Date: 2022-03-04T17:37:42+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];

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

     27: PetscErrorCode MatGetColumnReductions_SeqAIJ(Mat A,PetscInt type,PetscReal *reductions)
     28: {
     30:   PetscInt       i,m,n;
     31:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

     34:   MatGetSize(A,&m,&n);
     35:   PetscArrayzero(reductions,n);
     36:   if (type == NORM_2) {
     37:     for (i=0; i<aij->i[m]; i++) {
     38:       reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]);
     39:     }
     40:   } else if (type == NORM_1) {
     41:     for (i=0; i<aij->i[m]; i++) {
     42:       reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]);
     43:     }
     44:   } else if (type == NORM_INFINITY) {
     45:     for (i=0; i<aij->i[m]; i++) {
     46:       reductions[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),reductions[aij->j[i]]);
     47:     }
     48:   } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
     49:     for (i=0; i<aij->i[m]; i++) {
     50:       reductions[aij->j[i]] += PetscRealPart(aij->a[i]);
     51:     }
     52:   } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
     53:     for (i=0; i<aij->i[m]; i++) {
     54:       reductions[aij->j[i]] += PetscImaginaryPart(aij->a[i]);
     55:     }
     56:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown reduction type");

     58:   if (type == NORM_2) {
     59:     for (i=0; i<n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
     60:   } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
     61:     for (i=0; i<n; i++) reductions[i] /= m;
     62:   }
     63:   return(0);
     64: }

     66: PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A,IS *is)
     67: {
     68:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
     69:   PetscInt        i,m=A->rmap->n,cnt = 0, bs = A->rmap->bs;
     70:   const PetscInt  *jj = a->j,*ii = a->i;
     71:   PetscInt        *rows;
     72:   PetscErrorCode  ierr;

     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:       cnt++;
     78:     }
     79:   }
     80:   PetscMalloc1(cnt,&rows);
     81:   cnt  = 0;
     82:   for (i=0; i<m; i++) {
     83:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
     84:       rows[cnt] = i;
     85:       cnt++;
     86:     }
     87:   }
     88:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);
     89:   return(0);
     90: }

     92: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows)
     93: {
     94:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
     95:   const MatScalar *aa = a->a;
     96:   PetscInt        i,m=A->rmap->n,cnt = 0;
     97:   const PetscInt  *ii = a->i,*jj = a->j,*diag;
     98:   PetscInt        *rows;
     99:   PetscErrorCode  ierr;

    102:   MatMarkDiagonal_SeqAIJ(A);
    103:   diag = a->diag;
    104:   for (i=0; i<m; i++) {
    105:     if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
    106:       cnt++;
    107:     }
    108:   }
    109:   PetscMalloc1(cnt,&rows);
    110:   cnt  = 0;
    111:   for (i=0; i<m; i++) {
    112:     if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
    113:       rows[cnt++] = i;
    114:     }
    115:   }
    116:   *nrows = cnt;
    117:   *zrows = rows;
    118:   return(0);
    119: }

    121: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
    122: {
    123:   PetscInt       nrows,*rows;

    127:   *zrows = NULL;
    128:   MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
    129:   ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);
    130:   return(0);
    131: }

    133: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
    134: {
    135:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    136:   const MatScalar *aa;
    137:   PetscInt        m=A->rmap->n,cnt = 0;
    138:   const PetscInt  *ii;
    139:   PetscInt        n,i,j,*rows;
    140:   PetscErrorCode  ierr;

    143:   MatSeqAIJGetArrayRead(A,&aa);
    144:   *keptrows = NULL;
    145:   ii        = a->i;
    146:   for (i=0; i<m; i++) {
    147:     n = ii[i+1] - ii[i];
    148:     if (!n) {
    149:       cnt++;
    150:       goto ok1;
    151:     }
    152:     for (j=ii[i]; j<ii[i+1]; j++) {
    153:       if (aa[j] != 0.0) goto ok1;
    154:     }
    155:     cnt++;
    156: ok1:;
    157:   }
    158:   if (!cnt) {
    159:     MatSeqAIJRestoreArrayRead(A,&aa);
    160:     return(0);
    161:   }
    162:   PetscMalloc1(A->rmap->n-cnt,&rows);
    163:   cnt  = 0;
    164:   for (i=0; i<m; i++) {
    165:     n = ii[i+1] - ii[i];
    166:     if (!n) continue;
    167:     for (j=ii[i]; j<ii[i+1]; j++) {
    168:       if (aa[j] != 0.0) {
    169:         rows[cnt++] = i;
    170:         break;
    171:       }
    172:     }
    173:   }
    174:   MatSeqAIJRestoreArrayRead(A,&aa);
    175:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);
    176:   return(0);
    177: }

    179: PetscErrorCode  MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
    180: {
    181:   PetscErrorCode    ierr;
    182:   Mat_SeqAIJ        *aij = (Mat_SeqAIJ*) Y->data;
    183:   PetscInt          i,m = Y->rmap->n;
    184:   const PetscInt    *diag;
    185:   MatScalar         *aa;
    186:   const PetscScalar *v;
    187:   PetscBool         missing;
    188: #if defined(PETSC_HAVE_DEVICE)
    189:   PetscBool         inserted = PETSC_FALSE;
    190: #endif

    193:   if (Y->assembled) {
    194:     MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
    195:     if (!missing) {
    196:       diag = aij->diag;
    197:       VecGetArrayRead(D,&v);
    198:       MatSeqAIJGetArray(Y,&aa);
    199:       if (is == INSERT_VALUES) {
    200: #if defined(PETSC_HAVE_DEVICE)
    201:         inserted = PETSC_TRUE;
    202: #endif
    203:         for (i=0; i<m; i++) {
    204:           aa[diag[i]] = v[i];
    205:         }
    206:       } else {
    207:         for (i=0; i<m; i++) {
    208: #if defined(PETSC_HAVE_DEVICE)
    209:           if (v[i] != 0.0) inserted = PETSC_TRUE;
    210: #endif
    211:           aa[diag[i]] += v[i];
    212:         }
    213:       }
    214:       MatSeqAIJRestoreArray(Y,&aa);
    215: #if defined(PETSC_HAVE_DEVICE)
    216:       if (inserted) Y->offloadmask = PETSC_OFFLOAD_CPU;
    217: #endif
    218:       VecRestoreArrayRead(D,&v);
    219:       return(0);
    220:     }
    221:     MatSeqAIJInvalidateDiagonal(Y);
    222:   }
    223:   MatDiagonalSet_Default(Y,D,is);
    224:   return(0);
    225: }

    227: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    228: {
    229:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    231:   PetscInt       i,ishift;

    234:   *m = A->rmap->n;
    235:   if (!ia) return(0);
    236:   ishift = 0;
    237:   if (symmetric && !A->structurally_symmetric) {
    238:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);
    239:   } else if (oshift == 1) {
    240:     PetscInt *tia;
    241:     PetscInt nz = a->i[A->rmap->n];
    242:     /* malloc space and  add 1 to i and j indices */
    243:     PetscMalloc1(A->rmap->n+1,&tia);
    244:     for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1;
    245:     *ia = tia;
    246:     if (ja) {
    247:       PetscInt *tja;
    248:       PetscMalloc1(nz+1,&tja);
    249:       for (i=0; i<nz; i++) tja[i] = a->j[i] + 1;
    250:       *ja = tja;
    251:     }
    252:   } else {
    253:     *ia = a->i;
    254:     if (ja) *ja = a->j;
    255:   }
    256:   return(0);
    257: }

    259: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    260: {

    264:   if (!ia) return(0);
    265:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
    266:     PetscFree(*ia);
    267:     if (ja) {PetscFree(*ja);}
    268:   }
    269:   return(0);
    270: }

    272: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    273: {
    274:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    276:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
    277:   PetscInt       nz = a->i[m],row,*jj,mr,col;

    280:   *nn = n;
    281:   if (!ia) return(0);
    282:   if (symmetric) {
    283:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,0,oshift,(PetscInt**)ia,(PetscInt**)ja);
    284:   } else {
    285:     PetscCalloc1(n,&collengths);
    286:     PetscMalloc1(n+1,&cia);
    287:     PetscMalloc1(nz,&cja);
    288:     jj   = a->j;
    289:     for (i=0; i<nz; i++) {
    290:       collengths[jj[i]]++;
    291:     }
    292:     cia[0] = oshift;
    293:     for (i=0; i<n; i++) {
    294:       cia[i+1] = cia[i] + collengths[i];
    295:     }
    296:     PetscArrayzero(collengths,n);
    297:     jj   = a->j;
    298:     for (row=0; row<m; row++) {
    299:       mr = a->i[row+1] - a->i[row];
    300:       for (i=0; i<mr; i++) {
    301:         col = *jj++;

    303:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
    304:       }
    305:     }
    306:     PetscFree(collengths);
    307:     *ia  = cia; *ja = cja;
    308:   }
    309:   return(0);
    310: }

    312: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    313: {

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

    319:   PetscFree(*ia);
    320:   PetscFree(*ja);
    321:   return(0);
    322: }

    324: /*
    325:  MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
    326:  MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
    327:  spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
    328: */
    329: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
    330: {
    331:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    333:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
    334:   PetscInt       nz = a->i[m],row,mr,col,tmp;
    335:   PetscInt       *cspidx;
    336:   const PetscInt *jj;

    339:   *nn = n;
    340:   if (!ia) return(0);

    342:   PetscCalloc1(n,&collengths);
    343:   PetscMalloc1(n+1,&cia);
    344:   PetscMalloc1(nz,&cja);
    345:   PetscMalloc1(nz,&cspidx);
    346:   jj   = a->j;
    347:   for (i=0; i<nz; i++) {
    348:     collengths[jj[i]]++;
    349:   }
    350:   cia[0] = oshift;
    351:   for (i=0; i<n; i++) {
    352:     cia[i+1] = cia[i] + collengths[i];
    353:   }
    354:   PetscArrayzero(collengths,n);
    355:   jj   = a->j;
    356:   for (row=0; row<m; row++) {
    357:     mr = a->i[row+1] - a->i[row];
    358:     for (i=0; i<mr; i++) {
    359:       col         = *jj++;
    360:       tmp         = cia[col] + collengths[col]++ - oshift;
    361:       cspidx[tmp] = a->i[row] + i; /* index of a->j */
    362:       cja[tmp]    = row + oshift;
    363:     }
    364:   }
    365:   PetscFree(collengths);
    366:   *ia    = cia;
    367:   *ja    = cja;
    368:   *spidx = cspidx;
    369:   return(0);
    370: }

    372: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
    373: {

    377:   MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);
    378:   PetscFree(*spidx);
    379:   return(0);
    380: }

    382: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
    383: {
    384:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
    385:   PetscInt       *ai = a->i;

    389:   PetscArraycpy(a->a+ai[row],v,ai[row+1]-ai[row]);
    390: #if defined(PETSC_HAVE_DEVICE)
    391:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && ai[row+1]-ai[row]) A->offloadmask = PETSC_OFFLOAD_CPU;
    392: #endif
    393:   return(0);
    394: }

    396: /*
    397:     MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions

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

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

    406: */

    408: #include <petsc/private/isimpl.h>
    409: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
    410: {
    411:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    412:   PetscInt       low,high,t,row,nrow,i,col,l;
    413:   const PetscInt *rp,*ai = a->i,*ailen = a->ilen,*aj = a->j;
    414:   PetscInt       lastcol = -1;
    415:   MatScalar      *ap,value,*aa = a->a;
    416:   const PetscInt *ridx = A->rmap->mapping->indices,*cidx = A->cmap->mapping->indices;

    418:   row  = ridx[im[0]];
    419:   rp   = aj + ai[row];
    420:   ap   = aa + ai[row];
    421:   nrow = ailen[row];
    422:   low  = 0;
    423:   high = nrow;
    424:   for (l=0; l<n; l++) { /* loop over added columns */
    425:     col = cidx[in[l]];
    426:     value = v[l];

    428:     if (col <= lastcol) low = 0;
    429:     else high = nrow;
    430:     lastcol = col;
    431:     while (high-low > 5) {
    432:       t = (low+high)/2;
    433:       if (rp[t] > col) high = t;
    434:       else low = t;
    435:     }
    436:     for (i=low; i<high; i++) {
    437:       if (rp[i] == col) {
    438:         ap[i] += value;
    439:         low = i + 1;
    440:         break;
    441:       }
    442:     }
    443:   }
    444: #if defined(PETSC_HAVE_DEVICE)
    445:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
    446: #endif
    447:   return 0;
    448: }

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

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

    489:       if (col <= lastcol) low = 0;
    490:       else high = nrow;
    491:       lastcol = col;
    492:       while (high-low > 5) {
    493:         t = (low+high)/2;
    494:         if (rp[t] > col) high = t;
    495:         else low = t;
    496:       }
    497:       for (i=low; i<high; i++) {
    498:         if (rp[i] > col) break;
    499:         if (rp[i] == col) {
    500:           if (!A->structure_only) {
    501:             if (is == ADD_VALUES) {
    502:               ap[i] += value;
    503:               (void)PetscLogFlops(1.0);
    504:             }
    505:             else ap[i] = value;
    506: #if defined(PETSC_HAVE_DEVICE)
    507:             inserted = PETSC_TRUE;
    508: #endif
    509:           }
    510:           low = i + 1;
    511:           goto noinsert;
    512:         }
    513:       }
    514:       if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
    515:       if (nonew == 1) goto noinsert;
    516:       if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
    517:       if (A->structure_only) {
    518:         MatSeqXAIJReallocateAIJ_structure_only(A,A->rmap->n,1,nrow,row,col,rmax,ai,aj,rp,imax,nonew,MatScalar);
    519:       } else {
    520:         MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
    521:       }
    522:       N = nrow++ - 1; a->nz++; high++;
    523:       /* shift up all the later entries in this row */
    524:       PetscArraymove(rp+i+1,rp+i,N-i+1);
    525:       rp[i] = col;
    526:       if (!A->structure_only) {
    527:         PetscArraymove(ap+i+1,ap+i,N-i+1);
    528:         ap[i] = value;
    529:       }
    530:       low = i + 1;
    531:       A->nonzerostate++;
    532: #if defined(PETSC_HAVE_DEVICE)
    533:       inserted = PETSC_TRUE;
    534: #endif
    535: noinsert:;
    536:     }
    537:     ailen[row] = nrow;
    538:   }
    539: #if defined(PETSC_HAVE_DEVICE)
    540:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && inserted) A->offloadmask = PETSC_OFFLOAD_CPU;
    541: #endif
    542:   return(0);
    543: }

    545: PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
    546: {
    547:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    548:   PetscInt       *rp,k,row;
    549:   PetscInt       *ai = a->i;
    551:   PetscInt       *aj = a->j;
    552:   MatScalar      *aa = a->a,*ap;

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

    562:     PetscMemcpy(rp,in,n*sizeof(PetscInt));
    563:     if (!A->structure_only) {
    564:       if (v) {
    565:         PetscMemcpy(ap,v,n*sizeof(PetscScalar));
    566:         v   += n;
    567:       } else {
    568:         PetscMemzero(ap,n*sizeof(PetscScalar));
    569:       }
    570:     }
    571:     a->ilen[row] = n;
    572:     a->imax[row] = n;
    573:     a->i[row+1]  = a->i[row]+n;
    574:     a->nz       += n;
    575:   }
    576: #if defined(PETSC_HAVE_DEVICE)
    577:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
    578: #endif
    579:   return(0);
    580: }

    582: /*@
    583:     MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.

    585:   Input Parameters:
    586: +  A - the SeqAIJ matrix
    587: -  nztotal - bound on the number of nonzeros

    589:   Level: advanced

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

    596: .seealso: MatSetOption(), MAT_SORTED_FULL, MatSetValues(), MatSeqAIJSetPreallocation()
    597: @*/

    599: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A,PetscInt nztotal)
    600: {
    602:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    605:   PetscLayoutSetUp(A->rmap);
    606:   PetscLayoutSetUp(A->cmap);
    607:   a->maxnz  = nztotal;
    608:   if (!a->imax) {
    609:     PetscMalloc1(A->rmap->n,&a->imax);
    610:     PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
    611:   }
    612:   if (!a->ilen) {
    613:     PetscMalloc1(A->rmap->n,&a->ilen);
    614:     PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
    615:   } else {
    616:     PetscMemzero(a->ilen,A->rmap->n*sizeof(PetscInt));
    617:   }

    619:   /* allocate the matrix space */
    620:   if (A->structure_only) {
    621:     PetscMalloc1(nztotal,&a->j);
    622:     PetscMalloc1(A->rmap->n+1,&a->i);
    623:     PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*sizeof(PetscInt));
    624:   } else {
    625:     PetscMalloc3(nztotal,&a->a,nztotal,&a->j,A->rmap->n+1,&a->i);
    626:     PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*(sizeof(PetscScalar)+sizeof(PetscInt)));
    627:   }
    628:   a->i[0] = 0;
    629:   if (A->structure_only) {
    630:     a->singlemalloc = PETSC_FALSE;
    631:     a->free_a       = PETSC_FALSE;
    632:   } else {
    633:     a->singlemalloc = PETSC_TRUE;
    634:     a->free_a       = PETSC_TRUE;
    635:   }
    636:   a->free_ij         = PETSC_TRUE;
    637:   A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
    638:   A->preallocated   = PETSC_TRUE;
    639:   return(0);
    640: }

    642: PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
    643: {
    644:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    645:   PetscInt       *rp,k,row;
    646:   PetscInt       *ai = a->i,*ailen = a->ilen;
    648:   PetscInt       *aj = a->j;
    649:   MatScalar      *aa = a->a,*ap;

    652:   for (k=0; k<m; k++) { /* loop over added rows */
    653:     row  = im[k];
    654:     if (PetscUnlikelyDebug(n > a->imax[row])) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Preallocation for row %D does not match number of columns provided",n);
    655:     rp   = aj + ai[row];
    656:     ap   = aa + ai[row];
    657:     if (!A->was_assembled) {
    658:       PetscMemcpy(rp,in,n*sizeof(PetscInt));
    659:     }
    660:     if (!A->structure_only) {
    661:       if (v) {
    662:         PetscMemcpy(ap,v,n*sizeof(PetscScalar));
    663:         v   += n;
    664:       } else {
    665:         PetscMemzero(ap,n*sizeof(PetscScalar));
    666:       }
    667:     }
    668:     ailen[row] = n;
    669:     a->nz      += n;
    670:   }
    671: #if defined(PETSC_HAVE_DEVICE)
    672:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
    673: #endif
    674:   return(0);
    675: }

    677: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
    678: {
    679:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
    680:   PetscInt   *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
    681:   PetscInt   *ai = a->i,*ailen = a->ilen;
    682:   MatScalar  *ap,*aa = a->a;

    685:   for (k=0; k<m; k++) { /* loop over rows */
    686:     row = im[k];
    687:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
    688:     if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
    689:     rp   = aj + ai[row]; ap = aa + ai[row];
    690:     nrow = ailen[row];
    691:     for (l=0; l<n; l++) { /* loop over columns */
    692:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
    693:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
    694:       col  = in[l];
    695:       high = nrow; low = 0; /* assume unsorted */
    696:       while (high-low > 5) {
    697:         t = (low+high)/2;
    698:         if (rp[t] > col) high = t;
    699:         else low = t;
    700:       }
    701:       for (i=low; i<high; i++) {
    702:         if (rp[i] > col) break;
    703:         if (rp[i] == col) {
    704:           *v++ = ap[i];
    705:           goto finished;
    706:         }
    707:       }
    708:       *v++ = 0.0;
    709: finished:;
    710:     }
    711:   }
    712:   return(0);
    713: }

    715: PetscErrorCode MatView_SeqAIJ_Binary(Mat mat,PetscViewer viewer)
    716: {
    717:   Mat_SeqAIJ        *A = (Mat_SeqAIJ*)mat->data;
    718:   const PetscScalar *av;
    719:   PetscInt          header[4],M,N,m,nz,i;
    720:   PetscInt          *rowlens;
    721:   PetscErrorCode    ierr;

    724:   PetscViewerSetUp(viewer);

    726:   M  = mat->rmap->N;
    727:   N  = mat->cmap->N;
    728:   m  = mat->rmap->n;
    729:   nz = A->nz;

    731:   /* write matrix header */
    732:   header[0] = MAT_FILE_CLASSID;
    733:   header[1] = M; header[2] = N; header[3] = nz;
    734:   PetscViewerBinaryWrite(viewer,header,4,PETSC_INT);

    736:   /* fill in and store row lengths */
    737:   PetscMalloc1(m,&rowlens);
    738:   for (i=0; i<m; i++) rowlens[i] = A->i[i+1] - A->i[i];
    739:   PetscViewerBinaryWrite(viewer,rowlens,m,PETSC_INT);
    740:   PetscFree(rowlens);
    741:   /* store column indices */
    742:   PetscViewerBinaryWrite(viewer,A->j,nz,PETSC_INT);
    743:   /* store nonzero values */
    744:   MatSeqAIJGetArrayRead(mat,&av);
    745:   PetscViewerBinaryWrite(viewer,av,nz,PETSC_SCALAR);
    746:   MatSeqAIJRestoreArrayRead(mat,&av);

    748:   /* write block size option to the viewer's .info file */
    749:   MatView_Binary_BlockSizes(mat,viewer);
    750:   return(0);
    751: }

    753: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A,PetscViewer viewer)
    754: {
    756:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    757:   PetscInt       i,k,m=A->rmap->N;

    760:   PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    761:   for (i=0; i<m; i++) {
    762:     PetscViewerASCIIPrintf(viewer,"row %D:",i);
    763:     for (k=a->i[i]; k<a->i[i+1]; k++) {
    764:       PetscViewerASCIIPrintf(viewer," (%D) ",a->j[k]);
    765:     }
    766:     PetscViewerASCIIPrintf(viewer,"\n");
    767:   }
    768:   PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    769:   return(0);
    770: }

    772: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

    774: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
    775: {
    776:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    777:   const PetscScalar *av;
    778:   PetscErrorCode    ierr;
    779:   PetscInt          i,j,m = A->rmap->n;
    780:   const char        *name;
    781:   PetscViewerFormat format;

    784:   if (A->structure_only) {
    785:     MatView_SeqAIJ_ASCII_structonly(A,viewer);
    786:     return(0);
    787:   }

