ii[i];
1239:     if (usecprow) {
1240:       alpha = x[ridx[i]];
1241:     } else {
1242:       alpha = x[i];
1243:     }
1244:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1245:   }
1246: #endif
1247:   PetscLogFlops(2.0*a->nz);
1248:   VecRestoreArrayRead(xx,&x);
1249:   VecRestoreArray(yy,&y);
1250:   return(0);
1251: }

1253: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1254: {

1258:   VecSet(yy,0.0);
1259:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1260:   return(0);
1261: }

1263: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>

1265: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1266: {
1267:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1268:   PetscScalar       *y;
1269:   const PetscScalar *x;
1270:   const MatScalar   *aa;
1271:   PetscErrorCode    ierr;
1272:   PetscInt          m=A->rmap->n;
1273:   const PetscInt    *aj,*ii,*ridx=NULL;
1274:   PetscInt          n,i;
1275:   PetscScalar       sum;
1276:   PetscBool         usecprow=a->compressedrow.use;

1278: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1279: #pragma disjoint(*x,*y,*aa)
1280: #endif

1283:   VecGetArrayRead(xx,&x);
1284:   VecGetArray(yy,&y);
1285:   ii   = a->i;
1286:   if (usecprow) { /* use compressed row format */
1287:     PetscMemzero(y,m*sizeof(PetscScalar));
1288:     m    = a->compressedrow.nrows;
1289:     ii   = a->compressedrow.i;
1290:     ridx = a->compressedrow.rindex;
1291:     for (i=0; i<m; i++) {
1292:       n           = ii[i+1] - ii[i];
1293:       aj          = a->j + ii[i];
1294:       aa          = a->a + ii[i];
1295:       sum         = 0.0;
1296:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1297:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1298:       y[*ridx++] = sum;
1299:     }
1300:   } else { /* do not use compressed row format */
1301: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1302:     aj   = a->j;
1303:     aa   = a->a;
1304:     fortranmultaij_(&m,x,ii,aj,aa,y);
1305: #else
1306:     for (i=0; i<m; i++) {
1307:       n           = ii[i+1] - ii[i];
1308:       aj          = a->j + ii[i];
1309:       aa          = a->a + ii[i];
1310:       sum         = 0.0;
1311:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1312:       y[i] = sum;
1313:     }
1314: #endif
1315:   }
1316:   PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
1317:   VecRestoreArrayRead(xx,&x);
1318:   VecRestoreArray(yy,&y);
1319:   return(0);
1320: }

1322: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
1323: {
1324:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1325:   PetscScalar       *y;
1326:   const PetscScalar *x;
1327:   const MatScalar   *aa;
1328:   PetscErrorCode    ierr;
1329:   PetscInt          m=A->rmap->n;
1330:   const PetscInt    *aj,*ii,*ridx=NULL;
1331:   PetscInt          n,i,nonzerorow=0;
1332:   PetscScalar       sum;
1333:   PetscBool         usecprow=a->compressedrow.use;

1335: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1336: #pragma disjoint(*x,*y,*aa)
1337: #endif

1340:   VecGetArrayRead(xx,&x);
1341:   VecGetArray(yy,&y);
1342:   if (usecprow) { /* use compressed row format */
1343:     m    = a->compressedrow.nrows;
1344:     ii   = a->compressedrow.i;
1345:     ridx = a->compressedrow.rindex;
1346:     for (i=0; i<m; i++) {
1347:       n           = ii[i+1] - ii[i];
1348:       aj          = a->j + ii[i];
1349:       aa          = a->a + ii[i];
1350:       sum         = 0.0;
1351:       nonzerorow += (n>0);
1352:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1353:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1354:       y[*ridx++] = sum;
1355:     }
1356:   } else { /* do not use compressed row format */
1357:     ii = a->i;
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:       y[i] = sum;
1366:     }
1367:   }
1368:   PetscLogFlops(2.0*a->nz - nonzerorow);
1369:   VecRestoreArrayRead(xx,&x);
1370:   VecRestoreArray(yy,&y);
1371:   return(0);
1372: }

1374: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1375: {
1376:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1377:   PetscScalar       *y,*z;
1378:   const PetscScalar *x;
1379:   const MatScalar   *aa;
1380:   PetscErrorCode    ierr;
1381:   PetscInt          m = A->rmap->n,*aj,*ii;
1382:   PetscInt          n,i,*ridx=NULL;
1383:   PetscScalar       sum;
1384:   PetscBool         usecprow=a->compressedrow.use;

1387:   VecGetArrayRead(xx,&x);
1388:   VecGetArrayPair(yy,zz,&y,&z);
1389:   if (usecprow) { /* use compressed row format */
1390:     if (zz != yy) {
1391:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
1392:     }
1393:     m    = a->compressedrow.nrows;
1394:     ii   = a->compressedrow.i;
1395:     ridx = a->compressedrow.rindex;
1396:     for (i=0; i<m; i++) {
1397:       n   = ii[i+1] - ii[i];
1398:       aj  = a->j + ii[i];
1399:       aa  = a->a + ii[i];
1400:       sum = y[*ridx];
1401:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1402:       z[*ridx++] = sum;
1403:     }
1404:   } else { /* do not use compressed row format */
1405:     ii = a->i;
1406:     for (i=0; i<m; i++) {
1407:       n   = ii[i+1] - ii[i];
1408:       aj  = a->j + ii[i];
1409:       aa  = a->a + ii[i];
1410:       sum = y[i];
1411:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1412:       z[i] = sum;
1413:     }
1414:   }
1415:   PetscLogFlops(2.0*a->nz);
1416:   VecRestoreArrayRead(xx,&x);
1417:   VecRestoreArrayPair(yy,zz,&y,&z);
1418:   return(0);
1419: }

1421: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1422: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1423: {
1424:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1425:   PetscScalar       *y,*z;
1426:   const PetscScalar *x;
1427:   const MatScalar   *aa;
1428:   PetscErrorCode    ierr;
1429:   const PetscInt    *aj,*ii,*ridx=NULL;
1430:   PetscInt          m = A->rmap->n,n,i;
1431:   PetscScalar       sum;
1432:   PetscBool         usecprow=a->compressedrow.use;

1435:   VecGetArrayRead(xx,&x);
1436:   VecGetArrayPair(yy,zz,&y,&z);
1437:   if (usecprow) { /* use compressed row format */
1438:     if (zz != yy) {
1439:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
1440:     }
1441:     m    = a->compressedrow.nrows;
1442:     ii   = a->compressedrow.i;
1443:     ridx = a->compressedrow.rindex;
1444:     for (i=0; i<m; i++) {
1445:       n   = ii[i+1] - ii[i];
1446:       aj  = a->j + ii[i];
1447:       aa  = a->a + ii[i];
1448:       sum = y[*ridx];
1449:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1450:       z[*ridx++] = sum;
1451:     }
1452:   } else { /* do not use compressed row format */
1453:     ii = a->i;
1454: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1455:     aj = a->j;
1456:     aa = a->a;
1457:     fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1458: #else
1459:     for (i=0; i<m; i++) {
1460:       n   = ii[i+1] - ii[i];
1461:       aj  = a->j + ii[i];
1462:       aa  = a->a + ii[i];
1463:       sum = y[i];
1464:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1465:       z[i] = sum;
1466:     }
1467: #endif
1468:   }
1469:   PetscLogFlops(2.0*a->nz);
1470:   VecRestoreArrayRead(xx,&x);
1471:   VecRestoreArrayPair(yy,zz,&y,&z);
1472: #if defined(PETSC_HAVE_CUSP)
1473:   /*
1474:   VecView(xx,0);
1475:   VecView(zz,0);
1476:   MatView(A,0);
1477:   */
1478: #endif
1479:   return(0);
1480: }

1482: /*
1483:      Adds diagonal pointers to sparse matrix structure.
1484: */
1485: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1486: {
1487:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1489:   PetscInt       i,j,m = A->rmap->n;

1492:   if (!a->diag) {
1493:     PetscMalloc1(m,&a->diag);
1494:     PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
1495:   }
1496:   for (i=0; i<A->rmap->n; i++) {
1497:     a->diag[i] = a->i[i+1];
1498:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1499:       if (a->j[j] == i) {
1500:         a->diag[i] = j;
1501:         break;
1502:       }
1503:     }
1504:   }
1505:   return(0);
1506: }

1508: /*
1509:      Checks for missing diagonals
1510: */
1511: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
1512: {
1513:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1514:   PetscInt   *diag,*ii = a->i,i;

1517:   *missing = PETSC_FALSE;
1518:   if (A->rmap->n > 0 && !ii) {
1519:     *missing = PETSC_TRUE;
1520:     if (d) *d = 0;
1521:     PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
1522:   } else {
1523:     diag = a->diag;
1524:     for (i=0; i<A->rmap->n; i++) {
1525:       if (diag[i] >= ii[i+1]) {
1526:         *missing = PETSC_TRUE;
1527:         if (d) *d = i;
1528:         PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
1529:         break;
1530:       }
1531:     }
1532:   }
1533:   return(0);
1534: }

1536: /*
1537:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1538: */
1539: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1540: {
1541:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
1543:   PetscInt       i,*diag,m = A->rmap->n;
1544:   MatScalar      *v = a->a;
1545:   PetscScalar    *idiag,*mdiag;

1548:   if (a->idiagvalid) return(0);
1549:   MatMarkDiagonal_SeqAIJ(A);
1550:   diag = a->diag;
1551:   if (!a->idiag) {
1552:     PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
1553:     PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));
1554:     v    = a->a;
1555:   }
1556:   mdiag = a->mdiag;
1557:   idiag = a->idiag;

1559:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1560:     for (i=0; i<m; i++) {
1561:       mdiag[i] = v[diag[i]];
1562:       if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1563:         if (PetscRealPart(fshift)) {
1564:           PetscInfo1(A,"Zero diagonal on row %D\n",i);
1565:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1566:           A->factorerror_zeropivot_value = 0.0;
1567:           A->factorerror_zeropivot_row   = i;
1568:         } SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1569:       }
1570:       idiag[i] = 1.0/v[diag[i]];
1571:     }
1572:     PetscLogFlops(m);
1573:   } else {
1574:     for (i=0; i<m; i++) {
1575:       mdiag[i] = v[diag[i]];
1576:       idiag[i] = omega/(fshift + v[diag[i]]);
1577:     }
1578:     PetscLogFlops(2.0*m);
1579:   }
1580:   a->idiagvalid = PETSC_TRUE;
1581:   return(0);
1582: }

1584: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1585: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1586: {
1587:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1588:   PetscScalar       *x,d,sum,*t,scale;
1589:   const MatScalar   *v,*idiag=0,*mdiag;
1590:   const PetscScalar *b, *bs,*xb, *ts;
1591:   PetscErrorCode    ierr;
1592:   PetscInt          n,m = A->rmap->n,i;
1593:   const PetscInt    *idx,*diag;

1596:   its = its*lits;

1598:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1599:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1600:   a->fshift = fshift;
1601:   a->omega  = omega;

1603:   diag  = a->diag;
1604:   t     = a->ssor_work;
1605:   idiag = a->idiag;
1606:   mdiag = a->mdiag;

1608:   VecGetArray(xx,&x);
1609:   VecGetArrayRead(bb,&b);
1610:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1611:   if (flag == SOR_APPLY_UPPER) {
1612:     /* apply (U + D/omega) to the vector */
1613:     bs = b;
1614:     for (i=0; i<m; i++) {
1615:       d   = fshift + mdiag[i];
1616:       n   = a->i[i+1] - diag[i] - 1;
1617:       idx = a->j + diag[i] + 1;
1618:       v   = a->a + diag[i] + 1;
1619:       sum = b[i]*d/omega;
1620:       PetscSparseDensePlusDot(sum,bs,v,idx,n);
1621:       x[i] = sum;
1622:     }
1623:     VecRestoreArray(xx,&x);
1624:     VecRestoreArrayRead(bb,&b);
1625:     PetscLogFlops(a->nz);
1626:     return(0);
1627:   }

1629:   if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1630:   else if (flag & SOR_EISENSTAT) {
1631:     /* Let  A = L + U + D; where L is lower trianglar,
1632:     U is upper triangular, E = D/omega; This routine applies

