Date: 2022-03-31T04:46:44+00:00

Actual source code: aij.c

      1: /*
      2:     Defines the basic matrix operations for the AIJ (compressed row)
      3:   matrix storage format.
      4: */

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

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

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

     24: PetscErrorCode MatGetColumnReductions_SeqAIJ(Mat A,PetscInt type,PetscReal *reductions)
     25: {
     26:   PetscInt       i,m,n;
     27:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

     29:   MatGetSize(A,&m,&n);
     30:   PetscArrayzero(reductions,n);
     31:   if (type == NORM_2) {
     32:     for (i=0; i<aij->i[m]; i++) {
     33:       reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]);
     34:     }
     35:   } else if (type == NORM_1) {
     36:     for (i=0; i<aij->i[m]; i++) {
     37:       reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]);
     38:     }
     39:   } else if (type == NORM_INFINITY) {
     40:     for (i=0; i<aij->i[m]; i++) {
     41:       reductions[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),reductions[aij->j[i]]);
     42:     }
     43:   } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
     44:     for (i=0; i<aij->i[m]; i++) {
     45:       reductions[aij->j[i]] += PetscRealPart(aij->a[i]);
     46:     }
     47:   } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
     48:     for (i=0; i<aij->i[m]; i++) {
     49:       reductions[aij->j[i]] += PetscImaginaryPart(aij->a[i]);
     50:     }
     51:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown reduction type");

     53:   if (type == NORM_2) {
     54:     for (i=0; i<n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
     55:   } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
     56:     for (i=0; i<n; i++) reductions[i] /= m;
     57:   }
     58:   return 0;
     59: }

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

     68:   for (i=0; i<m; i++) {
     69:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
     70:       cnt++;
     71:     }
     72:   }
     73:   PetscMalloc1(cnt,&rows);
     74:   cnt  = 0;
     75:   for (i=0; i<m; i++) {
     76:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
     77:       rows[cnt] = i;
     78:       cnt++;
     79:     }
     80:   }
     81:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);
     82:   return 0;
     83: }

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

     93:   MatSeqAIJGetArrayRead(A,&aa);
     94:   MatMarkDiagonal_SeqAIJ(A);
     95:   diag = a->diag;
     96:   for (i=0; i<m; i++) {
     97:     if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
     98:       cnt++;
     99:     }
    100:   }
    101:   PetscMalloc1(cnt,&rows);
    102:   cnt  = 0;
    103:   for (i=0; i<m; i++) {
    104:     if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
    105:       rows[cnt++] = i;
    106:     }
    107:   }
    108:   *nrows = cnt;
    109:   *zrows = rows;
    110:   MatSeqAIJRestoreArrayRead(A,&aa);
    111:   return 0;
    112: }

    114: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
    115: {
    116:   PetscInt       nrows,*rows;

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

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

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

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

    177:   if (Y->assembled) {
    178:     MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
    179:     if (!missing) {
    180:       diag = aij->diag;
    181:       VecGetArrayRead(D,&v);
    182:       MatSeqAIJGetArray(Y,&aa);
    183:       if (is == INSERT_VALUES) {
    184:         for (i=0; i<m; i++) {
    185:           aa[diag[i]] = v[i];
    186:         }
    187:       } else {
    188:         for (i=0; i<m; i++) {
    189:           aa[diag[i]] += v[i];
    190:         }
    191:       }
    192:       MatSeqAIJRestoreArray(Y,&aa);
    193:       VecRestoreArrayRead(D,&v);
    194:       return 0;
    195:     }
    196:     MatSeqAIJInvalidateDiagonal(Y);
    197:   }
    198:   MatDiagonalSet_Default(Y,D,is);
    199:   return 0;
    200: }

    202: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    203: {
    204:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    205:   PetscInt       i,ishift;

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

    232: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    233: {
    234:   if (!ia) return 0;
    235:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
    236:     PetscFree(*ia);
    237:     if (ja) PetscFree(*ja);
    238:   }
    239:   return 0;
    240: }

    242: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    243: {
    244:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    245:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
    246:   PetscInt       nz = a->i[m],row,*jj,mr,col;

    248:   *nn = n;
    249:   if (!ia) return 0;
    250:   if (symmetric) {
    251:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,0,oshift,(PetscInt**)ia,(PetscInt**)ja);
    252:   } else {
    253:     PetscCalloc1(n,&collengths);
    254:     PetscMalloc1(n+1,&cia);
    255:     PetscMalloc1(nz,&cja);
    256:     jj   = a->j;
    257:     for (i=0; i<nz; i++) {
    258:       collengths[jj[i]]++;
    259:     }
    260:     cia[0] = oshift;
    261:     for (i=0; i<n; i++) {
    262:       cia[i+1] = cia[i] + collengths[i];
    263:     }
    264:     PetscArrayzero(collengths,n);
    265:     jj   = a->j;
    266:     for (row=0; row<m; row++) {
    267:       mr = a->i[row+1] - a->i[row];
    268:       for (i=0; i<mr; i++) {
    269:         col = *jj++;

    271:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
    272:       }
    273:     }
    274:     PetscFree(collengths);
    275:     *ia  = cia; *ja = cja;
    276:   }
    277:   return 0;
    278: }

    280: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    281: {
    282:   if (!ia) return 0;

    284:   PetscFree(*ia);
    285:   PetscFree(*ja);
    286:   return 0;
    287: }

    289: /*
    290:  MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
    291:  MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
    292:  spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
    293: */
    294: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
    295: {
    296:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    297:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
    298:   PetscInt       nz = a->i[m],row,mr,col,tmp;
    299:   PetscInt       *cspidx;
    300:   const PetscInt *jj;

    302:   *nn = n;
    303:   if (!ia) return 0;

    305:   PetscCalloc1(n,&collengths);
    306:   PetscMalloc1(n+1,&cia);
    307:   PetscMalloc1(nz,&cja);
    308:   PetscMalloc1(nz,&cspidx);
    309:   jj   = a->j;
    310:   for (i=0; i<nz; i++) {
    311:     collengths[jj[i]]++;
    312:   }
    313:   cia[0] = oshift;
    314:   for (i=0; i<n; i++) {
    315:     cia[i+1] = cia[i] + collengths[i];
    316:   }
    317:   PetscArrayzero(collengths,n);
    318:   jj   = a->j;
    319:   for (row=0; row<m; row++) {
    320:     mr = a->i[row+1] - a->i[row];
    321:     for (i=0; i<mr; i++) {
    322:       col         = *jj++;
    323:       tmp         = cia[col] + collengths[col]++ - oshift;
    324:       cspidx[tmp] = a->i[row] + i; /* index of a->j */
    325:       cja[tmp]    = row + oshift;
    326:     }
    327:   }
    328:   PetscFree(collengths);
    329:   *ia    = cia;
    330:   *ja    = cja;
    331:   *spidx = cspidx;
    332:   return 0;
    333: }

    335: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
    336: {
    337:   MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);
    338:   PetscFree(*spidx);
    339:   return 0;
    340: }

    342: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
    343: {
    344:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
    345:   PetscInt       *ai = a->i;
    346:   PetscScalar    *aa;

    348:   MatSeqAIJGetArray(A,&aa);
    349:   PetscArraycpy(aa+ai[row],v,ai[row+1]-ai[row]);
    350:   MatSeqAIJRestoreArray(A,&aa);
    351:   return 0;
    352: }

    354: /*
    355:     MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions

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

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

    364: */

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

    376:   MatSeqAIJGetArray(A,&aa);
    377:   row  = ridx[im[0]];
    378:   rp   = aj + ai[row];
    379:   ap   = aa + ai[row];
    380:   nrow = ailen[row];
    381:   low  = 0;
    382:   high = nrow;
    383:   for (l=0; l<n; l++) { /* loop over added columns */
    384:     col = cidx[in[l]];
    385:     value = v[l];

    387:     if (col <= lastcol) low = 0;
    388:     else high = nrow;
    389:     lastcol = col;
    390:     while (high-low > 5) {
    391:       t = (low+high)/2;
    392:       if (rp[t] > col) high = t;
    393:       else low = t;
    394:     }
    395:     for (i=low; i<high; i++) {
    396:       if (rp[i] == col) {
    397:         ap[i] += value;
    398:         low = i + 1;
    399:         break;
    400:       }
    401:     }
    402:   }
    403:   MatSeqAIJRestoreArray(A,&aa);
    404:   return 0;
    405: }

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

    417:   MatSeqAIJGetArray(A,&aa);
    418:   for (k=0; k<m; k++) { /* loop over added rows */
    419:     row = im[k];
    420:     if (row < 0) continue;
    422:     rp   = aj + ai[row];
    423:     if (!A->structure_only) ap = aa + ai[row];
    424:     rmax = imax[row]; nrow = ailen[row];
    425:     low  = 0;
    426:     high = nrow;
    427:     for (l=0; l<n; l++) { /* loop over added columns */
    428:       if (in[l] < 0) continue;
    430:       col = in[l];
    431:       if (v && !A->structure_only) value = roworiented ? v[l + k*n] : v[k + l*m];
    432:       if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue;

    434:       if (col <= lastcol) low = 0;
    435:       else high = nrow;
    436:       lastcol = col;
    437:       while (high-low > 5) {
    438:         t = (low+high)/2;
    439:         if (rp[t] > col) high = t;
    440:         else low = t;
    441:       }
    442:       for (i=low; i<high; i++) {
    443:         if (rp[i] > col) break;
    444:         if (rp[i] == col) {
    445:           if (!A->structure_only) {
    446:             if (is == ADD_VALUES) {
    447:               ap[i] += value;
    448:               (void)PetscLogFlops(1.0);
    449:             }
    450:             else ap[i] = value;
    451:           }
    452:           low = i + 1;
    453:           goto noinsert;
    454:         }
    455:       }
    456:       if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
    457:       if (nonew == 1) goto noinsert;
    459:       if (A->structure_only) {
    460:         MatSeqXAIJReallocateAIJ_structure_only(A,A->rmap->n,1,nrow,row,col,rmax,ai,aj,rp,imax,nonew,MatScalar);
    461:       } else {
    462:         MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
    463:       }
    464:       N = nrow++ - 1; a->nz++; high++;
    465:       /* shift up all the later entries in this row */
    466:       PetscArraymove(rp+i+1,rp+i,N-i+1);
    467:       rp[i] = col;
    468:       if (!A->structure_only) {
    469:         PetscArraymove(ap+i+1,ap+i,N-i+1);
    470:         ap[i] = value;
    471:       }
    472:       low = i + 1;
    473:       A->nonzerostate++;
    474: noinsert:;
    475:     }
    476:     ailen[row] = nrow;
    477:   }
    478:   MatSeqAIJRestoreArray(A,&aa);
    479:   return 0;
    480: }

    482: PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
    483: {
    484:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    485:   PetscInt       *rp,k,row;
    486:   PetscInt       *ai = a->i;
    487:   PetscInt       *aj = a->j;
    488:   MatScalar      *aa,*ap;

    493:   MatSeqAIJGetArray(A,&aa);
    494:   for (k=0; k<m; k++) { /* loop over added rows */
    495:     row  = im[k];
    496:     rp   = aj + ai[row];
    497:     ap   = aa + ai[row];

    499:     PetscMemcpy(rp,in,n*sizeof(PetscInt));
    500:     if (!A->structure_only) {
    501:       if (v) {
    502:         PetscMemcpy(ap,v,n*sizeof(PetscScalar));
    503:         v   += n;
    504:       } else {
    505:         PetscMemzero(ap,n*sizeof(PetscScalar));
    506:       }
    507:     }
    508:     a->ilen[row] = n;
    509:     a->imax[row] = n;
    510:     a->i[row+1]  = a->i[row]+n;
    511:     a->nz       += n;
    512:   }
    513:   MatSeqAIJRestoreArray(A,&aa);
    514:   return 0;
    515: }

    517: /*@
    518:     MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.

    520:   Input Parameters:
    521: +  A - the SeqAIJ matrix
    522: -  nztotal - bound on the number of nonzeros

    524:   Level: advanced

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

    531: .seealso: MatSetOption(), MAT_SORTED_FULL, MatSetValues(), MatSeqAIJSetPreallocation()
    532: @*/

    534: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A,PetscInt nztotal)
    535: {
    536:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    538:   PetscLayoutSetUp(A->rmap);
    539:   PetscLayoutSetUp(A->cmap);
    540:   a->maxnz  = nztotal;
    541:   if (!a->imax) {
    542:     PetscMalloc1(A->rmap->n,&a->imax);
    543:     PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
    544:   }
    545:   if (!a->ilen) {
    546:     PetscMalloc1(A->rmap->n,&a->ilen);
    547:     PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
    548:   } else {
    549:     PetscMemzero(a->ilen,A->rmap->n*sizeof(PetscInt));
    550:   }

    552:   /* allocate the matrix space */
    553:   if (A->structure_only) {
    554:     PetscMalloc1(nztotal,&a->j);
    555:     PetscMalloc1(A->rmap->n+1,&a->i);
    556:     PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*sizeof(PetscInt));
    557:   } else {
    558:     PetscMalloc3(nztotal,&a->a,nztotal,&a->j,A->rmap->n+1,&a->i);
    559:     PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*(sizeof(PetscScalar)+sizeof(PetscInt)));
    560:   }
    561:   a->i[0] = 0;
    562:   if (A->structure_only) {
    563:     a->singlemalloc = PETSC_FALSE;
    564:     a->free_a       = PETSC_FALSE;
    565:   } else {
    566:     a->singlemalloc = PETSC_TRUE;
    567:     a->free_a       = PETSC_TRUE;
    568:   }
    569:   a->free_ij        = PETSC_TRUE;
    570:   A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
    571:   A->preallocated   = PETSC_TRUE;
    572:   return 0;
    573: }

    575: PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
    576: {
    577:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    578:   PetscInt       *rp,k,row;
    579:   PetscInt       *ai = a->i,*ailen = a->ilen;
    580:   PetscInt       *aj = a->j;
    581:   MatScalar      *aa,*ap;

    583:   MatSeqAIJGetArray(A,&aa);
    584:   for (k=0; k<m; k++) { /* loop over added rows */
    585:     row  = im[k];
    587:     rp   = aj + ai[row];
    588:     ap   = aa + ai[row];
    589:     if (!A->was_assembled) {
    590:       PetscMemcpy(rp,in,n*sizeof(PetscInt));
    591:     }
    592:     if (!A->structure_only) {
    593:       if (v) {
    594:         PetscMemcpy(ap,v,n*sizeof(PetscScalar));
    595:         v   += n;
    596:       } else {
    597:         PetscMemzero(ap,n*sizeof(PetscScalar));
    598:       }
    599:     }
    600:     ailen[row] = n;
    601:     a->nz      += n;
    602:   }
    603:   MatSeqAIJRestoreArray(A,&aa);
    604:   return 0;
    605: }

    607: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
    608: {
    609:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    610:   PetscInt       *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
    611:   PetscInt       *ai = a->i,*ailen = a->ilen;
    612:   MatScalar      *ap,*aa;

    614:   MatSeqAIJGetArray(A,&aa);
    615:   for (k=0; k<m; k++) { /* loop over rows */
    616:     row = im[k];
    617:     if (row < 0) {v += n; continue;} /* negative row */
    619:     rp   = aj + ai[row]; ap = aa + ai[row];
    620:     nrow = ailen[row];
    621:     for (l=0; l<n; l++) { /* loop over columns */
    622:       if (in[l] < 0) {v++; continue;} /* negative column */
    624:       col  = in[l];
    625:       high = nrow; low = 0; /* assume unsorted */
    626:       while (high-low > 5) {
    627:         t = (low+high)/2;
    628:         if (rp[t] > col) high = t;
    629:         else low = t;
    630:       }
    631:       for (i=low; i<high; i++) {
    632:         if (rp[i] > col) break;
    633:         if (rp[i] == col) {
    634:           *v++ = ap[i];
    635:           goto finished;
    636:         }
    637:       }
    638:       *v++ = 0.0;
    639: finished:;
    640:     }
    641:   }
    642:   MatSeqAIJRestoreArray(A,&aa);
    643:   return 0;
    644: }

    646: PetscErrorCode MatView_SeqAIJ_Binary(Mat mat,PetscViewer viewer)
    647: {
    648:   Mat_SeqAIJ        *A = (Mat_SeqAIJ*)mat->data;
    649:   const PetscScalar *av;
    650:   PetscInt          header[4],M,N,m,nz,i;
    651:   PetscInt          *rowlens;

    653:   PetscViewerSetUp(viewer);

    655:   M  = mat->rmap->N;
    656:   N  = mat->cmap->N;
    657:   m  = mat->rmap->n;
    658:   nz = A->nz;

    660:   /* write matrix header */
    661:   header[0] = MAT_FILE_CLASSID;
    662:   header[1] = M; header[2] = N; header[3] = nz;
    663:   PetscViewerBinaryWrite(viewer,header,4,PETSC_INT);

    665:   /* fill in and store row lengths */
    666:   PetscMalloc1(m,&rowlens);
    667:   for (i=0; i<m; i++) rowlens[i] = A->i[i+1] - A->i[i];
    668:   PetscViewerBinaryWrite(viewer,rowlens,m,PETSC_INT);
    669:   PetscFree(rowlens);
    670:   /* store column indices */
    671:   PetscViewerBinaryWrite(viewer,A->j,nz,PETSC_INT);
    672:   /* store nonzero values */
    673:   MatSeqAIJGetArrayRead(mat,&av);
    674:   PetscViewerBinaryWrite(viewer,av,nz,PETSC_SCALAR);
    675:   MatSeqAIJRestoreArrayRead(mat,&av);

    677:   /* write block size option to the viewer's .info file */
    678:   MatView_Binary_BlockSizes(mat,viewer);
    679:   return 0;
    680: }

    682: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A,PetscViewer viewer)
    683: {
    684:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    685:   PetscInt       i,k,m=A->rmap->N;

    687:   PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    688:   for (i=0; i<m; i++) {
    689:     PetscViewerASCIIPrintf(viewer,"row %" PetscInt_FMT ":",i);
    690:     for (k=a->i[i]; k<a->i[i+1]; k++) {
    691:       PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ") ",a->j[k]);
    692:     }
    693:     PetscViewerASCIIPrintf(viewer,"\n");
    694:   }
    695:   PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    696:   return 0;
    697: }

    699: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

    701: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
    702: {
    703:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    704:   const PetscScalar *av;
    705:   PetscInt          i,j,m = A->rmap->n;
    706:   const char        *name;
    707:   PetscViewerFormat format;

    709:   if (A->structure_only) {
    710:     MatView_SeqAIJ_ASCII_structonly(A,viewer);
    711:     return 0;
    712:   }

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

    717:   /* trigger copy to CPU if needed */
    718:   MatSeqAIJGetArrayRead(A,&av);
    719:   MatSeqAIJRestoreArrayRead(A,&av);
    720:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
    721:     PetscInt nofinalvalue = 0;
    722:     if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) {
    723:       /* Need a dummy value to ensure the dimension of the matrix. */
    724:       nofinalvalue = 1;
    725:     }
    726:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    727:     PetscViewerASCIIPrintf(viewer,"%% Size = %" PetscInt_FMT " %" PetscInt_FMT " \n",m,A->cmap->n);
    728:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %" PetscInt_FMT " \n",a->nz);
    729: #if defined(PETSC_USE_COMPLEX)
    730:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%" PetscInt_FMT ",4);\n",a->nz+nofinalvalue);
    731: #else
    732:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%" PetscInt_FMT ",3);\n",a->nz+nofinalvalue);
    733: #endif
    734:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

    736:     for (i=0; i<m; i++) {
    737:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    738: #if defined(PETSC_USE_COMPLEX)
    739:         PetscViewerASCIIPrintf(viewer,"%" PetscInt_FMT " %" PetscInt_FMT "  %18.16e %18.16e\n",i+1,a->j[j]+1,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    740: #else
    741:         PetscViewerASCIIPrintf(viewer,"%" PetscInt_FMT " %" PetscInt_FMT "  %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);
    742: #endif
    743:       }
    744:     }
    745:     if (nofinalvalue) {
    746: #if defined(PETSC_USE_COMPLEX)
    747:       PetscViewerASCIIPrintf(viewer,"%" PetscInt_FMT " %" PetscInt_FMT "  %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
    748: #else
    749:       PetscViewerASCIIPrintf(viewer,"%" PetscInt_FMT " %" PetscInt_FMT "  %18.16e\n",m,A->cmap->n,0.0);
    750: #endif
    751:     }
    752:     PetscObjectGetName((PetscObject)A,&name);
    753:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
    754:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    755:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
    756:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    757:     for (i=0; i<m; i++) {
    758:       PetscViewerASCIIPrintf(viewer,"row %" PetscInt_FMT ":",i);
    759:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    760: #if defined(PETSC_USE_COMPLEX)
    761:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
    762:           PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    763:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
    764:           PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
    765:         } else if (PetscRealPart(a->a[j]) != 0.0) {
    766:           PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    767:         }
    768: #else
    769:         if (a->a[j] != 0.0) PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g) ",a->j[j],(double)a->a[j]);
    770: #endif
    771:       }
    772:       PetscViewerASCIIPrintf(viewer,"\n");
    773:     }
    774:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    775:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
    776:     PetscInt nzd=0,fshift=1,*sptr;
    777:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    778:     PetscMalloc1(m+1,&sptr);
    779:     for (i=0; i<m; i++) {
    780:       sptr[i] = nzd+1;
    781:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    782:         if (a->j[j] >= i) {
    783: #if defined(PETSC_USE_COMPLEX)
    784:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
    785: #else
    786:           if (a->a[j] != 0.0) nzd++;
    787: #endif
    788:         }
    789:       }
    790:     }
    791:     sptr[m] = nzd+1;
    792:     PetscViewerASCIIPrintf(viewer," %" PetscInt_FMT " %" PetscInt_FMT "\n\n",m,nzd);
    793:     for (i=0; i<m+1; i+=6) {
    794:       if (i+4<m) {
    795:         PetscViewerASCIIPrintf(viewer," %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);
    796:       } else if (i+3<m) {
    797:         PetscViewerASCIIPrintf(viewer," %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
    798:       } else if (i+2<m) {
    799:         PetscViewerASCIIPrintf(viewer," %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
    800:       } else if (i+1<m) {
    801:         PetscViewerASCIIPrintf(viewer," %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n",sptr[i],sptr[i+1],sptr[i+2]);
    802:       } else if (i<m) {
    803:         PetscViewerASCIIPrintf(viewer," %" PetscInt_FMT " %" PetscInt_FMT "\n",sptr[i],sptr[i+1]);
    804:       } else {
    805:         PetscViewerASCIIPrintf(viewer," %" PetscInt_FMT "\n",sptr[i]);
    806:       }
    807:     }
    808:     PetscViewerASCIIPrintf(viewer,"\n");
    809:     PetscFree(sptr);
    810:     for (i=0; i<m; i++) {
    811:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    812:         if (a->j[j] >= i) PetscViewerASCIIPrintf(viewer," %" PetscInt_FMT " ",a->j[j]+fshift);
    813:       }
    814:       PetscViewerASCIIPrintf(viewer,"\n");
    815:     }
    816:     PetscViewerASCIIPrintf(viewer,"\n");
    817:     for (i=0; i<m; i++) {
    818:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    819:         if (a->j[j] >= i) {
    820: #if defined(PETSC_USE_COMPLEX)
    821:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
    822:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    823:           }
    824: #else
    825:           if (a->a[j] != 0.0) PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);
    826: #endif
    827:         }
    828:       }
    829:       PetscViewerASCIIPrintf(viewer,"\n");
    830:     }
    831:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    832:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
    833:     PetscInt    cnt = 0,jcnt;
    834:     PetscScalar value;
    835: #if defined(PETSC_USE_COMPLEX)
    836:     PetscBool   realonly = PETSC_TRUE;

