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: /* defines MatSetValues_Seq_Hash(), MatAssemblyEnd_Seq_Hash(), MatSetUp_Seq_Hash() */
     12: #define TYPE AIJ
     13: #define TYPE_BS
     14: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
     15: #include "../src/mat/impls/aij/seq/seqhashmat.h"
     16: #undef TYPE
     17: #undef TYPE_BS

     19: static PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
     20: {
     21:   PetscBool flg;
     22:   char      type[256];

     24:   PetscFunctionBegin;
     25:   PetscObjectOptionsBegin((PetscObject)A);
     26:   PetscCall(PetscOptionsFList("-mat_seqaij_type", "Matrix SeqAIJ type", "MatSeqAIJSetType", MatSeqAIJList, "seqaij", type, 256, &flg));
     27:   if (flg) PetscCall(MatSeqAIJSetType(A, type));
     28:   PetscOptionsEnd();
     29:   PetscFunctionReturn(PETSC_SUCCESS);
     30: }

     32: static PetscErrorCode MatGetColumnReductions_SeqAIJ(Mat A, PetscInt type, PetscReal *reductions)
     33: {
     34:   PetscInt    i, m, n;
     35:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

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

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

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

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

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

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

    109: static PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A, IS *zrows)
    110: {
    111:   PetscInt nrows, *rows;

    113:   PetscFunctionBegin;
    114:   *zrows = NULL;
    115:   PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(A, &nrows, &rows));
    116:   PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), nrows, rows, PETSC_OWN_POINTER, zrows));
    117:   PetscFunctionReturn(PETSC_SUCCESS);
    118: }

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

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

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

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

    196: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
    197: {
    198:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
    199:   PetscInt    i, ishift;

    201:   PetscFunctionBegin;
    202:   if (m) *m = A->rmap->n;
    203:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
    204:   ishift = 0;
    205:   if (symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) {
    206:     PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, ishift, oshift, (PetscInt **)ia, (PetscInt **)ja));
    207:   } else if (oshift == 1) {
    208:     PetscInt *tia;
    209:     PetscInt  nz = a->i[A->rmap->n];
    210:     /* malloc space and  add 1 to i and j indices */
    211:     PetscCall(PetscMalloc1(A->rmap->n + 1, &tia));
    212:     for (i = 0; i < A->rmap->n + 1; i++) tia[i] = a->i[i] + 1;
    213:     *ia = tia;
    214:     if (ja) {
    215:       PetscInt *tja;
    216:       PetscCall(PetscMalloc1(nz + 1, &tja));
    217:       for (i = 0; i < nz; i++) tja[i] = a->j[i] + 1;
    218:       *ja = tja;
    219:     }
    220:   } else {
    221:     *ia = a->i;
    222:     if (ja) *ja = a->j;
    223:   }
    224:   PetscFunctionReturn(PETSC_SUCCESS);
    225: }

    227: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
    228: {
    229:   PetscFunctionBegin;
    230:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
    231:   if ((symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) || oshift == 1) {
    232:     PetscCall(PetscFree(*ia));
    233:     if (ja) PetscCall(PetscFree(*ja));
    234:   }
    235:   PetscFunctionReturn(PETSC_SUCCESS);
    236: }

    238: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
    239: {
    240:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
    241:   PetscInt    i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n;
    242:   PetscInt    nz = a->i[m], row, *jj, mr, col;

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

    264:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
    265:       }
    266:     }
    267:     PetscCall(PetscFree(collengths));
    268:     *ia = cia;
    269:     *ja = cja;
    270:   }
    271:   PetscFunctionReturn(PETSC_SUCCESS);
    272: }

    274: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
    275: {
    276:   PetscFunctionBegin;
    277:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);

    279:   PetscCall(PetscFree(*ia));
    280:   PetscCall(PetscFree(*ja));
    281:   PetscFunctionReturn(PETSC_SUCCESS);
    282: }

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

    297:   PetscFunctionBegin;
    298:   *nn = n;
    299:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);

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

    327: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
    328: {
    329:   PetscFunctionBegin;
    330:   PetscCall(MatRestoreColumnIJ_SeqAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done));
    331:   PetscCall(PetscFree(*spidx));
    332:   PetscFunctionReturn(PETSC_SUCCESS);
    333: }

    335: static PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A, PetscInt row, const PetscScalar v[])
    336: {
    337:   Mat_SeqAIJ  *a  = (Mat_SeqAIJ *)A->data;
    338:   PetscInt    *ai = a->i;
    339:   PetscScalar *aa;

    341:   PetscFunctionBegin;
    342:   PetscCall(MatSeqAIJGetArray(A, &aa));
    343:   PetscCall(PetscArraycpy(aa + ai[row], v, ai[row + 1] - ai[row]));
    344:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
    345:   PetscFunctionReturn(PETSC_SUCCESS);
    346: }

    348: /*
    349:     MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions

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

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

    358: */

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

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

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

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

    412:   PetscFunctionBegin;
    413:   PetscCall(MatSeqAIJGetArray(A, &aa));
    414:   for (k = 0; k < m; k++) { /* loop over added rows */
    415:     row = im[k];
    416:     if (row < 0) continue;
    417:     PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
    418:     rp = PetscSafePointerPlusOffset(aj, ai[row]);
    419:     if (!A->structure_only) ap = PetscSafePointerPlusOffset(aa, ai[row]);
    420:     rmax = imax[row];
    421:     nrow = ailen[row];
    422:     low  = 0;
    423:     high = nrow;
    424:     for (l = 0; l < n; l++) { /* loop over added columns */
    425:       if (in[l] < 0) continue;
    426:       PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
    427:       col = in[l];
    428:       if (v && !A->structure_only) value = roworiented ? v[l + k * n] : v[k + l * m];
    429:       if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue;

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

    479: static PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
    480: {
    481:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
    482:   PetscInt   *rp, k, row;
    483:   PetscInt   *ai = a->i;
    484:   PetscInt   *aj = a->j;
    485:   MatScalar  *aa, *ap;

    487:   PetscFunctionBegin;
    488:   PetscCheck(!A->was_assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot call on assembled matrix.");
    489:   PetscCheck(m * n + a->nz <= a->maxnz, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of entries in matrix will be larger than maximum nonzeros allocated for %" PetscInt_FMT " in MatSeqAIJSetTotalPreallocation()", a->maxnz);

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

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

    515: /*@
    516:   MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.

    518:   Input Parameters:
    519: + A       - the `MATSEQAIJ` matrix
    520: - nztotal - bound on the number of nonzeros

    522:   Level: advanced

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

    529: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MAT_SORTED_FULL`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`
    530: @*/
    531: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A, PetscInt nztotal)
    532: {
    533:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

    535:   PetscFunctionBegin;
    536:   PetscCall(PetscLayoutSetUp(A->rmap));
    537:   PetscCall(PetscLayoutSetUp(A->cmap));
    538:   a->maxnz = nztotal;
    539:   if (!a->imax) { PetscCall(PetscMalloc1(A->rmap->n, &a->imax)); }
    540:   if (!a->ilen) {
    541:     PetscCall(PetscMalloc1(A->rmap->n, &a->ilen));
    542:   } else {
    543:     PetscCall(PetscMemzero(a->ilen, A->rmap->n * sizeof(PetscInt)));
    544:   }

    546:   /* allocate the matrix space */
    547:   PetscCall(PetscShmgetAllocateArray(A->rmap->n + 1, sizeof(PetscInt), (void **)&a->i));
    548:   PetscCall(PetscShmgetAllocateArray(nztotal, sizeof(PetscInt), (void **)&a->j));
    549:   a->free_ij = PETSC_TRUE;
    550:   if (A->structure_only) {
    551:     a->free_a = PETSC_FALSE;
    552:   } else {
    553:     PetscCall(PetscShmgetAllocateArray(nztotal, sizeof(PetscScalar), (void **)&a->a));
    554:     a->free_a = PETSC_TRUE;
    555:   }
    556:   a->i[0]           = 0;
    557:   A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
    558:   A->preallocated   = PETSC_TRUE;
    559:   PetscFunctionReturn(PETSC_SUCCESS);
    560: }

    562: static PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
    563: {
    564:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
    565:   PetscInt   *rp, k, row;
    566:   PetscInt   *ai = a->i, *ailen = a->ilen;
    567:   PetscInt   *aj = a->j;
    568:   MatScalar  *aa, *ap;

    570:   PetscFunctionBegin;
    571:   PetscCall(MatSeqAIJGetArray(A, &aa));
    572:   for (k = 0; k < m; k++) { /* loop over added rows */
    573:     row = im[k];
    574:     PetscCheck(n <= a->imax[row], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Preallocation for row %" PetscInt_FMT " does not match number of columns provided", n);
    575:     rp = aj + ai[row];
    576:     ap = aa + ai[row];
    577:     if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
    578:     if (!A->structure_only) {
    579:       if (v) {
    580:         PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
    581:         v += n;
    582:       } else {
    583:         PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
    584:       }
    585:     }
    586:     ailen[row] = n;
    587:     a->nz += n;
    588:   }
    589:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
    590:   PetscFunctionReturn(PETSC_SUCCESS);
    591: }

    593: static PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
    594: {
    595:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
    596:   PetscInt        *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
    597:   PetscInt        *ai = a->i, *ailen = a->ilen;
    598:   const MatScalar *ap, *aa;

    600:   PetscFunctionBegin;
    601:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    602:   for (k = 0; k < m; k++) { /* loop over rows */
    603:     row = im[k];
    604:     if (row < 0) {
    605:       v += n;
    606:       continue;
    607:     } /* negative row */
    608:     PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
    609:     rp   = PetscSafePointerPlusOffset(aj, ai[row]);
    610:     ap   = PetscSafePointerPlusOffset(aa, ai[row]);
    611:     nrow = ailen[row];
    612:     for (l = 0; l < n; l++) { /* loop over columns */
    613:       if (in[l] < 0) {
    614:         v++;
    615:         continue;
    616:       } /* negative column */
    617:       PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
    618:       col  = in[l];
    619:       high = nrow;
    620:       low  = 0; /* assume unsorted */
    621:       while (high - low > 5) {
    622:         t = (low + high) / 2;
    623:         if (rp[t] > col) high = t;
    624:         else low = t;
    625:       }
    626:       for (i = low; i < high; i++) {
    627:         if (rp[i] > col) break;
    628:         if (rp[i] == col) {
    629:           *v++ = ap[i];
    630:           goto finished;
    631:         }
    632:       }
    633:       *v++ = 0.0;
    634:     finished:;
    635:     }
    636:   }
    637:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    638:   PetscFunctionReturn(PETSC_SUCCESS);
    639: }

    641: static PetscErrorCode MatView_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
    642: {
    643:   Mat_SeqAIJ        *A = (Mat_SeqAIJ *)mat->data;
    644:   const PetscScalar *av;
    645:   PetscInt           header[4], M, N, m, nz, i;
    646:   PetscInt          *rowlens;

    648:   PetscFunctionBegin;
    649:   PetscCall(PetscViewerSetUp(viewer));

    651:   M  = mat->rmap->N;
    652:   N  = mat->cmap->N;
    653:   m  = mat->rmap->n;
    654:   nz = A->nz;

    656:   /* write matrix header */
    657:   header[0] = MAT_FILE_CLASSID;
    658:   header[1] = M;
    659:   header[2] = N;
    660:   header[3] = nz;
    661:   PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));

    663:   /* fill in and store row lengths */
    664:   PetscCall(PetscMalloc1(m, &rowlens));
    665:   for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i];
    666:   if (PetscDefined(USE_DEBUG)) {
    667:     PetscInt mnz = 0;

    669:     for (i = 0; i < m; i++) mnz += rowlens[i];
    670:     PetscCheck(nz == mnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row lens %" PetscInt_FMT " do not sum to nz %" PetscInt_FMT, mnz, nz);
    671:   }
    672:   PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT));
    673:   PetscCall(PetscFree(rowlens));
    674:   /* store column indices */
    675:   PetscCall(PetscViewerBinaryWrite(viewer, A->j, nz, PETSC_INT));
    676:   /* store nonzero values */
    677:   PetscCall(MatSeqAIJGetArrayRead(mat, &av));
    678:   PetscCall(PetscViewerBinaryWrite(viewer, av, nz, PETSC_SCALAR));
    679:   PetscCall(MatSeqAIJRestoreArrayRead(mat, &av));

    681:   /* write block size option to the viewer's .info file */
    682:   PetscCall(MatView_Binary_BlockSizes(mat, viewer));
    683:   PetscFunctionReturn(PETSC_SUCCESS);
    684: }

    686: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A, PetscViewer viewer)
    687: {
    688:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
    689:   PetscInt    i, k, m = A->rmap->N;

    691:   PetscFunctionBegin;
    692:   PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
    693:   for (i = 0; i < m; i++) {
    694:     PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
    695:     for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ") ", a->j[k]));
    696:     PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
    697:   }
    698:   PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
    699:   PetscFunctionReturn(PETSC_SUCCESS);
    700: }

    702: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat, PetscViewer);

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

    712:   PetscFunctionBegin;
    713:   if (A->structure_only) {
    714:     PetscCall(MatView_SeqAIJ_ASCII_structonly(A, viewer));
    715:     PetscFunctionReturn(PETSC_SUCCESS);
    716:   }

    718:   PetscCall(PetscViewerGetFormat(viewer, &format));
    719:   // By petsc's rule, even PETSC_VIEWER_ASCII_INFO_DETAIL doesn't print matrix entries
    720:   if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(PETSC_SUCCESS);

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

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

    841:     for (i = 0; i < a->i[m]; i++) {
    842:       if (PetscImaginaryPart(a->a[i]) != 0.0) {
    843:         realonly = PETSC_FALSE;
    844:         break;
    845:       }
    846:     }
    847: #endif

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

    924:         /* U part */
    925:         for (j = a->diag[i + 1] + 1; j < a->diag[i]; j++) {
    926: #if defined(PETSC_USE_COMPLEX)
    927:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    928:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
    929:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    930:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
    931:           } else {
    932:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
    933:           }
    934: #else
    935:           PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
    936: #endif
    937:         }
    938:         PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
    939:       }
    940:     } else {
    941:       for (i = 0; i < m; i++) {
    942:         PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
    943:         for (j = a->i[i]; j < a->i[i + 1]; j++) {
    944: #if defined(PETSC_USE_COMPLEX)
    945:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    946:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
    947:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    948:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
    949:           } else {
    950:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
    951:           }
    952: #else
    953:           PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
    954: #endif
    955:         }
    956:         PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
    957:       }
    958:     }
    959:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
    960:   }
    961:   PetscCall(PetscViewerFlush(viewer));
    962:   PetscFunctionReturn(PETSC_SUCCESS);
    963: }

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

    977:   PetscFunctionBegin;
    978:   PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer));
    979:   PetscCall(PetscViewerGetFormat(viewer, &format));
    980:   PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr));

    982:   /* loop over matrix elements drawing boxes */
    983:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    984:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
    985:     PetscDrawCollectiveBegin(draw);
    986:     /* Blue for negative, Cyan for zero and  Red for positive */
    987:     color = PETSC_DRAW_BLUE;
    988:     for (i = 0; i < m; i++) {
    989:       y_l = m - i - 1.0;
    990:       y_r = y_l + 1.0;
    991:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
    992:         x_l = a->j[j];
    993:         x_r = x_l + 1.0;
    994:         if (PetscRealPart(aa[j]) >= 0.) continue;
    995:         PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
    996:       }
    997:     }
    998:     color = PETSC_DRAW_CYAN;
    999:     for (i = 0; i < m; i++) {
    1000:       y_l = m - i - 1.0;
    1001:       y_r = y_l + 1.0;
    1002:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
    1003:         x_l = a->j[j];
    1004:         x_r = x_l + 1.0;
    1005:         if (aa[j] != 0.) continue;
    1006:         PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
    1007:       }
    1008:     }
    1009:     color = PETSC_DRAW_RED;
    1010:     for (i = 0; i < m; i++) {
    1011:       y_l = m - i - 1.0;
    1012:       y_r = y_l + 1.0;
    1013:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
    1014:         x_l = a->j[j];
    1015:         x_r = x_l + 1.0;
    1016:         if (PetscRealPart(aa[j]) <= 0.) continue;
    1017:         PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
    1018:       }
    1019:     }
    1020:     PetscDrawCollectiveEnd(draw);
    1021:   } else {
    1022:     /* use contour shading to indicate magnitude of values */
    1023:     /* first determine max of all nonzero values */
    1024:     PetscReal minv = 0.0, maxv = 0.0;
    1025:     PetscInt  nz = a->nz, count = 0;
    1026:     PetscDraw popup;

    1028:     for (i = 0; i < nz; i++) {
    1029:       if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]);
    1030:     }
    1031:     if (minv >= maxv) maxv = minv + PETSC_SMALL;
    1032:     PetscCall(PetscDrawGetPopup(draw, &popup));
    1033:     PetscCall(PetscDrawScalePopup(popup, minv, maxv));

    1035:     PetscDrawCollectiveBegin(draw);
    1036:     for (i = 0; i < m; i++) {
    1037:       y_l = m - i - 1.0;
    1038:       y_r = y_l + 1.0;
    1039:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
    1040:         x_l   = a->j[j];
    1041:         x_r   = x_l + 1.0;
    1042:         color = PetscDrawRealToColor(PetscAbsScalar(aa[count]), minv, maxv);
    1043:         PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
    1044:         count++;
    1045:       }
    1046:     }
    1047:     PetscDrawCollectiveEnd(draw);
    1048:   }
    1049:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1050:   PetscFunctionReturn(PETSC_SUCCESS);
    1051: }

    1053: #include <petscdraw.h>
    1054: static PetscErrorCode MatView_SeqAIJ_Draw(Mat A, PetscViewer viewer)
    1055: {
    1056:   PetscDraw draw;
    1057:   PetscReal xr, yr, xl, yl, h, w;
    1058:   PetscBool isnull;

    1060:   PetscFunctionBegin;
    1061:   PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
    1062:   PetscCall(PetscDrawIsNull(draw, &isnull));
    1063:   if (isnull) PetscFunctionReturn(PETSC_SUCCESS);

    1065:   xr = A->cmap->n;
    1066:   yr = A->rmap->n;
    1067:   h  = yr / 10.0;
    1068:   w  = xr / 10.0;
    1069:   xr += w;
    1070:   yr += h;
    1071:   xl = -w;
    1072:   yl = -h;
    1073:   PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
    1074:   PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
    1075:   PetscCall(PetscDrawZoom(draw, MatView_SeqAIJ_Draw_Zoom, A));
    1076:   PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
    1077:   PetscCall(PetscDrawSave(draw));
    1078:   PetscFunctionReturn(PETSC_SUCCESS);
    1079: }

    1081: PetscErrorCode MatView_SeqAIJ(Mat A, PetscViewer viewer)
    1082: {
    1083:   PetscBool iascii, isbinary, isdraw;

    1085:   PetscFunctionBegin;
    1086:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
    1087:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
    1088:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
    1089:   if (iascii) PetscCall(MatView_SeqAIJ_ASCII(A, viewer));
    1090:   else if (isbinary) PetscCall(MatView_SeqAIJ_Binary(A, viewer));
    1091:   else if (isdraw) PetscCall(MatView_SeqAIJ_Draw(A, viewer));
    1092:   PetscCall(MatView_SeqAIJ_Inode(A, viewer));
    1093:   PetscFunctionReturn(PETSC_SUCCESS);
    1094: }

    1096: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A, MatAssemblyType mode)
    1097: {
    1098:   Mat_SeqAIJ *a      = (Mat_SeqAIJ *)A->data;
    1099:   PetscInt    fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
    1100:   PetscInt    m = A->rmap->n, *ip, N, *ailen = a->ilen, rmax = 0, n;
    1101:   MatScalar  *aa    = a->a, *ap;
    1102:   PetscReal   ratio = 0.6;

    1104:   PetscFunctionBegin;
    1105:   if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
    1106:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
    1107:   if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
    1108:     /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
    1109:     PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
    1110:     PetscFunctionReturn(PETSC_SUCCESS);
    1111:   }

    1113:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
    1114:   for (i = 1; i < m; i++) {
    1115:     /* move each row back by the amount of empty slots (fshift) before it*/
    1116:     fshift += imax[i - 1] - ailen[i - 1];
    1117:     rmax = PetscMax(rmax, ailen[i]);
    1118:     if (fshift) {
    1119:       ip = aj + ai[i];
    1120:       ap = aa + ai[i];
    1121:       N  = ailen[i];
    1122:       PetscCall(PetscArraymove(ip - fshift, ip, N));
    1123:       if (!A->structure_only) PetscCall(PetscArraymove(ap - fshift, ap, N));
    1124:     }
    1125:     ai[i] = ai[i - 1] + ailen[i - 1];
    1126:   }
    1127:   if (m) {
    1128:     fshift += imax[m - 1] - ailen[m - 1];
    1129:     ai[m] = ai[m - 1] + ailen[m - 1];
    1130:   }
    1131:   /* reset ilen and imax for each row */
    1132:   a->nonzerorowcnt = 0;
    1133:   if (A->structure_only) {
    1134:     PetscCall(PetscFree(a->imax));
    1135:     PetscCall(PetscFree(a->ilen));
    1136:   } else { /* !A->structure_only */
    1137:     for (i = 0; i < m; i++) {
    1138:       ailen[i] = imax[i] = ai[i + 1] - ai[i];
    1139:       a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
    1140:     }
    1141:   }
    1142:   a->nz = ai[m];
    1143:   PetscCheck(!fshift || a->nounused != -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unused space detected in matrix: %" PetscInt_FMT " X %" PetscInt_FMT ", %" PetscInt_FMT " unneeded", m, A->cmap->n, fshift);
    1144:   PetscCall(MatMarkDiagonal_SeqAIJ(A)); // since diagonal info is used a lot, it is helpful to set them up at the end of assembly
    1145:   a->diagonaldense = PETSC_TRUE;
    1146:   n                = PetscMin(A->rmap->n, A->cmap->n);
    1147:   for (i = 0; i < n; i++) {
    1148:     if (a->diag[i] >= ai[i + 1]) {
    1149:       a->diagonaldense = PETSC_FALSE;
    1150:       break;
    1151:     }
    1152:   }
    1153:   PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; storage space: %" PetscInt_FMT " unneeded,%" PetscInt_FMT " used\n", m, A->cmap->n, fshift, a->nz));
    1154:   PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs));
    1155:   PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax));

    1157:   A->info.mallocs += a->reallocs;
    1158:   a->reallocs         = 0;
    1159:   A->info.nz_unneeded = (PetscReal)fshift;
    1160:   a->rmax             = rmax;