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

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

    811:     for (i=0; i<m; i++) {
    812:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    813: #if defined(PETSC_USE_COMPLEX)
    814:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e %18.16e\n",i+1,a->j[j]+1,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    815: #else
    816:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);
    817: #endif
    818:       }
    819:     }
    820:     if (nofinalvalue) {
    821: #if defined(PETSC_USE_COMPLEX)
    822:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
    823: #else
    824:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap->n,0.0);
    825: #endif
    826:     }
    827:     PetscObjectGetName((PetscObject)A,&name);
    828:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
    829:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    830:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
    831:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    832:     for (i=0; i<m; i++) {
    833:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
    834:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    835: #if defined(PETSC_USE_COMPLEX)
    836:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
    837:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    838:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
    839:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
    840:         } else if (PetscRealPart(a->a[j]) != 0.0) {
    841:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    842:         }
    843: #else
    844:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);}
    845: #endif
    846:       }
    847:       PetscViewerASCIIPrintf(viewer,"\n");
    848:     }
    849:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    850:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
    851:     PetscInt nzd=0,fshift=1,*sptr;
    852:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    853:     PetscMalloc1(m+1,&sptr);
    854:     for (i=0; i<m; i++) {
    855:       sptr[i] = nzd+1;
    856:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    857:         if (a->j[j] >= i) {
    858: #if defined(PETSC_USE_COMPLEX)
    859:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
    860: #else
    861:           if (a->a[j] != 0.0) nzd++;
    862: #endif
    863:         }
    864:       }
    865:     }
    866:     sptr[m] = nzd+1;
    867:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
    868:     for (i=0; i<m+1; i+=6) {
    869:       if (i+4<m) {
    870:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);
    871:       } else if (i+3<m) {
    872:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
    873:       } else if (i+2<m) {
    874:         PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
    875:       } else if (i+1<m) {
    876:         PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);
    877:       } else if (i<m) {
    878:         PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);
    879:       } else {
    880:         PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);
    881:       }
    882:     }
    883:     PetscViewerASCIIPrintf(viewer,"\n");
    884:     PetscFree(sptr);
    885:     for (i=0; i<m; i++) {
    886:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    887:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
    888:       }
    889:       PetscViewerASCIIPrintf(viewer,"\n");
    890:     }
    891:     PetscViewerASCIIPrintf(viewer,"\n");
    892:     for (i=0; i<m; i++) {
    893:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    894:         if (a->j[j] >= i) {
    895: #if defined(PETSC_USE_COMPLEX)
    896:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
    897:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    898:           }
    899: #else
    900:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);}
    901: #endif
    902:         }
    903:       }
    904:       PetscViewerASCIIPrintf(viewer,"\n");
    905:     }
    906:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    907:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
    908:     PetscInt    cnt = 0,jcnt;
    909:     PetscScalar value;
    910: #if defined(PETSC_USE_COMPLEX)
    911:     PetscBool   realonly = PETSC_TRUE;

    913:     for (i=0; i<a->i[m]; i++) {
    914:       if (PetscImaginaryPart(a->a[i]) != 0.0) {
    915:         realonly = PETSC_FALSE;
    916:         break;
    917:       }
    918:     }
    919: #endif

    921:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    922:     for (i=0; i<m; i++) {
    923:       jcnt = 0;
    924:       for (j=0; j<A->cmap->n; j++) {
    925:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
    926:           value = a->a[cnt++];
    927:           jcnt++;
    928:         } else {
    929:           value = 0.0;
    930:         }
    931: #if defined(PETSC_USE_COMPLEX)
    932:         if (realonly) {
    933:           PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
    934:         } else {
    935:           PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
    936:         }
    937: #else
    938:         PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
    939: #endif
    940:       }
    941:       PetscViewerASCIIPrintf(viewer,"\n");
    942:     }
    943:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    944:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
    945:     PetscInt fshift=1;
    946:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    947: #if defined(PETSC_USE_COMPLEX)
    948:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
    949: #else
    950:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
    951: #endif
    952:     PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
    953:     for (i=0; i<m; i++) {
    954:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    955: #if defined(PETSC_USE_COMPLEX)
    956:         PetscViewerASCIIPrintf(viewer,"%D %D %g %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    957: #else
    958:         PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);
    959: #endif
    960:       }
    961:     }
    962:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    963:   } else {
    964:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    965:     if (A->factortype) {
    966:       for (i=0; i<m; i++) {
    967:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
    968:         /* L part */
    969:         for (j=a->i[i]; j<a->i[i+1]; j++) {
    970: #if defined(PETSC_USE_COMPLEX)
    971:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    972:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    973:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    974:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
    975:           } else {
    976:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    977:           }
    978: #else
    979:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
    980: #endif
    981:         }
    982:         /* diagonal */
    983:         j = a->diag[i];
    984: #if defined(PETSC_USE_COMPLEX)
    985:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
    986:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)PetscImaginaryPart(1.0/a->a[j]));
    987:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    988:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)(-PetscImaginaryPart(1.0/a->a[j])));
    989:         } else {
    990:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));
    991:         }
    992: #else
    993:         PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)(1.0/a->a[j]));
    994: #endif

    996:         /* U part */
    997:         for (j=a->diag[i+1]+1; j<a->diag[i]; j++) {
    998: #if defined(PETSC_USE_COMPLEX)
    999:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    1000:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    1001:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    1002:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
    1003:           } else {
    1004:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    1005:           }
    1006: #else
    1007:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
    1008: #endif
    1009:         }
    1010:         PetscViewerASCIIPrintf(viewer,"\n");
    1011:       }
    1012:     } else {
    1013:       for (i=0; i<m; i++) {
    1014:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
    1015:         for (j=a->i[i]; j<a->i[i+1]; j++) {
    1016: #if defined(PETSC_USE_COMPLEX)
    1017:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    1018:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    1019:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    1020:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
    1021:           } else {
    1022:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    1023:           }
    1024: #else
    1025:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
    1026: #endif
    1027:         }
    1028:         PetscViewerASCIIPrintf(viewer,"\n");
    1029:       }
    1030:     }
    1031:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    1032:   }
    1033:   PetscViewerFlush(viewer);
    1034:   return(0);
    1035: }

    1037: #include <petscdraw.h>
    1038: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
    1039: {
    1040:   Mat               A  = (Mat) Aa;
    1041:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1042:   PetscErrorCode    ierr;
    1043:   PetscInt          i,j,m = A->rmap->n;
    1044:   int               color;
    1045:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r;
    1046:   PetscViewer       viewer;
    1047:   PetscViewerFormat format;

    1050:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
    1051:   PetscViewerGetFormat(viewer,&format);
    1052:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);

    1054:   /* loop over matrix elements drawing boxes */

    1056:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
    1057:     PetscDrawCollectiveBegin(draw);
    1058:     /* Blue for negative, Cyan for zero and  Red for positive */
    1059:     color = PETSC_DRAW_BLUE;
    1060:     for (i=0; i<m; i++) {
    1061:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    1062:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    1063:         x_l = a->j[j]; x_r = x_l + 1.0;
    1064:         if (PetscRealPart(a->a[j]) >=  0.) continue;
    1065:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    1066:       }
    1067:     }
    1068:     color = PETSC_DRAW_CYAN;
    1069:     for (i=0; i<m; i++) {
    1070:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    1071:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    1072:         x_l = a->j[j]; x_r = x_l + 1.0;
    1073:         if (a->a[j] !=  0.) continue;
    1074:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    1075:       }
    1076:     }
    1077:     color = PETSC_DRAW_RED;
    1078:     for (i=0; i<m; i++) {
    1079:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    1080:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    1081:         x_l = a->j[j]; x_r = x_l + 1.0;
    1082:         if (PetscRealPart(a->a[j]) <=  0.) continue;
    1083:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    1084:       }
    1085:     }
    1086:     PetscDrawCollectiveEnd(draw);
    1087:   } else {
    1088:     /* use contour shading to indicate magnitude of values */
    1089:     /* first determine max of all nonzero values */
    1090:     PetscReal minv = 0.0, maxv = 0.0;
    1091:     PetscInt  nz = a->nz, count = 0;
    1092:     PetscDraw popup;

    1094:     for (i=0; i<nz; i++) {
    1095:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
    1096:     }
    1097:     if (minv >= maxv) maxv = minv + PETSC_SMALL;
    1098:     PetscDrawGetPopup(draw,&popup);
    1099:     PetscDrawScalePopup(popup,minv,maxv);

    1101:     PetscDrawCollectiveBegin(draw);
    1102:     for (i=0; i<m; i++) {
    1103:       y_l = m - i - 1.0;
    1104:       y_r = y_l + 1.0;
    1105:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    1106:         x_l = a->j[j];
    1107:         x_r = x_l + 1.0;
    1108:         color = PetscDrawRealToColor(PetscAbsScalar(a->a[count]),minv,maxv);
    1109:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    1110:         count++;
    1111:       }
    1112:     }
    1113:     PetscDrawCollectiveEnd(draw);
    1114:   }
    1115:   return(0);
    1116: }

    1118: #include <petscdraw.h>
    1119: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
    1120: {
    1122:   PetscDraw      draw;
    1123:   PetscReal      xr,yr,xl,yl,h,w;
    1124:   PetscBool      isnull;

    1127:   PetscViewerDrawGetDraw(viewer,0,&draw);
    1128:   PetscDrawIsNull(draw,&isnull);
    1129:   if (isnull) return(0);

    1131:   xr   = A->cmap->n; yr  = A->rmap->n; h = yr/10.0; w = xr/10.0;
    1132:   xr  += w;          yr += h;         xl = -w;     yl = -h;
    1133:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
    1134:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
    1135:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
    1136:   PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
    1137:   PetscDrawSave(draw);
    1138:   return(0);
    1139: }

    1141: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
    1142: {
    1144:   PetscBool      iascii,isbinary,isdraw;

    1147:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    1148:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    1149:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
    1150:   if (iascii) {
    1151:     MatView_SeqAIJ_ASCII(A,viewer);
    1152:   } else if (isbinary) {
    1153:     MatView_SeqAIJ_Binary(A,viewer);
    1154:   } else if (isdraw) {
    1155:     MatView_SeqAIJ_Draw(A,viewer);
    1156:   }
    1157:   MatView_SeqAIJ_Inode(A,viewer);
    1158:   return(0);
    1159: }

    1161: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
    1162: {
    1163:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1165:   PetscInt       fshift = 0,i,*ai = a->i,*aj = a->j,*imax = a->imax;
    1166:   PetscInt       m      = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
    1167:   MatScalar      *aa    = a->a,*ap;
    1168:   PetscReal      ratio  = 0.6;

    1171:   if (mode == MAT_FLUSH_ASSEMBLY) return(0);
    1172:   MatSeqAIJInvalidateDiagonal(A);
    1173:   if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
    1174:     /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
    1175:     MatAssemblyEnd_SeqAIJ_Inode(A,mode);
    1176:     return(0);
    1177:   }

    1179:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
    1180:   for (i=1; i<m; i++) {
    1181:     /* move each row back by the amount of empty slots (fshift) before it*/
    1182:     fshift += imax[i-1] - ailen[i-1];
    1183:     rmax    = PetscMax(rmax,ailen[i]);
    1184:     if (fshift) {
    1185:       ip = aj + ai[i];
    1186:       ap = aa + ai[i];
    1187:       N  = ailen[i];
    1188:       PetscArraymove(ip-fshift,ip,N);
    1189:       if (!A->structure_only) {
    1190:         PetscArraymove(ap-fshift,ap,N);
    1191:       }
    1192:     }
    1193:     ai[i] = ai[i-1] + ailen[i-1];
    1194:   }
    1195:   if (m) {
    1196:     fshift += imax[m-1] - ailen[m-1];
    1197:     ai[m]   = ai[m-1] + ailen[m-1];
    1198:   }

    1200:   /* reset ilen and imax for each row */
    1201:   a->nonzerorowcnt = 0;
    1202:   if (A->structure_only) {
    1203:     PetscFree(a->imax);
    1204:     PetscFree(a->ilen);
    1205:   } else { /* !A->structure_only */
    1206:     for (i=0; i<m; i++) {
    1207:       ailen[i] = imax[i] = ai[i+1] - ai[i];
    1208:       a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0);
    1209:     }
    1210:   }
    1211:   a->nz = ai[m];
    1212:   if (fshift && a->nounused == -1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D, %D unneeded", m, A->cmap->n, fshift);

    1214:   MatMarkDiagonal_SeqAIJ(A);
    1215:   PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
    1216:   PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
    1217:   PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);

    1219:   A->info.mallocs    += a->reallocs;
    1220:   a->reallocs         = 0;
    1221:   A->info.nz_unneeded = (PetscReal)fshift;
    1222:   a->rmax             = rmax;

    1224:   if (!A->structure_only) {
    1225:     MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);
    1226:   }
    1227:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);
    1228:   return(0);
    1229: }

    1231: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
    1232: {
    1233:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1234:   PetscInt       i,nz = a->nz;
    1235:   MatScalar      *aa;

    1239:   MatSeqAIJGetArray(A,&aa);
    1240:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
    1241:   MatSeqAIJRestoreArray(A,&aa);
    1242:   MatSeqAIJInvalidateDiagonal(A);
    1243: #if defined(PETSC_HAVE_DEVICE)
    1244:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    1245: #endif
    1246:   return(0);
    1247: }

    1249: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
    1250: {
    1251:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1252:   PetscInt       i,nz = a->nz;
    1253:   MatScalar      *aa;

    1257:   MatSeqAIJGetArray(A,&aa);
    1258:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
    1259:   MatSeqAIJRestoreArray(A,&aa);
    1260:   MatSeqAIJInvalidateDiagonal(A);
    1261: #if defined(PETSC_HAVE_DEVICE)
    1262:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    1263: #endif
    1264:   return(0);
    1265: }

    1267: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
    1268: {
    1269:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    1273:   PetscArrayzero(a->a,a->i[A->rmap->n]);
    1274:   MatSeqAIJInvalidateDiagonal(A);
    1275: #if defined(PETSC_HAVE_DEVICE)
    1276:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    1277: #endif
    1278:   return(0);
    1279: }

    1281: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
    1282: {
    1283:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    1287: #if defined(PETSC_USE_LOG)
    1288:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
    1289: #endif
    1290:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
    1291:   ISDestroy(&a->row);
    1292:   ISDestroy(&a->col);
    1293:   PetscFree(a->diag);
    1294:   PetscFree(a->ibdiag);
    1295:   PetscFree(a->imax);
    1296:   PetscFree(a->ilen);
    1297:   PetscFree(a->ipre);
    1298:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
    1299:   PetscFree(a->solve_work);
    1300:   ISDestroy(&a->icol);
    1301:   PetscFree(a->saved_values);
    1302:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);

    1304:   MatDestroy_SeqAIJ_Inode(A);
    1305:   PetscFree(A->data);

    1307:   /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
    1308:      That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
    1309:      that is hard to properly add this data to the MatProduct data. We free it here to avoid
    1310:      users reusing the matrix object with different data to incur in obscure segmentation faults
    1311:      due to different matrix sizes */
    1312:   PetscObjectCompose((PetscObject)A,"__PETSc__ab_dense",NULL);

    1314:   PetscObjectChangeTypeName((PetscObject)A,NULL);
    1315:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
    1316:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
    1317:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
    1318:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
    1319:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
    1320:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);
    1321: #if defined(PETSC_HAVE_CUDA)
    1322:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcusparse_C",NULL);
    1323:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",NULL);
    1324:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaijcusparse_C",NULL);
    1325: #endif
    1326: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    1327:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijkokkos_C",NULL);
    1328: #endif
    1329:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcrl_C",NULL);
    1330: #if defined(PETSC_HAVE_ELEMENTAL)
    1331:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);
    1332: #endif
    1333: #if defined(PETSC_HAVE_SCALAPACK)
    1334:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_scalapack_C",NULL);
    1335: #endif
    1336: #if defined(PETSC_HAVE_HYPRE)
    1337:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_hypre_C",NULL);
    1338:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",NULL);
    1339: #endif
    1340:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);
    1341:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsell_C",NULL);
    1342:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_is_C",NULL);
    1343:   PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
    1344:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
    1345:   PetscObjectComposeFunction((PetscObject)A,"MatResetPreallocation_C",NULL);
    1346:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
    1347:   PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
    1348:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_is_seqaij_C",NULL);
    1349:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqdense_seqaij_C",NULL);
    1350:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaij_C",NULL);
    1351:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJKron_C",NULL);
    1352:   return(0);
    1353: }

    1355: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
    1356: {
    1357:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    1361:   switch (op) {
    1362:   case MAT_ROW_ORIENTED:
    1363:     a->roworiented = flg;
    1364:     break;
    1365:   case MAT_KEEP_NONZERO_PATTERN:
    1366:     a->keepnonzeropattern = flg;
    1367:     break;
    1368:   case MAT_NEW_NONZERO_LOCATIONS:
    1369:     a->nonew = (flg ? 0 : 1);
    1370:     break;
    1371:   case MAT_NEW_NONZERO_LOCATION_ERR:
    1372:     a->nonew = (flg ? -1 : 0);
    1373:     break;
    1374:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
    1375:     a->nonew = (flg ? -2 : 0);
    1376:     break;
    1377:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
    1378:     a->nounused = (flg ? -1 : 0);
    1379:     break;
    1380:   case MAT_IGNORE_ZERO_ENTRIES:
    1381:     a->ignorezeroentries = flg;
    1382:     break;
    1383:   case MAT_SPD:
    1384:   case MAT_SYMMETRIC:
    1385:   case MAT_STRUCTURALLY_SYMMETRIC:
    1386:   case MAT_HERMITIAN:
    1387:   case MAT_SYMMETRY_ETERNAL:
    1388:   case MAT_STRUCTURE_ONLY:
    1389:     /* These options are handled directly by MatSetOption() */
    1390:     break;
    1391:   case MAT_FORCE_DIAGONAL_ENTRIES:
    1392:   case MAT_IGNORE_OFF_PROC_ENTRIES:
    1393:   case MAT_USE_HASH_TABLE:
    1394:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
    1395:     break;
    1396:   case MAT_USE_INODES:
    1397:     MatSetOption_SeqAIJ_Inode(A,MAT_USE_INODES,flg);
    1398:     break;
    1399:   case MAT_SUBMAT_SINGLEIS:
    1400:     A->submat_singleis = flg;
    1401:     break;
    1402:   case MAT_SORTED_FULL:
    1403:     if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
    1404:     else     A->ops->setvalues = MatSetValues_SeqAIJ;
    1405:     break;
    1406:   case MAT_FORM_EXPLICIT_TRANSPOSE:
    1407:     A->form_explicit_transpose = flg;
    1408:     break;
    1409:   default:
    1410:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
    1411:   }
    1412:   return(0);
    1413: }

    1415: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
    1416: {
    1417:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1418:   PetscErrorCode    ierr;
    1419:   PetscInt          i,j,n,*ai=a->i,*aj=a->j;
    1420:   PetscScalar       *x;
    1421:   const PetscScalar *aa;

    1424:   VecGetLocalSize(v,&n);
    1425:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    1426:   MatSeqAIJGetArrayRead(A,&aa);
    1427:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
    1428:     PetscInt *diag=a->diag;
    1429:     VecGetArrayWrite(v,&x);
    1430:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
    1431:     VecRestoreArrayWrite(v,&x);
    1432:     MatSeqAIJRestoreArrayRead(A,&aa);
    1433:     return(0);
    1434:   }

    1436:   VecGetArrayWrite(v,&x);
    1437:   for (i=0; i<n; i++) {
    1438:     x[i] = 0.0;
    1439:     for (j=ai[i]; j<ai[i+1]; j++) {
    1440:       if (aj[j] == i) {
    1441:         x[i] = aa[j];
    1442:         break;
    1443:       }
    1444:     }
    1445:   }
    1446:   VecRestoreArrayWrite(v,&x);
    1447:   MatSeqAIJRestoreArrayRead(A,&aa);
    1448:   return(0);
    1449: }

    1451: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
    1452: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
    1453: {
    1454:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1455:   PetscScalar       *y;
    1456:   const PetscScalar *x;
    1457:   PetscErrorCode    ierr;
    1458:   PetscInt          m = A->rmap->n;
    1459: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    1460:   const MatScalar   *v;
    1461:   PetscScalar       alpha;
    1462:   PetscInt          n,i,j;
    1463:   const PetscInt    *idx,*ii,*ridx=NULL;
    1464:   Mat_CompressedRow cprow    = a->compressedrow;
    1465:   PetscBool         usecprow = cprow.use;
    1466: #endif

    1469:   if (zz != yy) {VecCopy(zz,yy);}
    1470:   VecGetArrayRead(xx,&x);
    1471:   VecGetArray(yy,&y);

    1473: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    1474:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
    1475: #else
    1476:   if (usecprow) {
    1477:     m    = cprow.nrows;
    1478:     ii   = cprow.i;
    1479:     ridx = cprow.rindex;
    1480:   } else {
    1481:     ii = a->i;
    1482:   }
    1483:   for (i=0; i<m; i++) {
    1484:     idx = a->j + ii[i];
    1485:     v   = a->a + ii[i];
    1486:     n   = ii[i+1] - ii[i];
    1487:     if (usecprow) {
    1488:       alpha = x[ridx[i]];
    1489:     } else {
    1490:       alpha = x[i];
    1491:     }
    1492:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
    1493:   }
    1494: #endif
    1495:   PetscLogFlops(2.0*a->nz);
    1496:   VecRestoreArrayRead(xx,&x);
    1497:   VecRestoreArray(yy,&y);
    1498:   return(0);
    1499: }

    1501: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
    1502: {

    1506:   VecSet(yy,0.0);
    1507:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
    1508:   return(0);
    1509: }

    1511: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>

    1513: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
    1514: {
    1515:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1516:   PetscScalar       *y;
    1517:   const PetscScalar *x;
    1518:   const MatScalar   *aa;
    1519:   PetscErrorCode    ierr;
    1520:   PetscInt          m=A->rmap->n;
    1521:   const PetscInt    *aj,*ii,*ridx=NULL;
    1522:   PetscInt          n,i;
    1523:   PetscScalar       sum;
    1524:   PetscBool         usecprow=a->compressedrow.use;

    1526: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
    1527: #pragma disjoint(*x,*y,*aa)
    1528: #endif

    1531:   if (a->inode.use && a->inode.checked) {
    1532:     MatMult_SeqAIJ_Inode(A,xx,yy);
    1533:     return(0);
    1534:   }
    1535:   VecGetArrayRead(xx,&x);
    1536:   VecGetArray(yy,&y);
    1537:   ii   = a->i;
    1538:   if (usecprow) { /* use compressed row format */
    1539:     PetscArrayzero(y,m);
    1540:     m    = a->compressedrow.nrows;
    1541:     ii   = a->compressedrow.i;
    1542:     ridx = a->compressedrow.rindex;
    1543:     for (i=0; i<m; i++) {
    1544:       n           = ii[i+1] - ii[i];
    1545:       aj          = a->j + ii[i];
    1546:       aa          = a->a + ii[i];
    1547:       sum         = 0.0;
    1548:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1549:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
    1550:       y[*ridx++] = sum;
    1551:     }
    1552:   } else { /* do not use compressed row format */
    1553: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
    1554:     aj   = a->j;
    1555:     aa   = a->a;
    1556:     fortranmultaij_(&m,x,ii,aj,aa,y);
    1557: #else
    1558:     for (i=0; i<m; i++) {
    1559:       n           = ii[i+1] - ii[i];
    1560:       aj          = a->j + ii[i];
    1561:       aa          = a->a + ii[i];
    1562:       sum         = 0.0;
    1563:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1564:       y[i] = sum;
    1565:     }
    1566: #endif
    1567:   }
    1568:   PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
    1569:   VecRestoreArrayRead(xx,&x);
    1570:   VecRestoreArray(yy,&y);
    1571:   return(0);
    1572: }