1634:             (L + E)^{-1} A (U + E)^{-1}

1636:     to a vector efficiently using Eisenstat's trick.
1637:     */
1638:     scale = (2.0/omega) - 1.0;

1640:     /*  x = (E + U)^{-1} b */
1641:     for (i=m-1; i>=0; i--) {
1642:       n   = a->i[i+1] - diag[i] - 1;
1643:       idx = a->j + diag[i] + 1;
1644:       v   = a->a + diag[i] + 1;
1645:       sum = b[i];
1646:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
1647:       x[i] = sum*idiag[i];
1648:     }

1650:     /*  t = b - (2*E - D)x */
1651:     v = a->a;
1652:     for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];

1654:     /*  t = (E + L)^{-1}t */
1655:     ts   = t;
1656:     diag = a->diag;
1657:     for (i=0; i<m; i++) {
1658:       n   = diag[i] - a->i[i];
1659:       idx = a->j + a->i[i];
1660:       v   = a->a + a->i[i];
1661:       sum = t[i];
1662:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
1663:       t[i] = sum*idiag[i];
1664:       /*  x = x + t */
1665:       x[i] += t[i];
1666:     }

1668:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
1669:     VecRestoreArray(xx,&x);
1670:     VecRestoreArrayRead(bb,&b);
1671:     return(0);
1672:   }
1673:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1674:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1675:       for (i=0; i<m; i++) {
1676:         n   = diag[i] - a->i[i];
1677:         idx = a->j + a->i[i];
1678:         v   = a->a + a->i[i];
1679:         sum = b[i];
1680:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1681:         t[i] = sum;
1682:         x[i] = sum*idiag[i];
1683:       }
1684:       xb   = t;
1685:       PetscLogFlops(a->nz);
1686:     } else xb = b;
1687:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1688:       for (i=m-1; i>=0; i--) {
1689:         n   = a->i[i+1] - diag[i] - 1;
1690:         idx = a->j + diag[i] + 1;
1691:         v   = a->a + diag[i] + 1;
1692:         sum = xb[i];
1693:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1694:         if (xb == b) {
1695:           x[i] = sum*idiag[i];
1696:         } else {
1697:           x[i] = (1-omega)*x[i] + sum*idiag[i];  /* omega in idiag */
1698:         }
1699:       }
1700:       PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1701:     }
1702:     its--;
1703:   }
1704:   while (its--) {
1705:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1706:       for (i=0; i<m; i++) {
1707:         /* lower */
1708:         n   = diag[i] - a->i[i];
1709:         idx = a->j + a->i[i];
1710:         v   = a->a + a->i[i];
1711:         sum = b[i];
1712:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1713:         t[i] = sum;             /* save application of the lower-triangular part */
1714:         /* upper */
1715:         n   = a->i[i+1] - diag[i] - 1;
1716:         idx = a->j + diag[i] + 1;
1717:         v   = a->a + diag[i] + 1;
1718:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1719:         x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1720:       }
1721:       xb   = t;
1722:       PetscLogFlops(2.0*a->nz);
1723:     } else xb = b;
1724:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1725:       for (i=m-1; i>=0; i--) {
1726:         sum = xb[i];
1727:         if (xb == b) {
1728:           /* whole matrix (no checkpointing available) */
1729:           n   = a->i[i+1] - a->i[i];
1730:           idx = a->j + a->i[i];
1731:           v   = a->a + a->i[i];
1732:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
1733:           x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1734:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
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:       }
1742:       if (xb == b) {
1743:         PetscLogFlops(2.0*a->nz);
1744:       } else {
1745:         PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1746:       }
1747:     }
1748:   }
1749:   VecRestoreArray(xx,&x);
1750:   VecRestoreArrayRead(bb,&b);
1751:   return(0);
1752: }


1755: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1756: {
1757:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1760:   info->block_size   = 1.0;
1761:   info->nz_allocated = (double)a->maxnz;
1762:   info->nz_used      = (double)a->nz;
1763:   info->nz_unneeded  = (double)(a->maxnz - a->nz);
1764:   info->assemblies   = (double)A->num_ass;
1765:   info->mallocs      = (double)A->info.mallocs;
1766:   info->memory       = ((PetscObject)A)->mem;
1767:   if (A->factortype) {
1768:     info->fill_ratio_given  = A->info.fill_ratio_given;
1769:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1770:     info->factor_mallocs    = A->info.factor_mallocs;
1771:   } else {
1772:     info->fill_ratio_given  = 0;
1773:     info->fill_ratio_needed = 0;
1774:     info->factor_mallocs    = 0;
1775:   }
1776:   return(0);
1777: }

1779: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1780: {
1781:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1782:   PetscInt          i,m = A->rmap->n - 1;
1783:   PetscErrorCode    ierr;
1784:   const PetscScalar *xx;
1785:   PetscScalar       *bb;
1786:   PetscInt          d = 0;

1789:   if (x && b) {
1790:     VecGetArrayRead(x,&xx);
1791:     VecGetArray(b,&bb);
1792:     for (i=0; i<N; i++) {
1793:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1794:       bb[rows[i]] = diag*xx[rows[i]];
1795:     }
1796:     VecRestoreArrayRead(x,&xx);
1797:     VecRestoreArray(b,&bb);
1798:   }

1800:   if (a->keepnonzeropattern) {
1801:     for (i=0; i<N; i++) {
1802:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1803:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1804:     }
1805:     if (diag != 0.0) {
1806:       for (i=0; i<N; i++) {
1807:         d = rows[i];
1808:         if (a->diag[d] >= a->i[d+1]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in the zeroed row %D",d);
1809:       }
1810:       for (i=0; i<N; i++) {
1811:         a->a[a->diag[rows[i]]] = diag;
1812:       }
1813:     }
1814:   } else {
1815:     if (diag != 0.0) {
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:         if (a->ilen[rows[i]] > 0) {
1819:           a->ilen[rows[i]]    = 1;
1820:           a->a[a->i[rows[i]]] = diag;
1821:           a->j[a->i[rows[i]]] = rows[i];
1822:         } else { /* in case row was completely empty */
1823:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1824:         }
1825:       }
1826:     } else {
1827:       for (i=0; i<N; i++) {
1828:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1829:         a->ilen[rows[i]] = 0;
1830:       }
1831:     }
1832:     A->nonzerostate++;
1833:   }
1834:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1835:   return(0);
1836: }

1838: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1839: {
1840:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1841:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
1842:   PetscErrorCode    ierr;
1843:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
1844:   const PetscScalar *xx;
1845:   PetscScalar       *bb;

1848:   if (x && b) {
1849:     VecGetArrayRead(x,&xx);
1850:     VecGetArray(b,&bb);
1851:     vecs = PETSC_TRUE;
1852:   }
1853:   PetscCalloc1(A->rmap->n,&zeroed);
1854:   for (i=0; i<N; i++) {
1855:     if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1856:     PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));

1858:     zeroed[rows[i]] = PETSC_TRUE;
1859:   }
1860:   for (i=0; i<A->rmap->n; i++) {
1861:     if (!zeroed[i]) {
1862:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1863:         if (zeroed[a->j[j]]) {
1864:           if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
1865:           a->a[j] = 0.0;
1866:         }
1867:       }
1868:     } else if (vecs) bb[i] = diag*xx[i];
1869:   }
1870:   if (x && b) {
1871:     VecRestoreArrayRead(x,&xx);
1872:     VecRestoreArray(b,&bb);
1873:   }
1874:   PetscFree(zeroed);
1875:   if (diag != 0.0) {
1876:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1877:     if (missing) {
1878:       if (a->nonew) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1879:       else {
1880:         for (i=0; i<N; i++) {
1881:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1882:         }
1883:       }
1884:     } else {
1885:       for (i=0; i<N; i++) {
1886:         a->a[a->diag[rows[i]]] = diag;
1887:       }
1888:     }
1889:   }
1890:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1891:   return(0);
1892: }

1894: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1895: {
1896:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1897:   PetscInt   *itmp;

1900:   if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

1902:   *nz = a->i[row+1] - a->i[row];
1903:   if (v) *v = a->a + a->i[row];
1904:   if (idx) {
1905:     itmp = a->j + a->i[row];
1906:     if (*nz) *idx = itmp;
1907:     else *idx = 0;
1908:   }
1909:   return(0);
1910: }

1912: /* remove this function? */
1913: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1914: {
1916:   return(0);
1917: }

1919: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1920: {
1921:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
1922:   MatScalar      *v  = a->a;
1923:   PetscReal      sum = 0.0;
1925:   PetscInt       i,j;

1928:   if (type == NORM_FROBENIUS) {
1929: #if defined(PETSC_USE_REAL___FP16)
1930:     PetscBLASInt one = 1,nz = a->nz;
1931:     *nrm = BLASnrm2_(&nz,v,&one);
1932: #else
1933:     for (i=0; i<a->nz; i++) {
1934:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1935:     }
1936:     *nrm = PetscSqrtReal(sum);
1937: #endif
1938:     PetscLogFlops(2*a->nz);
1939:   } else if (type == NORM_1) {
1940:     PetscReal *tmp;
1941:     PetscInt  *jj = a->j;
1942:     PetscCalloc1(A->cmap->n+1,&tmp);
1943:     *nrm = 0.0;
1944:     for (j=0; j<a->nz; j++) {
1945:       tmp[*jj++] += PetscAbsScalar(*v);  v++;
1946:     }
1947:     for (j=0; j<A->cmap->n; j++) {
1948:       if (tmp[j] > *nrm) *nrm = tmp[j];
1949:     }
1950:     PetscFree(tmp);
1951:     PetscLogFlops(PetscMax(a->nz-1,0));
1952:   } else if (type == NORM_INFINITY) {
1953:     *nrm = 0.0;
1954:     for (j=0; j<A->rmap->n; j++) {
1955:       v   = a->a + a->i[j];
1956:       sum = 0.0;
1957:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1958:         sum += PetscAbsScalar(*v); v++;
1959:       }
1960:       if (sum > *nrm) *nrm = sum;
1961:     }
1962:     PetscLogFlops(PetscMax(a->nz-1,0));
1963:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
1964:   return(0);
1965: }

1967: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
1968: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
1969: {
1971:   PetscInt       i,j,anzj;
1972:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b;
1973:   PetscInt       an=A->cmap->N,am=A->rmap->N;
1974:   PetscInt       *ati,*atj,*atfill,*ai=a->i,*aj=a->j;

1977:   /* Allocate space for symbolic transpose info and work array */
1978:   PetscCalloc1(an+1,&ati);
1979:   PetscMalloc1(ai[am],&atj);
1980:   PetscMalloc1(an,&atfill);

1982:   /* Walk through aj and count ## of non-zeros in each row of A^T. */
1983:   /* Note: offset by 1 for fast conversion into csr format. */
1984:   for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
1985:   /* Form ati for csr format of A^T. */
1986:   for (i=0;i<an;i++) ati[i+1] += ati[i];

1988:   /* Copy ati into atfill so we have locations of the next free space in atj */
1989:   PetscMemcpy(atfill,ati,an*sizeof(PetscInt));

1991:   /* Walk through A row-wise and mark nonzero entries of A^T. */
1992:   for (i=0;i<am;i++) {
1993:     anzj = ai[i+1] - ai[i];
1994:     for (j=0;j<anzj;j++) {
1995:       atj[atfill[*aj]] = i;
1996:       atfill[*aj++]   += 1;
1997:     }
1998:   }

2000:   /* Clean up temporary space and complete requests. */
2001:   PetscFree(atfill);
2002:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
2003:   MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));

2005:   b          = (Mat_SeqAIJ*)((*B)->data);
2006:   b->free_a  = PETSC_FALSE;
2007:   b->free_ij = PETSC_TRUE;
2008:   b->nonew   = 0;
2009:   return(0);
2010: }

2012: PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B)
2013: {
2014:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2015:   Mat            C;
2017:   PetscInt       i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col;
2018:   MatScalar      *array = a->a;

2021:   if (reuse == MAT_INPLACE_MATRIX && m != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");

2023:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
2024:     PetscCalloc1(1+A->cmap->n,&col);