    1162:   if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio));
    1163:   PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
    1164:   PetscFunctionReturn(PETSC_SUCCESS);
    1165: }

    1167: static PetscErrorCode MatRealPart_SeqAIJ(Mat A)
    1168: {
    1169:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
    1170:   PetscInt    i, nz = a->nz;
    1171:   MatScalar  *aa;

    1173:   PetscFunctionBegin;
    1174:   PetscCall(MatSeqAIJGetArray(A, &aa));
    1175:   for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
    1176:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
    1177:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
    1178:   PetscFunctionReturn(PETSC_SUCCESS);
    1179: }

    1181: static PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
    1182: {
    1183:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
    1184:   PetscInt    i, nz = a->nz;
    1185:   MatScalar  *aa;

    1187:   PetscFunctionBegin;
    1188:   PetscCall(MatSeqAIJGetArray(A, &aa));
    1189:   for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
    1190:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
    1191:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
    1192:   PetscFunctionReturn(PETSC_SUCCESS);
    1193: }

    1195: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
    1196: {
    1197:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
    1198:   MatScalar  *aa;

    1200:   PetscFunctionBegin;
    1201:   PetscCall(MatSeqAIJGetArrayWrite(A, &aa));
    1202:   PetscCall(PetscArrayzero(aa, a->i[A->rmap->n]));
    1203:   PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa));
    1204:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
    1205:   PetscFunctionReturn(PETSC_SUCCESS);
    1206: }

    1208: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
    1209: {
    1210:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

    1212:   PetscFunctionBegin;
    1213:   if (A->hash_active) {
    1214:     A->ops[0] = a->cops;
    1215:     PetscCall(PetscHMapIJVDestroy(&a->ht));
    1216:     PetscCall(PetscFree(a->dnz));
    1217:     A->hash_active = PETSC_FALSE;
    1218:   }

    1220:   PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
    1221:   PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
    1222:   PetscCall(ISDestroy(&a->row));
    1223:   PetscCall(ISDestroy(&a->col));
    1224:   PetscCall(PetscFree(a->diag));
    1225:   PetscCall(PetscFree(a->ibdiag));
    1226:   PetscCall(PetscFree(a->imax));
    1227:   PetscCall(PetscFree(a->ilen));
    1228:   PetscCall(PetscFree(a->ipre));
    1229:   PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work));
    1230:   PetscCall(PetscFree(a->solve_work));
    1231:   PetscCall(ISDestroy(&a->icol));
    1232:   PetscCall(PetscFree(a->saved_values));
    1233:   PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
    1234:   PetscCall(MatDestroy_SeqAIJ_Inode(A));
    1235:   PetscCall(PetscFree(A->data));

    1237:   /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
    1238:      That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
    1239:      that is hard to properly add this data to the MatProduct data. We free it here to avoid
    1240:      users reusing the matrix object with different data to incur in obscure segmentation faults
    1241:      due to different matrix sizes */
    1242:   PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL));

    1244:   PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
    1245:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL));
    1246:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL));
    1247:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL));
    1248:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
    1249:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
    1250:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL));
    1251:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL));
    1252:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL));
    1253:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL));
    1254: #if defined(PETSC_HAVE_MKL_SPARSE)
    1255:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL));
    1256: #endif
    1257: #if defined(PETSC_HAVE_CUDA)
    1258:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL));
    1259:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL));
    1260:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL));
    1261: #endif
    1262: #if defined(PETSC_HAVE_HIP)
    1263:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL));
    1264:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL));
    1265:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL));
    1266: #endif
    1267: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    1268:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL));
    1269: #endif
    1270:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL));
    1271: #if defined(PETSC_HAVE_ELEMENTAL)
    1272:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL));
    1273: #endif
    1274: #if defined(PETSC_HAVE_SCALAPACK)
    1275:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL));
    1276: #endif
    1277: #if defined(PETSC_HAVE_HYPRE)
    1278:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL));
    1279:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL));
    1280: #endif
    1281:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL));
    1282:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL));
    1283:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL));
    1284:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
    1285:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL));
    1286:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL));
    1287:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL));
    1288:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL));
    1289:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL));
    1290:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL));
    1291:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL));
    1292:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL));
    1293:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL));
    1294:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
    1295:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
    1296:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
    1297:   /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */
    1298:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL));
    1299:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
    1300:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
    1301:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
    1302:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
    1303:   PetscFunctionReturn(PETSC_SUCCESS);
    1304: }

    1306: PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg)
    1307: {
    1308:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

    1310:   PetscFunctionBegin;
    1311:   switch (op) {
    1312:   case MAT_ROW_ORIENTED:
    1313:     a->roworiented = flg;
    1314:     break;
    1315:   case MAT_KEEP_NONZERO_PATTERN:
    1316:     a->keepnonzeropattern = flg;
    1317:     break;
    1318:   case MAT_NEW_NONZERO_LOCATIONS:
    1319:     a->nonew = (flg ? 0 : 1);
    1320:     break;
    1321:   case MAT_NEW_NONZERO_LOCATION_ERR:
    1322:     a->nonew = (flg ? -1 : 0);
    1323:     break;
    1324:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
    1325:     a->nonew = (flg ? -2 : 0);
    1326:     break;
    1327:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
    1328:     a->nounused = (flg ? -1 : 0);
    1329:     break;
    1330:   case MAT_IGNORE_ZERO_ENTRIES:
    1331:     a->ignorezeroentries = flg;
    1332:     break;
    1333:   case MAT_SPD:
    1334:   case MAT_SYMMETRIC:
    1335:   case MAT_STRUCTURALLY_SYMMETRIC:
    1336:   case MAT_HERMITIAN:
    1337:   case MAT_SYMMETRY_ETERNAL:
    1338:   case MAT_STRUCTURE_ONLY:
    1339:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
    1340:   case MAT_SPD_ETERNAL:
    1341:     break;
    1342:   case MAT_FORCE_DIAGONAL_ENTRIES:
    1343:   case MAT_IGNORE_OFF_PROC_ENTRIES:
    1344:   case MAT_USE_HASH_TABLE:
    1345:     PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
    1346:     break;
    1347:   case MAT_USE_INODES:
    1348:     PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg));
    1349:     break;
    1350:   case MAT_SUBMAT_SINGLEIS:
    1351:     A->submat_singleis = flg;
    1352:     break;
    1353:   case MAT_SORTED_FULL:
    1354:     if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
    1355:     else A->ops->setvalues = MatSetValues_SeqAIJ;
    1356:     break;
    1357:   case MAT_FORM_EXPLICIT_TRANSPOSE:
    1358:     A->form_explicit_transpose = flg;
    1359:     break;
    1360:   default:
    1361:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
    1362:   }
    1363:   PetscFunctionReturn(PETSC_SUCCESS);
    1364: }

    1366: static PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v)
    1367: {
    1368:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    1369:   PetscInt           i, j, n, *ai = a->i, *aj = a->j;
    1370:   PetscScalar       *x;
    1371:   const PetscScalar *aa;

    1373:   PetscFunctionBegin;
    1374:   PetscCall(VecGetLocalSize(v, &n));
    1375:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
    1376:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1377:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
    1378:     PetscInt *diag = a->diag;
    1379:     PetscCall(VecGetArrayWrite(v, &x));
    1380:     for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]];
    1381:     PetscCall(VecRestoreArrayWrite(v, &x));
    1382:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1383:     PetscFunctionReturn(PETSC_SUCCESS);
    1384:   }

    1386:   PetscCall(VecGetArrayWrite(v, &x));
    1387:   for (i = 0; i < n; i++) {
    1388:     x[i] = 0.0;
    1389:     for (j = ai[i]; j < ai[i + 1]; j++) {
    1390:       if (aj[j] == i) {
    1391:         x[i] = aa[j];
    1392:         break;
    1393:       }
    1394:     }
    1395:   }
    1396:   PetscCall(VecRestoreArrayWrite(v, &x));
    1397:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1398:   PetscFunctionReturn(PETSC_SUCCESS);
    1399: }

    1401: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
    1402: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy)
    1403: {
    1404:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    1405:   const MatScalar   *aa;
    1406:   PetscScalar       *y;
    1407:   const PetscScalar *x;
    1408:   PetscInt           m = A->rmap->n;
    1409: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    1410:   const MatScalar  *v;
    1411:   PetscScalar       alpha;
    1412:   PetscInt          n, i, j;
    1413:   const PetscInt   *idx, *ii, *ridx = NULL;
    1414:   Mat_CompressedRow cprow    = a->compressedrow;
    1415:   PetscBool         usecprow = cprow.use;
    1416: #endif

    1418:   PetscFunctionBegin;
    1419:   if (zz != yy) PetscCall(VecCopy(zz, yy));
    1420:   PetscCall(VecGetArrayRead(xx, &x));
    1421:   PetscCall(VecGetArray(yy, &y));
    1422:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));

    1424: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    1425:   fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y);
    1426: #else
    1427:   if (usecprow) {
    1428:     m    = cprow.nrows;
    1429:     ii   = cprow.i;
    1430:     ridx = cprow.rindex;
    1431:   } else {
    1432:     ii = a->i;
    1433:   }
    1434:   for (i = 0; i < m; i++) {
    1435:     idx = a->j + ii[i];
    1436:     v   = aa + ii[i];
    1437:     n   = ii[i + 1] - ii[i];
    1438:     if (usecprow) {
    1439:       alpha = x[ridx[i]];
    1440:     } else {
    1441:       alpha = x[i];
    1442:     }
    1443:     for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j];
    1444:   }
    1445: #endif
    1446:   PetscCall(PetscLogFlops(2.0 * a->nz));
    1447:   PetscCall(VecRestoreArrayRead(xx, &x));
    1448:   PetscCall(VecRestoreArray(yy, &y));
    1449:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1450:   PetscFunctionReturn(PETSC_SUCCESS);
    1451: }

    1453: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy)
    1454: {
    1455:   PetscFunctionBegin;
    1456:   PetscCall(VecSet(yy, 0.0));
    1457:   PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy));
    1458:   PetscFunctionReturn(PETSC_SUCCESS);
    1459: }

    1461: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>

    1463: PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy)
    1464: {
    1465:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    1466:   PetscScalar       *y;
    1467:   const PetscScalar *x;
    1468:   const MatScalar   *a_a;
    1469:   PetscInt           m = A->rmap->n;
    1470:   const PetscInt    *ii, *ridx = NULL;
    1471:   PetscBool          usecprow = a->compressedrow.use;

    1473: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
    1474:   #pragma disjoint(*x, *y, *aa)
    1475: #endif

    1477:   PetscFunctionBegin;
    1478:   if (a->inode.use && a->inode.checked) {
    1479:     PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
    1480:     PetscFunctionReturn(PETSC_SUCCESS);
    1481:   }
    1482:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
    1483:   PetscCall(VecGetArrayRead(xx, &x));
    1484:   PetscCall(VecGetArray(yy, &y));
    1485:   ii = a->i;
    1486:   if (usecprow) { /* use compressed row format */
    1487:     PetscCall(PetscArrayzero(y, m));
    1488:     m    = a->compressedrow.nrows;
    1489:     ii   = a->compressedrow.i;
    1490:     ridx = a->compressedrow.rindex;
    1491:     PetscPragmaUseOMPKernels(parallel for)
    1492:     for (PetscInt i = 0; i < m; i++) {
    1493:       PetscInt           n   = ii[i + 1] - ii[i];
    1494:       const PetscInt    *aj  = a->j + ii[i];
    1495:       const PetscScalar *aa  = a_a + ii[i];
    1496:       PetscScalar        sum = 0.0;
    1497:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
    1498:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
    1499:       y[*ridx++] = sum;
    1500:     }
    1501:   } else { /* do not use compressed row format */
    1502: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
    1503:     fortranmultaij_(&m, x, ii, a->j, a_a, y);
    1504: #else
    1505:     PetscPragmaUseOMPKernels(parallel for)
    1506:     for (PetscInt i = 0; i < m; i++) {
    1507:       PetscInt           n   = ii[i + 1] - ii[i];
    1508:       const PetscInt    *aj  = a->j + ii[i];
    1509:       const PetscScalar *aa  = a_a + ii[i];
    1510:       PetscScalar        sum = 0.0;
    1511:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
    1512:       y[i] = sum;
    1513:     }
    1514: #endif
    1515:   }
    1516:   PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
    1517:   PetscCall(VecRestoreArrayRead(xx, &x));
    1518:   PetscCall(VecRestoreArray(yy, &y));
    1519:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
    1520:   PetscFunctionReturn(PETSC_SUCCESS);
    1521: }

    1523: // HACK!!!!! Used by src/mat/tests/ex170.c
    1524: PETSC_EXTERN PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
    1525: {
    1526:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    1527:   PetscScalar       *y;
    1528:   const PetscScalar *x;
    1529:   const MatScalar   *aa, *a_a;
    1530:   PetscInt           m = A->rmap->n;
    1531:   const PetscInt    *aj, *ii, *ridx   = NULL;
    1532:   PetscInt           n, i, nonzerorow = 0;
    1533:   PetscScalar        sum;
    1534:   PetscBool          usecprow = a->compressedrow.use;

    1536: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
    1537:   #pragma disjoint(*x, *y, *aa)
    1538: #endif

    1540:   PetscFunctionBegin;
    1541:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
    1542:   PetscCall(VecGetArrayRead(xx, &x));
    1543:   PetscCall(VecGetArray(yy, &y));
    1544:   if (usecprow) { /* use compressed row format */
    1545:     m    = a->compressedrow.nrows;
    1546:     ii   = a->compressedrow.i;
    1547:     ridx = a->compressedrow.rindex;
    1548:     for (i = 0; i < m; i++) {
    1549:       n   = ii[i + 1] - ii[i];
    1550:       aj  = a->j + ii[i];
    1551:       aa  = a_a + ii[i];
    1552:       sum = 0.0;
    1553:       nonzerorow += (n > 0);
    1554:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
    1555:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
    1556:       y[*ridx++] = sum;
    1557:     }
    1558:   } else { /* do not use compressed row format */
    1559:     ii = a->i;
    1560:     for (i = 0; i < m; i++) {
    1561:       n   = ii[i + 1] - ii[i];
    1562:       aj  = a->j + ii[i];
    1563:       aa  = a_a + ii[i];
    1564:       sum = 0.0;
    1565:       nonzerorow += (n > 0);
    1566:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
    1567:       y[i] = sum;
    1568:     }
    1569:   }
    1570:   PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
    1571:   PetscCall(VecRestoreArrayRead(xx, &x));
    1572:   PetscCall(VecRestoreArray(yy, &y));
    1573:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
    1574:   PetscFunctionReturn(PETSC_SUCCESS);
    1575: }

    1577: // HACK!!!!! Used by src/mat/tests/ex170.c
    1578: PETSC_EXTERN PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
    1579: {
    1580:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    1581:   PetscScalar       *y, *z;
    1582:   const PetscScalar *x;
    1583:   const MatScalar   *aa, *a_a;
    1584:   PetscInt           m = A->rmap->n, *aj, *ii;
    1585:   PetscInt           n, i, *ridx = NULL;
    1586:   PetscScalar        sum;
    1587:   PetscBool          usecprow = a->compressedrow.use;

    1589:   PetscFunctionBegin;
    1590:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
    1591:   PetscCall(VecGetArrayRead(xx, &x));
    1592:   PetscCall(VecGetArrayPair(yy, zz, &y, &z));
    1593:   if (usecprow) { /* use compressed row format */
    1594:     if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
    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 = y[*ridx];
    1603:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
    1604:       z[*ridx++] = sum;
    1605:     }
    1606:   } else { /* do not use compressed row format */
    1607:     ii = a->i;
    1608:     for (i = 0; i < m; i++) {
    1609:       n   = ii[i + 1] - ii[i];
    1610:       aj  = a->j + ii[i];
    1611:       aa  = a_a + ii[i];
    1612:       sum = y[i];
    1613:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
    1614:       z[i] = sum;
    1615:     }
    1616:   }
    1617:   PetscCall(PetscLogFlops(2.0 * a->nz));
    1618:   PetscCall(VecRestoreArrayRead(xx, &x));
    1619:   PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
    1620:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
    1621:   PetscFunctionReturn(PETSC_SUCCESS);
    1622: }

    1624: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
    1625: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
    1626: {
    1627:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    1628:   PetscScalar       *y, *z;
    1629:   const PetscScalar *x;
    1630:   const MatScalar   *a_a;
    1631:   const PetscInt    *ii, *ridx = NULL;
    1632:   PetscInt           m        = A->rmap->n;
    1633:   PetscBool          usecprow = a->compressedrow.use;

    1635:   PetscFunctionBegin;
    1636:   if (a->inode.use && a->inode.checked) {
    1637:     PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
    1638:     PetscFunctionReturn(PETSC_SUCCESS);
    1639:   }
    1640:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
    1641:   PetscCall(VecGetArrayRead(xx, &x));
    1642:   PetscCall(VecGetArrayPair(yy, zz, &y, &z));
    1643:   if (usecprow) { /* use compressed row format */
    1644:     if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
    1645:     m    = a->compressedrow.nrows;
    1646:     ii   = a->compressedrow.i;
    1647:     ridx = a->compressedrow.rindex;
    1648:     for (PetscInt i = 0; i < m; i++) {
    1649:       PetscInt           n   = ii[i + 1] - ii[i];
    1650:       const PetscInt    *aj  = a->j + ii[i];
    1651:       const PetscScalar *aa  = a_a + ii[i];
    1652:       PetscScalar        sum = y[*ridx];
    1653:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
    1654:       z[*ridx++] = sum;
    1655:     }
    1656:   } else { /* do not use compressed row format */
    1657:     ii = a->i;
    1658: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
    1659:     fortranmultaddaij_(&m, x, ii, a->j, a_a, y, z);
    1660: #else
    1661:     PetscPragmaUseOMPKernels(parallel for)
    1662:     for (PetscInt i = 0; i < m; i++) {
    1663:       PetscInt           n   = ii[i + 1] - ii[i];
    1664:       const PetscInt    *aj  = a->j + ii[i];
    1665:       const PetscScalar *aa  = a_a + ii[i];
    1666:       PetscScalar        sum = y[i];
    1667:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
    1668:       z[i] = sum;
    1669:     }
    1670: #endif
    1671:   }
    1672:   PetscCall(PetscLogFlops(2.0 * a->nz));
    1673:   PetscCall(VecRestoreArrayRead(xx, &x));
    1674:   PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
    1675:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
    1676:   PetscFunctionReturn(PETSC_SUCCESS);
    1677: }

    1679: /*
    1680:      Adds diagonal pointers to sparse matrix nonzero structure.
    1681: */
    1682: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
    1683: {
    1684:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
    1685:   PetscInt    i, j, m = A->rmap->n;
    1686:   PetscBool   alreadySet = PETSC_TRUE;

    1688:   PetscFunctionBegin;
    1689:   if (!a->diag) {
    1690:     PetscCall(PetscMalloc1(m, &a->diag));
    1691:     alreadySet = PETSC_FALSE;
    1692:   }
    1693:   for (i = 0; i < A->rmap->n; i++) {
    1694:     /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
    1695:     if (alreadySet) {
    1696:       PetscInt pos = a->diag[i];
    1697:       if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
    1698:     }

    1700:     a->diag[i] = a->i[i + 1];
    1701:     for (j = a->i[i]; j < a->i[i + 1]; j++) {
    1702:       if (a->j[j] == i) {
    1703:         a->diag[i] = j;
    1704:         break;
    1705:       }
    1706:     }
    1707:   }
    1708:   PetscFunctionReturn(PETSC_SUCCESS);
    1709: }

    1711: static PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
    1712: {
    1713:   Mat_SeqAIJ     *a    = (Mat_SeqAIJ *)A->data;
    1714:   const PetscInt *diag = (const PetscInt *)a->diag;
    1715:   const PetscInt *ii   = (const PetscInt *)a->i;
    1716:   PetscInt        i, *mdiag = NULL;
    1717:   PetscInt        cnt = 0; /* how many diagonals are missing */

    1719:   PetscFunctionBegin;
    1720:   if (!A->preallocated || !a->nz) {
    1721:     PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
    1722:     PetscCall(MatShift_Basic(A, v));
    1723:     PetscFunctionReturn(PETSC_SUCCESS);
    1724:   }

    1726:   if (a->diagonaldense) {
    1727:     cnt = 0;
    1728:   } else {
    1729:     PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
    1730:     for (i = 0; i < A->rmap->n; i++) {
    1731:       if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
    1732:         cnt++;
    1733:         mdiag[i] = 1;
    1734:       }
    1735:     }
    1736:   }
    1737:   if (!cnt) {
    1738:     PetscCall(MatShift_Basic(A, v));
    1739:   } else {
    1740:     PetscScalar       *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
    1741:     PetscInt          *oldj = a->j, *oldi = a->i;
    1742:     PetscBool          free_a = a->free_a, free_ij = a->free_ij;
    1743:     const PetscScalar *Aa;

    1745:     PetscCall(MatSeqAIJGetArrayRead(A, &Aa)); // sync the host
    1746:     PetscCall(MatSeqAIJRestoreArrayRead(A, &Aa));

    1748:     a->a = NULL;
    1749:     a->j = NULL;
    1750:     a->i = NULL;
    1751:     /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
    1752:     for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
    1753:     PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));

    1755:     /* copy old values into new matrix data structure */
    1756:     for (i = 0; i < A->rmap->n; i++) {
    1757:       PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
    1758:       if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
    1759:     }
    1760:     PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
    1761:     PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
    1762:     if (free_a) PetscCall(PetscShmgetDeallocateArray((void **)&olda));
    1763:     if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldj));
    1764:     if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldi));
    1765:   }
    1766:   PetscCall(PetscFree(mdiag));
    1767:   a->diagonaldense = PETSC_TRUE;
    1768:   PetscFunctionReturn(PETSC_SUCCESS);
    1769: }

    1771: /*
    1772:      Checks for missing diagonals
    1773: */
    1774: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
    1775: {
    1776:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
    1777:   PetscInt   *diag, *ii = a->i, i;

    1779:   PetscFunctionBegin;
    1780:   *missing = PETSC_FALSE;
    1781:   if (A->rmap->n > 0 && !ii) {
    1782:     *missing = PETSC_TRUE;
    1783:     if (d) *d = 0;
    1784:     PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
    1785:   } else {
    1786:     PetscInt n;
    1787:     n    = PetscMin(A->rmap->n, A->cmap->n);
    1788:     diag = a->diag;
    1789:     for (i = 0; i < n; i++) {
    1790:       if (diag[i] >= ii[i + 1]) {
    1791:         *missing = PETSC_TRUE;
    1792:         if (d) *d = i;
    1793:         PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
    1794:         break;
    1795:       }
    1796:     }
    1797:   }
    1798:   PetscFunctionReturn(PETSC_SUCCESS);
    1799: }