    1574: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
    1575: {
    1576:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1577:   PetscScalar       *y;
    1578:   const PetscScalar *x;
    1579:   const MatScalar   *aa;
    1580:   PetscErrorCode    ierr;
    1581:   PetscInt          m=A->rmap->n;
    1582:   const PetscInt    *aj,*ii,*ridx=NULL;
    1583:   PetscInt          n,i,nonzerorow=0;
    1584:   PetscScalar       sum;
    1585:   PetscBool         usecprow=a->compressedrow.use;

    1587: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
    1588: #pragma disjoint(*x,*y,*aa)
    1589: #endif

    1592:   VecGetArrayRead(xx,&x);
    1593:   VecGetArray(yy,&y);
    1594:   if (usecprow) { /* use compressed row format */
    1595:     m    = a->compressedrow.nrows;
    1596:     ii   = a->compressedrow.i;
    1597:     ridx = a->compressedrow.rindex;
    1598:     for (i=0; i<m; i++) {
    1599:       n           = ii[i+1] - ii[i];
    1600:       aj          = a->j + ii[i];
    1601:       aa          = a->a + ii[i];
    1602:       sum         = 0.0;
    1603:       nonzerorow += (n>0);
    1604:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1605:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
    1606:       y[*ridx++] = sum;
    1607:     }
    1608:   } else { /* do not use compressed row format */
    1609:     ii = a->i;
    1610:     for (i=0; i<m; i++) {
    1611:       n           = ii[i+1] - ii[i];
    1612:       aj          = a->j + ii[i];
    1613:       aa          = a->a + ii[i];
    1614:       sum         = 0.0;
    1615:       nonzerorow += (n>0);
    1616:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1617:       y[i] = sum;
    1618:     }
    1619:   }
    1620:   PetscLogFlops(2.0*a->nz - nonzerorow);
    1621:   VecRestoreArrayRead(xx,&x);
    1622:   VecRestoreArray(yy,&y);
    1623:   return(0);
    1624: }

    1626: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    1627: {
    1628:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1629:   PetscScalar       *y,*z;
    1630:   const PetscScalar *x;
    1631:   const MatScalar   *aa;
    1632:   PetscErrorCode    ierr;
    1633:   PetscInt          m = A->rmap->n,*aj,*ii;
    1634:   PetscInt          n,i,*ridx=NULL;
    1635:   PetscScalar       sum;
    1636:   PetscBool         usecprow=a->compressedrow.use;

    1639:   VecGetArrayRead(xx,&x);
    1640:   VecGetArrayPair(yy,zz,&y,&z);
    1641:   if (usecprow) { /* use compressed row format */
    1642:     if (zz != yy) {
    1643:       PetscArraycpy(z,y,m);
    1644:     }
    1645:     m    = a->compressedrow.nrows;
    1646:     ii   = a->compressedrow.i;
    1647:     ridx = a->compressedrow.rindex;
    1648:     for (i=0; i<m; i++) {
    1649:       n   = ii[i+1] - ii[i];
    1650:       aj  = a->j + ii[i];
    1651:       aa  = a->a + ii[i];
    1652:       sum = y[*ridx];
    1653:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1654:       z[*ridx++] = sum;
    1655:     }
    1656:   } else { /* do not use compressed row format */
    1657:     ii = a->i;
    1658:     for (i=0; i<m; i++) {
    1659:       n   = ii[i+1] - ii[i];
    1660:       aj  = a->j + ii[i];
    1661:       aa  = a->a + ii[i];
    1662:       sum = y[i];
    1663:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1664:       z[i] = sum;
    1665:     }
    1666:   }
    1667:   PetscLogFlops(2.0*a->nz);
    1668:   VecRestoreArrayRead(xx,&x);
    1669:   VecRestoreArrayPair(yy,zz,&y,&z);
    1670:   return(0);
    1671: }

    1673: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
    1674: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    1675: {
    1676:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1677:   PetscScalar       *y,*z;
    1678:   const PetscScalar *x;
    1679:   const MatScalar   *aa;
    1680:   PetscErrorCode    ierr;
    1681:   const PetscInt    *aj,*ii,*ridx=NULL;
    1682:   PetscInt          m = A->rmap->n,n,i;
    1683:   PetscScalar       sum;
    1684:   PetscBool         usecprow=a->compressedrow.use;

    1687:   if (a->inode.use && a->inode.checked) {
    1688:     MatMultAdd_SeqAIJ_Inode(A,xx,yy,zz);
    1689:     return(0);
    1690:   }
    1691:   VecGetArrayRead(xx,&x);
    1692:   VecGetArrayPair(yy,zz,&y,&z);
    1693:   if (usecprow) { /* use compressed row format */
    1694:     if (zz != yy) {
    1695:       PetscArraycpy(z,y,m);
    1696:     }
    1697:     m    = a->compressedrow.nrows;
    1698:     ii   = a->compressedrow.i;
    1699:     ridx = a->compressedrow.rindex;
    1700:     for (i=0; i<m; i++) {
    1701:       n   = ii[i+1] - ii[i];
    1702:       aj  = a->j + ii[i];
    1703:       aa  = a->a + ii[i];
    1704:       sum = y[*ridx];
    1705:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1706:       z[*ridx++] = sum;
    1707:     }
    1708:   } else { /* do not use compressed row format */
    1709:     ii = a->i;
    1710: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
    1711:     aj = a->j;
    1712:     aa = a->a;
    1713:     fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
    1714: #else
    1715:     for (i=0; i<m; i++) {
    1716:       n   = ii[i+1] - ii[i];
    1717:       aj  = a->j + ii[i];
    1718:       aa  = a->a + ii[i];
    1719:       sum = y[i];
    1720:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1721:       z[i] = sum;
    1722:     }
    1723: #endif
    1724:   }
    1725:   PetscLogFlops(2.0*a->nz);
    1726:   VecRestoreArrayRead(xx,&x);
    1727:   VecRestoreArrayPair(yy,zz,&y,&z);
    1728:   return(0);
    1729: }

    1731: /*
    1732:      Adds diagonal pointers to sparse matrix structure.
    1733: */
    1734: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
    1735: {
    1736:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1738:   PetscInt       i,j,m = A->rmap->n;

    1741:   if (!a->diag) {
    1742:     PetscMalloc1(m,&a->diag);
    1743:     PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
    1744:   }
    1745:   for (i=0; i<A->rmap->n; i++) {
    1746:     a->diag[i] = a->i[i+1];
    1747:     for (j=a->i[i]; j<a->i[i+1]; j++) {
    1748:       if (a->j[j] == i) {
    1749:         a->diag[i] = j;
    1750:         break;
    1751:       }
    1752:     }
    1753:   }
    1754:   return(0);
    1755: }

    1757: PetscErrorCode MatShift_SeqAIJ(Mat A,PetscScalar v)
    1758: {
    1759:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1760:   const PetscInt    *diag = (const PetscInt*)a->diag;
    1761:   const PetscInt    *ii = (const PetscInt*) a->i;
    1762:   PetscInt          i,*mdiag = NULL;
    1763:   PetscErrorCode    ierr;
    1764:   PetscInt          cnt = 0; /* how many diagonals are missing */

    1767:   if (!A->preallocated || !a->nz) {
    1768:     MatSeqAIJSetPreallocation(A,1,NULL);
    1769:     MatShift_Basic(A,v);
    1770:     return(0);
    1771:   }

    1773:   if (a->diagonaldense) {
    1774:     cnt = 0;
    1775:   } else {
    1776:     PetscCalloc1(A->rmap->n,&mdiag);
    1777:     for (i=0; i<A->rmap->n; i++) {
    1778:       if (diag[i] >= ii[i+1]) {
    1779:         cnt++;
    1780:         mdiag[i] = 1;
    1781:       }
    1782:     }
    1783:   }
    1784:   if (!cnt) {
    1785:     MatShift_Basic(A,v);
    1786:   } else {
    1787:     PetscScalar *olda = a->a;  /* preserve pointers to current matrix nonzeros structure and values */
    1788:     PetscInt    *oldj = a->j, *oldi = a->i;
    1789:     PetscBool   singlemalloc = a->singlemalloc,free_a = a->free_a,free_ij = a->free_ij;

    1791:     a->a = NULL;
    1792:     a->j = NULL;
    1793:     a->i = NULL;
    1794:     /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
    1795:     for (i=0; i<A->rmap->n; i++) {
    1796:       a->imax[i] += mdiag[i];
    1797:       a->imax[i] = PetscMin(a->imax[i],A->cmap->n);
    1798:     }
    1799:     MatSeqAIJSetPreallocation_SeqAIJ(A,0,a->imax);

    1801:     /* copy old values into new matrix data structure */
    1802:     for (i=0; i<A->rmap->n; i++) {
    1803:       MatSetValues(A,1,&i,a->imax[i] - mdiag[i],&oldj[oldi[i]],&olda[oldi[i]],ADD_VALUES);
    1804:       if (i < A->cmap->n) {
    1805:         MatSetValue(A,i,i,v,ADD_VALUES);
    1806:       }
    1807:     }
    1808:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    1809:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    1810:     if (singlemalloc) {
    1811:       PetscFree3(olda,oldj,oldi);
    1812:     } else {
    1813:       if (free_a)  {PetscFree(olda);}
    1814:       if (free_ij) {PetscFree(oldj);}
    1815:       if (free_ij) {PetscFree(oldi);}
    1816:     }
    1817:   }
    1818:   PetscFree(mdiag);
    1819:   a->diagonaldense = PETSC_TRUE;
    1820:   return(0);
    1821: }

    1823: /*
    1824:      Checks for missing diagonals
    1825: */
    1826: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
    1827: {
    1828:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1829:   PetscInt       *diag,*ii = a->i,i;

    1833:   *missing = PETSC_FALSE;
    1834:   if (A->rmap->n > 0 && !ii) {
    1835:     *missing = PETSC_TRUE;
    1836:     if (d) *d = 0;
    1837:     PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
    1838:   } else {
    1839:     PetscInt n;
    1840:     n = PetscMin(A->rmap->n, A->cmap->n);
    1841:     diag = a->diag;
    1842:     for (i=0; i<n; i++) {
    1843:       if (diag[i] >= ii[i+1]) {
    1844:         *missing = PETSC_TRUE;
    1845:         if (d) *d = i;
    1846:         PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
    1847:         break;
    1848:       }
    1849:     }
    1850:   }
    1851:   return(0);
    1852: }

    1854: #include <petscblaslapack.h>
    1855: #include <petsc/private/kernels/blockinvert.h>

    1857: /*
    1858:     Note that values is allocated externally by the PC and then passed into this routine
    1859: */
    1860: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A,PetscInt nblocks,const PetscInt *bsizes,PetscScalar *diag)
    1861: {
    1862:   PetscErrorCode  ierr;
    1863:   PetscInt        n = A->rmap->n, i, ncnt = 0, *indx,j,bsizemax = 0,*v_pivots;
    1864:   PetscBool       allowzeropivot,zeropivotdetected=PETSC_FALSE;
    1865:   const PetscReal shift = 0.0;
    1866:   PetscInt        ipvt[5];
    1867:   PetscScalar     work[25],*v_work;

    1870:   allowzeropivot = PetscNot(A->erroriffailure);
    1871:   for (i=0; i<nblocks; i++) ncnt += bsizes[i];
    1872:   if (ncnt != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Total blocksizes %D doesn't match number matrix rows %D",ncnt,n);
    1873:   for (i=0; i<nblocks; i++) {
    1874:     bsizemax = PetscMax(bsizemax,bsizes[i]);
    1875:   }
    1876:   PetscMalloc1(bsizemax,&indx);
    1877:   if (bsizemax > 7) {
    1878:     PetscMalloc2(bsizemax,&v_work,bsizemax,&v_pivots);
    1879:   }
    1880:   ncnt = 0;
    1881:   for (i=0; i<nblocks; i++) {
    1882:     for (j=0; j<bsizes[i]; j++) indx[j] = ncnt+j;
    1883:     MatGetValues(A,bsizes[i],indx,bsizes[i],indx,diag);
    1884:     switch (bsizes[i]) {
    1885:     case 1:
    1886:       *diag = 1.0/(*diag);
    1887:       break;
    1888:     case 2:
    1889:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
    1890:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1891:       PetscKernel_A_gets_transpose_A_2(diag);
    1892:       break;
    1893:     case 3:
    1894:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
    1895:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1896:       PetscKernel_A_gets_transpose_A_3(diag);
    1897:       break;
    1898:     case 4:
    1899:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
    1900:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1901:       PetscKernel_A_gets_transpose_A_4(diag);
    1902:       break;
    1903:     case 5:
    1904:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
    1905:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1906:       PetscKernel_A_gets_transpose_A_5(diag);
    1907:       break;
    1908:     case 6:
    1909:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
    1910:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1911:       PetscKernel_A_gets_transpose_A_6(diag);
    1912:       break;
    1913:     case 7:
    1914:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
    1915:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1916:       PetscKernel_A_gets_transpose_A_7(diag);
    1917:       break;
    1918:     default:
    1919:       PetscKernel_A_gets_inverse_A(bsizes[i],diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
    1920:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1921:       PetscKernel_A_gets_transpose_A_N(diag,bsizes[i]);
    1922:     }
    1923:     ncnt   += bsizes[i];
    1924:     diag += bsizes[i]*bsizes[i];
    1925:   }
    1926:   if (bsizemax > 7) {
    1927:     PetscFree2(v_work,v_pivots);
    1928:   }
    1929:   PetscFree(indx);
    1930:   return(0);
    1931: }

    1933: /*
    1934:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
    1935: */
    1936: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
    1937: {
    1938:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*) A->data;
    1939:   PetscErrorCode  ierr;
    1940:   PetscInt        i,*diag,m = A->rmap->n;
    1941:   const MatScalar *v;
    1942:   PetscScalar     *idiag,*mdiag;

    1945:   if (a->idiagvalid) return(0);
    1946:   MatMarkDiagonal_SeqAIJ(A);
    1947:   diag = a->diag;
    1948:   if (!a->idiag) {
    1949:     PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
    1950:     PetscLogObjectMemory((PetscObject)A,3*m*sizeof(PetscScalar));
    1951:   }

    1953:   mdiag = a->mdiag;
    1954:   idiag = a->idiag;
    1955:   MatSeqAIJGetArrayRead(A,&v);
    1956:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
    1957:     for (i=0; i<m; i++) {
    1958:       mdiag[i] = v[diag[i]];
    1959:       if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
    1960:         if (PetscRealPart(fshift)) {
    1961:           PetscInfo1(A,"Zero diagonal on row %D\n",i);
    1962:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1963:           A->factorerror_zeropivot_value = 0.0;
    1964:           A->factorerror_zeropivot_row   = i;
    1965:         } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
    1966:       }
    1967:       idiag[i] = 1.0/v[diag[i]];
    1968:     }
    1969:     PetscLogFlops(m);
    1970:   } else {
    1971:     for (i=0; i<m; i++) {
    1972:       mdiag[i] = v[diag[i]];
    1973:       idiag[i] = omega/(fshift + v[diag[i]]);
    1974:     }
    1975:     PetscLogFlops(2.0*m);
    1976:   }
    1977:   a->idiagvalid = PETSC_TRUE;
    1978:   MatSeqAIJRestoreArrayRead(A,&v);
    1979:   return(0);
    1980: }

    1982: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
    1983: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
    1984: {
    1985:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1986:   PetscScalar       *x,d,sum,*t,scale;
    1987:   const MatScalar   *v,*idiag=NULL,*mdiag,*aa;
    1988:   const PetscScalar *b, *bs,*xb, *ts;
    1989:   PetscErrorCode    ierr;
    1990:   PetscInt          n,m = A->rmap->n,i;
    1991:   const PetscInt    *idx,*diag;

    1994:   if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
    1995:     MatSOR_SeqAIJ_Inode(A,bb,omega,flag,fshift,its,lits,xx);
    1996:     return(0);
    1997:   }
    1998:   its = its*lits;

    2000:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
    2001:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
    2002:   a->fshift = fshift;
    2003:   a->omega  = omega;

    2005:   diag  = a->diag;
    2006:   t     = a->ssor_work;
    2007:   idiag = a->idiag;
    2008:   mdiag = a->mdiag;

    2010:   MatSeqAIJGetArrayRead(A,&aa);
    2011:   VecGetArray(xx,&x);
    2012:   VecGetArrayRead(bb,&b);
    2013:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
    2014:   if (flag == SOR_APPLY_UPPER) {
    2015:     /* apply (U + D/omega) to the vector */
    2016:     bs = b;
    2017:     for (i=0; i<m; i++) {
    2018:       d   = fshift + mdiag[i];
    2019:       n   = a->i[i+1] - diag[i] - 1;
    2020:       idx = a->j + diag[i] + 1;
    2021:       v   = aa + diag[i] + 1;
    2022:       sum = b[i]*d/omega;
    2023:       PetscSparseDensePlusDot(sum,bs,v,idx,n);
    2024:       x[i] = sum;
    2025:     }
    2026:     VecRestoreArray(xx,&x);
    2027:     VecRestoreArrayRead(bb,&b);
    2028:     MatSeqAIJRestoreArrayRead(A,&aa);
    2029:     PetscLogFlops(a->nz);
    2030:     return(0);
    2031:   }

    2033:   if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
    2034:   else if (flag & SOR_EISENSTAT) {
    2035:     /* Let  A = L + U + D; where L is lower triangular,
    2036:     U is upper triangular, E = D/omega; This routine applies

    2038:             (L + E)^{-1} A (U + E)^{-1}

    2040:     to a vector efficiently using Eisenstat's trick.
    2041:     */
    2042:     scale = (2.0/omega) - 1.0;

    2044:     /*  x = (E + U)^{-1} b */
    2045:     for (i=m-1; i>=0; i--) {
    2046:       n   = a->i[i+1] - diag[i] - 1;
    2047:       idx = a->j + diag[i] + 1;
    2048:       v   = aa + diag[i] + 1;
    2049:       sum = b[i];
    2050:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2051:       x[i] = sum*idiag[i];
    2052:     }

    2054:     /*  t = b - (2*E - D)x */
    2055:     v = aa;
    2056:     for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];

    2058:     /*  t = (E + L)^{-1}t */
    2059:     ts   = t;
    2060:     diag = a->diag;
    2061:     for (i=0; i<m; i++) {
    2062:       n   = diag[i] - a->i[i];
    2063:       idx = a->j + a->i[i];
    2064:       v   = aa + a->i[i];
    2065:       sum = t[i];
    2066:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
    2067:       t[i] = sum*idiag[i];
    2068:       /*  x = x + t */
    2069:       x[i] += t[i];
    2070:     }

    2072:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
    2073:     VecRestoreArray(xx,&x);
    2074:     VecRestoreArrayRead(bb,&b);
    2075:     return(0);
    2076:   }
    2077:   if (flag & SOR_ZERO_INITIAL_GUESS) {
    2078:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
    2079:       for (i=0; i<m; i++) {
    2080:         n   = diag[i] - a->i[i];
    2081:         idx = a->j + a->i[i];
    2082:         v   = aa + a->i[i];
    2083:         sum = b[i];
    2084:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2085:         t[i] = sum;
    2086:         x[i] = sum*idiag[i];
    2087:       }
    2088:       xb   = t;
    2089:       PetscLogFlops(a->nz);
    2090:     } else xb = b;
    2091:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
    2092:       for (i=m-1; i>=0; i--) {
    2093:         n   = a->i[i+1] - diag[i] - 1;
    2094:         idx = a->j + diag[i] + 1;
    2095:         v   = aa + diag[i] + 1;
    2096:         sum = xb[i];
    2097:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2098:         if (xb == b) {
    2099:           x[i] = sum*idiag[i];
    2100:         } else {
    2101:           x[i] = (1-omega)*x[i] + sum*idiag[i];  /* omega in idiag */
    2102:         }
    2103:       }
    2104:       PetscLogFlops(a->nz); /* assumes 1/2 in upper */
    2105:     }
    2106:     its--;
    2107:   }
    2108:   while (its--) {
    2109:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
    2110:       for (i=0; i<m; i++) {
    2111:         /* lower */
    2112:         n   = diag[i] - a->i[i];
    2113:         idx = a->j + a->i[i];
    2114:         v   = aa + a->i[i];
    2115:         sum = b[i];
    2116:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2117:         t[i] = sum;             /* save application of the lower-triangular part */
    2118:         /* upper */
    2119:         n   = a->i[i+1] - diag[i] - 1;
    2120:         idx = a->j + diag[i] + 1;
    2121:         v   = aa + diag[i] + 1;
    2122:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2123:         x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
    2124:       }
    2125:       xb   = t;
    2126:       PetscLogFlops(2.0*a->nz);
    2127:     } else xb = b;
    2128:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
    2129:       for (i=m-1; i>=0; i--) {
    2130:         sum = xb[i];
    2131:         if (xb == b) {
    2132:           /* whole matrix (no checkpointing available) */
    2133:           n   = a->i[i+1] - a->i[i];
    2134:           idx = a->j + a->i[i];
    2135:           v   = aa + a->i[i];
    2136:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2137:           x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
    2138:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
    2139:           n   = a->i[i+1] - diag[i] - 1;
    2140:           idx = a->j + diag[i] + 1;
    2141:           v   = aa + diag[i] + 1;
    2142:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2143:           x[i] = (1. - omega)*x[i] + sum*idiag[i];  /* omega in idiag */
    2144:         }
    2145:       }
    2146:       if (xb == b) {
    2147:         PetscLogFlops(2.0*a->nz);
    2148:       } else {
    2149:         PetscLogFlops(a->nz); /* assumes 1/2 in upper */
    2150:       }
    2151:     }
    2152:   }
    2153:   MatSeqAIJRestoreArrayRead(A,&aa);
    2154:   VecRestoreArray(xx,&x);
    2155:   VecRestoreArrayRead(bb,&b);
    2156:   return(0);
    2157: }

    2159: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
    2160: {
    2161:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