    838:     for (i=0; i<a->i[m]; i++) {
    839:       if (PetscImaginaryPart(a->a[i]) != 0.0) {
    840:         realonly = PETSC_FALSE;
    841:         break;
    842:       }
    843:     }
    844: #endif

    846:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    847:     for (i=0; i<m; i++) {
    848:       jcnt = 0;
    849:       for (j=0; j<A->cmap->n; j++) {
    850:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
    851:           value = a->a[cnt++];
    852:           jcnt++;
    853:         } else {
    854:           value = 0.0;
    855:         }
    856: #if defined(PETSC_USE_COMPLEX)
    857:         if (realonly) {
    858:           PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
    859:         } else {
    860:           PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
    861:         }
    862: #else
    863:         PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
    864: #endif
    865:       }
    866:       PetscViewerASCIIPrintf(viewer,"\n");
    867:     }
    868:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    869:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
    870:     PetscInt fshift=1;
    871:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    872: #if defined(PETSC_USE_COMPLEX)
    873:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
    874: #else
    875:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
    876: #endif
    877:     PetscViewerASCIIPrintf(viewer,"%" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", m, A->cmap->n, a->nz);
    878:     for (i=0; i<m; i++) {
    879:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    880: #if defined(PETSC_USE_COMPLEX)
    881:         PetscViewerASCIIPrintf(viewer,"%" PetscInt_FMT " %" PetscInt_FMT " %g %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    882: #else
    883:         PetscViewerASCIIPrintf(viewer,"%" PetscInt_FMT " %" PetscInt_FMT " %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);
    884: #endif
    885:       }
    886:     }
    887:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    888:   } else {
    889:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    890:     if (A->factortype) {
    891:       for (i=0; i<m; i++) {
    892:         PetscViewerASCIIPrintf(viewer,"row %" PetscInt_FMT ":",i);
    893:         /* L part */
    894:         for (j=a->i[i]; j<a->i[i+1]; j++) {
    895: #if defined(PETSC_USE_COMPLEX)
    896:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    897:             PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    898:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    899:             PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
    900:           } else {
    901:             PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    902:           }
    903: #else
    904:           PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g) ",a->j[j],(double)a->a[j]);
    905: #endif
    906:         }
    907:         /* diagonal */
    908:         j = a->diag[i];
    909: #if defined(PETSC_USE_COMPLEX)
    910:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
    911:           PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g + %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)PetscImaginaryPart(1.0/a->a[j]));
    912:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    913:           PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g - %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)(-PetscImaginaryPart(1.0/a->a[j])));
    914:         } else {
    915:           PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));
    916:         }
    917: #else
    918:         PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g) ",a->j[j],(double)(1.0/a->a[j]));
    919: #endif

    921:         /* U part */
    922:         for (j=a->diag[i+1]+1; j<a->diag[i]; j++) {
    923: #if defined(PETSC_USE_COMPLEX)
    924:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    925:             PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    926:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    927:             PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
    928:           } else {
    929:             PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    930:           }
    931: #else
    932:           PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g) ",a->j[j],(double)a->a[j]);
    933: #endif
    934:         }
    935:         PetscViewerASCIIPrintf(viewer,"\n");
    936:       }
    937:     } else {
    938:       for (i=0; i<m; i++) {
    939:         PetscViewerASCIIPrintf(viewer,"row %" PetscInt_FMT ":",i);
    940:         for (j=a->i[i]; j<a->i[i+1]; j++) {
    941: #if defined(PETSC_USE_COMPLEX)
    942:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    943:             PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    944:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    945:             PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
    946:           } else {
    947:             PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    948:           }
    949: #else
    950:           PetscViewerASCIIPrintf(viewer," (%" PetscInt_FMT ", %g) ",a->j[j],(double)a->a[j]);
    951: #endif
    952:         }
    953:         PetscViewerASCIIPrintf(viewer,"\n");
    954:       }
    955:     }
    956:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    957:   }
    958:   PetscViewerFlush(viewer);
    959:   return 0;
    960: }

    962: #include <petscdraw.h>
    963: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
    964: {
    965:   Mat               A  = (Mat) Aa;
    966:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    967:   PetscInt          i,j,m = A->rmap->n;
    968:   int               color;
    969:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r;
    970:   PetscViewer       viewer;
    971:   PetscViewerFormat format;
    972:   const PetscScalar *aa;

    974:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
    975:   PetscViewerGetFormat(viewer,&format);
    976:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);

    978:   /* loop over matrix elements drawing boxes */
    979:   MatSeqAIJGetArrayRead(A,&aa);
    980:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
    982:     PetscDrawCollectiveBegin(draw);
    983:     /* Blue for negative, Cyan for zero and  Red for positive */
    984:     color = PETSC_DRAW_BLUE;
    985:     for (i=0; i<m; i++) {
    986:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    987:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    988:         x_l = a->j[j]; x_r = x_l + 1.0;
    989:         if (PetscRealPart(aa[j]) >=  0.) continue;
    990:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    991:       }
    992:     }
    993:     color = PETSC_DRAW_CYAN;
    994:     for (i=0; i<m; i++) {
    995:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    996:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    997:         x_l = a->j[j]; x_r = x_l + 1.0;
    998:         if (aa[j] !=  0.) continue;
    999:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    1000:       }
    1001:     }
    1002:     color = PETSC_DRAW_RED;
    1003:     for (i=0; i<m; i++) {
    1004:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    1005:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    1006:         x_l = a->j[j]; x_r = x_l + 1.0;
    1007:         if (PetscRealPart(aa[j]) <=  0.) continue;
    1008:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    1009:       }
    1010:     }
    1011:     PetscDrawCollectiveEnd(draw);
    1012:   } else {
    1013:     /* use contour shading to indicate magnitude of values */
    1014:     /* first determine max of all nonzero values */
    1015:     PetscReal      minv = 0.0, maxv = 0.0;
    1016:     PetscInt       nz   = a->nz, count = 0;
    1017:     PetscDraw      popup;

    1020:     for (i=0; i<nz; i++) {
    1021:       if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]);
    1022:     }
    1023:     if (minv >= maxv) maxv = minv + PETSC_SMALL;
    1024:     PetscDrawGetPopup(draw,&popup);
    1025:     PetscDrawScalePopup(popup,minv,maxv);

    1027:     PetscDrawCollectiveBegin(draw);
    1028:     for (i=0; i<m; i++) {
    1029:       y_l = m - i - 1.0;
    1030:       y_r = y_l + 1.0;
    1031:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    1032:         x_l = a->j[j];
    1033:         x_r = x_l + 1.0;
    1034:         color = PetscDrawRealToColor(PetscAbsScalar(aa[count]),minv,maxv);
    1035:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    1036:         count++;
    1037:       }
    1038:     }
    1039:     PetscDrawCollectiveEnd(draw);
    1040:   }
    1041:   MatSeqAIJRestoreArrayRead(A,&aa);
    1042:   return 0;
    1043: }

    1045: #include <petscdraw.h>
    1046: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
    1047: {
    1048:   PetscDraw      draw;
    1049:   PetscReal      xr,yr,xl,yl,h,w;
    1050:   PetscBool      isnull;

    1052:   PetscViewerDrawGetDraw(viewer,0,&draw);
    1053:   PetscDrawIsNull(draw,&isnull);
    1054:   if (isnull) return 0;

    1056:   xr   = A->cmap->n; yr  = A->rmap->n; h = yr/10.0; w = xr/10.0;
    1057:   xr  += w;          yr += h;         xl = -w;     yl = -h;
    1058:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
    1059:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
    1060:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
    1061:   PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
    1062:   PetscDrawSave(draw);
    1063:   return 0;
    1064: }

    1066: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
    1067: {
    1068:   PetscBool      iascii,isbinary,isdraw;

    1070:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    1071:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    1072:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
    1073:   if (iascii) {
    1074:     MatView_SeqAIJ_ASCII(A,viewer);
    1075:   } else if (isbinary) {
    1076:     MatView_SeqAIJ_Binary(A,viewer);
    1077:   } else if (isdraw) {
    1078:     MatView_SeqAIJ_Draw(A,viewer);
    1079:   }
    1080:   MatView_SeqAIJ_Inode(A,viewer);
    1081:   return 0;
    1082: }

    1084: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
    1085: {
    1086:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1087:   PetscInt       fshift = 0,i,*ai = a->i,*aj = a->j,*imax = a->imax;
    1088:   PetscInt       m      = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
    1089:   MatScalar      *aa    = a->a,*ap;
    1090:   PetscReal      ratio  = 0.6;

    1092:   if (mode == MAT_FLUSH_ASSEMBLY) return 0;
    1093:   MatSeqAIJInvalidateDiagonal(A);
    1094:   if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
    1095:     /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
    1096:     MatAssemblyEnd_SeqAIJ_Inode(A,mode);
    1097:     return 0;
    1098:   }

    1100:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
    1101:   for (i=1; i<m; i++) {
    1102:     /* move each row back by the amount of empty slots (fshift) before it*/
    1103:     fshift += imax[i-1] - ailen[i-1];
    1104:     rmax    = PetscMax(rmax,ailen[i]);
    1105:     if (fshift) {
    1106:       ip = aj + ai[i];
    1107:       ap = aa + ai[i];
    1108:       N  = ailen[i];
    1109:       PetscArraymove(ip-fshift,ip,N);
    1110:       if (!A->structure_only) {
    1111:         PetscArraymove(ap-fshift,ap,N);
    1112:       }
    1113:     }
    1114:     ai[i] = ai[i-1] + ailen[i-1];
    1115:   }
    1116:   if (m) {
    1117:     fshift += imax[m-1] - ailen[m-1];
    1118:     ai[m]   = ai[m-1] + ailen[m-1];
    1119:   }

    1121:   /* reset ilen and imax for each row */
    1122:   a->nonzerorowcnt = 0;
    1123:   if (A->structure_only) {
    1124:     PetscFree(a->imax);
    1125:     PetscFree(a->ilen);
    1126:   } else { /* !A->structure_only */
    1127:     for (i=0; i<m; i++) {
    1128:       ailen[i] = imax[i] = ai[i+1] - ai[i];
    1129:       a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0);
    1130:     }
    1131:   }
    1132:   a->nz = ai[m];

    1135:   MatMarkDiagonal_SeqAIJ(A);
    1136:   PetscInfo(A,"Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; storage space: %" PetscInt_FMT " unneeded,%" PetscInt_FMT " used\n",m,A->cmap->n,fshift,a->nz);
    1137:   PetscInfo(A,"Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n",a->reallocs);
    1138:   PetscInfo(A,"Maximum nonzeros in any row is %" PetscInt_FMT "\n",rmax);

    1140:   A->info.mallocs    += a->reallocs;
    1141:   a->reallocs         = 0;
    1142:   A->info.nz_unneeded = (PetscReal)fshift;
    1143:   a->rmax             = rmax;

    1145:   if (!A->structure_only) {
    1146:     MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);
    1147:   }
    1148:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);
    1149:   return 0;
    1150: }

    1152: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
    1153: {
    1154:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1155:   PetscInt       i,nz = a->nz;
    1156:   MatScalar      *aa;

    1158:   MatSeqAIJGetArray(A,&aa);
    1159:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
    1160:   MatSeqAIJRestoreArray(A,&aa);
    1161:   MatSeqAIJInvalidateDiagonal(A);
    1162:   return 0;
    1163: }

    1165: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
    1166: {
    1167:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1168:   PetscInt       i,nz = a->nz;
    1169:   MatScalar      *aa;

    1171:   MatSeqAIJGetArray(A,&aa);
    1172:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
    1173:   MatSeqAIJRestoreArray(A,&aa);
    1174:   MatSeqAIJInvalidateDiagonal(A);
    1175:   return 0;
    1176: }

    1178: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
    1179: {
    1180:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1181:   MatScalar      *aa;

    1183:   MatSeqAIJGetArrayWrite(A,&aa);
    1184:   PetscArrayzero(aa,a->i[A->rmap->n]);
    1185:   MatSeqAIJRestoreArrayWrite(A,&aa);
    1186:   MatSeqAIJInvalidateDiagonal(A);
    1187:   return 0;
    1188: }

    1190: PETSC_INTERN PetscErrorCode MatResetPreallocationCOO_SeqAIJ(Mat A)
    1191: {
    1192:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    1194:   PetscFree(a->perm);
    1195:   PetscFree(a->jmap);
    1196:   return 0;
    1197: }

    1199: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
    1200: {
    1201:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    1203: #if defined(PETSC_USE_LOG)
    1204:   PetscLogObjectState((PetscObject)A,"Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT,A->rmap->n,A->cmap->n,a->nz);
    1205: #endif
    1206:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
    1207:   MatResetPreallocationCOO_SeqAIJ(A);
    1208:   ISDestroy(&a->row);
    1209:   ISDestroy(&a->col);
    1210:   PetscFree(a->diag);
    1211:   PetscFree(a->ibdiag);
    1212:   PetscFree(a->imax);
    1213:   PetscFree(a->ilen);
    1214:   PetscFree(a->ipre);
    1215:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
    1216:   PetscFree(a->solve_work);
    1217:   ISDestroy(&a->icol);
    1218:   PetscFree(a->saved_values);
    1219:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);
    1220:   MatDestroy_SeqAIJ_Inode(A);
    1221:   PetscFree(A->data);

    1223:   /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
    1224:      That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
    1225:      that is hard to properly add this data to the MatProduct data. We free it here to avoid
    1226:      users reusing the matrix object with different data to incur in obscure segmentation faults
    1227:      due to different matrix sizes */
    1228:   PetscObjectCompose((PetscObject)A,"__PETSc__ab_dense",NULL);

    1230:   PetscObjectChangeTypeName((PetscObject)A,NULL);
    1231:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
    1232:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
    1233:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
    1234:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
    1235:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
    1236:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);
    1237: #if defined(PETSC_HAVE_CUDA)
    1238:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcusparse_C",NULL);
    1239:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",NULL);
    1240:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaijcusparse_C",NULL);
    1241: #endif
    1242: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    1243:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijkokkos_C",NULL);
    1244: #endif
    1245:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcrl_C",NULL);
    1246: #if defined(PETSC_HAVE_ELEMENTAL)
    1247:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);
    1248: #endif
    1249: #if defined(PETSC_HAVE_SCALAPACK)
    1250:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_scalapack_C",NULL);
    1251: #endif
    1252: #if defined(PETSC_HAVE_HYPRE)
    1253:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_hypre_C",NULL);
    1254:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",NULL);
    1255: #endif
    1256:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);
    1257:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsell_C",NULL);
    1258:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_is_C",NULL);
    1259:   PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
    1260:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
    1261:   PetscObjectComposeFunction((PetscObject)A,"MatResetPreallocation_C",NULL);
    1262:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
    1263:   PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
    1264:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_is_seqaij_C",NULL);
    1265:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqdense_seqaij_C",NULL);
    1266:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaij_C",NULL);
    1267:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJKron_C",NULL);
    1268:   PetscObjectComposeFunction((PetscObject)A,"MatSetPreallocationCOO_C",NULL);
    1269:   PetscObjectComposeFunction((PetscObject)A,"MatSetValuesCOO_C",NULL);
    1270:   return 0;
    1271: }

    1273: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
    1274: {
    1275:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    1277:   switch (op) {
    1278:   case MAT_ROW_ORIENTED:
    1279:     a->roworiented = flg;
    1280:     break;
    1281:   case MAT_KEEP_NONZERO_PATTERN:
    1282:     a->keepnonzeropattern = flg;
    1283:     break;
    1284:   case MAT_NEW_NONZERO_LOCATIONS:
    1285:     a->nonew = (flg ? 0 : 1);
    1286:     break;
    1287:   case MAT_NEW_NONZERO_LOCATION_ERR:
    1288:     a->nonew = (flg ? -1 : 0);
    1289:     break;
    1290:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
    1291:     a->nonew = (flg ? -2 : 0);
    1292:     break;
    1293:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
    1294:     a->nounused = (flg ? -1 : 0);
    1295:     break;
    1296:   case MAT_IGNORE_ZERO_ENTRIES:
    1297:     a->ignorezeroentries = flg;
    1298:     break;
    1299:   case MAT_SPD:
    1300:   case MAT_SYMMETRIC:
    1301:   case MAT_STRUCTURALLY_SYMMETRIC:
    1302:   case MAT_HERMITIAN:
    1303:   case MAT_SYMMETRY_ETERNAL:
    1304:   case MAT_STRUCTURE_ONLY:
    1305:     /* These options are handled directly by MatSetOption() */
    1306:     break;
    1307:   case MAT_FORCE_DIAGONAL_ENTRIES:
    1308:   case MAT_IGNORE_OFF_PROC_ENTRIES:
    1309:   case MAT_USE_HASH_TABLE:
    1310:     PetscInfo(A,"Option %s ignored\n",MatOptions[op]);
    1311:     break;
    1312:   case MAT_USE_INODES:
    1313:     MatSetOption_SeqAIJ_Inode(A,MAT_USE_INODES,flg);
    1314:     break;
    1315:   case MAT_SUBMAT_SINGLEIS:
    1316:     A->submat_singleis = flg;
    1317:     break;
    1318:   case MAT_SORTED_FULL:
    1319:     if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
    1320:     else     A->ops->setvalues = MatSetValues_SeqAIJ;
    1321:     break;
    1322:   case MAT_FORM_EXPLICIT_TRANSPOSE:
    1323:     A->form_explicit_transpose = flg;
    1324:     break;
    1325:   default:
    1326:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
    1327:   }
    1328:   return 0;
    1329: }

    1331: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
    1332: {
    1333:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1334:   PetscInt          i,j,n,*ai=a->i,*aj=a->j;
    1335:   PetscScalar       *x;
    1336:   const PetscScalar *aa;

    1338:   VecGetLocalSize(v,&n);
    1340:   MatSeqAIJGetArrayRead(A,&aa);
    1341:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
    1342:     PetscInt *diag=a->diag;
    1343:     VecGetArrayWrite(v,&x);
    1344:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
    1345:     VecRestoreArrayWrite(v,&x);
    1346:     MatSeqAIJRestoreArrayRead(A,&aa);
    1347:     return 0;
    1348:   }

    1350:   VecGetArrayWrite(v,&x);
    1351:   for (i=0; i<n; i++) {
    1352:     x[i] = 0.0;
    1353:     for (j=ai[i]; j<ai[i+1]; j++) {
    1354:       if (aj[j] == i) {
    1355:         x[i] = aa[j];
    1356:         break;
    1357:       }
    1358:     }
    1359:   }
    1360:   VecRestoreArrayWrite(v,&x);
    1361:   MatSeqAIJRestoreArrayRead(A,&aa);
    1362:   return 0;
    1363: }

    1365: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
    1366: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
    1367: {
    1368:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1369:   const MatScalar   *aa;
    1370:   PetscScalar       *y;
    1371:   const PetscScalar *x;
    1372:   PetscInt          m = A->rmap->n;
    1373: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    1374:   const MatScalar   *v;
    1375:   PetscScalar       alpha;
    1376:   PetscInt          n,i,j;
    1377:   const PetscInt    *idx,*ii,*ridx=NULL;
    1378:   Mat_CompressedRow cprow    = a->compressedrow;
    1379:   PetscBool         usecprow = cprow.use;
    1380: #endif

    1382:   if (zz != yy) VecCopy(zz,yy);
    1383:   VecGetArrayRead(xx,&x);
    1384:   VecGetArray(yy,&y);
    1385:   MatSeqAIJGetArrayRead(A,&aa);

    1387: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    1388:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,aa,y);
    1389: #else
    1390:   if (usecprow) {
    1391:     m    = cprow.nrows;
    1392:     ii   = cprow.i;
    1393:     ridx = cprow.rindex;
    1394:   } else {
    1395:     ii = a->i;
    1396:   }
    1397:   for (i=0; i<m; i++) {
    1398:     idx = a->j + ii[i];
    1399:     v   = aa + ii[i];
    1400:     n   = ii[i+1] - ii[i];
    1401:     if (usecprow) {
    1402:       alpha = x[ridx[i]];
    1403:     } else {
    1404:       alpha = x[i];
    1405:     }
    1406:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
    1407:   }
    1408: #endif
    1409:   PetscLogFlops(2.0*a->nz);
    1410:   VecRestoreArrayRead(xx,&x);
    1411:   VecRestoreArray(yy,&y);
    1412:   MatSeqAIJRestoreArrayRead(A,&aa);
    1413:   return 0;
    1414: }

    1416: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
    1417: {
    1418:   VecSet(yy,0.0);
    1419:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
    1420:   return 0;
    1421: }

    1423: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>

    1425: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
    1426: {
    1427:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1428:   PetscScalar       *y;
    1429:   const PetscScalar *x;
    1430:   const MatScalar   *aa,*a_a;
    1431:   PetscInt          m=A->rmap->n;
    1432:   const PetscInt    *aj,*ii,*ridx=NULL;
    1433:   PetscInt          n,i;
    1434:   PetscScalar       sum;
    1435:   PetscBool         usecprow=a->compressedrow.use;

    1437: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
    1438: #pragma disjoint(*x,*y,*aa)
    1439: #endif

    1441:   if (a->inode.use && a->inode.checked) {
    1442:     MatMult_SeqAIJ_Inode(A,xx,yy);
    1443:     return 0;
    1444:   }
    1445:   MatSeqAIJGetArrayRead(A,&a_a);
    1446:   VecGetArrayRead(xx,&x);
    1447:   VecGetArray(yy,&y);
    1448:   ii   = a->i;
    1449:   if (usecprow) { /* use compressed row format */
    1450:     PetscArrayzero(y,m);
    1451:     m    = a->compressedrow.nrows;
    1452:     ii   = a->compressedrow.i;
    1453:     ridx = a->compressedrow.rindex;
    1454:     for (i=0; i<m; i++) {
    1455:       n           = ii[i+1] - ii[i];
    1456:       aj          = a->j + ii[i];
    1457:       aa          = a_a + ii[i];
    1458:       sum         = 0.0;
    1459:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1460:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
    1461:       y[*ridx++] = sum;
    1462:     }
    1463:   } else { /* do not use compressed row format */
    1464: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
    1465:     aj   = a->j;
    1466:     aa   = a_a;
    1467:     fortranmultaij_(&m,x,ii,aj,aa,y);
    1468: #else
    1469:     for (i=0; i<m; i++) {
    1470:       n           = ii[i+1] - ii[i];
    1471:       aj          = a->j + ii[i];
    1472:       aa          = a_a + ii[i];
    1473:       sum         = 0.0;
    1474:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1475:       y[i] = sum;
    1476:     }
    1477: #endif
    1478:   }
    1479:   PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
    1480:   VecRestoreArrayRead(xx,&x);
    1481:   VecRestoreArray(yy,&y);
    1482:   MatSeqAIJRestoreArrayRead(A,&a_a);
    1483:   return 0;
    1484: }

    1486: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
    1487: {
    1488:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1489:   PetscScalar       *y;
    1490:   const PetscScalar *x;
    1491:   const MatScalar   *aa,*a_a;
    1492:   PetscInt          m=A->rmap->n;
    1493:   const PetscInt    *aj,*ii,*ridx=NULL;
    1494:   PetscInt          n,i,nonzerorow=0;
    1495:   PetscScalar       sum;
    1496:   PetscBool         usecprow=a->compressedrow.use;

    1498: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
    1499: #pragma disjoint(*x,*y,*aa)
    1500: #endif

