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:       A->nonzerostate++;
472:     noinsert:;
473:     }
474:     ailen[row] = nrow;
475:   }
476:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
477:   PetscFunctionReturn(PETSC_SUCCESS);
478: }

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

488:   PetscFunctionBegin;
489:   PetscCheck(!A->was_assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot call on assembled matrix.");
490:   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);

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

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

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

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

523:   Level: advanced

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

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

536:   PetscFunctionBegin;
537:   PetscCall(PetscLayoutSetUp(A->rmap));
538:   PetscCall(PetscLayoutSetUp(A->cmap));
539:   a->maxnz = nztotal;
540:   if (!a->imax) { PetscCall(PetscMalloc1(A->rmap->n, &a->imax)); }
541:   if (!a->ilen) {
542:     PetscCall(PetscMalloc1(A->rmap->n, &a->ilen));
543:   } else {
544:     PetscCall(PetscMemzero(a->ilen, A->rmap->n * sizeof(PetscInt)));
545:   }

547:   /* allocate the matrix space */
548:   if (A->structure_only) {
549:     PetscCall(PetscMalloc1(nztotal, &a->j));
550:     PetscCall(PetscMalloc1(A->rmap->n + 1, &a->i));
551:   } else {
552:     PetscCall(PetscMalloc3(nztotal, &a->a, nztotal, &a->j, A->rmap->n + 1, &a->i));
553:   }
554:   a->i[0] = 0;
555:   if (A->structure_only) {
556:     a->singlemalloc = PETSC_FALSE;
557:     a->free_a       = PETSC_FALSE;
558:   } else {
559:     a->singlemalloc = PETSC_TRUE;
560:     a->free_a       = PETSC_TRUE;
561:   }
562:   a->free_ij        = PETSC_TRUE;
563:   A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
564:   A->preallocated   = PETSC_TRUE;
565:   PetscFunctionReturn(PETSC_SUCCESS);
566: }

568: static PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
569: {
570:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
571:   PetscInt   *rp, k, row;
572:   PetscInt   *ai = a->i, *ailen = a->ilen;
573:   PetscInt   *aj = a->j;
574:   MatScalar  *aa, *ap;

576:   PetscFunctionBegin;
577:   PetscCall(MatSeqAIJGetArray(A, &aa));
578:   for (k = 0; k < m; k++) { /* loop over added rows */
579:     row = im[k];
580:     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);
581:     rp = aj + ai[row];
582:     ap = aa + ai[row];
583:     if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
584:     if (!A->structure_only) {
585:       if (v) {
586:         PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
587:         v += n;
588:       } else {
589:         PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
590:       }
591:     }
592:     ailen[row] = n;
593:     a->nz += n;
594:   }
595:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
596:   PetscFunctionReturn(PETSC_SUCCESS);
597: }

599: static PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
600: {
601:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
602:   PetscInt        *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
603:   PetscInt        *ai = a->i, *ailen = a->ilen;
604:   const MatScalar *ap, *aa;

606:   PetscFunctionBegin;
607:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
608:   for (k = 0; k < m; k++) { /* loop over rows */
609:     row = im[k];
610:     if (row < 0) {
611:       v += n;
612:       continue;
613:     } /* negative row */
614:     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);
615:     rp   = PetscSafePointerPlusOffset(aj, ai[row]);
616:     ap   = PetscSafePointerPlusOffset(aa, ai[row]);
617:     nrow = ailen[row];
618:     for (l = 0; l < n; l++) { /* loop over columns */
619:       if (in[l] < 0) {
620:         v++;
621:         continue;
622:       } /* negative column */
623:       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);
624:       col  = in[l];
625:       high = nrow;
626:       low  = 0; /* assume unsorted */
627:       while (high - low > 5) {
628:         t = (low + high) / 2;
629:         if (rp[t] > col) high = t;
630:         else low = t;
631:       }
632:       for (i = low; i < high; i++) {
633:         if (rp[i] > col) break;
634:         if (rp[i] == col) {
635:           *v++ = ap[i];
636:           goto finished;
637:         }
638:       }
639:       *v++ = 0.0;
640:     finished:;
641:     }
642:   }
643:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
644:   PetscFunctionReturn(PETSC_SUCCESS);
645: }

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

654:   PetscFunctionBegin;
655:   PetscCall(PetscViewerSetUp(viewer));

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

662:   /* write matrix header */
663:   header[0] = MAT_FILE_CLASSID;
664:   header[1] = M;
665:   header[2] = N;
666:   header[3] = nz;
667:   PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));

669:   /* fill in and store row lengths */
670:   PetscCall(PetscMalloc1(m, &rowlens));
671:   for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i];
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:   if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(PETSC_SUCCESS);

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

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

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

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

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

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

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

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

1027:     for (i = 0; i < nz; i++) {
1028:       if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]);
1029:     }
1030:     if (minv >= maxv) maxv = minv + PETSC_SMALL;
1031:     PetscCall(PetscDrawGetPopup(draw, &popup));
1032:     PetscCall(PetscDrawScalePopup(popup, minv, maxv));

1034:     PetscDrawCollectiveBegin(draw);
1035:     for (i = 0; i < m; i++) {
1036:       y_l = m - i - 1.0;
1037:       y_r = y_l + 1.0;
1038:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
1039:         x_l   = a->j[j];
1040:         x_r   = x_l + 1.0;
1041:         color = PetscDrawRealToColor(PetscAbsScalar(aa[count]), minv, maxv);
1042:         PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1043:         count++;
1044:       }
1045:     }
1046:     PetscDrawCollectiveEnd(draw);
1047:   }
1048:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1049:   PetscFunctionReturn(PETSC_SUCCESS);
1050: }

1052: #include <petscdraw.h>
1053: static PetscErrorCode MatView_SeqAIJ_Draw(Mat A, PetscViewer viewer)
1054: {
1055:   PetscDraw draw;
1056:   PetscReal xr, yr, xl, yl, h, w;
1057:   PetscBool isnull;

1059:   PetscFunctionBegin;
1060:   PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1061:   PetscCall(PetscDrawIsNull(draw, &isnull));
1062:   if (isnull) PetscFunctionReturn(PETSC_SUCCESS);

1064:   xr = A->cmap->n;
1065:   yr = A->rmap->n;
1066:   h  = yr / 10.0;
1067:   w  = xr / 10.0;
1068:   xr += w;
1069:   yr += h;
1070:   xl = -w;
1071:   yl = -h;
1072:   PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
1073:   PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
1074:   PetscCall(PetscDrawZoom(draw, MatView_SeqAIJ_Draw_Zoom, A));
1075:   PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
1076:   PetscCall(PetscDrawSave(draw));
1077:   PetscFunctionReturn(PETSC_SUCCESS);
1078: }