    1801: #include <petscblaslapack.h>
    1802: #include <petsc/private/kernels/blockinvert.h>

    1804: /*
    1805:     Note that values is allocated externally by the PC and then passed into this routine
    1806: */
    1807: static PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
    1808: {
    1809:   PetscInt        n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
    1810:   PetscBool       allowzeropivot, zeropivotdetected = PETSC_FALSE;
    1811:   const PetscReal shift = 0.0;
    1812:   PetscInt        ipvt[5];
    1813:   PetscCount      flops = 0;
    1814:   PetscScalar     work[25], *v_work;

    1816:   PetscFunctionBegin;
    1817:   allowzeropivot = PetscNot(A->erroriffailure);
    1818:   for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
    1819:   PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
    1820:   for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
    1821:   PetscCall(PetscMalloc1(bsizemax, &indx));
    1822:   if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
    1823:   ncnt = 0;
    1824:   for (i = 0; i < nblocks; i++) {
    1825:     for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
    1826:     PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
    1827:     switch (bsizes[i]) {
    1828:     case 1:
    1829:       *diag = 1.0 / (*diag);
    1830:       break;
    1831:     case 2:
    1832:       PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
    1833:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1834:       PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
    1835:       break;
    1836:     case 3:
    1837:       PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
    1838:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1839:       PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
    1840:       break;
    1841:     case 4:
    1842:       PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
    1843:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1844:       PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
    1845:       break;
    1846:     case 5:
    1847:       PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
    1848:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1849:       PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
    1850:       break;
    1851:     case 6:
    1852:       PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
    1853:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1854:       PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
    1855:       break;
    1856:     case 7:
    1857:       PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
    1858:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1859:       PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
    1860:       break;
    1861:     default:
    1862:       PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
    1863:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1864:       PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
    1865:     }
    1866:     ncnt += bsizes[i];
    1867:     diag += bsizes[i] * bsizes[i];
    1868:     flops += 2 * PetscPowInt64(bsizes[i], 3) / 3;
    1869:   }
    1870:   PetscCall(PetscLogFlops(flops));
    1871:   if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
    1872:   PetscCall(PetscFree(indx));
    1873:   PetscFunctionReturn(PETSC_SUCCESS);
    1874: }

    1876: /*
    1877:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
    1878: */
    1879: static PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
    1880: {
    1881:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
    1882:   PetscInt         i, *diag, m = A->rmap->n;
    1883:   const MatScalar *v;
    1884:   PetscScalar     *idiag, *mdiag;

    1886:   PetscFunctionBegin;
    1887:   if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
    1888:   PetscCall(MatMarkDiagonal_SeqAIJ(A));
    1889:   diag = a->diag;
    1890:   if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); }

    1892:   mdiag = a->mdiag;
    1893:   idiag = a->idiag;
    1894:   PetscCall(MatSeqAIJGetArrayRead(A, &v));
    1895:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
    1896:     for (i = 0; i < m; i++) {
    1897:       mdiag[i] = v[diag[i]];
    1898:       if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
    1899:         if (PetscRealPart(fshift)) {
    1900:           PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
    1901:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1902:           A->factorerror_zeropivot_value = 0.0;
    1903:           A->factorerror_zeropivot_row   = i;
    1904:         } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
    1905:       }
    1906:       idiag[i] = 1.0 / v[diag[i]];
    1907:     }
    1908:     PetscCall(PetscLogFlops(m));
    1909:   } else {
    1910:     for (i = 0; i < m; i++) {
    1911:       mdiag[i] = v[diag[i]];
    1912:       idiag[i] = omega / (fshift + v[diag[i]]);
    1913:     }
    1914:     PetscCall(PetscLogFlops(2.0 * m));
    1915:   }
    1916:   a->idiagvalid = PETSC_TRUE;
    1917:   PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
    1918:   PetscFunctionReturn(PETSC_SUCCESS);
    1919: }

    1921: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
    1922: {
    1923:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    1924:   PetscScalar       *x, d, sum, *t, scale;
    1925:   const MatScalar   *v, *idiag = NULL, *mdiag, *aa;
    1926:   const PetscScalar *b, *bs, *xb, *ts;
    1927:   PetscInt           n, m = A->rmap->n, i;
    1928:   const PetscInt    *idx, *diag;

    1930:   PetscFunctionBegin;
    1931:   if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
    1932:     PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
    1933:     PetscFunctionReturn(PETSC_SUCCESS);
    1934:   }
    1935:   its = its * lits;

    1937:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
    1938:   if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
    1939:   a->fshift = fshift;
    1940:   a->omega  = omega;

    1942:   diag  = a->diag;
    1943:   t     = a->ssor_work;
    1944:   idiag = a->idiag;
    1945:   mdiag = a->mdiag;

    1947:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1948:   PetscCall(VecGetArray(xx, &x));
    1949:   PetscCall(VecGetArrayRead(bb, &b));
    1950:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
    1951:   if (flag == SOR_APPLY_UPPER) {
    1952:     /* apply (U + D/omega) to the vector */
    1953:     bs = b;
    1954:     for (i = 0; i < m; i++) {
    1955:       d   = fshift + mdiag[i];
    1956:       n   = a->i[i + 1] - diag[i] - 1;
    1957:       idx = a->j + diag[i] + 1;
    1958:       v   = aa + diag[i] + 1;
    1959:       sum = b[i] * d / omega;
    1960:       PetscSparseDensePlusDot(sum, bs, v, idx, n);
    1961:       x[i] = sum;
    1962:     }
    1963:     PetscCall(VecRestoreArray(xx, &x));
    1964:     PetscCall(VecRestoreArrayRead(bb, &b));
    1965:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1966:     PetscCall(PetscLogFlops(a->nz));
    1967:     PetscFunctionReturn(PETSC_SUCCESS);
    1968:   }

    1970:   PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
    1971:   if (flag & SOR_EISENSTAT) {
    1972:     /* Let  A = L + U + D; where L is lower triangular,
    1973:     U is upper triangular, E = D/omega; This routine applies

    1975:             (L + E)^{-1} A (U + E)^{-1}

    1977:     to a vector efficiently using Eisenstat's trick.
    1978:     */
    1979:     scale = (2.0 / omega) - 1.0;

    1981:     /*  x = (E + U)^{-1} b */
    1982:     for (i = m - 1; i >= 0; i--) {
    1983:       n   = a->i[i + 1] - diag[i] - 1;
    1984:       idx = a->j + diag[i] + 1;
    1985:       v   = aa + diag[i] + 1;
    1986:       sum = b[i];
    1987:       PetscSparseDenseMinusDot(sum, x, v, idx, n);
    1988:       x[i] = sum * idiag[i];
    1989:     }

    1991:     /*  t = b - (2*E - D)x */
    1992:     v = aa;
    1993:     for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];

    1995:     /*  t = (E + L)^{-1}t */
    1996:     ts   = t;
    1997:     diag = a->diag;
    1998:     for (i = 0; i < m; i++) {
    1999:       n   = diag[i] - a->i[i];
    2000:       idx = a->j + a->i[i];
    2001:       v   = aa + a->i[i];
    2002:       sum = t[i];
    2003:       PetscSparseDenseMinusDot(sum, ts, v, idx, n);
    2004:       t[i] = sum * idiag[i];
    2005:       /*  x = x + t */
    2006:       x[i] += t[i];
    2007:     }

    2009:     PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
    2010:     PetscCall(VecRestoreArray(xx, &x));
    2011:     PetscCall(VecRestoreArrayRead(bb, &b));
    2012:     PetscFunctionReturn(PETSC_SUCCESS);
    2013:   }
    2014:   if (flag & SOR_ZERO_INITIAL_GUESS) {
    2015:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
    2016:       for (i = 0; i < m; i++) {
    2017:         n   = diag[i] - a->i[i];
    2018:         idx = a->j + a->i[i];
    2019:         v   = aa + a->i[i];
    2020:         sum = b[i];
    2021:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
    2022:         t[i] = sum;
    2023:         x[i] = sum * idiag[i];
    2024:       }
    2025:       xb = t;
    2026:       PetscCall(PetscLogFlops(a->nz));
    2027:     } else xb = b;
    2028:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
    2029:       for (i = m - 1; i >= 0; i--) {
    2030:         n   = a->i[i + 1] - diag[i] - 1;
    2031:         idx = a->j + diag[i] + 1;
    2032:         v   = aa + diag[i] + 1;
    2033:         sum = xb[i];
    2034:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
    2035:         if (xb == b) {
    2036:           x[i] = sum * idiag[i];
    2037:         } else {
    2038:           x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
    2039:         }
    2040:       }
    2041:       PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
    2042:     }
    2043:     its--;
    2044:   }
    2045:   while (its--) {
    2046:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
    2047:       for (i = 0; i < m; i++) {
    2048:         /* lower */
    2049:         n   = diag[i] - a->i[i];
    2050:         idx = a->j + a->i[i];
    2051:         v   = aa + a->i[i];
    2052:         sum = b[i];
    2053:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
    2054:         t[i] = sum; /* save application of the lower-triangular part */
    2055:         /* upper */
    2056:         n   = a->i[i + 1] - diag[i] - 1;
    2057:         idx = a->j + diag[i] + 1;
    2058:         v   = aa + diag[i] + 1;
    2059:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
    2060:         x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
    2061:       }
    2062:       xb = t;
    2063:       PetscCall(PetscLogFlops(2.0 * a->nz));
    2064:     } else xb = b;
    2065:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
    2066:       for (i = m - 1; i >= 0; i--) {
    2067:         sum = xb[i];
    2068:         if (xb == b) {
    2069:           /* whole matrix (no checkpointing available) */
    2070:           n   = a->i[i + 1] - a->i[i];
    2071:           idx = a->j + a->i[i];
    2072:           v   = aa + a->i[i];
    2073:           PetscSparseDenseMinusDot(sum, x, v, idx, n);
    2074:           x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
    2075:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
    2076:           n   = a->i[i + 1] - diag[i] - 1;
    2077:           idx = a->j + diag[i] + 1;
    2078:           v   = aa + diag[i] + 1;
    2079:           PetscSparseDenseMinusDot(sum, x, v, idx, n);
    2080:           x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
    2081:         }
    2082:       }
    2083:       if (xb == b) {
    2084:         PetscCall(PetscLogFlops(2.0 * a->nz));
    2085:       } else {
    2086:         PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
    2087:       }
    2088:     }
    2089:   }
    2090:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    2091:   PetscCall(VecRestoreArray(xx, &x));
    2092:   PetscCall(VecRestoreArrayRead(bb, &b));
    2093:   PetscFunctionReturn(PETSC_SUCCESS);
    2094: }

    2096: static PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
    2097: {
    2098:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

    2100:   PetscFunctionBegin;
    2101:   info->block_size   = 1.0;
    2102:   info->nz_allocated = a->maxnz;
    2103:   info->nz_used      = a->nz;
    2104:   info->nz_unneeded  = (a->maxnz - a->nz);
    2105:   info->assemblies   = A->num_ass;
    2106:   info->mallocs      = A->info.mallocs;
    2107:   info->memory       = 0; /* REVIEW ME */
    2108:   if (A->factortype) {
    2109:     info->fill_ratio_given  = A->info.fill_ratio_given;
    2110:     info->fill_ratio_needed = A->info.fill_ratio_needed;
    2111:     info->factor_mallocs    = A->info.factor_mallocs;
    2112:   } else {
    2113:     info->fill_ratio_given  = 0;
    2114:     info->fill_ratio_needed = 0;
    2115:     info->factor_mallocs    = 0;
    2116:   }
    2117:   PetscFunctionReturn(PETSC_SUCCESS);
    2118: }

    2120: static PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
    2121: {
    2122:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    2123:   PetscInt           i, m = A->rmap->n - 1;
    2124:   const PetscScalar *xx;
    2125:   PetscScalar       *bb, *aa;
    2126:   PetscInt           d = 0;

    2128:   PetscFunctionBegin;
    2129:   if (x && b) {
    2130:     PetscCall(VecGetArrayRead(x, &xx));
    2131:     PetscCall(VecGetArray(b, &bb));
    2132:     for (i = 0; i < N; i++) {
    2133:       PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
    2134:       if (rows[i] >= A->cmap->n) continue;
    2135:       bb[rows[i]] = diag * xx[rows[i]];
    2136:     }
    2137:     PetscCall(VecRestoreArrayRead(x, &xx));
    2138:     PetscCall(VecRestoreArray(b, &bb));
    2139:   }

    2141:   PetscCall(MatSeqAIJGetArray(A, &aa));
    2142:   if (a->keepnonzeropattern) {
    2143:     for (i = 0; i < N; i++) {
    2144:       PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
    2145:       PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
    2146:     }
    2147:     if (diag != 0.0) {
    2148:       for (i = 0; i < N; i++) {
    2149:         d = rows[i];
    2150:         if (rows[i] >= A->cmap->n) continue;
    2151:         PetscCheck(a->diag[d] < a->i[d + 1], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry in the zeroed row %" PetscInt_FMT, d);
    2152:       }
    2153:       for (i = 0; i < N; i++) {
    2154:         if (rows[i] >= A->cmap->n) continue;
    2155:         aa[a->diag[rows[i]]] = diag;
    2156:       }
    2157:     }
    2158:   } else {
    2159:     if (diag != 0.0) {
    2160:       for (i = 0; i < N; i++) {
    2161:         PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
    2162:         if (a->ilen[rows[i]] > 0) {
    2163:           if (rows[i] >= A->cmap->n) {
    2164:             a->ilen[rows[i]] = 0;
    2165:           } else {
    2166:             a->ilen[rows[i]]    = 1;
    2167:             aa[a->i[rows[i]]]   = diag;
    2168:             a->j[a->i[rows[i]]] = rows[i];
    2169:           }
    2170:         } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
    2171:           PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
    2172:         }
    2173:       }
    2174:     } else {
    2175:       for (i = 0; i < N; i++) {
    2176:         PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
    2177:         a->ilen[rows[i]] = 0;
    2178:       }
    2179:     }
    2180:     A->nonzerostate++;
    2181:   }
    2182:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
    2183:   PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
    2184:   PetscFunctionReturn(PETSC_SUCCESS);
    2185: }

    2187: static PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
    2188: {
    2189:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    2190:   PetscInt           i, j, m = A->rmap->n - 1, d = 0;
    2191:   PetscBool          missing, *zeroed, vecs = PETSC_FALSE;
    2192:   const PetscScalar *xx;
    2193:   PetscScalar       *bb, *aa;

    2195:   PetscFunctionBegin;
    2196:   if (!N) PetscFunctionReturn(PETSC_SUCCESS);
    2197:   PetscCall(MatSeqAIJGetArray(A, &aa));
    2198:   if (x && b) {
    2199:     PetscCall(VecGetArrayRead(x, &xx));
    2200:     PetscCall(VecGetArray(b, &bb));
    2201:     vecs = PETSC_TRUE;
    2202:   }
    2203:   PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
    2204:   for (i = 0; i < N; i++) {
    2205:     PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
    2206:     PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aa, a->i[rows[i]]), a->ilen[rows[i]]));

    2208:     zeroed[rows[i]] = PETSC_TRUE;
    2209:   }
    2210:   for (i = 0; i < A->rmap->n; i++) {
    2211:     if (!zeroed[i]) {
    2212:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
    2213:         if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
    2214:           if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
    2215:           aa[j] = 0.0;
    2216:         }
    2217:       }
    2218:     } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
    2219:   }
    2220:   if (x && b) {
    2221:     PetscCall(VecRestoreArrayRead(x, &xx));
    2222:     PetscCall(VecRestoreArray(b, &bb));
    2223:   }
    2224:   PetscCall(PetscFree(zeroed));
    2225:   if (diag != 0.0) {
    2226:     PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
    2227:     if (missing) {
    2228:       for (i = 0; i < N; i++) {
    2229:         if (rows[i] >= A->cmap->N) continue;
    2230:         PetscCheck(!a->nonew || rows[i] < d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry in row %" PetscInt_FMT " (%" PetscInt_FMT ")", d, rows[i]);
    2231:         PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
    2232:       }
    2233:     } else {
    2234:       for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
    2235:     }
    2236:   }
    2237:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
    2238:   PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
    2239:   PetscFunctionReturn(PETSC_SUCCESS);
    2240: }

    2242: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
    2243: {
    2244:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    2245:   const PetscScalar *aa;

    2247:   PetscFunctionBegin;
    2248:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    2249:   *nz = a->i[row + 1] - a->i[row];
    2250:   if (v) *v = PetscSafePointerPlusOffset((PetscScalar *)aa, a->i[row]);
    2251:   if (idx) {
    2252:     if (*nz && a->j) *idx = a->j + a->i[row];
    2253:     else *idx = NULL;
    2254:   }
    2255:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    2256:   PetscFunctionReturn(PETSC_SUCCESS);
    2257: }

    2259: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
    2260: {
    2261:   PetscFunctionBegin;
    2262:   PetscFunctionReturn(PETSC_SUCCESS);
    2263: }

    2265: static PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
    2266: {
    2267:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
    2268:   const MatScalar *v;
    2269:   PetscReal        sum = 0.0;
    2270:   PetscInt         i, j;

    2272:   PetscFunctionBegin;
    2273:   PetscCall(MatSeqAIJGetArrayRead(A, &v));
    2274:   if (type == NORM_FROBENIUS) {
    2275: #if defined(PETSC_USE_REAL___FP16)
    2276:     PetscBLASInt one = 1, nz = a->nz;
    2277:     PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
    2278: #else
    2279:     for (i = 0; i < a->nz; i++) {
    2280:       sum += PetscRealPart(PetscConj(*v) * (*v));
    2281:       v++;
    2282:     }
    2283:     *nrm = PetscSqrtReal(sum);
    2284: #endif
    2285:     PetscCall(PetscLogFlops(2.0 * a->nz));
    2286:   } else if (type == NORM_1) {
    2287:     PetscReal *tmp;
    2288:     PetscInt  *jj = a->j;
    2289:     PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp));
    2290:     *nrm = 0.0;
    2291:     for (j = 0; j < a->nz; j++) {
    2292:       tmp[*jj++] += PetscAbsScalar(*v);
    2293:       v++;
    2294:     }
    2295:     for (j = 0; j < A->cmap->n; j++) {
    2296:       if (tmp[j] > *nrm) *nrm = tmp[j];
    2297:     }
    2298:     PetscCall(PetscFree(tmp));
    2299:     PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
    2300:   } else if (type == NORM_INFINITY) {
    2301:     *nrm = 0.0;
    2302:     for (j = 0; j < A->rmap->n; j++) {
    2303:       const PetscScalar *v2 = PetscSafePointerPlusOffset(v, a->i[j]);
    2304:       sum                   = 0.0;
    2305:       for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
    2306:         sum += PetscAbsScalar(*v2);
    2307:         v2++;
    2308:       }
    2309:       if (sum > *nrm) *nrm = sum;
    2310:     }
    2311:     PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
    2312:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
    2313:   PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
    2314:   PetscFunctionReturn(PETSC_SUCCESS);
    2315: }

    2317: static PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
    2318: {
    2319:   Mat_SeqAIJ      *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
    2320:   PetscInt        *adx, *bdx, *aii, *bii, *aptr, *bptr;
    2321:   const MatScalar *va, *vb;
    2322:   PetscInt         ma, na, mb, nb, i;

    2324:   PetscFunctionBegin;
    2325:   PetscCall(MatGetSize(A, &ma, &na));
    2326:   PetscCall(MatGetSize(B, &mb, &nb));
    2327:   if (ma != nb || na != mb) {
    2328:     *f = PETSC_FALSE;
    2329:     PetscFunctionReturn(PETSC_SUCCESS);
    2330:   }
    2331:   PetscCall(MatSeqAIJGetArrayRead(A, &va));
    2332:   PetscCall(MatSeqAIJGetArrayRead(B, &vb));
    2333:   aii = aij->i;
    2334:   bii = bij->i;
    2335:   adx = aij->j;
    2336:   bdx = bij->j;
    2337:   PetscCall(PetscMalloc1(ma, &aptr));
    2338:   PetscCall(PetscMalloc1(mb, &bptr));
    2339:   for (i = 0; i < ma; i++) aptr[i] = aii[i];
    2340:   for (i = 0; i < mb; i++) bptr[i] = bii[i];

    2342:   *f = PETSC_TRUE;
    2343:   for (i = 0; i < ma; i++) {
    2344:     while (aptr[i] < aii[i + 1]) {
    2345:       PetscInt    idc, idr;
    2346:       PetscScalar vc, vr;
    2347:       /* column/row index/value */
    2348:       idc = adx[aptr[i]];
    2349:       idr = bdx[bptr[idc]];
    2350:       vc  = va[aptr[i]];
    2351:       vr  = vb[bptr[idc]];
    2352:       if (i != idr || PetscAbsScalar(vc - vr) > tol) {
    2353:         *f = PETSC_FALSE;
    2354:         goto done;
    2355:       } else {
    2356:         aptr[i]++;
    2357:         if (B || i != idc) bptr[idc]++;
    2358:       }
    2359:     }
    2360:   }
    2361: done:
    2362:   PetscCall(PetscFree(aptr));
    2363:   PetscCall(PetscFree(bptr));
    2364:   PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
    2365:   PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
    2366:   PetscFunctionReturn(PETSC_SUCCESS);
    2367: }

    2369: static PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
    2370: {
    2371:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
    2372:   PetscInt   *adx, *bdx, *aii, *bii, *aptr, *bptr;
    2373:   MatScalar  *va, *vb;
    2374:   PetscInt    ma, na, mb, nb, i;

    2376:   PetscFunctionBegin;
    2377:   PetscCall(MatGetSize(A, &ma, &na));
    2378:   PetscCall(MatGetSize(B, &mb, &nb));
    2379:   if (ma != nb || na != mb) {
    2380:     *f = PETSC_FALSE;
    2381:     PetscFunctionReturn(PETSC_SUCCESS);
    2382:   }
    2383:   aii = aij->i;
    2384:   bii = bij->i;
    2385:   adx = aij->j;
    2386:   bdx = bij->j;
    2387:   va  = aij->a;
    2388:   vb  = bij->a;
    2389:   PetscCall(PetscMalloc1(ma, &aptr));
    2390:   PetscCall(PetscMalloc1(mb, &bptr));
    2391:   for (i = 0; i < ma; i++) aptr[i] = aii[i];
    2392:   for (i = 0; i < mb; i++) bptr[i] = bii[i];