    1502:   MatSeqAIJGetArrayRead(A,&a_a);
    1503:   VecGetArrayRead(xx,&x);
    1504:   VecGetArray(yy,&y);
    1505:   if (usecprow) { /* use compressed row format */
    1506:     m    = a->compressedrow.nrows;
    1507:     ii   = a->compressedrow.i;
    1508:     ridx = a->compressedrow.rindex;
    1509:     for (i=0; i<m; i++) {
    1510:       n           = ii[i+1] - ii[i];
    1511:       aj          = a->j + ii[i];
    1512:       aa          = a_a + ii[i];
    1513:       sum         = 0.0;
    1514:       nonzerorow += (n>0);
    1515:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1516:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
    1517:       y[*ridx++] = sum;
    1518:     }
    1519:   } else { /* do not use compressed row format */
    1520:     ii = a->i;
    1521:     for (i=0; i<m; i++) {
    1522:       n           = ii[i+1] - ii[i];
    1523:       aj          = a->j + ii[i];
    1524:       aa          = a_a + ii[i];
    1525:       sum         = 0.0;
    1526:       nonzerorow += (n>0);
    1527:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1528:       y[i] = sum;
    1529:     }
    1530:   }
    1531:   PetscLogFlops(2.0*a->nz - nonzerorow);
    1532:   VecRestoreArrayRead(xx,&x);
    1533:   VecRestoreArray(yy,&y);
    1534:   MatSeqAIJRestoreArrayRead(A,&a_a);
    1535:   return 0;
    1536: }

    1538: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    1539: {
    1540:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1541:   PetscScalar       *y,*z;
    1542:   const PetscScalar *x;
    1543:   const MatScalar   *aa,*a_a;
    1544:   PetscInt          m = A->rmap->n,*aj,*ii;
    1545:   PetscInt          n,i,*ridx=NULL;
    1546:   PetscScalar       sum;
    1547:   PetscBool         usecprow=a->compressedrow.use;

    1549:   MatSeqAIJGetArrayRead(A,&a_a);
    1550:   VecGetArrayRead(xx,&x);
    1551:   VecGetArrayPair(yy,zz,&y,&z);
    1552:   if (usecprow) { /* use compressed row format */
    1553:     if (zz != yy) {
    1554:       PetscArraycpy(z,y,m);
    1555:     }
    1556:     m    = a->compressedrow.nrows;
    1557:     ii   = a->compressedrow.i;
    1558:     ridx = a->compressedrow.rindex;
    1559:     for (i=0; i<m; i++) {
    1560:       n   = ii[i+1] - ii[i];
    1561:       aj  = a->j + ii[i];
    1562:       aa  = a_a + ii[i];
    1563:       sum = y[*ridx];
    1564:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1565:       z[*ridx++] = sum;
    1566:     }
    1567:   } else { /* do not use compressed row format */
    1568:     ii = a->i;
    1569:     for (i=0; i<m; i++) {
    1570:       n   = ii[i+1] - ii[i];
    1571:       aj  = a->j + ii[i];
    1572:       aa  = a_a + ii[i];
    1573:       sum = y[i];
    1574:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1575:       z[i] = sum;
    1576:     }
    1577:   }
    1578:   PetscLogFlops(2.0*a->nz);
    1579:   VecRestoreArrayRead(xx,&x);
    1580:   VecRestoreArrayPair(yy,zz,&y,&z);
    1581:   MatSeqAIJRestoreArrayRead(A,&a_a);
    1582:   return 0;
    1583: }

    1585: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
    1586: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    1587: {
    1588:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1589:   PetscScalar       *y,*z;
    1590:   const PetscScalar *x;
    1591:   const MatScalar   *aa,*a_a;
    1592:   const PetscInt    *aj,*ii,*ridx=NULL;
    1593:   PetscInt          m = A->rmap->n,n,i;
    1594:   PetscScalar       sum;
    1595:   PetscBool         usecprow=a->compressedrow.use;

    1597:   if (a->inode.use && a->inode.checked) {
    1598:     MatMultAdd_SeqAIJ_Inode(A,xx,yy,zz);
    1599:     return 0;
    1600:   }
    1601:   MatSeqAIJGetArrayRead(A,&a_a);
    1602:   VecGetArrayRead(xx,&x);
    1603:   VecGetArrayPair(yy,zz,&y,&z);
    1604:   if (usecprow) { /* use compressed row format */
    1605:     if (zz != yy) {
    1606:       PetscArraycpy(z,y,m);
    1607:     }
    1608:     m    = a->compressedrow.nrows;
    1609:     ii   = a->compressedrow.i;
    1610:     ridx = a->compressedrow.rindex;
    1611:     for (i=0; i<m; i++) {
    1612:       n   = ii[i+1] - ii[i];
    1613:       aj  = a->j + ii[i];
    1614:       aa  = a_a + ii[i];
    1615:       sum = y[*ridx];
    1616:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1617:       z[*ridx++] = sum;
    1618:     }
    1619:   } else { /* do not use compressed row format */
    1620:     ii = a->i;
    1621: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
    1622:     aj = a->j;
    1623:     aa = a_a;
    1624:     fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
    1625: #else
    1626:     for (i=0; i<m; i++) {
    1627:       n   = ii[i+1] - ii[i];
    1628:       aj  = a->j + ii[i];
    1629:       aa  = a_a + ii[i];
    1630:       sum = y[i];
    1631:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1632:       z[i] = sum;
    1633:     }
    1634: #endif
    1635:   }
    1636:   PetscLogFlops(2.0*a->nz);
    1637:   VecRestoreArrayRead(xx,&x);
    1638:   VecRestoreArrayPair(yy,zz,&y,&z);
    1639:   MatSeqAIJRestoreArrayRead(A,&a_a);
    1640:   return 0;
    1641: }

    1643: /*
    1644:      Adds diagonal pointers to sparse matrix structure.
    1645: */
    1646: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
    1647: {
    1648:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1649:   PetscInt       i,j,m = A->rmap->n;
    1650:   PetscBool      alreadySet = PETSC_TRUE;

    1652:   if (!a->diag) {
    1653:     PetscMalloc1(m,&a->diag);
    1654:     PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
    1655:     alreadySet = PETSC_FALSE;
    1656:   }
    1657:   for (i=0; i<A->rmap->n; i++) {
    1658:     /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
    1659:     if (alreadySet) {
    1660:       PetscInt pos = a->diag[i];
    1661:       if (pos >= a->i[i] && pos < a->i[i+1] && a->j[pos] == i) continue;
    1662:     }

    1664:     a->diag[i] = a->i[i+1];
    1665:     for (j=a->i[i]; j<a->i[i+1]; j++) {
    1666:       if (a->j[j] == i) {
    1667:         a->diag[i] = j;
    1668:         break;
    1669:       }
    1670:     }
    1671:   }
    1672:   return 0;
    1673: }

    1675: PetscErrorCode MatShift_SeqAIJ(Mat A,PetscScalar v)
    1676: {
    1677:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1678:   const PetscInt    *diag = (const PetscInt*)a->diag;
    1679:   const PetscInt    *ii = (const PetscInt*) a->i;
    1680:   PetscInt          i,*mdiag = NULL;
    1681:   PetscInt          cnt = 0; /* how many diagonals are missing */

    1683:   if (!A->preallocated || !a->nz) {
    1684:     MatSeqAIJSetPreallocation(A,1,NULL);
    1685:     MatShift_Basic(A,v);
    1686:     return 0;
    1687:   }

    1689:   if (a->diagonaldense) {
    1690:     cnt = 0;
    1691:   } else {
    1692:     PetscCalloc1(A->rmap->n,&mdiag);
    1693:     for (i=0; i<A->rmap->n; i++) {
    1694:       if (diag[i] >= ii[i+1]) {
    1695:         cnt++;
    1696:         mdiag[i] = 1;
    1697:       }
    1698:     }
    1699:   }
    1700:   if (!cnt) {
    1701:     MatShift_Basic(A,v);
    1702:   } else {
    1703:     PetscScalar *olda = a->a;  /* preserve pointers to current matrix nonzeros structure and values */
    1704:     PetscInt    *oldj = a->j, *oldi = a->i;
    1705:     PetscBool   singlemalloc = a->singlemalloc,free_a = a->free_a,free_ij = a->free_ij;

    1707:     a->a = NULL;
    1708:     a->j = NULL;
    1709:     a->i = NULL;
    1710:     /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
    1711:     for (i=0; i<A->rmap->n; i++) {
    1712:       a->imax[i] += mdiag[i];
    1713:       a->imax[i] = PetscMin(a->imax[i],A->cmap->n);
    1714:     }
    1715:     MatSeqAIJSetPreallocation_SeqAIJ(A,0,a->imax);

    1717:     /* copy old values into new matrix data structure */
    1718:     for (i=0; i<A->rmap->n; i++) {
    1719:       MatSetValues(A,1,&i,a->imax[i] - mdiag[i],&oldj[oldi[i]],&olda[oldi[i]],ADD_VALUES);
    1720:       if (i < A->cmap->n) {
    1721:         MatSetValue(A,i,i,v,ADD_VALUES);
    1722:       }
    1723:     }
    1724:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    1725:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    1726:     if (singlemalloc) {
    1727:       PetscFree3(olda,oldj,oldi);
    1728:     } else {
    1729:       if (free_a)  PetscFree(olda);
    1730:       if (free_ij) PetscFree(oldj);
    1731:       if (free_ij) PetscFree(oldi);
    1732:     }
    1733:   }
    1734:   PetscFree(mdiag);
    1735:   a->diagonaldense = PETSC_TRUE;
    1736:   return 0;
    1737: }

    1739: /*
    1740:      Checks for missing diagonals
    1741: */
    1742: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
    1743: {
    1744:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1745:   PetscInt       *diag,*ii = a->i,i;

    1747:   *missing = PETSC_FALSE;
    1748:   if (A->rmap->n > 0 && !ii) {
    1749:     *missing = PETSC_TRUE;
    1750:     if (d) *d = 0;
    1751:     PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
    1752:   } else {
    1753:     PetscInt n;
    1754:     n = PetscMin(A->rmap->n, A->cmap->n);
    1755:     diag = a->diag;
    1756:     for (i=0; i<n; i++) {
    1757:       if (diag[i] >= ii[i+1]) {
    1758:         *missing = PETSC_TRUE;
    1759:         if (d) *d = i;
    1760:         PetscInfo(A,"Matrix is missing diagonal number %" PetscInt_FMT "\n",i);
    1761:         break;
    1762:       }
    1763:     }
    1764:   }
    1765:   return 0;
    1766: }

    1768: #include <petscblaslapack.h>
    1769: #include <petsc/private/kernels/blockinvert.h>

    1771: /*
    1772:     Note that values is allocated externally by the PC and then passed into this routine
    1773: */
    1774: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A,PetscInt nblocks,const PetscInt *bsizes,PetscScalar *diag)
    1775: {
    1776:   PetscInt        n = A->rmap->n, i, ncnt = 0, *indx,j,bsizemax = 0,*v_pivots;
    1777:   PetscBool       allowzeropivot,zeropivotdetected=PETSC_FALSE;
    1778:   const PetscReal shift = 0.0;
    1779:   PetscInt        ipvt[5];
    1780:   PetscScalar     work[25],*v_work;

    1782:   allowzeropivot = PetscNot(A->erroriffailure);
    1783:   for (i=0; i<nblocks; i++) ncnt += bsizes[i];
    1785:   for (i=0; i<nblocks; i++) {
    1786:     bsizemax = PetscMax(bsizemax,bsizes[i]);
    1787:   }
    1788:   PetscMalloc1(bsizemax,&indx);
    1789:   if (bsizemax > 7) {
    1790:     PetscMalloc2(bsizemax,&v_work,bsizemax,&v_pivots);
    1791:   }
    1792:   ncnt = 0;
    1793:   for (i=0; i<nblocks; i++) {
    1794:     for (j=0; j<bsizes[i]; j++) indx[j] = ncnt+j;
    1795:     MatGetValues(A,bsizes[i],indx,bsizes[i],indx,diag);
    1796:     switch (bsizes[i]) {
    1797:     case 1:
    1798:       *diag = 1.0/(*diag);
    1799:       break;
    1800:     case 2:
    1801:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
    1802:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1803:       PetscKernel_A_gets_transpose_A_2(diag);
    1804:       break;
    1805:     case 3:
    1806:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
    1807:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1808:       PetscKernel_A_gets_transpose_A_3(diag);
    1809:       break;
    1810:     case 4:
    1811:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
    1812:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1813:       PetscKernel_A_gets_transpose_A_4(diag);
    1814:       break;
    1815:     case 5:
    1816:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
    1817:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1818:       PetscKernel_A_gets_transpose_A_5(diag);
    1819:       break;
    1820:     case 6:
    1821:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
    1822:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1823:       PetscKernel_A_gets_transpose_A_6(diag);
    1824:       break;
    1825:     case 7:
    1826:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
    1827:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1828:       PetscKernel_A_gets_transpose_A_7(diag);
    1829:       break;
    1830:     default:
    1831:       PetscKernel_A_gets_inverse_A(bsizes[i],diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
    1832:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1833:       PetscKernel_A_gets_transpose_A_N(diag,bsizes[i]);
    1834:     }
    1835:     ncnt   += bsizes[i];
    1836:     diag += bsizes[i]*bsizes[i];
    1837:   }
    1838:   if (bsizemax > 7) {
    1839:     PetscFree2(v_work,v_pivots);
    1840:   }
    1841:   PetscFree(indx);
    1842:   return 0;
    1843: }

    1845: /*
    1846:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
    1847: */
    1848: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
    1849: {
    1850:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*) A->data;
    1851:   PetscInt        i,*diag,m = A->rmap->n;
    1852:   const MatScalar *v;
    1853:   PetscScalar     *idiag,*mdiag;

    1855:   if (a->idiagvalid) return 0;
    1856:   MatMarkDiagonal_SeqAIJ(A);
    1857:   diag = a->diag;
    1858:   if (!a->idiag) {
    1859:     PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
    1860:     PetscLogObjectMemory((PetscObject)A,3*m*sizeof(PetscScalar));
    1861:   }

    1863:   mdiag = a->mdiag;
    1864:   idiag = a->idiag;
    1865:   MatSeqAIJGetArrayRead(A,&v);
    1866:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
    1867:     for (i=0; i<m; i++) {
    1868:       mdiag[i] = v[diag[i]];
    1869:       if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
    1870:         if (PetscRealPart(fshift)) {
    1871:           PetscInfo(A,"Zero diagonal on row %" PetscInt_FMT "\n",i);
    1872:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1873:           A->factorerror_zeropivot_value = 0.0;
    1874:           A->factorerror_zeropivot_row   = i;
    1875:         } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %" PetscInt_FMT,i);
    1876:       }
    1877:       idiag[i] = 1.0/v[diag[i]];
    1878:     }
    1879:     PetscLogFlops(m);
    1880:   } else {
    1881:     for (i=0; i<m; i++) {
    1882:       mdiag[i] = v[diag[i]];
    1883:       idiag[i] = omega/(fshift + v[diag[i]]);
    1884:     }
    1885:     PetscLogFlops(2.0*m);
    1886:   }
    1887:   a->idiagvalid = PETSC_TRUE;
    1888:   MatSeqAIJRestoreArrayRead(A,&v);
    1889:   return 0;
    1890: }

    1892: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
    1893: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
    1894: {
    1895:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1896:   PetscScalar       *x,d,sum,*t,scale;
    1897:   const MatScalar   *v,*idiag=NULL,*mdiag,*aa;
    1898:   const PetscScalar *b, *bs,*xb, *ts;
    1899:   PetscInt          n,m = A->rmap->n,i;
    1900:   const PetscInt    *idx,*diag;

    1902:   if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
    1903:     MatSOR_SeqAIJ_Inode(A,bb,omega,flag,fshift,its,lits,xx);
    1904:     return 0;
    1905:   }
    1906:   its = its*lits;

    1908:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
    1909:   if (!a->idiagvalid) MatInvertDiagonal_SeqAIJ(A,omega,fshift);
    1910:   a->fshift = fshift;
    1911:   a->omega  = omega;

    1913:   diag  = a->diag;
    1914:   t     = a->ssor_work;
    1915:   idiag = a->idiag;
    1916:   mdiag = a->mdiag;

    1918:   MatSeqAIJGetArrayRead(A,&aa);
    1919:   VecGetArray(xx,&x);
    1920:   VecGetArrayRead(bb,&b);
    1921:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
    1922:   if (flag == SOR_APPLY_UPPER) {
    1923:     /* apply (U + D/omega) to the vector */
    1924:     bs = b;
    1925:     for (i=0; i<m; i++) {
    1926:       d   = fshift + mdiag[i];
    1927:       n   = a->i[i+1] - diag[i] - 1;
    1928:       idx = a->j + diag[i] + 1;
    1929:       v   = aa + diag[i] + 1;
    1930:       sum = b[i]*d/omega;
    1931:       PetscSparseDensePlusDot(sum,bs,v,idx,n);
    1932:       x[i] = sum;
    1933:     }
    1934:     VecRestoreArray(xx,&x);
    1935:     VecRestoreArrayRead(bb,&b);
    1936:     MatSeqAIJRestoreArrayRead(A,&aa);
    1937:     PetscLogFlops(a->nz);
    1938:     return 0;
    1939:   }

    1942:   else if (flag & SOR_EISENSTAT) {
    1943:     /* Let  A = L + U + D; where L is lower triangular,
    1944:     U is upper triangular, E = D/omega; This routine applies

    1946:             (L + E)^{-1} A (U + E)^{-1}

    1948:     to a vector efficiently using Eisenstat's trick.
    1949:     */
    1950:     scale = (2.0/omega) - 1.0;

    1952:     /*  x = (E + U)^{-1} b */
    1953:     for (i=m-1; i>=0; i--) {
    1954:       n   = a->i[i+1] - diag[i] - 1;
    1955:       idx = a->j + diag[i] + 1;
    1956:       v   = aa + diag[i] + 1;
    1957:       sum = b[i];
    1958:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
    1959:       x[i] = sum*idiag[i];
    1960:     }

    1962:     /*  t = b - (2*E - D)x */
    1963:     v = aa;
    1964:     for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];

    1966:     /*  t = (E + L)^{-1}t */
    1967:     ts   = t;
    1968:     diag = a->diag;
    1969:     for (i=0; i<m; i++) {
    1970:       n   = diag[i] - a->i[i];
    1971:       idx = a->j + a->i[i];
    1972:       v   = aa + a->i[i];
    1973:       sum = t[i];
    1974:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
    1975:       t[i] = sum*idiag[i];
    1976:       /*  x = x + t */
    1977:       x[i] += t[i];
    1978:     }

    1980:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
    1981:     VecRestoreArray(xx,&x);
    1982:     VecRestoreArrayRead(bb,&b);
    1983:     return 0;
    1984:   }
    1985:   if (flag & SOR_ZERO_INITIAL_GUESS) {
    1986:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
    1987:       for (i=0; i<m; i++) {
    1988:         n   = diag[i] - a->i[i];
    1989:         idx = a->j + a->i[i];
    1990:         v   = aa + a->i[i];
    1991:         sum = b[i];
    1992:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    1993:         t[i] = sum;
    1994:         x[i] = sum*idiag[i];
    1995:       }
    1996:       xb   = t;
    1997:       PetscLogFlops(a->nz);
    1998:     } else xb = b;
    1999:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
    2000:       for (i=m-1; i>=0; i--) {
    2001:         n   = a->i[i+1] - diag[i] - 1;
    2002:         idx = a->j + diag[i] + 1;
    2003:         v   = aa + diag[i] + 1;
    2004:         sum = xb[i];
    2005:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2006:         if (xb == b) {
    2007:           x[i] = sum*idiag[i];
    2008:         } else {
    2009:           x[i] = (1-omega)*x[i] + sum*idiag[i];  /* omega in idiag */
    2010:         }
    2011:       }
    2012:       PetscLogFlops(a->nz); /* assumes 1/2 in upper */
    2013:     }
    2014:     its--;
    2015:   }
    2016:   while (its--) {
    2017:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
    2018:       for (i=0; i<m; i++) {
    2019:         /* lower */
    2020:         n   = diag[i] - a->i[i];
    2021:         idx = a->j + a->i[i];
    2022:         v   = aa + a->i[i];
    2023:         sum = b[i];
    2024:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2025:         t[i] = sum;             /* save application of the lower-triangular part */
    2026:         /* upper */
    2027:         n   = a->i[i+1] - diag[i] - 1;
    2028:         idx = a->j + diag[i] + 1;
    2029:         v   = aa + diag[i] + 1;
    2030:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2031:         x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
    2032:       }
    2033:       xb   = t;
    2034:       PetscLogFlops(2.0*a->nz);
    2035:     } else xb = b;
    2036:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
    2037:       for (i=m-1; i>=0; i--) {
    2038:         sum = xb[i];
    2039:         if (xb == b) {
    2040:           /* whole matrix (no checkpointing available) */
    2041:           n   = a->i[i+1] - a->i[i];
    2042:           idx = a->j + a->i[i];
    2043:           v   = aa + a->i[i];
    2044:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2045:           x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
    2046:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
    2047:           n   = a->i[i+1] - diag[i] - 1;
    2048:           idx = a->j + diag[i] + 1;
    2049:           v   = aa + diag[i] + 1;
    2050:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2051:           x[i] = (1. - omega)*x[i] + sum*idiag[i];  /* omega in idiag */
    2052:         }
    2053:       }
    2054:       if (xb == b) {
    2055:         PetscLogFlops(2.0*a->nz);
    2056:       } else {
    2057:         PetscLogFlops(a->nz); /* assumes 1/2 in upper */
    2058:       }
    2059:     }
    2060:   }
    2061:   MatSeqAIJRestoreArrayRead(A,&aa);
    2062:   VecRestoreArray(xx,&x);
    2063:   VecRestoreArrayRead(bb,&b);
    2064:   return 0;
    2065: }

    2067: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
    2068: {
    2069:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

    2071:   info->block_size   = 1.0;
    2072:   info->nz_allocated = a->maxnz;
    2073:   info->nz_used      = a->nz;
    2074:   info->nz_unneeded  = (a->maxnz - a->nz);
    2075:   info->assemblies   = A->num_ass;
    2076:   info->mallocs      = A->info.mallocs;
    2077:   info->memory       = ((PetscObject)A)->mem;
    2078:   if (A->factortype) {
    2079:     info->fill_ratio_given  = A->info.fill_ratio_given;
    2080:     info->fill_ratio_needed = A->info.fill_ratio_needed;
    2081:     info->factor_mallocs    = A->info.factor_mallocs;
    2082:   } else {
    2083:     info->fill_ratio_given  = 0;
    2084:     info->fill_ratio_needed = 0;
    2085:     info->factor_mallocs    = 0;
    2086:   }
    2087:   return 0;
    2088: }

    2090: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    2091: {
    2092:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2093:   PetscInt          i,m = A->rmap->n - 1;
    2094:   const PetscScalar *xx;
    2095:   PetscScalar       *bb,*aa;
    2096:   PetscInt          d = 0;

    2098:   if (x && b) {
    2099:     VecGetArrayRead(x,&xx);
    2100:     VecGetArray(b,&bb);
    2101:     for (i=0; i<N; i++) {
    2103:       if (rows[i] >= A->cmap->n) continue;
    2104:       bb[rows[i]] = diag*xx[rows[i]];
    2105:     }
    2106:     VecRestoreArrayRead(x,&xx);
    2107:     VecRestoreArray(b,&bb);
    2108:   }