1080: PetscErrorCode MatView_SeqAIJ(Mat A, PetscViewer viewer)
1081: {
1082:   PetscBool iascii, isbinary, isdraw;

1084:   PetscFunctionBegin;
1085:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1086:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1087:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1088:   if (iascii) PetscCall(MatView_SeqAIJ_ASCII(A, viewer));
1089:   else if (isbinary) PetscCall(MatView_SeqAIJ_Binary(A, viewer));
1090:   else if (isdraw) PetscCall(MatView_SeqAIJ_Draw(A, viewer));
1091:   PetscCall(MatView_SeqAIJ_Inode(A, viewer));
1092:   PetscFunctionReturn(PETSC_SUCCESS);
1093: }

1095: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A, MatAssemblyType mode)
1096: {
1097:   Mat_SeqAIJ *a      = (Mat_SeqAIJ *)A->data;
1098:   PetscInt    fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
1099:   PetscInt    m = A->rmap->n, *ip, N, *ailen = a->ilen, rmax = 0, n;
1100:   MatScalar  *aa    = a->a, *ap;
1101:   PetscReal   ratio = 0.6;

1103:   PetscFunctionBegin;
1104:   if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
1105:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
1106:   if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
1107:     /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
1108:     PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1109:     PetscFunctionReturn(PETSC_SUCCESS);
1110:   }

1112:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1113:   for (i = 1; i < m; i++) {
1114:     /* move each row back by the amount of empty slots (fshift) before it*/
1115:     fshift += imax[i - 1] - ailen[i - 1];
1116:     rmax = PetscMax(rmax, ailen[i]);
1117:     if (fshift) {
1118:       ip = aj + ai[i];
1119:       ap = aa + ai[i];
1120:       N  = ailen[i];
1121:       PetscCall(PetscArraymove(ip - fshift, ip, N));
1122:       if (!A->structure_only) PetscCall(PetscArraymove(ap - fshift, ap, N));
1123:     }
1124:     ai[i] = ai[i - 1] + ailen[i - 1];
1125:   }
1126:   if (m) {
1127:     fshift += imax[m - 1] - ailen[m - 1];
1128:     ai[m] = ai[m - 1] + ailen[m - 1];
1129:   }
1130:   /* reset ilen and imax for each row */
1131:   a->nonzerorowcnt = 0;
1132:   if (A->structure_only) {
1133:     PetscCall(PetscFree(a->imax));
1134:     PetscCall(PetscFree(a->ilen));
1135:   } else { /* !A->structure_only */
1136:     for (i = 0; i < m; i++) {
1137:       ailen[i] = imax[i] = ai[i + 1] - ai[i];
1138:       a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
1139:     }
1140:   }
1141:   a->nz = ai[m];
1142:   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);
1143:   PetscCall(MatMarkDiagonal_SeqAIJ(A)); // since diagonal info is used a lot, it is helpful to set them up at the end of assembly
1144:   a->diagonaldense = PETSC_TRUE;
1145:   n                = PetscMin(A->rmap->n, A->cmap->n);
1146:   for (i = 0; i < n; i++) {
1147:     if (a->diag[i] >= ai[i + 1]) {
1148:       a->diagonaldense = PETSC_FALSE;
1149:       break;
1150:     }
1151:   }
1152:   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));
1153:   PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs));
1154:   PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax));

1156:   A->info.mallocs += a->reallocs;
1157:   a->reallocs         = 0;
1158:   A->info.nz_unneeded = (PetscReal)fshift;
1159:   a->rmax             = rmax;

1161:   if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio));
1162:   PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1163:   PetscFunctionReturn(PETSC_SUCCESS);
1164: }

1166: static PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1167: {
1168:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1169:   PetscInt    i, nz = a->nz;
1170:   MatScalar  *aa;

1172:   PetscFunctionBegin;
1173:   PetscCall(MatSeqAIJGetArray(A, &aa));
1174:   for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
1175:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
1176:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
1177:   PetscFunctionReturn(PETSC_SUCCESS);
1178: }

1180: static PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1181: {
1182:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1183:   PetscInt    i, nz = a->nz;
1184:   MatScalar  *aa;

1186:   PetscFunctionBegin;
1187:   PetscCall(MatSeqAIJGetArray(A, &aa));
1188:   for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1189:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
1190:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
1191:   PetscFunctionReturn(PETSC_SUCCESS);
1192: }

1194: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1195: {
1196:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1197:   MatScalar  *aa;

1199:   PetscFunctionBegin;
1200:   PetscCall(MatSeqAIJGetArrayWrite(A, &aa));
1201:   PetscCall(PetscArrayzero(aa, a->i[A->rmap->n]));
1202:   PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa));
1203:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
1204:   PetscFunctionReturn(PETSC_SUCCESS);
1205: }

1207: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1208: {
1209:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

1211:   PetscFunctionBegin;
1212:   if (A->hash_active) {
1213:     A->ops[0] = a->cops;
1214:     PetscCall(PetscHMapIJVDestroy(&a->ht));
1215:     PetscCall(PetscFree(a->dnz));
1216:     A->hash_active = PETSC_FALSE;
1217:   }

1219:   PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
1220:   PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1221:   PetscCall(ISDestroy(&a->row));
1222:   PetscCall(ISDestroy(&a->col));
1223:   PetscCall(PetscFree(a->diag));
1224:   PetscCall(PetscFree(a->ibdiag));
1225:   PetscCall(PetscFree(a->imax));
1226:   PetscCall(PetscFree(a->ilen));
1227:   PetscCall(PetscFree(a->ipre));
1228:   PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work));
1229:   PetscCall(PetscFree(a->solve_work));
1230:   PetscCall(ISDestroy(&a->icol));
1231:   PetscCall(PetscFree(a->saved_values));
1232:   PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1233:   PetscCall(MatDestroy_SeqAIJ_Inode(A));
1234:   PetscCall(PetscFree(A->data));

1236:   /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1237:      That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1238:      that is hard to properly add this data to the MatProduct data. We free it here to avoid
1239:      users reusing the matrix object with different data to incur in obscure segmentation faults
1240:      due to different matrix sizes */
1241:   PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL));