    2394:   *f = PETSC_TRUE;
    2395:   for (i = 0; i < ma; i++) {
    2396:     while (aptr[i] < aii[i + 1]) {
    2397:       PetscInt    idc, idr;
    2398:       PetscScalar vc, vr;
    2399:       /* column/row index/value */
    2400:       idc = adx[aptr[i]];
    2401:       idr = bdx[bptr[idc]];
    2402:       vc  = va[aptr[i]];
    2403:       vr  = vb[bptr[idc]];
    2404:       if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
    2405:         *f = PETSC_FALSE;
    2406:         goto done;
    2407:       } else {
    2408:         aptr[i]++;
    2409:         if (B || i != idc) bptr[idc]++;
    2410:       }
    2411:     }
    2412:   }
    2413: done:
    2414:   PetscCall(PetscFree(aptr));
    2415:   PetscCall(PetscFree(bptr));
    2416:   PetscFunctionReturn(PETSC_SUCCESS);
    2417: }

    2419: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
    2420: {
    2421:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    2422:   const PetscScalar *l, *r;
    2423:   PetscScalar        x;
    2424:   MatScalar         *v;
    2425:   PetscInt           i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
    2426:   const PetscInt    *jj;

    2428:   PetscFunctionBegin;
    2429:   if (ll) {
    2430:     /* The local size is used so that VecMPI can be passed to this routine
    2431:        by MatDiagonalScale_MPIAIJ */
    2432:     PetscCall(VecGetLocalSize(ll, &m));
    2433:     PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
    2434:     PetscCall(VecGetArrayRead(ll, &l));
    2435:     PetscCall(MatSeqAIJGetArray(A, &v));
    2436:     for (i = 0; i < m; i++) {
    2437:       x = l[i];
    2438:       M = a->i[i + 1] - a->i[i];
    2439:       for (j = 0; j < M; j++) (*v++) *= x;
    2440:     }
    2441:     PetscCall(VecRestoreArrayRead(ll, &l));
    2442:     PetscCall(PetscLogFlops(nz));
    2443:     PetscCall(MatSeqAIJRestoreArray(A, &v));
    2444:   }
    2445:   if (rr) {
    2446:     PetscCall(VecGetLocalSize(rr, &n));
    2447:     PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
    2448:     PetscCall(VecGetArrayRead(rr, &r));
    2449:     PetscCall(MatSeqAIJGetArray(A, &v));
    2450:     jj = a->j;
    2451:     for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
    2452:     PetscCall(MatSeqAIJRestoreArray(A, &v));
    2453:     PetscCall(VecRestoreArrayRead(rr, &r));
    2454:     PetscCall(PetscLogFlops(nz));
    2455:   }
    2456:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
    2457:   PetscFunctionReturn(PETSC_SUCCESS);
    2458: }

    2460: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
    2461: {
    2462:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data, *c;
    2463:   PetscInt          *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
    2464:   PetscInt           row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
    2465:   const PetscInt    *irow, *icol;
    2466:   const PetscScalar *aa;
    2467:   PetscInt           nrows, ncols;
    2468:   PetscInt          *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
    2469:   MatScalar         *a_new, *mat_a, *c_a;
    2470:   Mat                C;
    2471:   PetscBool          stride;

    2473:   PetscFunctionBegin;
    2474:   PetscCall(ISGetIndices(isrow, &irow));
    2475:   PetscCall(ISGetLocalSize(isrow, &nrows));
    2476:   PetscCall(ISGetLocalSize(iscol, &ncols));

    2478:   PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
    2479:   if (stride) {
    2480:     PetscCall(ISStrideGetInfo(iscol, &first, &step));
    2481:   } else {
    2482:     first = 0;
    2483:     step  = 0;
    2484:   }
    2485:   if (stride && step == 1) {
    2486:     /* special case of contiguous rows */
    2487:     PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
    2488:     /* loop over new rows determining lens and starting points */
    2489:     for (i = 0; i < nrows; i++) {
    2490:       kstart    = ai[irow[i]];
    2491:       kend      = kstart + ailen[irow[i]];
    2492:       starts[i] = kstart;
    2493:       for (k = kstart; k < kend; k++) {
    2494:         if (aj[k] >= first) {
    2495:           starts[i] = k;
    2496:           break;
    2497:         }
    2498:       }
    2499:       sum = 0;
    2500:       while (k < kend) {
    2501:         if (aj[k++] >= first + ncols) break;
    2502:         sum++;
    2503:       }
    2504:       lens[i] = sum;
    2505:     }
    2506:     /* create submatrix */
    2507:     if (scall == MAT_REUSE_MATRIX) {
    2508:       PetscInt n_cols, n_rows;
    2509:       PetscCall(MatGetSize(*B, &n_rows, &n_cols));
    2510:       PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
    2511:       PetscCall(MatZeroEntries(*B));
    2512:       C = *B;
    2513:     } else {
    2514:       PetscInt rbs, cbs;
    2515:       PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
    2516:       PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
    2517:       PetscCall(ISGetBlockSize(isrow, &rbs));
    2518:       PetscCall(ISGetBlockSize(iscol, &cbs));
    2519:       PetscCall(MatSetBlockSizes(C, rbs, cbs));
    2520:       PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
    2521:       PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
    2522:     }
    2523:     c = (Mat_SeqAIJ *)C->data;

    2525:     /* loop over rows inserting into submatrix */
    2526:     PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
    2527:     j_new = c->j;
    2528:     i_new = c->i;
    2529:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    2530:     for (i = 0; i < nrows; i++) {
    2531:       ii    = starts[i];
    2532:       lensi = lens[i];
    2533:       if (lensi) {
    2534:         for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
    2535:         PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
    2536:         a_new += lensi;
    2537:       }
    2538:       i_new[i + 1] = i_new[i] + lensi;
    2539:       c->ilen[i]   = lensi;
    2540:     }
    2541:     PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
    2542:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    2543:     PetscCall(PetscFree2(lens, starts));
    2544:   } else {
    2545:     PetscCall(ISGetIndices(iscol, &icol));
    2546:     PetscCall(PetscCalloc1(oldcols, &smap));
    2547:     PetscCall(PetscMalloc1(1 + nrows, &lens));
    2548:     for (i = 0; i < ncols; i++) {
    2549:       PetscCheck(icol[i] < oldcols, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Requesting column beyond largest column icol[%" PetscInt_FMT "] %" PetscInt_FMT " >= A->cmap->n %" PetscInt_FMT, i, icol[i], oldcols);
    2550:       smap[icol[i]] = i + 1;
    2551:     }

    2553:     /* determine lens of each row */
    2554:     for (i = 0; i < nrows; i++) {
    2555:       kstart  = ai[irow[i]];
    2556:       kend    = kstart + a->ilen[irow[i]];
    2557:       lens[i] = 0;
    2558:       for (k = kstart; k < kend; k++) {
    2559:         if (smap[aj[k]]) lens[i]++;
    2560:       }
    2561:     }
    2562:     /* Create and fill new matrix */
    2563:     if (scall == MAT_REUSE_MATRIX) {
    2564:       PetscBool equal;

    2566:       c = (Mat_SeqAIJ *)((*B)->data);
    2567:       PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
    2568:       PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
    2569:       PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong number of nonzeros");
    2570:       PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
    2571:       C = *B;
    2572:     } else {
    2573:       PetscInt rbs, cbs;
    2574:       PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
    2575:       PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
    2576:       PetscCall(ISGetBlockSize(isrow, &rbs));
    2577:       PetscCall(ISGetBlockSize(iscol, &cbs));
    2578:       if (rbs > 1 || cbs > 1) PetscCall(MatSetBlockSizes(C, rbs, cbs));
    2579:       PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
    2580:       PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
    2581:     }
    2582:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));

    2584:     c = (Mat_SeqAIJ *)C->data;
    2585:     PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
    2586:     for (i = 0; i < nrows; i++) {
    2587:       row      = irow[i];
    2588:       kstart   = ai[row];
    2589:       kend     = kstart + a->ilen[row];
    2590:       mat_i    = c->i[i];
    2591:       mat_j    = PetscSafePointerPlusOffset(c->j, mat_i);
    2592:       mat_a    = PetscSafePointerPlusOffset(c_a, mat_i);
    2593:       mat_ilen = c->ilen + i;
    2594:       for (k = kstart; k < kend; k++) {
    2595:         if ((tcol = smap[a->j[k]])) {
    2596:           *mat_j++ = tcol - 1;
    2597:           *mat_a++ = aa[k];
    2598:           (*mat_ilen)++;
    2599:         }
    2600:       }
    2601:     }
    2602:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    2603:     /* Free work space */
    2604:     PetscCall(ISRestoreIndices(iscol, &icol));
    2605:     PetscCall(PetscFree(smap));
    2606:     PetscCall(PetscFree(lens));
    2607:     /* sort */
    2608:     for (i = 0; i < nrows; i++) {
    2609:       PetscInt ilen;

    2611:       mat_i = c->i[i];
    2612:       mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
    2613:       mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
    2614:       ilen  = c->ilen[i];
    2615:       PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
    2616:     }
    2617:     PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
    2618:   }
    2619: #if defined(PETSC_HAVE_DEVICE)
    2620:   PetscCall(MatBindToCPU(C, A->boundtocpu));
    2621: #endif
    2622:   PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
    2623:   PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));

    2625:   PetscCall(ISRestoreIndices(isrow, &irow));
    2626:   *B = C;
    2627:   PetscFunctionReturn(PETSC_SUCCESS);
    2628: }

    2630: static PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
    2631: {
    2632:   Mat B;

    2634:   PetscFunctionBegin;
    2635:   if (scall == MAT_INITIAL_MATRIX) {
    2636:     PetscCall(MatCreate(subComm, &B));
    2637:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
    2638:     PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
    2639:     PetscCall(MatSetType(B, MATSEQAIJ));
    2640:     PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
    2641:     *subMat = B;
    2642:   } else {
    2643:     PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
    2644:   }
    2645:   PetscFunctionReturn(PETSC_SUCCESS);
    2646: }

    2648: static PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
    2649: {
    2650:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
    2651:   Mat         outA;
    2652:   PetscBool   row_identity, col_identity;

    2654:   PetscFunctionBegin;
    2655:   PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");

    2657:   PetscCall(ISIdentity(row, &row_identity));
    2658:   PetscCall(ISIdentity(col, &col_identity));

    2660:   outA             = inA;
    2661:   outA->factortype = MAT_FACTOR_LU;
    2662:   PetscCall(PetscFree(inA->solvertype));
    2663:   PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));

    2665:   PetscCall(PetscObjectReference((PetscObject)row));
    2666:   PetscCall(ISDestroy(&a->row));

    2668:   a->row = row;

    2670:   PetscCall(PetscObjectReference((PetscObject)col));
    2671:   PetscCall(ISDestroy(&a->col));

    2673:   a->col = col;

    2675:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
    2676:   PetscCall(ISDestroy(&a->icol));
    2677:   PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));

    2679:   if (!a->solve_work) { /* this matrix may have been factored before */
    2680:     PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
    2681:   }

    2683:   PetscCall(MatMarkDiagonal_SeqAIJ(inA));
    2684:   if (row_identity && col_identity) {
    2685:     PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
    2686:   } else {
    2687:     PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
    2688:   }
    2689:   PetscFunctionReturn(PETSC_SUCCESS);
    2690: }

    2692: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
    2693: {
    2694:   Mat_SeqAIJ  *a = (Mat_SeqAIJ *)inA->data;
    2695:   PetscScalar *v;
    2696:   PetscBLASInt one = 1, bnz;

    2698:   PetscFunctionBegin;
    2699:   PetscCall(MatSeqAIJGetArray(inA, &v));
    2700:   PetscCall(PetscBLASIntCast(a->nz, &bnz));
    2701:   PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
    2702:   PetscCall(PetscLogFlops(a->nz));
    2703:   PetscCall(MatSeqAIJRestoreArray(inA, &v));
    2704:   PetscCall(MatSeqAIJInvalidateDiagonal(inA));
    2705:   PetscFunctionReturn(PETSC_SUCCESS);
    2706: }

    2708: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
    2709: {
    2710:   PetscInt i;

    2712:   PetscFunctionBegin;
    2713:   if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
    2714:     PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));

    2716:     for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
    2717:     PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));

    2719:     if (submatj->rbuf1) {
    2720:       PetscCall(PetscFree(submatj->rbuf1[0]));
    2721:       PetscCall(PetscFree(submatj->rbuf1));
    2722:     }

    2724:     for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
    2725:     PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
    2726:     PetscCall(PetscFree(submatj->pa));
    2727:   }

    2729: #if defined(PETSC_USE_CTABLE)
    2730:   PetscCall(PetscHMapIDestroy(&submatj->rmap));
    2731:   if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
    2732:   PetscCall(PetscFree(submatj->rmap_loc));
    2733: #else
    2734:   PetscCall(PetscFree(submatj->rmap));
    2735: #endif

    2737:   if (!submatj->allcolumns) {
    2738: #if defined(PETSC_USE_CTABLE)
    2739:     PetscCall(PetscHMapIDestroy((PetscHMapI *)&submatj->cmap));
    2740: #else
    2741:     PetscCall(PetscFree(submatj->cmap));
    2742: #endif
    2743:   }
    2744:   PetscCall(PetscFree(submatj->row2proc));

    2746:   PetscCall(PetscFree(submatj));
    2747:   PetscFunctionReturn(PETSC_SUCCESS);
    2748: }

    2750: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
    2751: {
    2752:   Mat_SeqAIJ  *c       = (Mat_SeqAIJ *)C->data;
    2753:   Mat_SubSppt *submatj = c->submatis1;

    2755:   PetscFunctionBegin;
    2756:   PetscCall((*submatj->destroy)(C));
    2757:   PetscCall(MatDestroySubMatrix_Private(submatj));
    2758:   PetscFunctionReturn(PETSC_SUCCESS);
    2759: }

    2761: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
    2762: static PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
    2763: {
    2764:   PetscInt     i;
    2765:   Mat          C;
    2766:   Mat_SeqAIJ  *c;
    2767:   Mat_SubSppt *submatj;

    2769:   PetscFunctionBegin;
    2770:   for (i = 0; i < n; i++) {
    2771:     C       = (*mat)[i];
    2772:     c       = (Mat_SeqAIJ *)C->data;
    2773:     submatj = c->submatis1;
    2774:     if (submatj) {
    2775:       if (--((PetscObject)C)->refct <= 0) {
    2776:         PetscCall(PetscFree(C->factorprefix));
    2777:         PetscCall((*submatj->destroy)(C));
    2778:         PetscCall(MatDestroySubMatrix_Private(submatj));
    2779:         PetscCall(PetscFree(C->defaultvectype));
    2780:         PetscCall(PetscFree(C->defaultrandtype));
    2781:         PetscCall(PetscLayoutDestroy(&C->rmap));
    2782:         PetscCall(PetscLayoutDestroy(&C->cmap));
    2783:         PetscCall(PetscHeaderDestroy(&C));
    2784:       }
    2785:     } else {
    2786:       PetscCall(MatDestroy(&C));
    2787:     }
    2788:   }

    2790:   /* Destroy Dummy submatrices created for reuse */
    2791:   PetscCall(MatDestroySubMatrices_Dummy(n, mat));

    2793:   PetscCall(PetscFree(*mat));
    2794:   PetscFunctionReturn(PETSC_SUCCESS);
    2795: }

    2797: static PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
    2798: {
    2799:   PetscInt i;

    2801:   PetscFunctionBegin;
    2802:   if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));

    2804:   for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
    2805:   PetscFunctionReturn(PETSC_SUCCESS);
    2806: }

    2808: static PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
    2809: {
    2810:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
    2811:   PetscInt        row, i, j, k, l, ll, m, n, *nidx, isz, val;
    2812:   const PetscInt *idx;
    2813:   PetscInt        start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1;
    2814:   PetscBT         table;

    2816:   PetscFunctionBegin;
    2817:   m  = A->rmap->n / bs;
    2818:   ai = a->i;
    2819:   aj = a->j;

    2821:   PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");

    2823:   PetscCall(PetscMalloc1(m + 1, &nidx));
    2824:   PetscCall(PetscBTCreate(m, &table));

    2826:   for (i = 0; i < is_max; i++) {
    2827:     /* Initialize the two local arrays */
    2828:     isz = 0;
    2829:     PetscCall(PetscBTMemzero(m, table));

    2831:     /* Extract the indices, assume there can be duplicate entries */
    2832:     PetscCall(ISGetIndices(is[i], &idx));
    2833:     PetscCall(ISGetLocalSize(is[i], &n));

    2835:     if (bs > 1) {
    2836:       /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
    2837:       for (j = 0; j < n; ++j) {
    2838:         if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
    2839:       }
    2840:       PetscCall(ISRestoreIndices(is[i], &idx));
    2841:       PetscCall(ISDestroy(&is[i]));

    2843:       k = 0;
    2844:       for (j = 0; j < ov; j++) { /* for each overlap */
    2845:         n = isz;
    2846:         for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
    2847:           for (ll = 0; ll < bs; ll++) {
    2848:             row   = bs * nidx[k] + ll;
    2849:             start = ai[row];
    2850:             end   = ai[row + 1];
    2851:             for (l = start; l < end; l++) {
    2852:               val = aj[l] / bs;
    2853:               if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
    2854:             }
    2855:           }
    2856:         }
    2857:       }
    2858:       PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, is + i));
    2859:     } else {
    2860:       /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
    2861:       for (j = 0; j < n; ++j) {
    2862:         if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
    2863:       }
    2864:       PetscCall(ISRestoreIndices(is[i], &idx));
    2865:       PetscCall(ISDestroy(&is[i]));

    2867:       k = 0;
    2868:       for (j = 0; j < ov; j++) { /* for each overlap */
    2869:         n = isz;
    2870:         for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
    2871:           row   = nidx[k];
    2872:           start = ai[row];
    2873:           end   = ai[row + 1];
    2874:           for (l = start; l < end; l++) {
    2875:             val = aj[l];
    2876:             if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
    2877:           }
    2878:         }
    2879:       }
    2880:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, is + i));
    2881:     }
    2882:   }
    2883:   PetscCall(PetscBTDestroy(&table));
    2884:   PetscCall(PetscFree(nidx));
    2885:   PetscFunctionReturn(PETSC_SUCCESS);
    2886: }

    2888: static PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
    2889: {
    2890:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
    2891:   PetscInt        i, nz = 0, m = A->rmap->n, n = A->cmap->n;
    2892:   const PetscInt *row, *col;
    2893:   PetscInt       *cnew, j, *lens;
    2894:   IS              icolp, irowp;
    2895:   PetscInt       *cwork = NULL;
    2896:   PetscScalar    *vwork = NULL;

    2898:   PetscFunctionBegin;
    2899:   PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
    2900:   PetscCall(ISGetIndices(irowp, &row));
    2901:   PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
    2902:   PetscCall(ISGetIndices(icolp, &col));

    2904:   /* determine lengths of permuted rows */
    2905:   PetscCall(PetscMalloc1(m + 1, &lens));
    2906:   for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
    2907:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
    2908:   PetscCall(MatSetSizes(*B, m, n, m, n));
    2909:   PetscCall(MatSetBlockSizesFromMats(*B, A, A));
    2910:   PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
    2911:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
    2912:   PetscCall(PetscFree(lens));

    2914:   PetscCall(PetscMalloc1(n, &cnew));
    2915:   for (i = 0; i < m; i++) {
    2916:     PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
    2917:     for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
    2918:     PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
    2919:     PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
    2920:   }
    2921:   PetscCall(PetscFree(cnew));

    2923:   (*B)->assembled = PETSC_FALSE;

    2925: #if defined(PETSC_HAVE_DEVICE)
    2926:   PetscCall(MatBindToCPU(*B, A->boundtocpu));
    2927: #endif
    2928:   PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
    2929:   PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
    2930:   PetscCall(ISRestoreIndices(irowp, &row));
    2931:   PetscCall(ISRestoreIndices(icolp, &col));
    2932:   PetscCall(ISDestroy(&irowp));
    2933:   PetscCall(ISDestroy(&icolp));
    2934:   if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
    2935:   PetscFunctionReturn(PETSC_SUCCESS);
    2936: }

    2938: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
    2939: {
    2940:   PetscFunctionBegin;
    2941:   /* If the two matrices have the same copy implementation, use fast copy. */
    2942:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
    2943:     Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    2944:     Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;
    2945:     const PetscScalar *aa;

    2947:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    2948:     PetscCheck(a->i[A->rmap->n] == b->i[B->rmap->n], PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Number of nonzeros in two matrices are different %" PetscInt_FMT " != %" PetscInt_FMT, a->i[A->rmap->n], b->i[B->rmap->n]);
    2949:     PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n]));
    2950:     PetscCall(PetscObjectStateIncrease((PetscObject)B));
    2951:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    2952:   } else {
    2953:     PetscCall(MatCopy_Basic(A, B, str));
    2954:   }
    2955:   PetscFunctionReturn(PETSC_SUCCESS);
    2956: }

    2958: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
    2959: {
    2960:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

    2962:   PetscFunctionBegin;
    2963:   *array = a->a;
    2964:   PetscFunctionReturn(PETSC_SUCCESS);
    2965: }

    2967: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
    2968: {
    2969:   PetscFunctionBegin;
    2970:   *array = NULL;
    2971:   PetscFunctionReturn(PETSC_SUCCESS);
    2972: }

    2974: /*
    2975:    Computes the number of nonzeros per row needed for preallocation when X and Y
    2976:    have different nonzero structure.
    2977: */
    2978: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
    2979: {
    2980:   PetscInt i, j, k, nzx, nzy;

    2982:   PetscFunctionBegin;
    2983:   /* Set the number of nonzeros in the new matrix */
    2984:   for (i = 0; i < m; i++) {
    2985:     const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
    2986:     nzx    = xi[i + 1] - xi[i];
    2987:     nzy    = yi[i + 1] - yi[i];
    2988:     nnz[i] = 0;
    2989:     for (j = 0, k = 0; j < nzx; j++) {                  /* Point in X */
    2990:       for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
    2991:       if (k < nzy && yjj[k] == xjj[j]) k++;             /* Skip duplicate */
    2992:       nnz[i]++;
    2993:     }
    2994:     for (; k < nzy; k++) nnz[i]++;
    2995:   }
    2996:   PetscFunctionReturn(PETSC_SUCCESS);
    2997: }

    2999: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
    3000: {
    3001:   PetscInt    m = Y->rmap->N;
    3002:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
    3003:   Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;

    3005:   PetscFunctionBegin;
    3006:   /* Set the number of nonzeros in the new matrix */
    3007:   PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
    3008:   PetscFunctionReturn(PETSC_SUCCESS);
    3009: }

    3011: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
    3012: {
    3013:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;