2026:     for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
2027:     MatCreate(PetscObjectComm((PetscObject)A),&C);
2028:     MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);
2029:     MatSetBlockSizes(C,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2030:     MatSetType(C,((PetscObject)A)->type_name);
2031:     MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
2032:     PetscFree(col);
2033:   } else {
2034:     C = *B;
2035:   }

2037:   for (i=0; i<m; i++) {
2038:     len    = ai[i+1]-ai[i];
2039:     MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
2040:     array += len;
2041:     aj    += len;
2042:   }
2043:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2044:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

2046:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
2047:     *B = C;
2048:   } else {
2049:     MatHeaderMerge(A,&C);
2050:   }
2051:   return(0);
2052: }

2054: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
2055: {
2056:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2057:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
2058:   MatScalar      *va,*vb;
2060:   PetscInt       ma,na,mb,nb, i;

2063:   MatGetSize(A,&ma,&na);
2064:   MatGetSize(B,&mb,&nb);
2065:   if (ma!=nb || na!=mb) {
2066:     *f = PETSC_FALSE;
2067:     return(0);
2068:   }
2069:   aii  = aij->i; bii = bij->i;
2070:   adx  = aij->j; bdx = bij->j;
2071:   va   = aij->a; vb = bij->a;
2072:   PetscMalloc1(ma,&aptr);
2073:   PetscMalloc1(mb,&bptr);
2074:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2075:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2077:   *f = PETSC_TRUE;
2078:   for (i=0; i<ma; i++) {
2079:     while (aptr[i]<aii[i+1]) {
2080:       PetscInt    idc,idr;
2081:       PetscScalar vc,vr;
2082:       /* column/row index/value */
2083:       idc = adx[aptr[i]];
2084:       idr = bdx[bptr[idc]];
2085:       vc  = va[aptr[i]];
2086:       vr  = vb[bptr[idc]];
2087:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
2088:         *f = PETSC_FALSE;
2089:         goto done;
2090:       } else {
2091:         aptr[i]++;
2092:         if (B || i!=idc) bptr[idc]++;
2093:       }
2094:     }
2095:   }
2096: done:
2097:   PetscFree(aptr);
2098:   PetscFree(bptr);
2099:   return(0);
2100: }

2102: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
2103: {
2104:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2105:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
2106:   MatScalar      *va,*vb;
2108:   PetscInt       ma,na,mb,nb, i;

2111:   MatGetSize(A,&ma,&na);
2112:   MatGetSize(B,&mb,&nb);
2113:   if (ma!=nb || na!=mb) {
2114:     *f = PETSC_FALSE;
2115:     return(0);
2116:   }
2117:   aii  = aij->i; bii = bij->i;
2118:   adx  = aij->j; bdx = bij->j;
2119:   va   = aij->a; vb = bij->a;
2120:   PetscMalloc1(ma,&aptr);
2121:   PetscMalloc1(mb,&bptr);
2122:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2123:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2125:   *f = PETSC_TRUE;
2126:   for (i=0; i<ma; i++) {
2127:     while (aptr[i]<aii[i+1]) {
2128:       PetscInt    idc,idr;
2129:       PetscScalar vc,vr;
2130:       /* column/row index/value */
2131:       idc = adx[aptr[i]];
2132:       idr = bdx[bptr[idc]];
2133:       vc  = va[aptr[i]];
2134:       vr  = vb[bptr[idc]];
2135:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2136:         *f = PETSC_FALSE;
2137:         goto done;
2138:       } else {
2139:         aptr[i]++;
2140:         if (B || i!=idc) bptr[idc]++;
2141:       }
2142:     }
2143:   }
2144: done:
2145:   PetscFree(aptr);
2146:   PetscFree(bptr);
2147:   return(0);
2148: }

2150: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2151: {

2155:   MatIsTranspose_SeqAIJ(A,A,tol,f);
2156:   return(0);
2157: }

2159: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2160: {

2164:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2165:   return(0);
2166: }

2168: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
2169: {
2170:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2171:   PetscScalar    *l,*r,x;
2172:   MatScalar      *v;
2174:   PetscInt       i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz,*jj;

2177:   if (ll) {
2178:     /* The local size is used so that VecMPI can be passed to this routine
2179:        by MatDiagonalScale_MPIAIJ */
2180:     VecGetLocalSize(ll,&m);
2181:     if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
2182:     VecGetArray(ll,&l);
2183:     v    = a->a;
2184:     for (i=0; i<m; i++) {
2185:       x = l[i];
2186:       M = a->i[i+1] - a->i[i];
2187:       for (j=0; j<M; j++) (*v++) *= x;
2188:     }
2189:     VecRestoreArray(ll,&l);
2190:     PetscLogFlops(nz);
2191:   }
2192:   if (rr) {
2193:     VecGetLocalSize(rr,&n);
2194:     if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
2195:     VecGetArray(rr,&r);
2196:     v    = a->a; jj = a->j;
2197:     for (i=0; i<nz; i++) (*v++) *= r[*jj++];
2198:     VecRestoreArray(rr,&r);
2199:     PetscLogFlops(nz);
2200:   }
2201:   MatSeqAIJInvalidateDiagonal(A);
2202:   return(0);
2203: }

2205: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2206: {
2207:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
2209:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2210:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2211:   const PetscInt *irow,*icol;
2212:   PetscInt       nrows,ncols;
2213:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2214:   MatScalar      *a_new,*mat_a;
2215:   Mat            C;
2216:   PetscBool      stride;


2220:   ISGetIndices(isrow,&irow);
2221:   ISGetLocalSize(isrow,&nrows);
2222:   ISGetLocalSize(iscol,&ncols);

2224:   PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2225:   if (stride) {
2226:     ISStrideGetInfo(iscol,&first,&step);
2227:   } else {
2228:     first = 0;
2229:     step  = 0;
2230:   }
2231:   if (stride && step == 1) {
2232:     /* special case of contiguous rows */
2233:     PetscMalloc2(nrows,&lens,nrows,&starts);
2234:     /* loop over new rows determining lens and starting points */
2235:     for (i=0; i<nrows; i++) {
2236:       kstart = ai[irow[i]];
2237:       kend   = kstart + ailen[irow[i]];
2238:       starts[i] = kstart;
2239:       for (k=kstart; k<kend; k++) {
2240:         if (aj[k] >= first) {
2241:           starts[i] = k;
2242:           break;
2243:         }
2244:       }
2245:       sum = 0;
2246:       while (k < kend) {
2247:         if (aj[k++] >= first+ncols) break;
2248:         sum++;
2249:       }
2250:       lens[i] = sum;
2251:     }
2252:     /* create submatrix */
2253:     if (scall == MAT_REUSE_MATRIX) {
2254:       PetscInt n_cols,n_rows;
2255:       MatGetSize(*B,&n_rows,&n_cols);
2256:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2257:       MatZeroEntries(*B);
2258:       C    = *B;
2259:     } else {
2260:       PetscInt rbs,cbs;
2261:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2262:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2263:       ISGetBlockSize(isrow,&rbs);
2264:       ISGetBlockSize(iscol,&cbs);
2265:       MatSetBlockSizes(C,rbs,cbs);
2266:       MatSetType(C,((PetscObject)A)->type_name);
2267:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2268:     }
2269:     c = (Mat_SeqAIJ*)C->data;

2271:     /* loop over rows inserting into submatrix */
2272:     a_new = c->a;
2273:     j_new = c->j;
2274:     i_new = c->i;

2276:     for (i=0; i<nrows; i++) {
2277:       ii    = starts[i];
2278:       lensi = lens[i];
2279:       for (k=0; k<lensi; k++) {
2280:         *j_new++ = aj[ii+k] - first;
2281:       }
2282:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
2283:       a_new     += lensi;
2284:       i_new[i+1] = i_new[i] + lensi;
2285:       c->ilen[i] = lensi;
2286:     }
2287:     PetscFree2(lens,starts);
2288:   } else {
2289:     ISGetIndices(iscol,&icol);
2290:     PetscCalloc1(oldcols,&smap);
2291:     PetscMalloc1(1+nrows,&lens);
2292:     for (i=0; i<ncols; i++) {
2293: #if defined(PETSC_USE_DEBUG)
2294:       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);
2295: #endif
2296:       smap[icol[i]] = i+1;
2297:     }

2299:     /* determine lens of each row */
2300:     for (i=0; i<nrows; i++) {
2301:       kstart  = ai[irow[i]];
2302:       kend    = kstart + a->ilen[irow[i]];
2303:       lens[i] = 0;
2304:       for (k=kstart; k<kend; k++) {
2305:         if (smap[aj[k]]) {
2306:           lens[i]++;
2307:         }
2308:       }
2309:     }
2310:     /* Create and fill new matrix */
2311:     if (scall == MAT_REUSE_MATRIX) {
2312:       PetscBool equal;

2314:       c = (Mat_SeqAIJ*)((*B)->data);
2315:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2316:       PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);
2317:       if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2318:       PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));
2319:       C    = *B;
2320:     } else {
2321:       PetscInt rbs,cbs;
2322:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2323:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2324:       ISGetBlockSize(isrow,&rbs);
2325:       ISGetBlockSize(iscol,&cbs);
2326:       MatSetBlockSizes(C,rbs,cbs);
2327:       MatSetType(C,((PetscObject)A)->type_name);
2328:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2329:     }
2330:     c = (Mat_SeqAIJ*)(C->data);
2331:     for (i=0; i<nrows; i++) {
2332:       row      = irow[i];
2333:       kstart   = ai[row];
2334:       kend     = kstart + a->ilen[row];
2335:       mat_i    = c->i[i];
2336:       mat_j    = c->j + mat_i;
2337:       mat_a    = c->a + mat_i;
2338:       mat_ilen = c->ilen + i;
2339:       for (k=kstart; k<kend; k++) {
2340:         if ((tcol=smap[a->j[k]])) {
2341:           *mat_j++ = tcol - 1;
2342:           *mat_a++ = a->a[k];
2343:           (*mat_ilen)++;

2345:         }
2346:       }
2347:     }
2348:     /* Free work space */
2349:     ISRestoreIndices(iscol,&icol);
2350:     PetscFree(smap);
2351:     PetscFree(lens);
2352:     /* sort */
2353:     for (i = 0; i < nrows; i++) {
2354:       PetscInt ilen;

2356:       mat_i = c->i[i];
2357:       mat_j = c->j + mat_i;
2358:       mat_a = c->a + mat_i;
2359:       ilen  = c->ilen[i];
2360:       PetscSortIntWithScalarArray(ilen,mat_j,mat_a);
2361:     }
2362:   }
2363:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2364:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

2366:   ISRestoreIndices(isrow,&irow);
2367:   *B   = C;
2368:   return(0);
2369: }

2371: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
2372: {
2374:   Mat            B;

2377:   if (scall == MAT_INITIAL_MATRIX) {
2378:     MatCreate(subComm,&B);
2379:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2380:     MatSetBlockSizesFromMats(B,mat,mat);
2381:     MatSetType(B,MATSEQAIJ);
2382:     MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2383:     *subMat = B;
2384:   } else {
2385:     MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
2386:   }
2387:   return(0);
2388: }

2390: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2391: {
2392:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
2394:   Mat            outA;
2395:   PetscBool      row_identity,col_identity;

2398:   if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

2400:   ISIdentity(row,&row_identity);
2401:   ISIdentity(col,&col_identity);

2403:   outA             = inA;
2404:   outA->factortype = MAT_FACTOR_LU;
2405:   PetscFree(inA->solvertype);
2406:   PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);

2408:   PetscObjectReference((PetscObject)row);
2409:   ISDestroy(&a->row);

2411:   a->row = row;

2413:   PetscObjectReference((PetscObject)col);
2414:   ISDestroy(&a->col);

2416:   a->col = col;

2418:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2419:   ISDestroy(&a->icol);
2420:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2421:   PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);

2423:   if (!a->solve_work) { /* this matrix may have been factored before */
2424:     PetscMalloc1(inA->rmap->n+1,&a->solve_work);
2425:     PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2426:   }

2428:   MatMarkDiagonal_SeqAIJ(inA);
2429:   if (row_identity && col_identity) {
2430:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2431:   } else {
2432:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2433:   }
2434:   return(0);
2435: }

2437: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2438: {
2439:   Mat_SeqAIJ     *a     = (Mat_SeqAIJ*)inA->data;
2440:   PetscScalar    oalpha = alpha;
2442:   PetscBLASInt   one = 1,bnz;