    2164:   info->block_size   = 1.0;
    2165:   info->nz_allocated = a->maxnz;
    2166:   info->nz_used      = a->nz;
    2167:   info->nz_unneeded  = (a->maxnz - a->nz);
    2168:   info->assemblies   = A->num_ass;
    2169:   info->mallocs      = A->info.mallocs;
    2170:   info->memory       = ((PetscObject)A)->mem;
    2171:   if (A->factortype) {
    2172:     info->fill_ratio_given  = A->info.fill_ratio_given;
    2173:     info->fill_ratio_needed = A->info.fill_ratio_needed;
    2174:     info->factor_mallocs    = A->info.factor_mallocs;
    2175:   } else {
    2176:     info->fill_ratio_given  = 0;
    2177:     info->fill_ratio_needed = 0;
    2178:     info->factor_mallocs    = 0;
    2179:   }
    2180:   return(0);
    2181: }

    2183: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    2184: {
    2185:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2186:   PetscInt          i,m = A->rmap->n - 1;
    2187:   PetscErrorCode    ierr;
    2188:   const PetscScalar *xx;
    2189:   PetscScalar       *bb,*aa;
    2190:   PetscInt          d = 0;

    2193:   if (x && b) {
    2194:     VecGetArrayRead(x,&xx);
    2195:     VecGetArray(b,&bb);
    2196:     for (i=0; i<N; i++) {
    2197:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    2198:       if (rows[i] >= A->cmap->n) continue;
    2199:       bb[rows[i]] = diag*xx[rows[i]];
    2200:     }
    2201:     VecRestoreArrayRead(x,&xx);
    2202:     VecRestoreArray(b,&bb);
    2203:   }

    2205:   MatSeqAIJGetArray(A,&aa);
    2206:   if (a->keepnonzeropattern) {
    2207:     for (i=0; i<N; i++) {
    2208:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    2209:       PetscArrayzero(&aa[a->i[rows[i]]],a->ilen[rows[i]]);
    2210:     }
    2211:     if (diag != 0.0) {
    2212:       for (i=0; i<N; i++) {
    2213:         d = rows[i];
    2214:         if (rows[i] >= A->cmap->n) continue;
    2215:         if (a->diag[d] >= a->i[d+1]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in the zeroed row %D",d);
    2216:       }
    2217:       for (i=0; i<N; i++) {
    2218:         if (rows[i] >= A->cmap->n) continue;
    2219:         aa[a->diag[rows[i]]] = diag;
    2220:       }
    2221:     }
    2222:   } else {
    2223:     if (diag != 0.0) {
    2224:       for (i=0; i<N; i++) {
    2225:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    2226:         if (a->ilen[rows[i]] > 0) {
    2227:           if (rows[i] >= A->cmap->n) {
    2228:             a->ilen[rows[i]] = 0;
    2229:           } else {
    2230:             a->ilen[rows[i]]    = 1;
    2231:             aa[a->i[rows[i]]]   = diag;
    2232:             a->j[a->i[rows[i]]] = rows[i];
    2233:           }
    2234:         } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
    2235:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
    2236:         }
    2237:       }
    2238:     } else {
    2239:       for (i=0; i<N; i++) {
    2240:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    2241:         a->ilen[rows[i]] = 0;
    2242:       }
    2243:     }
    2244:     A->nonzerostate++;
    2245:   }
    2246:   MatSeqAIJRestoreArray(A,&aa);
    2247: #if defined(PETSC_HAVE_DEVICE)
    2248:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    2249: #endif
    2250:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
    2251:   return(0);
    2252: }

    2254: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    2255: {
    2256:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2257:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
    2258:   PetscErrorCode    ierr;
    2259:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
    2260:   const PetscScalar *xx;
    2261:   PetscScalar       *bb,*aa;

    2264:   if (!N) return(0);
    2265:   MatSeqAIJGetArray(A,&aa);
    2266:   if (x && b) {
    2267:     VecGetArrayRead(x,&xx);
    2268:     VecGetArray(b,&bb);
    2269:     vecs = PETSC_TRUE;
    2270:   }
    2271:   PetscCalloc1(A->rmap->n,&zeroed);
    2272:   for (i=0; i<N; i++) {
    2273:     if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    2274:     PetscArrayzero(&aa[a->i[rows[i]]],a->ilen[rows[i]]);

    2276:     zeroed[rows[i]] = PETSC_TRUE;
    2277:   }
    2278:   for (i=0; i<A->rmap->n; i++) {
    2279:     if (!zeroed[i]) {
    2280:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    2281:         if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
    2282:           if (vecs) bb[i] -= aa[j]*xx[a->j[j]];
    2283:           aa[j] = 0.0;
    2284:         }
    2285:       }
    2286:     } else if (vecs && i < A->cmap->N) bb[i] = diag*xx[i];
    2287:   }
    2288:   if (x && b) {
    2289:     VecRestoreArrayRead(x,&xx);
    2290:     VecRestoreArray(b,&bb);
    2291:   }
    2292:   PetscFree(zeroed);
    2293:   if (diag != 0.0) {
    2294:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
    2295:     if (missing) {
    2296:       for (i=0; i<N; i++) {
    2297:         if (rows[i] >= A->cmap->N) continue;
    2298:         if (a->nonew && rows[i] >= d) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D (%D)",d,rows[i]);
    2299:         MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
    2300:       }
    2301:     } else {
    2302:       for (i=0; i<N; i++) {
    2303:         aa[a->diag[rows[i]]] = diag;
    2304:       }
    2305:     }
    2306:   }
    2307:   MatSeqAIJRestoreArray(A,&aa);
    2308: #if defined(PETSC_HAVE_DEVICE)
    2309:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    2310: #endif
    2311:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
    2312:   return(0);
    2313: }

    2315: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    2316: {
    2317:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2318:   const PetscScalar *aa = a->a;
    2319:   PetscInt          *itmp;
    2320: #if defined(PETSC_HAVE_DEVICE)
    2321:   PetscErrorCode    ierr;
    2322:   PetscBool         rest = PETSC_FALSE;
    2323: #endif

    2326: #if defined(PETSC_HAVE_DEVICE)
    2327:   if (v && A->offloadmask == PETSC_OFFLOAD_GPU) {
    2328:     /* triggers copy to CPU */
    2329:     rest = PETSC_TRUE;
    2330:     MatSeqAIJGetArrayRead(A,&aa);
    2331:   } else aa = a->a;
    2332: #endif
    2333:   *nz = a->i[row+1] - a->i[row];
    2334:   if (v) *v = (PetscScalar*)(aa + a->i[row]);
    2335:   if (idx) {
    2336:     itmp = a->j + a->i[row];
    2337:     if (*nz) *idx = itmp;
    2338:     else *idx = NULL;
    2339:   }
    2340: #if defined(PETSC_HAVE_DEVICE)
    2341:   if (rest) {
    2342:     MatSeqAIJRestoreArrayRead(A,&aa);
    2343:   }
    2344: #endif
    2345:   return(0);
    2346: }

    2348: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    2349: {
    2351:   if (nz)  *nz = 0;
    2352:   if (idx) *idx = NULL;
    2353:   if (v)   *v = NULL;
    2354:   return(0);
    2355: }

    2357: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
    2358: {
    2359:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
    2360:   const MatScalar *v;
    2361:   PetscReal       sum = 0.0;
    2362:   PetscErrorCode  ierr;
    2363:   PetscInt        i,j;

    2366:   MatSeqAIJGetArrayRead(A,&v);
    2367:   if (type == NORM_FROBENIUS) {
    2368: #if defined(PETSC_USE_REAL___FP16)
    2369:     PetscBLASInt one = 1,nz = a->nz;
    2370:     PetscStackCallBLAS("BLASnrm2",*nrm = BLASnrm2_(&nz,v,&one));
    2371: #else
    2372:     for (i=0; i<a->nz; i++) {
    2373:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
    2374:     }
    2375:     *nrm = PetscSqrtReal(sum);
    2376: #endif
    2377:     PetscLogFlops(2.0*a->nz);
    2378:   } else if (type == NORM_1) {
    2379:     PetscReal *tmp;
    2380:     PetscInt  *jj = a->j;
    2381:     PetscCalloc1(A->cmap->n+1,&tmp);
    2382:     *nrm = 0.0;
    2383:     for (j=0; j<a->nz; j++) {
    2384:       tmp[*jj++] += PetscAbsScalar(*v);  v++;
    2385:     }
    2386:     for (j=0; j<A->cmap->n; j++) {
    2387:       if (tmp[j] > *nrm) *nrm = tmp[j];
    2388:     }
    2389:     PetscFree(tmp);
    2390:     PetscLogFlops(PetscMax(a->nz-1,0));
    2391:   } else if (type == NORM_INFINITY) {
    2392:     *nrm = 0.0;
    2393:     for (j=0; j<A->rmap->n; j++) {
    2394:       const PetscScalar *v2 = v + a->i[j];
    2395:       sum = 0.0;
    2396:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
    2397:         sum += PetscAbsScalar(*v2); v2++;
    2398:       }
    2399:       if (sum > *nrm) *nrm = sum;
    2400:     }
    2401:     PetscLogFlops(PetscMax(a->nz-1,0));
    2402:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
    2403:   MatSeqAIJRestoreArrayRead(A,&v);
    2404:   return(0);
    2405: }

    2407: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
    2408: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
    2409: {
    2411:   PetscInt       i,j,anzj;
    2412:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b;
    2413:   PetscInt       an=A->cmap->N,am=A->rmap->N;
    2414:   PetscInt       *ati,*atj,*atfill,*ai=a->i,*aj=a->j;

    2417:   /* Allocate space for symbolic transpose info and work array */
    2418:   PetscCalloc1(an+1,&ati);
    2419:   PetscMalloc1(ai[am],&atj);
    2420:   PetscMalloc1(an,&atfill);

    2422:   /* Walk through aj and count ## of non-zeros in each row of A^T. */
    2423:   /* Note: offset by 1 for fast conversion into csr format. */
    2424:   for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
    2425:   /* Form ati for csr format of A^T. */
    2426:   for (i=0;i<an;i++) ati[i+1] += ati[i];

    2428:   /* Copy ati into atfill so we have locations of the next free space in atj */
    2429:   PetscArraycpy(atfill,ati,an);

    2431:   /* Walk through A row-wise and mark nonzero entries of A^T. */
    2432:   for (i=0;i<am;i++) {
    2433:     anzj = ai[i+1] - ai[i];
    2434:     for (j=0;j<anzj;j++) {
    2435:       atj[atfill[*aj]] = i;
    2436:       atfill[*aj++]   += 1;
    2437:     }
    2438:   }

    2440:   /* Clean up temporary space and complete requests. */
    2441:   PetscFree(atfill);
    2442:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
    2443:   MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
    2444:   MatSetType(*B,((PetscObject)A)->type_name);

    2446:   b          = (Mat_SeqAIJ*)((*B)->data);
    2447:   b->free_a  = PETSC_FALSE;
    2448:   b->free_ij = PETSC_TRUE;
    2449:   b->nonew   = 0;
    2450:   return(0);
    2451: }

    2453: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
    2454: {
    2455:   Mat_SeqAIJ      *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
    2456:   PetscInt        *adx,*bdx,*aii,*bii,*aptr,*bptr;
    2457:   const MatScalar *va,*vb;
    2458:   PetscErrorCode  ierr;
    2459:   PetscInt        ma,na,mb,nb, i;

    2462:   MatGetSize(A,&ma,&na);
    2463:   MatGetSize(B,&mb,&nb);
    2464:   if (ma!=nb || na!=mb) {
    2465:     *f = PETSC_FALSE;
    2466:     return(0);
    2467:   }
    2468:   MatSeqAIJGetArrayRead(A,&va);
    2469:   MatSeqAIJGetArrayRead(B,&vb);
    2470:   aii  = aij->i; bii = bij->i;
    2471:   adx  = aij->j; bdx = bij->j;
    2472:   PetscMalloc1(ma,&aptr);
    2473:   PetscMalloc1(mb,&bptr);
    2474:   for (i=0; i<ma; i++) aptr[i] = aii[i];
    2475:   for (i=0; i<mb; i++) bptr[i] = bii[i];

    2477:   *f = PETSC_TRUE;
    2478:   for (i=0; i<ma; i++) {
    2479:     while (aptr[i]<aii[i+1]) {
    2480:       PetscInt    idc,idr;
    2481:       PetscScalar vc,vr;
    2482:       /* column/row index/value */
    2483:       idc = adx[aptr[i]];
    2484:       idr = bdx[bptr[idc]];
    2485:       vc  = va[aptr[i]];
    2486:       vr  = vb[bptr[idc]];
    2487:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
    2488:         *f = PETSC_FALSE;
    2489:         goto done;
    2490:       } else {
    2491:         aptr[i]++;
    2492:         if (B || i!=idc) bptr[idc]++;
    2493:       }
    2494:     }
    2495:   }
    2496: done:
    2497:   PetscFree(aptr);
    2498:   PetscFree(bptr);
    2499:   MatSeqAIJRestoreArrayRead(A,&va);
    2500:   MatSeqAIJRestoreArrayRead(B,&vb);
    2501:   return(0);
    2502: }

    2504: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
    2505: {
    2506:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
    2507:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
    2508:   MatScalar      *va,*vb;
    2510:   PetscInt       ma,na,mb,nb, i;

    2513:   MatGetSize(A,&ma,&na);
    2514:   MatGetSize(B,&mb,&nb);
    2515:   if (ma!=nb || na!=mb) {
    2516:     *f = PETSC_FALSE;
    2517:     return(0);
    2518:   }
    2519:   aii  = aij->i; bii = bij->i;
    2520:   adx  = aij->j; bdx = bij->j;
    2521:   va   = aij->a; vb = bij->a;
    2522:   PetscMalloc1(ma,&aptr);
    2523:   PetscMalloc1(mb,&bptr);
    2524:   for (i=0; i<ma; i++) aptr[i] = aii[i];
    2525:   for (i=0; i<mb; i++) bptr[i] = bii[i];

    2527:   *f = PETSC_TRUE;
    2528:   for (i=0; i<ma; i++) {
    2529:     while (aptr[i]<aii[i+1]) {
    2530:       PetscInt    idc,idr;
    2531:       PetscScalar vc,vr;
    2532:       /* column/row index/value */
    2533:       idc = adx[aptr[i]];
    2534:       idr = bdx[bptr[idc]];
    2535:       vc  = va[aptr[i]];
    2536:       vr  = vb[bptr[idc]];
    2537:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
    2538:         *f = PETSC_FALSE;
    2539:         goto done;
    2540:       } else {
    2541:         aptr[i]++;
    2542:         if (B || i!=idc) bptr[idc]++;
    2543:       }
    2544:     }
    2545:   }
    2546: done:
    2547:   PetscFree(aptr);
    2548:   PetscFree(bptr);
    2549:   return(0);
    2550: }

    2552: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
    2553: {

    2557:   MatIsTranspose_SeqAIJ(A,A,tol,f);
    2558:   return(0);
    2559: }

    2561: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
    2562: {

    2566:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
    2567:   return(0);
    2568: }

    2570: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
    2571: {
    2572:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2573:   const PetscScalar *l,*r;
    2574:   PetscScalar       x;
    2575:   MatScalar         *v;
    2576:   PetscErrorCode    ierr;
    2577:   PetscInt          i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz;
    2578:   const PetscInt    *jj;

    2581:   if (ll) {
    2582:     /* The local size is used so that VecMPI can be passed to this routine
    2583:        by MatDiagonalScale_MPIAIJ */
    2584:     VecGetLocalSize(ll,&m);
    2585:     if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
    2586:     VecGetArrayRead(ll,&l);
    2587:     MatSeqAIJGetArray(A,&v);
    2588:     for (i=0; i<m; i++) {
    2589:       x = l[i];
    2590:       M = a->i[i+1] - a->i[i];
    2591:       for (j=0; j<M; j++) (*v++) *= x;
    2592:     }
    2593:     VecRestoreArrayRead(ll,&l);
    2594:     PetscLogFlops(nz);
    2595:     MatSeqAIJRestoreArray(A,&v);
    2596:   }
    2597:   if (rr) {
    2598:     VecGetLocalSize(rr,&n);
    2599:     if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
    2600:     VecGetArrayRead(rr,&r);
    2601:     MatSeqAIJGetArray(A,&v);
    2602:     jj = a->j;
    2603:     for (i=0; i<nz; i++) (*v++) *= r[*jj++];
    2604:     MatSeqAIJRestoreArray(A,&v);
    2605:     VecRestoreArrayRead(rr,&r);
    2606:     PetscLogFlops(nz);
    2607:   }
    2608:   MatSeqAIJInvalidateDiagonal(A);
    2609: #if defined(PETSC_HAVE_DEVICE)
    2610:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    2611: #endif
    2612:   return(0);
    2613: }

    2615: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
    2616: {
    2617:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data,*c;
    2618:   PetscErrorCode    ierr;
    2619:   PetscInt          *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
    2620:   PetscInt          row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
    2621:   const PetscInt    *irow,*icol;
    2622:   const PetscScalar *aa;
    2623:   PetscInt          nrows,ncols;
    2624:   PetscInt          *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
    2625:   MatScalar         *a_new,*mat_a;
    2626:   Mat               C;
    2627:   PetscBool         stride;

    2630:   ISGetIndices(isrow,&irow);
    2631:   ISGetLocalSize(isrow,&nrows);
    2632:   ISGetLocalSize(iscol,&ncols);

    2634:   PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
    2635:   if (stride) {
    2636:     ISStrideGetInfo(iscol,&first,&step);
    2637:   } else {
    2638:     first = 0;
    2639:     step  = 0;
    2640:   }
    2641:   if (stride && step == 1) {
    2642:     /* special case of contiguous rows */
    2643:     PetscMalloc2(nrows,&lens,nrows,&starts);
    2644:     /* loop over new rows determining lens and starting points */
    2645:     for (i=0; i<nrows; i++) {
    2646:       kstart = ai[irow[i]];
    2647:       kend   = kstart + ailen[irow[i]];
    2648:       starts[i] = kstart;
    2649:       for (k=kstart; k<kend; k++) {
    2650:         if (aj[k] >= first) {
    2651:           starts[i] = k;
    2652:           break;
    2653:         }
    2654:       }
    2655:       sum = 0;
    2656:       while (k < kend) {
    2657:         if (aj[k++] >= first+ncols) break;
    2658:         sum++;
    2659:       }
    2660:       lens[i] = sum;
    2661:     }
    2662:     /* create submatrix */
    2663:     if (scall == MAT_REUSE_MATRIX) {
    2664:       PetscInt n_cols,n_rows;
    2665:       MatGetSize(*B,&n_rows,&n_cols);
    2666:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
    2667:       MatZeroEntries(*B);
    2668:       C    = *B;
    2669:     } else {
    2670:       PetscInt rbs,cbs;
    2671:       MatCreate(PetscObjectComm((PetscObject)A),&C);
    2672:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
    2673:       ISGetBlockSize(isrow,&rbs);
    2674:       ISGetBlockSize(iscol,&cbs);
    2675:       MatSetBlockSizes(C,rbs,cbs);
    2676:       MatSetType(C,((PetscObject)A)->type_name);
    2677:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
    2678:     }
    2679:     c = (Mat_SeqAIJ*)C->data;

    2681:     /* loop over rows inserting into submatrix */
    2682:     a_new = c->a;
    2683:     j_new = c->j;
    2684:     i_new = c->i;
    2685:     MatSeqAIJGetArrayRead(A,&aa);
    2686:     for (i=0; i<nrows; i++) {
    2687:       ii    = starts[i];
    2688:       lensi = lens[i];
    2689:       for (k=0; k<lensi; k++) {
    2690:         *j_new++ = aj[ii+k] - first;
    2691:       }
    2692:       PetscArraycpy(a_new,aa + starts[i],lensi);
    2693:       a_new     += lensi;
    2694:       i_new[i+1] = i_new[i] + lensi;
    2695:       c->ilen[i] = lensi;
    2696:     }
    2697:     MatSeqAIJRestoreArrayRead(A,&aa);
    2698:     PetscFree2(lens,starts);
    2699:   } else {
    2700:     ISGetIndices(iscol,&icol);
    2701:     PetscCalloc1(oldcols,&smap);
    2702:     PetscMalloc1(1+nrows,&lens);
    2703:     for (i=0; i<ncols; i++) {
    2704:       if (PetscUnlikelyDebug(icol[i] >= oldcols)) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Requesting column beyond largest column icol[%D] %D >= A->cmap->n %D",i,icol[i],oldcols);
    2705:       smap[icol[i]] = i+1;
    2706:     }

    2708:     /* determine lens of each row */
    2709:     for (i=0; i<nrows; i++) {
    2710:       kstart  = ai[irow[i]];
    2711:       kend    = kstart + a->ilen[irow[i]];
    2712:       lens[i] = 0;
    2713:       for (k=kstart; k<kend; k++) {
    2714:         if (smap[aj[k]]) {
    2715:           lens[i]++;
    2716:         }
    2717:       }
    2718:     }
    2719:     /* Create and fill new matrix */
    2720:     if (scall == MAT_REUSE_MATRIX) {
    2721:       PetscBool equal;

    2723:       c = (Mat_SeqAIJ*)((*B)->data);
    2724:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
    2725:       PetscArraycmp(c->ilen,lens,(*B)->rmap->n,&equal);
    2726:       if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
    2727:       PetscArrayzero(c->ilen,(*B)->rmap->n);
    2728:       C    = *B;
    2729:     } else {
    2730:       PetscInt rbs,cbs;
    2731:       MatCreate(PetscObjectComm((PetscObject)A),&C);
    2732:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
    2733:       ISGetBlockSize(isrow,&rbs);
    2734:       ISGetBlockSize(iscol,&cbs);
    2735:       MatSetBlockSizes(C,rbs,cbs);
    2736:       MatSetType(C,((PetscObject)A)->type_name);
    2737:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
    2738:     }
    2739:     MatSeqAIJGetArrayRead(A,&aa);
    2740:     c = (Mat_SeqAIJ*)(C->data);
    2741:     for (i=0; i<nrows; i++) {
    2742:       row      = irow[i];
    2743:       kstart   = ai[row];
    2744:       kend     = kstart + a->ilen[row];
    2745:       mat_i    = c->i[i];
    2746:       mat_j    = c->j + mat_i;
    2747:       mat_a    = c->a + mat_i;
    2748:       mat_ilen = c->ilen + i;
    2749:       for (k=kstart; k<kend; k++) {
    2750:         if ((tcol=smap[a->j[k]])) {
    2751:           *mat_j++ = tcol - 1;
    2752:           *mat_a++ = aa[k];
    2753:           (*mat_ilen)++;

    2755:         }
    2756:       }
    2757:     }
    2758:     MatSeqAIJRestoreArrayRead(A,&aa);
    2759:     /* Free work space */
    2760:     ISRestoreIndices(iscol,&icol);
    2761:     PetscFree(smap);
    2762:     PetscFree(lens);
    2763:     /* sort */
    2764:     for (i = 0; i < nrows; i++) {
    2765:       PetscInt ilen;