    3015:   PetscFunctionBegin;
    3016:   if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
    3017:     PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
    3018:     if (e) {
    3019:       PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
    3020:       if (e) {
    3021:         PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
    3022:         if (e) str = SAME_NONZERO_PATTERN;
    3023:       }
    3024:     }
    3025:     if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
    3026:   }
    3027:   if (str == SAME_NONZERO_PATTERN) {
    3028:     const PetscScalar *xa;
    3029:     PetscScalar       *ya, alpha = a;
    3030:     PetscBLASInt       one = 1, bnz;

    3032:     PetscCall(PetscBLASIntCast(x->nz, &bnz));
    3033:     PetscCall(MatSeqAIJGetArray(Y, &ya));
    3034:     PetscCall(MatSeqAIJGetArrayRead(X, &xa));
    3035:     PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
    3036:     PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
    3037:     PetscCall(MatSeqAIJRestoreArray(Y, &ya));
    3038:     PetscCall(PetscLogFlops(2.0 * bnz));
    3039:     PetscCall(MatSeqAIJInvalidateDiagonal(Y));
    3040:     PetscCall(PetscObjectStateIncrease((PetscObject)Y));
    3041:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
    3042:     PetscCall(MatAXPY_Basic(Y, a, X, str));
    3043:   } else {
    3044:     Mat       B;
    3045:     PetscInt *nnz;
    3046:     PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
    3047:     PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
    3048:     PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
    3049:     PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
    3050:     PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
    3051:     PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
    3052:     PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
    3053:     PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
    3054:     PetscCall(MatHeaderMerge(Y, &B));
    3055:     PetscCall(MatSeqAIJCheckInode(Y));
    3056:     PetscCall(PetscFree(nnz));
    3057:   }
    3058:   PetscFunctionReturn(PETSC_SUCCESS);
    3059: }

    3061: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
    3062: {
    3063: #if defined(PETSC_USE_COMPLEX)
    3064:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ *)mat->data;
    3065:   PetscInt     i, nz;
    3066:   PetscScalar *a;

    3068:   PetscFunctionBegin;
    3069:   nz = aij->nz;
    3070:   PetscCall(MatSeqAIJGetArray(mat, &a));
    3071:   for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
    3072:   PetscCall(MatSeqAIJRestoreArray(mat, &a));
    3073: #else
    3074:   PetscFunctionBegin;
    3075: #endif
    3076:   PetscFunctionReturn(PETSC_SUCCESS);
    3077: }

    3079: static PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
    3080: {
    3081:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
    3082:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
    3083:   PetscReal        atmp;
    3084:   PetscScalar     *x;
    3085:   const MatScalar *aa, *av;

    3087:   PetscFunctionBegin;
    3088:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    3089:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
    3090:   aa = av;
    3091:   ai = a->i;
    3092:   aj = a->j;

    3094:   PetscCall(VecGetArrayWrite(v, &x));
    3095:   PetscCall(VecGetLocalSize(v, &n));
    3096:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
    3097:   for (i = 0; i < m; i++) {
    3098:     ncols = ai[1] - ai[0];
    3099:     ai++;
    3100:     x[i] = 0;
    3101:     for (j = 0; j < ncols; j++) {
    3102:       atmp = PetscAbsScalar(*aa);
    3103:       if (PetscAbsScalar(x[i]) < atmp) {
    3104:         x[i] = atmp;
    3105:         if (idx) idx[i] = *aj;
    3106:       }
    3107:       aa++;
    3108:       aj++;
    3109:     }
    3110:   }
    3111:   PetscCall(VecRestoreArrayWrite(v, &x));
    3112:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
    3113:   PetscFunctionReturn(PETSC_SUCCESS);
    3114: }

    3116: static PetscErrorCode MatGetRowSumAbs_SeqAIJ(Mat A, Vec v)
    3117: {
    3118:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
    3119:   PetscInt         i, j, m = A->rmap->n, *ai, ncols, n;
    3120:   PetscScalar     *x;
    3121:   const MatScalar *aa, *av;

    3123:   PetscFunctionBegin;
    3124:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    3125:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
    3126:   aa = av;
    3127:   ai = a->i;

    3129:   PetscCall(VecGetArrayWrite(v, &x));
    3130:   PetscCall(VecGetLocalSize(v, &n));
    3131:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
    3132:   for (i = 0; i < m; i++) {
    3133:     ncols = ai[1] - ai[0];
    3134:     ai++;
    3135:     x[i] = 0;
    3136:     for (j = 0; j < ncols; j++) {
    3137:       x[i] += PetscAbsScalar(*aa);
    3138:       aa++;
    3139:     }
    3140:   }
    3141:   PetscCall(VecRestoreArrayWrite(v, &x));
    3142:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
    3143:   PetscFunctionReturn(PETSC_SUCCESS);
    3144: }

    3146: static PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
    3147: {
    3148:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
    3149:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
    3150:   PetscScalar     *x;
    3151:   const MatScalar *aa, *av;

    3153:   PetscFunctionBegin;
    3154:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    3155:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
    3156:   aa = av;
    3157:   ai = a->i;
    3158:   aj = a->j;

    3160:   PetscCall(VecGetArrayWrite(v, &x));
    3161:   PetscCall(VecGetLocalSize(v, &n));
    3162:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
    3163:   for (i = 0; i < m; i++) {
    3164:     ncols = ai[1] - ai[0];
    3165:     ai++;
    3166:     if (ncols == A->cmap->n) { /* row is dense */
    3167:       x[i] = *aa;
    3168:       if (idx) idx[i] = 0;
    3169:     } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
    3170:       x[i] = 0.0;
    3171:       if (idx) {
    3172:         for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
    3173:           if (aj[j] > j) {
    3174:             idx[i] = j;
    3175:             break;
    3176:           }
    3177:         }
    3178:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3179:         if (j == ncols && j < A->cmap->n) idx[i] = j;
    3180:       }
    3181:     }
    3182:     for (j = 0; j < ncols; j++) {
    3183:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
    3184:         x[i] = *aa;
    3185:         if (idx) idx[i] = *aj;
    3186:       }
    3187:       aa++;
    3188:       aj++;
    3189:     }
    3190:   }
    3191:   PetscCall(VecRestoreArrayWrite(v, &x));
    3192:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
    3193:   PetscFunctionReturn(PETSC_SUCCESS);
    3194: }

    3196: static PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
    3197: {
    3198:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
    3199:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
    3200:   PetscScalar     *x;
    3201:   const MatScalar *aa, *av;

    3203:   PetscFunctionBegin;
    3204:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
    3205:   aa = av;
    3206:   ai = a->i;
    3207:   aj = a->j;

    3209:   PetscCall(VecGetArrayWrite(v, &x));
    3210:   PetscCall(VecGetLocalSize(v, &n));
    3211:   PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
    3212:   for (i = 0; i < m; i++) {
    3213:     ncols = ai[1] - ai[0];
    3214:     ai++;
    3215:     if (ncols == A->cmap->n) { /* row is dense */
    3216:       x[i] = *aa;
    3217:       if (idx) idx[i] = 0;
    3218:     } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
    3219:       x[i] = 0.0;
    3220:       if (idx) { /* find first implicit 0.0 in the row */
    3221:         for (j = 0; j < ncols; j++) {
    3222:           if (aj[j] > j) {
    3223:             idx[i] = j;
    3224:             break;
    3225:           }
    3226:         }
    3227:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3228:         if (j == ncols && j < A->cmap->n) idx[i] = j;
    3229:       }
    3230:     }
    3231:     for (j = 0; j < ncols; j++) {
    3232:       if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
    3233:         x[i] = *aa;
    3234:         if (idx) idx[i] = *aj;
    3235:       }
    3236:       aa++;
    3237:       aj++;
    3238:     }
    3239:   }
    3240:   PetscCall(VecRestoreArrayWrite(v, &x));
    3241:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
    3242:   PetscFunctionReturn(PETSC_SUCCESS);
    3243: }

    3245: static PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
    3246: {
    3247:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
    3248:   PetscInt         i, j, m = A->rmap->n, ncols, n;
    3249:   const PetscInt  *ai, *aj;
    3250:   PetscScalar     *x;
    3251:   const MatScalar *aa, *av;

    3253:   PetscFunctionBegin;
    3254:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    3255:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
    3256:   aa = av;
    3257:   ai = a->i;
    3258:   aj = a->j;

    3260:   PetscCall(VecGetArrayWrite(v, &x));
    3261:   PetscCall(VecGetLocalSize(v, &n));
    3262:   PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
    3263:   for (i = 0; i < m; i++) {
    3264:     ncols = ai[1] - ai[0];
    3265:     ai++;
    3266:     if (ncols == A->cmap->n) { /* row is dense */
    3267:       x[i] = *aa;
    3268:       if (idx) idx[i] = 0;
    3269:     } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
    3270:       x[i] = 0.0;
    3271:       if (idx) { /* find first implicit 0.0 in the row */
    3272:         for (j = 0; j < ncols; j++) {
    3273:           if (aj[j] > j) {
    3274:             idx[i] = j;
    3275:             break;
    3276:           }
    3277:         }
    3278:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3279:         if (j == ncols && j < A->cmap->n) idx[i] = j;
    3280:       }
    3281:     }
    3282:     for (j = 0; j < ncols; j++) {
    3283:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
    3284:         x[i] = *aa;
    3285:         if (idx) idx[i] = *aj;
    3286:       }
    3287:       aa++;
    3288:       aj++;
    3289:     }
    3290:   }
    3291:   PetscCall(VecRestoreArrayWrite(v, &x));
    3292:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
    3293:   PetscFunctionReturn(PETSC_SUCCESS);
    3294: }

    3296: static PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
    3297: {
    3298:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
    3299:   PetscInt        i, bs = PetscAbs(A->rmap->bs), mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
    3300:   MatScalar      *diag, work[25], *v_work;
    3301:   const PetscReal shift = 0.0;
    3302:   PetscBool       allowzeropivot, zeropivotdetected = PETSC_FALSE;

    3304:   PetscFunctionBegin;
    3305:   allowzeropivot = PetscNot(A->erroriffailure);
    3306:   if (a->ibdiagvalid) {
    3307:     if (values) *values = a->ibdiag;
    3308:     PetscFunctionReturn(PETSC_SUCCESS);
    3309:   }
    3310:   PetscCall(MatMarkDiagonal_SeqAIJ(A));
    3311:   if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); }
    3312:   diag = a->ibdiag;
    3313:   if (values) *values = a->ibdiag;
    3314:   /* factor and invert each block */
    3315:   switch (bs) {
    3316:   case 1:
    3317:     for (i = 0; i < mbs; i++) {
    3318:       PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
    3319:       if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
    3320:         if (allowzeropivot) {
    3321:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3322:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
    3323:           A->factorerror_zeropivot_row   = i;
    3324:           PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
    3325:         } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON);
    3326:       }
    3327:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
    3328:     }
    3329:     break;
    3330:   case 2:
    3331:     for (i = 0; i < mbs; i++) {
    3332:       ij[0] = 2 * i;
    3333:       ij[1] = 2 * i + 1;
    3334:       PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
    3335:       PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
    3336:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3337:       PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
    3338:       diag += 4;
    3339:     }
    3340:     break;
    3341:   case 3:
    3342:     for (i = 0; i < mbs; i++) {
    3343:       ij[0] = 3 * i;
    3344:       ij[1] = 3 * i + 1;
    3345:       ij[2] = 3 * i + 2;
    3346:       PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
    3347:       PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
    3348:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3349:       PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
    3350:       diag += 9;
    3351:     }
    3352:     break;
    3353:   case 4:
    3354:     for (i = 0; i < mbs; i++) {
    3355:       ij[0] = 4 * i;
    3356:       ij[1] = 4 * i + 1;
    3357:       ij[2] = 4 * i + 2;
    3358:       ij[3] = 4 * i + 3;
    3359:       PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
    3360:       PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
    3361:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3362:       PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
    3363:       diag += 16;
    3364:     }
    3365:     break;
    3366:   case 5:
    3367:     for (i = 0; i < mbs; i++) {
    3368:       ij[0] = 5 * i;
    3369:       ij[1] = 5 * i + 1;
    3370:       ij[2] = 5 * i + 2;
    3371:       ij[3] = 5 * i + 3;
    3372:       ij[4] = 5 * i + 4;
    3373:       PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
    3374:       PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
    3375:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3376:       PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
    3377:       diag += 25;
    3378:     }
    3379:     break;
    3380:   case 6:
    3381:     for (i = 0; i < mbs; i++) {
    3382:       ij[0] = 6 * i;
    3383:       ij[1] = 6 * i + 1;
    3384:       ij[2] = 6 * i + 2;
    3385:       ij[3] = 6 * i + 3;
    3386:       ij[4] = 6 * i + 4;
    3387:       ij[5] = 6 * i + 5;
    3388:       PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
    3389:       PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
    3390:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3391:       PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
    3392:       diag += 36;
    3393:     }
    3394:     break;
    3395:   case 7:
    3396:     for (i = 0; i < mbs; i++) {
    3397:       ij[0] = 7 * i;
    3398:       ij[1] = 7 * i + 1;
    3399:       ij[2] = 7 * i + 2;
    3400:       ij[3] = 7 * i + 3;
    3401:       ij[4] = 7 * i + 4;
    3402:       ij[5] = 7 * i + 5;
    3403:       ij[6] = 7 * i + 6;
    3404:       PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
    3405:       PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
    3406:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3407:       PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
    3408:       diag += 49;
    3409:     }
    3410:     break;
    3411:   default:
    3412:     PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
    3413:     for (i = 0; i < mbs; i++) {
    3414:       for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
    3415:       PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
    3416:       PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
    3417:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3418:       PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
    3419:       diag += bs2;
    3420:     }
    3421:     PetscCall(PetscFree3(v_work, v_pivots, IJ));
    3422:   }
    3423:   a->ibdiagvalid = PETSC_TRUE;
    3424:   PetscFunctionReturn(PETSC_SUCCESS);
    3425: }

    3427: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
    3428: {
    3429:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
    3430:   PetscScalar a, *aa;
    3431:   PetscInt    m, n, i, j, col;

    3433:   PetscFunctionBegin;
    3434:   if (!x->assembled) {
    3435:     PetscCall(MatGetSize(x, &m, &n));
    3436:     for (i = 0; i < m; i++) {
    3437:       for (j = 0; j < aij->imax[i]; j++) {
    3438:         PetscCall(PetscRandomGetValue(rctx, &a));
    3439:         col = (PetscInt)(n * PetscRealPart(a));
    3440:         PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
    3441:       }
    3442:     }
    3443:   } else {
    3444:     PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
    3445:     for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
    3446:     PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
    3447:   }
    3448:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
    3449:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
    3450:   PetscFunctionReturn(PETSC_SUCCESS);
    3451: }

    3453: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
    3454: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
    3455: {
    3456:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
    3457:   PetscScalar a;
    3458:   PetscInt    m, n, i, j, col, nskip;

    3460:   PetscFunctionBegin;
    3461:   nskip = high - low;
    3462:   PetscCall(MatGetSize(x, &m, &n));
    3463:   n -= nskip; /* shrink number of columns where nonzeros can be set */
    3464:   for (i = 0; i < m; i++) {
    3465:     for (j = 0; j < aij->imax[i]; j++) {
    3466:       PetscCall(PetscRandomGetValue(rctx, &a));
    3467:       col = (PetscInt)(n * PetscRealPart(a));
    3468:       if (col >= low) col += nskip; /* shift col rightward to skip the hole */
    3469:       PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
    3470:     }
    3471:   }
    3472:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
    3473:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
    3474:   PetscFunctionReturn(PETSC_SUCCESS);
    3475: }

    3477: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
    3478:                                        MatGetRow_SeqAIJ,
    3479:                                        MatRestoreRow_SeqAIJ,
    3480:                                        MatMult_SeqAIJ,
    3481:                                        /*  4*/ MatMultAdd_SeqAIJ,
    3482:                                        MatMultTranspose_SeqAIJ,
    3483:                                        MatMultTransposeAdd_SeqAIJ,
    3484:                                        NULL,
    3485:                                        NULL,
    3486:                                        NULL,
    3487:                                        /* 10*/ NULL,
    3488:                                        MatLUFactor_SeqAIJ,
    3489:                                        NULL,
    3490:                                        MatSOR_SeqAIJ,
    3491:                                        MatTranspose_SeqAIJ,
    3492:                                        /*1 5*/ MatGetInfo_SeqAIJ,
    3493:                                        MatEqual_SeqAIJ,
    3494:                                        MatGetDiagonal_SeqAIJ,
    3495:                                        MatDiagonalScale_SeqAIJ,
    3496:                                        MatNorm_SeqAIJ,
    3497:                                        /* 20*/ NULL,
    3498:                                        MatAssemblyEnd_SeqAIJ,
    3499:                                        MatSetOption_SeqAIJ,
    3500:                                        MatZeroEntries_SeqAIJ,
    3501:                                        /* 24*/ MatZeroRows_SeqAIJ,
    3502:                                        NULL,
    3503:                                        NULL,
    3504:                                        NULL,
    3505:                                        NULL,
    3506:                                        /* 29*/ MatSetUp_Seq_Hash,
    3507:                                        NULL,
    3508:                                        NULL,
    3509:                                        NULL,
    3510:                                        NULL,
    3511:                                        /* 34*/ MatDuplicate_SeqAIJ,
    3512:                                        NULL,
    3513:                                        NULL,
    3514:                                        MatILUFactor_SeqAIJ,
    3515:                                        NULL,
    3516:                                        /* 39*/ MatAXPY_SeqAIJ,
    3517:                                        MatCreateSubMatrices_SeqAIJ,
    3518:                                        MatIncreaseOverlap_SeqAIJ,
    3519:                                        MatGetValues_SeqAIJ,
    3520:                                        MatCopy_SeqAIJ,
    3521:                                        /* 44*/ MatGetRowMax_SeqAIJ,
    3522:                                        MatScale_SeqAIJ,
    3523:                                        MatShift_SeqAIJ,
    3524:                                        MatDiagonalSet_SeqAIJ,
    3525:                                        MatZeroRowsColumns_SeqAIJ,
    3526:                                        /* 49*/ MatSetRandom_SeqAIJ,
    3527:                                        MatGetRowIJ_SeqAIJ,
    3528:                                        MatRestoreRowIJ_SeqAIJ,
    3529:                                        MatGetColumnIJ_SeqAIJ,
    3530:                                        MatRestoreColumnIJ_SeqAIJ,
    3531:                                        /* 54*/ MatFDColoringCreate_SeqXAIJ,
    3532:                                        NULL,
    3533:                                        NULL,
    3534:                                        MatPermute_SeqAIJ,
    3535:                                        NULL,
    3536:                                        /* 59*/ NULL,
    3537:                                        MatDestroy_SeqAIJ,
    3538:                                        MatView_SeqAIJ,
    3539:                                        NULL,
    3540:                                        NULL,
    3541:                                        /* 64*/ NULL,
    3542:                                        MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
    3543:                                        NULL,
    3544:                                        NULL,
    3545:                                        NULL,
    3546:                                        /* 69*/ MatGetRowMaxAbs_SeqAIJ,
    3547:                                        MatGetRowMinAbs_SeqAIJ,
    3548:                                        NULL,
    3549:                                        NULL,
    3550:                                        NULL,
    3551:                                        /* 74*/ NULL,
    3552:                                        MatFDColoringApply_AIJ,
    3553:                                        NULL,
    3554:                                        NULL,
    3555:                                        NULL,
    3556:                                        /* 79*/ MatFindZeroDiagonals_SeqAIJ,
    3557:                                        NULL,
    3558:                                        NULL,
    3559:                                        NULL,
    3560:                                        MatLoad_SeqAIJ,
    3561:                                        /* 84*/ NULL,
    3562:                                        NULL,
    3563:                                        NULL,
    3564:                                        NULL,
    3565:                                        NULL,
    3566:                                        /* 89*/ NULL,
    3567:                                        NULL,
    3568:                                        MatMatMultNumeric_SeqAIJ_SeqAIJ,
    3569:                                        NULL,
    3570:                                        NULL,
    3571:                                        /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
    3572:                                        NULL,
    3573:                                        NULL,
    3574:                                        MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
    3575:                                        NULL,
    3576:                                        /* 99*/ MatProductSetFromOptions_SeqAIJ,
    3577:                                        NULL,
    3578:                                        NULL,
    3579:                                        MatConjugate_SeqAIJ,
    3580:                                        NULL,
    3581:                                        /*104*/ MatSetValuesRow_SeqAIJ,
    3582:                                        MatRealPart_SeqAIJ,
    3583:                                        MatImaginaryPart_SeqAIJ,
    3584:                                        NULL,
    3585:                                        NULL,
    3586:                                        /*109*/ MatMatSolve_SeqAIJ,
    3587:                                        NULL,
    3588:                                        MatGetRowMin_SeqAIJ,
    3589:                                        NULL,
    3590:                                        MatMissingDiagonal_SeqAIJ,
    3591:                                        /*114*/ NULL,
    3592:                                        NULL,
    3593:                                        NULL,
    3594:                                        NULL,
    3595:                                        NULL,
    3596:                                        /*119*/ NULL,
    3597:                                        NULL,
    3598:                                        NULL,
    3599:                                        NULL,
    3600:                                        MatGetMultiProcBlock_SeqAIJ,
    3601:                                        /*124*/ MatFindNonzeroRows_SeqAIJ,
    3602:                                        MatGetColumnReductions_SeqAIJ,
    3603:                                        MatInvertBlockDiagonal_SeqAIJ,
    3604:                                        MatInvertVariableBlockDiagonal_SeqAIJ,
    3605:                                        NULL,
    3606:                                        /*129*/ NULL,
    3607:                                        NULL,
    3608:                                        NULL,
    3609:                                        MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
    3610:                                        MatTransposeColoringCreate_SeqAIJ,
    3611:                                        /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
    3612:                                        MatTransColoringApplyDenToSp_SeqAIJ,
    3613:                                        NULL,
    3614:                                        NULL,
    3615:                                        MatRARtNumeric_SeqAIJ_SeqAIJ,
    3616:                                        /*139*/ NULL,
    3617:                                        NULL,
    3618:                                        NULL,
    3619:                                        MatFDColoringSetUp_SeqXAIJ,
    3620:                                        MatFindOffBlockDiagonalEntries_SeqAIJ,
    3621:                                        MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
    3622:                                        /*145*/ MatDestroySubMatrices_SeqAIJ,
    3623:                                        NULL,
    3624:                                        NULL,
    3625:                                        MatCreateGraph_Simple_AIJ,
    3626:                                        NULL,
    3627:                                        /*150*/ MatTransposeSymbolic_SeqAIJ,
    3628:                                        MatEliminateZeros_SeqAIJ,
    3629:                                        MatGetRowSumAbs_SeqAIJ,
    3630:                                        NULL,
    3631:                                        NULL,
    3632:                                        NULL};

    3634: static PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
    3635: {
    3636:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
    3637:   PetscInt    i, nz, n;

    3639:   PetscFunctionBegin;
    3640:   nz = aij->maxnz;
    3641:   n  = mat->rmap->n;
    3642:   for (i = 0; i < nz; i++) aij->j[i] = indices[i];
    3643:   aij->nz = nz;
    3644:   for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
    3645:   PetscFunctionReturn(PETSC_SUCCESS);
    3646: }

    3648: /*
    3649:  * Given a sparse matrix with global column indices, compact it by using a local column space.
    3650:  * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
    3651:  */
    3652: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
    3653: {
    3654:   Mat_SeqAIJ   *aij = (Mat_SeqAIJ *)mat->data;
    3655:   PetscHMapI    gid1_lid1;
    3656:   PetscHashIter tpos;
    3657:   PetscInt      gid, lid, i, ec, nz = aij->nz;
    3658:   PetscInt     *garray, *jj = aij->j;

    3660:   PetscFunctionBegin;
    3662:   PetscAssertPointer(mapping, 2);
    3663:   /* use a table */
    3664:   PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
    3665:   ec = 0;
    3666:   for (i = 0; i < nz; i++) {
    3667:     PetscInt data, gid1 = jj[i] + 1;
    3668:     PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
    3669:     if (!data) {
    3670:       /* one based table */
    3671:       PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
    3672:     }
    3673:   }
    3674:   /* form array of columns we need */
    3675:   PetscCall(PetscMalloc1(ec, &garray));
    3676:   PetscHashIterBegin(gid1_lid1, tpos);
    3677:   while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
    3678:     PetscHashIterGetKey(gid1_lid1, tpos, gid);
    3679:     PetscHashIterGetVal(gid1_lid1, tpos, lid);
    3680:     PetscHashIterNext(gid1_lid1, tpos);
    3681:     gid--;
    3682:     lid--;
    3683:     garray[lid] = gid;
    3684:   }
    3685:   PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
    3686:   PetscCall(PetscHMapIClear(gid1_lid1));
    3687:   for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
    3688:   /* compact out the extra columns in B */
    3689:   for (i = 0; i < nz; i++) {
    3690:     PetscInt gid1 = jj[i] + 1;
    3691:     PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
    3692:     lid--;
    3693:     jj[i] = lid;
    3694:   }
    3695:   PetscCall(PetscLayoutDestroy(&mat->cmap));
    3696:   PetscCall(PetscHMapIDestroy(&gid1_lid1));
    3697:   PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
    3698:   PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
    3699:   PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
    3700:   PetscFunctionReturn(PETSC_SUCCESS);
    3701: }

    3703: /*@
    3704:   MatSeqAIJSetColumnIndices - Set the column indices for all the rows
    3705:   in the matrix.