    2110:   MatSeqAIJGetArray(A,&aa);
    2111:   if (a->keepnonzeropattern) {
    2112:     for (i=0; i<N; i++) {
    2114:       PetscArrayzero(&aa[a->i[rows[i]]],a->ilen[rows[i]]);
    2115:     }
    2116:     if (diag != 0.0) {
    2117:       for (i=0; i<N; i++) {
    2118:         d = rows[i];
    2119:         if (rows[i] >= A->cmap->n) continue;
    2121:       }
    2122:       for (i=0; i<N; i++) {
    2123:         if (rows[i] >= A->cmap->n) continue;
    2124:         aa[a->diag[rows[i]]] = diag;
    2125:       }
    2126:     }
    2127:   } else {
    2128:     if (diag != 0.0) {
    2129:       for (i=0; i<N; i++) {
    2131:         if (a->ilen[rows[i]] > 0) {
    2132:           if (rows[i] >= A->cmap->n) {
    2133:             a->ilen[rows[i]] = 0;
    2134:           } else {
    2135:             a->ilen[rows[i]]    = 1;
    2136:             aa[a->i[rows[i]]]   = diag;
    2137:             a->j[a->i[rows[i]]] = rows[i];
    2138:           }
    2139:         } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
    2140:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
    2141:         }
    2142:       }
    2143:     } else {
    2144:       for (i=0; i<N; i++) {
    2146:         a->ilen[rows[i]] = 0;
    2147:       }
    2148:     }
    2149:     A->nonzerostate++;
    2150:   }
    2151:   MatSeqAIJRestoreArray(A,&aa);
    2152:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
    2153:   return 0;
    2154: }

    2156: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    2157: {
    2158:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2159:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
    2160:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
    2161:   const PetscScalar *xx;
    2162:   PetscScalar       *bb,*aa;

    2164:   if (!N) return 0;
    2165:   MatSeqAIJGetArray(A,&aa);
    2166:   if (x && b) {
    2167:     VecGetArrayRead(x,&xx);
    2168:     VecGetArray(b,&bb);
    2169:     vecs = PETSC_TRUE;
    2170:   }
    2171:   PetscCalloc1(A->rmap->n,&zeroed);
    2172:   for (i=0; i<N; i++) {
    2174:     PetscArrayzero(&aa[a->i[rows[i]]],a->ilen[rows[i]]);

    2176:     zeroed[rows[i]] = PETSC_TRUE;
    2177:   }
    2178:   for (i=0; i<A->rmap->n; i++) {
    2179:     if (!zeroed[i]) {
    2180:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    2181:         if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
    2182:           if (vecs) bb[i] -= aa[j]*xx[a->j[j]];
    2183:           aa[j] = 0.0;
    2184:         }
    2185:       }
    2186:     } else if (vecs && i < A->cmap->N) bb[i] = diag*xx[i];
    2187:   }
    2188:   if (x && b) {
    2189:     VecRestoreArrayRead(x,&xx);
    2190:     VecRestoreArray(b,&bb);
    2191:   }
    2192:   PetscFree(zeroed);
    2193:   if (diag != 0.0) {
    2194:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
    2195:     if (missing) {
    2196:       for (i=0; i<N; i++) {
    2197:         if (rows[i] >= A->cmap->N) continue;
    2199:         MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
    2200:       }
    2201:     } else {
    2202:       for (i=0; i<N; i++) {
    2203:         aa[a->diag[rows[i]]] = diag;
    2204:       }
    2205:     }
    2206:   }
    2207:   MatSeqAIJRestoreArray(A,&aa);
    2208:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
    2209:   return 0;
    2210: }

    2212: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    2213: {
    2214:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2215:   const PetscScalar *aa;
    2216:   PetscInt          *itmp;

    2218:   MatSeqAIJGetArrayRead(A,&aa);
    2219:   *nz = a->i[row+1] - a->i[row];
    2220:   if (v) *v = (PetscScalar*)(aa + a->i[row]);
    2221:   if (idx) {
    2222:     itmp = a->j + a->i[row];
    2223:     if (*nz) *idx = itmp;
    2224:     else *idx = NULL;
    2225:   }
    2226:   MatSeqAIJRestoreArrayRead(A,&aa);
    2227:   return 0;
    2228: }

    2230: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    2231: {
    2232:   if (nz)  *nz = 0;
    2233:   if (idx) *idx = NULL;
    2234:   if (v)   *v = NULL;
    2235:   return 0;
    2236: }

    2238: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
    2239: {
    2240:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
    2241:   const MatScalar *v;
    2242:   PetscReal       sum = 0.0;
    2243:   PetscInt        i,j;

    2245:   MatSeqAIJGetArrayRead(A,&v);
    2246:   if (type == NORM_FROBENIUS) {
    2247: #if defined(PETSC_USE_REAL___FP16)
    2248:     PetscBLASInt one = 1,nz = a->nz;
    2249:     PetscStackCallBLAS("BLASnrm2",*nrm = BLASnrm2_(&nz,v,&one));
    2250: #else
    2251:     for (i=0; i<a->nz; i++) {
    2252:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
    2253:     }
    2254:     *nrm = PetscSqrtReal(sum);
    2255: #endif
    2256:     PetscLogFlops(2.0*a->nz);
    2257:   } else if (type == NORM_1) {
    2258:     PetscReal *tmp;
    2259:     PetscInt  *jj = a->j;
    2260:     PetscCalloc1(A->cmap->n+1,&tmp);
    2261:     *nrm = 0.0;
    2262:     for (j=0; j<a->nz; j++) {
    2263:       tmp[*jj++] += PetscAbsScalar(*v);  v++;
    2264:     }
    2265:     for (j=0; j<A->cmap->n; j++) {
    2266:       if (tmp[j] > *nrm) *nrm = tmp[j];
    2267:     }
    2268:     PetscFree(tmp);
    2269:     PetscLogFlops(PetscMax(a->nz-1,0));
    2270:   } else if (type == NORM_INFINITY) {
    2271:     *nrm = 0.0;
    2272:     for (j=0; j<A->rmap->n; j++) {
    2273:       const PetscScalar *v2 = v + a->i[j];
    2274:       sum = 0.0;
    2275:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
    2276:         sum += PetscAbsScalar(*v2); v2++;
    2277:       }
    2278:       if (sum > *nrm) *nrm = sum;
    2279:     }
    2280:     PetscLogFlops(PetscMax(a->nz-1,0));
    2281:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
    2282:   MatSeqAIJRestoreArrayRead(A,&v);
    2283:   return 0;
    2284: }

    2286: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
    2287: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
    2288: {
    2289:   PetscInt       i,j,anzj;
    2290:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b;
    2291:   PetscInt       an=A->cmap->N,am=A->rmap->N;
    2292:   PetscInt       *ati,*atj,*atfill,*ai=a->i,*aj=a->j;

    2294:   /* Allocate space for symbolic transpose info and work array */
    2295:   PetscCalloc1(an+1,&ati);
    2296:   PetscMalloc1(ai[am],&atj);
    2297:   PetscMalloc1(an,&atfill);

    2299:   /* Walk through aj and count ## of non-zeros in each row of A^T. */
    2300:   /* Note: offset by 1 for fast conversion into csr format. */
    2301:   for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
    2302:   /* Form ati for csr format of A^T. */
    2303:   for (i=0;i<an;i++) ati[i+1] += ati[i];

    2305:   /* Copy ati into atfill so we have locations of the next free space in atj */
    2306:   PetscArraycpy(atfill,ati,an);

    2308:   /* Walk through A row-wise and mark nonzero entries of A^T. */
    2309:   for (i=0;i<am;i++) {
    2310:     anzj = ai[i+1] - ai[i];
    2311:     for (j=0;j<anzj;j++) {
    2312:       atj[atfill[*aj]] = i;
    2313:       atfill[*aj++]   += 1;
    2314:     }
    2315:   }

    2317:   /* Clean up temporary space and complete requests. */
    2318:   PetscFree(atfill);
    2319:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
    2320:   MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
    2321:   MatSetType(*B,((PetscObject)A)->type_name);

    2323:   b          = (Mat_SeqAIJ*)((*B)->data);
    2324:   b->free_a  = PETSC_FALSE;
    2325:   b->free_ij = PETSC_TRUE;
    2326:   b->nonew   = 0;
    2327:   return 0;
    2328: }

    2330: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
    2331: {
    2332:   Mat_SeqAIJ      *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
    2333:   PetscInt        *adx,*bdx,*aii,*bii,*aptr,*bptr;
    2334:   const MatScalar *va,*vb;
    2335:   PetscInt        ma,na,mb,nb, i;

    2337:   MatGetSize(A,&ma,&na);
    2338:   MatGetSize(B,&mb,&nb);
    2339:   if (ma!=nb || na!=mb) {
    2340:     *f = PETSC_FALSE;
    2341:     return 0;
    2342:   }
    2343:   MatSeqAIJGetArrayRead(A,&va);
    2344:   MatSeqAIJGetArrayRead(B,&vb);
    2345:   aii  = aij->i; bii = bij->i;
    2346:   adx  = aij->j; bdx = bij->j;
    2347:   PetscMalloc1(ma,&aptr);
    2348:   PetscMalloc1(mb,&bptr);
    2349:   for (i=0; i<ma; i++) aptr[i] = aii[i];
    2350:   for (i=0; i<mb; i++) bptr[i] = bii[i];

    2352:   *f = PETSC_TRUE;
    2353:   for (i=0; i<ma; i++) {
    2354:     while (aptr[i]<aii[i+1]) {
    2355:       PetscInt    idc,idr;
    2356:       PetscScalar vc,vr;
    2357:       /* column/row index/value */
    2358:       idc = adx[aptr[i]];
    2359:       idr = bdx[bptr[idc]];
    2360:       vc  = va[aptr[i]];
    2361:       vr  = vb[bptr[idc]];
    2362:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
    2363:         *f = PETSC_FALSE;
    2364:         goto done;
    2365:       } else {
    2366:         aptr[i]++;
    2367:         if (B || i!=idc) bptr[idc]++;
    2368:       }
    2369:     }
    2370:   }
    2371: done:
    2372:   PetscFree(aptr);
    2373:   PetscFree(bptr);
    2374:   MatSeqAIJRestoreArrayRead(A,&va);
    2375:   MatSeqAIJRestoreArrayRead(B,&vb);
    2376:   return 0;
    2377: }

    2379: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
    2380: {
    2381:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
    2382:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
    2383:   MatScalar      *va,*vb;
    2384:   PetscInt       ma,na,mb,nb, i;

    2386:   MatGetSize(A,&ma,&na);
    2387:   MatGetSize(B,&mb,&nb);
    2388:   if (ma!=nb || na!=mb) {
    2389:     *f = PETSC_FALSE;
    2390:     return 0;
    2391:   }
    2392:   aii  = aij->i; bii = bij->i;
    2393:   adx  = aij->j; bdx = bij->j;
    2394:   va   = aij->a; vb = bij->a;
    2395:   PetscMalloc1(ma,&aptr);
    2396:   PetscMalloc1(mb,&bptr);
    2397:   for (i=0; i<ma; i++) aptr[i] = aii[i];
    2398:   for (i=0; i<mb; i++) bptr[i] = bii[i];

    2400:   *f = PETSC_TRUE;
    2401:   for (i=0; i<ma; i++) {
    2402:     while (aptr[i]<aii[i+1]) {
    2403:       PetscInt    idc,idr;
    2404:       PetscScalar vc,vr;
    2405:       /* column/row index/value */
    2406:       idc = adx[aptr[i]];
    2407:       idr = bdx[bptr[idc]];
    2408:       vc  = va[aptr[i]];
    2409:       vr  = vb[bptr[idc]];
    2410:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
    2411:         *f = PETSC_FALSE;
    2412:         goto done;
    2413:       } else {
    2414:         aptr[i]++;
    2415:         if (B || i!=idc) bptr[idc]++;
    2416:       }
    2417:     }
    2418:   }
    2419: done:
    2420:   PetscFree(aptr);
    2421:   PetscFree(bptr);
    2422:   return 0;
    2423: }

    2425: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
    2426: {
    2427:   MatIsTranspose_SeqAIJ(A,A,tol,f);
    2428:   return 0;
    2429: }

    2431: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
    2432: {
    2433:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
    2434:   return 0;
    2435: }

    2437: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
    2438: {
    2439:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2440:   const PetscScalar *l,*r;
    2441:   PetscScalar       x;
    2442:   MatScalar         *v;
    2443:   PetscInt          i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz;
    2444:   const PetscInt    *jj;

    2446:   if (ll) {
    2447:     /* The local size is used so that VecMPI can be passed to this routine
    2448:        by MatDiagonalScale_MPIAIJ */
    2449:     VecGetLocalSize(ll,&m);
    2451:     VecGetArrayRead(ll,&l);
    2452:     MatSeqAIJGetArray(A,&v);
    2453:     for (i=0; i<m; i++) {
    2454:       x = l[i];
    2455:       M = a->i[i+1] - a->i[i];
    2456:       for (j=0; j<M; j++) (*v++) *= x;
    2457:     }
    2458:     VecRestoreArrayRead(ll,&l);
    2459:     PetscLogFlops(nz);
    2460:     MatSeqAIJRestoreArray(A,&v);
    2461:   }
    2462:   if (rr) {
    2463:     VecGetLocalSize(rr,&n);
    2465:     VecGetArrayRead(rr,&r);
    2466:     MatSeqAIJGetArray(A,&v);
    2467:     jj = a->j;
    2468:     for (i=0; i<nz; i++) (*v++) *= r[*jj++];
    2469:     MatSeqAIJRestoreArray(A,&v);
    2470:     VecRestoreArrayRead(rr,&r);
    2471:     PetscLogFlops(nz);
    2472:   }
    2473:   MatSeqAIJInvalidateDiagonal(A);
    2474:   return 0;
    2475: }

    2477: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
    2478: {
    2479:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data,*c;
    2480:   PetscInt          *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
    2481:   PetscInt          row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
    2482:   const PetscInt    *irow,*icol;
    2483:   const PetscScalar *aa;
    2484:   PetscInt          nrows,ncols;
    2485:   PetscInt          *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
    2486:   MatScalar         *a_new,*mat_a;
    2487:   Mat               C;
    2488:   PetscBool         stride;

    2490:   ISGetIndices(isrow,&irow);
    2491:   ISGetLocalSize(isrow,&nrows);
    2492:   ISGetLocalSize(iscol,&ncols);

    2494:   PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
    2495:   if (stride) {
    2496:     ISStrideGetInfo(iscol,&first,&step);
    2497:   } else {
    2498:     first = 0;
    2499:     step  = 0;
    2500:   }
    2501:   if (stride && step == 1) {
    2502:     /* special case of contiguous rows */
    2503:     PetscMalloc2(nrows,&lens,nrows,&starts);
    2504:     /* loop over new rows determining lens and starting points */
    2505:     for (i=0; i<nrows; i++) {
    2506:       kstart = ai[irow[i]];
    2507:       kend   = kstart + ailen[irow[i]];
    2508:       starts[i] = kstart;
    2509:       for (k=kstart; k<kend; k++) {
    2510:         if (aj[k] >= first) {
    2511:           starts[i] = k;
    2512:           break;
    2513:         }
    2514:       }
    2515:       sum = 0;
    2516:       while (k < kend) {
    2517:         if (aj[k++] >= first+ncols) break;
    2518:         sum++;
    2519:       }
    2520:       lens[i] = sum;
    2521:     }
    2522:     /* create submatrix */
    2523:     if (scall == MAT_REUSE_MATRIX) {
    2524:       PetscInt n_cols,n_rows;
    2525:       MatGetSize(*B,&n_rows,&n_cols);
    2527:       MatZeroEntries(*B);
    2528:       C    = *B;
    2529:     } else {
    2530:       PetscInt rbs,cbs;
    2531:       MatCreate(PetscObjectComm((PetscObject)A),&C);
    2532:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
    2533:       ISGetBlockSize(isrow,&rbs);
    2534:       ISGetBlockSize(iscol,&cbs);
    2535:       MatSetBlockSizes(C,rbs,cbs);
    2536:       MatSetType(C,((PetscObject)A)->type_name);
    2537:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
    2538:     }
    2539:     c = (Mat_SeqAIJ*)C->data;

    2541:     /* loop over rows inserting into submatrix */
    2542:     a_new = c->a;
    2543:     j_new = c->j;
    2544:     i_new = c->i;
    2545:     MatSeqAIJGetArrayRead(A,&aa);
    2546:     for (i=0; i<nrows; i++) {
    2547:       ii    = starts[i];
    2548:       lensi = lens[i];
    2549:       for (k=0; k<lensi; k++) {
    2550:         *j_new++ = aj[ii+k] - first;
    2551:       }
    2552:       PetscArraycpy(a_new,aa + starts[i],lensi);
    2553:       a_new     += lensi;
    2554:       i_new[i+1] = i_new[i] + lensi;
    2555:       c->ilen[i] = lensi;
    2556:     }
    2557:     MatSeqAIJRestoreArrayRead(A,&aa);
    2558:     PetscFree2(lens,starts);
    2559:   } else {
    2560:     ISGetIndices(iscol,&icol);
    2561:     PetscCalloc1(oldcols,&smap);
    2562:     PetscMalloc1(1+nrows,&lens);
    2563:     for (i=0; i<ncols; i++) {
    2565:       smap[icol[i]] = i+1;
    2566:     }

    2568:     /* determine lens of each row */
    2569:     for (i=0; i<nrows; i++) {
    2570:       kstart  = ai[irow[i]];
    2571:       kend    = kstart + a->ilen[irow[i]];
    2572:       lens[i] = 0;
    2573:       for (k=kstart; k<kend; k++) {
    2574:         if (smap[aj[k]]) {
    2575:           lens[i]++;
    2576:         }
    2577:       }
    2578:     }
    2579:     /* Create and fill new matrix */
    2580:     if (scall == MAT_REUSE_MATRIX) {
    2581:       PetscBool equal;

    2583:       c = (Mat_SeqAIJ*)((*B)->data);
    2585:       PetscArraycmp(c->ilen,lens,(*B)->rmap->n,&equal);
    2587:       PetscArrayzero(c->ilen,(*B)->rmap->n);
    2588:       C    = *B;
    2589:     } else {
    2590:       PetscInt rbs,cbs;
    2591:       MatCreate(PetscObjectComm((PetscObject)A),&C);
    2592:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
    2593:       ISGetBlockSize(isrow,&rbs);
    2594:       ISGetBlockSize(iscol,&cbs);
    2595:       MatSetBlockSizes(C,rbs,cbs);
    2596:       MatSetType(C,((PetscObject)A)->type_name);
    2597:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
    2598:     }
    2599:     MatSeqAIJGetArrayRead(A,&aa);
    2600:     c = (Mat_SeqAIJ*)(C->data);
    2601:     for (i=0; i<nrows; i++) {
    2602:       row      = irow[i];
    2603:       kstart   = ai[row];
    2604:       kend     = kstart + a->ilen[row];
    2605:       mat_i    = c->i[i];
    2606:       mat_j    = c->j + mat_i;
    2607:       mat_a    = c->a + mat_i;
    2608:       mat_ilen = c->ilen + i;
    2609:       for (k=kstart; k<kend; k++) {
    2610:         if ((tcol=smap[a->j[k]])) {
    2611:           *mat_j++ = tcol - 1;
    2612:           *mat_a++ = aa[k];
    2613:           (*mat_ilen)++;

    2615:         }
    2616:       }
    2617:     }
    2618:     MatSeqAIJRestoreArrayRead(A,&aa);
    2619:     /* Free work space */
    2620:     ISRestoreIndices(iscol,&icol);
    2621:     PetscFree(smap);
    2622:     PetscFree(lens);
    2623:     /* sort */
    2624:     for (i = 0; i < nrows; i++) {
    2625:       PetscInt ilen;

    2627:       mat_i = c->i[i];
    2628:       mat_j = c->j + mat_i;
    2629:       mat_a = c->a + mat_i;
    2630:       ilen  = c->ilen[i];
    2631:       PetscSortIntWithScalarArray(ilen,mat_j,mat_a);
    2632:     }
    2633:   }
    2634: #if defined(PETSC_HAVE_DEVICE)
    2635:   MatBindToCPU(C,A->boundtocpu);
    2636: #endif
    2637:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    2638:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

    2640:   ISRestoreIndices(isrow,&irow);
    2641:   *B   = C;
    2642:   return 0;
    2643: }

    2645: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
    2646: {
    2647:   Mat            B;

    2649:   if (scall == MAT_INITIAL_MATRIX) {
    2650:     MatCreate(subComm,&B);
    2651:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
    2652:     MatSetBlockSizesFromMats(B,mat,mat);
    2653:     MatSetType(B,MATSEQAIJ);
    2654:     MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
    2655:     *subMat = B;
    2656:   } else {
    2657:     MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
    2658:   }
    2659:   return 0;
    2660: }

    2662: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
    2663: {
    2664:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
    2665:   Mat            outA;
    2666:   PetscBool      row_identity,col_identity;

    2670:   ISIdentity(row,&row_identity);
    2671:   ISIdentity(col,&col_identity);

    2673:   outA             = inA;
    2674:   outA->factortype = MAT_FACTOR_LU;
    2675:   PetscFree(inA->solvertype);
    2676:   PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);

    2678:   PetscObjectReference((PetscObject)row);
    2679:   ISDestroy(&a->row);

    2681:   a->row = row;

    2683:   PetscObjectReference((PetscObject)col);
    2684:   ISDestroy(&a->col);

    2686:   a->col = col;

    2688:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
    2689:   ISDestroy(&a->icol);
    2690:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
    2691:   PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);

    2693:   if (!a->solve_work) { /* this matrix may have been factored before */
    2694:     PetscMalloc1(inA->rmap->n+1,&a->solve_work);
    2695:     PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
    2696:   }

    2698:   MatMarkDiagonal_SeqAIJ(inA);
    2699:   if (row_identity && col_identity) {
    2700:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
    2701:   } else {
    2702:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
    2703:   }
    2704:   return 0;
    2705: }

    2707: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
    2708: {
    2709:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
    2710:   PetscScalar    *v;
    2711:   PetscBLASInt   one = 1,bnz;

    2713:   MatSeqAIJGetArray(inA,&v);
    2714:   PetscBLASIntCast(a->nz,&bnz);
    2715:   PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&alpha,v,&one));
    2716:   PetscLogFlops(a->nz);
    2717:   MatSeqAIJRestoreArray(inA,&v);
    2718:   MatSeqAIJInvalidateDiagonal(inA);
    2719:   return 0;
    2720: }

    2722: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
    2723: {
    2724:   PetscInt       i;

    2726:   if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
    2727:     PetscFree4(submatj->sbuf1,submatj->ptr,submatj->tmp,submatj->ctr);

    2729:     for (i=0; i<submatj->nrqr; ++i) {
    2730:       PetscFree(submatj->sbuf2[i]);
    2731:     }
    2732:     PetscFree3(submatj->sbuf2,submatj->req_size,submatj->req_source1);

    2734:     if (submatj->rbuf1) {
    2735:       PetscFree(submatj->rbuf1[0]);
    2736:       PetscFree(submatj->rbuf1);
    2737:     }

    2739:     for (i=0; i<submatj->nrqs; ++i) {
    2740:       PetscFree(submatj->rbuf3[i]);
    2741:     }
    2742:     PetscFree3(submatj->req_source2,submatj->rbuf2,submatj->rbuf3);
    2743:     PetscFree(submatj->pa);
    2744:   }

    2746: #if defined(PETSC_USE_CTABLE)
    2747:   PetscTableDestroy((PetscTable*)&submatj->rmap);
    2748:   if (submatj->cmap_loc) PetscFree(submatj->cmap_loc);
    2749:   PetscFree(submatj->rmap_loc);
    2750: #else
    2751:   PetscFree(submatj->rmap);
    2752: #endif

    2754:   if (!submatj->allcolumns) {
    2755: #if defined(PETSC_USE_CTABLE)
    2756:     PetscTableDestroy((PetscTable*)&submatj->cmap);
    2757: #else
    2758:     PetscFree(submatj->cmap);
    2759: #endif
    2760:   }
    2761:   PetscFree(submatj->row2proc);

    2763:   PetscFree(submatj);
    2764:   return 0;
    2765: }

    2767: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
    2768: {
    2769:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data;
    2770:   Mat_SubSppt    *submatj = c->submatis1;