    2767:       mat_i = c->i[i];
    2768:       mat_j = c->j + mat_i;
    2769:       mat_a = c->a + mat_i;
    2770:       ilen  = c->ilen[i];
    2771:       PetscSortIntWithScalarArray(ilen,mat_j,mat_a);
    2772:     }
    2773:   }
    2774: #if defined(PETSC_HAVE_DEVICE)
    2775:   MatBindToCPU(C,A->boundtocpu);
    2776: #endif
    2777:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    2778:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

    2780:   ISRestoreIndices(isrow,&irow);
    2781:   *B   = C;
    2782:   return(0);
    2783: }

    2785: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
    2786: {
    2788:   Mat            B;

    2791:   if (scall == MAT_INITIAL_MATRIX) {
    2792:     MatCreate(subComm,&B);
    2793:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
    2794:     MatSetBlockSizesFromMats(B,mat,mat);
    2795:     MatSetType(B,MATSEQAIJ);
    2796:     MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
    2797:     *subMat = B;
    2798:   } else {
    2799:     MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
    2800:   }
    2801:   return(0);
    2802: }

    2804: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
    2805: {
    2806:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
    2808:   Mat            outA;
    2809:   PetscBool      row_identity,col_identity;

    2812:   if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

    2814:   ISIdentity(row,&row_identity);
    2815:   ISIdentity(col,&col_identity);

    2817:   outA             = inA;
    2818:   outA->factortype = MAT_FACTOR_LU;
    2819:   PetscFree(inA->solvertype);
    2820:   PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);

    2822:   PetscObjectReference((PetscObject)row);
    2823:   ISDestroy(&a->row);

    2825:   a->row = row;

    2827:   PetscObjectReference((PetscObject)col);
    2828:   ISDestroy(&a->col);

    2830:   a->col = col;

    2832:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
    2833:   ISDestroy(&a->icol);
    2834:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
    2835:   PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);

    2837:   if (!a->solve_work) { /* this matrix may have been factored before */
    2838:     PetscMalloc1(inA->rmap->n+1,&a->solve_work);
    2839:     PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
    2840:   }

    2842:   MatMarkDiagonal_SeqAIJ(inA);
    2843:   if (row_identity && col_identity) {
    2844:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
    2845:   } else {
    2846:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
    2847:   }
    2848:   return(0);
    2849: }

    2851: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
    2852: {
    2853:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
    2854:   PetscScalar    *v;
    2856:   PetscBLASInt   one = 1,bnz;

    2859:   MatSeqAIJGetArray(inA,&v);
    2860:   PetscBLASIntCast(a->nz,&bnz);
    2861:   PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&alpha,v,&one));
    2862:   PetscLogFlops(a->nz);
    2863:   MatSeqAIJRestoreArray(inA,&v);
    2864:   MatSeqAIJInvalidateDiagonal(inA);
    2865:   return(0);
    2866: }

    2868: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
    2869: {
    2871:   PetscInt       i;

    2874:   if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
    2875:     PetscFree4(submatj->sbuf1,submatj->ptr,submatj->tmp,submatj->ctr);

    2877:     for (i=0; i<submatj->nrqr; ++i) {
    2878:       PetscFree(submatj->sbuf2[i]);
    2879:     }
    2880:     PetscFree3(submatj->sbuf2,submatj->req_size,submatj->req_source1);

    2882:     if (submatj->rbuf1) {
    2883:       PetscFree(submatj->rbuf1[0]);
    2884:       PetscFree(submatj->rbuf1);
    2885:     }

    2887:     for (i=0; i<submatj->nrqs; ++i) {
    2888:       PetscFree(submatj->rbuf3[i]);
    2889:     }
    2890:     PetscFree3(submatj->req_source2,submatj->rbuf2,submatj->rbuf3);
    2891:     PetscFree(submatj->pa);
    2892:   }

    2894: #if defined(PETSC_USE_CTABLE)
    2895:   PetscTableDestroy((PetscTable*)&submatj->rmap);
    2896:   if (submatj->cmap_loc) {PetscFree(submatj->cmap_loc);}
    2897:   PetscFree(submatj->rmap_loc);
    2898: #else
    2899:   PetscFree(submatj->rmap);
    2900: #endif

    2902:   if (!submatj->allcolumns) {
    2903: #if defined(PETSC_USE_CTABLE)
    2904:     PetscTableDestroy((PetscTable*)&submatj->cmap);
    2905: #else
    2906:     PetscFree(submatj->cmap);
    2907: #endif
    2908:   }
    2909:   PetscFree(submatj->row2proc);

    2911:   PetscFree(submatj);
    2912:   return(0);
    2913: }

    2915: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
    2916: {
    2918:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data;
    2919:   Mat_SubSppt    *submatj = c->submatis1;

    2922:   (*submatj->destroy)(C);
    2923:   MatDestroySubMatrix_Private(submatj);
    2924:   return(0);
    2925: }

    2927: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n,Mat *mat[])
    2928: {
    2930:   PetscInt       i;
    2931:   Mat            C;
    2932:   Mat_SeqAIJ     *c;
    2933:   Mat_SubSppt    *submatj;

    2936:   for (i=0; i<n; i++) {
    2937:     C       = (*mat)[i];
    2938:     c       = (Mat_SeqAIJ*)C->data;
    2939:     submatj = c->submatis1;
    2940:     if (submatj) {
    2941:       if (--((PetscObject)C)->refct <= 0) {
    2942:         (*submatj->destroy)(C);
    2943:         MatDestroySubMatrix_Private(submatj);
    2944:         PetscFree(C->defaultvectype);
    2945:         PetscLayoutDestroy(&C->rmap);
    2946:         PetscLayoutDestroy(&C->cmap);
    2947:         PetscHeaderDestroy(&C);
    2948:       }
    2949:     } else {
    2950:       MatDestroy(&C);
    2951:     }
    2952:   }

    2954:   /* Destroy Dummy submatrices created for reuse */
    2955:   MatDestroySubMatrices_Dummy(n,mat);

    2957:   PetscFree(*mat);
    2958:   return(0);
    2959: }

    2961: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
    2962: {
    2964:   PetscInt       i;

    2967:   if (scall == MAT_INITIAL_MATRIX) {
    2968:     PetscCalloc1(n+1,B);
    2969:   }

    2971:   for (i=0; i<n; i++) {
    2972:     MatCreateSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
    2973:   }
    2974:   return(0);
    2975: }

    2977: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
    2978: {
    2979:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2981:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
    2982:   const PetscInt *idx;
    2983:   PetscInt       start,end,*ai,*aj;
    2984:   PetscBT        table;

    2987:   m  = A->rmap->n;
    2988:   ai = a->i;
    2989:   aj = a->j;

    2991:   if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

    2993:   PetscMalloc1(m+1,&nidx);
    2994:   PetscBTCreate(m,&table);

    2996:   for (i=0; i<is_max; i++) {
    2997:     /* Initialize the two local arrays */
    2998:     isz  = 0;
    2999:     PetscBTMemzero(m,table);

    3001:     /* Extract the indices, assume there can be duplicate entries */
    3002:     ISGetIndices(is[i],&idx);
    3003:     ISGetLocalSize(is[i],&n);

    3005:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
    3006:     for (j=0; j<n; ++j) {
    3007:       if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
    3008:     }
    3009:     ISRestoreIndices(is[i],&idx);
    3010:     ISDestroy(&is[i]);

    3012:     k = 0;
    3013:     for (j=0; j<ov; j++) { /* for each overlap */
    3014:       n = isz;
    3015:       for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
    3016:         row   = nidx[k];
    3017:         start = ai[row];
    3018:         end   = ai[row+1];
    3019:         for (l = start; l<end; l++) {
    3020:           val = aj[l];
    3021:           if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
    3022:         }
    3023:       }
    3024:     }
    3025:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
    3026:   }
    3027:   PetscBTDestroy(&table);
    3028:   PetscFree(nidx);
    3029:   return(0);
    3030: }

    3032: /* -------------------------------------------------------------- */
    3033: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
    3034: {
    3035:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    3037:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
    3038:   const PetscInt *row,*col;
    3039:   PetscInt       *cnew,j,*lens;
    3040:   IS             icolp,irowp;
    3041:   PetscInt       *cwork = NULL;
    3042:   PetscScalar    *vwork = NULL;

    3045:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
    3046:   ISGetIndices(irowp,&row);
    3047:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
    3048:   ISGetIndices(icolp,&col);

    3050:   /* determine lengths of permuted rows */
    3051:   PetscMalloc1(m+1,&lens);
    3052:   for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
    3053:   MatCreate(PetscObjectComm((PetscObject)A),B);
    3054:   MatSetSizes(*B,m,n,m,n);
    3055:   MatSetBlockSizesFromMats(*B,A,A);
    3056:   MatSetType(*B,((PetscObject)A)->type_name);
    3057:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
    3058:   PetscFree(lens);

    3060:   PetscMalloc1(n,&cnew);
    3061:   for (i=0; i<m; i++) {
    3062:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
    3063:     for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
    3064:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
    3065:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
    3066:   }
    3067:   PetscFree(cnew);

    3069:   (*B)->assembled = PETSC_FALSE;

    3071: #if defined(PETSC_HAVE_DEVICE)
    3072:   MatBindToCPU(*B,A->boundtocpu);
    3073: #endif
    3074:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
    3075:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
    3076:   ISRestoreIndices(irowp,&row);
    3077:   ISRestoreIndices(icolp,&col);
    3078:   ISDestroy(&irowp);
    3079:   ISDestroy(&icolp);
    3080:   if (rowp == colp) {
    3081:     MatPropagateSymmetryOptions(A,*B);
    3082:   }
    3083:   return(0);
    3084: }

    3086: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
    3087: {

    3091:   /* If the two matrices have the same copy implementation, use fast copy. */
    3092:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
    3093:     Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    3094:     Mat_SeqAIJ        *b = (Mat_SeqAIJ*)B->data;
    3095:     const PetscScalar *aa;

    3097:     MatSeqAIJGetArrayRead(A,&aa);
    3098:     if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different %D != %D",a->i[A->rmap->n],b->i[B->rmap->n]);
    3099:     PetscArraycpy(b->a,aa,a->i[A->rmap->n]);
    3100:     PetscObjectStateIncrease((PetscObject)B);
    3101:     MatSeqAIJRestoreArrayRead(A,&aa);
    3102:   } else {
    3103:     MatCopy_Basic(A,B,str);
    3104:   }
    3105:   return(0);
    3106: }

    3108: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
    3109: {

    3113:   MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,NULL);
    3114:   return(0);
    3115: }

    3117: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
    3118: {
    3119:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

    3122:   *array = a->a;
    3123:   return(0);
    3124: }

    3126: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
    3127: {
    3129:   *array = NULL;
    3130:   return(0);
    3131: }

    3133: /*
    3134:    Computes the number of nonzeros per row needed for preallocation when X and Y
    3135:    have different nonzero structure.
    3136: */
    3137: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
    3138: {
    3139:   PetscInt       i,j,k,nzx,nzy;

    3142:   /* Set the number of nonzeros in the new matrix */
    3143:   for (i=0; i<m; i++) {
    3144:     const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
    3145:     nzx = xi[i+1] - xi[i];
    3146:     nzy = yi[i+1] - yi[i];
    3147:     nnz[i] = 0;
    3148:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
    3149:       for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
    3150:       if (k<nzy && yjj[k]==xjj[j]) k++;             /* Skip duplicate */
    3151:       nnz[i]++;
    3152:     }
    3153:     for (; k<nzy; k++) nnz[i]++;
    3154:   }
    3155:   return(0);
    3156: }

    3158: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
    3159: {
    3160:   PetscInt       m = Y->rmap->N;
    3161:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data;
    3162:   Mat_SeqAIJ     *y = (Mat_SeqAIJ*)Y->data;

    3166:   /* Set the number of nonzeros in the new matrix */
    3167:   MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
    3168:   return(0);
    3169: }

    3171: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
    3172: {
    3174:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;

    3177:   if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
    3178:     PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
    3179:     if (e) {
    3180:       PetscArraycmp(x->i,y->i,Y->rmap->n+1,&e);
    3181:       if (e) {
    3182:         PetscArraycmp(x->j,y->j,y->nz,&e);
    3183:         if (e) str = SAME_NONZERO_PATTERN;
    3184:       }
    3185:     }
    3186:     if (!e && str == SAME_NONZERO_PATTERN) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"MatStructure is not SAME_NONZERO_PATTERN");
    3187:   }
    3188:   if (str == SAME_NONZERO_PATTERN) {
    3189:     const PetscScalar *xa;
    3190:     PetscScalar       *ya,alpha = a;
    3191:     PetscBLASInt      one = 1,bnz;

    3193:     PetscBLASIntCast(x->nz,&bnz);
    3194:     MatSeqAIJGetArray(Y,&ya);
    3195:     MatSeqAIJGetArrayRead(X,&xa);
    3196:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,xa,&one,ya,&one));
    3197:     MatSeqAIJRestoreArrayRead(X,&xa);
    3198:     MatSeqAIJRestoreArray(Y,&ya);
    3199:     PetscLogFlops(2.0*bnz);
    3200:     MatSeqAIJInvalidateDiagonal(Y);
    3201:     PetscObjectStateIncrease((PetscObject)Y);
    3202:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
    3203:     MatAXPY_Basic(Y,a,X,str);
    3204:   } else {
    3205:     Mat      B;
    3206:     PetscInt *nnz;
    3207:     PetscMalloc1(Y->rmap->N,&nnz);
    3208:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
    3209:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
    3210:     MatSetLayouts(B,Y->rmap,Y->cmap);
    3211:     MatSetType(B,((PetscObject)Y)->type_name);
    3212:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
    3213:     MatSeqAIJSetPreallocation(B,0,nnz);
    3214:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
    3215:     MatHeaderReplace(Y,&B);
    3216:     PetscFree(nnz);
    3217:   }
    3218:   return(0);
    3219: }

    3221: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
    3222: {
    3223: #if defined(PETSC_USE_COMPLEX)
    3224:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
    3225:   PetscInt       i,nz;
    3226:   PetscScalar    *a;

    3230:   nz = aij->nz;
    3231:   MatSeqAIJGetArray(mat,&a);
    3232:   for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
    3233:   MatSeqAIJRestoreArray(mat,&a);
    3234: #else
    3236: #endif
    3237:   return(0);
    3238: }

    3240: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3241: {
    3242:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3243:   PetscErrorCode  ierr;
    3244:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    3245:   PetscReal       atmp;
    3246:   PetscScalar     *x;
    3247:   const MatScalar *aa,*av;

    3250:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3251:   MatSeqAIJGetArrayRead(A,&av);
    3252:   aa = av;
    3253:   ai = a->i;
    3254:   aj = a->j;

    3256:   VecSet(v,0.0);
    3257:   VecGetArrayWrite(v,&x);
    3258:   VecGetLocalSize(v,&n);
    3259:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    3260:   for (i=0; i<m; i++) {
    3261:     ncols = ai[1] - ai[0]; ai++;
    3262:     for (j=0; j<ncols; j++) {
    3263:       atmp = PetscAbsScalar(*aa);
    3264:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
    3265:       aa++; aj++;
    3266:     }
    3267:   }
    3268:   VecRestoreArrayWrite(v,&x);
    3269:   MatSeqAIJRestoreArrayRead(A,&av);
    3270:   return(0);
    3271: }

    3273: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3274: {
    3275:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3276:   PetscErrorCode  ierr;
    3277:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    3278:   PetscScalar     *x;
    3279:   const MatScalar *aa,*av;

    3282:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3283:   MatSeqAIJGetArrayRead(A,&av);
    3284:   aa = av;
    3285:   ai = a->i;
    3286:   aj = a->j;

    3288:   VecSet(v,0.0);
    3289:   VecGetArrayWrite(v,&x);
    3290:   VecGetLocalSize(v,&n);
    3291:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    3292:   for (i=0; i<m; i++) {
    3293:     ncols = ai[1] - ai[0]; ai++;
    3294:     if (ncols == A->cmap->n) { /* row is dense */
    3295:       x[i] = *aa; if (idx) idx[i] = 0;
    3296:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
    3297:       x[i] = 0.0;
    3298:       if (idx) {
    3299:         for (j=0; j<ncols; j++) { /* find first implicit 0.0 in the row */
    3300:           if (aj[j] > j) {
    3301:             idx[i] = j;
    3302:             break;
    3303:           }
    3304:         }
    3305:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3306:         if (j==ncols && j < A->cmap->n) idx[i] = j;
    3307:       }
    3308:     }
    3309:     for (j=0; j<ncols; j++) {
    3310:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
    3311:       aa++; aj++;
    3312:     }
    3313:   }
    3314:   VecRestoreArrayWrite(v,&x);
    3315:   MatSeqAIJRestoreArrayRead(A,&av);
    3316:   return(0);
    3317: }

    3319: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3320: {
    3321:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3322:   PetscErrorCode  ierr;
    3323:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    3324:   PetscScalar     *x;
    3325:   const MatScalar *aa,*av;

    3328:   MatSeqAIJGetArrayRead(A,&av);
    3329:   aa = av;
    3330:   ai = a->i;
    3331:   aj = a->j;

    3333:   VecSet(v,0.0);
    3334:   VecGetArrayWrite(v,&x);
    3335:   VecGetLocalSize(v,&n);
    3336:   if (n != m) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %D vs. %D rows", m, n);
    3337:   for (i=0; i<m; i++) {
    3338:     ncols = ai[1] - ai[0]; ai++;
    3339:     if (ncols == A->cmap->n) { /* row is dense */
    3340:       x[i] = *aa; if (idx) idx[i] = 0;
    3341:     } else {  /* row is sparse so already KNOW minimum is 0.0 or higher */
    3342:       x[i] = 0.0;
    3343:       if (idx) {   /* find first implicit 0.0 in the row */
    3344:         for (j=0; j<ncols; j++) {
    3345:           if (aj[j] > j) {
    3346:             idx[i] = j;
    3347:             break;
    3348:           }
    3349:         }
    3350:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3351:         if (j==ncols && j < A->cmap->n) idx[i] = j;
    3352:       }
    3353:     }
    3354:     for (j=0; j<ncols; j++) {
    3355:       if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
    3356:       aa++; aj++;
    3357:     }
    3358:   }
    3359:   VecRestoreArrayWrite(v,&x);
    3360:   MatSeqAIJRestoreArrayRead(A,&av);
    3361:   return(0);
    3362: }

    3364: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3365: {
    3366:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3367:   PetscErrorCode  ierr;
    3368:   PetscInt        i,j,m = A->rmap->n,ncols,n;
    3369:   const PetscInt  *ai,*aj;
    3370:   PetscScalar     *x;
    3371:   const MatScalar *aa,*av;

    3374:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3375:   MatSeqAIJGetArrayRead(A,&av);
    3376:   aa = av;
    3377:   ai = a->i;
    3378:   aj = a->j;

    3380:   VecSet(v,0.0);
    3381:   VecGetArrayWrite(v,&x);
    3382:   VecGetLocalSize(v,&n);
    3383:   if (n != m) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    3384:   for (i=0; i<m; i++) {
    3385:     ncols = ai[1] - ai[0]; ai++;
    3386:     if (ncols == A->cmap->n) { /* row is dense */
    3387:       x[i] = *aa; if (idx) idx[i] = 0;
    3388:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
    3389:       x[i] = 0.0;
    3390:       if (idx) {   /* find first implicit 0.0 in the row */
    3391:         for (j=0; j<ncols; j++) {
    3392:           if (aj[j] > j) {
    3393:             idx[i] = j;
    3394:             break;
    3395:           }
    3396:         }
    3397:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3398:         if (j==ncols && j < A->cmap->n) idx[i] = j;
    3399:       }
    3400:     }
    3401:     for (j=0; j<ncols; j++) {
    3402:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
    3403:       aa++; aj++;
    3404:     }
    3405:   }
    3406:   VecRestoreArrayWrite(v,&x);
    3407:   MatSeqAIJRestoreArrayRead(A,&av);
    3408:   return(0);
    3409: }

    3411: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
    3412: {
    3413:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*) A->data;
    3414:   PetscErrorCode  ierr;
    3415:   PetscInt        i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
    3416:   MatScalar       *diag,work[25],*v_work;
    3417:   const PetscReal shift = 0.0;
    3418:   PetscBool       allowzeropivot,zeropivotdetected=PETSC_FALSE;

    3421:   allowzeropivot = PetscNot(A->erroriffailure);
    3422:   if (a->ibdiagvalid) {
    3423:     if (values) *values = a->ibdiag;
    3424:     return(0);
    3425:   }
    3426:   MatMarkDiagonal_SeqAIJ(A);
    3427:   if (!a->ibdiag) {
    3428:     PetscMalloc1(bs2*mbs,&a->ibdiag);
    3429:     PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
    3430:   }
    3431:   diag = a->ibdiag;
    3432:   if (values) *values = a->ibdiag;
    3433:   /* factor and invert each block */
    3434:   switch (bs) {
    3435:   case 1:
    3436:     for (i=0; i<mbs; i++) {
    3437:       MatGetValues(A,1,&i,1,&i,diag+i);
    3438:       if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
    3439:         if (allowzeropivot) {
    3440:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3441:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
    3442:           A->factorerror_zeropivot_row   = i;
    3443:           PetscInfo3(A,"Zero pivot, row %D pivot %g tolerance %g\n",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
    3444:         } else SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot, row %D pivot %g tolerance %g",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
    3445:       }
    3446:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
    3447:     }
    3448:     break;
    3449:   case 2:
    3450:     for (i=0; i<mbs; i++) {
    3451:       ij[0] = 2*i; ij[1] = 2*i + 1;
    3452:       MatGetValues(A,2,ij,2,ij,diag);
    3453:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
    3454:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3455:       PetscKernel_A_gets_transpose_A_2(diag);
    3456:       diag += 4;
    3457:     }
    3458:     break;
    3459:   case 3:
    3460:     for (i=0; i<mbs; i++) {
    3461:       ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
    3462:       MatGetValues(A,3,ij,3,ij,diag);
    3463:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
    3464:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3465:       PetscKernel_A_gets_transpose_A_3(diag);
    3466:       diag += 9;
    3467:     }
    3468:     break;
    3469:   case 4:
    3470:     for (i=0; i<mbs; i++) {
    3471:       ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
    3472:       MatGetValues(A,4,ij,4,ij,diag);
    3473:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
    3474:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3475:       PetscKernel_A_gets_transpose_A_4(diag);
    3476:       diag += 16;
    3477:     }
    3478:     break;
    3479:   case 5:
    3480:     for (i=0; i<mbs; i++) {
    3481:       ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
    3482:       MatGetValues(A,5,ij,5,ij,diag);
    3483:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
    3484:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3485:       PetscKernel_A_gets_transpose_A_5(diag);
    3486:       diag += 25;
    3487:     }
    3488:     break;
    3489:   case 6:
    3490:     for (i=0; i<mbs; i++) {
    3491:       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;
    3492:       MatGetValues(A,6,ij,6,ij,diag);
    3493:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
    3494:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3495:       PetscKernel_A_gets_transpose_A_6(diag);
    3496:       diag += 36;
    3497:     }
    3498:     break;
    3499:   case 7:
    3500:     for (i=0; i<mbs; i++) {
    3501:       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;
    3502:       MatGetValues(A,7,ij,7,ij,diag);
    3503:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
    3504:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3505:       PetscKernel_A_gets_transpose_A_7(diag);
    3506:       diag += 49;
    3507:     }
    3508:     break;
    3509:   default:
    3510:     PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
    3511:     for (i=0; i<mbs; i++) {
    3512:       for (j=0; j<bs; j++) {
    3513:         IJ[j] = bs*i + j;
    3514:       }
    3515:       MatGetValues(A,bs,IJ,bs,IJ,diag);
    3516:       PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
    3517:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3518:       PetscKernel_A_gets_transpose_A_N(diag,bs);
    3519:       diag += bs2;
    3520:     }
    3521:     PetscFree3(v_work,v_pivots,IJ);
    3522:   }
    3523:   a->ibdiagvalid = PETSC_TRUE;
    3524:   return(0);
    3525: }