    3707:   Input Parameters:
    3708: + mat     - the `MATSEQAIJ` matrix
    3709: - indices - the column indices

    3711:   Level: advanced

    3713:   Notes:
    3714:   This can be called if you have precomputed the nonzero structure of the
    3715:   matrix and want to provide it to the matrix object to improve the performance
    3716:   of the `MatSetValues()` operation.

    3718:   You MUST have set the correct numbers of nonzeros per row in the call to
    3719:   `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.

    3721:   MUST be called before any calls to `MatSetValues()`

    3723:   The indices should start with zero, not one.

    3725: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`
    3726: @*/
    3727: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
    3728: {
    3729:   PetscFunctionBegin;
    3731:   PetscAssertPointer(indices, 2);
    3732:   PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
    3733:   PetscFunctionReturn(PETSC_SUCCESS);
    3734: }

    3736: static PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
    3737: {
    3738:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
    3739:   size_t      nz  = aij->i[mat->rmap->n];

    3741:   PetscFunctionBegin;
    3742:   PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

    3744:   /* allocate space for values if not already there */
    3745:   if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); }

    3747:   /* copy values over */
    3748:   PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
    3749:   PetscFunctionReturn(PETSC_SUCCESS);
    3750: }

    3752: /*@
    3753:   MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
    3754:   nonlinear portion.

    3756:   Logically Collect

    3758:   Input Parameter:
    3759: . mat - the matrix (currently only `MATAIJ` matrices support this option)

    3761:   Level: advanced

    3763:   Example Usage:
    3764: .vb
    3765:     Using SNES
    3766:     Create Jacobian matrix
    3767:     Set linear terms into matrix
    3768:     Apply boundary conditions to matrix, at this time matrix must have
    3769:       final nonzero structure (i.e. setting the nonlinear terms and applying
    3770:       boundary conditions again will not change the nonzero structure
    3771:     MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
    3772:     MatStoreValues(mat);
    3773:     Call SNESSetJacobian() with matrix
    3774:     In your Jacobian routine
    3775:       MatRetrieveValues(mat);
    3776:       Set nonlinear terms in matrix

    3778:     Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
    3779:     // build linear portion of Jacobian
    3780:     MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
    3781:     MatStoreValues(mat);
    3782:     loop over nonlinear iterations
    3783:        MatRetrieveValues(mat);
    3784:        // call MatSetValues(mat,...) to set nonliner portion of Jacobian
    3785:        // call MatAssemblyBegin/End() on matrix
    3786:        Solve linear system with Jacobian
    3787:     endloop
    3788: .ve

    3790:   Notes:
    3791:   Matrix must already be assembled before calling this routine
    3792:   Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
    3793:   calling this routine.

    3795:   When this is called multiple times it overwrites the previous set of stored values
    3796:   and does not allocated additional space.

    3798: .seealso: [](ch_matrices), `Mat`, `MatRetrieveValues()`
    3799: @*/
    3800: PetscErrorCode MatStoreValues(Mat mat)
    3801: {
    3802:   PetscFunctionBegin;
    3804:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
    3805:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    3806:   PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
    3807:   PetscFunctionReturn(PETSC_SUCCESS);
    3808: }

    3810: static PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
    3811: {
    3812:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
    3813:   PetscInt    nz  = aij->i[mat->rmap->n];

    3815:   PetscFunctionBegin;
    3816:   PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
    3817:   PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
    3818:   /* copy values over */
    3819:   PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
    3820:   PetscFunctionReturn(PETSC_SUCCESS);
    3821: }

    3823: /*@
    3824:   MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`

    3826:   Logically Collect

    3828:   Input Parameter:
    3829: . mat - the matrix (currently only `MATAIJ` matrices support this option)

    3831:   Level: advanced

    3833: .seealso: [](ch_matrices), `Mat`, `MatStoreValues()`
    3834: @*/
    3835: PetscErrorCode MatRetrieveValues(Mat mat)
    3836: {
    3837:   PetscFunctionBegin;
    3839:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
    3840:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    3841:   PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
    3842:   PetscFunctionReturn(PETSC_SUCCESS);
    3843: }

    3845: /*@
    3846:   MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
    3847:   (the default parallel PETSc format).  For good matrix assembly performance
    3848:   the user should preallocate the matrix storage by setting the parameter `nz`
    3849:   (or the array `nnz`).

    3851:   Collective

    3853:   Input Parameters:
    3854: + comm - MPI communicator, set to `PETSC_COMM_SELF`
    3855: . m    - number of rows
    3856: . n    - number of columns
    3857: . nz   - number of nonzeros per row (same for all rows)
    3858: - nnz  - array containing the number of nonzeros in the various rows
    3859:          (possibly different for each row) or NULL

    3861:   Output Parameter:
    3862: . A - the matrix

    3864:   Options Database Keys:
    3865: + -mat_no_inode            - Do not use inodes
    3866: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)

    3868:   Level: intermediate

    3870:   Notes:
    3871:   It is recommend to use `MatCreateFromOptions()` instead of this routine

    3873:   If `nnz` is given then `nz` is ignored

    3875:   The `MATSEQAIJ` format, also called
    3876:   compressed row storage, is fully compatible with standard Fortran
    3877:   storage.  That is, the stored row and column indices can begin at
    3878:   either one (as in Fortran) or zero.

    3880:   Specify the preallocated storage with either `nz` or `nnz` (not both).
    3881:   Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
    3882:   allocation.

    3884:   By default, this format uses inodes (identical nodes) when possible, to
    3885:   improve numerical efficiency of matrix-vector products and solves. We
    3886:   search for consecutive rows with the same nonzero structure, thereby
    3887:   reusing matrix information to achieve increased efficiency.

    3889: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
    3890: @*/
    3891: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
    3892: {
    3893:   PetscFunctionBegin;
    3894:   PetscCall(MatCreate(comm, A));
    3895:   PetscCall(MatSetSizes(*A, m, n, m, n));
    3896:   PetscCall(MatSetType(*A, MATSEQAIJ));
    3897:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
    3898:   PetscFunctionReturn(PETSC_SUCCESS);
    3899: }

    3901: /*@
    3902:   MatSeqAIJSetPreallocation - For good matrix assembly performance
    3903:   the user should preallocate the matrix storage by setting the parameter nz
    3904:   (or the array nnz).  By setting these parameters accurately, performance
    3905:   during matrix assembly can be increased by more than a factor of 50.

    3907:   Collective

    3909:   Input Parameters:
    3910: + B   - The matrix
    3911: . nz  - number of nonzeros per row (same for all rows)
    3912: - nnz - array containing the number of nonzeros in the various rows
    3913:          (possibly different for each row) or NULL

    3915:   Options Database Keys:
    3916: + -mat_no_inode            - Do not use inodes
    3917: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)

    3919:   Level: intermediate

    3921:   Notes:
    3922:   If `nnz` is given then `nz` is ignored

    3924:   The `MATSEQAIJ` format also called
    3925:   compressed row storage, is fully compatible with standard Fortran
    3926:   storage.  That is, the stored row and column indices can begin at
    3927:   either one (as in Fortran) or zero.  See the users' manual for details.

    3929:   Specify the preallocated storage with either `nz` or `nnz` (not both).
    3930:   Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
    3931:   allocation.

    3933:   You can call `MatGetInfo()` to get information on how effective the preallocation was;
    3934:   for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
    3935:   You can also run with the option -info and look for messages with the string
    3936:   malloc in them to see if additional memory allocation was needed.

    3938:   Developer Notes:
    3939:   Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
    3940:   entries or columns indices

    3942:   By default, this format uses inodes (identical nodes) when possible, to
    3943:   improve numerical efficiency of matrix-vector products and solves. We
    3944:   search for consecutive rows with the same nonzero structure, thereby
    3945:   reusing matrix information to achieve increased efficiency.

    3947: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
    3948:           `MatSeqAIJSetTotalPreallocation()`
    3949: @*/
    3950: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
    3951: {
    3952:   PetscFunctionBegin;
    3955:   PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
    3956:   PetscFunctionReturn(PETSC_SUCCESS);
    3957: }

    3959: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
    3960: {
    3961:   Mat_SeqAIJ *b              = (Mat_SeqAIJ *)B->data;
    3962:   PetscBool   skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
    3963:   PetscInt    i;

    3965:   PetscFunctionBegin;
    3966:   if (B->hash_active) {
    3967:     B->ops[0] = b->cops;
    3968:     PetscCall(PetscHMapIJVDestroy(&b->ht));
    3969:     PetscCall(PetscFree(b->dnz));
    3970:     B->hash_active = PETSC_FALSE;
    3971:   }
    3972:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
    3973:   if (nz == MAT_SKIP_ALLOCATION) {
    3974:     skipallocation = PETSC_TRUE;
    3975:     nz             = 0;
    3976:   }
    3977:   PetscCall(PetscLayoutSetUp(B->rmap));
    3978:   PetscCall(PetscLayoutSetUp(B->cmap));

    3980:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
    3981:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
    3982:   if (nnz) {
    3983:     for (i = 0; i < B->rmap->n; i++) {
    3984:       PetscCheck(nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be less than 0: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, nnz[i]);
    3985:       PetscCheck(nnz[i] <= B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be greater than row length: local row %" PetscInt_FMT " value %" PetscInt_FMT " rowlength %" PetscInt_FMT, i, nnz[i], B->cmap->n);
    3986:     }
    3987:   }

    3989:   B->preallocated = PETSC_TRUE;
    3990:   if (!skipallocation) {
    3991:     if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); }
    3992:     if (!b->ilen) {
    3993:       /* b->ilen will count nonzeros in each row so far. */
    3994:       PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
    3995:     } else {
    3996:       PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
    3997:     }
    3998:     if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
    3999:     if (!nnz) {
    4000:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
    4001:       else if (nz < 0) nz = 1;
    4002:       nz = PetscMin(nz, B->cmap->n);
    4003:       for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
    4004:       PetscCall(PetscIntMultError(nz, B->rmap->n, &nz));
    4005:     } else {
    4006:       PetscInt64 nz64 = 0;
    4007:       for (i = 0; i < B->rmap->n; i++) {
    4008:         b->imax[i] = nnz[i];
    4009:         nz64 += nnz[i];
    4010:       }
    4011:       PetscCall(PetscIntCast(nz64, &nz));
    4012:     }

    4014:     /* allocate the matrix space */
    4015:     PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
    4016:     PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscInt), (void **)&b->j));
    4017:     PetscCall(PetscShmgetAllocateArray(B->rmap->n + 1, sizeof(PetscInt), (void **)&b->i));
    4018:     b->free_ij = PETSC_TRUE;
    4019:     if (B->structure_only) {
    4020:       b->free_a = PETSC_FALSE;
    4021:     } else {
    4022:       PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscScalar), (void **)&b->a));
    4023:       b->free_a = PETSC_TRUE;
    4024:     }
    4025:     b->i[0] = 0;
    4026:     for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
    4027:   } else {
    4028:     b->free_a  = PETSC_FALSE;
    4029:     b->free_ij = PETSC_FALSE;
    4030:   }

    4032:   if (b->ipre && nnz != b->ipre && b->imax) {
    4033:     /* reserve user-requested sparsity */
    4034:     PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
    4035:   }

    4037:   b->nz               = 0;
    4038:   b->maxnz            = nz;
    4039:   B->info.nz_unneeded = (double)b->maxnz;
    4040:   if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
    4041:   B->was_assembled = PETSC_FALSE;
    4042:   B->assembled     = PETSC_FALSE;
    4043:   /* We simply deem preallocation has changed nonzero state. Updating the state
    4044:      will give clients (like AIJKokkos) a chance to know something has happened.
    4045:   */
    4046:   B->nonzerostate++;
    4047:   PetscFunctionReturn(PETSC_SUCCESS);
    4048: }

    4050: PetscErrorCode MatResetPreallocation_SeqAIJ_Private(Mat A, PetscBool *memoryreset)
    4051: {
    4052:   Mat_SeqAIJ *a;
    4053:   PetscInt    i;
    4054:   PetscBool   skipreset;

    4056:   PetscFunctionBegin;

    4059:   PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
    4060:   if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);

    4062:   /* Check local size. If zero, then return */
    4063:   if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);

    4065:   a = (Mat_SeqAIJ *)A->data;
    4066:   /* if no saved info, we error out */
    4067:   PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");

    4069:   PetscCheck(a->i && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and cannot reset preallocation ");

    4071:   PetscCall(PetscArraycmp(a->ipre, a->ilen, A->rmap->n, &skipreset));
    4072:   if (skipreset) PetscCall(MatZeroEntries(A));
    4073:   else {
    4074:     PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
    4075:     PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
    4076:     a->i[0] = 0;
    4077:     for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
    4078:     A->preallocated     = PETSC_TRUE;
    4079:     a->nz               = 0;
    4080:     a->maxnz            = a->i[A->rmap->n];
    4081:     A->info.nz_unneeded = (double)a->maxnz;
    4082:     A->was_assembled    = PETSC_FALSE;
    4083:     A->assembled        = PETSC_FALSE;
    4084:     A->nonzerostate++;
    4085:     /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
    4086:     PetscCall(PetscObjectStateIncrease((PetscObject)A));
    4087:   }
    4088:   if (memoryreset) *memoryreset = (PetscBool)!skipreset;
    4089:   PetscFunctionReturn(PETSC_SUCCESS);
    4090: }

    4092: static PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
    4093: {
    4094:   PetscFunctionBegin;
    4095:   PetscCall(MatResetPreallocation_SeqAIJ_Private(A, NULL));
    4096:   PetscFunctionReturn(PETSC_SUCCESS);
    4097: }

    4099: /*@
    4100:   MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.

    4102:   Input Parameters:
    4103: + B - the matrix
    4104: . i - the indices into `j` for the start of each row (indices start with zero)
    4105: . j - the column indices for each row (indices start with zero) these must be sorted for each row
    4106: - v - optional values in the matrix, use `NULL` if not provided

    4108:   Level: developer

    4110:   Notes:
    4111:   The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`

    4113:   This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
    4114:   structure will be the union of all the previous nonzero structures.

    4116:   Developer Notes:
    4117:   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
    4118:   then just copies the `v` values directly with `PetscMemcpy()`.

    4120:   This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.

    4122: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MATSEQAIJ`, `MatResetPreallocation()`
    4123: @*/
    4124: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
    4125: {
    4126:   PetscFunctionBegin;
    4129:   PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
    4130:   PetscFunctionReturn(PETSC_SUCCESS);
    4131: }

    4133: static PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
    4134: {
    4135:   PetscInt  i;
    4136:   PetscInt  m, n;
    4137:   PetscInt  nz;
    4138:   PetscInt *nnz;

    4140:   PetscFunctionBegin;
    4141:   PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);

    4143:   PetscCall(PetscLayoutSetUp(B->rmap));
    4144:   PetscCall(PetscLayoutSetUp(B->cmap));

    4146:   PetscCall(MatGetSize(B, &m, &n));
    4147:   PetscCall(PetscMalloc1(m + 1, &nnz));
    4148:   for (i = 0; i < m; i++) {
    4149:     nz = Ii[i + 1] - Ii[i];
    4150:     PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
    4151:     nnz[i] = nz;
    4152:   }
    4153:   PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
    4154:   PetscCall(PetscFree(nnz));

    4156:   for (i = 0; i < m; i++) PetscCall(MatSetValues_SeqAIJ(B, 1, &i, Ii[i + 1] - Ii[i], J + Ii[i], PetscSafePointerPlusOffset(v, Ii[i]), INSERT_VALUES));

    4158:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
    4159:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));

    4161:   PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
    4162:   PetscFunctionReturn(PETSC_SUCCESS);
    4163: }

    4165: /*@
    4166:   MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.

    4168:   Input Parameters:
    4169: + A     - left-hand side matrix
    4170: . B     - right-hand side matrix
    4171: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

    4173:   Output Parameter:
    4174: . C - Kronecker product of `A` and `B`

    4176:   Level: intermediate

    4178:   Note:
    4179:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.

    4181: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
    4182: @*/
    4183: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
    4184: {
    4185:   PetscFunctionBegin;
    4190:   PetscAssertPointer(C, 4);
    4191:   if (reuse == MAT_REUSE_MATRIX) {
    4194:   }
    4195:   PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
    4196:   PetscFunctionReturn(PETSC_SUCCESS);
    4197: }

    4199: static PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
    4200: {
    4201:   Mat                newmat;
    4202:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    4203:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;
    4204:   PetscScalar       *v;
    4205:   const PetscScalar *aa, *ba;
    4206:   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;
    4207:   PetscBool          flg;

    4209:   PetscFunctionBegin;
    4210:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    4211:   PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
    4212:   PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    4213:   PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
    4214:   PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
    4215:   PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
    4216:   PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
    4217:   if (reuse == MAT_INITIAL_MATRIX) {
    4218:     PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
    4219:     PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
    4220:     PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
    4221:     PetscCall(MatSetType(newmat, MATAIJ));
    4222:     i[0] = 0;
    4223:     for (m = 0; m < am; ++m) {
    4224:       for (p = 0; p < bm; ++p) {
    4225:         i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
    4226:         for (n = a->i[m]; n < a->i[m + 1]; ++n) {
    4227:           for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
    4228:         }
    4229:       }
    4230:     }
    4231:     PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
    4232:     *C = newmat;
    4233:     PetscCall(PetscFree2(i, j));
    4234:     nnz = 0;
    4235:   }
    4236:   PetscCall(MatSeqAIJGetArray(*C, &v));
    4237:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    4238:   PetscCall(MatSeqAIJGetArrayRead(B, &ba));
    4239:   for (m = 0; m < am; ++m) {
    4240:     for (p = 0; p < bm; ++p) {
    4241:       for (n = a->i[m]; n < a->i[m + 1]; ++n) {
    4242:         for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
    4243:       }
    4244:     }
    4245:   }
    4246:   PetscCall(MatSeqAIJRestoreArray(*C, &v));
    4247:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    4248:   PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
    4249:   PetscFunctionReturn(PETSC_SUCCESS);
    4250: }

    4252: #include <../src/mat/impls/dense/seq/dense.h>
    4253: #include <petsc/private/kernels/petscaxpy.h>

    4255: /*
    4256:     Computes (B'*A')' since computing B*A directly is untenable

    4258:                n                       p                          p
    4259:         [             ]       [             ]         [                 ]
    4260:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
    4261:         [             ]       [             ]         [                 ]

    4263: */
    4264: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
    4265: {
    4266:   Mat_SeqDense      *sub_a = (Mat_SeqDense *)A->data;
    4267:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ *)B->data;
    4268:   Mat_SeqDense      *sub_c = (Mat_SeqDense *)C->data;
    4269:   PetscInt           i, j, n, m, q, p;
    4270:   const PetscInt    *ii, *idx;
    4271:   const PetscScalar *b, *a, *a_q;
    4272:   PetscScalar       *c, *c_q;
    4273:   PetscInt           clda = sub_c->lda;
    4274:   PetscInt           alda = sub_a->lda;

    4276:   PetscFunctionBegin;
    4277:   m = A->rmap->n;
    4278:   n = A->cmap->n;
    4279:   p = B->cmap->n;
    4280:   a = sub_a->v;
    4281:   b = sub_b->a;
    4282:   c = sub_c->v;
    4283:   if (clda == m) {
    4284:     PetscCall(PetscArrayzero(c, m * p));
    4285:   } else {
    4286:     for (j = 0; j < p; j++)
    4287:       for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
    4288:   }
    4289:   ii  = sub_b->i;
    4290:   idx = sub_b->j;
    4291:   for (i = 0; i < n; i++) {
    4292:     q = ii[i + 1] - ii[i];
    4293:     while (q-- > 0) {
    4294:       c_q = c + clda * (*idx);
    4295:       a_q = a + alda * i;
    4296:       PetscKernelAXPY(c_q, *b, a_q, m);
    4297:       idx++;
    4298:       b++;
    4299:     }
    4300:   }
    4301:   PetscFunctionReturn(PETSC_SUCCESS);
    4302: }

    4304: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
    4305: {
    4306:   PetscInt  m = A->rmap->n, n = B->cmap->n;
    4307:   PetscBool cisdense;

    4309:   PetscFunctionBegin;
    4310:   PetscCheck(A->cmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "A->cmap->n %" PetscInt_FMT " != B->rmap->n %" PetscInt_FMT, A->cmap->n, B->rmap->n);
    4311:   PetscCall(MatSetSizes(C, m, n, m, n));
    4312:   PetscCall(MatSetBlockSizesFromMats(C, A, B));
    4313:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
    4314:   if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
    4315:   PetscCall(MatSetUp(C));

    4317:   C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
    4318:   PetscFunctionReturn(PETSC_SUCCESS);
    4319: }

    4321: /*MC
    4322:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
    4323:    based on compressed sparse row format.