2445:   PetscBLASIntCast(a->nz,&bnz);
2446:   PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one));
2447:   PetscLogFlops(a->nz);
2448:   MatSeqAIJInvalidateDiagonal(inA);
2449:   return(0);
2450: }

2452: PetscErrorCode MatDestroySubMatrices_Private(Mat_SubSppt *submatj)
2453: {
2455:   PetscInt       i;

2458:   if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2459:     PetscFree4(submatj->sbuf1,submatj->ptr,submatj->tmp,submatj->ctr);

2461:     for (i=0; i<submatj->nrqr; ++i) {
2462:       PetscFree(submatj->sbuf2[i]);
2463:     }
2464:     PetscFree3(submatj->sbuf2,submatj->req_size,submatj->req_source1);

2466:     if (submatj->rbuf1) {
2467:       PetscFree(submatj->rbuf1[0]);
2468:       PetscFree(submatj->rbuf1);
2469:     }

2471:     for (i=0; i<submatj->nrqs; ++i) {
2472:       PetscFree(submatj->rbuf3[i]);
2473:     }
2474:     PetscFree3(submatj->req_source2,submatj->rbuf2,submatj->rbuf3);
2475:     PetscFree(submatj->pa);
2476:   }

2478: #if defined(PETSC_USE_CTABLE)
2479:   PetscTableDestroy((PetscTable*)&submatj->rmap);
2480:   if (submatj->cmap_loc) {PetscFree(submatj->cmap_loc);}
2481:   PetscFree(submatj->rmap_loc);
2482: #else
2483:   PetscFree(submatj->rmap);
2484: #endif

2486:   if (!submatj->allcolumns) {
2487: #if defined(PETSC_USE_CTABLE)
2488:     PetscTableDestroy((PetscTable*)&submatj->cmap);
2489: #else
2490:     PetscFree(submatj->cmap);
2491: #endif
2492:   }
2493:   PetscFree(submatj->row2proc);

2495:   PetscFree(submatj);
2496:   return(0);
2497: }

2499: PetscErrorCode MatDestroy_SeqAIJ_Submatrices(Mat C)
2500: {
2502:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data;
2503:   Mat_SubSppt    *submatj = c->submatis1;

2506:   submatj->destroy(C);
2507:   MatDestroySubMatrices_Private(submatj);
2508:   return(0);
2509: }

2511: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2512: {
2514:   PetscInt       i;

2517:   if (scall == MAT_INITIAL_MATRIX) {
2518:     PetscCalloc1(n+1,B);
2519:   }

2521:   for (i=0; i<n; i++) {
2522:     MatCreateSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2523:   }
2524:   return(0);
2525: }

2527: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2528: {
2529:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2531:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
2532:   const PetscInt *idx;
2533:   PetscInt       start,end,*ai,*aj;
2534:   PetscBT        table;

2537:   m  = A->rmap->n;
2538:   ai = a->i;
2539:   aj = a->j;

2541:   if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

2543:   PetscMalloc1(m+1,&nidx);
2544:   PetscBTCreate(m,&table);

2546:   for (i=0; i<is_max; i++) {
2547:     /* Initialize the two local arrays */
2548:     isz  = 0;
2549:     PetscBTMemzero(m,table);

2551:     /* Extract the indices, assume there can be duplicate entries */
2552:     ISGetIndices(is[i],&idx);
2553:     ISGetLocalSize(is[i],&n);

2555:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2556:     for (j=0; j<n; ++j) {
2557:       if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
2558:     }
2559:     ISRestoreIndices(is[i],&idx);
2560:     ISDestroy(&is[i]);

2562:     k = 0;
2563:     for (j=0; j<ov; j++) { /* for each overlap */
2564:       n = isz;
2565:       for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
2566:         row   = nidx[k];
2567:         start = ai[row];
2568:         end   = ai[row+1];
2569:         for (l = start; l<end; l++) {
2570:           val = aj[l];
2571:           if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
2572:         }
2573:       }
2574:     }
2575:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
2576:   }
2577:   PetscBTDestroy(&table);
2578:   PetscFree(nidx);
2579:   return(0);
2580: }

2582: /* -------------------------------------------------------------- */
2583: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2584: {
2585:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2587:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2588:   const PetscInt *row,*col;
2589:   PetscInt       *cnew,j,*lens;
2590:   IS             icolp,irowp;
2591:   PetscInt       *cwork = NULL;
2592:   PetscScalar    *vwork = NULL;

2595:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2596:   ISGetIndices(irowp,&row);
2597:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2598:   ISGetIndices(icolp,&col);

2600:   /* determine lengths of permuted rows */
2601:   PetscMalloc1(m+1,&lens);
2602:   for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
2603:   MatCreate(PetscObjectComm((PetscObject)A),B);
2604:   MatSetSizes(*B,m,n,m,n);
2605:   MatSetBlockSizesFromMats(*B,A,A);
2606:   MatSetType(*B,((PetscObject)A)->type_name);
2607:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2608:   PetscFree(lens);

2610:   PetscMalloc1(n,&cnew);
2611:   for (i=0; i<m; i++) {
2612:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2613:     for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
2614:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2615:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2616:   }
2617:   PetscFree(cnew);

2619:   (*B)->assembled = PETSC_FALSE;

2621:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2622:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2623:   ISRestoreIndices(irowp,&row);
2624:   ISRestoreIndices(icolp,&col);
2625:   ISDestroy(&irowp);
2626:   ISDestroy(&icolp);
2627:   return(0);
2628: }

2630: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
2631: {

2635:   /* If the two matrices have the same copy implementation, use fast copy. */
2636:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2637:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2638:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

2640:     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");
2641:     PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
2642:   } else {
2643:     MatCopy_Basic(A,B,str);
2644:   }
2645:   return(0);
2646: }

2648: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
2649: {

2653:    MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2654:   return(0);
2655: }

2657: PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2658: {
2659:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

2662:   *array = a->a;
2663:   return(0);
2664: }

2666: PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2667: {
2669:   return(0);
2670: }

2672: /*
2673:    Computes the number of nonzeros per row needed for preallocation when X and Y
2674:    have different nonzero structure.
2675: */
2676: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
2677: {
2678:   PetscInt       i,j,k,nzx,nzy;

2681:   /* Set the number of nonzeros in the new matrix */
2682:   for (i=0; i<m; i++) {
2683:     const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
2684:     nzx = xi[i+1] - xi[i];
2685:     nzy = yi[i+1] - yi[i];
2686:     nnz[i] = 0;
2687:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
2688:       for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
2689:       if (k<nzy && yjj[k]==xjj[j]) k++;             /* Skip duplicate */
2690:       nnz[i]++;
2691:     }
2692:     for (; k<nzy; k++) nnz[i]++;
2693:   }
2694:   return(0);
2695: }

2697: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
2698: {
2699:   PetscInt       m = Y->rmap->N;
2700:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data;
2701:   Mat_SeqAIJ     *y = (Mat_SeqAIJ*)Y->data;

2705:   /* Set the number of nonzeros in the new matrix */
2706:   MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
2707:   return(0);
2708: }

2710: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2711: {
2713:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;
2714:   PetscBLASInt   one=1,bnz;

2717:   PetscBLASIntCast(x->nz,&bnz);
2718:   if (str == SAME_NONZERO_PATTERN) {
2719:     PetscScalar alpha = a;
2720:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2721:     MatSeqAIJInvalidateDiagonal(Y);
2722:     PetscObjectStateIncrease((PetscObject)Y);
2723:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2724:     MatAXPY_Basic(Y,a,X,str);
2725:   } else {
2726:     Mat      B;
2727:     PetscInt *nnz;
2728:     PetscMalloc1(Y->rmap->N,&nnz);
2729:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
2730:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2731:     MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2732:     MatSetBlockSizesFromMats(B,Y,Y);
2733:     MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
2734:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
2735:     MatSeqAIJSetPreallocation(B,0,nnz);
2736:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2737:     MatHeaderReplace(Y,&B);
2738:     PetscFree(nnz);
2739:   }
2740:   return(0);
2741: }

2743: PetscErrorCode  MatConjugate_SeqAIJ(Mat mat)
2744: {
2745: #if defined(PETSC_USE_COMPLEX)
2746:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ*)mat->data;
2747:   PetscInt    i,nz;
2748:   PetscScalar *a;

2751:   nz = aij->nz;
2752:   a  = aij->a;
2753:   for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
2754: #else
2756: #endif
2757:   return(0);
2758: }

2760: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2761: {
2762:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2764:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2765:   PetscReal      atmp;
2766:   PetscScalar    *x;
2767:   MatScalar      *aa;

2770:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2771:   aa = a->a;
2772:   ai = a->i;
2773:   aj = a->j;

2775:   VecSet(v,0.0);
2776:   VecGetArray(v,&x);
2777:   VecGetLocalSize(v,&n);
2778:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2779:   for (i=0; i<m; i++) {
2780:     ncols = ai[1] - ai[0]; ai++;
2781:     x[i]  = 0.0;
2782:     for (j=0; j<ncols; j++) {
2783:       atmp = PetscAbsScalar(*aa);
2784:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2785:       aa++; aj++;
2786:     }
2787:   }
2788:   VecRestoreArray(v,&x);
2789:   return(0);
2790: }

2792: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2793: {
2794:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2796:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2797:   PetscScalar    *x;
2798:   MatScalar      *aa;

2801:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2802:   aa = a->a;
2803:   ai = a->i;
2804:   aj = a->j;

2806:   VecSet(v,0.0);
2807:   VecGetArray(v,&x);
2808:   VecGetLocalSize(v,&n);
2809:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2810:   for (i=0; i<m; i++) {
2811:     ncols = ai[1] - ai[0]; ai++;
2812:     if (ncols == A->cmap->n) { /* row is dense */
2813:       x[i] = *aa; if (idx) idx[i] = 0;
2814:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
2815:       x[i] = 0.0;
2816:       if (idx) {
2817:         idx[i] = 0; /* in case ncols is zero */
2818:         for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
2819:           if (aj[j] > j) {
2820:             idx[i] = j;
2821:             break;
2822:           }
2823:         }
2824:       }
2825:     }
2826:     for (j=0; j<ncols; j++) {
2827:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2828:       aa++; aj++;
2829:     }
2830:   }
2831:   VecRestoreArray(v,&x);
2832:   return(0);
2833: }

2835: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2836: {
2837:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2839:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2840:   PetscReal      atmp;
2841:   PetscScalar    *x;
2842:   MatScalar      *aa;

2845:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2846:   aa = a->a;
2847:   ai = a->i;
2848:   aj = a->j;

2850:   VecSet(v,0.0);
2851:   VecGetArray(v,&x);
2852:   VecGetLocalSize(v,&n);
2853:   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);
2854:   for (i=0; i<m; i++) {
2855:     ncols = ai[1] - ai[0]; ai++;
2856:     if (ncols) {
2857:       /* Get first nonzero */
2858:       for (j = 0; j < ncols; j++) {
2859:         atmp = PetscAbsScalar(aa[j]);
2860:         if (atmp > 1.0e-12) {
2861:           x[i] = atmp;
2862:           if (idx) idx[i] = aj[j];
2863:           break;
2864:         }
2865:       }
2866:       if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;}
2867:     } else {
2868:       x[i] = 0.0; if (idx) idx[i] = 0;
2869:     }
2870:     for (j = 0; j < ncols; j++) {
2871:       atmp = PetscAbsScalar(*aa);
2872:       if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2873:       aa++; aj++;
2874:     }
2875:   }
2876:   VecRestoreArray(v,&x);
2877:   return(0);
2878: }

2880: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2881: {
2882:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
2883:   PetscErrorCode  ierr;
2884:   PetscInt        i,j,m = A->rmap->n,ncols,n;
2885:   const PetscInt  *ai,*aj;
2886:   PetscScalar     *x;
2887:   const MatScalar *aa;

2890:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2891:   aa = a->a;
2892:   ai = a->i;
2893:   aj = a->j;