    2772:   (*submatj->destroy)(C);
    2773:   MatDestroySubMatrix_Private(submatj);
    2774:   return 0;
    2775: }

    2777: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n,Mat *mat[])
    2778: {
    2779:   PetscInt       i;
    2780:   Mat            C;
    2781:   Mat_SeqAIJ     *c;
    2782:   Mat_SubSppt    *submatj;

    2784:   for (i=0; i<n; i++) {
    2785:     C       = (*mat)[i];
    2786:     c       = (Mat_SeqAIJ*)C->data;
    2787:     submatj = c->submatis1;
    2788:     if (submatj) {
    2789:       if (--((PetscObject)C)->refct <= 0) {
    2790:         (*submatj->destroy)(C);
    2791:         MatDestroySubMatrix_Private(submatj);
    2792:         PetscFree(C->defaultvectype);
    2793:         PetscLayoutDestroy(&C->rmap);
    2794:         PetscLayoutDestroy(&C->cmap);
    2795:         PetscHeaderDestroy(&C);
    2796:       }
    2797:     } else {
    2798:       MatDestroy(&C);
    2799:     }
    2800:   }

    2802:   /* Destroy Dummy submatrices created for reuse */
    2803:   MatDestroySubMatrices_Dummy(n,mat);

    2805:   PetscFree(*mat);
    2806:   return 0;
    2807: }

    2809: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
    2810: {
    2811:   PetscInt       i;

    2813:   if (scall == MAT_INITIAL_MATRIX) {
    2814:     PetscCalloc1(n+1,B);
    2815:   }

    2817:   for (i=0; i<n; i++) {
    2818:     MatCreateSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
    2819:   }
    2820:   return 0;
    2821: }

    2823: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
    2824: {
    2825:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2826:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
    2827:   const PetscInt *idx;
    2828:   PetscInt       start,end,*ai,*aj;
    2829:   PetscBT        table;

    2831:   m  = A->rmap->n;
    2832:   ai = a->i;
    2833:   aj = a->j;

    2837:   PetscMalloc1(m+1,&nidx);
    2838:   PetscBTCreate(m,&table);

    2840:   for (i=0; i<is_max; i++) {
    2841:     /* Initialize the two local arrays */
    2842:     isz  = 0;
    2843:     PetscBTMemzero(m,table);

    2845:     /* Extract the indices, assume there can be duplicate entries */
    2846:     ISGetIndices(is[i],&idx);
    2847:     ISGetLocalSize(is[i],&n);

    2849:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
    2850:     for (j=0; j<n; ++j) {
    2851:       if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
    2852:     }
    2853:     ISRestoreIndices(is[i],&idx);
    2854:     ISDestroy(&is[i]);

    2856:     k = 0;
    2857:     for (j=0; j<ov; j++) { /* for each overlap */
    2858:       n = isz;
    2859:       for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
    2860:         row   = nidx[k];
    2861:         start = ai[row];
    2862:         end   = ai[row+1];
    2863:         for (l = start; l<end; l++) {
    2864:           val = aj[l];
    2865:           if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
    2866:         }
    2867:       }
    2868:     }
    2869:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
    2870:   }
    2871:   PetscBTDestroy(&table);
    2872:   PetscFree(nidx);
    2873:   return 0;
    2874: }

    2876: /* -------------------------------------------------------------- */
    2877: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
    2878: {
    2879:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2880:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
    2881:   const PetscInt *row,*col;
    2882:   PetscInt       *cnew,j,*lens;
    2883:   IS             icolp,irowp;
    2884:   PetscInt       *cwork = NULL;
    2885:   PetscScalar    *vwork = NULL;

    2887:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
    2888:   ISGetIndices(irowp,&row);
    2889:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
    2890:   ISGetIndices(icolp,&col);

    2892:   /* determine lengths of permuted rows */
    2893:   PetscMalloc1(m+1,&lens);
    2894:   for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
    2895:   MatCreate(PetscObjectComm((PetscObject)A),B);
    2896:   MatSetSizes(*B,m,n,m,n);
    2897:   MatSetBlockSizesFromMats(*B,A,A);
    2898:   MatSetType(*B,((PetscObject)A)->type_name);
    2899:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
    2900:   PetscFree(lens);

    2902:   PetscMalloc1(n,&cnew);
    2903:   for (i=0; i<m; i++) {
    2904:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
    2905:     for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
    2906:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
    2907:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
    2908:   }
    2909:   PetscFree(cnew);

    2911:   (*B)->assembled = PETSC_FALSE;

    2913: #if defined(PETSC_HAVE_DEVICE)
    2914:   MatBindToCPU(*B,A->boundtocpu);
    2915: #endif
    2916:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
    2917:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
    2918:   ISRestoreIndices(irowp,&row);
    2919:   ISRestoreIndices(icolp,&col);
    2920:   ISDestroy(&irowp);
    2921:   ISDestroy(&icolp);
    2922:   if (rowp == colp) {
    2923:     MatPropagateSymmetryOptions(A,*B);
    2924:   }
    2925:   return 0;
    2926: }

    2928: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
    2929: {
    2930:   /* If the two matrices have the same copy implementation, use fast copy. */
    2931:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
    2932:     Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2933:     Mat_SeqAIJ        *b = (Mat_SeqAIJ*)B->data;
    2934:     const PetscScalar *aa;

    2936:     MatSeqAIJGetArrayRead(A,&aa);
    2938:     PetscArraycpy(b->a,aa,a->i[A->rmap->n]);
    2939:     PetscObjectStateIncrease((PetscObject)B);
    2940:     MatSeqAIJRestoreArrayRead(A,&aa);
    2941:   } else {
    2942:     MatCopy_Basic(A,B,str);
    2943:   }
    2944:   return 0;
    2945: }

    2947: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
    2948: {
    2949:   MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,NULL);
    2950:   return 0;
    2951: }

    2953: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
    2954: {
    2955:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

    2957:   *array = a->a;
    2958:   return 0;
    2959: }

    2961: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
    2962: {
    2963:   *array = NULL;
    2964:   return 0;
    2965: }

    2967: /*
    2968:    Computes the number of nonzeros per row needed for preallocation when X and Y
    2969:    have different nonzero structure.
    2970: */
    2971: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
    2972: {
    2973:   PetscInt       i,j,k,nzx,nzy;

    2975:   /* Set the number of nonzeros in the new matrix */
    2976:   for (i=0; i<m; i++) {
    2977:     const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
    2978:     nzx = xi[i+1] - xi[i];
    2979:     nzy = yi[i+1] - yi[i];
    2980:     nnz[i] = 0;
    2981:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
    2982:       for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
    2983:       if (k<nzy && yjj[k]==xjj[j]) k++;             /* Skip duplicate */
    2984:       nnz[i]++;
    2985:     }
    2986:     for (; k<nzy; k++) nnz[i]++;
    2987:   }
    2988:   return 0;
    2989: }

    2991: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
    2992: {
    2993:   PetscInt       m = Y->rmap->N;
    2994:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data;
    2995:   Mat_SeqAIJ     *y = (Mat_SeqAIJ*)Y->data;

    2997:   /* Set the number of nonzeros in the new matrix */
    2998:   MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
    2999:   return 0;
    3000: }

    3002: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
    3003: {
    3004:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;

    3006:   if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
    3007:     PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
    3008:     if (e) {
    3009:       PetscArraycmp(x->i,y->i,Y->rmap->n+1,&e);
    3010:       if (e) {
    3011:         PetscArraycmp(x->j,y->j,y->nz,&e);
    3012:         if (e) str = SAME_NONZERO_PATTERN;
    3013:       }
    3014:     }
    3016:   }
    3017:   if (str == SAME_NONZERO_PATTERN) {
    3018:     const PetscScalar *xa;
    3019:     PetscScalar       *ya,alpha = a;
    3020:     PetscBLASInt      one = 1,bnz;

    3022:     PetscBLASIntCast(x->nz,&bnz);
    3023:     MatSeqAIJGetArray(Y,&ya);
    3024:     MatSeqAIJGetArrayRead(X,&xa);
    3025:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,xa,&one,ya,&one));
    3026:     MatSeqAIJRestoreArrayRead(X,&xa);
    3027:     MatSeqAIJRestoreArray(Y,&ya);
    3028:     PetscLogFlops(2.0*bnz);
    3029:     MatSeqAIJInvalidateDiagonal(Y);
    3030:     PetscObjectStateIncrease((PetscObject)Y);
    3031:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
    3032:     MatAXPY_Basic(Y,a,X,str);
    3033:   } else {
    3034:     Mat      B;
    3035:     PetscInt *nnz;
    3036:     PetscMalloc1(Y->rmap->N,&nnz);
    3037:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
    3038:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
    3039:     MatSetLayouts(B,Y->rmap,Y->cmap);
    3040:     MatSetType(B,((PetscObject)Y)->type_name);
    3041:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
    3042:     MatSeqAIJSetPreallocation(B,0,nnz);
    3043:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
    3044:     MatHeaderMerge(Y,&B);
    3045:     PetscFree(nnz);
    3046:   }
    3047:   return 0;
    3048: }

    3050: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
    3051: {
    3052: #if defined(PETSC_USE_COMPLEX)
    3053:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ*)mat->data;
    3054:   PetscInt     i,nz;
    3055:   PetscScalar *a;

    3057:   nz = aij->nz;
    3058:   MatSeqAIJGetArray(mat,&a);
    3059:   for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
    3060:   MatSeqAIJRestoreArray(mat,&a);
    3061: #else
    3062: #endif
    3063:   return 0;
    3064: }

    3066: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3067: {
    3068:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3069:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    3070:   PetscReal       atmp;
    3071:   PetscScalar     *x;
    3072:   const MatScalar *aa,*av;

    3075:   MatSeqAIJGetArrayRead(A,&av);
    3076:   aa = av;
    3077:   ai = a->i;
    3078:   aj = a->j;

    3080:   VecSet(v,0.0);
    3081:   VecGetArrayWrite(v,&x);
    3082:   VecGetLocalSize(v,&n);
    3084:   for (i=0; i<m; i++) {
    3085:     ncols = ai[1] - ai[0]; ai++;
    3086:     for (j=0; j<ncols; j++) {
    3087:       atmp = PetscAbsScalar(*aa);
    3088:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
    3089:       aa++; aj++;
    3090:     }
    3091:   }
    3092:   VecRestoreArrayWrite(v,&x);
    3093:   MatSeqAIJRestoreArrayRead(A,&av);
    3094:   return 0;
    3095: }

    3097: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3098: {
    3099:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3100:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    3101:   PetscScalar     *x;
    3102:   const MatScalar *aa,*av;

    3105:   MatSeqAIJGetArrayRead(A,&av);
    3106:   aa = av;
    3107:   ai = a->i;
    3108:   aj = a->j;

    3110:   VecSet(v,0.0);
    3111:   VecGetArrayWrite(v,&x);
    3112:   VecGetLocalSize(v,&n);
    3114:   for (i=0; i<m; i++) {
    3115:     ncols = ai[1] - ai[0]; ai++;
    3116:     if (ncols == A->cmap->n) { /* row is dense */
    3117:       x[i] = *aa; if (idx) idx[i] = 0;
    3118:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
    3119:       x[i] = 0.0;
    3120:       if (idx) {
    3121:         for (j=0; j<ncols; j++) { /* find first implicit 0.0 in the row */
    3122:           if (aj[j] > j) {
    3123:             idx[i] = j;
    3124:             break;
    3125:           }
    3126:         }
    3127:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3128:         if (j==ncols && j < A->cmap->n) idx[i] = j;
    3129:       }
    3130:     }
    3131:     for (j=0; j<ncols; j++) {
    3132:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
    3133:       aa++; aj++;
    3134:     }
    3135:   }
    3136:   VecRestoreArrayWrite(v,&x);
    3137:   MatSeqAIJRestoreArrayRead(A,&av);
    3138:   return 0;
    3139: }

    3141: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3142: {
    3143:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3144:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    3145:   PetscScalar     *x;
    3146:   const MatScalar *aa,*av;

    3148:   MatSeqAIJGetArrayRead(A,&av);
    3149:   aa = av;
    3150:   ai = a->i;
    3151:   aj = a->j;

    3153:   VecSet(v,0.0);
    3154:   VecGetArrayWrite(v,&x);
    3155:   VecGetLocalSize(v,&n);
    3157:   for (i=0; i<m; i++) {
    3158:     ncols = ai[1] - ai[0]; ai++;
    3159:     if (ncols == A->cmap->n) { /* row is dense */
    3160:       x[i] = *aa; if (idx) idx[i] = 0;
    3161:     } else {  /* row is sparse so already KNOW minimum is 0.0 or higher */
    3162:       x[i] = 0.0;
    3163:       if (idx) {   /* find first implicit 0.0 in the row */
    3164:         for (j=0; j<ncols; j++) {
    3165:           if (aj[j] > j) {
    3166:             idx[i] = j;
    3167:             break;
    3168:           }
    3169:         }
    3170:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3171:         if (j==ncols && j < A->cmap->n) idx[i] = j;
    3172:       }
    3173:     }
    3174:     for (j=0; j<ncols; j++) {
    3175:       if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
    3176:       aa++; aj++;
    3177:     }
    3178:   }
    3179:   VecRestoreArrayWrite(v,&x);
    3180:   MatSeqAIJRestoreArrayRead(A,&av);
    3181:   return 0;
    3182: }

    3184: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3185: {
    3186:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3187:   PetscInt        i,j,m = A->rmap->n,ncols,n;
    3188:   const PetscInt  *ai,*aj;
    3189:   PetscScalar     *x;
    3190:   const MatScalar *aa,*av;

    3193:   MatSeqAIJGetArrayRead(A,&av);
    3194:   aa = av;
    3195:   ai = a->i;
    3196:   aj = a->j;

    3198:   VecSet(v,0.0);
    3199:   VecGetArrayWrite(v,&x);
    3200:   VecGetLocalSize(v,&n);
    3202:   for (i=0; i<m; i++) {
    3203:     ncols = ai[1] - ai[0]; ai++;
    3204:     if (ncols == A->cmap->n) { /* row is dense */
    3205:       x[i] = *aa; if (idx) idx[i] = 0;
    3206:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
    3207:       x[i] = 0.0;
    3208:       if (idx) {   /* find first implicit 0.0 in the row */
    3209:         for (j=0; j<ncols; j++) {
    3210:           if (aj[j] > j) {
    3211:             idx[i] = j;
    3212:             break;
    3213:           }
    3214:         }
    3215:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3216:         if (j==ncols && j < A->cmap->n) idx[i] = j;
    3217:       }
    3218:     }
    3219:     for (j=0; j<ncols; j++) {
    3220:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
    3221:       aa++; aj++;
    3222:     }
    3223:   }
    3224:   VecRestoreArrayWrite(v,&x);
    3225:   MatSeqAIJRestoreArrayRead(A,&av);
    3226:   return 0;
    3227: }

    3229: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
    3230: {
    3231:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*) A->data;
    3232:   PetscInt        i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
    3233:   MatScalar       *diag,work[25],*v_work;
    3234:   const PetscReal shift = 0.0;
    3235:   PetscBool       allowzeropivot,zeropivotdetected=PETSC_FALSE;

    3237:   allowzeropivot = PetscNot(A->erroriffailure);
    3238:   if (a->ibdiagvalid) {
    3239:     if (values) *values = a->ibdiag;
    3240:     return 0;
    3241:   }
    3242:   MatMarkDiagonal_SeqAIJ(A);
    3243:   if (!a->ibdiag) {
    3244:     PetscMalloc1(bs2*mbs,&a->ibdiag);
    3245:     PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
    3246:   }
    3247:   diag = a->ibdiag;
    3248:   if (values) *values = a->ibdiag;
    3249:   /* factor and invert each block */
    3250:   switch (bs) {
    3251:   case 1:
    3252:     for (i=0; i<mbs; i++) {
    3253:       MatGetValues(A,1,&i,1,&i,diag+i);
    3254:       if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
    3255:         if (allowzeropivot) {
    3256:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3257:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
    3258:           A->factorerror_zeropivot_row   = i;
    3259:           PetscInfo(A,"Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
    3260:         } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
    3261:       }
    3262:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
    3263:     }
    3264:     break;
    3265:   case 2:
    3266:     for (i=0; i<mbs; i++) {
    3267:       ij[0] = 2*i; ij[1] = 2*i + 1;
    3268:       MatGetValues(A,2,ij,2,ij,diag);
    3269:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
    3270:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3271:       PetscKernel_A_gets_transpose_A_2(diag);
    3272:       diag += 4;
    3273:     }
    3274:     break;
    3275:   case 3:
    3276:     for (i=0; i<mbs; i++) {
    3277:       ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
    3278:       MatGetValues(A,3,ij,3,ij,diag);
    3279:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
    3280:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3281:       PetscKernel_A_gets_transpose_A_3(diag);
    3282:       diag += 9;
    3283:     }
    3284:     break;
    3285:   case 4:
    3286:     for (i=0; i<mbs; i++) {
    3287:       ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
    3288:       MatGetValues(A,4,ij,4,ij,diag);
    3289:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
    3290:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3291:       PetscKernel_A_gets_transpose_A_4(diag);
    3292:       diag += 16;
    3293:     }
    3294:     break;
    3295:   case 5:
    3296:     for (i=0; i<mbs; i++) {
    3297:       ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
    3298:       MatGetValues(A,5,ij,5,ij,diag);
    3299:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
    3300:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3301:       PetscKernel_A_gets_transpose_A_5(diag);
    3302:       diag += 25;
    3303:     }
    3304:     break;
    3305:   case 6:
    3306:     for (i=0; i<mbs; i++) {
    3307:       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;
    3308:       MatGetValues(A,6,ij,6,ij,diag);
    3309:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
    3310:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3311:       PetscKernel_A_gets_transpose_A_6(diag);
    3312:       diag += 36;
    3313:     }
    3314:     break;
    3315:   case 7:
    3316:     for (i=0; i<mbs; i++) {
    3317:       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;
    3318:       MatGetValues(A,7,ij,7,ij,diag);
    3319:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
    3320:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3321:       PetscKernel_A_gets_transpose_A_7(diag);
    3322:       diag += 49;
    3323:     }
    3324:     break;
    3325:   default:
    3326:     PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
    3327:     for (i=0; i<mbs; i++) {
    3328:       for (j=0; j<bs; j++) {
    3329:         IJ[j] = bs*i + j;
    3330:       }
    3331:       MatGetValues(A,bs,IJ,bs,IJ,diag);
    3332:       PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
    3333:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3334:       PetscKernel_A_gets_transpose_A_N(diag,bs);
    3335:       diag += bs2;
    3336:     }
    3337:     PetscFree3(v_work,v_pivots,IJ);
    3338:   }
    3339:   a->ibdiagvalid = PETSC_TRUE;
    3340:   return 0;
    3341: }

    3343: static PetscErrorCode  MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
    3344: {
    3345:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
    3346:   PetscScalar    a,*aa;
    3347:   PetscInt       m,n,i,j,col;

    3349:   if (!x->assembled) {
    3350:     MatGetSize(x,&m,&n);
    3351:     for (i=0; i<m; i++) {
    3352:       for (j=0; j<aij->imax[i]; j++) {
    3353:         PetscRandomGetValue(rctx,&a);
    3354:         col  = (PetscInt)(n*PetscRealPart(a));
    3355:         MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
    3356:       }
    3357:     }
    3358:   } else {
    3359:     MatSeqAIJGetArrayWrite(x,&aa);
    3360:     for (i=0; i<aij->nz; i++) PetscRandomGetValue(rctx,aa+i);
    3361:     MatSeqAIJRestoreArrayWrite(x,&aa);
    3362:   }
    3363:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
    3364:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
    3365:   return 0;
    3366: }

    3368: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
    3369: PetscErrorCode  MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x,PetscInt low,PetscInt high,PetscRandom rctx)
    3370: {
    3371:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
    3372:   PetscScalar    a;
    3373:   PetscInt       m,n,i,j,col,nskip;

    3375:   nskip = high - low;
    3376:   MatGetSize(x,&m,&n);
    3377:   n    -= nskip; /* shrink number of columns where nonzeros can be set */
    3378:   for (i=0; i<m; i++) {
    3379:     for (j=0; j<aij->imax[i]; j++) {
    3380:       PetscRandomGetValue(rctx,&a);
    3381:       col  = (PetscInt)(n*PetscRealPart(a));
    3382:       if (col >= low) col += nskip; /* shift col rightward to skip the hole */
    3383:       MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
    3384:     }
    3385:   }
    3386:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
    3387:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
    3388:   return 0;
    3389: }