    4325:    Options Database Key:
    4326: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

    4328:    Level: beginner

    4330:    Notes:
    4331:     `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
    4332:     in this case the values associated with the rows and columns one passes in are set to zero
    4333:     in the matrix

    4335:     `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
    4336:     space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored

    4338:   Developer Note:
    4339:     It would be nice if all matrix formats supported passing `NULL` in for the numerical values

    4341: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
    4342: M*/

    4344: /*MC
    4345:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

    4347:    This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
    4348:    and `MATMPIAIJ` otherwise.  As a result, for single process communicators,
    4349:    `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
    4350:    for communicators controlling multiple processes.  It is recommended that you call both of
    4351:    the above preallocation routines for simplicity.

    4353:    Options Database Key:
    4354: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`

    4356:   Level: beginner

    4358:    Note:
    4359:    Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
    4360:    enough exist.

    4362: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
    4363: M*/

    4365: /*MC
    4366:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

    4368:    Options Database Key:
    4369: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`

    4371:   Level: beginner

    4373:    Note:
    4374:    This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
    4375:    and `MATMPIAIJCRL` otherwise.  As a result, for single process communicators,
    4376:    `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
    4377:    for communicators controlling multiple processes.  It is recommended that you call both of
    4378:    the above preallocation routines for simplicity.

    4380: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
    4381: M*/

    4383: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
    4384: #if defined(PETSC_HAVE_ELEMENTAL)
    4385: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
    4386: #endif
    4387: #if defined(PETSC_HAVE_SCALAPACK)
    4388: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
    4389: #endif
    4390: #if defined(PETSC_HAVE_HYPRE)
    4391: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
    4392: #endif

    4394: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
    4395: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
    4396: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

    4398: /*@C
    4399:   MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored

    4401:   Not Collective

    4403:   Input Parameter:
    4404: . A - a `MATSEQAIJ` matrix

    4406:   Output Parameter:
    4407: . array - pointer to the data

    4409:   Level: intermediate

    4411:   Fortran Notes:
    4412:   `MatSeqAIJGetArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJGetArrayF90()`

    4414: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
    4415: @*/
    4416: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar *array[])
    4417: {
    4418:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

    4420:   PetscFunctionBegin;
    4421:   if (aij->ops->getarray) {
    4422:     PetscCall((*aij->ops->getarray)(A, array));
    4423:   } else {
    4424:     *array = aij->a;
    4425:   }
    4426:   PetscFunctionReturn(PETSC_SUCCESS);
    4427: }

    4429: /*@C
    4430:   MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`

    4432:   Not Collective

    4434:   Input Parameters:
    4435: + A     - a `MATSEQAIJ` matrix
    4436: - array - pointer to the data

    4438:   Level: intermediate

    4440:   Fortran Notes:
    4441:   `MatSeqAIJRestoreArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJRestoreArrayF90()`

    4443: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()`
    4444: @*/
    4445: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar *array[])
    4446: {
    4447:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

    4449:   PetscFunctionBegin;
    4450:   if (aij->ops->restorearray) {
    4451:     PetscCall((*aij->ops->restorearray)(A, array));
    4452:   } else {
    4453:     *array = NULL;
    4454:   }
    4455:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
    4456:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
    4457:   PetscFunctionReturn(PETSC_SUCCESS);
    4458: }

    4460: /*@C
    4461:   MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored

    4463:   Not Collective; No Fortran Support

    4465:   Input Parameter:
    4466: . A - a `MATSEQAIJ` matrix

    4468:   Output Parameter:
    4469: . array - pointer to the data

    4471:   Level: intermediate

    4473: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
    4474: @*/
    4475: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar *array[])
    4476: {
    4477:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

    4479:   PetscFunctionBegin;
    4480:   if (aij->ops->getarrayread) {
    4481:     PetscCall((*aij->ops->getarrayread)(A, array));
    4482:   } else {
    4483:     *array = aij->a;
    4484:   }
    4485:   PetscFunctionReturn(PETSC_SUCCESS);
    4486: }

    4488: /*@C
    4489:   MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`

    4491:   Not Collective; No Fortran Support

    4493:   Input Parameter:
    4494: . A - a `MATSEQAIJ` matrix

    4496:   Output Parameter:
    4497: . array - pointer to the data

    4499:   Level: intermediate

    4501: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
    4502: @*/
    4503: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar *array[])
    4504: {
    4505:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

    4507:   PetscFunctionBegin;
    4508:   if (aij->ops->restorearrayread) {
    4509:     PetscCall((*aij->ops->restorearrayread)(A, array));
    4510:   } else {
    4511:     *array = NULL;
    4512:   }
    4513:   PetscFunctionReturn(PETSC_SUCCESS);
    4514: }

    4516: /*@C
    4517:   MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored

    4519:   Not Collective; No Fortran Support

    4521:   Input Parameter:
    4522: . A - a `MATSEQAIJ` matrix

    4524:   Output Parameter:
    4525: . array - pointer to the data

    4527:   Level: intermediate

    4529: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
    4530: @*/
    4531: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar *array[])
    4532: {
    4533:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

    4535:   PetscFunctionBegin;
    4536:   if (aij->ops->getarraywrite) {
    4537:     PetscCall((*aij->ops->getarraywrite)(A, array));
    4538:   } else {
    4539:     *array = aij->a;
    4540:   }
    4541:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
    4542:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
    4543:   PetscFunctionReturn(PETSC_SUCCESS);
    4544: }

    4546: /*@C
    4547:   MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead

    4549:   Not Collective; No Fortran Support

    4551:   Input Parameter:
    4552: . A - a MATSEQAIJ matrix

    4554:   Output Parameter:
    4555: . array - pointer to the data

    4557:   Level: intermediate

    4559: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
    4560: @*/
    4561: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar *array[])
    4562: {
    4563:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

    4565:   PetscFunctionBegin;
    4566:   if (aij->ops->restorearraywrite) {
    4567:     PetscCall((*aij->ops->restorearraywrite)(A, array));
    4568:   } else {
    4569:     *array = NULL;
    4570:   }
    4571:   PetscFunctionReturn(PETSC_SUCCESS);
    4572: }

    4574: /*@C
    4575:   MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix

    4577:   Not Collective; No Fortran Support

    4579:   Input Parameter:
    4580: . mat - a matrix of type `MATSEQAIJ` or its subclasses

    4582:   Output Parameters:
    4583: + i     - row map array of the matrix
    4584: . j     - column index array of the matrix
    4585: . a     - data array of the matrix
    4586: - mtype - memory type of the arrays

    4588:   Level: developer

    4590:   Notes:
    4591:   Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
    4592:   If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.

    4594:   One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
    4595:   If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.

    4597: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
    4598: @*/
    4599: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt *i[], const PetscInt *j[], PetscScalar *a[], PetscMemType *mtype)
    4600: {
    4601:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;

    4603:   PetscFunctionBegin;
    4604:   PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
    4605:   if (aij->ops->getcsrandmemtype) {
    4606:     PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
    4607:   } else {
    4608:     if (i) *i = aij->i;
    4609:     if (j) *j = aij->j;
    4610:     if (a) *a = aij->a;
    4611:     if (mtype) *mtype = PETSC_MEMTYPE_HOST;
    4612:   }
    4613:   PetscFunctionReturn(PETSC_SUCCESS);
    4614: }

    4616: /*@
    4617:   MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

    4619:   Not Collective

    4621:   Input Parameter:
    4622: . A - a `MATSEQAIJ` matrix

    4624:   Output Parameter:
    4625: . nz - the maximum number of nonzeros in any row

    4627:   Level: intermediate

    4629: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
    4630: @*/
    4631: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
    4632: {
    4633:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

    4635:   PetscFunctionBegin;
    4636:   *nz = aij->rmax;
    4637:   PetscFunctionReturn(PETSC_SUCCESS);
    4638: }

    4640: static PetscErrorCode MatCOOStructDestroy_SeqAIJ(void *data)
    4641: {
    4642:   MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)data;

    4644:   PetscFunctionBegin;
    4645:   PetscCall(PetscFree(coo->perm));
    4646:   PetscCall(PetscFree(coo->jmap));
    4647:   PetscCall(PetscFree(coo));
    4648:   PetscFunctionReturn(PETSC_SUCCESS);
    4649: }

    4651: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
    4652: {
    4653:   MPI_Comm             comm;
    4654:   PetscInt            *i, *j;
    4655:   PetscInt             M, N, row, iprev;
    4656:   PetscCount           k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
    4657:   PetscInt            *Ai;                             /* Change to PetscCount once we use it for row pointers */
    4658:   PetscInt            *Aj;
    4659:   PetscScalar         *Aa;
    4660:   Mat_SeqAIJ          *seqaij = (Mat_SeqAIJ *)mat->data;
    4661:   MatType              rtype;
    4662:   PetscCount          *perm, *jmap;
    4663:   MatCOOStruct_SeqAIJ *coo;
    4664:   PetscBool            isorted;
    4665:   PetscBool            hypre;
    4666:   const char          *name;

    4668:   PetscFunctionBegin;
    4669:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
    4670:   PetscCall(MatGetSize(mat, &M, &N));
    4671:   i = coo_i;
    4672:   j = coo_j;
    4673:   PetscCall(PetscMalloc1(coo_n, &perm));

    4675:   /* Ignore entries with negative row or col indices; at the same time, check if i[] is already sorted (e.g., MatConvert_AlJ_HYPRE results in this case) */
    4676:   isorted = PETSC_TRUE;
    4677:   iprev   = PETSC_INT_MIN;
    4678:   for (k = 0; k < coo_n; k++) {
    4679:     if (j[k] < 0) i[k] = -1;
    4680:     if (isorted) {
    4681:       if (i[k] < iprev) isorted = PETSC_FALSE;
    4682:       else iprev = i[k];
    4683:     }
    4684:     perm[k] = k;
    4685:   }

    4687:   /* Sort by row if not already */
    4688:   if (!isorted) PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));

    4690:   /* Advance k to the first row with a non-negative index */
    4691:   for (k = 0; k < coo_n; k++)
    4692:     if (i[k] >= 0) break;
    4693:   nneg = k;
    4694:   PetscCall(PetscMalloc1(coo_n - nneg + 1, &jmap)); /* +1 to make a CSR-like data structure. jmap[i] originally is the number of repeats for i-th nonzero */
    4695:   nnz = 0;                                          /* Total number of unique nonzeros to be counted */
    4696:   jmap++;                                           /* Inc jmap by 1 for convenience */

    4698:   PetscCall(PetscShmgetAllocateArray(M + 1, sizeof(PetscInt), (void **)&Ai)); /* CSR of A */
    4699:   PetscCall(PetscArrayzero(Ai, M + 1));
    4700:   PetscCall(PetscShmgetAllocateArray(coo_n - nneg, sizeof(PetscInt), (void **)&Aj)); /* We have at most coo_n-nneg unique nonzeros */

    4702:   PetscCall(PetscObjectGetName((PetscObject)mat, &name));
    4703:   PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre));

    4705:   /* In each row, sort by column, then unique column indices to get row length */
    4706:   Ai++;  /* Inc by 1 for convenience */
    4707:   q = 0; /* q-th unique nonzero, with q starting from 0 */
    4708:   while (k < coo_n) {
    4709:     PetscBool strictly_sorted; // this row is strictly sorted?
    4710:     PetscInt  jprev;

    4712:     /* get [start,end) indices for this row; also check if cols in this row are strictly sorted */
    4713:     row             = i[k];
    4714:     start           = k;
    4715:     jprev           = PETSC_INT_MIN;
    4716:     strictly_sorted = PETSC_TRUE;
    4717:     while (k < coo_n && i[k] == row) {
    4718:       if (strictly_sorted) {
    4719:         if (j[k] <= jprev) strictly_sorted = PETSC_FALSE;
    4720:         else jprev = j[k];
    4721:       }
    4722:       k++;
    4723:     }
    4724:     end = k;

    4726:     /* hack for HYPRE: swap min column to diag so that diagonal values will go first */
    4727:     if (hypre) {
    4728:       PetscInt  minj    = PETSC_INT_MAX;
    4729:       PetscBool hasdiag = PETSC_FALSE;

    4731:       if (strictly_sorted) { // fast path to swap the first and the diag
    4732:         PetscCount tmp;
    4733:         for (p = start; p < end; p++) {
    4734:           if (j[p] == row && p != start) {
    4735:             j[p]        = j[start];
    4736:             j[start]    = row;
    4737:             tmp         = perm[start];
    4738:             perm[start] = perm[p];
    4739:             perm[p]     = tmp;
    4740:             break;
    4741:           }
    4742:         }
    4743:       } else {
    4744:         for (p = start; p < end; p++) {
    4745:           hasdiag = (PetscBool)(hasdiag || (j[p] == row));
    4746:           minj    = PetscMin(minj, j[p]);
    4747:         }

    4749:         if (hasdiag) {
    4750:           for (p = start; p < end; p++) {
    4751:             if (j[p] == minj) j[p] = row;
    4752:             else if (j[p] == row) j[p] = minj;
    4753:           }
    4754:         }
    4755:       }
    4756:     }
    4757:     // sort by columns in a row
    4758:     if (!strictly_sorted) PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));

    4760:     if (strictly_sorted) { // fast path to set Aj[], jmap[], Ai[], nnz, q
    4761:       for (p = start; p < end; p++, q++) {
    4762:         Aj[q]   = j[p];
    4763:         jmap[q] = 1;
    4764:       }
    4765:       PetscCall(PetscIntCast(end - start, Ai + row));
    4766:       nnz += Ai[row]; // q is already advanced
    4767:     } else {
    4768:       /* Find number of unique col entries in this row */
    4769:       Aj[q]   = j[start]; /* Log the first nonzero in this row */
    4770:       jmap[q] = 1;        /* Number of repeats of this nonzero entry */
    4771:       Ai[row] = 1;
    4772:       nnz++;

    4774:       for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
    4775:         if (j[p] != j[p - 1]) {           /* Meet a new nonzero */
    4776:           q++;
    4777:           jmap[q] = 1;
    4778:           Aj[q]   = j[p];
    4779:           Ai[row]++;
    4780:           nnz++;
    4781:         } else {
    4782:           jmap[q]++;
    4783:         }
    4784:       }
    4785:       q++; /* Move to next row and thus next unique nonzero */
    4786:     }
    4787:   }

    4789:   Ai--; /* Back to the beginning of Ai[] */
    4790:   for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
    4791:   jmap--; // Back to the beginning of jmap[]
    4792:   jmap[0] = 0;
    4793:   for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];

    4795:   if (nnz < coo_n - nneg) { /* Reallocate with actual number of unique nonzeros */
    4796:     PetscCount *jmap_new;
    4797:     PetscInt   *Aj_new;

    4799:     PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
    4800:     PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
    4801:     PetscCall(PetscFree(jmap));
    4802:     jmap = jmap_new;

    4804:     PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscInt), (void **)&Aj_new));
    4805:     PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
    4806:     PetscCall(PetscShmgetDeallocateArray((void **)&Aj));
    4807:     Aj = Aj_new;
    4808:   }

    4810:   if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
    4811:     PetscCount *perm_new;

    4813:     PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
    4814:     PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
    4815:     PetscCall(PetscFree(perm));
    4816:     perm = perm_new;
    4817:   }

    4819:   PetscCall(MatGetRootType_Private(mat, &rtype));
    4820:   PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscScalar), (void **)&Aa));
    4821:   PetscCall(PetscArrayzero(Aa, nnz));
    4822:   PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));

    4824:   seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */

    4826:   // Put the COO struct in a container and then attach that to the matrix
    4827:   PetscCall(PetscMalloc1(1, &coo));
    4828:   PetscCall(PetscIntCast(nnz, &coo->nz));
    4829:   coo->n    = coo_n;
    4830:   coo->Atot = coo_n - nneg; // Annz is seqaij->nz, so no need to record that again
    4831:   coo->jmap = jmap;         // of length nnz+1
    4832:   coo->perm = perm;
    4833:   PetscCall(PetscObjectContainerCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", coo, MatCOOStructDestroy_SeqAIJ));
    4834:   PetscFunctionReturn(PETSC_SUCCESS);
    4835: }

    4837: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
    4838: {
    4839:   Mat_SeqAIJ          *aseq = (Mat_SeqAIJ *)A->data;
    4840:   PetscCount           i, j, Annz = aseq->nz;
    4841:   PetscCount          *perm, *jmap;
    4842:   PetscScalar         *Aa;
    4843:   PetscContainer       container;
    4844:   MatCOOStruct_SeqAIJ *coo;

    4846:   PetscFunctionBegin;
    4847:   PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
    4848:   PetscCheck(container, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
    4849:   PetscCall(PetscContainerGetPointer(container, (void **)&coo));
    4850:   perm = coo->perm;
    4851:   jmap = coo->jmap;
    4852:   PetscCall(MatSeqAIJGetArray(A, &Aa));
    4853:   for (i = 0; i < Annz; i++) {
    4854:     PetscScalar sum = 0.0;
    4855:     for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
    4856:     Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
    4857:   }
    4858:   PetscCall(MatSeqAIJRestoreArray(A, &Aa));
    4859:   PetscFunctionReturn(PETSC_SUCCESS);
    4860: }

    4862: #if defined(PETSC_HAVE_CUDA)
    4863: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
    4864: #endif
    4865: #if defined(PETSC_HAVE_HIP)
    4866: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
    4867: #endif
    4868: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    4869: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
    4870: #endif

    4872: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
    4873: {
    4874:   Mat_SeqAIJ *b;
    4875:   PetscMPIInt size;

    4877:   PetscFunctionBegin;
    4878:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
    4879:   PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");

    4881:   PetscCall(PetscNew(&b));

    4883:   B->data   = (void *)b;
    4884:   B->ops[0] = MatOps_Values;
    4885:   if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;

    4887:   b->row                = NULL;
    4888:   b->col                = NULL;
    4889:   b->icol               = NULL;
    4890:   b->reallocs           = 0;
    4891:   b->ignorezeroentries  = PETSC_FALSE;
    4892:   b->roworiented        = PETSC_TRUE;
    4893:   b->nonew              = 0;
    4894:   b->diag               = NULL;
    4895:   b->solve_work         = NULL;
    4896:   B->spptr              = NULL;
    4897:   b->saved_values       = NULL;
    4898:   b->idiag              = NULL;
    4899:   b->mdiag              = NULL;
    4900:   b->ssor_work          = NULL;
    4901:   b->omega              = 1.0;
    4902:   b->fshift             = 0.0;
    4903:   b->idiagvalid         = PETSC_FALSE;
    4904:   b->ibdiagvalid        = PETSC_FALSE;
    4905:   b->keepnonzeropattern = PETSC_FALSE;

    4907:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
    4908: #if defined(PETSC_HAVE_MATLAB)
    4909:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
    4910:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
    4911: #endif
    4912:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
    4913:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
    4914:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
    4915:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
    4916:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
    4917:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
    4918:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
    4919: #if defined(PETSC_HAVE_MKL_SPARSE)
    4920:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
    4921: #endif
    4922: #if defined(PETSC_HAVE_CUDA)
    4923:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
    4924:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
    4925:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
    4926: #endif
    4927: #if defined(PETSC_HAVE_HIP)
    4928:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
    4929:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
    4930:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
    4931: #endif
    4932: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    4933:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
    4934: #endif
    4935:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
    4936: #if defined(PETSC_HAVE_ELEMENTAL)
    4937:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
    4938: #endif
    4939: #if defined(PETSC_HAVE_SCALAPACK)
    4940:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
    4941: #endif
    4942: #if defined(PETSC_HAVE_HYPRE)
    4943:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
    4944:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
    4945: #endif
    4946:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
    4947:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
    4948:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
    4949:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
    4950:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsHermitianTranspose_SeqAIJ));
    4951:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
    4952:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
    4953:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
    4954:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
    4955:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
    4956:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
    4957:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
    4958:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
    4959:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
    4960:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
    4961:   PetscCall(MatCreate_SeqAIJ_Inode(B));
    4962:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
    4963:   PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
    4964:   PetscFunctionReturn(PETSC_SUCCESS);
    4965: }

    4967: /*
    4968:     Given a matrix generated with MatGetFactor() duplicates all the information in A into C
    4969: */
    4970: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
    4971: {
    4972:   Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
    4973:   PetscInt    m = A->rmap->n, i;

    4975:   PetscFunctionBegin;
    4976:   PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");

    4978:   C->factortype    = A->factortype;
    4979:   c->row           = NULL;
    4980:   c->col           = NULL;
    4981:   c->icol          = NULL;
    4982:   c->reallocs      = 0;
    4983:   c->diagonaldense = a->diagonaldense;

    4985:   C->assembled = A->assembled;

    4987:   if (A->preallocated) {
    4988:     PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
    4989:     PetscCall(PetscLayoutReference(A->cmap, &C->cmap));

    4991:     if (!A->hash_active) {
    4992:       PetscCall(PetscMalloc1(m, &c->imax));
    4993:       PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
    4994:       PetscCall(PetscMalloc1(m, &c->ilen));
    4995:       PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));

    4997:       /* allocate the matrix space */
    4998:       if (mallocmatspace) {
    4999:         PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscScalar), (void **)&c->a));
    5000:         PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscInt), (void **)&c->j));
    5001:         PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
    5002:         PetscCall(PetscArraycpy(c->i, a->i, m + 1));
    5003:         c->free_a  = PETSC_TRUE;
    5004:         c->free_ij = PETSC_TRUE;
    5005:         if (m > 0) {
    5006:           PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
    5007:           if (cpvalues == MAT_COPY_VALUES) {
    5008:             const PetscScalar *aa;

    5010:             PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    5011:             PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
    5012:             PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    5013:           } else {
    5014:             PetscCall(PetscArrayzero(c->a, a->i[m]));
    5015:           }
    5016:         }
    5017:       }
    5018:       C->preallocated = PETSC_TRUE;
    5019:     } else {
    5020:       PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
    5021:       PetscCall(MatSetUp(C));
    5022:     }