1243:   PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1244:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL));
1245:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL));
1246:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL));
1247:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1248:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1249:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL));
1250:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL));
1251:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL));
1252:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL));
1253: #if defined(PETSC_HAVE_MKL_SPARSE)
1254:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL));
1255: #endif
1256: #if defined(PETSC_HAVE_CUDA)
1257:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL));
1258:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL));
1259:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL));
1260: #endif
1261: #if defined(PETSC_HAVE_HIP)
1262:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL));
1263:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL));
1264:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL));
1265: #endif
1266: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1267:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL));
1268: #endif
1269:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL));
1270: #if defined(PETSC_HAVE_ELEMENTAL)
1271:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL));
1272: #endif
1273: #if defined(PETSC_HAVE_SCALAPACK)
1274:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL));
1275: #endif
1276: #if defined(PETSC_HAVE_HYPRE)
1277:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL));
1278:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL));
1279: #endif
1280:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL));
1281:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL));
1282:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL));
1283:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1284:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL));
1285:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL));
1286:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL));
1287:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL));
1288:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL));
1289:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL));
1290:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL));
1291:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL));
1292:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL));
1293:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1294:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1295:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1296:   /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */
1297:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL));
1298:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
1299:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
1300:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
1301:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
1302:   PetscFunctionReturn(PETSC_SUCCESS);
1303: }

1305: PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg)
1306: {
1307:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

1309:   PetscFunctionBegin;
1310:   switch (op) {
1311:   case MAT_ROW_ORIENTED:
1312:     a->roworiented = flg;
1313:     break;
1314:   case MAT_KEEP_NONZERO_PATTERN:
1315:     a->keepnonzeropattern = flg;
1316:     break;
1317:   case MAT_NEW_NONZERO_LOCATIONS:
1318:     a->nonew = (flg ? 0 : 1);
1319:     break;
1320:   case MAT_NEW_NONZERO_LOCATION_ERR:
1321:     a->nonew = (flg ? -1 : 0);
1322:     break;
1323:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1324:     a->nonew = (flg ? -2 : 0);
1325:     break;
1326:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1327:     a->nounused = (flg ? -1 : 0);
1328:     break;
1329:   case MAT_IGNORE_ZERO_ENTRIES:
1330:     a->ignorezeroentries = flg;
1331:     break;
1332:   case MAT_SPD:
1333:   case MAT_SYMMETRIC:
1334:   case MAT_STRUCTURALLY_SYMMETRIC:
1335:   case MAT_HERMITIAN:
1336:   case MAT_SYMMETRY_ETERNAL:
1337:   case MAT_STRUCTURE_ONLY:
1338:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1339:   case MAT_SPD_ETERNAL:
1340:     /* if the diagonal matrix is square it inherits some of the properties above */
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   *aa, *a_a;
1469:   PetscInt           m = A->rmap->n;
1470:   const PetscInt    *aj, *ii, *ridx = NULL;
1471:   PetscInt           n, i;
1472:   PetscScalar        sum;
1473:   PetscBool          usecprow = a->compressedrow.use;

1475: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1476:   #pragma disjoint(*x, *y, *aa)
1477: #endif

1479:   PetscFunctionBegin;
1480:   if (a->inode.use && a->inode.checked) {
1481:     PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
1482:     PetscFunctionReturn(PETSC_SUCCESS);
1483:   }
1484:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1485:   PetscCall(VecGetArrayRead(xx, &x));
1486:   PetscCall(VecGetArray(yy, &y));
1487:   ii = a->i;
1488:   if (usecprow) { /* use compressed row format */
1489:     PetscCall(PetscArrayzero(y, m));
1490:     m    = a->compressedrow.nrows;
1491:     ii   = a->compressedrow.i;
1492:     ridx = a->compressedrow.rindex;
1493:     for (i = 0; i < m; i++) {
1494:       n   = ii[i + 1] - ii[i];
1495:       aj  = a->j + ii[i];
1496:       aa  = a_a + ii[i];
1497:       sum = 0.0;
1498:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
1499:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1500:       y[*ridx++] = sum;
1501:     }
1502:   } else { /* do not use compressed row format */
1503: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1504:     aj = a->j;
1505:     aa = a_a;
1506:     fortranmultaij_(&m, x, ii, aj, aa, y);
1507: #else
1508:     for (i = 0; i < m; i++) {
1509:       n   = ii[i + 1] - ii[i];
1510:       aj  = a->j + ii[i];
1511:       aa  = a_a + ii[i];
1512:       sum = 0.0;
1513:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
1514:       y[i] = sum;
1515:     }
1516: #endif
1517:   }
1518:   PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
1519:   PetscCall(VecRestoreArrayRead(xx, &x));
1520:   PetscCall(VecRestoreArray(yy, &y));
1521:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1522:   PetscFunctionReturn(PETSC_SUCCESS);
1523: }

1525: // HACK!!!!! Used by src/mat/tests/ex170.c
1526: PETSC_EXTERN PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1527: {
1528:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1529:   PetscScalar       *y;
1530:   const PetscScalar *x;
1531:   const MatScalar   *aa, *a_a;
1532:   PetscInt           m = A->rmap->n;
1533:   const PetscInt    *aj, *ii, *ridx   = NULL;
1534:   PetscInt           n, i, nonzerorow = 0;
1535:   PetscScalar        sum;
1536:   PetscBool          usecprow = a->compressedrow.use;

1538: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1539:   #pragma disjoint(*x, *y, *aa)
1540: #endif

1542:   PetscFunctionBegin;
1543:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1544:   PetscCall(VecGetArrayRead(xx, &x));
1545:   PetscCall(VecGetArray(yy, &y));
1546:   if (usecprow) { /* use compressed row format */
1547:     m    = a->compressedrow.nrows;
1548:     ii   = a->compressedrow.i;
1549:     ridx = a->compressedrow.rindex;
1550:     for (i = 0; i < m; i++) {
1551:       n   = ii[i + 1] - ii[i];
1552:       aj  = a->j + ii[i];
1553:       aa  = a_a + ii[i];
1554:       sum = 0.0;
1555:       nonzerorow += (n > 0);
1556:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1557:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1558:       y[*ridx++] = sum;
1559:     }
1560:   } else { /* do not use compressed row format */
1561:     ii = a->i;
1562:     for (i = 0; i < m; i++) {
1563:       n   = ii[i + 1] - ii[i];
1564:       aj  = a->j + ii[i];
1565:       aa  = a_a + ii[i];
1566:       sum = 0.0;
1567:       nonzerorow += (n > 0);
1568:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1569:       y[i] = sum;
1570:     }
1571:   }
1572:   PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1573:   PetscCall(VecRestoreArrayRead(xx, &x));
1574:   PetscCall(VecRestoreArray(yy, &y));
1575:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1576:   PetscFunctionReturn(PETSC_SUCCESS);
1577: }

1579: // HACK!!!!! Used by src/mat/tests/ex170.c
1580: PETSC_EXTERN PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1581: {
1582:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1583:   PetscScalar       *y, *z;
1584:   const PetscScalar *x;
1585:   const MatScalar   *aa, *a_a;
1586:   PetscInt           m = A->rmap->n, *aj, *ii;
1587:   PetscInt           n, i, *ridx = NULL;
1588:   PetscScalar        sum;
1589:   PetscBool          usecprow = a->compressedrow.use;