    3391: /* -------------------------------------------------------------------*/
    3392: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
    3393:                                         MatGetRow_SeqAIJ,
    3394:                                         MatRestoreRow_SeqAIJ,
    3395:                                         MatMult_SeqAIJ,
    3396:                                 /*  4*/ MatMultAdd_SeqAIJ,
    3397:                                         MatMultTranspose_SeqAIJ,
    3398:                                         MatMultTransposeAdd_SeqAIJ,
    3399:                                         NULL,
    3400:                                         NULL,
    3401:                                         NULL,
    3402:                                 /* 10*/ NULL,
    3403:                                         MatLUFactor_SeqAIJ,
    3404:                                         NULL,
    3405:                                         MatSOR_SeqAIJ,
    3406:                                         MatTranspose_SeqAIJ,
    3407:                                 /*1 5*/ MatGetInfo_SeqAIJ,
    3408:                                         MatEqual_SeqAIJ,
    3409:                                         MatGetDiagonal_SeqAIJ,
    3410:                                         MatDiagonalScale_SeqAIJ,
    3411:                                         MatNorm_SeqAIJ,
    3412:                                 /* 20*/ NULL,
    3413:                                         MatAssemblyEnd_SeqAIJ,
    3414:                                         MatSetOption_SeqAIJ,
    3415:                                         MatZeroEntries_SeqAIJ,
    3416:                                 /* 24*/ MatZeroRows_SeqAIJ,
    3417:                                         NULL,
    3418:                                         NULL,
    3419:                                         NULL,
    3420:                                         NULL,
    3421:                                 /* 29*/ MatSetUp_SeqAIJ,
    3422:                                         NULL,
    3423:                                         NULL,
    3424:                                         NULL,
    3425:                                         NULL,
    3426:                                 /* 34*/ MatDuplicate_SeqAIJ,
    3427:                                         NULL,
    3428:                                         NULL,
    3429:                                         MatILUFactor_SeqAIJ,
    3430:                                         NULL,
    3431:                                 /* 39*/ MatAXPY_SeqAIJ,
    3432:                                         MatCreateSubMatrices_SeqAIJ,
    3433:                                         MatIncreaseOverlap_SeqAIJ,
    3434:                                         MatGetValues_SeqAIJ,
    3435:                                         MatCopy_SeqAIJ,
    3436:                                 /* 44*/ MatGetRowMax_SeqAIJ,
    3437:                                         MatScale_SeqAIJ,
    3438:                                         MatShift_SeqAIJ,
    3439:                                         MatDiagonalSet_SeqAIJ,
    3440:                                         MatZeroRowsColumns_SeqAIJ,
    3441:                                 /* 49*/ MatSetRandom_SeqAIJ,
    3442:                                         MatGetRowIJ_SeqAIJ,
    3443:                                         MatRestoreRowIJ_SeqAIJ,
    3444:                                         MatGetColumnIJ_SeqAIJ,
    3445:                                         MatRestoreColumnIJ_SeqAIJ,
    3446:                                 /* 54*/ MatFDColoringCreate_SeqXAIJ,
    3447:                                         NULL,
    3448:                                         NULL,
    3449:                                         MatPermute_SeqAIJ,
    3450:                                         NULL,
    3451:                                 /* 59*/ NULL,
    3452:                                         MatDestroy_SeqAIJ,
    3453:                                         MatView_SeqAIJ,
    3454:                                         NULL,
    3455:                                         NULL,
    3456:                                 /* 64*/ NULL,
    3457:                                         MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
    3458:                                         NULL,
    3459:                                         NULL,
    3460:                                         NULL,
    3461:                                 /* 69*/ MatGetRowMaxAbs_SeqAIJ,
    3462:                                         MatGetRowMinAbs_SeqAIJ,
    3463:                                         NULL,
    3464:                                         NULL,
    3465:                                         NULL,
    3466:                                 /* 74*/ NULL,
    3467:                                         MatFDColoringApply_AIJ,
    3468:                                         NULL,
    3469:                                         NULL,
    3470:                                         NULL,
    3471:                                 /* 79*/ MatFindZeroDiagonals_SeqAIJ,
    3472:                                         NULL,
    3473:                                         NULL,
    3474:                                         NULL,
    3475:                                         MatLoad_SeqAIJ,
    3476:                                 /* 84*/ MatIsSymmetric_SeqAIJ,
    3477:                                         MatIsHermitian_SeqAIJ,
    3478:                                         NULL,
    3479:                                         NULL,
    3480:                                         NULL,
    3481:                                 /* 89*/ NULL,
    3482:                                         NULL,
    3483:                                         MatMatMultNumeric_SeqAIJ_SeqAIJ,
    3484:                                         NULL,
    3485:                                         NULL,
    3486:                                 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
    3487:                                         NULL,
    3488:                                         NULL,
    3489:                                         MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
    3490:                                         NULL,
    3491:                                 /* 99*/ MatProductSetFromOptions_SeqAIJ,
    3492:                                         NULL,
    3493:                                         NULL,
    3494:                                         MatConjugate_SeqAIJ,
    3495:                                         NULL,
    3496:                                 /*104*/ MatSetValuesRow_SeqAIJ,
    3497:                                         MatRealPart_SeqAIJ,
    3498:                                         MatImaginaryPart_SeqAIJ,
    3499:                                         NULL,
    3500:                                         NULL,
    3501:                                 /*109*/ MatMatSolve_SeqAIJ,
    3502:                                         NULL,
    3503:                                         MatGetRowMin_SeqAIJ,
    3504:                                         NULL,
    3505:                                         MatMissingDiagonal_SeqAIJ,
    3506:                                 /*114*/ NULL,
    3507:                                         NULL,
    3508:                                         NULL,
    3509:                                         NULL,
    3510:                                         NULL,
    3511:                                 /*119*/ NULL,
    3512:                                         NULL,
    3513:                                         NULL,
    3514:                                         NULL,
    3515:                                         MatGetMultiProcBlock_SeqAIJ,
    3516:                                 /*124*/ MatFindNonzeroRows_SeqAIJ,
    3517:                                         MatGetColumnReductions_SeqAIJ,
    3518:                                         MatInvertBlockDiagonal_SeqAIJ,
    3519:                                         MatInvertVariableBlockDiagonal_SeqAIJ,
    3520:                                         NULL,
    3521:                                 /*129*/ NULL,
    3522:                                         NULL,
    3523:                                         NULL,
    3524:                                         MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
    3525:                                         MatTransposeColoringCreate_SeqAIJ,
    3526:                                 /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
    3527:                                         MatTransColoringApplyDenToSp_SeqAIJ,
    3528:                                         NULL,
    3529:                                         NULL,
    3530:                                         MatRARtNumeric_SeqAIJ_SeqAIJ,
    3531:                                  /*139*/NULL,
    3532:                                         NULL,
    3533:                                         NULL,
    3534:                                         MatFDColoringSetUp_SeqXAIJ,
    3535:                                         MatFindOffBlockDiagonalEntries_SeqAIJ,
    3536:                                         MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
    3537:                                  /*145*/MatDestroySubMatrices_SeqAIJ,
    3538:                                         NULL,
    3539:                                         NULL
    3540: };

    3542: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
    3543: {
    3544:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
    3545:   PetscInt   i,nz,n;

    3547:   nz = aij->maxnz;
    3548:   n  = mat->rmap->n;
    3549:   for (i=0; i<nz; i++) {
    3550:     aij->j[i] = indices[i];
    3551:   }
    3552:   aij->nz = nz;
    3553:   for (i=0; i<n; i++) {
    3554:     aij->ilen[i] = aij->imax[i];
    3555:   }
    3556:   return 0;
    3557: }

    3559: /*
    3560:  * Given a sparse matrix with global column indices, compact it by using a local column space.
    3561:  * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
    3562:  */
    3563: PetscErrorCode  MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
    3564: {
    3565:   Mat_SeqAIJ         *aij = (Mat_SeqAIJ*)mat->data;
    3566:   PetscTable         gid1_lid1;
    3567:   PetscTablePosition tpos;
    3568:   PetscInt           gid,lid,i,ec,nz = aij->nz;
    3569:   PetscInt           *garray,*jj = aij->j;

    3573:   /* use a table */
    3574:   PetscTableCreate(mat->rmap->n,mat->cmap->N+1,&gid1_lid1);
    3575:   ec = 0;
    3576:   for (i=0; i<nz; i++) {
    3577:     PetscInt data,gid1 = jj[i] + 1;
    3578:     PetscTableFind(gid1_lid1,gid1,&data);
    3579:     if (!data) {
    3580:       /* one based table */
    3581:       PetscTableAdd(gid1_lid1,gid1,++ec,INSERT_VALUES);
    3582:     }
    3583:   }
    3584:   /* form array of columns we need */
    3585:   PetscMalloc1(ec,&garray);
    3586:   PetscTableGetHeadPosition(gid1_lid1,&tpos);
    3587:   while (tpos) {
    3588:     PetscTableGetNext(gid1_lid1,&tpos,&gid,&lid);
    3589:     gid--;
    3590:     lid--;
    3591:     garray[lid] = gid;
    3592:   }
    3593:   PetscSortInt(ec,garray); /* sort, and rebuild */
    3594:   PetscTableRemoveAll(gid1_lid1);
    3595:   for (i=0; i<ec; i++) {
    3596:     PetscTableAdd(gid1_lid1,garray[i]+1,i+1,INSERT_VALUES);
    3597:   }
    3598:   /* compact out the extra columns in B */
    3599:   for (i=0; i<nz; i++) {
    3600:     PetscInt gid1 = jj[i] + 1;
    3601:     PetscTableFind(gid1_lid1,gid1,&lid);
    3602:     lid--;
    3603:     jj[i] = lid;
    3604:   }
    3605:   PetscLayoutDestroy(&mat->cmap);
    3606:   PetscTableDestroy(&gid1_lid1);
    3607:   PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat),ec,ec,1,&mat->cmap);
    3608:   ISLocalToGlobalMappingCreate(PETSC_COMM_SELF,mat->cmap->bs,mat->cmap->n,garray,PETSC_OWN_POINTER,mapping);
    3609:   ISLocalToGlobalMappingSetType(*mapping,ISLOCALTOGLOBALMAPPINGHASH);
    3610:   return 0;
    3611: }

    3613: /*@
    3614:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
    3615:        in the matrix.

    3617:   Input Parameters:
    3618: +  mat - the SeqAIJ matrix
    3619: -  indices - the column indices

    3621:   Level: advanced

    3623:   Notes:
    3624:     This can be called if you have precomputed the nonzero structure of the
    3625:   matrix and want to provide it to the matrix object to improve the performance
    3626:   of the MatSetValues() operation.

    3628:     You MUST have set the correct numbers of nonzeros per row in the call to
    3629:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

    3631:     MUST be called before any calls to MatSetValues();

    3633:     The indices should start with zero, not one.

    3635: @*/
    3636: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
    3637: {
    3640:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
    3641:   return 0;
    3642: }

    3644: /* ----------------------------------------------------------------------------------------*/

    3646: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
    3647: {
    3648:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
    3649:   size_t         nz = aij->i[mat->rmap->n];

    3653:   /* allocate space for values if not already there */
    3654:   if (!aij->saved_values) {
    3655:     PetscMalloc1(nz+1,&aij->saved_values);
    3656:     PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
    3657:   }

    3659:   /* copy values over */
    3660:   PetscArraycpy(aij->saved_values,aij->a,nz);
    3661:   return 0;
    3662: }

    3664: /*@
    3665:     MatStoreValues - Stashes a copy of the matrix values; this allows, for
    3666:        example, reuse of the linear part of a Jacobian, while recomputing the
    3667:        nonlinear portion.

    3669:    Collect on Mat

    3671:   Input Parameters:
    3672: .  mat - the matrix (currently only AIJ matrices support this option)

    3674:   Level: advanced

    3676:   Common Usage, with SNESSolve():
    3677: $    Create Jacobian matrix
    3678: $    Set linear terms into matrix
    3679: $    Apply boundary conditions to matrix, at this time matrix must have
    3680: $      final nonzero structure (i.e. setting the nonlinear terms and applying
    3681: $      boundary conditions again will not change the nonzero structure
    3682: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
    3683: $    MatStoreValues(mat);
    3684: $    Call SNESSetJacobian() with matrix
    3685: $    In your Jacobian routine
    3686: $      MatRetrieveValues(mat);
    3687: $      Set nonlinear terms in matrix

    3689:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
    3690: $    // build linear portion of Jacobian
    3691: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
    3692: $    MatStoreValues(mat);
    3693: $    loop over nonlinear iterations
    3694: $       MatRetrieveValues(mat);
    3695: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
    3696: $       // call MatAssemblyBegin/End() on matrix
    3697: $       Solve linear system with Jacobian
    3698: $    endloop

    3700:   Notes:
    3701:     Matrix must already be assemblied before calling this routine
    3702:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
    3703:     calling this routine.

    3705:     When this is called multiple times it overwrites the previous set of stored values
    3706:     and does not allocated additional space.

    3708: .seealso: MatRetrieveValues()

    3710: @*/
    3711: PetscErrorCode  MatStoreValues(Mat mat)
    3712: {
    3716:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
    3717:   return 0;
    3718: }

    3720: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
    3721: {
    3722:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
    3723:   PetscInt       nz = aij->i[mat->rmap->n];

    3727:   /* copy values over */
    3728:   PetscArraycpy(aij->a,aij->saved_values,nz);
    3729:   return 0;
    3730: }

    3732: /*@
    3733:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
    3734:        example, reuse of the linear part of a Jacobian, while recomputing the
    3735:        nonlinear portion.

    3737:    Collect on Mat

    3739:   Input Parameters:
    3740: .  mat - the matrix (currently only AIJ matrices support this option)

    3742:   Level: advanced

    3744: .seealso: MatStoreValues()

    3746: @*/
    3747: PetscErrorCode  MatRetrieveValues(Mat mat)
    3748: {
    3752:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
    3753:   return 0;
    3754: }

    3756: /* --------------------------------------------------------------------------------*/
    3757: /*@C
    3758:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
    3759:    (the default parallel PETSc format).  For good matrix assembly performance
    3760:    the user should preallocate the matrix storage by setting the parameter nz
    3761:    (or the array nnz).  By setting these parameters accurately, performance
    3762:    during matrix assembly can be increased by more than a factor of 50.

    3764:    Collective

    3766:    Input Parameters:
    3767: +  comm - MPI communicator, set to PETSC_COMM_SELF
    3768: .  m - number of rows
    3769: .  n - number of columns
    3770: .  nz - number of nonzeros per row (same for all rows)
    3771: -  nnz - array containing the number of nonzeros in the various rows
    3772:          (possibly different for each row) or NULL

    3774:    Output Parameter:
    3775: .  A - the matrix

    3777:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
    3778:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
    3779:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

    3781:    Notes:
    3782:    If nnz is given then nz is ignored

    3784:    The AIJ format (also called the Yale sparse matrix format or
    3785:    compressed row storage), is fully compatible with standard Fortran 77
    3786:    storage.  That is, the stored row and column indices can begin at
    3787:    either one (as in Fortran) or zero.  See the users' manual for details.

    3789:    Specify the preallocated storage with either nz or nnz (not both).
    3790:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
    3791:    allocation.  For large problems you MUST preallocate memory or you
    3792:    will get TERRIBLE performance, see the users' manual chapter on matrices.

    3794:    By default, this format uses inodes (identical nodes) when possible, to
    3795:    improve numerical efficiency of matrix-vector products and solves. We
    3796:    search for consecutive rows with the same nonzero structure, thereby
    3797:    reusing matrix information to achieve increased efficiency.

    3799:    Options Database Keys:
    3800: +  -mat_no_inode  - Do not use inodes
    3801: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

    3803:    Level: intermediate

    3805: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

    3807: @*/
    3808: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
    3809: {
    3810:   MatCreate(comm,A);
    3811:   MatSetSizes(*A,m,n,m,n);
    3812:   MatSetType(*A,MATSEQAIJ);
    3813:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
    3814:   return 0;
    3815: }

    3817: /*@C
    3818:    MatSeqAIJSetPreallocation - For good matrix assembly performance
    3819:    the user should preallocate the matrix storage by setting the parameter nz
    3820:    (or the array nnz).  By setting these parameters accurately, performance
    3821:    during matrix assembly can be increased by more than a factor of 50.

    3823:    Collective

    3825:    Input Parameters:
    3826: +  B - The matrix
    3827: .  nz - number of nonzeros per row (same for all rows)
    3828: -  nnz - array containing the number of nonzeros in the various rows
    3829:          (possibly different for each row) or NULL

    3831:    Notes:
    3832:      If nnz is given then nz is ignored

    3834:     The AIJ format (also called the Yale sparse matrix format or
    3835:    compressed row storage), is fully compatible with standard Fortran 77
    3836:    storage.  That is, the stored row and column indices can begin at
    3837:    either one (as in Fortran) or zero.  See the users' manual for details.

    3839:    Specify the preallocated storage with either nz or nnz (not both).
    3840:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
    3841:    allocation.  For large problems you MUST preallocate memory or you
    3842:    will get TERRIBLE performance, see the users' manual chapter on matrices.

    3844:    You can call MatGetInfo() to get information on how effective the preallocation was;
    3845:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
    3846:    You can also run with the option -info and look for messages with the string
    3847:    malloc in them to see if additional memory allocation was needed.

    3849:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
    3850:    entries or columns indices

    3852:    By default, this format uses inodes (identical nodes) when possible, to
    3853:    improve numerical efficiency of matrix-vector products and solves. We
    3854:    search for consecutive rows with the same nonzero structure, thereby
    3855:    reusing matrix information to achieve increased efficiency.

    3857:    Options Database Keys:
    3858: +  -mat_no_inode  - Do not use inodes
    3859: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

    3861:    Level: intermediate

    3863: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo(),
    3864:           MatSeqAIJSetTotalPreallocation()

    3866: @*/
    3867: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
    3868: {
    3871:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
    3872:   return 0;
    3873: }

    3875: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
    3876: {
    3877:   Mat_SeqAIJ     *b;
    3878:   PetscBool      skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
    3879:   PetscInt       i;

    3881:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
    3882:   if (nz == MAT_SKIP_ALLOCATION) {
    3883:     skipallocation = PETSC_TRUE;
    3884:     nz             = 0;
    3885:   }
    3886:   PetscLayoutSetUp(B->rmap);
    3887:   PetscLayoutSetUp(B->cmap);

    3889:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
    3891:   if (PetscUnlikelyDebug(nnz)) {
    3892:     for (i=0; i<B->rmap->n; i++) {
    3895:     }
    3896:   }

    3898:   B->preallocated = PETSC_TRUE;

    3900:   b = (Mat_SeqAIJ*)B->data;

    3902:   if (!skipallocation) {
    3903:     if (!b->imax) {
    3904:       PetscMalloc1(B->rmap->n,&b->imax);
    3905:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
    3906:     }
    3907:     if (!b->ilen) {
    3908:       /* b->ilen will count nonzeros in each row so far. */
    3909:       PetscCalloc1(B->rmap->n,&b->ilen);
    3910:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
    3911:     } else {
    3912:       PetscMemzero(b->ilen,B->rmap->n*sizeof(PetscInt));
    3913:     }
    3914:     if (!b->ipre) {
    3915:       PetscMalloc1(B->rmap->n,&b->ipre);
    3916:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
    3917:     }
    3918:     if (!nnz) {
    3919:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
    3920:       else if (nz < 0) nz = 1;
    3921:       nz = PetscMin(nz,B->cmap->n);
    3922:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
    3923:       nz = nz*B->rmap->n;
    3924:     } else {
    3925:       PetscInt64 nz64 = 0;
    3926:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz64 += nnz[i];}
    3927:       PetscIntCast(nz64,&nz);
    3928:     }

    3930:     /* allocate the matrix space */
    3931:     /* FIXME: should B's old memory be unlogged? */
    3932:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
    3933:     if (B->structure_only) {
    3934:       PetscMalloc1(nz,&b->j);
    3935:       PetscMalloc1(B->rmap->n+1,&b->i);
    3936:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
    3937:     } else {
    3938:       PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
    3939:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
    3940:     }
    3941:     b->i[0] = 0;
    3942:     for (i=1; i<B->rmap->n+1; i++) {
    3943:       b->i[i] = b->i[i-1] + b->imax[i-1];
    3944:     }
    3945:     if (B->structure_only) {
    3946:       b->singlemalloc = PETSC_FALSE;
    3947:       b->free_a       = PETSC_FALSE;
    3948:     } else {
    3949:       b->singlemalloc = PETSC_TRUE;
    3950:       b->free_a       = PETSC_TRUE;
    3951:     }
    3952:     b->free_ij      = PETSC_TRUE;
    3953:   } else {
    3954:     b->free_a  = PETSC_FALSE;
    3955:     b->free_ij = PETSC_FALSE;
    3956:   }

    3958:   if (b->ipre && nnz != b->ipre && b->imax) {
    3959:     /* reserve user-requested sparsity */
    3960:     PetscArraycpy(b->ipre,b->imax,B->rmap->n);
    3961:   }

    3963:   b->nz               = 0;
    3964:   b->maxnz            = nz;
    3965:   B->info.nz_unneeded = (double)b->maxnz;
    3966:   if (realalloc) {
    3967:     MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
    3968:   }
    3969:   B->was_assembled = PETSC_FALSE;
    3970:   B->assembled     = PETSC_FALSE;
    3971:   return 0;
    3972: }

    3974: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
    3975: {
    3976:   Mat_SeqAIJ     *a;
    3977:   PetscInt       i;

    3981:   /* Check local size. If zero, then return */
    3982:   if (!A->rmap->n) return 0;

    3984:   a = (Mat_SeqAIJ*)A->data;
    3985:   /* if no saved info, we error out */

    3990:   PetscArraycpy(a->imax,a->ipre,A->rmap->n);
    3991:   PetscArrayzero(a->ilen,A->rmap->n);
    3992:   a->i[0] = 0;
    3993:   for (i=1; i<A->rmap->n+1; i++) {
    3994:     a->i[i] = a->i[i-1] + a->imax[i-1];
    3995:   }
    3996:   A->preallocated     = PETSC_TRUE;
    3997:   a->nz               = 0;
    3998:   a->maxnz            = a->i[A->rmap->n];
    3999:   A->info.nz_unneeded = (double)a->maxnz;
    4000:   A->was_assembled    = PETSC_FALSE;
    4001:   A->assembled        = PETSC_FALSE;
    4002:   return 0;
    4003: }

    4005: /*@
    4006:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.

    4008:    Input Parameters:
    4009: +  B - the matrix
    4010: .  i - the indices into j for the start of each row (starts with zero)
    4011: .  j - the column indices for each row (starts with zero) these must be sorted for each row
    4012: -  v - optional values in the matrix

    4014:    Level: developer

    4016:    Notes:
    4017:       The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

    4019:       This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
    4020:       structure will be the union of all the previous nonzero structures.

    4022:     Developer Notes:
    4023:       An optimization could be added to the implementation where it checks if the i, and j are identical to the current i and j and
    4024:       then just copies the v values directly with PetscMemcpy().

    4026:       This routine could also take a PetscCopyMode argument to allow sharing the values instead of always copying them.

    4028: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), MATSEQAIJ, MatResetPreallocation()
    4029: @*/
    4030: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
    4031: {
    4034:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
    4035:   return 0;
    4036: }

    4038: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
    4039: {
    4040:   PetscInt       i;
    4041:   PetscInt       m,n;
    4042:   PetscInt       nz;
    4043:   PetscInt       *nnz;

    4047:   PetscLayoutSetUp(B->rmap);
    4048:   PetscLayoutSetUp(B->cmap);

    4050:   MatGetSize(B, &m, &n);
    4051:   PetscMalloc1(m+1, &nnz);
    4052:   for (i = 0; i < m; i++) {
    4053:     nz     = Ii[i+1]- Ii[i];
    4055:     nnz[i] = nz;
    4056:   }
    4057:   MatSeqAIJSetPreallocation(B, 0, nnz);
    4058:   PetscFree(nnz);

    4060:   for (i = 0; i < m; i++) {
    4061:     MatSetValues_SeqAIJ(B, 1, &i, Ii[i+1] - Ii[i], J+Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES);
    4062:   }

    4064:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    4065:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

    4067:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
    4068:   return 0;
    4069: }

    4071: /*@
    4072:    MatSeqAIJKron - Computes C, the Kronecker product of A and B.

    4074:    Input Parameters:
    4075: +  A - left-hand side matrix
    4076: .  B - right-hand side matrix
    4077: -  reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

    4079:    Output Parameter:
    4080: .  C - Kronecker product of A and B

    4082:    Level: intermediate

    4084:    Notes:
    4085:       MAT_REUSE_MATRIX can only be used when the nonzero structure of the product matrix has not changed from that last call to MatSeqAIJKron().