2895:   VecSet(v,0.0);
2896:   VecGetArray(v,&x);
2897:   VecGetLocalSize(v,&n);
2898:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2899:   for (i=0; i<m; i++) {
2900:     ncols = ai[1] - ai[0]; ai++;
2901:     if (ncols == A->cmap->n) { /* row is dense */
2902:       x[i] = *aa; if (idx) idx[i] = 0;
2903:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
2904:       x[i] = 0.0;
2905:       if (idx) {   /* find first implicit 0.0 in the row */
2906:         idx[i] = 0; /* in case ncols is zero */
2907:         for (j=0; j<ncols; j++) {
2908:           if (aj[j] > j) {
2909:             idx[i] = j;
2910:             break;
2911:           }
2912:         }
2913:       }
2914:     }
2915:     for (j=0; j<ncols; j++) {
2916:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2917:       aa++; aj++;
2918:     }
2919:   }
2920:   VecRestoreArray(v,&x);
2921:   return(0);
2922: }

2924:  #include <petscblaslapack.h>
2925:  #include <petsc/private/kernels/blockinvert.h>

2927: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
2928: {
2929:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
2931:   PetscInt       i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
2932:   MatScalar      *diag,work[25],*v_work;
2933:   PetscReal      shift = 0.0;
2934:   PetscBool      allowzeropivot,zeropivotdetected=PETSC_FALSE;

2937:   allowzeropivot = PetscNot(A->erroriffailure);
2938:   if (a->ibdiagvalid) {
2939:     if (values) *values = a->ibdiag;
2940:     return(0);
2941:   }
2942:   MatMarkDiagonal_SeqAIJ(A);
2943:   if (!a->ibdiag) {
2944:     PetscMalloc1(bs2*mbs,&a->ibdiag);
2945:     PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
2946:   }
2947:   diag = a->ibdiag;
2948:   if (values) *values = a->ibdiag;
2949:   /* factor and invert each block */
2950:   switch (bs) {
2951:   case 1:
2952:     for (i=0; i<mbs; i++) {
2953:       MatGetValues(A,1,&i,1,&i,diag+i);
2954:       if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
2955:         if (allowzeropivot) {
2956:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
2957:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
2958:           A->factorerror_zeropivot_row   = i;
2959:           PetscInfo3(A,"Zero pivot, row %D pivot %g tolerance %g\n",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
2960:         } else SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot, row %D pivot %g tolerance %g",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
2961:       }
2962:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
2963:     }
2964:     break;
2965:   case 2:
2966:     for (i=0; i<mbs; i++) {
2967:       ij[0] = 2*i; ij[1] = 2*i + 1;
2968:       MatGetValues(A,2,ij,2,ij,diag);
2969:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
2970:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
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,allowzeropivot,&zeropivotdetected);
2980:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
2981:       PetscKernel_A_gets_transpose_A_3(diag);
2982:       diag += 9;
2983:     }
2984:     break;
2985:   case 4:
2986:     for (i=0; i<mbs; i++) {
2987:       ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
2988:       MatGetValues(A,4,ij,4,ij,diag);
2989:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
2990:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
2991:       PetscKernel_A_gets_transpose_A_4(diag);
2992:       diag += 16;
2993:     }
2994:     break;
2995:   case 5:
2996:     for (i=0; i<mbs; i++) {
2997:       ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
2998:       MatGetValues(A,5,ij,5,ij,diag);
2999:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
3000:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3001:       PetscKernel_A_gets_transpose_A_5(diag);
3002:       diag += 25;
3003:     }
3004:     break;
3005:   case 6:
3006:     for (i=0; i<mbs; i++) {
3007:       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;
3008:       MatGetValues(A,6,ij,6,ij,diag);
3009:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
3010:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3011:       PetscKernel_A_gets_transpose_A_6(diag);
3012:       diag += 36;
3013:     }
3014:     break;
3015:   case 7:
3016:     for (i=0; i<mbs; i++) {
3017:       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;
3018:       MatGetValues(A,7,ij,7,ij,diag);
3019:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
3020:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3021:       PetscKernel_A_gets_transpose_A_7(diag);
3022:       diag += 49;
3023:     }
3024:     break;
3025:   default:
3026:     PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
3027:     for (i=0; i<mbs; i++) {
3028:       for (j=0; j<bs; j++) {
3029:         IJ[j] = bs*i + j;
3030:       }
3031:       MatGetValues(A,bs,IJ,bs,IJ,diag);
3032:       PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
3033:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3034:       PetscKernel_A_gets_transpose_A_N(diag,bs);
3035:       diag += bs2;
3036:     }
3037:     PetscFree3(v_work,v_pivots,IJ);
3038:   }
3039:   a->ibdiagvalid = PETSC_TRUE;
3040:   return(0);
3041: }

3043: static PetscErrorCode  MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
3044: {
3046:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
3047:   PetscScalar    a;
3048:   PetscInt       m,n,i,j,col;

3051:   if (!x->assembled) {
3052:     MatGetSize(x,&m,&n);
3053:     for (i=0; i<m; i++) {
3054:       for (j=0; j<aij->imax[i]; j++) {
3055:         PetscRandomGetValue(rctx,&a);
3056:         col  = (PetscInt)(n*PetscRealPart(a));
3057:         MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3058:       }
3059:     }
3060:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not yet coded");
3061:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3062:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3063:   return(0);
3064: }

3066: PetscErrorCode MatShift_SeqAIJ(Mat Y,PetscScalar a)
3067: {
3069:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)Y->data;

3072:   if (!Y->preallocated || !aij->nz) {
3073:     MatSeqAIJSetPreallocation(Y,1,NULL);
3074:   }
3075:   MatShift_Basic(Y,a);
3076:   return(0);
3077: }

3079: /* -------------------------------------------------------------------*/
3080: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
3081:                                         MatGetRow_SeqAIJ,
3082:                                         MatRestoreRow_SeqAIJ,
3083:                                         MatMult_SeqAIJ,
3084:                                 /*  4*/ MatMultAdd_SeqAIJ,
3085:                                         MatMultTranspose_SeqAIJ,
3086:                                         MatMultTransposeAdd_SeqAIJ,
3087:                                         0,
3088:                                         0,
3089:                                         0,
3090:                                 /* 10*/ 0,
3091:                                         MatLUFactor_SeqAIJ,
3092:                                         0,
3093:                                         MatSOR_SeqAIJ,
3094:                                         MatTranspose_SeqAIJ,
3095:                                 /*1 5*/ MatGetInfo_SeqAIJ,
3096:                                         MatEqual_SeqAIJ,
3097:                                         MatGetDiagonal_SeqAIJ,
3098:                                         MatDiagonalScale_SeqAIJ,
3099:                                         MatNorm_SeqAIJ,
3100:                                 /* 20*/ 0,
3101:                                         MatAssemblyEnd_SeqAIJ,
3102:                                         MatSetOption_SeqAIJ,
3103:                                         MatZeroEntries_SeqAIJ,
3104:                                 /* 24*/ MatZeroRows_SeqAIJ,
3105:                                         0,
3106:                                         0,
3107:                                         0,
3108:                                         0,
3109:                                 /* 29*/ MatSetUp_SeqAIJ,
3110:                                         0,
3111:                                         0,
3112:                                         0,
3113:                                         0,
3114:                                 /* 34*/ MatDuplicate_SeqAIJ,
3115:                                         0,
3116:                                         0,
3117:                                         MatILUFactor_SeqAIJ,
3118:                                         0,
3119:                                 /* 39*/ MatAXPY_SeqAIJ,
3120:                                         MatCreateSubMatrices_SeqAIJ,
3121:                                         MatIncreaseOverlap_SeqAIJ,
3122:                                         MatGetValues_SeqAIJ,
3123:                                         MatCopy_SeqAIJ,
3124:                                 /* 44*/ MatGetRowMax_SeqAIJ,
3125:                                         MatScale_SeqAIJ,
3126:                                         MatShift_SeqAIJ,
3127:                                         MatDiagonalSet_SeqAIJ,
3128:                                         MatZeroRowsColumns_SeqAIJ,
3129:                                 /* 49*/ MatSetRandom_SeqAIJ,
3130:                                         MatGetRowIJ_SeqAIJ,
3131:                                         MatRestoreRowIJ_SeqAIJ,
3132:                                         MatGetColumnIJ_SeqAIJ,
3133:                                         MatRestoreColumnIJ_SeqAIJ,
3134:                                 /* 54*/ MatFDColoringCreate_SeqXAIJ,
3135:                                         0,
3136:                                         0,
3137:                                         MatPermute_SeqAIJ,
3138:                                         0,
3139:                                 /* 59*/ 0,
3140:                                         MatDestroy_SeqAIJ,
3141:                                         MatView_SeqAIJ,
3142:                                         0,
3143:                                         MatMatMatMult_SeqAIJ_SeqAIJ_SeqAIJ,
3144:                                 /* 64*/ MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ,
3145:                                         MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3146:                                         0,
3147:                                         0,
3148:                                         0,
3149:                                 /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3150:                                         MatGetRowMinAbs_SeqAIJ,
3151:                                         0,
3152:                                         0,
3153:                                         0,
3154:                                 /* 74*/ 0,
3155:                                         MatFDColoringApply_AIJ,
3156:                                         0,
3157:                                         0,
3158:                                         0,
3159:                                 /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3160:                                         0,
3161:                                         0,
3162:                                         0,
3163:                                         MatLoad_SeqAIJ,
3164:                                 /* 84*/ MatIsSymmetric_SeqAIJ,
3165:                                         MatIsHermitian_SeqAIJ,
3166:                                         0,
3167:                                         0,
3168:                                         0,
3169:                                 /* 89*/ MatMatMult_SeqAIJ_SeqAIJ,
3170:                                         MatMatMultSymbolic_SeqAIJ_SeqAIJ,
3171:                                         MatMatMultNumeric_SeqAIJ_SeqAIJ,
3172:                                         MatPtAP_SeqAIJ_SeqAIJ,
3173:                                         MatPtAPSymbolic_SeqAIJ_SeqAIJ_DenseAxpy,
3174:                                 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ,
3175:                                         MatMatTransposeMult_SeqAIJ_SeqAIJ,
3176:                                         MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ,
3177:                                         MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3178:                                         0,
3179:                                 /* 99*/ 0,
3180:                                         0,
3181:                                         0,
3182:                                         MatConjugate_SeqAIJ,
3183:                                         0,
3184:                                 /*104*/ MatSetValuesRow_SeqAIJ,
3185:                                         MatRealPart_SeqAIJ,
3186:                                         MatImaginaryPart_SeqAIJ,
3187:                                         0,
3188:                                         0,
3189:                                 /*109*/ MatMatSolve_SeqAIJ,
3190:                                         0,
3191:                                         MatGetRowMin_SeqAIJ,
3192:                                         0,
3193:                                         MatMissingDiagonal_SeqAIJ,
3194:                                 /*114*/ 0,
3195:                                         0,
3196:                                         0,
3197:                                         0,
3198:                                         0,
3199:                                 /*119*/ 0,
3200:                                         0,
3201:                                         0,
3202:                                         0,
3203:                                         MatGetMultiProcBlock_SeqAIJ,
3204:                                 /*124*/ MatFindNonzeroRows_SeqAIJ,
3205:                                         MatGetColumnNorms_SeqAIJ,
3206:                                         MatInvertBlockDiagonal_SeqAIJ,
3207:                                         0,
3208:                                         0,
3209:                                 /*129*/ 0,
3210:                                         MatTransposeMatMult_SeqAIJ_SeqAIJ,
3211:                                         MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ,
3212:                                         MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3213:                                         MatTransposeColoringCreate_SeqAIJ,
3214:                                 /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3215:                                         MatTransColoringApplyDenToSp_SeqAIJ,
3216:                                         MatRARt_SeqAIJ_SeqAIJ,
3217:                                         MatRARtSymbolic_SeqAIJ_SeqAIJ,
3218:                                         MatRARtNumeric_SeqAIJ_SeqAIJ,
3219:                                  /*139*/0,
3220:                                         0,
3221:                                         0,
3222:                                         MatFDColoringSetUp_SeqXAIJ,
3223:                                         MatFindOffBlockDiagonalEntries_SeqAIJ,
3224:                                  /*144*/MatCreateMPIMatConcatenateSeqMat_SeqAIJ
3225: };

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: }

3245: /*@
3246:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3247:        in the matrix.