1591:   PetscFunctionBegin;
1592:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1593:   PetscCall(VecGetArrayRead(xx, &x));
1594:   PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1595:   if (usecprow) { /* use compressed row format */
1596:     if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1597:     m    = a->compressedrow.nrows;
1598:     ii   = a->compressedrow.i;
1599:     ridx = a->compressedrow.rindex;
1600:     for (i = 0; i < m; i++) {
1601:       n   = ii[i + 1] - ii[i];
1602:       aj  = a->j + ii[i];
1603:       aa  = a_a + ii[i];
1604:       sum = y[*ridx];
1605:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1606:       z[*ridx++] = sum;
1607:     }
1608:   } else { /* do not use compressed row format */
1609:     ii = a->i;
1610:     for (i = 0; i < m; i++) {
1611:       n   = ii[i + 1] - ii[i];
1612:       aj  = a->j + ii[i];
1613:       aa  = a_a + ii[i];
1614:       sum = y[i];
1615:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1616:       z[i] = sum;
1617:     }
1618:   }
1619:   PetscCall(PetscLogFlops(2.0 * a->nz));
1620:   PetscCall(VecRestoreArrayRead(xx, &x));
1621:   PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1622:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1623:   PetscFunctionReturn(PETSC_SUCCESS);
1624: }

1626: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1627: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1628: {
1629:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1630:   PetscScalar       *y, *z;
1631:   const PetscScalar *x;
1632:   const MatScalar   *aa, *a_a;
1633:   const PetscInt    *aj, *ii, *ridx = NULL;
1634:   PetscInt           m = A->rmap->n, n, i;
1635:   PetscScalar        sum;
1636:   PetscBool          usecprow = a->compressedrow.use;

1638:   PetscFunctionBegin;
1639:   if (a->inode.use && a->inode.checked) {
1640:     PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
1641:     PetscFunctionReturn(PETSC_SUCCESS);
1642:   }
1643:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1644:   PetscCall(VecGetArrayRead(xx, &x));
1645:   PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1646:   if (usecprow) { /* use compressed row format */
1647:     if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1648:     m    = a->compressedrow.nrows;
1649:     ii   = a->compressedrow.i;
1650:     ridx = a->compressedrow.rindex;
1651:     for (i = 0; i < m; i++) {
1652:       n   = ii[i + 1] - ii[i];
1653:       aj  = a->j + ii[i];
1654:       aa  = a_a + ii[i];
1655:       sum = y[*ridx];
1656:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
1657:       z[*ridx++] = sum;
1658:     }
1659:   } else { /* do not use compressed row format */
1660:     ii = a->i;
1661: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1662:     aj = a->j;
1663:     aa = a_a;
1664:     fortranmultaddaij_(&m, x, ii, aj, aa, y, z);
1665: #else
1666:     for (i = 0; i < m; i++) {
1667:       n   = ii[i + 1] - ii[i];
1668:       aj  = a->j + ii[i];
1669:       aa  = a_a + ii[i];
1670:       sum = y[i];
1671:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
1672:       z[i] = sum;
1673:     }
1674: #endif
1675:   }
1676:   PetscCall(PetscLogFlops(2.0 * a->nz));
1677:   PetscCall(VecRestoreArrayRead(xx, &x));
1678:   PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1679:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1680:   PetscFunctionReturn(PETSC_SUCCESS);
1681: }

1683: /*
1684:      Adds diagonal pointers to sparse matrix structure.
1685: */
1686: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1687: {
1688:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1689:   PetscInt    i, j, m = A->rmap->n;
1690:   PetscBool   alreadySet = PETSC_TRUE;

1692:   PetscFunctionBegin;
1693:   if (!a->diag) {
1694:     PetscCall(PetscMalloc1(m, &a->diag));
1695:     alreadySet = PETSC_FALSE;
1696:   }
1697:   for (i = 0; i < A->rmap->n; i++) {
1698:     /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
1699:     if (alreadySet) {
1700:       PetscInt pos = a->diag[i];
1701:       if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
1702:     }

1704:     a->diag[i] = a->i[i + 1];
1705:     for (j = a->i[i]; j < a->i[i + 1]; j++) {
1706:       if (a->j[j] == i) {
1707:         a->diag[i] = j;
1708:         break;
1709:       }
1710:     }
1711:   }
1712:   PetscFunctionReturn(PETSC_SUCCESS);
1713: }

1715: static PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1716: {
1717:   Mat_SeqAIJ     *a    = (Mat_SeqAIJ *)A->data;
1718:   const PetscInt *diag = (const PetscInt *)a->diag;
1719:   const PetscInt *ii   = (const PetscInt *)a->i;
1720:   PetscInt        i, *mdiag = NULL;
1721:   PetscInt        cnt = 0; /* how many diagonals are missing */

1723:   PetscFunctionBegin;
1724:   if (!A->preallocated || !a->nz) {
1725:     PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1726:     PetscCall(MatShift_Basic(A, v));
1727:     PetscFunctionReturn(PETSC_SUCCESS);
1728:   }

1730:   if (a->diagonaldense) {
1731:     cnt = 0;
1732:   } else {
1733:     PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1734:     for (i = 0; i < A->rmap->n; i++) {
1735:       if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1736:         cnt++;
1737:         mdiag[i] = 1;
1738:       }
1739:     }
1740:   }
1741:   if (!cnt) {
1742:     PetscCall(MatShift_Basic(A, v));
1743:   } else {
1744:     PetscScalar       *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1745:     PetscInt          *oldj = a->j, *oldi = a->i;
1746:     PetscBool          singlemalloc = a->singlemalloc, free_a = a->free_a, free_ij = a->free_ij;
1747:     const PetscScalar *Aa;

1749:     PetscCall(MatSeqAIJGetArrayRead(A, &Aa)); // sync the host
1750:     PetscCall(MatSeqAIJRestoreArrayRead(A, &Aa));

1752:     a->a = NULL;
1753:     a->j = NULL;
1754:     a->i = NULL;
1755:     /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1756:     for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1757:     PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));

1759:     /* copy old values into new matrix data structure */
1760:     for (i = 0; i < A->rmap->n; i++) {
1761:       PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1762:       if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1763:     }
1764:     PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1765:     PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1766:     if (singlemalloc) {
1767:       PetscCall(PetscFree3(olda, oldj, oldi));
1768:     } else {
1769:       if (free_a) PetscCall(PetscFree(olda));
1770:       if (free_ij) PetscCall(PetscFree(oldj));
1771:       if (free_ij) PetscCall(PetscFree(oldi));
1772:     }
1773:   }
1774:   PetscCall(PetscFree(mdiag));
1775:   a->diagonaldense = PETSC_TRUE;
1776:   PetscFunctionReturn(PETSC_SUCCESS);
1777: }