    3527: static PetscErrorCode  MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
    3528: {
    3530:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
    3531:   PetscScalar    a;
    3532:   PetscInt       m,n,i,j,col;

    3535:   if (!x->assembled) {
    3536:     MatGetSize(x,&m,&n);
    3537:     for (i=0; i<m; i++) {
    3538:       for (j=0; j<aij->imax[i]; j++) {
    3539:         PetscRandomGetValue(rctx,&a);
    3540:         col  = (PetscInt)(n*PetscRealPart(a));
    3541:         MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
    3542:       }
    3543:     }
    3544:   } else {
    3545:     for (i=0; i<aij->nz; i++) {PetscRandomGetValue(rctx,aij->a+i);}
    3546:   }
    3547: #if defined(PETSC_HAVE_DEVICE)
    3548:   if (x->offloadmask != PETSC_OFFLOAD_UNALLOCATED) x->offloadmask = PETSC_OFFLOAD_CPU;
    3549: #endif
    3550:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
    3551:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
    3552:   return(0);
    3553: }

    3555: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
    3556: PetscErrorCode  MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x,PetscInt low,PetscInt high,PetscRandom rctx)
    3557: {
    3559:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
    3560:   PetscScalar    a;
    3561:   PetscInt       m,n,i,j,col,nskip;

    3564:   nskip = high - low;
    3565:   MatGetSize(x,&m,&n);
    3566:   n    -= nskip; /* shrink number of columns where nonzeros can be set */
    3567:   for (i=0; i<m; i++) {
    3568:     for (j=0; j<aij->imax[i]; j++) {
    3569:       PetscRandomGetValue(rctx,&a);
    3570:       col  = (PetscInt)(n*PetscRealPart(a));
    3571:       if (col >= low) col += nskip; /* shift col rightward to skip the hole */
    3572:       MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
    3573:     }
    3574:   }
    3575:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
    3576:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
    3577:   return(0);
    3578: }

    3580: /* -------------------------------------------------------------------*/
    3581: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
    3582:                                         MatGetRow_SeqAIJ,
    3583:                                         MatRestoreRow_SeqAIJ,
    3584:                                         MatMult_SeqAIJ,
    3585:                                 /*  4*/ MatMultAdd_SeqAIJ,
    3586:                                         MatMultTranspose_SeqAIJ,
    3587:                                         MatMultTransposeAdd_SeqAIJ,
    3588:                                         NULL,
    3589:                                         NULL,
    3590:                                         NULL,
    3591:                                 /* 10*/ NULL,
    3592:                                         MatLUFactor_SeqAIJ,
    3593:                                         NULL,
    3594:                                         MatSOR_SeqAIJ,
    3595:                                         MatTranspose_SeqAIJ,
    3596:                                 /*1 5*/ MatGetInfo_SeqAIJ,
    3597:                                         MatEqual_SeqAIJ,
    3598:                                         MatGetDiagonal_SeqAIJ,
    3599:                                         MatDiagonalScale_SeqAIJ,
    3600:                                         MatNorm_SeqAIJ,
    3601:                                 /* 20*/ NULL,
    3602:                                         MatAssemblyEnd_SeqAIJ,
    3603:                                         MatSetOption_SeqAIJ,
    3604:                                         MatZeroEntries_SeqAIJ,
    3605:                                 /* 24*/ MatZeroRows_SeqAIJ,
    3606:                                         NULL,
    3607:                                         NULL,
    3608:                                         NULL,
    3609:                                         NULL,
    3610:                                 /* 29*/ MatSetUp_SeqAIJ,
    3611:                                         NULL,
    3612:                                         NULL,
    3613:                                         NULL,
    3614:                                         NULL,
    3615:                                 /* 34*/ MatDuplicate_SeqAIJ,
    3616:                                         NULL,
    3617:                                         NULL,
    3618:                                         MatILUFactor_SeqAIJ,
    3619:                                         NULL,
    3620:                                 /* 39*/ MatAXPY_SeqAIJ,
    3621:                                         MatCreateSubMatrices_SeqAIJ,
    3622:                                         MatIncreaseOverlap_SeqAIJ,
    3623:                                         MatGetValues_SeqAIJ,
    3624:                                         MatCopy_SeqAIJ,
    3625:                                 /* 44*/ MatGetRowMax_SeqAIJ,
    3626:                                         MatScale_SeqAIJ,
    3627:                                         MatShift_SeqAIJ,
    3628:                                         MatDiagonalSet_SeqAIJ,
    3629:                                         MatZeroRowsColumns_SeqAIJ,
    3630:                                 /* 49*/ MatSetRandom_SeqAIJ,
    3631:                                         MatGetRowIJ_SeqAIJ,
    3632:                                         MatRestoreRowIJ_SeqAIJ,
    3633:                                         MatGetColumnIJ_SeqAIJ,
    3634:                                         MatRestoreColumnIJ_SeqAIJ,
    3635:                                 /* 54*/ MatFDColoringCreate_SeqXAIJ,
    3636:                                         NULL,
    3637:                                         NULL,
    3638:                                         MatPermute_SeqAIJ,
    3639:                                         NULL,
    3640:                                 /* 59*/ NULL,
    3641:                                         MatDestroy_SeqAIJ,
    3642:                                         MatView_SeqAIJ,
    3643:                                         NULL,
    3644:                                         NULL,
    3645:                                 /* 64*/ NULL,
    3646:                                         MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
    3647:                                         NULL,
    3648:                                         NULL,
    3649:                                         NULL,
    3650:                                 /* 69*/ MatGetRowMaxAbs_SeqAIJ,
    3651:                                         MatGetRowMinAbs_SeqAIJ,
    3652:                                         NULL,
    3653:                                         NULL,
    3654:                                         NULL,
    3655:                                 /* 74*/ NULL,
    3656:                                         MatFDColoringApply_AIJ,
    3657:                                         NULL,
    3658:                                         NULL,
    3659:                                         NULL,
    3660:                                 /* 79*/ MatFindZeroDiagonals_SeqAIJ,
    3661:                                         NULL,
    3662:                                         NULL,
    3663:                                         NULL,
    3664:                                         MatLoad_SeqAIJ,
    3665:                                 /* 84*/ MatIsSymmetric_SeqAIJ,
    3666:                                         MatIsHermitian_SeqAIJ,
    3667:                                         NULL,
    3668:                                         NULL,
    3669:                                         NULL,
    3670:                                 /* 89*/ NULL,
    3671:                                         NULL,
    3672:                                         MatMatMultNumeric_SeqAIJ_SeqAIJ,
    3673:                                         NULL,
    3674:                                         NULL,
    3675:                                 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
    3676:                                         NULL,
    3677:                                         NULL,
    3678:                                         MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
    3679:                                         NULL,
    3680:                                 /* 99*/ MatProductSetFromOptions_SeqAIJ,
    3681:                                         NULL,
    3682:                                         NULL,
    3683:                                         MatConjugate_SeqAIJ,
    3684:                                         NULL,
    3685:                                 /*104*/ MatSetValuesRow_SeqAIJ,
    3686:                                         MatRealPart_SeqAIJ,
    3687:                                         MatImaginaryPart_SeqAIJ,
    3688:                                         NULL,
    3689:                                         NULL,
    3690:                                 /*109*/ MatMatSolve_SeqAIJ,
    3691:                                         NULL,
    3692:                                         MatGetRowMin_SeqAIJ,
    3693:                                         NULL,
    3694:                                         MatMissingDiagonal_SeqAIJ,
    3695:                                 /*114*/ NULL,
    3696:                                         NULL,
    3697:                                         NULL,
    3698:                                         NULL,
    3699:                                         NULL,
    3700:                                 /*119*/ NULL,
    3701:                                         NULL,
    3702:                                         NULL,
    3703:                                         NULL,
    3704:                                         MatGetMultiProcBlock_SeqAIJ,
    3705:                                 /*124*/ MatFindNonzeroRows_SeqAIJ,
    3706:                                         MatGetColumnReductions_SeqAIJ,
    3707:                                         MatInvertBlockDiagonal_SeqAIJ,
    3708:                                         MatInvertVariableBlockDiagonal_SeqAIJ,
    3709:                                         NULL,
    3710:                                 /*129*/ NULL,
    3711:                                         NULL,
    3712:                                         NULL,
    3713:                                         MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
    3714:                                         MatTransposeColoringCreate_SeqAIJ,
    3715:                                 /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
    3716:                                         MatTransColoringApplyDenToSp_SeqAIJ,
    3717:                                         NULL,
    3718:                                         NULL,
    3719:                                         MatRARtNumeric_SeqAIJ_SeqAIJ,
    3720:                                  /*139*/NULL,
    3721:                                         NULL,
    3722:                                         NULL,
    3723:                                         MatFDColoringSetUp_SeqXAIJ,
    3724:                                         MatFindOffBlockDiagonalEntries_SeqAIJ,
    3725:                                         MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
    3726:                                  /*145*/MatDestroySubMatrices_SeqAIJ,
    3727:                                         NULL,
    3728:                                         NULL
    3729: };

    3731: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
    3732: {
    3733:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
    3734:   PetscInt   i,nz,n;

    3737:   nz = aij->maxnz;
    3738:   n  = mat->rmap->n;
    3739:   for (i=0; i<nz; i++) {
    3740:     aij->j[i] = indices[i];
    3741:   }
    3742:   aij->nz = nz;
    3743:   for (i=0; i<n; i++) {
    3744:     aij->ilen[i] = aij->imax[i];
    3745:   }
    3746:   return(0);
    3747: }

    3749: /*
    3750:  * Given a sparse matrix with global column indices, compact it by using a local column space.
    3751:  * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
    3752:  */
    3753: PetscErrorCode  MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
    3754: {
    3755:   Mat_SeqAIJ         *aij = (Mat_SeqAIJ*)mat->data;
    3756:   PetscTable         gid1_lid1;
    3757:   PetscTablePosition tpos;
    3758:   PetscInt           gid,lid,i,ec,nz = aij->nz;
    3759:   PetscInt           *garray,*jj = aij->j;
    3760:   PetscErrorCode     ierr;

    3765:   /* use a table */
    3766:   PetscTableCreate(mat->rmap->n,mat->cmap->N+1,&gid1_lid1);
    3767:   ec = 0;
    3768:   for (i=0; i<nz; i++) {
    3769:     PetscInt data,gid1 = jj[i] + 1;
    3770:     PetscTableFind(gid1_lid1,gid1,&data);
    3771:     if (!data) {
    3772:       /* one based table */
    3773:       PetscTableAdd(gid1_lid1,gid1,++ec,INSERT_VALUES);
    3774:     }
    3775:   }
    3776:   /* form array of columns we need */
    3777:   PetscMalloc1(ec,&garray);
    3778:   PetscTableGetHeadPosition(gid1_lid1,&tpos);
    3779:   while (tpos) {
    3780:     PetscTableGetNext(gid1_lid1,&tpos,&gid,&lid);
    3781:     gid--;
    3782:     lid--;
    3783:     garray[lid] = gid;
    3784:   }
    3785:   PetscSortInt(ec,garray); /* sort, and rebuild */
    3786:   PetscTableRemoveAll(gid1_lid1);
    3787:   for (i=0; i<ec; i++) {
    3788:     PetscTableAdd(gid1_lid1,garray[i]+1,i+1,INSERT_VALUES);
    3789:   }
    3790:   /* compact out the extra columns in B */
    3791:   for (i=0; i<nz; i++) {
    3792:     PetscInt gid1 = jj[i] + 1;
    3793:     PetscTableFind(gid1_lid1,gid1,&lid);
    3794:     lid--;
    3795:     jj[i] = lid;
    3796:   }
    3797:   PetscLayoutDestroy(&mat->cmap);
    3798:   PetscTableDestroy(&gid1_lid1);
    3799:   PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat),ec,ec,1,&mat->cmap);
    3800:   ISLocalToGlobalMappingCreate(PETSC_COMM_SELF,mat->cmap->bs,mat->cmap->n,garray,PETSC_OWN_POINTER,mapping);
    3801:   ISLocalToGlobalMappingSetType(*mapping,ISLOCALTOGLOBALMAPPINGHASH);
    3802:   return(0);
    3803: }

    3805: /*@
    3806:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
    3807:        in the matrix.

    3809:   Input Parameters:
    3810: +  mat - the SeqAIJ matrix
    3811: -  indices - the column indices

    3813:   Level: advanced

    3815:   Notes:
    3816:     This can be called if you have precomputed the nonzero structure of the
    3817:   matrix and want to provide it to the matrix object to improve the performance
    3818:   of the MatSetValues() operation.

    3820:     You MUST have set the correct numbers of nonzeros per row in the call to
    3821:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

    3823:     MUST be called before any calls to MatSetValues();

    3825:     The indices should start with zero, not one.

    3827: @*/
    3828: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
    3829: {

    3835:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
    3836:   return(0);
    3837: }

    3839: /* ----------------------------------------------------------------------------------------*/

    3841: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
    3842: {
    3843:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
    3845:   size_t         nz = aij->i[mat->rmap->n];

    3848:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

    3850:   /* allocate space for values if not already there */
    3851:   if (!aij->saved_values) {
    3852:     PetscMalloc1(nz+1,&aij->saved_values);
    3853:     PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
    3854:   }

    3856:   /* copy values over */
    3857:   PetscArraycpy(aij->saved_values,aij->a,nz);
    3858:   return(0);
    3859: }

    3861: /*@
    3862:     MatStoreValues - Stashes a copy of the matrix values; this allows, for
    3863:        example, reuse of the linear part of a Jacobian, while recomputing the
    3864:        nonlinear portion.

    3866:    Collect on Mat

    3868:   Input Parameters:
    3869: .  mat - the matrix (currently only AIJ matrices support this option)

    3871:   Level: advanced

    3873:   Common Usage, with SNESSolve():
    3874: $    Create Jacobian matrix
    3875: $    Set linear terms into matrix
    3876: $    Apply boundary conditions to matrix, at this time matrix must have
    3877: $      final nonzero structure (i.e. setting the nonlinear terms and applying
    3878: $      boundary conditions again will not change the nonzero structure
    3879: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
    3880: $    MatStoreValues(mat);
    3881: $    Call SNESSetJacobian() with matrix
    3882: $    In your Jacobian routine
    3883: $      MatRetrieveValues(mat);
    3884: $      Set nonlinear terms in matrix

    3886:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
    3887: $    // build linear portion of Jacobian
    3888: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
    3889: $    MatStoreValues(mat);
    3890: $    loop over nonlinear iterations
    3891: $       MatRetrieveValues(mat);
    3892: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
    3893: $       // call MatAssemblyBegin/End() on matrix
    3894: $       Solve linear system with Jacobian
    3895: $    endloop

    3897:   Notes:
    3898:     Matrix must already be assemblied before calling this routine
    3899:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
    3900:     calling this routine.

    3902:     When this is called multiple times it overwrites the previous set of stored values
    3903:     and does not allocated additional space.

    3905: .seealso: MatRetrieveValues()

    3907: @*/
    3908: PetscErrorCode  MatStoreValues(Mat mat)
    3909: {

    3914:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
    3915:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3916:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
    3917:   return(0);
    3918: }

    3920: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
    3921: {
    3922:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
    3924:   PetscInt       nz = aij->i[mat->rmap->n];

    3927:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
    3928:   if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
    3929:   /* copy values over */
    3930:   PetscArraycpy(aij->a,aij->saved_values,nz);
    3931:   return(0);
    3932: }

    3934: /*@
    3935:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
    3936:        example, reuse of the linear part of a Jacobian, while recomputing the
    3937:        nonlinear portion.

    3939:    Collect on Mat

    3941:   Input Parameters:
    3942: .  mat - the matrix (currently only AIJ matrices support this option)

    3944:   Level: advanced

    3946: .seealso: MatStoreValues()

    3948: @*/
    3949: PetscErrorCode  MatRetrieveValues(Mat mat)
    3950: {

    3955:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
    3956:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3957:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
    3958:   return(0);
    3959: }

    3961: /* --------------------------------------------------------------------------------*/
    3962: /*@C
    3963:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
    3964:    (the default parallel PETSc format).  For good matrix assembly performance
    3965:    the user should preallocate the matrix storage by setting the parameter nz
    3966:    (or the array nnz).  By setting these parameters accurately, performance
    3967:    during matrix assembly can be increased by more than a factor of 50.

    3969:    Collective

    3971:    Input Parameters:
    3972: +  comm - MPI communicator, set to PETSC_COMM_SELF
    3973: .  m - number of rows
    3974: .  n - number of columns
    3975: .  nz - number of nonzeros per row (same for all rows)
    3976: -  nnz - array containing the number of nonzeros in the various rows
    3977:          (possibly different for each row) or NULL

    3979:    Output Parameter:
    3980: .  A - the matrix

    3982:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
    3983:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
    3984:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

    3986:    Notes:
    3987:    If nnz is given then nz is ignored

    3989:    The AIJ format (also called the Yale sparse matrix format or
    3990:    compressed row storage), is fully compatible with standard Fortran 77
    3991:    storage.  That is, the stored row and column indices can begin at
    3992:    either one (as in Fortran) or zero.  See the users' manual for details.

    3994:    Specify the preallocated storage with either nz or nnz (not both).
    3995:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
    3996:    allocation.  For large problems you MUST preallocate memory or you
    3997:    will get TERRIBLE performance, see the users' manual chapter on matrices.

    3999:    By default, this format uses inodes (identical nodes) when possible, to
    4000:    improve numerical efficiency of matrix-vector products and solves. We
    4001:    search for consecutive rows with the same nonzero structure, thereby
    4002:    reusing matrix information to achieve increased efficiency.

    4004:    Options Database Keys:
    4005: +  -mat_no_inode  - Do not use inodes
    4006: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

    4008:    Level: intermediate

    4010: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

    4012: @*/
    4013: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
    4014: {

    4018:   MatCreate(comm,A);
    4019:   MatSetSizes(*A,m,n,m,n);
    4020:   MatSetType(*A,MATSEQAIJ);
    4021:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
    4022:   return(0);
    4023: }

    4025: /*@C
    4026:    MatSeqAIJSetPreallocation - For good matrix assembly performance
    4027:    the user should preallocate the matrix storage by setting the parameter nz
    4028:    (or the array nnz).  By setting these parameters accurately, performance
    4029:    during matrix assembly can be increased by more than a factor of 50.

    4031:    Collective

    4033:    Input Parameters:
    4034: +  B - The matrix
    4035: .  nz - number of nonzeros per row (same for all rows)
    4036: -  nnz - array containing the number of nonzeros in the various rows
    4037:          (possibly different for each row) or NULL

    4039:    Notes:
    4040:      If nnz is given then nz is ignored

    4042:     The AIJ format (also called the Yale sparse matrix format or
    4043:    compressed row storage), is fully compatible with standard Fortran 77
    4044:    storage.  That is, the stored row and column indices can begin at
    4045:    either one (as in Fortran) or zero.  See the users' manual for details.

    4047:    Specify the preallocated storage with either nz or nnz (not both).
    4048:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
    4049:    allocation.  For large problems you MUST preallocate memory or you
    4050:    will get TERRIBLE performance, see the users' manual chapter on matrices.

    4052:    You can call MatGetInfo() to get information on how effective the preallocation was;
    4053:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
    4054:    You can also run with the option -info and look for messages with the string
    4055:    malloc in them to see if additional memory allocation was needed.

    4057:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
    4058:    entries or columns indices

    4060:    By default, this format uses inodes (identical nodes) when possible, to
    4061:    improve numerical efficiency of matrix-vector products and solves. We
    4062:    search for consecutive rows with the same nonzero structure, thereby
    4063:    reusing matrix information to achieve increased efficiency.