3249:   Input Parameters:
3250: +  mat - the SeqAIJ matrix
3251: -  indices - the column indices

3253:   Level: advanced

3255:   Notes:
3256:     This can be called if you have precomputed the nonzero structure of the
3257:   matrix and want to provide it to the matrix object to improve the performance
3258:   of the MatSetValues() operation.

3260:     You MUST have set the correct numbers of nonzeros per row in the call to
3261:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

3263:     MUST be called before any calls to MatSetValues();

3265:     The indices should start with zero, not one.

3267: @*/
3268: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3269: {

3275:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
3276:   return(0);
3277: }

3279: /* ----------------------------------------------------------------------------------------*/

3281: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
3282: {
3283:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3285:   size_t         nz = aij->i[mat->rmap->n];

3288:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

3290:   /* allocate space for values if not already there */
3291:   if (!aij->saved_values) {
3292:     PetscMalloc1(nz+1,&aij->saved_values);
3293:     PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
3294:   }

3296:   /* copy values over */
3297:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
3298:   return(0);
3299: }

3301: /*@
3302:     MatStoreValues - Stashes a copy of the matrix values; this allows, for
3303:        example, reuse of the linear part of a Jacobian, while recomputing the
3304:        nonlinear portion.

3306:    Collect on Mat

3308:   Input Parameters:
3309: .  mat - the matrix (currently only AIJ matrices support this option)

3311:   Level: advanced

3313:   Common Usage, with SNESSolve():
3314: $    Create Jacobian matrix
3315: $    Set linear terms into matrix
3316: $    Apply boundary conditions to matrix, at this time matrix must have
3317: $      final nonzero structure (i.e. setting the nonlinear terms and applying
3318: $      boundary conditions again will not change the nonzero structure
3319: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3320: $    MatStoreValues(mat);
3321: $    Call SNESSetJacobian() with matrix
3322: $    In your Jacobian routine
3323: $      MatRetrieveValues(mat);
3324: $      Set nonlinear terms in matrix

3326:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3327: $    // build linear portion of Jacobian
3328: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3329: $    MatStoreValues(mat);
3330: $    loop over nonlinear iterations
3331: $       MatRetrieveValues(mat);
3332: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3333: $       // call MatAssemblyBegin/End() on matrix
3334: $       Solve linear system with Jacobian
3335: $    endloop

3337:   Notes:
3338:     Matrix must already be assemblied before calling this routine
3339:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
3340:     calling this routine.

3342:     When this is called multiple times it overwrites the previous set of stored values
3343:     and does not allocated additional space.

3345: .seealso: MatRetrieveValues()

3347: @*/
3348: PetscErrorCode  MatStoreValues(Mat mat)
3349: {

3354:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3355:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3356:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3357:   return(0);
3358: }

3360: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
3361: {
3362:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3364:   PetscInt       nz = aij->i[mat->rmap->n];

3367:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3368:   if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3369:   /* copy values over */
3370:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
3371:   return(0);
3372: }

3374: /*@
3375:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3376:        example, reuse of the linear part of a Jacobian, while recomputing the
3377:        nonlinear portion.

3379:    Collect on Mat

3381:   Input Parameters:
3382: .  mat - the matrix (currently only AIJ matrices support this option)

3384:   Level: advanced

3386: .seealso: MatStoreValues()

3388: @*/
3389: PetscErrorCode  MatRetrieveValues(Mat mat)
3390: {

3395:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3396:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3397:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3398:   return(0);
3399: }


3402: /* --------------------------------------------------------------------------------*/
3403: /*@C
3404:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3405:    (the default parallel PETSc format).  For good matrix assembly performance
3406:    the user should preallocate the matrix storage by setting the parameter nz
3407:    (or the array nnz).  By setting these parameters accurately, performance
3408:    during matrix assembly can be increased by more than a factor of 50.

3410:    Collective on MPI_Comm

3412:    Input Parameters:
3413: +  comm - MPI communicator, set to PETSC_COMM_SELF
3414: .  m - number of rows
3415: .  n - number of columns
3416: .  nz - number of nonzeros per row (same for all rows)
3417: -  nnz - array containing the number of nonzeros in the various rows
3418:          (possibly different for each row) or NULL

3420:    Output Parameter:
3421: .  A - the matrix

3423:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3424:    MatXXXXSetPreallocation() paradgm instead of this routine directly.
3425:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

3427:    Notes:
3428:    If nnz is given then nz is ignored

3430:    The AIJ format (also called the Yale sparse matrix format or
3431:    compressed row storage), is fully compatible with standard Fortran 77
3432:    storage.  That is, the stored row and column indices can begin at
3433:    either one (as in Fortran) or zero.  See the users' manual for details.

3435:    Specify the preallocated storage with either nz or nnz (not both).
3436:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3437:    allocation.  For large problems you MUST preallocate memory or you
3438:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3440:    By default, this format uses inodes (identical nodes) when possible, to
3441:    improve numerical efficiency of matrix-vector products and solves. We
3442:    search for consecutive rows with the same nonzero structure, thereby
3443:    reusing matrix information to achieve increased efficiency.

3445:    Options Database Keys:
3446: +  -mat_no_inode  - Do not use inodes
3447: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3449:    Level: intermediate

3451: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

3453: @*/
3454: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3455: {

3459:   MatCreate(comm,A);
3460:   MatSetSizes(*A,m,n,m,n);
3461:   MatSetType(*A,MATSEQAIJ);
3462:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
3463:   return(0);
3464: }

3466: /*@C
3467:    MatSeqAIJSetPreallocation - For good matrix assembly performance
3468:    the user should preallocate the matrix storage by setting the parameter nz
3469:    (or the array nnz).  By setting these parameters accurately, performance
3470:    during matrix assembly can be increased by more than a factor of 50.

3472:    Collective on MPI_Comm

3474:    Input Parameters:
3475: +  B - The matrix
3476: .  nz - number of nonzeros per row (same for all rows)
3477: -  nnz - array containing the number of nonzeros in the various rows
3478:          (possibly different for each row) or NULL

3480:    Notes:
3481:      If nnz is given then nz is ignored

3483:     The AIJ format (also called the Yale sparse matrix format or
3484:    compressed row storage), is fully compatible with standard Fortran 77
3485:    storage.  That is, the stored row and column indices can begin at
3486:    either one (as in Fortran) or zero.  See the users' manual for details.

3488:    Specify the preallocated storage with either nz or nnz (not both).
3489:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3490:    allocation.  For large problems you MUST preallocate memory or you
3491:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3493:    You can call MatGetInfo() to get information on how effective the preallocation was;
3494:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3495:    You can also run with the option -info and look for messages with the string
3496:    malloc in them to see if additional memory allocation was needed.

3498:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
3499:    entries or columns indices

3501:    By default, this format uses inodes (identical nodes) when possible, to
3502:    improve numerical efficiency of matrix-vector products and solves. We
3503:    search for consecutive rows with the same nonzero structure, thereby
3504:    reusing matrix information to achieve increased efficiency.

3506:    Options Database Keys:
3507: +  -mat_no_inode  - Do not use inodes
3508: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3509: -  -mat_aij_oneindex - Internally use indexing starting at 1
3510:         rather than 0.  Note that when calling MatSetValues(),
3511:         the user still MUST index entries starting at 0!

3513:    Level: intermediate

3515: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()

3517: @*/
3518: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
3519: {

3525:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
3526:   return(0);
3527: }

3529: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
3530: {
3531:   Mat_SeqAIJ     *b;
3532:   PetscBool      skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
3534:   PetscInt       i;

3537:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3538:   if (nz == MAT_SKIP_ALLOCATION) {
3539:     skipallocation = PETSC_TRUE;
3540:     nz             = 0;
3541:   }
3542:   PetscLayoutSetUp(B->rmap);
3543:   PetscLayoutSetUp(B->cmap);

3545:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3546:   if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
3547:   if (nnz) {
3548:     for (i=0; i<B->rmap->n; i++) {
3549:       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]);
3550:       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);
3551:     }
3552:   }

3554:   B->preallocated = PETSC_TRUE;

3556:   b = (Mat_SeqAIJ*)B->data;

3558:   if (!skipallocation) {
3559:     if (!b->imax) {
3560:       PetscMalloc2(B->rmap->n,&b->imax,B->rmap->n,&b->ilen);
3561:       PetscLogObjectMemory((PetscObject)B,2*B->rmap->n*sizeof(PetscInt));
3562:     }
3563:     if (!nnz) {
3564:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3565:       else if (nz < 0) nz = 1;
3566:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
3567:       nz = nz*B->rmap->n;
3568:     } else {
3569:       nz = 0;
3570:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
3571:     }
3572:     /* b->ilen will count nonzeros in each row so far. */
3573:     for (i=0; i<B->rmap->n; i++) b->ilen[i] = 0;

3575:     /* allocate the matrix space */
3576:     /* FIXME: should B's old memory be unlogged? */
3577:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3578:     if (B->structure_only) {
3579:       PetscMalloc1(nz,&b->j);
3580:       PetscMalloc1(B->rmap->n+1,&b->i);
3581:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
3582:     } else {
3583:       PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
3584:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
3585:     }
3586:     b->i[0] = 0;
3587:     for (i=1; i<B->rmap->n+1; i++) {
3588:       b->i[i] = b->i[i-1] + b->imax[i-1];
3589:     }
3590:     if (B->structure_only) {
3591:       b->singlemalloc = PETSC_FALSE;
3592:       b->free_a       = PETSC_FALSE;
3593:     } else {
3594:       b->singlemalloc = PETSC_TRUE;
3595:       b->free_a       = PETSC_TRUE;
3596:     }
3597:     b->free_ij      = PETSC_TRUE;
3598:   } else {
3599:     b->free_a  = PETSC_FALSE;
3600:     b->free_ij = PETSC_FALSE;
3601:   }

3603:   b->nz               = 0;
3604:   b->maxnz            = nz;
3605:   B->info.nz_unneeded = (double)b->maxnz;
3606:   if (realalloc) {
3607:     MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
3608:   }
3609:   B->was_assembled = PETSC_FALSE;
3610:   B->assembled     = PETSC_FALSE;
3611:   return(0);
3612: }

3614: /*@
3615:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.

3617:    Input Parameters:
3618: +  B - the matrix
3619: .  i - the indices into j for the start of each row (starts with zero)
3620: .  j - the column indices for each row (starts with zero) these must be sorted for each row
3621: -  v - optional values in the matrix

3623:    Level: developer

3625:    The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

3627: .keywords: matrix, aij, compressed row, sparse, sequential

3629: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ
3630: @*/
3631: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
3632: {

3638:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
3639:   return(0);
3640: }

3642: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3643: {
3644:   PetscInt       i;
3645:   PetscInt       m,n;
3646:   PetscInt       nz;
3647:   PetscInt       *nnz, nz_max = 0;
3648:   PetscScalar    *values;

3652:   if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);

3654:   PetscLayoutSetUp(B->rmap);
3655:   PetscLayoutSetUp(B->cmap);

3657:   MatGetSize(B, &m, &n);
3658:   PetscMalloc1(m+1, &nnz);
3659:   for (i = 0; i < m; i++) {
3660:     nz     = Ii[i+1]- Ii[i];
3661:     nz_max = PetscMax(nz_max, nz);
3662:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
3663:     nnz[i] = nz;
3664:   }
3665:   MatSeqAIJSetPreallocation(B, 0, nnz);
3666:   PetscFree(nnz);

3668:   if (v) {
3669:     values = (PetscScalar*) v;
3670:   } else {
3671:     PetscCalloc1(nz_max, &values);
3672:   }

3674:   for (i = 0; i < m; i++) {
3675:     nz   = Ii[i+1] - Ii[i];
3676:     MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
3677:   }

3679:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3680:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

3682:   if (!v) {
3683:     PetscFree(values);
3684:   }
3685:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3686:   return(0);
3687: }

3689:  #include <../src/mat/impls/dense/seq/dense.h>
3690:  #include <petsc/private/kernels/petscaxpy.h>

3692: /*
3693:     Computes (B'*A')' since computing B*A directly is untenable

3695:                n                       p                          p
3696:         (              )       (              )         (                  )
3697:       m (      A       )  *  n (       B      )   =   m (         C        )
3698:         (              )       (              )         (                  )