1779: /*
1780:      Checks for missing diagonals
1781: */
1782: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
1783: {
1784:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1785:   PetscInt   *diag, *ii = a->i, i;

1787:   PetscFunctionBegin;
1788:   *missing = PETSC_FALSE;
1789:   if (A->rmap->n > 0 && !ii) {
1790:     *missing = PETSC_TRUE;
1791:     if (d) *d = 0;
1792:     PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1793:   } else {
1794:     PetscInt n;
1795:     n    = PetscMin(A->rmap->n, A->cmap->n);
1796:     diag = a->diag;
1797:     for (i = 0; i < n; i++) {
1798:       if (diag[i] >= ii[i + 1]) {
1799:         *missing = PETSC_TRUE;
1800:         if (d) *d = i;
1801:         PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
1802:         break;
1803:       }
1804:     }
1805:   }
1806:   PetscFunctionReturn(PETSC_SUCCESS);
1807: }

1809: #include <petscblaslapack.h>
1810: #include <petsc/private/kernels/blockinvert.h>

1812: /*
1813:     Note that values is allocated externally by the PC and then passed into this routine
1814: */
1815: static PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1816: {
1817:   PetscInt        n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1818:   PetscBool       allowzeropivot, zeropivotdetected = PETSC_FALSE;
1819:   const PetscReal shift = 0.0;
1820:   PetscInt        ipvt[5];
1821:   PetscCount      flops = 0;
1822:   PetscScalar     work[25], *v_work;

1824:   PetscFunctionBegin;
1825:   allowzeropivot = PetscNot(A->erroriffailure);
1826:   for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1827:   PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1828:   for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1829:   PetscCall(PetscMalloc1(bsizemax, &indx));
1830:   if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1831:   ncnt = 0;
1832:   for (i = 0; i < nblocks; i++) {
1833:     for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1834:     PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1835:     switch (bsizes[i]) {
1836:     case 1:
1837:       *diag = 1.0 / (*diag);
1838:       break;
1839:     case 2:
1840:       PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1841:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1842:       PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1843:       break;
1844:     case 3:
1845:       PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1846:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1847:       PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1848:       break;
1849:     case 4:
1850:       PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1851:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1852:       PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1853:       break;
1854:     case 5:
1855:       PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1856:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1857:       PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1858:       break;
1859:     case 6:
1860:       PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1861:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1862:       PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1863:       break;
1864:     case 7:
1865:       PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1866:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1867:       PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1868:       break;
1869:     default:
1870:       PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1871:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1872:       PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1873:     }
1874:     ncnt += bsizes[i];
1875:     diag += bsizes[i] * bsizes[i];
1876:     flops += 2 * PetscPowInt(bsizes[i], 3) / 3;
1877:   }
1878:   PetscCall(PetscLogFlops(flops));
1879:   if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1880:   PetscCall(PetscFree(indx));
1881:   PetscFunctionReturn(PETSC_SUCCESS);
1882: }

1884: /*
1885:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1886: */
1887: static PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1888: {
1889:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
1890:   PetscInt         i, *diag, m = A->rmap->n;
1891:   const MatScalar *v;
1892:   PetscScalar     *idiag, *mdiag;

1894:   PetscFunctionBegin;
1895:   if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
1896:   PetscCall(MatMarkDiagonal_SeqAIJ(A));
1897:   diag = a->diag;
1898:   if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); }

1900:   mdiag = a->mdiag;
1901:   idiag = a->idiag;
1902:   PetscCall(MatSeqAIJGetArrayRead(A, &v));
1903:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1904:     for (i = 0; i < m; i++) {
1905:       mdiag[i] = v[diag[i]];
1906:       if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1907:         if (PetscRealPart(fshift)) {
1908:           PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1909:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1910:           A->factorerror_zeropivot_value = 0.0;
1911:           A->factorerror_zeropivot_row   = i;
1912:         } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1913:       }
1914:       idiag[i] = 1.0 / v[diag[i]];
1915:     }
1916:     PetscCall(PetscLogFlops(m));
1917:   } else {
1918:     for (i = 0; i < m; i++) {
1919:       mdiag[i] = v[diag[i]];
1920:       idiag[i] = omega / (fshift + v[diag[i]]);
1921:     }
1922:     PetscCall(PetscLogFlops(2.0 * m));
1923:   }
1924:   a->idiagvalid = PETSC_TRUE;
1925:   PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1926:   PetscFunctionReturn(PETSC_SUCCESS);
1927: }

1929: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1930: {
1931:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1932:   PetscScalar       *x, d, sum, *t, scale;
1933:   const MatScalar   *v, *idiag = NULL, *mdiag, *aa;
1934:   const PetscScalar *b, *bs, *xb, *ts;
1935:   PetscInt           n, m = A->rmap->n, i;
1936:   const PetscInt    *idx, *diag;

1938:   PetscFunctionBegin;
1939:   if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1940:     PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1941:     PetscFunctionReturn(PETSC_SUCCESS);
1942:   }
1943:   its = its * lits;

1945:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1946:   if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
1947:   a->fshift = fshift;
1948:   a->omega  = omega;

1950:   diag  = a->diag;
1951:   t     = a->ssor_work;
1952:   idiag = a->idiag;
1953:   mdiag = a->mdiag;

1955:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1956:   PetscCall(VecGetArray(xx, &x));
1957:   PetscCall(VecGetArrayRead(bb, &b));
1958:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1959:   if (flag == SOR_APPLY_UPPER) {
1960:     /* apply (U + D/omega) to the vector */
1961:     bs = b;
1962:     for (i = 0; i < m; i++) {
1963:       d   = fshift + mdiag[i];
1964:       n   = a->i[i + 1] - diag[i] - 1;
1965:       idx = a->j + diag[i] + 1;
1966:       v   = aa + diag[i] + 1;
1967:       sum = b[i] * d / omega;
1968:       PetscSparseDensePlusDot(sum, bs, v, idx, n);
1969:       x[i] = sum;
1970:     }
1971:     PetscCall(VecRestoreArray(xx, &x));
1972:     PetscCall(VecRestoreArrayRead(bb, &b));
1973:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1974:     PetscCall(PetscLogFlops(a->nz));
1975:     PetscFunctionReturn(PETSC_SUCCESS);
1976:   }

1978:   PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1979:   if (flag & SOR_EISENSTAT) {
1980:     /* Let  A = L + U + D; where L is lower triangular,
1981:     U is upper triangular, E = D/omega; This routine applies

1983:             (L + E)^{-1} A (U + E)^{-1}

1985:     to a vector efficiently using Eisenstat's trick.
1986:     */
1987:     scale = (2.0 / omega) - 1.0;