    4087: .seealso: MatCreateSeqAIJ(), MATSEQAIJ, MATKAIJ, MatReuse
    4088: @*/
    4089: PetscErrorCode MatSeqAIJKron(Mat A,Mat B,MatReuse reuse,Mat *C)
    4090: {
    4096:   if (reuse == MAT_REUSE_MATRIX) {
    4099:   }
    4100:   PetscTryMethod(A,"MatSeqAIJKron_C",(Mat,Mat,MatReuse,Mat*),(A,B,reuse,C));
    4101:   return 0;
    4102: }

    4104: PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A,Mat B,MatReuse reuse,Mat *C)
    4105: {
    4106:   Mat                newmat;
    4107:   Mat_SeqAIJ         *a = (Mat_SeqAIJ*)A->data;
    4108:   Mat_SeqAIJ         *b = (Mat_SeqAIJ*)B->data;
    4109:   PetscScalar        *v;
    4110:   const PetscScalar  *aa,*ba;
    4111:   PetscInt           *i,*j,m,n,p,q,nnz = 0,am = A->rmap->n,bm = B->rmap->n,an = A->cmap->n, bn = B->cmap->n;
    4112:   PetscBool          flg;

    4118:   PetscObjectTypeCompare((PetscObject)B,MATSEQAIJ,&flg);
    4121:   if (reuse == MAT_INITIAL_MATRIX) {
    4122:     PetscMalloc2(am*bm+1,&i,a->i[am]*b->i[bm],&j);
    4123:     MatCreate(PETSC_COMM_SELF,&newmat);
    4124:     MatSetSizes(newmat,am*bm,an*bn,am*bm,an*bn);
    4125:     MatSetType(newmat,MATAIJ);
    4126:     i[0] = 0;
    4127:     for (m = 0; m < am; ++m) {
    4128:       for (p = 0; p < bm; ++p) {
    4129:         i[m*bm + p + 1] = i[m*bm + p] + (a->i[m+1] - a->i[m]) * (b->i[p+1] - b->i[p]);
    4130:         for (n = a->i[m]; n < a->i[m+1]; ++n) {
    4131:           for (q = b->i[p]; q < b->i[p+1]; ++q) {
    4132:             j[nnz++] = a->j[n]*bn + b->j[q];
    4133:           }
    4134:         }
    4135:       }
    4136:     }
    4137:     MatSeqAIJSetPreallocationCSR(newmat,i,j,NULL);
    4138:     *C = newmat;
    4139:     PetscFree2(i,j);
    4140:     nnz = 0;
    4141:   }
    4142:   MatSeqAIJGetArray(*C,&v);
    4143:   MatSeqAIJGetArrayRead(A,&aa);
    4144:   MatSeqAIJGetArrayRead(B,&ba);
    4145:   for (m = 0; m < am; ++m) {
    4146:     for (p = 0; p < bm; ++p) {
    4147:       for (n = a->i[m]; n < a->i[m+1]; ++n) {
    4148:         for (q = b->i[p]; q < b->i[p+1]; ++q) {
    4149:           v[nnz++] = aa[n] * ba[q];
    4150:         }
    4151:       }
    4152:     }
    4153:   }
    4154:   MatSeqAIJRestoreArray(*C,&v);
    4155:   MatSeqAIJRestoreArrayRead(A,&aa);
    4156:   MatSeqAIJRestoreArrayRead(B,&ba);
    4157:   return 0;
    4158: }

    4160: #include <../src/mat/impls/dense/seq/dense.h>
    4161: #include <petsc/private/kernels/petscaxpy.h>

    4163: /*
    4164:     Computes (B'*A')' since computing B*A directly is untenable

    4166:                n                       p                          p
    4167:         [             ]       [             ]         [                 ]
    4168:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
    4169:         [             ]       [             ]         [                 ]

    4171: */
    4172: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
    4173: {
    4174:   Mat_SeqDense      *sub_a = (Mat_SeqDense*)A->data;
    4175:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ*)B->data;
    4176:   Mat_SeqDense      *sub_c = (Mat_SeqDense*)C->data;
    4177:   PetscInt          i,j,n,m,q,p;
    4178:   const PetscInt    *ii,*idx;
    4179:   const PetscScalar *b,*a,*a_q;
    4180:   PetscScalar       *c,*c_q;
    4181:   PetscInt          clda = sub_c->lda;
    4182:   PetscInt          alda = sub_a->lda;

    4184:   m    = A->rmap->n;
    4185:   n    = A->cmap->n;
    4186:   p    = B->cmap->n;
    4187:   a    = sub_a->v;
    4188:   b    = sub_b->a;
    4189:   c    = sub_c->v;
    4190:   if (clda == m) {
    4191:     PetscArrayzero(c,m*p);
    4192:   } else {
    4193:     for (j=0;j<p;j++)
    4194:       for (i=0;i<m;i++)
    4195:         c[j*clda + i] = 0.0;
    4196:   }
    4197:   ii  = sub_b->i;
    4198:   idx = sub_b->j;
    4199:   for (i=0; i<n; i++) {
    4200:     q = ii[i+1] - ii[i];
    4201:     while (q-->0) {
    4202:       c_q = c + clda*(*idx);
    4203:       a_q = a + alda*i;
    4204:       PetscKernelAXPY(c_q,*b,a_q,m);
    4205:       idx++;
    4206:       b++;
    4207:     }
    4208:   }
    4209:   return 0;
    4210: }

    4212: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)
    4213: {
    4214:   PetscInt       m=A->rmap->n,n=B->cmap->n;
    4215:   PetscBool      cisdense;

    4218:   MatSetSizes(C,m,n,m,n);
    4219:   MatSetBlockSizesFromMats(C,A,B);
    4220:   PetscObjectTypeCompareAny((PetscObject)C,&cisdense,MATSEQDENSE,MATSEQDENSECUDA,"");
    4221:   if (!cisdense) {
    4222:     MatSetType(C,MATDENSE);
    4223:   }
    4224:   MatSetUp(C);

    4226:   C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
    4227:   return 0;
    4228: }

    4230: /* ----------------------------------------------------------------*/
    4231: /*MC
    4232:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
    4233:    based on compressed sparse row format.

    4235:    Options Database Keys:
    4236: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

    4238:    Level: beginner

    4240:    Notes:
    4241:     MatSetValues() may be called for this matrix type with a NULL argument for the numerical values,
    4242:     in this case the values associated with the rows and columns one passes in are set to zero
    4243:     in the matrix

    4245:     MatSetOptions(,MAT_STRUCTURE_ONLY,PETSC_TRUE) may be called for this matrix type. In this no
    4246:     space is allocated for the nonzero entries and any entries passed with MatSetValues() are ignored

    4248:   Developer Notes:
    4249:     It would be nice if all matrix formats supported passing NULL in for the numerical values

    4251: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType, MATSELL, MATSEQSELL, MATMPISELL
    4252: M*/

    4254: /*MC
    4255:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

    4257:    This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
    4258:    and MATMPIAIJ otherwise.  As a result, for single process communicators,
    4259:    MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
    4260:    for communicators controlling multiple processes.  It is recommended that you call both of
    4261:    the above preallocation routines for simplicity.

    4263:    Options Database Keys:
    4264: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()

    4266:   Developer Notes:
    4267:     Subclasses include MATAIJCUSPARSE, MATAIJPERM, MATAIJSELL, MATAIJMKL, MATAIJCRL, and also automatically switches over to use inodes when
    4268:    enough exist.

    4270:   Level: beginner

    4272: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ, MATMPIAIJ, MATSELL, MATSEQSELL, MATMPISELL
    4273: M*/

    4275: /*MC
    4276:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

    4278:    This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
    4279:    and MATMPIAIJCRL otherwise.  As a result, for single process communicators,
    4280:    MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
    4281:    for communicators controlling multiple processes.  It is recommended that you call both of
    4282:    the above preallocation routines for simplicity.

    4284:    Options Database Keys:
    4285: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()

    4287:   Level: beginner

    4289: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
    4290: M*/

    4292: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
    4293: #if defined(PETSC_HAVE_ELEMENTAL)
    4294: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
    4295: #endif
    4296: #if defined(PETSC_HAVE_SCALAPACK)
    4297: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat,MatType,MatReuse,Mat*);
    4298: #endif
    4299: #if defined(PETSC_HAVE_HYPRE)
    4300: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A,MatType,MatReuse,Mat*);
    4301: #endif

    4303: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat,MatType,MatReuse,Mat*);
    4304: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat,MatType,MatReuse,Mat*);
    4305: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

    4307: /*@C
    4308:    MatSeqAIJGetArray - gives read/write access to the array where the data for a MATSEQAIJ matrix is stored

    4310:    Not Collective

    4312:    Input Parameter:
    4313: .  mat - a MATSEQAIJ matrix

    4315:    Output Parameter:
    4316: .   array - pointer to the data

    4318:    Level: intermediate

    4320: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
    4321: @*/
    4322: PetscErrorCode  MatSeqAIJGetArray(Mat A,PetscScalar **array)
    4323: {
    4324:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

    4326:   if (aij->ops->getarray) {
    4327:     (*aij->ops->getarray)(A,array);
    4328:   } else {
    4329:     *array = aij->a;
    4330:   }
    4331:   return 0;
    4332: }

    4334: /*@C
    4335:    MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()

    4337:    Not Collective

    4339:    Input Parameters:
    4340: +  mat - a MATSEQAIJ matrix
    4341: -  array - pointer to the data

    4343:    Level: intermediate

    4345: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
    4346: @*/
    4347: PetscErrorCode  MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
    4348: {
    4349:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

    4351:   if (aij->ops->restorearray) {
    4352:     (*aij->ops->restorearray)(A,array);
    4353:   } else {
    4354:     *array = NULL;
    4355:   }
    4356:   MatSeqAIJInvalidateDiagonal(A);
    4357:   PetscObjectStateIncrease((PetscObject)A);
    4358:   return 0;
    4359: }

    4361: /*@C
    4362:    MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a MATSEQAIJ matrix is stored

    4364:    Not Collective

    4366:    Input Parameter:
    4367: .  mat - a MATSEQAIJ matrix

    4369:    Output Parameter:
    4370: .   array - pointer to the data

    4372:    Level: intermediate

    4374: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayRead()
    4375: @*/
    4376: PetscErrorCode  MatSeqAIJGetArrayRead(Mat A,const PetscScalar **array)
    4377: {
    4378:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

    4380:   if (aij->ops->getarrayread) {
    4381:     (*aij->ops->getarrayread)(A,array);
    4382:   } else {
    4383:     *array = aij->a;
    4384:   }
    4385:   return 0;
    4386: }

    4388: /*@C
    4389:    MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from MatSeqAIJGetArrayRead

    4391:    Not Collective

    4393:    Input Parameter:
    4394: .  mat - a MATSEQAIJ matrix

    4396:    Output Parameter:
    4397: .   array - pointer to the data

    4399:    Level: intermediate

    4401: .seealso: MatSeqAIJGetArray(), MatSeqAIJGetArrayRead()
    4402: @*/
    4403: PetscErrorCode  MatSeqAIJRestoreArrayRead(Mat A,const PetscScalar **array)
    4404: {
    4405:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

    4407:   if (aij->ops->restorearrayread) {
    4408:     (*aij->ops->restorearrayread)(A,array);
    4409:   } else {
    4410:     *array = NULL;
    4411:   }
    4412:   return 0;
    4413: }

    4415: /*@C
    4416:    MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a MATSEQAIJ matrix is stored

    4418:    Not Collective

    4420:    Input Parameter:
    4421: .  mat - a MATSEQAIJ matrix

    4423:    Output Parameter:
    4424: .   array - pointer to the data

    4426:    Level: intermediate

    4428: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayRead()
    4429: @*/
    4430: PetscErrorCode  MatSeqAIJGetArrayWrite(Mat A,PetscScalar **array)
    4431: {
    4432:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

    4434:   if (aij->ops->getarraywrite) {
    4435:     (*aij->ops->getarraywrite)(A,array);
    4436:   } else {
    4437:     *array = aij->a;
    4438:   }
    4439:   MatSeqAIJInvalidateDiagonal(A);
    4440:   PetscObjectStateIncrease((PetscObject)A);
    4441:   return 0;
    4442: }

    4444: /*@C
    4445:    MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead

    4447:    Not Collective

    4449:    Input Parameter:
    4450: .  mat - a MATSEQAIJ matrix

    4452:    Output Parameter:
    4453: .   array - pointer to the data

    4455:    Level: intermediate

    4457: .seealso: MatSeqAIJGetArray(), MatSeqAIJGetArrayRead()
    4458: @*/
    4459: PetscErrorCode  MatSeqAIJRestoreArrayWrite(Mat A,PetscScalar **array)
    4460: {
    4461:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

    4463:   if (aij->ops->restorearraywrite) {
    4464:     (*aij->ops->restorearraywrite)(A,array);
    4465:   } else {
    4466:     *array = NULL;
    4467:   }
    4468:   return 0;
    4469: }

    4471: /*@C
    4472:    MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the SEQAIJ matrix

    4474:    Not Collective

    4476:    Input Parameter:
    4477: .  mat - a matrix of type MATSEQAIJ or its subclasses

    4479:    Output Parameters:
    4480: +  i - row map array of the matrix
    4481: .  j - column index array of the matrix
    4482: .  a - data array of the matrix
    4483: -  memtype - memory type of the arrays

    4485:   Notes:
    4486:    Any of the output parameters can be NULL, in which case the corresponding value is not returned.
    4487:    If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.

    4489:    One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
    4490:    If the matrix is assembled, the data array 'a' is guaranteed to have the latest values of the matrix.

    4492:    Level: Developer

    4494: .seealso: MatSeqAIJGetArray(), MatSeqAIJGetArrayRead()
    4495: @*/
    4496: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat,const PetscInt **i,const PetscInt **j,PetscScalar **a,PetscMemType *mtype)
    4497: {
    4498:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;

    4501:   if (aij->ops->getcsrandmemtype) {
    4502:     (*aij->ops->getcsrandmemtype)(mat,i,j,a,mtype);
    4503:   } else {
    4504:     if (i) *i = aij->i;
    4505:     if (j) *j = aij->j;
    4506:     if (a) *a = aij->a;
    4507:     if (mtype) *mtype = PETSC_MEMTYPE_HOST;
    4508:   }
    4509:   return 0;
    4510: }

    4512: /*@C
    4513:    MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

    4515:    Not Collective

    4517:    Input Parameter:
    4518: .  mat - a MATSEQAIJ matrix

    4520:    Output Parameter:
    4521: .   nz - the maximum number of nonzeros in any row

    4523:    Level: intermediate

    4525: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
    4526: @*/
    4527: PetscErrorCode  MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
    4528: {
    4529:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

    4531:   *nz = aij->rmax;
    4532:   return 0;
    4533: }

    4535: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, const PetscInt coo_i[], const PetscInt coo_j[])
    4536: {
    4537:   MPI_Comm                  comm;
    4538:   PetscInt                  *i,*j;
    4539:   PetscInt                  M,N,row;
    4540:   PetscCount                k,p,q,nneg,nnz,start,end; /* Index the coo array, so use PetscCount as their type */
    4541:   PetscInt                  *Ai; /* Change to PetscCount once we use it for row pointers */
    4542:   PetscInt                  *Aj;
    4543:   PetscScalar               *Aa;
    4544:   Mat_SeqAIJ                *seqaij = (Mat_SeqAIJ*)(mat->data);
    4545:   MatType                   rtype;
    4546:   PetscCount                *perm,*jmap;

    4548:   MatResetPreallocationCOO_SeqAIJ(mat);
    4549:   PetscObjectGetComm((PetscObject)mat,&comm);
    4550:   MatGetSize(mat,&M,&N);
    4551:   PetscMalloc2(coo_n,&i,coo_n,&j);
    4552:   PetscArraycpy(i,coo_i,coo_n); /* Make a copy since we'll modify it */
    4553:   PetscArraycpy(j,coo_j,coo_n);
    4554:   PetscMalloc1(coo_n,&perm);
    4555:   for (k=0; k<coo_n; k++) { /* Ignore entries with negative row or col indices */
    4556:     if (j[k] < 0) i[k] = -1;
    4557:     perm[k] = k;
    4558:   }

    4560:   /* Sort by row */
    4561:   PetscSortIntWithIntCountArrayPair(coo_n,i,j,perm);
    4562:   for (k=0; k<coo_n; k++) {if (i[k] >= 0) break;} /* Advance k to the first row with a non-negative index */
    4563:   nneg = k;
    4564:   PetscMalloc1(coo_n-nneg+1,&jmap); /* +1 to make a CSR-like data structure. jmap[i] originally is the number of repeats for i-th nonzero */
    4565:   nnz  = 0; /* Total number of unique nonzeros to be counted */
    4566:   jmap++; /* Inc jmap by 1 for convinience */

    4568:   PetscCalloc1(M+1,&Ai); /* CSR of A */
    4569:   PetscMalloc1(coo_n-nneg,&Aj); /* We have at most coo_n-nneg unique nonzeros */

    4571:   /* In each row, sort by column, then unique column indices to get row length */
    4572:   Ai++; /* Inc by 1 for convinience */
    4573:   q = 0; /* q-th unique nonzero, with q starting from 0 */
    4574:   while (k<coo_n) {
    4575:     row   = i[k];
    4576:     start = k; /* [start,end) indices for this row */
    4577:     while (k<coo_n && i[k] == row) k++;
    4578:     end   = k;
    4579:     PetscSortIntWithCountArray(end-start,j+start,perm+start);
    4580:     /* Find number of unique col entries in this row */
    4581:     Aj[q]   = j[start]; /* Log the first nonzero in this row */
    4582:     jmap[q] = 1; /* Number of repeats of this nozero entry */
    4583:     Ai[row] = 1;
    4584:     nnz++;

    4586:     for (p=start+1; p<end; p++) { /* Scan remaining nonzero in this row */
    4587:       if (j[p] != j[p-1]) { /* Meet a new nonzero */
    4588:         q++;
    4589:         jmap[q] = 1;
    4590:         Aj[q]   = j[p];
    4591:         Ai[row]++;
    4592:         nnz++;
    4593:       } else {
    4594:         jmap[q]++;
    4595:       }
    4596:     }
    4597:     q++; /* Move to next row and thus next unique nonzero */
    4598:   }
    4599:   PetscFree2(i,j);

    4601:   Ai--; /* Back to the beginning of Ai[] */
    4602:   for (k=0; k<M; k++) Ai[k+1] += Ai[k];
    4603:   jmap--; /* Back to the beginning of jmap[] */
    4604:   jmap[0] = 0;
    4605:   for (k=0; k<nnz; k++) jmap[k+1] += jmap[k];
    4606:   if (nnz < coo_n-nneg) { /* Realloc with actual number of unique nonzeros */
    4607:     PetscCount *jmap_new;
    4608:     PetscInt   *Aj_new;

    4610:     PetscMalloc1(nnz+1,&jmap_new);
    4611:     PetscArraycpy(jmap_new,jmap,nnz+1);
    4612:     PetscFree(jmap);
    4613:     jmap = jmap_new;

    4615:     PetscMalloc1(nnz,&Aj_new);
    4616:     PetscArraycpy(Aj_new,Aj,nnz);
    4617:     PetscFree(Aj);
    4618:     Aj   = Aj_new;
    4619:   }

    4621:   if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
    4622:     PetscCount *perm_new;

    4624:     PetscMalloc1(coo_n-nneg,&perm_new);
    4625:     PetscArraycpy(perm_new,perm+nneg,coo_n-nneg);
    4626:     PetscFree(perm);
    4627:     perm = perm_new;
    4628:   }

    4630:   MatGetRootType_Private(mat,&rtype);
    4631:   PetscCalloc1(nnz,&Aa); /* Zero the matrix */
    4632:   MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF,M,N,Ai,Aj,Aa,rtype,mat);

    4634:   seqaij->singlemalloc = PETSC_FALSE; /* Ai, Aj and Aa are not allocated in one big malloc */
    4635:   seqaij->free_a       = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */
    4636:   /* Record COO fields */
    4637:   seqaij->coo_n        = coo_n;
    4638:   seqaij->Atot         = coo_n-nneg; /* Annz is seqaij->nz, so no need to record that again */
    4639:   seqaij->jmap         = jmap; /* of length nnz+1 */
    4640:   seqaij->perm         = perm;
    4641:   return 0;
    4642: }

    4644: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A,const PetscScalar v[],InsertMode imode)
    4645: {
    4646:   Mat_SeqAIJ                  *aseq = (Mat_SeqAIJ*)A->data;
    4647:   PetscCount                  i,j,Annz = aseq->nz;
    4648:   PetscCount                  *perm = aseq->perm,*jmap = aseq->jmap;
    4649:   PetscScalar                 *Aa;

    4651:   MatSeqAIJGetArray(A,&Aa);
    4652:   for (i=0; i<Annz; i++) {
    4653:     PetscScalar sum = 0.0;
    4654:     for (j=jmap[i]; j<jmap[i+1]; j++) sum += v[perm[j]];
    4655:     Aa[i] = (imode == INSERT_VALUES? 0.0 : Aa[i]) + sum;
    4656:   }
    4657:   MatSeqAIJRestoreArray(A,&Aa);
    4658:   return 0;
    4659: }

    4661: #if defined(PETSC_HAVE_CUDA)
    4662: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat,MatType,MatReuse,Mat*);
    4663: #endif
    4664: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    4665: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat,MatType,MatReuse,Mat*);
    4666: #endif

    4668: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
    4669: {
    4670:   Mat_SeqAIJ     *b;
    4671:   PetscMPIInt    size;

    4673:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);

    4676:   PetscNewLog(B,&b);

    4678:   B->data = (void*)b;

    4680:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
    4681:   if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;

    4683:   b->row                = NULL;
    4684:   b->col                = NULL;
    4685:   b->icol               = NULL;
    4686:   b->reallocs           = 0;
    4687:   b->ignorezeroentries  = PETSC_FALSE;
    4688:   b->roworiented        = PETSC_TRUE;
    4689:   b->nonew              = 0;
    4690:   b->diag               = NULL;
    4691:   b->solve_work         = NULL;
    4692:   B->spptr              = NULL;
    4693:   b->saved_values       = NULL;
    4694:   b->idiag              = NULL;
    4695:   b->mdiag              = NULL;
    4696:   b->ssor_work          = NULL;
    4697:   b->omega              = 1.0;
    4698:   b->fshift             = 0.0;
    4699:   b->idiagvalid         = PETSC_FALSE;
    4700:   b->ibdiagvalid        = PETSC_FALSE;
    4701:   b->keepnonzeropattern = PETSC_FALSE;

    4703:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);

    4705: #if defined(PETSC_HAVE_MATLAB_ENGINE)
    4706:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
    4707:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
    4708: #endif

    4710:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
    4711:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
    4712:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
    4713:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
    4714:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
    4715:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
    4716:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijsell_C",MatConvert_SeqAIJ_SeqAIJSELL);
    4717: #if defined(PETSC_HAVE_MKL_SPARSE)
    4718:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijmkl_C",MatConvert_SeqAIJ_SeqAIJMKL);
    4719: #endif
    4720: #if defined(PETSC_HAVE_CUDA)
    4721:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcusparse_C",MatConvert_SeqAIJ_SeqAIJCUSPARSE);
    4722:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",MatProductSetFromOptions_SeqAIJ);
    4723:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaijcusparse_C",MatProductSetFromOptions_SeqAIJ);
    4724: #endif
    4725: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    4726:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijkokkos_C",MatConvert_SeqAIJ_SeqAIJKokkos);
    4727: #endif
    4728:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
    4729: #if defined(PETSC_HAVE_ELEMENTAL)
    4730:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
    4731: #endif
    4732: #if defined(PETSC_HAVE_SCALAPACK)
    4733:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_scalapack_C",MatConvert_AIJ_ScaLAPACK);
    4734: #endif
    4735: #if defined(PETSC_HAVE_HYPRE)
    4736:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_hypre_C",MatConvert_AIJ_HYPRE);
    4737:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",MatProductSetFromOptions_Transpose_AIJ_AIJ);
    4738: #endif
    4739:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
    4740:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsell_C",MatConvert_SeqAIJ_SeqSELL);
    4741:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_is_C",MatConvert_XAIJ_IS);
    4742:   PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
    4743:   PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
    4744:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
    4745:   PetscObjectComposeFunction((PetscObject)B,"MatResetPreallocation_C",MatResetPreallocation_SeqAIJ);
    4746:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
    4747:   PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
    4748:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_is_seqaij_C",MatProductSetFromOptions_IS_XAIJ);
    4749:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqdense_seqaij_C",MatProductSetFromOptions_SeqDense_SeqAIJ);
    4750:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaij_C",MatProductSetFromOptions_SeqAIJ);
    4751:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJKron_C",MatSeqAIJKron_SeqAIJ);
    4752:   PetscObjectComposeFunction((PetscObject)B,"MatSetPreallocationCOO_C",MatSetPreallocationCOO_SeqAIJ);
    4753:   PetscObjectComposeFunction((PetscObject)B,"MatSetValuesCOO_C",MatSetValuesCOO_SeqAIJ);
    4754:   MatCreate_SeqAIJ_Inode(B);
    4755:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
    4756:   MatSeqAIJSetTypeFromOptions(B);  /* this allows changing the matrix subtype to say MATSEQAIJPERM */
    4757:   return 0;
    4758: }

    4760: /*
    4761:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
    4762: */
    4763: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
    4764: {
    4765:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data,*a = (Mat_SeqAIJ*)A->data;
    4766:   PetscInt       m = A->rmap->n,i;

    4770:   C->factortype = A->factortype;
    4771:   c->row        = NULL;
    4772:   c->col        = NULL;
    4773:   c->icol       = NULL;
    4774:   c->reallocs   = 0;

    4776:   C->assembled    = A->assembled;
    4777:   C->preallocated = A->preallocated;

    4779:   if (A->preallocated) {
    4780:     PetscLayoutReference(A->rmap,&C->rmap);
    4781:     PetscLayoutReference(A->cmap,&C->cmap);