3700: */
3701: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
3702: {
3703:   PetscErrorCode    ierr;
3704:   Mat_SeqDense      *sub_a = (Mat_SeqDense*)A->data;
3705:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ*)B->data;
3706:   Mat_SeqDense      *sub_c = (Mat_SeqDense*)C->data;
3707:   PetscInt          i,n,m,q,p;
3708:   const PetscInt    *ii,*idx;
3709:   const PetscScalar *b,*a,*a_q;
3710:   PetscScalar       *c,*c_q;

3713:   m    = A->rmap->n;
3714:   n    = A->cmap->n;
3715:   p    = B->cmap->n;
3716:   a    = sub_a->v;
3717:   b    = sub_b->a;
3718:   c    = sub_c->v;
3719:   PetscMemzero(c,m*p*sizeof(PetscScalar));

3721:   ii  = sub_b->i;
3722:   idx = sub_b->j;
3723:   for (i=0; i<n; i++) {
3724:     q = ii[i+1] - ii[i];
3725:     while (q-->0) {
3726:       c_q = c + m*(*idx);
3727:       a_q = a + m*i;
3728:       PetscKernelAXPY(c_q,*b,a_q,m);
3729:       idx++;
3730:       b++;
3731:     }
3732:   }
3733:   return(0);
3734: }

3736: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
3737: {
3739:   PetscInt       m=A->rmap->n,n=B->cmap->n;
3740:   Mat            Cmat;

3743:   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);
3744:   MatCreate(PetscObjectComm((PetscObject)A),&Cmat);
3745:   MatSetSizes(Cmat,m,n,m,n);
3746:   MatSetBlockSizesFromMats(Cmat,A,B);
3747:   MatSetType(Cmat,MATSEQDENSE);
3748:   MatSeqDenseSetPreallocation(Cmat,NULL);

3750:   Cmat->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;

3752:   *C = Cmat;
3753:   return(0);
3754: }

3756: /* ----------------------------------------------------------------*/
3757: PETSC_INTERN PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
3758: {

3762:   if (scall == MAT_INITIAL_MATRIX) {
3763:     PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
3764:     MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);
3765:     PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
3766:   }
3767:   PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
3768:   MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);
3769:   PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
3770:   return(0);
3771: }


3774: /*MC
3775:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
3776:    based on compressed sparse row format.

3778:    Options Database Keys:
3779: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

3781:   Level: beginner

3783: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
3784: M*/

3786: /*MC
3787:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

3789:    This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
3790:    and MATMPIAIJ otherwise.  As a result, for single process communicators,
3791:   MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
3792:   for communicators controlling multiple processes.  It is recommended that you call both of
3793:   the above preallocation routines for simplicity.

3795:    Options Database Keys:
3796: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()

3798:   Developer Notes: Subclasses include MATAIJCUSP, MATAIJPERM, MATAIJCRL, and also automatically switches over to use inodes when
3799:    enough exist.

3801:   Level: beginner

3803: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
3804: M*/

3806: /*MC
3807:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

3809:    This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
3810:    and MATMPIAIJCRL otherwise.  As a result, for single process communicators,
3811:    MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
3812:   for communicators controlling multiple processes.  It is recommended that you call both of
3813:   the above preallocation routines for simplicity.

3815:    Options Database Keys:
3816: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()

3818:   Level: beginner

3820: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
3821: M*/

3823: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
3824: #if defined(PETSC_HAVE_ELEMENTAL)
3825: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
3826: #endif
3827: #if defined(PETSC_HAVE_HYPRE)
3828: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A,MatType,MatReuse,Mat*);
3829: PETSC_INTERN PetscErrorCode MatMatMatMult_Transpose_AIJ_AIJ(Mat,Mat,Mat,MatReuse,PetscReal,Mat*);
3830: #endif
3831: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat,MatType,MatReuse,Mat*);

3833: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3834: PETSC_EXTERN PetscErrorCode  MatlabEnginePut_SeqAIJ(PetscObject,void*);
3835: PETSC_EXTERN PetscErrorCode  MatlabEngineGet_SeqAIJ(PetscObject,void*);
3836: #endif


3839: /*@C
3840:    MatSeqAIJGetArray - gives access to the array where the data for a MATSEQAIJ matrix is stored

3842:    Not Collective

3844:    Input Parameter:
3845: .  mat - a MATSEQAIJ matrix

3847:    Output Parameter:
3848: .   array - pointer to the data

3850:    Level: intermediate

3852: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
3853: @*/
3854: PetscErrorCode  MatSeqAIJGetArray(Mat A,PetscScalar **array)
3855: {

3859:   PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
3860:   return(0);
3861: }

3863: /*@C
3864:    MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

3866:    Not Collective

3868:    Input Parameter:
3869: .  mat - a MATSEQAIJ matrix

3871:    Output Parameter:
3872: .   nz - the maximum number of nonzeros in any row

3874:    Level: intermediate

3876: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
3877: @*/
3878: PetscErrorCode  MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
3879: {
3880:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

3883:   *nz = aij->rmax;
3884:   return(0);
3885: }

3887: /*@C
3888:    MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()

3890:    Not Collective

3892:    Input Parameters:
3893: .  mat - a MATSEQAIJ matrix
3894: .  array - pointer to the data

3896:    Level: intermediate

3898: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
3899: @*/
3900: PetscErrorCode  MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
3901: {

3905:   PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
3906:   return(0);
3907: }

3909: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
3910: {
3911:   Mat_SeqAIJ     *b;
3913:   PetscMPIInt    size;

3916:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
3917:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

3919:   PetscNewLog(B,&b);

3921:   B->data = (void*)b;

3923:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));

3925:   b->row                = 0;
3926:   b->col                = 0;
3927:   b->icol               = 0;
3928:   b->reallocs           = 0;
3929:   b->ignorezeroentries  = PETSC_FALSE;
3930:   b->roworiented        = PETSC_TRUE;
3931:   b->nonew              = 0;
3932:   b->diag               = 0;
3933:   b->solve_work         = 0;
3934:   B->spptr              = 0;
3935:   b->saved_values       = 0;
3936:   b->idiag              = 0;
3937:   b->mdiag              = 0;
3938:   b->ssor_work          = 0;
3939:   b->omega              = 1.0;
3940:   b->fshift             = 0.0;
3941:   b->idiagvalid         = PETSC_FALSE;
3942:   b->ibdiagvalid        = PETSC_FALSE;
3943:   b->keepnonzeropattern = PETSC_FALSE;

3945:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3946:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
3947:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);

3949: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3950:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
3951:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
3952: #endif

3954:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
3955:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
3956:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
3957:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
3958:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
3959:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
3960:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
3961: #if defined(PETSC_HAVE_ELEMENTAL)
3962:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
3963: #endif
3964: #if defined(PETSC_HAVE_HYPRE)
3965:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_hypre_C",MatConvert_AIJ_HYPRE);
3966:   PetscObjectComposeFunction((PetscObject)B,"MatMatMatMult_transpose_seqaij_seqaij_C",MatMatMatMult_Transpose_AIJ_AIJ);
3967: #endif
3968:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
3969:   PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
3970:   PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
3971:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
3972:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
3973:   PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
3974:   PetscObjectComposeFunction((PetscObject)B,"MatMatMult_seqdense_seqaij_C",MatMatMult_SeqDense_SeqAIJ);
3975:   PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C",MatMatMultSymbolic_SeqDense_SeqAIJ);
3976:   PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C",MatMatMultNumeric_SeqDense_SeqAIJ);
3977:   MatCreate_SeqAIJ_Inode(B);
3978:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3979:   return(0);
3980: }

3982: /*
3983:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
3984: */
3985: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
3986: {
3987:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
3989:   PetscInt       i,m = A->rmap->n;

3992:   c = (Mat_SeqAIJ*)C->data;

3994:   C->factortype = A->factortype;
3995:   c->row        = 0;
3996:   c->col        = 0;
3997:   c->icol       = 0;
3998:   c->reallocs   = 0;

4000:   C->assembled = PETSC_TRUE;

4002:   PetscLayoutReference(A->rmap,&C->rmap);
4003:   PetscLayoutReference(A->cmap,&C->cmap);

4005:   PetscMalloc2(m,&c->imax,m,&c->ilen);
4006:   PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));
4007:   for (i=0; i<m; i++) {
4008:     c->imax[i] = a->imax[i];
4009:     c->ilen[i] = a->ilen[i];
4010:   }

4012:   /* allocate the matrix space */
4013:   if (mallocmatspace) {
4014:     PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
4015:     PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));

4017:     c->singlemalloc = PETSC_TRUE;

4019:     PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
4020:     if (m > 0) {
4021:       PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
4022:       if (cpvalues == MAT_COPY_VALUES) {
4023:         PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
4024:       } else {
4025:         PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
4026:       }
4027:     }
4028:   }

4030:   c->ignorezeroentries = a->ignorezeroentries;
4031:   c->roworiented       = a->roworiented;
4032:   c->nonew             = a->nonew;
4033:   if (a->diag) {
4034:     PetscMalloc1(m+1,&c->diag);
4035:     PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
4036:     for (i=0; i<m; i++) {
4037:       c->diag[i] = a->diag[i];
4038:     }
4039:   } else c->diag = 0;

4041:   c->solve_work         = 0;
4042:   c->saved_values       = 0;
4043:   c->idiag              = 0;
4044:   c->ssor_work          = 0;
4045:   c->keepnonzeropattern = a->keepnonzeropattern;
4046:   c->free_a             = PETSC_TRUE;
4047:   c->free_ij            = PETSC_TRUE;

4049:   c->rmax         = a->rmax;
4050:   c->nz           = a->nz;
4051:   c->maxnz        = a->nz;       /* Since we allocate exactly the right amount */
4052:   C->preallocated = PETSC_TRUE;

4054:   c->compressedrow.use   = a->compressedrow.use;
4055:   c->compressedrow.nrows = a->compressedrow.nrows;
4056:   if (a->compressedrow.use) {
4057:     i    = a->compressedrow.nrows;
4058:     PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
4059:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
4060:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
4061:   } else {
4062:     c->compressedrow.use    = PETSC_FALSE;
4063:     c->compressedrow.i      = NULL;
4064:     c->compressedrow.rindex = NULL;
4065:   }
4066:   c->nonzerorowcnt = a->nonzerorowcnt;
4067:   C->nonzerostate  = A->nonzerostate;

4069:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
4070:   PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
4071:   return(0);
4072: }

4074: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
4075: {

4079:   MatCreate(PetscObjectComm((PetscObject)A),B);
4080:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
4081:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
4082:     MatSetBlockSizesFromMats(*B,A,A);
4083:   }
4084:   MatSetType(*B,((PetscObject)A)->type_name);
4085:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4086:   return(0);
4087: }

4089: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4090: {
4091:   Mat_SeqAIJ     *a;
4093:   PetscInt       i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols;
4094:   int            fd;
4095:   PetscMPIInt    size;
4096:   MPI_Comm       comm;
4097:   PetscInt       bs = newMat->rmap->bs;

4100:   /* force binary viewer to load .info file if it has not yet done so */
4101:   PetscViewerSetUp(viewer);
4102:   PetscObjectGetComm((PetscObject)viewer,&comm);
4103:   MPI_Comm_size(comm,&size);
4104:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor");

4106:   PetscOptionsBegin(comm,NULL,"Options for loading SEQAIJ matrix","Mat");
4107:   PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
4108:   PetscOptionsEnd();
4109:   if (bs < 0) bs = 1;
4110:   MatSetBlockSize(newMat,bs);

4112:   PetscViewerBinaryGetDescriptor(viewer,&fd);
4113:   PetscBinaryRead(fd,header,4,PETSC_INT);
4114:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
4115:   M = header[1]; N = header[2]; nz = header[3];

4117:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");

4119:   /* read in row lengths */
4120:   PetscMalloc1(M,&rowlengths);
4121:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

4123:   /* check if sum of rowlengths is same as nz */
4124:   for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
4125:   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);

4127:   /* set global size if not set already*/
4128:   if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) {
4129:     MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);
4130:   } else {
4131:     /* if sizes and type are already set, check if the matrix  global sizes are correct */
4132:     MatGetSize(newMat,&rows,&cols);
4133:     if (rows < 0 && cols < 0) { /* user might provide local size instead of global size */
4134:       MatGetLocalSize(newMat,&rows,&cols);
4135:     }
4136:     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);
4137:   }
4138:   MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);
4139:   a    = (Mat_SeqAIJ*)newMat->data;

4141:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

4143:   /* read in nonzero values */
4144:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

4146:   /* set matrix "i" values */
4147:   a->i[0] = 0;
4148:   for (i=1; i<= M; i++) {
4149:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
4150:     a->ilen[i-1] = rowlengths[i-1];
4151:   }
4152:   PetscFree(rowlengths);

4154:   MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
4155:   MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
4156:   return(0);
4157: }

4159: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4160: {
4161:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
4163: #if defined(PETSC_USE_COMPLEX)
4164:   PetscInt k;
4165: #endif

4168:   /* If the  matrix dimensions are not equal,or no of nonzeros */
4169:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4170:     *flg = PETSC_FALSE;
4171:     return(0);
4172:   }

4174:   /* if the a->i are the same */
4175:   PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);
4176:   if (!*flg) return(0);

4178:   /* if a->j are the same */
4179:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
4180:   if (!*flg) return(0);

4182:   /* if a->a are the same */
4183: #if defined(PETSC_USE_COMPLEX)
4184:   for (k=0; k<a->nz; k++) {
4185:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
4186:       *flg = PETSC_FALSE;
4187:       return(0);
4188:     }
4189:   }
4190: #else
4191:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
4192: #endif
4193:   return(0);
4194: }

4196: /*@
4197:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4198:               provided by the user.