1989:     /*  x = (E + U)^{-1} b */
1990:     for (i = m - 1; i >= 0; i--) {
1991:       n   = a->i[i + 1] - diag[i] - 1;
1992:       idx = a->j + diag[i] + 1;
1993:       v   = aa + diag[i] + 1;
1994:       sum = b[i];
1995:       PetscSparseDenseMinusDot(sum, x, v, idx, n);
1996:       x[i] = sum * idiag[i];
1997:     }

1999:     /*  t = b - (2*E - D)x */
2000:     v = aa;
2001:     for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];

2003:     /*  t = (E + L)^{-1}t */
2004:     ts   = t;
2005:     diag = a->diag;
2006:     for (i = 0; i < m; i++) {
2007:       n   = diag[i] - a->i[i];
2008:       idx = a->j + a->i[i];
2009:       v   = aa + a->i[i];
2010:       sum = t[i];
2011:       PetscSparseDenseMinusDot(sum, ts, v, idx, n);
2012:       t[i] = sum * idiag[i];
2013:       /*  x = x + t */
2014:       x[i] += t[i];
2015:     }

2017:     PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
2018:     PetscCall(VecRestoreArray(xx, &x));
2019:     PetscCall(VecRestoreArrayRead(bb, &b));
2020:     PetscFunctionReturn(PETSC_SUCCESS);
2021:   }
2022:   if (flag & SOR_ZERO_INITIAL_GUESS) {
2023:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2024:       for (i = 0; i < m; i++) {
2025:         n   = diag[i] - a->i[i];
2026:         idx = a->j + a->i[i];
2027:         v   = aa + a->i[i];
2028:         sum = b[i];
2029:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2030:         t[i] = sum;
2031:         x[i] = sum * idiag[i];
2032:       }
2033:       xb = t;
2034:       PetscCall(PetscLogFlops(a->nz));
2035:     } else xb = b;
2036:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2037:       for (i = m - 1; i >= 0; i--) {
2038:         n   = a->i[i + 1] - diag[i] - 1;
2039:         idx = a->j + diag[i] + 1;
2040:         v   = aa + diag[i] + 1;
2041:         sum = xb[i];
2042:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2043:         if (xb == b) {
2044:           x[i] = sum * idiag[i];
2045:         } else {
2046:           x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2047:         }
2048:       }
2049:       PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2050:     }
2051:     its--;
2052:   }
2053:   while (its--) {
2054:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2055:       for (i = 0; i < m; i++) {
2056:         /* lower */
2057:         n   = diag[i] - a->i[i];
2058:         idx = a->j + a->i[i];
2059:         v   = aa + a->i[i];
2060:         sum = b[i];
2061:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2062:         t[i] = sum; /* save application of the lower-triangular part */
2063:         /* upper */
2064:         n   = a->i[i + 1] - diag[i] - 1;
2065:         idx = a->j + diag[i] + 1;
2066:         v   = aa + diag[i] + 1;
2067:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2068:         x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2069:       }
2070:       xb = t;
2071:       PetscCall(PetscLogFlops(2.0 * a->nz));
2072:     } else xb = b;
2073:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2074:       for (i = m - 1; i >= 0; i--) {
2075:         sum = xb[i];
2076:         if (xb == b) {
2077:           /* whole matrix (no checkpointing available) */
2078:           n   = a->i[i + 1] - a->i[i];
2079:           idx = a->j + a->i[i];
2080:           v   = aa + a->i[i];
2081:           PetscSparseDenseMinusDot(sum, x, v, idx, n);
2082:           x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2083:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2084:           n   = a->i[i + 1] - diag[i] - 1;
2085:           idx = a->j + diag[i] + 1;
2086:           v   = aa + diag[i] + 1;
2087:           PetscSparseDenseMinusDot(sum, x, v, idx, n);
2088:           x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2089:         }
2090:       }
2091:       if (xb == b) {
2092:         PetscCall(PetscLogFlops(2.0 * a->nz));
2093:       } else {
2094:         PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2095:       }
2096:     }
2097:   }
2098:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2099:   PetscCall(VecRestoreArray(xx, &x));
2100:   PetscCall(VecRestoreArrayRead(bb, &b));
2101:   PetscFunctionReturn(PETSC_SUCCESS);
2102: }

2104: static PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2105: {
2106:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

2108:   PetscFunctionBegin;
2109:   info->block_size   = 1.0;
2110:   info->nz_allocated = a->maxnz;
2111:   info->nz_used      = a->nz;
2112:   info->nz_unneeded  = (a->maxnz - a->nz);
2113:   info->assemblies   = A->num_ass;
2114:   info->mallocs      = A->info.mallocs;
2115:   info->memory       = 0; /* REVIEW ME */
2116:   if (A->factortype) {
2117:     info->fill_ratio_given  = A->info.fill_ratio_given;
2118:     info->fill_ratio_needed = A->info.fill_ratio_needed;
2119:     info->factor_mallocs    = A->info.factor_mallocs;
2120:   } else {
2121:     info->fill_ratio_given  = 0;
2122:     info->fill_ratio_needed = 0;
2123:     info->factor_mallocs    = 0;
2124:   }
2125:   PetscFunctionReturn(PETSC_SUCCESS);
2126: }

2128: static PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2129: {
2130:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2131:   PetscInt           i, m = A->rmap->n - 1;
2132:   const PetscScalar *xx;
2133:   PetscScalar       *bb, *aa;
2134:   PetscInt           d = 0;

2136:   PetscFunctionBegin;
2137:   if (x && b) {
2138:     PetscCall(VecGetArrayRead(x, &xx));
2139:     PetscCall(VecGetArray(b, &bb));
2140:     for (i = 0; i < N; i++) {
2141:       PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2142:       if (rows[i] >= A->cmap->n) continue;
2143:       bb[rows[i]] = diag * xx[rows[i]];
2144:     }
2145:     PetscCall(VecRestoreArrayRead(x, &xx));
2146:     PetscCall(VecRestoreArray(b, &bb));
2147:   }

2149:   PetscCall(MatSeqAIJGetArray(A, &aa));
2150:   if (a->keepnonzeropattern) {
2151:     for (i = 0; i < N; i++) {
2152:       PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2153:       PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2154:     }
2155:     if (diag != 0.0) {
2156:       for (i = 0; i < N; i++) {
2157:         d = rows[i];
2158:         if (rows[i] >= A->cmap->n) continue;
2159:         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);
2160:       }
2161:       for (i = 0; i < N; i++) {
2162:         if (rows[i] >= A->cmap->n) continue;
2163:         aa[a->diag[rows[i]]] = diag;
2164:       }
2165:     }
2166:   } else {
2167:     if (diag != 0.0) {
2168:       for (i = 0; i < N; i++) {
2169:         PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2170:         if (a->ilen[rows[i]] > 0) {
2171:           if (rows[i] >= A->cmap->n) {
2172:             a->ilen[rows[i]] = 0;
2173:           } else {
2174:             a->ilen[rows[i]]    = 1;
2175:             aa[a->i[rows[i]]]   = diag;
2176:             a->j[a->i[rows[i]]] = rows[i];
2177:           }
2178:         } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2179:           PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2180:         }
2181:       }
2182:     } else {
2183:       for (i = 0; i < N; i++) {
2184:         PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2185:         a->ilen[rows[i]] = 0;
2186:       }
2187:     }
2188:     A->nonzerostate++;
2189:   }
2190:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
2191:   PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2192:   PetscFunctionReturn(PETSC_SUCCESS);
2193: }