    4065:    Options Database Keys:
    4066: +  -mat_no_inode  - Do not use inodes
    4067: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

    4069:    Level: intermediate

    4071: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo(),
    4072:           MatSeqAIJSetTotalPreallocation()

    4074: @*/
    4075: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
    4076: {

    4082:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
    4083:   return(0);
    4084: }

    4086: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
    4087: {
    4088:   Mat_SeqAIJ     *b;
    4089:   PetscBool      skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
    4091:   PetscInt       i;

    4094:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
    4095:   if (nz == MAT_SKIP_ALLOCATION) {
    4096:     skipallocation = PETSC_TRUE;
    4097:     nz             = 0;
    4098:   }
    4099:   PetscLayoutSetUp(B->rmap);
    4100:   PetscLayoutSetUp(B->cmap);

    4102:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
    4103:   if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
    4104:   if (PetscUnlikelyDebug(nnz)) {
    4105:     for (i=0; i<B->rmap->n; i++) {
    4106:       if (nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
    4107:       if (nnz[i] > B->cmap->n) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %D value %d rowlength %D",i,nnz[i],B->cmap->n);
    4108:     }
    4109:   }

    4111:   B->preallocated = PETSC_TRUE;

    4113:   b = (Mat_SeqAIJ*)B->data;

    4115:   if (!skipallocation) {
    4116:     if (!b->imax) {
    4117:       PetscMalloc1(B->rmap->n,&b->imax);
    4118:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
    4119:     }
    4120:     if (!b->ilen) {
    4121:       /* b->ilen will count nonzeros in each row so far. */
    4122:       PetscCalloc1(B->rmap->n,&b->ilen);
    4123:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
    4124:     } else {
    4125:       PetscMemzero(b->ilen,B->rmap->n*sizeof(PetscInt));
    4126:     }
    4127:     if (!b->ipre) {
    4128:       PetscMalloc1(B->rmap->n,&b->ipre);
    4129:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
    4130:     }
    4131:     if (!nnz) {
    4132:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
    4133:       else if (nz < 0) nz = 1;
    4134:       nz = PetscMin(nz,B->cmap->n);
    4135:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
    4136:       nz = nz*B->rmap->n;
    4137:     } else {
    4138:       PetscInt64 nz64 = 0;
    4139:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz64 += nnz[i];}
    4140:       PetscIntCast(nz64,&nz);
    4141:     }

    4143:     /* allocate the matrix space */
    4144:     /* FIXME: should B's old memory be unlogged? */
    4145:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
    4146:     if (B->structure_only) {
    4147:       PetscMalloc1(nz,&b->j);
    4148:       PetscMalloc1(B->rmap->n+1,&b->i);
    4149:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
    4150:     } else {
    4151:       PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
    4152:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
    4153:     }
    4154:     b->i[0] = 0;
    4155:     for (i=1; i<B->rmap->n+1; i++) {
    4156:       b->i[i] = b->i[i-1] + b->imax[i-1];
    4157:     }
    4158:     if (B->structure_only) {
    4159:       b->singlemalloc = PETSC_FALSE;
    4160:       b->free_a       = PETSC_FALSE;
    4161:     } else {
    4162:       b->singlemalloc = PETSC_TRUE;
    4163:       b->free_a       = PETSC_TRUE;
    4164:     }
    4165:     b->free_ij      = PETSC_TRUE;
    4166:   } else {
    4167:     b->free_a  = PETSC_FALSE;
    4168:     b->free_ij = PETSC_FALSE;
    4169:   }

    4171:   if (b->ipre && nnz != b->ipre  && b->imax) {
    4172:     /* reserve user-requested sparsity */
    4173:     PetscArraycpy(b->ipre,b->imax,B->rmap->n);
    4174:   }

    4176:   b->nz               = 0;
    4177:   b->maxnz            = nz;
    4178:   B->info.nz_unneeded = (double)b->maxnz;
    4179:   if (realalloc) {
    4180:     MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
    4181:   }
    4182:   B->was_assembled = PETSC_FALSE;
    4183:   B->assembled     = PETSC_FALSE;
    4184:   return(0);
    4185: }

    4187: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
    4188: {
    4189:   Mat_SeqAIJ     *a;
    4190:   PetscInt       i;

    4196:   /* Check local size. If zero, then return */
    4197:   if (!A->rmap->n) return(0);

    4199:   a = (Mat_SeqAIJ*)A->data;
    4200:   /* if no saved info, we error out */
    4201:   if (!a->ipre) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"No saved preallocation info \n");

    4203:   if (!a->i || !a->j || !a->a || !a->imax || !a->ilen) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"Memory info is incomplete, and can not reset preallocation \n");

    4205:   PetscArraycpy(a->imax,a->ipre,A->rmap->n);
    4206:   PetscArrayzero(a->ilen,A->rmap->n);
    4207:   a->i[0] = 0;
    4208:   for (i=1; i<A->rmap->n+1; i++) {
    4209:     a->i[i] = a->i[i-1] + a->imax[i-1];
    4210:   }
    4211:   A->preallocated     = PETSC_TRUE;
    4212:   a->nz               = 0;
    4213:   a->maxnz            = a->i[A->rmap->n];
    4214:   A->info.nz_unneeded = (double)a->maxnz;
    4215:   A->was_assembled    = PETSC_FALSE;
    4216:   A->assembled        = PETSC_FALSE;
    4217:   return(0);
    4218: }

    4220: /*@
    4221:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.

    4223:    Input Parameters:
    4224: +  B - the matrix
    4225: .  i - the indices into j for the start of each row (starts with zero)
    4226: .  j - the column indices for each row (starts with zero) these must be sorted for each row
    4227: -  v - optional values in the matrix

    4229:    Level: developer

    4231:    Notes:
    4232:       The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

    4234:       This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
    4235:       structure will be the union of all the previous nonzero structures.

    4237:     Developer Notes:
    4238:       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
    4239:       then just copies the v values directly with PetscMemcpy().

    4241:       This routine could also take a PetscCopyMode argument to allow sharing the values instead of always copying them.

    4243: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), MATSEQAIJ, MatResetPreallocation()
    4244: @*/
    4245: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
    4246: {

    4252:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
    4253:   return(0);
    4254: }

    4256: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
    4257: {
    4258:   PetscInt       i;
    4259:   PetscInt       m,n;
    4260:   PetscInt       nz;
    4261:   PetscInt       *nnz;

    4265:   if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);

    4267:   PetscLayoutSetUp(B->rmap);
    4268:   PetscLayoutSetUp(B->cmap);

    4270:   MatGetSize(B, &m, &n);
    4271:   PetscMalloc1(m+1, &nnz);
    4272:   for (i = 0; i < m; i++) {
    4273:     nz     = Ii[i+1]- Ii[i];
    4274:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
    4275:     nnz[i] = nz;
    4276:   }
    4277:   MatSeqAIJSetPreallocation(B, 0, nnz);
    4278:   PetscFree(nnz);

    4280:   for (i = 0; i < m; i++) {
    4281:     MatSetValues_SeqAIJ(B, 1, &i, Ii[i+1] - Ii[i], J+Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES);
    4282:   }

    4284:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    4285:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

    4287:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
    4288:   return(0);
    4289: }

    4291: /*@
    4292:    MatSeqAIJKron - Computes C, the Kronecker product of A and B.

    4294:    Input Parameters:
    4295: +  A - left-hand side matrix
    4296: .  B - right-hand side matrix
    4297: -  reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

    4299:    Output Parameter:
    4300: .  C - Kronecker product of A and B

    4302:    Level: intermediate

    4304:    Notes:
    4305:       MAT_REUSE_MATRIX can only be used when the nonzero structure of the product matrix has not changed from that last call to MatSeqAIJKron().

    4307: .seealso: MatCreateSeqAIJ(), MATSEQAIJ, MATKAIJ, MatReuse
    4308: @*/
    4309: PetscErrorCode MatSeqAIJKron(Mat A,Mat B,MatReuse reuse,Mat *C)
    4310: {

    4319:   if (reuse == MAT_REUSE_MATRIX) {
    4322:   }
    4323:   PetscTryMethod(A,"MatSeqAIJKron_C",(Mat,Mat,MatReuse,Mat*),(A,B,reuse,C));
    4324:   return(0);
    4325: }

    4327: PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A,Mat B,MatReuse reuse,Mat *C)
    4328: {
    4329:   Mat            newmat;
    4330:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    4331:   Mat_SeqAIJ     *b = (Mat_SeqAIJ*)B->data;
    4332:   PetscScalar    *v;
    4333:   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;
    4334:   PetscBool      flg;

    4338:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    4339:   if (!A->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
    4340:   if (B->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    4341:   if (!B->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
    4342:   PetscObjectTypeCompare((PetscObject)B,MATSEQAIJ,&flg);
    4343:   if (!flg) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatType %s",((PetscObject)B)->type_name);
    4344:   if (reuse != MAT_INITIAL_MATRIX && reuse != MAT_REUSE_MATRIX) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatReuse %d",(int)reuse);
    4345:   if (reuse == MAT_INITIAL_MATRIX) {
    4346:     PetscMalloc2(am*bm+1,&i,a->i[am]*b->i[bm],&j);
    4347:     MatCreate(PETSC_COMM_SELF,&newmat);
    4348:     MatSetSizes(newmat,am*bm,an*bn,am*bm,an*bn);
    4349:     MatSetType(newmat,MATAIJ);
    4350:     i[0] = 0;
    4351:     for (m = 0; m < am; ++m) {
    4352:       for (p = 0; p < bm; ++p) {
    4353:         i[m*bm + p + 1] = i[m*bm + p] + (a->i[m+1] - a->i[m]) * (b->i[p+1] - b->i[p]);
    4354:         for (n = a->i[m]; n < a->i[m+1]; ++n) {
    4355:           for (q = b->i[p]; q < b->i[p+1]; ++q) {
    4356:             j[nnz++] = a->j[n]*bn + b->j[q];
    4357:           }
    4358:         }
    4359:       }
    4360:     }
    4361:     MatSeqAIJSetPreallocationCSR(newmat,i,j,NULL);
    4362:     *C = newmat;
    4363:     PetscFree2(i,j);
    4364:     nnz = 0;
    4365:   }
    4366:   MatSeqAIJGetArray(*C,&v);
    4367:   for (m = 0; m < am; ++m) {
    4368:     for (p = 0; p < bm; ++p) {
    4369:       for (n = a->i[m]; n < a->i[m+1]; ++n) {
    4370:         for (q = b->i[p]; q < b->i[p+1]; ++q) {
    4371:           v[nnz++] = a->a[n] * b->a[q];
    4372:         }
    4373:       }
    4374:     }
    4375:   }
    4376:   MatSeqAIJRestoreArray(*C,&v);
    4377:   return(0);
    4378: }

    4380: #include <../src/mat/impls/dense/seq/dense.h>
    4381: #include <petsc/private/kernels/petscaxpy.h>

    4383: /*
    4384:     Computes (B'*A')' since computing B*A directly is untenable

    4386:                n                       p                          p
    4387:         [             ]       [             ]         [                 ]
    4388:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
    4389:         [             ]       [             ]         [                 ]

    4391: */
    4392: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
    4393: {
    4394:   PetscErrorCode    ierr;
    4395:   Mat_SeqDense      *sub_a = (Mat_SeqDense*)A->data;
    4396:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ*)B->data;
    4397:   Mat_SeqDense      *sub_c = (Mat_SeqDense*)C->data;
    4398:   PetscInt          i,j,n,m,q,p;
    4399:   const PetscInt    *ii,*idx;
    4400:   const PetscScalar *b,*a,*a_q;
    4401:   PetscScalar       *c,*c_q;
    4402:   PetscInt          clda = sub_c->lda;
    4403:   PetscInt          alda = sub_a->lda;

    4406:   m    = A->rmap->n;
    4407:   n    = A->cmap->n;
    4408:   p    = B->cmap->n;
    4409:   a    = sub_a->v;
    4410:   b    = sub_b->a;
    4411:   c    = sub_c->v;
    4412:   if (clda == m) {
    4413:     PetscArrayzero(c,m*p);
    4414:   } else {
    4415:     for (j=0;j<p;j++)
    4416:       for (i=0;i<m;i++)
    4417:         c[j*clda + i] = 0.0;
    4418:   }
    4419:   ii  = sub_b->i;
    4420:   idx = sub_b->j;
    4421:   for (i=0; i<n; i++) {
    4422:     q = ii[i+1] - ii[i];
    4423:     while (q-->0) {
    4424:       c_q = c + clda*(*idx);
    4425:       a_q = a + alda*i;
    4426:       PetscKernelAXPY(c_q,*b,a_q,m);
    4427:       idx++;
    4428:       b++;
    4429:     }
    4430:   }
    4431:   return(0);
    4432: }

    4434: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)
    4435: {
    4437:   PetscInt       m=A->rmap->n,n=B->cmap->n;
    4438:   PetscBool      cisdense;

    4441:   if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"A->cmap->n %D != B->rmap->n %D\n",A->cmap->n,B->rmap->n);
    4442:   MatSetSizes(C,m,n,m,n);
    4443:   MatSetBlockSizesFromMats(C,A,B);
    4444:   PetscObjectTypeCompareAny((PetscObject)C,&cisdense,MATSEQDENSE,MATSEQDENSECUDA,"");
    4445:   if (!cisdense) {
    4446:     MatSetType(C,MATDENSE);
    4447:   }
    4448:   MatSetUp(C);

    4450:   C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
    4451:   return(0);
    4452: }

    4454: /* ----------------------------------------------------------------*/
    4455: /*MC
    4456:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
    4457:    based on compressed sparse row format.

    4459:    Options Database Keys:
    4460: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

    4462:    Level: beginner

    4464:    Notes:
    4465:     MatSetValues() may be called for this matrix type with a NULL argument for the numerical values,
    4466:     in this case the values associated with the rows and columns one passes in are set to zero
    4467:     in the matrix

    4469:     MatSetOptions(,MAT_STRUCTURE_ONLY,PETSC_TRUE) may be called for this matrix type. In this no
    4470:     space is allocated for the nonzero entries and any entries passed with MatSetValues() are ignored

    4472:   Developer Notes:
    4473:     It would be nice if all matrix formats supported passing NULL in for the numerical values

    4475: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
    4476: M*/

    4478: /*MC
    4479:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

    4481:    This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
    4482:    and MATMPIAIJ otherwise.  As a result, for single process communicators,
    4483:   MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation() is supported
    4484:   for communicators controlling multiple processes.  It is recommended that you call both of
    4485:   the above preallocation routines for simplicity.

    4487:    Options Database Keys:
    4488: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()

    4490:   Developer Notes:
    4491:     Subclasses include MATAIJCUSPARSE, MATAIJPERM, MATAIJSELL, MATAIJMKL, MATAIJCRL, and also automatically switches over to use inodes when
    4492:    enough exist.

    4494:   Level: beginner

    4496: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
    4497: M*/

    4499: /*MC
    4500:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

    4502:    This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
    4503:    and MATMPIAIJCRL otherwise.  As a result, for single process communicators,
    4504:    MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
    4505:   for communicators controlling multiple processes.  It is recommended that you call both of
    4506:   the above preallocation routines for simplicity.

    4508:    Options Database Keys:
    4509: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()

    4511:   Level: beginner

    4513: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
    4514: M*/

    4516: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
    4517: #if defined(PETSC_HAVE_ELEMENTAL)
    4518: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
    4519: #endif
    4520: #if defined(PETSC_HAVE_SCALAPACK)
    4521: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat,MatType,MatReuse,Mat*);
    4522: #endif
    4523: #if defined(PETSC_HAVE_HYPRE)
    4524: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A,MatType,MatReuse,Mat*);
    4525: #endif

    4527: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat,MatType,MatReuse,Mat*);
    4528: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat,MatType,MatReuse,Mat*);
    4529: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

    4531: /*@C
    4532:    MatSeqAIJGetArray - gives read/write access to the array where the data for a MATSEQAIJ matrix is stored

    4534:    Not Collective

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

    4539:    Output Parameter:
    4540: .   array - pointer to the data

    4542:    Level: intermediate

    4544: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
    4545: @*/
    4546: PetscErrorCode  MatSeqAIJGetArray(Mat A,PetscScalar **array)
    4547: {

    4551:   PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
    4552: #if defined(PETSC_HAVE_DEVICE)
    4553:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    4554: #endif
    4555:   return(0);
    4556: }

    4558: /*@C
    4559:    MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a MATSEQAIJ matrix is stored

    4561:    Not Collective

    4563:    Input Parameter:
    4564: .  mat - a MATSEQAIJ matrix

    4566:    Output Parameter:
    4567: .   array - pointer to the data

    4569:    Level: intermediate

    4571: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayRead()
    4572: @*/
    4573: PetscErrorCode  MatSeqAIJGetArrayRead(Mat A,const PetscScalar **array)
    4574: {
    4575: #if defined(PETSC_HAVE_DEVICE)
    4576:   PetscOffloadMask oval;
    4577: #endif

    4581: #if defined(PETSC_HAVE_DEVICE)
    4582:   oval = A->offloadmask;
    4583: #endif
    4584:   MatSeqAIJGetArray(A,(PetscScalar**)array);
    4585: #if defined(PETSC_HAVE_DEVICE)
    4586:   if (oval == PETSC_OFFLOAD_GPU || oval == PETSC_OFFLOAD_BOTH) A->offloadmask = PETSC_OFFLOAD_BOTH;
    4587: #endif
    4588:   return(0);
    4589: }

    4591: /*@C
    4592:    MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from MatSeqAIJGetArrayRead

    4594:    Not Collective

    4596:    Input Parameter:
    4597: .  mat - a MATSEQAIJ matrix

    4599:    Output Parameter:
    4600: .   array - pointer to the data

    4602:    Level: intermediate

    4604: .seealso: MatSeqAIJGetArray(), MatSeqAIJGetArrayRead()
    4605: @*/
    4606: PetscErrorCode  MatSeqAIJRestoreArrayRead(Mat A,const PetscScalar **array)
    4607: {
    4608: #if defined(PETSC_HAVE_DEVICE)
    4609:   PetscOffloadMask oval;
    4610: #endif

    4614: #if defined(PETSC_HAVE_DEVICE)
    4615:   oval = A->offloadmask;
    4616: #endif
    4617:   MatSeqAIJRestoreArray(A,(PetscScalar**)array);
    4618: #if defined(PETSC_HAVE_DEVICE)
    4619:   A->offloadmask = oval;
    4620: #endif
    4621:   return(0);
    4622: }

    4624: /*@C
    4625:    MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

    4627:    Not Collective

    4629:    Input Parameter:
    4630: .  mat - a MATSEQAIJ matrix

    4632:    Output Parameter:
    4633: .   nz - the maximum number of nonzeros in any row

    4635:    Level: intermediate

    4637: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
    4638: @*/
    4639: PetscErrorCode  MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
    4640: {
    4641:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

    4644:   *nz = aij->rmax;
    4645:   return(0);
    4646: }

    4648: /*@C
    4649:    MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()

    4651:    Not Collective

    4653:    Input Parameters:
    4654: +  mat - a MATSEQAIJ matrix
    4655: -  array - pointer to the data

    4657:    Level: intermediate

    4659: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
    4660: @*/
    4661: PetscErrorCode  MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
    4662: {

    4666:   PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
    4667:   return(0);
    4668: }

    4670: #if defined(PETSC_HAVE_CUDA)
    4671: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat,MatType,MatReuse,Mat*);
    4672: #endif
    4673: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    4674: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat,MatType,MatReuse,Mat*);
    4675: #endif

    4677: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
    4678: {
    4679:   Mat_SeqAIJ     *b;
    4681:   PetscMPIInt    size;

    4684:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
    4685:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

    4687:   PetscNewLog(B,&b);

    4689:   B->data = (void*)b;

    4691:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
    4692:   if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;

    4694:   b->row                = NULL;
    4695:   b->col                = NULL;
    4696:   b->icol               = NULL;
    4697:   b->reallocs           = 0;
    4698:   b->ignorezeroentries  = PETSC_FALSE;
    4699:   b->roworiented        = PETSC_TRUE;
    4700:   b->nonew              = 0;
    4701:   b->diag               = NULL;
    4702:   b->solve_work         = NULL;
    4703:   B->spptr              = NULL;
    4704:   b->saved_values       = NULL;
    4705:   b->idiag              = NULL;
    4706:   b->mdiag              = NULL;
    4707:   b->ssor_work          = NULL;
    4708:   b->omega              = 1.0;
    4709:   b->fshift             = 0.0;
    4710:   b->idiagvalid         = PETSC_FALSE;
    4711:   b->ibdiagvalid        = PETSC_FALSE;
    4712:   b->keepnonzeropattern = PETSC_FALSE;

    4714:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
    4715:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
    4716:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);

    4718: #if defined(PETSC_HAVE_MATLAB_ENGINE)
    4719:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
    4720:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
    4721: #endif

    4723:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
    4724:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
    4725:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
    4726:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
    4727:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
    4728:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
    4729:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijsell_C",MatConvert_SeqAIJ_SeqAIJSELL);
    4730: #if defined(PETSC_HAVE_MKL_SPARSE)
    4731:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijmkl_C",MatConvert_SeqAIJ_SeqAIJMKL);
    4732: #endif
    4733: #if defined(PETSC_HAVE_CUDA)
    4734:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcusparse_C",MatConvert_SeqAIJ_SeqAIJCUSPARSE);
    4735:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",MatProductSetFromOptions_SeqAIJ);
    4736:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaijcusparse_C",MatProductSetFromOptions_SeqAIJ);
    4737: #endif
    4738: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    4739:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijkokkos_C",MatConvert_SeqAIJ_SeqAIJKokkos);
    4740: #endif
    4741:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
    4742: #if defined(PETSC_HAVE_ELEMENTAL)
    4743:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
    4744: #endif
    4745: #if defined(PETSC_HAVE_SCALAPACK)
    4746:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_scalapack_C",MatConvert_AIJ_ScaLAPACK);
    4747: #endif
    4748: #if defined(PETSC_HAVE_HYPRE)
    4749:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_hypre_C",MatConvert_AIJ_HYPRE);
    4750:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",MatProductSetFromOptions_Transpose_AIJ_AIJ);
    4751: #endif
    4752:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
    4753:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsell_C",MatConvert_SeqAIJ_SeqSELL);
    4754:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_is_C",MatConvert_XAIJ_IS);
    4755:   PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
    4756:   PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
    4757:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
    4758:   PetscObjectComposeFunction((PetscObject)B,"MatResetPreallocation_C",MatResetPreallocation_SeqAIJ);
    4759:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
    4760:   PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
    4761:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_is_seqaij_C",MatProductSetFromOptions_IS_XAIJ);
    4762:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqdense_seqaij_C",MatProductSetFromOptions_SeqDense_SeqAIJ);
    4763:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaij_C",MatProductSetFromOptions_SeqAIJ);
    4764:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJKron_C",MatSeqAIJKron_SeqAIJ);
    4765:   MatCreate_SeqAIJ_Inode(B);
    4766:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
    4767:   MatSeqAIJSetTypeFromOptions(B);  /* this allows changing the matrix subtype to say MATSEQAIJPERM */
    4768:   return(0);
    4769: }

    4771: /*
    4772:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
    4773: */
    4774: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
    4775: {
    4776:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data,*a = (Mat_SeqAIJ*)A->data;
    4778:   PetscInt       m = A->rmap->n,i;

    4781:   if (!A->assembled && cpvalues!=MAT_DO_NOT_COPY_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot duplicate unassembled matrix");

    4783:   C->factortype = A->factortype;
    4784:   c->row        = NULL;
    4785:   c->col        = NULL;
    4786:   c->icol       = NULL;
    4787:   c->reallocs   = 0;

    4789:   C->assembled = PETSC_TRUE;

    4791:   PetscLayoutReference(A->rmap,&C->rmap);
    4792:   PetscLayoutReference(A->cmap,&C->cmap);

    4794:   PetscMalloc1(m,&c->imax);
    4795:   PetscMemcpy(c->imax,a->imax,m*sizeof(PetscInt));
    4796:   PetscMalloc1(m,&c->ilen);
    4797:   PetscMemcpy(c->ilen,a->ilen,m*sizeof(PetscInt));
    4798:   PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));

    4800:   /* allocate the matrix space */
    4801:   if (mallocmatspace) {
    4802:     PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
    4803:     PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));

    4805:     c->singlemalloc = PETSC_TRUE;

    4807:     PetscArraycpy(c->i,a->i,m+1);
    4808:     if (m > 0) {
    4809:       PetscArraycpy(c->j,a->j,a->i[m]);
    4810:       if (cpvalues == MAT_COPY_VALUES) {
    4811:         const PetscScalar *aa;