4200:       Collective on MPI_Comm

4202:    Input Parameters:
4203: +   comm - must be an MPI communicator of size 1
4204: .   m - number of rows
4205: .   n - number of columns
4206: .   i - row indices
4207: .   j - column indices
4208: -   a - matrix values

4210:    Output Parameter:
4211: .   mat - the matrix

4213:    Level: intermediate

4215:    Notes:
4216:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
4217:     once the matrix is destroyed and not before

4219:        You cannot set new nonzero locations into this matrix, that will generate an error.

4221:        The i and j indices are 0 based

4223:        The format which is used for the sparse matrix input, is equivalent to a
4224:     row-major ordering.. i.e for the following matrix, the input data expected is
4225:     as shown

4227: $        1 0 0
4228: $        2 0 3
4229: $        4 5 6
4230: $
4231: $        i =  {0,1,3,6}  [size = nrow+1  = 3+1]
4232: $        j =  {0,0,2,0,1,2}  [size = 6]; values must be sorted for each row
4233: $        v =  {1,2,3,4,5,6}  [size = 6]


4236: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4238: @*/
4239: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
4240: {
4242:   PetscInt       ii;
4243:   Mat_SeqAIJ     *aij;
4244: #if defined(PETSC_USE_DEBUG)
4245:   PetscInt jj;
4246: #endif

4249:   if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4250:   MatCreate(comm,mat);
4251:   MatSetSizes(*mat,m,n,m,n);
4252:   /* MatSetBlockSizes(*mat,,); */
4253:   MatSetType(*mat,MATSEQAIJ);
4254:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
4255:   aij  = (Mat_SeqAIJ*)(*mat)->data;
4256:   PetscMalloc2(m,&aij->imax,m,&aij->ilen);

4258:   aij->i            = i;
4259:   aij->j            = j;
4260:   aij->a            = a;
4261:   aij->singlemalloc = PETSC_FALSE;
4262:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4263:   aij->free_a       = PETSC_FALSE;
4264:   aij->free_ij      = PETSC_FALSE;

4266:   for (ii=0; ii<m; ii++) {
4267:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4268: #if defined(PETSC_USE_DEBUG)
4269:     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]);
4270:     for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4271:       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);
4272:       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);
4273:     }
4274: #endif
4275:   }
4276: #if defined(PETSC_USE_DEBUG)
4277:   for (ii=0; ii<aij->i[m]; ii++) {
4278:     if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
4279:     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]);
4280:   }
4281: #endif

4283:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4284:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4285:   return(0);
4286: }
4287: /*@C
4288:      MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4289:               provided by the user.

4291:       Collective on MPI_Comm

4293:    Input Parameters:
4294: +   comm - must be an MPI communicator of size 1
4295: .   m   - number of rows
4296: .   n   - number of columns
4297: .   i   - row indices
4298: .   j   - column indices
4299: .   a   - matrix values
4300: .   nz  - number of nonzeros
4301: -   idx - 0 or 1 based

4303:    Output Parameter:
4304: .   mat - the matrix

4306:    Level: intermediate

4308:    Notes:
4309:        The i and j indices are 0 based

4311:        The format which is used for the sparse matrix input, is equivalent to a
4312:     row-major ordering.. i.e for the following matrix, the input data expected is
4313:     as shown:

4315:         1 0 0
4316:         2 0 3
4317:         4 5 6

4319:         i =  {0,1,1,2,2,2}
4320:         j =  {0,0,2,0,1,2}
4321:         v =  {1,2,3,4,5,6}


4324: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4326: @*/
4327: PetscErrorCode  MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat,PetscInt nz,PetscBool idx)
4328: {
4330:   PetscInt       ii, *nnz, one = 1,row,col;


4334:   PetscCalloc1(m,&nnz);
4335:   for (ii = 0; ii < nz; ii++) {
4336:     nnz[i[ii] - !!idx] += 1;
4337:   }
4338:   MatCreate(comm,mat);
4339:   MatSetSizes(*mat,m,n,m,n);
4340:   MatSetType(*mat,MATSEQAIJ);
4341:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
4342:   for (ii = 0; ii < nz; ii++) {
4343:     if (idx) {
4344:       row = i[ii] - 1;
4345:       col = j[ii] - 1;
4346:     } else {
4347:       row = i[ii];
4348:       col = j[ii];
4349:     }
4350:     MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
4351:   }
4352:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4353:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4354:   PetscFree(nnz);
4355:   return(0);
4356: }

4358: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
4359: {
4360:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data;

4364:   a->idiagvalid  = PETSC_FALSE;
4365:   a->ibdiagvalid = PETSC_FALSE;

4367:   MatSeqAIJInvalidateDiagonal_Inode(A);
4368:   return(0);
4369: }

4371: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
4372: {
4374:   PetscMPIInt    size;

4377:   MPI_Comm_size(comm,&size);
4378:   if (size == 1 && scall == MAT_REUSE_MATRIX) {
4379:     MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
4380:   } else {
4381:     MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
4382:   }
4383:   return(0);
4384: }

4386: /*
4387:  Permute A into C's *local* index space using rowemb,colemb.
4388:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
4389:  of [0,m), colemb is in [0,n).
4390:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
4391:  */
4392: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
4393: {
4394:   /* If making this function public, change the error returned in this function away from _PLIB. */
4396:   Mat_SeqAIJ     *Baij;
4397:   PetscBool      seqaij;
4398:   PetscInt       m,n,*nz,i,j,count;
4399:   PetscScalar    v;
4400:   const PetscInt *rowindices,*colindices;

4403:   if (!B) return(0);
4404:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
4405:   PetscObjectTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
4406:   if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
4407:   if (rowemb) {
4408:     ISGetLocalSize(rowemb,&m);
4409:     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);
4410:   } else {
4411:     if (C->rmap->n != B->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
4412:   }
4413:   if (colemb) {
4414:     ISGetLocalSize(colemb,&n);
4415:     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);
4416:   } else {
4417:     if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
4418:   }

4420:   Baij = (Mat_SeqAIJ*)(B->data);
4421:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
4422:     PetscMalloc1(B->rmap->n,&nz);
4423:     for (i=0; i<B->rmap->n; i++) {
4424:       nz[i] = Baij->i[i+1] - Baij->i[i];
4425:     }
4426:     MatSeqAIJSetPreallocation(C,0,nz);
4427:     PetscFree(nz);
4428:   }
4429:   if (pattern == SUBSET_NONZERO_PATTERN) {
4430:     MatZeroEntries(C);
4431:   }
4432:   count = 0;
4433:   rowindices = NULL;
4434:   colindices = NULL;
4435:   if (rowemb) {
4436:     ISGetIndices(rowemb,&rowindices);
4437:   }
4438:   if (colemb) {
4439:     ISGetIndices(colemb,&colindices);
4440:   }
4441:   for (i=0; i<B->rmap->n; i++) {
4442:     PetscInt row;
4443:     row = i;
4444:     if (rowindices) row = rowindices[i];
4445:     for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
4446:       PetscInt col;
4447:       col  = Baij->j[count];
4448:       if (colindices) col = colindices[col];
4449:       v    = Baij->a[count];
4450:       MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
4451:       ++count;
4452:     }
4453:   }
4454:   /* FIXME: set C's nonzerostate correctly. */
4455:   /* Assembly for C is necessary. */
4456:   C->preallocated = PETSC_TRUE;
4457:   C->assembled     = PETSC_TRUE;
4458:   C->was_assembled = PETSC_FALSE;
4459:   return(0);
4460: }


4463: /*
4464:     Special version for direct calls from Fortran
4465: */
4466:  #include <petsc/private/fortranimpl.h>
4467: #if defined(PETSC_HAVE_FORTRAN_CAPS)
4468: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
4469: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
4470: #define matsetvaluesseqaij_ matsetvaluesseqaij
4471: #endif

4473: /* Change these macros so can be used in void function */
4474: #undef CHKERRQ
4475: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
4476: #undef SETERRQ2
4477: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
4478: #undef SETERRQ3
4479: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)

4481: 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)
4482: {
4483:   Mat            A  = *AA;
4484:   PetscInt       m  = *mm, n = *nn;
4485:   InsertMode     is = *isis;
4486:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
4487:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
4488:   PetscInt       *imax,*ai,*ailen;
4490:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
4491:   MatScalar      *ap,value,*aa;
4492:   PetscBool      ignorezeroentries = a->ignorezeroentries;
4493:   PetscBool      roworiented       = a->roworiented;

4496:   MatCheckPreallocated(A,1);
4497:   imax  = a->imax;
4498:   ai    = a->i;
4499:   ailen = a->ilen;
4500:   aj    = a->j;
4501:   aa    = a->a;

4503:   for (k=0; k<m; k++) { /* loop over added rows */
4504:     row = im[k];
4505:     if (row < 0) continue;
4506: #if defined(PETSC_USE_DEBUG)
4507:     if (row >= A->rmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
4508: #endif
4509:     rp   = aj + ai[row]; ap = aa + ai[row];
4510:     rmax = imax[row]; nrow = ailen[row];
4511:     low  = 0;
4512:     high = nrow;
4513:     for (l=0; l<n; l++) { /* loop over added columns */
4514:       if (in[l] < 0) continue;
4515: #if defined(PETSC_USE_DEBUG)
4516:       if (in[l] >= A->cmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
4517: #endif
4518:       col = in[l];
4519:       if (roworiented) value = v[l + k*n];
4520:       else value = v[k + l*m];

4522:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

4524:       if (col <= lastcol) low = 0;
4525:       else high = nrow;
4526:       lastcol = col;
4527:       while (high-low > 5) {
4528:         t = (low+high)/2;
4529:         if (rp[t] > col) high = t;
4530:         else             low  = t;
4531:       }
4532:       for (i=low; i<high; i++) {
4533:         if (rp[i] > col) break;
4534:         if (rp[i] == col) {
4535:           if (is == ADD_VALUES) ap[i] += value;
4536:           else                  ap[i] = value;
4537:           goto noinsert;
4538:         }
4539:       }
4540:       if (value == 0.0 && ignorezeroentries) goto noinsert;
4541:       if (nonew == 1) goto noinsert;
4542:       if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
4543:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
4544:       N = nrow++ - 1; a->nz++; high++;
4545:       /* shift up all the later entries in this row */
4546:       for (ii=N; ii>=i; ii--) {
4547:         rp[ii+1] = rp[ii];
4548:         ap[ii+1] = ap[ii];
4549:       }
4550:       rp[i] = col;
4551:       ap[i] = value;
4552:       A->nonzerostate++;
4553: noinsert:;
4554:       low = i + 1;
4555:     }
4556:     ailen[row] = nrow;
4557:   }
4558:   PetscFunctionReturnVoid();
4559: }