2195: static PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2196: {
2197:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2198:   PetscInt           i, j, m = A->rmap->n - 1, d = 0;
2199:   PetscBool          missing, *zeroed, vecs = PETSC_FALSE;
2200:   const PetscScalar *xx;
2201:   PetscScalar       *bb, *aa;

2203:   PetscFunctionBegin;
2204:   if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2205:   PetscCall(MatSeqAIJGetArray(A, &aa));
2206:   if (x && b) {
2207:     PetscCall(VecGetArrayRead(x, &xx));
2208:     PetscCall(VecGetArray(b, &bb));
2209:     vecs = PETSC_TRUE;
2210:   }
2211:   PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2212:   for (i = 0; i < N; i++) {
2213:     PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2214:     PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aa, a->i[rows[i]]), a->ilen[rows[i]]));

2216:     zeroed[rows[i]] = PETSC_TRUE;
2217:   }
2218:   for (i = 0; i < A->rmap->n; i++) {
2219:     if (!zeroed[i]) {
2220:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
2221:         if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2222:           if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2223:           aa[j] = 0.0;
2224:         }
2225:       }
2226:     } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
2227:   }
2228:   if (x && b) {
2229:     PetscCall(VecRestoreArrayRead(x, &xx));
2230:     PetscCall(VecRestoreArray(b, &bb));
2231:   }
2232:   PetscCall(PetscFree(zeroed));
2233:   if (diag != 0.0) {
2234:     PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
2235:     if (missing) {
2236:       for (i = 0; i < N; i++) {
2237:         if (rows[i] >= A->cmap->N) continue;
2238:         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]);
2239:         PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2240:       }
2241:     } else {
2242:       for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
2243:     }
2244:   }
2245:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
2246:   PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2247:   PetscFunctionReturn(PETSC_SUCCESS);
2248: }

2250: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2251: {
2252:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2253:   const PetscScalar *aa;

2255:   PetscFunctionBegin;
2256:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2257:   *nz = a->i[row + 1] - a->i[row];
2258:   if (v) *v = PetscSafePointerPlusOffset((PetscScalar *)aa, a->i[row]);
2259:   if (idx) {
2260:     if (*nz && a->j) *idx = a->j + a->i[row];
2261:     else *idx = NULL;
2262:   }
2263:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2264:   PetscFunctionReturn(PETSC_SUCCESS);
2265: }

2267: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2268: {
2269:   PetscFunctionBegin;
2270:   PetscFunctionReturn(PETSC_SUCCESS);
2271: }

2273: static PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
2274: {
2275:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
2276:   const MatScalar *v;
2277:   PetscReal        sum = 0.0;
2278:   PetscInt         i, j;

2280:   PetscFunctionBegin;
2281:   PetscCall(MatSeqAIJGetArrayRead(A, &v));
2282:   if (type == NORM_FROBENIUS) {
2283: #if defined(PETSC_USE_REAL___FP16)
2284:     PetscBLASInt one = 1, nz = a->nz;
2285:     PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
2286: #else
2287:     for (i = 0; i < a->nz; i++) {
2288:       sum += PetscRealPart(PetscConj(*v) * (*v));
2289:       v++;
2290:     }
2291:     *nrm = PetscSqrtReal(sum);
2292: #endif
2293:     PetscCall(PetscLogFlops(2.0 * a->nz));
2294:   } else if (type == NORM_1) {
2295:     PetscReal *tmp;
2296:     PetscInt  *jj = a->j;
2297:     PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp));
2298:     *nrm = 0.0;
2299:     for (j = 0; j < a->nz; j++) {
2300:       tmp[*jj++] += PetscAbsScalar(*v);
2301:       v++;
2302:     }
2303:     for (j = 0; j < A->cmap->n; j++) {
2304:       if (tmp[j] > *nrm) *nrm = tmp[j];
2305:     }
2306:     PetscCall(PetscFree(tmp));
2307:     PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2308:   } else if (type == NORM_INFINITY) {
2309:     *nrm = 0.0;
2310:     for (j = 0; j < A->rmap->n; j++) {
2311:       const PetscScalar *v2 = PetscSafePointerPlusOffset(v, a->i[j]);
2312:       sum                   = 0.0;
2313:       for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
2314:         sum += PetscAbsScalar(*v2);
2315:         v2++;
2316:       }
2317:       if (sum > *nrm) *nrm = sum;
2318:     }
2319:     PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2320:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
2321:   PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
2322:   PetscFunctionReturn(PETSC_SUCCESS);
2323: }

2325: static PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2326: {
2327:   Mat_SeqAIJ      *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2328:   PetscInt        *adx, *bdx, *aii, *bii, *aptr, *bptr;
2329:   const MatScalar *va, *vb;
2330:   PetscInt         ma, na, mb, nb, i;

2332:   PetscFunctionBegin;
2333:   PetscCall(MatGetSize(A, &ma, &na));
2334:   PetscCall(MatGetSize(B, &mb, &nb));
2335:   if (ma != nb || na != mb) {
2336:     *f = PETSC_FALSE;
2337:     PetscFunctionReturn(PETSC_SUCCESS);
2338:   }
2339:   PetscCall(MatSeqAIJGetArrayRead(A, &va));
2340:   PetscCall(MatSeqAIJGetArrayRead(B, &vb));
2341:   aii = aij->i;
2342:   bii = bij->i;
2343:   adx = aij->j;
2344:   bdx = bij->j;
2345:   PetscCall(PetscMalloc1(ma, &aptr));
2346:   PetscCall(PetscMalloc1(mb, &bptr));
2347:   for (i = 0; i < ma; i++) aptr[i] = aii[i];
2348:   for (i = 0; i < mb; i++) bptr[i] = bii[i];