    4783:     PetscMalloc1(m,&c->imax);
    4784:     PetscMemcpy(c->imax,a->imax,m*sizeof(PetscInt));
    4785:     PetscMalloc1(m,&c->ilen);
    4786:     PetscMemcpy(c->ilen,a->ilen,m*sizeof(PetscInt));
    4787:     PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));

    4789:     /* allocate the matrix space */
    4790:     if (mallocmatspace) {
    4791:       PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
    4792:       PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));

    4794:       c->singlemalloc = PETSC_TRUE;

    4796:       PetscArraycpy(c->i,a->i,m+1);
    4797:       if (m > 0) {
    4798:         PetscArraycpy(c->j,a->j,a->i[m]);
    4799:         if (cpvalues == MAT_COPY_VALUES) {
    4800:           const PetscScalar *aa;

    4802:           MatSeqAIJGetArrayRead(A,&aa);
    4803:           PetscArraycpy(c->a,aa,a->i[m]);
    4804:           MatSeqAIJGetArrayRead(A,&aa);
    4805:         } else {
    4806:           PetscArrayzero(c->a,a->i[m]);
    4807:         }
    4808:       }
    4809:     }

    4811:     c->ignorezeroentries = a->ignorezeroentries;
    4812:     c->roworiented       = a->roworiented;
    4813:     c->nonew             = a->nonew;
    4814:     if (a->diag) {
    4815:       PetscMalloc1(m+1,&c->diag);
    4816:       PetscMemcpy(c->diag,a->diag,m*sizeof(PetscInt));
    4817:       PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
    4818:     } else c->diag = NULL;

    4820:     c->solve_work         = NULL;
    4821:     c->saved_values       = NULL;
    4822:     c->idiag              = NULL;
    4823:     c->ssor_work          = NULL;
    4824:     c->keepnonzeropattern = a->keepnonzeropattern;
    4825:     c->free_a             = PETSC_TRUE;
    4826:     c->free_ij            = PETSC_TRUE;

    4828:     c->rmax         = a->rmax;
    4829:     c->nz           = a->nz;
    4830:     c->maxnz        = a->nz;       /* Since we allocate exactly the right amount */

    4832:     c->compressedrow.use   = a->compressedrow.use;
    4833:     c->compressedrow.nrows = a->compressedrow.nrows;
    4834:     if (a->compressedrow.use) {
    4835:       i    = a->compressedrow.nrows;
    4836:       PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
    4837:       PetscArraycpy(c->compressedrow.i,a->compressedrow.i,i+1);
    4838:       PetscArraycpy(c->compressedrow.rindex,a->compressedrow.rindex,i);
    4839:     } else {
    4840:       c->compressedrow.use    = PETSC_FALSE;
    4841:       c->compressedrow.i      = NULL;
    4842:       c->compressedrow.rindex = NULL;
    4843:     }
    4844:     c->nonzerorowcnt = a->nonzerorowcnt;
    4845:     C->nonzerostate  = A->nonzerostate;

    4847:     MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
    4848:   }
    4849:   PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
    4850:   return 0;
    4851: }

    4853: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
    4854: {
    4855:   MatCreate(PetscObjectComm((PetscObject)A),B);
    4856:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
    4857:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
    4858:     MatSetBlockSizesFromMats(*B,A,A);
    4859:   }
    4860:   MatSetType(*B,((PetscObject)A)->type_name);
    4861:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
    4862:   return 0;
    4863: }

    4865: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
    4866: {
    4867:   PetscBool      isbinary, ishdf5;

    4871:   /* force binary viewer to load .info file if it has not yet done so */
    4872:   PetscViewerSetUp(viewer);
    4873:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    4874:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERHDF5,  &ishdf5);
    4875:   if (isbinary) {
    4876:     MatLoad_SeqAIJ_Binary(newMat,viewer);
    4877:   } else if (ishdf5) {
    4878: #if defined(PETSC_HAVE_HDF5)
    4879:     MatLoad_AIJ_HDF5(newMat,viewer);
    4880: #else
    4881:     SETERRQ(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
    4882: #endif
    4883:   } else {
    4884:     SETERRQ(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"Viewer type %s not yet supported for reading %s matrices",((PetscObject)viewer)->type_name,((PetscObject)newMat)->type_name);
    4885:   }
    4886:   return 0;
    4887: }

    4889: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
    4890: {
    4891:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)mat->data;
    4892:   PetscInt       header[4],*rowlens,M,N,nz,sum,rows,cols,i;

    4894:   PetscViewerSetUp(viewer);

    4896:   /* read in matrix header */
    4897:   PetscViewerBinaryRead(viewer,header,4,NULL,PETSC_INT);
    4899:   M = header[1]; N = header[2]; nz = header[3];

    4904:   /* set block sizes from the viewer's .info file */
    4905:   MatLoad_Binary_BlockSizes(mat,viewer);
    4906:   /* set local and global sizes if not set already */
    4907:   if (mat->rmap->n < 0) mat->rmap->n = M;
    4908:   if (mat->cmap->n < 0) mat->cmap->n = N;
    4909:   if (mat->rmap->N < 0) mat->rmap->N = M;
    4910:   if (mat->cmap->N < 0) mat->cmap->N = N;
    4911:   PetscLayoutSetUp(mat->rmap);
    4912:   PetscLayoutSetUp(mat->cmap);

    4914:   /* check if the matrix sizes are correct */
    4915:   MatGetSize(mat,&rows,&cols);

    4918:   /* read in row lengths */
    4919:   PetscMalloc1(M,&rowlens);
    4920:   PetscViewerBinaryRead(viewer,rowlens,M,NULL,PETSC_INT);
    4921:   /* check if sum(rowlens) is same as nz */
    4922:   sum = 0; for (i=0; i<M; i++) sum += rowlens[i];
    4924:   /* preallocate and check sizes */
    4925:   MatSeqAIJSetPreallocation_SeqAIJ(mat,0,rowlens);
    4926:   MatGetSize(mat,&rows,&cols);
    4928:   /* store row lengths */
    4929:   PetscArraycpy(a->ilen,rowlens,M);
    4930:   PetscFree(rowlens);

    4932:   /* fill in "i" row pointers */
    4933:   a->i[0] = 0; for (i=0; i<M; i++) a->i[i+1] = a->i[i] + a->ilen[i];
    4934:   /* read in "j" column indices */
    4935:   PetscViewerBinaryRead(viewer,a->j,nz,NULL,PETSC_INT);
    4936:   /* read in "a" nonzero values */
    4937:   PetscViewerBinaryRead(viewer,a->a,nz,NULL,PETSC_SCALAR);

    4939:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
    4940:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
    4941:   return 0;
    4942: }

    4944: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
    4945: {
    4946:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
    4947:   const PetscScalar *aa,*ba;
    4948: #if defined(PETSC_USE_COMPLEX)
    4949:   PetscInt k;
    4950: #endif

    4952:   /* If the  matrix dimensions are not equal,or no of nonzeros */
    4953:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
    4954:     *flg = PETSC_FALSE;
    4955:     return 0;
    4956:   }

    4958:   /* if the a->i are the same */
    4959:   PetscArraycmp(a->i,b->i,A->rmap->n+1,flg);
    4960:   if (!*flg) return 0;

    4962:   /* if a->j are the same */
    4963:   PetscArraycmp(a->j,b->j,a->nz,flg);
    4964:   if (!*flg) return 0;

    4966:   MatSeqAIJGetArrayRead(A,&aa);
    4967:   MatSeqAIJGetArrayRead(B,&ba);
    4968:   /* if a->a are the same */
    4969: #if defined(PETSC_USE_COMPLEX)
    4970:   for (k=0; k<a->nz; k++) {
    4971:     if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
    4972:       *flg = PETSC_FALSE;
    4973:       return 0;
    4974:     }
    4975:   }
    4976: #else
    4977:   PetscArraycmp(aa,ba,a->nz,flg);
    4978: #endif
    4979:   MatSeqAIJRestoreArrayRead(A,&aa);
    4980:   MatSeqAIJRestoreArrayRead(B,&ba);
    4981:   return 0;
    4982: }

    4984: /*@
    4985:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
    4986:               provided by the user.

    4988:       Collective

    4990:    Input Parameters:
    4991: +   comm - must be an MPI communicator of size 1
    4992: .   m - number of rows
    4993: .   n - number of columns
    4994: .   i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
    4995: .   j - column indices
    4996: -   a - matrix values

    4998:    Output Parameter:
    4999: .   mat - the matrix

    5001:    Level: intermediate

    5003:    Notes:
    5004:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
    5005:     once the matrix is destroyed and not before

    5007:        You cannot set new nonzero locations into this matrix, that will generate an error.

    5009:        The i and j indices are 0 based

    5011:        The format which is used for the sparse matrix input, is equivalent to a
    5012:     row-major ordering.. i.e for the following matrix, the input data expected is
    5013:     as shown

    5015: $        1 0 0
    5016: $        2 0 3
    5017: $        4 5 6
    5018: $
    5019: $        i =  {0,1,3,6}  [size = nrow+1  = 3+1]
    5020: $        j =  {0,0,2,0,1,2}  [size = 6]; values must be sorted for each row
    5021: $        v =  {1,2,3,4,5,6}  [size = 6]

    5023: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

    5025: @*/
    5026: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
    5027: {
    5028:   PetscInt       ii;
    5029:   Mat_SeqAIJ     *aij;
    5030:   PetscInt jj;

    5033:   MatCreate(comm,mat);
    5034:   MatSetSizes(*mat,m,n,m,n);
    5035:   /* MatSetBlockSizes(*mat,,); */
    5036:   MatSetType(*mat,MATSEQAIJ);
    5037:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,NULL);
    5038:   aij  = (Mat_SeqAIJ*)(*mat)->data;
    5039:   PetscMalloc1(m,&aij->imax);
    5040:   PetscMalloc1(m,&aij->ilen);

    5042:   aij->i            = i;
    5043:   aij->j            = j;
    5044:   aij->a            = a;
    5045:   aij->singlemalloc = PETSC_FALSE;
    5046:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
    5047:   aij->free_a       = PETSC_FALSE;
    5048:   aij->free_ij      = PETSC_FALSE;

    5050:   for (ii=0,aij->nonzerorowcnt=0,aij->rmax=0; ii<m; ii++) {
    5051:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
    5052:     if (PetscDefined(USE_DEBUG)) {
    5054:       for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
    5057:       }
    5058:     }
    5059:   }
    5060:   if (PetscDefined(USE_DEBUG)) {
    5061:     for (ii=0; ii<aij->i[m]; ii++) {
    5064:     }
    5065:   }

    5067:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
    5068:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
    5069:   return 0;
    5070: }

    5072: /*@C
    5073:      MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
    5074:               provided by the user.

    5076:       Collective

    5078:    Input Parameters:
    5079: +   comm - must be an MPI communicator of size 1
    5080: .   m   - number of rows
    5081: .   n   - number of columns
    5082: .   i   - row indices
    5083: .   j   - column indices
    5084: .   a   - matrix values
    5085: .   nz  - number of nonzeros
    5086: -   idx - 0 or 1 based

    5088:    Output Parameter:
    5089: .   mat - the matrix

    5091:    Level: intermediate

    5093:    Notes:
    5094:        The i and j indices are 0 based. The format which is used for the sparse matrix input, is equivalent to a row-major ordering. i.e for the following matrix,
    5095:        the input data expected is as shown
    5096: .vb
    5097:         1 0 0
    5098:         2 0 3
    5099:         4 5 6

    5101:         i =  {0,1,1,2,2,2}
    5102:         j =  {0,0,2,0,1,2}
    5103:         v =  {1,2,3,4,5,6}
    5104: .ve

    5106: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

    5108: @*/
    5109: PetscErrorCode  MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat,PetscInt nz,PetscBool idx)
    5110: {
    5111:   PetscInt       ii, *nnz, one = 1,row,col;

    5113:   PetscCalloc1(m,&nnz);
    5114:   for (ii = 0; ii < nz; ii++) {
    5115:     nnz[i[ii] - !!idx] += 1;
    5116:   }
    5117:   MatCreate(comm,mat);
    5118:   MatSetSizes(*mat,m,n,m,n);
    5119:   MatSetType(*mat,MATSEQAIJ);
    5120:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
    5121:   for (ii = 0; ii < nz; ii++) {
    5122:     if (idx) {
    5123:       row = i[ii] - 1;
    5124:       col = j[ii] - 1;
    5125:     } else {
    5126:       row = i[ii];
    5127:       col = j[ii];
    5128:     }
    5129:     MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
    5130:   }
    5131:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
    5132:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
    5133:   PetscFree(nnz);
    5134:   return 0;
    5135: }

    5137: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
    5138: {
    5139:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data;

    5141:   a->idiagvalid  = PETSC_FALSE;
    5142:   a->ibdiagvalid = PETSC_FALSE;

    5144:   MatSeqAIJInvalidateDiagonal_Inode(A);
    5145:   return 0;
    5146: }

    5148: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
    5149: {
    5150:   PetscMPIInt    size;

    5152:   MPI_Comm_size(comm,&size);
    5153:   if (size == 1) {
    5154:     if (scall == MAT_INITIAL_MATRIX) {
    5155:       MatDuplicate(inmat,MAT_COPY_VALUES,outmat);
    5156:     } else {
    5157:       MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
    5158:     }
    5159:   } else {
    5160:     MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
    5161:   }
    5162:   return 0;
    5163: }

    5165: /*
    5166:  Permute A into C's *local* index space using rowemb,colemb.
    5167:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
    5168:  of [0,m), colemb is in [0,n).
    5169:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
    5170:  */
    5171: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
    5172: {
    5173:   /* If making this function public, change the error returned in this function away from _PLIB. */
    5174:   Mat_SeqAIJ     *Baij;
    5175:   PetscBool      seqaij;
    5176:   PetscInt       m,n,*nz,i,j,count;
    5177:   PetscScalar    v;
    5178:   const PetscInt *rowindices,*colindices;

    5180:   if (!B) return 0;
    5181:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
    5182:   PetscObjectBaseTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
    5184:   if (rowemb) {
    5185:     ISGetLocalSize(rowemb,&m);
    5187:   } else {
    5189:   }
    5190:   if (colemb) {
    5191:     ISGetLocalSize(colemb,&n);
    5193:   } else {
    5195:   }

    5197:   Baij = (Mat_SeqAIJ*)(B->data);
    5198:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
    5199:     PetscMalloc1(B->rmap->n,&nz);
    5200:     for (i=0; i<B->rmap->n; i++) {
    5201:       nz[i] = Baij->i[i+1] - Baij->i[i];
    5202:     }
    5203:     MatSeqAIJSetPreallocation(C,0,nz);
    5204:     PetscFree(nz);
    5205:   }
    5206:   if (pattern == SUBSET_NONZERO_PATTERN) {
    5207:     MatZeroEntries(C);
    5208:   }
    5209:   count = 0;
    5210:   rowindices = NULL;
    5211:   colindices = NULL;
    5212:   if (rowemb) {
    5213:     ISGetIndices(rowemb,&rowindices);
    5214:   }
    5215:   if (colemb) {
    5216:     ISGetIndices(colemb,&colindices);
    5217:   }
    5218:   for (i=0; i<B->rmap->n; i++) {
    5219:     PetscInt row;
    5220:     row = i;
    5221:     if (rowindices) row = rowindices[i];
    5222:     for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
    5223:       PetscInt col;
    5224:       col  = Baij->j[count];
    5225:       if (colindices) col = colindices[col];
    5226:       v    = Baij->a[count];
    5227:       MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
    5228:       ++count;
    5229:     }
    5230:   }
    5231:   /* FIXME: set C's nonzerostate correctly. */
    5232:   /* Assembly for C is necessary. */
    5233:   C->preallocated = PETSC_TRUE;
    5234:   C->assembled     = PETSC_TRUE;
    5235:   C->was_assembled = PETSC_FALSE;
    5236:   return 0;
    5237: }

    5239: PetscFunctionList MatSeqAIJList = NULL;

    5241: /*@C
    5242:    MatSeqAIJSetType - Converts a MATSEQAIJ matrix to a subtype

    5244:    Collective on Mat

    5246:    Input Parameters:
    5247: +  mat      - the matrix object
    5248: -  matype   - matrix type

    5250:    Options Database Key:
    5251: .  -mat_seqai_type  <method> - for example seqaijcrl

    5253:   Level: intermediate

    5255: .seealso: PCSetType(), VecSetType(), MatCreate(), MatType, Mat
    5256: @*/
    5257: PetscErrorCode  MatSeqAIJSetType(Mat mat, MatType matype)
    5258: {
    5259:   PetscBool      sametype;
    5260:   PetscErrorCode (*r)(Mat,MatType,MatReuse,Mat*);

    5263:   PetscObjectTypeCompare((PetscObject)mat,matype,&sametype);
    5264:   if (sametype) return 0;

    5266:   PetscFunctionListFind(MatSeqAIJList,matype,&r);
    5268:   (*r)(mat,matype,MAT_INPLACE_MATRIX,&mat);
    5269:   return 0;
    5270: }

    5272: /*@C
    5273:   MatSeqAIJRegister -  - Adds a new sub-matrix type for sequential AIJ matrices

    5275:    Not Collective

    5277:    Input Parameters:
    5278: +  name - name of a new user-defined matrix type, for example MATSEQAIJCRL
    5279: -  function - routine to convert to subtype

    5281:    Notes:
    5282:    MatSeqAIJRegister() may be called multiple times to add several user-defined solvers.

    5284:    Then, your matrix can be chosen with the procedural interface at runtime via the option
    5285: $     -mat_seqaij_type my_mat

    5287:    Level: advanced

    5289: .seealso: MatSeqAIJRegisterAll()

    5291:   Level: advanced
    5292: @*/
    5293: PetscErrorCode  MatSeqAIJRegister(const char sname[],PetscErrorCode (*function)(Mat,MatType,MatReuse,Mat *))
    5294: {
    5295:   MatInitializePackage();
    5296:   PetscFunctionListAdd(&MatSeqAIJList,sname,function);
    5297:   return 0;
    5298: }

    5300: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;

    5302: /*@C
    5303:   MatSeqAIJRegisterAll - Registers all of the matrix subtypes of SeqAIJ

    5305:   Not Collective

    5307:   Level: advanced

    5309: .seealso:  MatRegisterAll(), MatSeqAIJRegister()
    5310: @*/
    5311: PetscErrorCode  MatSeqAIJRegisterAll(void)
    5312: {
    5313:   if (MatSeqAIJRegisterAllCalled) return 0;
    5314:   MatSeqAIJRegisterAllCalled = PETSC_TRUE;

    5316:   MatSeqAIJRegister(MATSEQAIJCRL,      MatConvert_SeqAIJ_SeqAIJCRL);
    5317:   MatSeqAIJRegister(MATSEQAIJPERM,     MatConvert_SeqAIJ_SeqAIJPERM);
    5318:   MatSeqAIJRegister(MATSEQAIJSELL,     MatConvert_SeqAIJ_SeqAIJSELL);
    5319: #if defined(PETSC_HAVE_MKL_SPARSE)
    5320:   MatSeqAIJRegister(MATSEQAIJMKL,      MatConvert_SeqAIJ_SeqAIJMKL);
    5321: #endif
    5322: #if defined(PETSC_HAVE_CUDA)
    5323:   MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE);
    5324: #endif
    5325: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    5326:   MatSeqAIJRegister(MATSEQAIJKOKKOS,   MatConvert_SeqAIJ_SeqAIJKokkos);
    5327: #endif
    5328: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
    5329:   MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL);
    5330: #endif
    5331:   return 0;
    5332: }

    5334: /*
    5335:     Special version for direct calls from Fortran
    5336: */
    5337: #include <petsc/private/fortranimpl.h>
    5338: #if defined(PETSC_HAVE_FORTRAN_CAPS)
    5339: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
    5340: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
    5341: #define matsetvaluesseqaij_ matsetvaluesseqaij
    5342: #endif

    5344: /* Change these macros so can be used in void function */

    5346: /* Change these macros so can be used in void function */
    5347: /* Identical to PetscCallVoid, except it assigns to *_ierr */
    5348: #undef  PetscCall
    5349: #define PetscCall(...) do {                                                                    \
    5350:     PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__;                                              \
    5351:     if (PetscUnlikely(ierr_msv_mpiaij)) {                                                      \
    5352:       *_PetscError(PETSC_COMM_SELF,__LINE__,PETSC_FUNCTION_NAME,__FILE__,ierr_msv_mpiaij,PETSC_ERROR_REPEAT," "); \
    5353:       return;                                                                                  \
    5354:     }                                                                                          \
    5355:   } while (0)

    5357: #undef SETERRQ
    5358: #define SETERRQ(comm,ierr,...) do {                                                            \
    5359:     *_PetscError(comm,__LINE__,PETSC_FUNCTION_NAME,__FILE__,ierr,PETSC_ERROR_INITIAL,__VA_ARGS__); \
    5360:     return;                                                                                    \
    5361:   } while (0)

    5363: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
    5364: {
    5365:   Mat            A  = *AA;
    5366:   PetscInt       m  = *mm, n = *nn;
    5367:   InsertMode     is = *isis;
    5368:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    5369:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
    5370:   PetscInt       *imax,*ai,*ailen;
    5371:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
    5372:   MatScalar      *ap,value,*aa;
    5373:   PetscBool      ignorezeroentries = a->ignorezeroentries;
    5374:   PetscBool      roworiented       = a->roworiented;

    5376:   MatCheckPreallocated(A,1);
    5377:   imax  = a->imax;
    5378:   ai    = a->i;
    5379:   ailen = a->ilen;
    5380:   aj    = a->j;
    5381:   aa    = a->a;

    5383:   for (k=0; k<m; k++) { /* loop over added rows */
    5384:     row = im[k];
    5385:     if (row < 0) continue;
    5387:     rp   = aj + ai[row]; ap = aa + ai[row];
    5388:     rmax = imax[row]; nrow = ailen[row];
    5389:     low  = 0;
    5390:     high = nrow;
    5391:     for (l=0; l<n; l++) { /* loop over added columns */
    5392:       if (in[l] < 0) continue;
    5394:       col = in[l];
    5395:       if (roworiented) value = v[l + k*n];
    5396:       else value = v[k + l*m];

    5398:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

    5400:       if (col <= lastcol) low = 0;
    5401:       else high = nrow;
    5402:       lastcol = col;
    5403:       while (high-low > 5) {
    5404:         t = (low+high)/2;
    5405:         if (rp[t] > col) high = t;
    5406:         else             low  = t;
    5407:       }
    5408:       for (i=low; i<high; i++) {
    5409:         if (rp[i] > col) break;
    5410:         if (rp[i] == col) {
    5411:           if (is == ADD_VALUES) ap[i] += value;
    5412:           else                  ap[i] = value;
    5413:           goto noinsert;
    5414:         }
    5415:       }
    5416:       if (value == 0.0 && ignorezeroentries) goto noinsert;
    5417:       if (nonew == 1) goto noinsert;
    5419:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
    5420:       N = nrow++ - 1; a->nz++; high++;
    5421:       /* shift up all the later entries in this row */
    5422:       for (ii=N; ii>=i; ii--) {
    5423:         rp[ii+1] = rp[ii];
    5424:         ap[ii+1] = ap[ii];
    5425:       }
    5426:       rp[i] = col;
    5427:       ap[i] = value;
    5428:       A->nonzerostate++;
    5429: noinsert:;
    5430:       low = i + 1;
    5431:     }
    5432:     ailen[row] = nrow;
    5433:   }
    5434:   return;
    5435: }
    5436: /* Undefining these here since they were redefined from their original definition above! No
    5437:  * other PETSc functions should be defined past this point, as it is impossible to recover the
    5438:  * original definitions */
    5439: #undef PetscCall
    5440: #undef SETERRQ