    5024:     c->ignorezeroentries = a->ignorezeroentries;
    5025:     c->roworiented       = a->roworiented;
    5026:     c->nonew             = a->nonew;
    5027:     if (a->diag) {
    5028:       PetscCall(PetscMalloc1(m + 1, &c->diag));
    5029:       PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
    5030:     } else c->diag = NULL;

    5032:     c->solve_work         = NULL;
    5033:     c->saved_values       = NULL;
    5034:     c->idiag              = NULL;
    5035:     c->ssor_work          = NULL;
    5036:     c->keepnonzeropattern = a->keepnonzeropattern;

    5038:     c->rmax  = a->rmax;
    5039:     c->nz    = a->nz;
    5040:     c->maxnz = a->nz; /* Since we allocate exactly the right amount */

    5042:     c->compressedrow.use   = a->compressedrow.use;
    5043:     c->compressedrow.nrows = a->compressedrow.nrows;
    5044:     if (a->compressedrow.use) {
    5045:       i = a->compressedrow.nrows;
    5046:       PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
    5047:       PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
    5048:       PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
    5049:     } else {
    5050:       c->compressedrow.use    = PETSC_FALSE;
    5051:       c->compressedrow.i      = NULL;
    5052:       c->compressedrow.rindex = NULL;
    5053:     }
    5054:     c->nonzerorowcnt = a->nonzerorowcnt;
    5055:     C->nonzerostate  = A->nonzerostate;

    5057:     PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
    5058:   }
    5059:   PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
    5060:   PetscFunctionReturn(PETSC_SUCCESS);
    5061: }

    5063: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
    5064: {
    5065:   PetscFunctionBegin;
    5066:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
    5067:   PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
    5068:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
    5069:   PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
    5070:   PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
    5071:   PetscFunctionReturn(PETSC_SUCCESS);
    5072: }

    5074: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
    5075: {
    5076:   PetscBool isbinary, ishdf5;

    5078:   PetscFunctionBegin;
    5081:   /* force binary viewer to load .info file if it has not yet done so */
    5082:   PetscCall(PetscViewerSetUp(viewer));
    5083:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
    5084:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
    5085:   if (isbinary) {
    5086:     PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
    5087:   } else if (ishdf5) {
    5088: #if defined(PETSC_HAVE_HDF5)
    5089:     PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
    5090: #else
    5091:     SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
    5092: #endif
    5093:   } else {
    5094:     SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)newMat)->type_name);
    5095:   }
    5096:   PetscFunctionReturn(PETSC_SUCCESS);
    5097: }

    5099: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
    5100: {
    5101:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
    5102:   PetscInt    header[4], *rowlens, M, N, nz, sum, rows, cols, i;

    5104:   PetscFunctionBegin;
    5105:   PetscCall(PetscViewerSetUp(viewer));

    5107:   /* read in matrix header */
    5108:   PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
    5109:   PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
    5110:   M  = header[1];
    5111:   N  = header[2];
    5112:   nz = header[3];
    5113:   PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
    5114:   PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
    5115:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");

    5117:   /* set block sizes from the viewer's .info file */
    5118:   PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
    5119:   /* set local and global sizes if not set already */
    5120:   if (mat->rmap->n < 0) mat->rmap->n = M;
    5121:   if (mat->cmap->n < 0) mat->cmap->n = N;
    5122:   if (mat->rmap->N < 0) mat->rmap->N = M;
    5123:   if (mat->cmap->N < 0) mat->cmap->N = N;
    5124:   PetscCall(PetscLayoutSetUp(mat->rmap));
    5125:   PetscCall(PetscLayoutSetUp(mat->cmap));

    5127:   /* check if the matrix sizes are correct */
    5128:   PetscCall(MatGetSize(mat, &rows, &cols));
    5129:   PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);

    5131:   /* read in row lengths */
    5132:   PetscCall(PetscMalloc1(M, &rowlens));
    5133:   PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
    5134:   /* check if sum(rowlens) is same as nz */
    5135:   sum = 0;
    5136:   for (i = 0; i < M; i++) sum += rowlens[i];
    5137:   PetscCheck(sum == nz, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Inconsistent matrix data in file: nonzeros = %" PetscInt_FMT ", sum-row-lengths = %" PetscInt_FMT, nz, sum);
    5138:   /* preallocate and check sizes */
    5139:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
    5140:   PetscCall(MatGetSize(mat, &rows, &cols));
    5141:   PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
    5142:   /* store row lengths */
    5143:   PetscCall(PetscArraycpy(a->ilen, rowlens, M));
    5144:   PetscCall(PetscFree(rowlens));

    5146:   /* fill in "i" row pointers */
    5147:   a->i[0] = 0;
    5148:   for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
    5149:   /* read in "j" column indices */
    5150:   PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
    5151:   /* read in "a" nonzero values */
    5152:   PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));

    5154:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
    5155:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
    5156:   PetscFunctionReturn(PETSC_SUCCESS);
    5157: }

    5159: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
    5160: {
    5161:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
    5162:   const PetscScalar *aa, *ba;
    5163: #if defined(PETSC_USE_COMPLEX)
    5164:   PetscInt k;
    5165: #endif

    5167:   PetscFunctionBegin;
    5168:   /* If the  matrix dimensions are not equal,or no of nonzeros */
    5169:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
    5170:     *flg = PETSC_FALSE;
    5171:     PetscFunctionReturn(PETSC_SUCCESS);
    5172:   }

    5174:   /* if the a->i are the same */
    5175:   PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
    5176:   if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);

    5178:   /* if a->j are the same */
    5179:   PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
    5180:   if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);

    5182:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    5183:   PetscCall(MatSeqAIJGetArrayRead(B, &ba));
    5184:   /* if a->a are the same */
    5185: #if defined(PETSC_USE_COMPLEX)
    5186:   for (k = 0; k < a->nz; k++) {
    5187:     if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
    5188:       *flg = PETSC_FALSE;
    5189:       PetscFunctionReturn(PETSC_SUCCESS);
    5190:     }
    5191:   }
    5192: #else
    5193:   PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
    5194: #endif
    5195:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    5196:   PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
    5197:   PetscFunctionReturn(PETSC_SUCCESS);
    5198: }

    5200: /*@
    5201:   MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
    5202:   provided by the user.

    5204:   Collective

    5206:   Input Parameters:
    5207: + comm - must be an MPI communicator of size 1
    5208: . m    - number of rows
    5209: . n    - number of columns
    5210: . i    - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
    5211: . j    - column indices
    5212: - a    - matrix values

    5214:   Output Parameter:
    5215: . mat - the matrix

    5217:   Level: intermediate

    5219:   Notes:
    5220:   The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
    5221:   once the matrix is destroyed and not before

    5223:   You cannot set new nonzero locations into this matrix, that will generate an error.

    5225:   The `i` and `j` indices are 0 based

    5227:   The format which is used for the sparse matrix input, is equivalent to a
    5228:   row-major ordering.. i.e for the following matrix, the input data expected is
    5229:   as shown
    5230: .vb
    5231:         1 0 0
    5232:         2 0 3
    5233:         4 5 6

    5235:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
    5236:         j =  {0,0,2,0,1,2}  [size = 6]; values must be sorted for each row
    5237:         v =  {1,2,3,4,5,6}  [size = 6]
    5238: .ve

    5240: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
    5241: @*/
    5242: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
    5243: {
    5244:   PetscInt    ii;
    5245:   Mat_SeqAIJ *aij;
    5246:   PetscInt    jj;

    5248:   PetscFunctionBegin;
    5249:   PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
    5250:   PetscCall(MatCreate(comm, mat));
    5251:   PetscCall(MatSetSizes(*mat, m, n, m, n));
    5252:   /* PetscCall(MatSetBlockSizes(*mat,,)); */
    5253:   PetscCall(MatSetType(*mat, MATSEQAIJ));
    5254:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
    5255:   aij = (Mat_SeqAIJ *)(*mat)->data;
    5256:   PetscCall(PetscMalloc1(m, &aij->imax));
    5257:   PetscCall(PetscMalloc1(m, &aij->ilen));

    5259:   aij->i       = i;
    5260:   aij->j       = j;
    5261:   aij->a       = a;
    5262:   aij->nonew   = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
    5263:   aij->free_a  = PETSC_FALSE;
    5264:   aij->free_ij = PETSC_FALSE;

    5266:   for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
    5267:     aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
    5268:     if (PetscDefined(USE_DEBUG)) {
    5269:       PetscCheck(i[ii + 1] - i[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row length in i (row indices) row = %" PetscInt_FMT " length = %" PetscInt_FMT, ii, i[ii + 1] - i[ii]);
    5270:       for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
    5271:         PetscCheck(j[jj] >= j[jj - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is not sorted", jj - i[ii], j[jj], ii);
    5272:         PetscCheck(j[jj] != j[jj - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is identical to previous entry", jj - i[ii], j[jj], ii);
    5273:       }
    5274:     }
    5275:   }
    5276:   if (PetscDefined(USE_DEBUG)) {
    5277:     for (ii = 0; ii < aij->i[m]; ii++) {
    5278:       PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
    5279:       PetscCheck(j[ii] <= n - 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index to large at location = %" PetscInt_FMT " index = %" PetscInt_FMT " last column = %" PetscInt_FMT, ii, j[ii], n - 1);
    5280:     }
    5281:   }

    5283:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
    5284:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
    5285:   PetscFunctionReturn(PETSC_SUCCESS);
    5286: }

    5288: /*@
    5289:   MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
    5290:   provided by the user.

    5292:   Collective

    5294:   Input Parameters:
    5295: + comm - must be an MPI communicator of size 1
    5296: . m    - number of rows
    5297: . n    - number of columns
    5298: . i    - row indices
    5299: . j    - column indices
    5300: . a    - matrix values
    5301: . nz   - number of nonzeros
    5302: - idx  - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`

    5304:   Output Parameter:
    5305: . mat - the matrix

    5307:   Level: intermediate

    5309:   Example:
    5310:   For the following matrix, the input data expected is as shown (using 0 based indexing)
    5311: .vb
    5312:         1 0 0
    5313:         2 0 3
    5314:         4 5 6

    5316:         i =  {0,1,1,2,2,2}
    5317:         j =  {0,0,2,0,1,2}
    5318:         v =  {1,2,3,4,5,6}
    5319: .ve

    5321:   Note:
    5322:   Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
    5323:   and are particularly useful in iterative applications.

    5325: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
    5326: @*/
    5327: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx)
    5328: {
    5329:   PetscInt ii, *nnz, one = 1, row, col;

    5331:   PetscFunctionBegin;
    5332:   PetscCall(PetscCalloc1(m, &nnz));
    5333:   for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
    5334:   PetscCall(MatCreate(comm, mat));
    5335:   PetscCall(MatSetSizes(*mat, m, n, m, n));
    5336:   PetscCall(MatSetType(*mat, MATSEQAIJ));
    5337:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
    5338:   for (ii = 0; ii < nz; ii++) {
    5339:     if (idx) {
    5340:       row = i[ii] - 1;
    5341:       col = j[ii] - 1;
    5342:     } else {
    5343:       row = i[ii];
    5344:       col = j[ii];
    5345:     }
    5346:     PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
    5347:   }
    5348:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
    5349:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
    5350:   PetscCall(PetscFree(nnz));
    5351:   PetscFunctionReturn(PETSC_SUCCESS);
    5352: }

    5354: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
    5355: {
    5356:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

    5358:   PetscFunctionBegin;
    5359:   a->idiagvalid  = PETSC_FALSE;
    5360:   a->ibdiagvalid = PETSC_FALSE;

    5362:   PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
    5363:   PetscFunctionReturn(PETSC_SUCCESS);
    5364: }

    5366: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
    5367: {
    5368:   PetscFunctionBegin;
    5369:   PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
    5370:   PetscFunctionReturn(PETSC_SUCCESS);
    5371: }

    5373: /*
    5374:  Permute A into C's *local* index space using rowemb,colemb.
    5375:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
    5376:  of [0,m), colemb is in [0,n).
    5377:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
    5378:  */
    5379: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
    5380: {
    5381:   /* If making this function public, change the error returned in this function away from _PLIB. */
    5382:   Mat_SeqAIJ     *Baij;
    5383:   PetscBool       seqaij;
    5384:   PetscInt        m, n, *nz, i, j, count;
    5385:   PetscScalar     v;
    5386:   const PetscInt *rowindices, *colindices;

    5388:   PetscFunctionBegin;
    5389:   if (!B) PetscFunctionReturn(PETSC_SUCCESS);
    5390:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
    5391:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
    5392:   PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
    5393:   if (rowemb) {
    5394:     PetscCall(ISGetLocalSize(rowemb, &m));
    5395:     PetscCheck(m == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row IS of size %" PetscInt_FMT " is incompatible with matrix row size %" PetscInt_FMT, m, B->rmap->n);
    5396:   } else {
    5397:     PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
    5398:   }
    5399:   if (colemb) {
    5400:     PetscCall(ISGetLocalSize(colemb, &n));
    5401:     PetscCheck(n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Diag col IS of size %" PetscInt_FMT " is incompatible with input matrix col size %" PetscInt_FMT, n, B->cmap->n);
    5402:   } else {
    5403:     PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
    5404:   }

    5406:   Baij = (Mat_SeqAIJ *)B->data;
    5407:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
    5408:     PetscCall(PetscMalloc1(B->rmap->n, &nz));
    5409:     for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
    5410:     PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
    5411:     PetscCall(PetscFree(nz));
    5412:   }
    5413:   if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
    5414:   count      = 0;
    5415:   rowindices = NULL;
    5416:   colindices = NULL;
    5417:   if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
    5418:   if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
    5419:   for (i = 0; i < B->rmap->n; i++) {
    5420:     PetscInt row;
    5421:     row = i;
    5422:     if (rowindices) row = rowindices[i];
    5423:     for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
    5424:       PetscInt col;
    5425:       col = Baij->j[count];
    5426:       if (colindices) col = colindices[col];
    5427:       v = Baij->a[count];
    5428:       PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
    5429:       ++count;
    5430:     }
    5431:   }
    5432:   /* FIXME: set C's nonzerostate correctly. */
    5433:   /* Assembly for C is necessary. */
    5434:   C->preallocated  = PETSC_TRUE;
    5435:   C->assembled     = PETSC_TRUE;
    5436:   C->was_assembled = PETSC_FALSE;
    5437:   PetscFunctionReturn(PETSC_SUCCESS);
    5438: }

    5440: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A, PetscBool keep)
    5441: {
    5442:   Mat_SeqAIJ *a  = (Mat_SeqAIJ *)A->data;
    5443:   MatScalar  *aa = a->a;
    5444:   PetscInt    m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
    5445:   PetscInt   *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;

    5447:   PetscFunctionBegin;
    5448:   PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
    5449:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
    5450:   for (i = 1; i <= m; i++) {
    5451:     /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
    5452:     for (k = ai[i - 1]; k < ai[i]; k++) {
    5453:       if (aa[k] == 0 && (aj[k] != i - 1 || !keep)) fshift++;
    5454:       else {
    5455:         if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
    5456:         aa[k - fshift] = aa[k];
    5457:         aj[k - fshift] = aj[k];
    5458:       }
    5459:     }
    5460:     ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
    5461:     fshift_prev = fshift;
    5462:     /* reset ilen and imax for each row */
    5463:     ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
    5464:     a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
    5465:     rmax = PetscMax(rmax, ailen[i - 1]);
    5466:   }
    5467:   if (fshift) {
    5468:     if (m) {
    5469:       ai[m] -= fshift;
    5470:       a->nz = ai[m];
    5471:     }
    5472:     PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; zeros eliminated: %" PetscInt_FMT "; nonzeros left: %" PetscInt_FMT "\n", m, A->cmap->n, fshift, a->nz));
    5473:     A->nonzerostate++;
    5474:     A->info.nz_unneeded += (PetscReal)fshift;
    5475:     a->rmax = rmax;
    5476:     if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
    5477:     PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
    5478:     PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
    5479:   }
    5480:   PetscFunctionReturn(PETSC_SUCCESS);
    5481: }

    5483: PetscFunctionList MatSeqAIJList = NULL;

    5485: /*@
    5486:   MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype

    5488:   Collective

    5490:   Input Parameters:
    5491: + mat    - the matrix object
    5492: - matype - matrix type

    5494:   Options Database Key:
    5495: . -mat_seqaij_type  <method> - for example seqaijcrl

    5497:   Level: intermediate

    5499: .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`
    5500: @*/
    5501: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
    5502: {
    5503:   PetscBool sametype;
    5504:   PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);

    5506:   PetscFunctionBegin;
    5508:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
    5509:   if (sametype) PetscFunctionReturn(PETSC_SUCCESS);

    5511:   PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
    5512:   PetscCheck(r, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
    5513:   PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
    5514:   PetscFunctionReturn(PETSC_SUCCESS);
    5515: }

    5517: /*@C
    5518:   MatSeqAIJRegister -  - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices

    5520:   Not Collective, No Fortran Support

    5522:   Input Parameters:
    5523: + sname    - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
    5524: - function - routine to convert to subtype

    5526:   Level: advanced

    5528:   Notes:
    5529:   `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.

    5531:   Then, your matrix can be chosen with the procedural interface at runtime via the option
    5532: $     -mat_seqaij_type my_mat

    5534: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()`
    5535: @*/
    5536: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
    5537: {
    5538:   PetscFunctionBegin;
    5539:   PetscCall(MatInitializePackage());
    5540:   PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
    5541:   PetscFunctionReturn(PETSC_SUCCESS);
    5542: }

    5544: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;

    5546: /*@C
    5547:   MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`

    5549:   Not Collective

    5551:   Level: advanced

    5553:   Note:
    5554:   This registers the versions of `MATSEQAIJ` for GPUs

    5556: .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
    5557: @*/
    5558: PetscErrorCode MatSeqAIJRegisterAll(void)
    5559: {
    5560:   PetscFunctionBegin;
    5561:   if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
    5562:   MatSeqAIJRegisterAllCalled = PETSC_TRUE;

    5564:   PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
    5565:   PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
    5566:   PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
    5567: #if defined(PETSC_HAVE_MKL_SPARSE)
    5568:   PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
    5569: #endif
    5570: #if defined(PETSC_HAVE_CUDA)
    5571:   PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
    5572: #endif
    5573: #if defined(PETSC_HAVE_HIP)
    5574:   PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
    5575: #endif
    5576: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    5577:   PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
    5578: #endif
    5579: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
    5580:   PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
    5581: #endif
    5582:   PetscFunctionReturn(PETSC_SUCCESS);
    5583: }

    5585: /*
    5586:     Special version for direct calls from Fortran
    5587: */
    5588: #if defined(PETSC_HAVE_FORTRAN_CAPS)
    5589:   #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
    5590: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
    5591:   #define matsetvaluesseqaij_ matsetvaluesseqaij
    5592: #endif

    5594: /* Change these macros so can be used in void function */

    5596: /* Change these macros so can be used in void function */
    5597: /* Identical to PetscCallVoid, except it assigns to *_ierr */
    5598: #undef PetscCall
    5599: #define PetscCall(...) \
    5600:   do { \
    5601:     PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
    5602:     if (PetscUnlikely(ierr_msv_mpiaij)) { \
    5603:       *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
    5604:       return; \
    5605:     } \
    5606:   } while (0)

    5608: #undef SETERRQ
    5609: #define SETERRQ(comm, ierr, ...) \
    5610:   do { \
    5611:     *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
    5612:     return; \
    5613:   } while (0)

    5615: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
    5616: {
    5617:   Mat         A = *AA;
    5618:   PetscInt    m = *mm, n = *nn;
    5619:   InsertMode  is = *isis;
    5620:   Mat_SeqAIJ *a  = (Mat_SeqAIJ *)A->data;
    5621:   PetscInt   *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
    5622:   PetscInt   *imax, *ai, *ailen;
    5623:   PetscInt   *aj, nonew = a->nonew, lastcol = -1;
    5624:   MatScalar  *ap, value, *aa;
    5625:   PetscBool   ignorezeroentries = a->ignorezeroentries;
    5626:   PetscBool   roworiented       = a->roworiented;

    5628:   PetscFunctionBegin;
    5629:   MatCheckPreallocated(A, 1);
    5630:   imax  = a->imax;
    5631:   ai    = a->i;
    5632:   ailen = a->ilen;
    5633:   aj    = a->j;
    5634:   aa    = a->a;

    5636:   for (k = 0; k < m; k++) { /* loop over added rows */
    5637:     row = im[k];
    5638:     if (row < 0) continue;
    5639:     PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
    5640:     rp   = aj + ai[row];
    5641:     ap   = aa + ai[row];
    5642:     rmax = imax[row];
    5643:     nrow = ailen[row];
    5644:     low  = 0;
    5645:     high = nrow;
    5646:     for (l = 0; l < n; l++) { /* loop over added columns */
    5647:       if (in[l] < 0) continue;
    5648:       PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
    5649:       col = in[l];
    5650:       if (roworiented) value = v[l + k * n];
    5651:       else value = v[k + l * m];

    5653:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

    5655:       if (col <= lastcol) low = 0;
    5656:       else high = nrow;
    5657:       lastcol = col;
    5658:       while (high - low > 5) {
    5659:         t = (low + high) / 2;
    5660:         if (rp[t] > col) high = t;
    5661:         else low = t;
    5662:       }
    5663:       for (i = low; i < high; i++) {
    5664:         if (rp[i] > col) break;
    5665:         if (rp[i] == col) {
    5666:           if (is == ADD_VALUES) ap[i] += value;
    5667:           else ap[i] = value;
    5668:           goto noinsert;
    5669:         }
    5670:       }
    5671:       if (value == 0.0 && ignorezeroentries) goto noinsert;
    5672:       if (nonew == 1) goto noinsert;
    5673:       PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
    5674:       MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
    5675:       N = nrow++ - 1;
    5676:       a->nz++;
    5677:       high++;
    5678:       /* shift up all the later entries in this row */
    5679:       for (ii = N; ii >= i; ii--) {
    5680:         rp[ii + 1] = rp[ii];
    5681:         ap[ii + 1] = ap[ii];
    5682:       }
    5683:       rp[i] = col;
    5684:       ap[i] = value;
    5685:     noinsert:;
    5686:       low = i + 1;
    5687:     }
    5688:     ailen[row] = nrow;
    5689:   }
    5690:   PetscFunctionReturnVoid();
    5691: }
    5692: /* Undefining these here since they were redefined from their original definition above! No
    5693:  * other PETSc functions should be defined past this point, as it is impossible to recover the
    5694:  * original definitions */
    5695: #undef PetscCall
    5696: #undef SETERRQ