    4813:         MatSeqAIJGetArrayRead(A,&aa);
    4814:         PetscArraycpy(c->a,aa,a->i[m]);
    4815:         MatSeqAIJGetArrayRead(A,&aa);
    4816:       } else {
    4817:         PetscArrayzero(c->a,a->i[m]);
    4818:       }
    4819:     }
    4820:   }

    4822:   c->ignorezeroentries = a->ignorezeroentries;
    4823:   c->roworiented       = a->roworiented;
    4824:   c->nonew             = a->nonew;
    4825:   if (a->diag) {
    4826:     PetscMalloc1(m+1,&c->diag);
    4827:     PetscMemcpy(c->diag,a->diag,m*sizeof(PetscInt));
    4828:     PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
    4829:   } else c->diag = NULL;

    4831:   c->solve_work         = NULL;
    4832:   c->saved_values       = NULL;
    4833:   c->idiag              = NULL;
    4834:   c->ssor_work          = NULL;
    4835:   c->keepnonzeropattern = a->keepnonzeropattern;
    4836:   c->free_a             = PETSC_TRUE;
    4837:   c->free_ij            = PETSC_TRUE;

    4839:   c->rmax         = a->rmax;
    4840:   c->nz           = a->nz;
    4841:   c->maxnz        = a->nz;       /* Since we allocate exactly the right amount */
    4842:   C->preallocated = PETSC_TRUE;

    4844:   c->compressedrow.use   = a->compressedrow.use;
    4845:   c->compressedrow.nrows = a->compressedrow.nrows;
    4846:   if (a->compressedrow.use) {
    4847:     i    = a->compressedrow.nrows;
    4848:     PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
    4849:     PetscArraycpy(c->compressedrow.i,a->compressedrow.i,i+1);
    4850:     PetscArraycpy(c->compressedrow.rindex,a->compressedrow.rindex,i);
    4851:   } else {
    4852:     c->compressedrow.use    = PETSC_FALSE;
    4853:     c->compressedrow.i      = NULL;
    4854:     c->compressedrow.rindex = NULL;
    4855:   }
    4856:   c->nonzerorowcnt = a->nonzerorowcnt;
    4857:   C->nonzerostate  = A->nonzerostate;

    4859:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
    4860:   PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
    4861:   return(0);
    4862: }

    4864: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
    4865: {

    4869:   MatCreate(PetscObjectComm((PetscObject)A),B);
    4870:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
    4871:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
    4872:     MatSetBlockSizesFromMats(*B,A,A);
    4873:   }
    4874:   MatSetType(*B,((PetscObject)A)->type_name);
    4875:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
    4876:   return(0);
    4877: }

    4879: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
    4880: {
    4881:   PetscBool      isbinary, ishdf5;

    4887:   /* force binary viewer to load .info file if it has not yet done so */
    4888:   PetscViewerSetUp(viewer);
    4889:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    4890:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERHDF5,  &ishdf5);
    4891:   if (isbinary) {
    4892:     MatLoad_SeqAIJ_Binary(newMat,viewer);
    4893:   } else if (ishdf5) {
    4894: #if defined(PETSC_HAVE_HDF5)
    4895:     MatLoad_AIJ_HDF5(newMat,viewer);
    4896: #else
    4897:     SETERRQ(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
    4898: #endif
    4899:   } else {
    4900:     SETERRQ2(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"Viewer type %s not yet supported for reading %s matrices",((PetscObject)viewer)->type_name,((PetscObject)newMat)->type_name);
    4901:   }
    4902:   return(0);
    4903: }

    4905: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
    4906: {
    4907:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)mat->data;
    4909:   PetscInt       header[4],*rowlens,M,N,nz,sum,rows,cols,i;

    4912:   PetscViewerSetUp(viewer);

    4914:   /* read in matrix header */
    4915:   PetscViewerBinaryRead(viewer,header,4,NULL,PETSC_INT);
    4916:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Not a matrix object in file");
    4917:   M = header[1]; N = header[2]; nz = header[3];
    4918:   if (M < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix row size (%D) in file is negative",M);
    4919:   if (N < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix column size (%D) in file is negative",N);
    4920:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk, cannot load as SeqAIJ");

    4922:   /* set block sizes from the viewer's .info file */
    4923:   MatLoad_Binary_BlockSizes(mat,viewer);
    4924:   /* set local and global sizes if not set already */
    4925:   if (mat->rmap->n < 0) mat->rmap->n = M;
    4926:   if (mat->cmap->n < 0) mat->cmap->n = N;
    4927:   if (mat->rmap->N < 0) mat->rmap->N = M;
    4928:   if (mat->cmap->N < 0) mat->cmap->N = N;
    4929:   PetscLayoutSetUp(mat->rmap);
    4930:   PetscLayoutSetUp(mat->cmap);

    4932:   /* check if the matrix sizes are correct */
    4933:   MatGetSize(mat,&rows,&cols);
    4934:   if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols);

    4936:   /* read in row lengths */
    4937:   PetscMalloc1(M,&rowlens);
    4938:   PetscViewerBinaryRead(viewer,rowlens,M,NULL,PETSC_INT);
    4939:   /* check if sum(rowlens) is same as nz */
    4940:   sum = 0; for (i=0; i<M; i++) sum += rowlens[i];
    4941:   if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Inconsistent matrix data in file: nonzeros = %D, sum-row-lengths = %D\n",nz,sum);
    4942:   /* preallocate and check sizes */
    4943:   MatSeqAIJSetPreallocation_SeqAIJ(mat,0,rowlens);
    4944:   MatGetSize(mat,&rows,&cols);
    4945:   if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols);
    4946:   /* store row lengths */
    4947:   PetscArraycpy(a->ilen,rowlens,M);
    4948:   PetscFree(rowlens);

    4950:   /* fill in "i" row pointers */
    4951:   a->i[0] = 0; for (i=0; i<M; i++) a->i[i+1] = a->i[i] + a->ilen[i];
    4952:   /* read in "j" column indices */
    4953:   PetscViewerBinaryRead(viewer,a->j,nz,NULL,PETSC_INT);
    4954:   /* read in "a" nonzero values */
    4955:   PetscViewerBinaryRead(viewer,a->a,nz,NULL,PETSC_SCALAR);

    4957:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
    4958:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
    4959:   return(0);
    4960: }

    4962: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
    4963: {
    4964:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
    4966: #if defined(PETSC_USE_COMPLEX)
    4967:   PetscInt k;
    4968: #endif

    4971:   /* If the  matrix dimensions are not equal,or no of nonzeros */
    4972:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
    4973:     *flg = PETSC_FALSE;
    4974:     return(0);
    4975:   }

    4977:   /* if the a->i are the same */
    4978:   PetscArraycmp(a->i,b->i,A->rmap->n+1,flg);
    4979:   if (!*flg) return(0);

    4981:   /* if a->j are the same */
    4982:   PetscArraycmp(a->j,b->j,a->nz,flg);
    4983:   if (!*flg) return(0);

    4985:   /* if a->a are the same */
    4986: #if defined(PETSC_USE_COMPLEX)
    4987:   for (k=0; k<a->nz; k++) {
    4988:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
    4989:       *flg = PETSC_FALSE;
    4990:       return(0);
    4991:     }
    4992:   }
    4993: #else
    4994:   PetscArraycmp(a->a,b->a,a->nz,flg);
    4995: #endif
    4996:   return(0);
    4997: }

    4999: /*@
    5000:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
    5001:               provided by the user.

    5003:       Collective

    5005:    Input Parameters:
    5006: +   comm - must be an MPI communicator of size 1
    5007: .   m - number of rows
    5008: .   n - number of columns
    5009: .   i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
    5010: .   j - column indices
    5011: -   a - matrix values

    5013:    Output Parameter:
    5014: .   mat - the matrix

    5016:    Level: intermediate

    5018:    Notes:
    5019:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
    5020:     once the matrix is destroyed and not before

    5022:        You cannot set new nonzero locations into this matrix, that will generate an error.

    5024:        The i and j indices are 0 based

    5026:        The format which is used for the sparse matrix input, is equivalent to a
    5027:     row-major ordering.. i.e for the following matrix, the input data expected is
    5028:     as shown

    5030: $        1 0 0
    5031: $        2 0 3
    5032: $        4 5 6
    5033: $
    5034: $        i =  {0,1,3,6}  [size = nrow+1  = 3+1]
    5035: $        j =  {0,0,2,0,1,2}  [size = 6]; values must be sorted for each row
    5036: $        v =  {1,2,3,4,5,6}  [size = 6]

    5038: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

    5040: @*/
    5041: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
    5042: {
    5044:   PetscInt       ii;
    5045:   Mat_SeqAIJ     *aij;
    5046:   PetscInt jj;

    5049:   if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
    5050:   MatCreate(comm,mat);
    5051:   MatSetSizes(*mat,m,n,m,n);
    5052:   /* MatSetBlockSizes(*mat,,); */
    5053:   MatSetType(*mat,MATSEQAIJ);
    5054:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,NULL);
    5055:   aij  = (Mat_SeqAIJ*)(*mat)->data;
    5056:   PetscMalloc1(m,&aij->imax);
    5057:   PetscMalloc1(m,&aij->ilen);

    5059:   aij->i            = i;
    5060:   aij->j            = j;
    5061:   aij->a            = a;
    5062:   aij->singlemalloc = PETSC_FALSE;
    5063:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
    5064:   aij->free_a       = PETSC_FALSE;
    5065:   aij->free_ij      = PETSC_FALSE;

    5067:   for (ii=0; ii<m; ii++) {
    5068:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
    5069:     if (PetscDefined(USE_DEBUG)) {
    5070:       if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %D length = %D",ii,i[ii+1] - i[ii]);
    5071:       for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
    5072:         if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual column %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
    5073:         if (j[jj] == j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual column %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
    5074:       }
    5075:     }
    5076:   }
    5077:   if (PetscDefined(USE_DEBUG)) {
    5078:     for (ii=0; ii<aij->i[m]; ii++) {
    5079:       if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
    5080:       if (j[ii] > n - 1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %D index = %D",ii,j[ii]);
    5081:     }
    5082:   }

    5084:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
    5085:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
    5086:   return(0);
    5087: }
    5088: /*@C
    5089:      MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
    5090:               provided by the user.

    5092:       Collective

    5094:    Input Parameters:
    5095: +   comm - must be an MPI communicator of size 1
    5096: .   m   - number of rows
    5097: .   n   - number of columns
    5098: .   i   - row indices
    5099: .   j   - column indices
    5100: .   a   - matrix values
    5101: .   nz  - number of nonzeros
    5102: -   idx - 0 or 1 based

    5104:    Output Parameter:
    5105: .   mat - the matrix

    5107:    Level: intermediate

    5109:    Notes:
    5110:        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,
    5111:        the input data expected is as shown
    5112: .vb
    5113:         1 0 0
    5114:         2 0 3
    5115:         4 5 6

    5117:         i =  {0,1,1,2,2,2}
    5118:         j =  {0,0,2,0,1,2}
    5119:         v =  {1,2,3,4,5,6}
    5120: .ve

    5122: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

    5124: @*/
    5125: PetscErrorCode  MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat,PetscInt nz,PetscBool idx)
    5126: {
    5128:   PetscInt       ii, *nnz, one = 1,row,col;

    5131:   PetscCalloc1(m,&nnz);
    5132:   for (ii = 0; ii < nz; ii++) {
    5133:     nnz[i[ii] - !!idx] += 1;
    5134:   }
    5135:   MatCreate(comm,mat);
    5136:   MatSetSizes(*mat,m,n,m,n);
    5137:   MatSetType(*mat,MATSEQAIJ);
    5138:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
    5139:   for (ii = 0; ii < nz; ii++) {
    5140:     if (idx) {
    5141:       row = i[ii] - 1;
    5142:       col = j[ii] - 1;
    5143:     } else {
    5144:       row = i[ii];
    5145:       col = j[ii];
    5146:     }
    5147:     MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
    5148:   }
    5149:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
    5150:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
    5151:   PetscFree(nnz);
    5152:   return(0);
    5153: }

    5155: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
    5156: {
    5157:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data;

    5161:   a->idiagvalid  = PETSC_FALSE;
    5162:   a->ibdiagvalid = PETSC_FALSE;

    5164:   MatSeqAIJInvalidateDiagonal_Inode(A);
    5165:   return(0);
    5166: }

    5168: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
    5169: {
    5171:   PetscMPIInt    size;

    5174:   MPI_Comm_size(comm,&size);
    5175:   if (size == 1) {
    5176:     if (scall == MAT_INITIAL_MATRIX) {
    5177:       MatDuplicate(inmat,MAT_COPY_VALUES,outmat);
    5178:     } else {
    5179:       MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
    5180:     }
    5181:   } else {
    5182:     MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
    5183:   }
    5184:   return(0);
    5185: }

    5187: /*
    5188:  Permute A into C's *local* index space using rowemb,colemb.
    5189:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
    5190:  of [0,m), colemb is in [0,n).
    5191:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
    5192:  */
    5193: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
    5194: {
    5195:   /* If making this function public, change the error returned in this function away from _PLIB. */
    5197:   Mat_SeqAIJ     *Baij;
    5198:   PetscBool      seqaij;
    5199:   PetscInt       m,n,*nz,i,j,count;
    5200:   PetscScalar    v;
    5201:   const PetscInt *rowindices,*colindices;

    5204:   if (!B) return(0);
    5205:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
    5206:   PetscObjectBaseTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
    5207:   if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
    5208:   if (rowemb) {
    5209:     ISGetLocalSize(rowemb,&m);
    5210:     if (m != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Row IS of size %D is incompatible with matrix row size %D",m,B->rmap->n);
    5211:   } else {
    5212:     if (C->rmap->n != B->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
    5213:   }
    5214:   if (colemb) {
    5215:     ISGetLocalSize(colemb,&n);
    5216:     if (n != B->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Diag col IS of size %D is incompatible with input matrix col size %D",n,B->cmap->n);
    5217:   } else {
    5218:     if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
    5219:   }

    5221:   Baij = (Mat_SeqAIJ*)(B->data);
    5222:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
    5223:     PetscMalloc1(B->rmap->n,&nz);
    5224:     for (i=0; i<B->rmap->n; i++) {
    5225:       nz[i] = Baij->i[i+1] - Baij->i[i];
    5226:     }
    5227:     MatSeqAIJSetPreallocation(C,0,nz);
    5228:     PetscFree(nz);
    5229:   }
    5230:   if (pattern == SUBSET_NONZERO_PATTERN) {
    5231:     MatZeroEntries(C);
    5232:   }
    5233:   count = 0;
    5234:   rowindices = NULL;
    5235:   colindices = NULL;
    5236:   if (rowemb) {
    5237:     ISGetIndices(rowemb,&rowindices);
    5238:   }
    5239:   if (colemb) {
    5240:     ISGetIndices(colemb,&colindices);
    5241:   }
    5242:   for (i=0; i<B->rmap->n; i++) {
    5243:     PetscInt row;
    5244:     row = i;
    5245:     if (rowindices) row = rowindices[i];
    5246:     for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
    5247:       PetscInt col;
    5248:       col  = Baij->j[count];
    5249:       if (colindices) col = colindices[col];
    5250:       v    = Baij->a[count];
    5251:       MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
    5252:       ++count;
    5253:     }
    5254:   }
    5255:   /* FIXME: set C's nonzerostate correctly. */
    5256:   /* Assembly for C is necessary. */
    5257:   C->preallocated = PETSC_TRUE;
    5258:   C->assembled     = PETSC_TRUE;
    5259:   C->was_assembled = PETSC_FALSE;
    5260:   return(0);
    5261: }

    5263: PetscFunctionList MatSeqAIJList = NULL;

    5265: /*@C
    5266:    MatSeqAIJSetType - Converts a MATSEQAIJ matrix to a subtype

    5268:    Collective on Mat

    5270:    Input Parameters:
    5271: +  mat      - the matrix object
    5272: -  matype   - matrix type

    5274:    Options Database Key:
    5275: .  -mat_seqai_type  <method> - for example seqaijcrl

    5277:   Level: intermediate

    5279: .seealso: PCSetType(), VecSetType(), MatCreate(), MatType, Mat
    5280: @*/
    5281: PetscErrorCode  MatSeqAIJSetType(Mat mat, MatType matype)
    5282: {
    5283:   PetscErrorCode ierr,(*r)(Mat,MatType,MatReuse,Mat*);
    5284:   PetscBool      sametype;

    5288:   PetscObjectTypeCompare((PetscObject)mat,matype,&sametype);
    5289:   if (sametype) return(0);

    5291:    PetscFunctionListFind(MatSeqAIJList,matype,&r);
    5292:   if (!r) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unknown Mat type given: %s",matype);
    5293:   (*r)(mat,matype,MAT_INPLACE_MATRIX,&mat);
    5294:   return(0);
    5295: }

    5297: /*@C
    5298:   MatSeqAIJRegister -  - Adds a new sub-matrix type for sequential AIJ matrices

    5300:    Not Collective

    5302:    Input Parameters:
    5303: +  name - name of a new user-defined matrix type, for example MATSEQAIJCRL
    5304: -  function - routine to convert to subtype

    5306:    Notes:
    5307:    MatSeqAIJRegister() may be called multiple times to add several user-defined solvers.

    5309:    Then, your matrix can be chosen with the procedural interface at runtime via the option
    5310: $     -mat_seqaij_type my_mat

    5312:    Level: advanced

    5314: .seealso: MatSeqAIJRegisterAll()

    5316:   Level: advanced
    5317: @*/
    5318: PetscErrorCode  MatSeqAIJRegister(const char sname[],PetscErrorCode (*function)(Mat,MatType,MatReuse,Mat *))
    5319: {

    5323:   MatInitializePackage();
    5324:   PetscFunctionListAdd(&MatSeqAIJList,sname,function);
    5325:   return(0);
    5326: }

    5328: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;

    5330: /*@C
    5331:   MatSeqAIJRegisterAll - Registers all of the matrix subtypes of SeqAIJ

    5333:   Not Collective

    5335:   Level: advanced

    5337: .seealso:  MatRegisterAll(), MatSeqAIJRegister()
    5338: @*/
    5339: PetscErrorCode  MatSeqAIJRegisterAll(void)
    5340: {

    5344:   if (MatSeqAIJRegisterAllCalled) return(0);
    5345:   MatSeqAIJRegisterAllCalled = PETSC_TRUE;

    5347:   MatSeqAIJRegister(MATSEQAIJCRL,      MatConvert_SeqAIJ_SeqAIJCRL);
    5348:   MatSeqAIJRegister(MATSEQAIJPERM,     MatConvert_SeqAIJ_SeqAIJPERM);
    5349:   MatSeqAIJRegister(MATSEQAIJSELL,     MatConvert_SeqAIJ_SeqAIJSELL);
    5350: #if defined(PETSC_HAVE_MKL_SPARSE)
    5351:   MatSeqAIJRegister(MATSEQAIJMKL,      MatConvert_SeqAIJ_SeqAIJMKL);
    5352: #endif
    5353: #if defined(PETSC_HAVE_CUDA)
    5354:   MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE);
    5355: #endif
    5356: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    5357:   MatSeqAIJRegister(MATSEQAIJKOKKOS,   MatConvert_SeqAIJ_SeqAIJKokkos);
    5358: #endif
    5359: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
    5360:   MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL);
    5361: #endif
    5362:   return(0);
    5363: }

    5365: /*
    5366:     Special version for direct calls from Fortran
    5367: */
    5368: #include <petsc/private/fortranimpl.h>
    5369: #if defined(PETSC_HAVE_FORTRAN_CAPS)
    5370: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
    5371: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
    5372: #define matsetvaluesseqaij_ matsetvaluesseqaij
    5373: #endif

    5375: /* Change these macros so can be used in void function */
    5376: #undef CHKERRQ
    5377: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
    5378: #undef SETERRQ2
    5379: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
    5380: #undef SETERRQ3
    5381: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)

    5383: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
    5384: {
    5385:   Mat            A  = *AA;
    5386:   PetscInt       m  = *mm, n = *nn;
    5387:   InsertMode     is = *isis;
    5388:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    5389:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
    5390:   PetscInt       *imax,*ai,*ailen;
    5392:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
    5393:   MatScalar      *ap,value,*aa;
    5394:   PetscBool      ignorezeroentries = a->ignorezeroentries;
    5395:   PetscBool      roworiented       = a->roworiented;

    5398:   MatCheckPreallocated(A,1);
    5399:   imax  = a->imax;
    5400:   ai    = a->i;
    5401:   ailen = a->ilen;
    5402:   aj    = a->j;
    5403:   aa    = a->a;

    5405:   for (k=0; k<m; k++) { /* loop over added rows */
    5406:     row = im[k];
    5407:     if (row < 0) continue;
    5408:     if (PetscUnlikelyDebug(row >= A->rmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
    5409:     rp   = aj + ai[row]; ap = aa + ai[row];
    5410:     rmax = imax[row]; nrow = ailen[row];
    5411:     low  = 0;
    5412:     high = nrow;
    5413:     for (l=0; l<n; l++) { /* loop over added columns */
    5414:       if (in[l] < 0) continue;
    5415:       if (PetscUnlikelyDebug(in[l] >= A->cmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
    5416:       col = in[l];
    5417:       if (roworiented) value = v[l + k*n];
    5418:       else value = v[k + l*m];

    5420:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

    5422:       if (col <= lastcol) low = 0;
    5423:       else high = nrow;
    5424:       lastcol = col;
    5425:       while (high-low > 5) {
    5426:         t = (low+high)/2;
    5427:         if (rp[t] > col) high = t;
    5428:         else             low  = t;
    5429:       }
    5430:       for (i=low; i<high; i++) {
    5431:         if (rp[i] > col) break;
    5432:         if (rp[i] == col) {
    5433:           if (is == ADD_VALUES) ap[i] += value;
    5434:           else                  ap[i] = value;
    5435:           goto noinsert;
    5436:         }
    5437:       }
    5438:       if (value == 0.0 && ignorezeroentries) goto noinsert;
    5439:       if (nonew == 1) goto noinsert;
    5440:       if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
    5441:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
    5442:       N = nrow++ - 1; a->nz++; high++;
    5443:       /* shift up all the later entries in this row */
    5444:       for (ii=N; ii>=i; ii--) {
    5445:         rp[ii+1] = rp[ii];
    5446:         ap[ii+1] = ap[ii];
    5447:       }
    5448:       rp[i] = col;
    5449:       ap[i] = value;
    5450:       A->nonzerostate++;
    5451: noinsert:;
    5452:       low = i + 1;
    5453:     }
    5454:     ailen[row] = nrow;
    5455:   }
    5456:   PetscFunctionReturnVoid();
    5457: }