2350:   *f = PETSC_TRUE;
2351:   for (i = 0; i < ma; i++) {
2352:     while (aptr[i] < aii[i + 1]) {
2353:       PetscInt    idc, idr;
2354:       PetscScalar vc, vr;
2355:       /* column/row index/value */
2356:       idc = adx[aptr[i]];
2357:       idr = bdx[bptr[idc]];
2358:       vc  = va[aptr[i]];
2359:       vr  = vb[bptr[idc]];
2360:       if (i != idr || PetscAbsScalar(vc - vr) > tol) {
2361:         *f = PETSC_FALSE;
2362:         goto done;
2363:       } else {
2364:         aptr[i]++;
2365:         if (B || i != idc) bptr[idc]++;
2366:       }
2367:     }
2368:   }
2369: done:
2370:   PetscCall(PetscFree(aptr));
2371:   PetscCall(PetscFree(bptr));
2372:   PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
2373:   PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
2374:   PetscFunctionReturn(PETSC_SUCCESS);
2375: }

2377: static PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2378: {
2379:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2380:   PetscInt   *adx, *bdx, *aii, *bii, *aptr, *bptr;
2381:   MatScalar  *va, *vb;
2382:   PetscInt    ma, na, mb, nb, i;

2384:   PetscFunctionBegin;
2385:   PetscCall(MatGetSize(A, &ma, &na));
2386:   PetscCall(MatGetSize(B, &mb, &nb));
2387:   if (ma != nb || na != mb) {
2388:     *f = PETSC_FALSE;
2389:     PetscFunctionReturn(PETSC_SUCCESS);
2390:   }
2391:   aii = aij->i;
2392:   bii = bij->i;
2393:   adx = aij->j;
2394:   bdx = bij->j;
2395:   va  = aij->a;
2396:   vb  = bij->a;
2397:   PetscCall(PetscMalloc1(ma, &aptr));
2398:   PetscCall(PetscMalloc1(mb, &bptr));
2399:   for (i = 0; i < ma; i++) aptr[i] = aii[i];
2400:   for (i = 0; i < mb; i++) bptr[i] = bii[i];

2402:   *f = PETSC_TRUE;
2403:   for (i = 0; i < ma; i++) {
2404:     while (aptr[i] < aii[i + 1]) {
2405:       PetscInt    idc, idr;
2406:       PetscScalar vc, vr;
2407:       /* column/row index/value */
2408:       idc = adx[aptr[i]];
2409:       idr = bdx[bptr[idc]];
2410:       vc  = va[aptr[i]];
2411:       vr  = vb[bptr[idc]];
2412:       if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
2413:         *f = PETSC_FALSE;
2414:         goto done;
2415:       } else {
2416:         aptr[i]++;
2417:         if (B || i != idc) bptr[idc]++;
2418:       }
2419:     }
2420:   }
2421: done:
2422:   PetscCall(PetscFree(aptr));
2423:   PetscCall(PetscFree(bptr));
2424:   PetscFunctionReturn(PETSC_SUCCESS);
2425: }

2427: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2428: {
2429:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2430:   const PetscScalar *l, *r;
2431:   PetscScalar        x;
2432:   MatScalar         *v;
2433:   PetscInt           i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2434:   const PetscInt    *jj;

2436:   PetscFunctionBegin;
2437:   if (ll) {
2438:     /* The local size is used so that VecMPI can be passed to this routine
2439:        by MatDiagonalScale_MPIAIJ */
2440:     PetscCall(VecGetLocalSize(ll, &m));
2441:     PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2442:     PetscCall(VecGetArrayRead(ll, &l));
2443:     PetscCall(MatSeqAIJGetArray(A, &v));
2444:     for (i = 0; i < m; i++) {
2445:       x = l[i];
2446:       M = a->i[i + 1] - a->i[i];
2447:       for (j = 0; j < M; j++) (*v++) *= x;
2448:     }
2449:     PetscCall(VecRestoreArrayRead(ll, &l));
2450:     PetscCall(PetscLogFlops(nz));
2451:     PetscCall(MatSeqAIJRestoreArray(A, &v));
2452:   }
2453:   if (rr) {
2454:     PetscCall(VecGetLocalSize(rr, &n));
2455:     PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2456:     PetscCall(VecGetArrayRead(rr, &r));
2457:     PetscCall(MatSeqAIJGetArray(A, &v));
2458:     jj = a->j;
2459:     for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2460:     PetscCall(MatSeqAIJRestoreArray(A, &v));
2461:     PetscCall(VecRestoreArrayRead(rr, &r));
2462:     PetscCall(PetscLogFlops(nz));
2463:   }
2464:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
2465:   PetscFunctionReturn(PETSC_SUCCESS);
2466: }

2468: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2469: {
2470:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data, *c;
2471:   PetscInt          *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2472:   PetscInt           row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2473:   const PetscInt    *irow, *icol;
2474:   const PetscScalar *aa;
2475:   PetscInt           nrows, ncols;
2476:   PetscInt          *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2477:   MatScalar         *a_new, *mat_a, *c_a;
2478:   Mat                C;
2479:   PetscBool          stride;

2481:   PetscFunctionBegin;
2482:   PetscCall(ISGetIndices(isrow, &irow));
2483:   PetscCall(ISGetLocalSize(isrow, &nrows));
2484:   PetscCall(ISGetLocalSize(iscol, &ncols));

2486:   PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2487:   if (stride) {
2488:     PetscCall(ISStrideGetInfo(iscol, &first, &step));
2489:   } else {
2490:     first = 0;
2491:     step  = 0;
2492:   }
2493:   if (stride && step == 1) {
2494:     /* special case of contiguous rows */
2495:     PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2496:     /* loop over new rows determining lens and starting points */
2497:     for (i = 0; i < nrows; i++) {
2498:       kstart    = ai[irow[i]];
2499:       kend      = kstart + ailen[irow[i]];
2500:       starts[i] = kstart;
2501:       for (k = kstart; k < kend; k++) {
2502:         if (aj[k] >= first) {
2503:           starts[i] = k;
2504:           break;
2505:         }
2506:       }
2507:       sum = 0;
2508:       while (k < kend) {
2509:         if (aj[k++] >= first + ncols) break;
2510:         sum++;
2511:       }
2512:       lens[i] = sum;
2513:     }
2514:     /* create submatrix */
2515:     if (scall == MAT_REUSE_MATRIX) {
2516:       PetscInt n_cols, n_rows;
2517:       PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2518:       PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2519:       PetscCall(MatZeroEntries(*B));
2520:       C = *B;
2521:     } else {
2522:       PetscInt rbs, cbs;
2523:       PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2524:       PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2525:       PetscCall(ISGetBlockSize(isrow, &rbs));
2526:       PetscCall(ISGetBlockSize(iscol, &cbs));
2527:       PetscCall(MatSetBlockSizes(C, rbs, cbs));
2528:       PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2529:       PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2530:     }
2531:     c = (Mat_SeqAIJ *)C->data;

2533:     /* loop over rows inserting into submatrix */
2534:     PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2535:     j_new = c->j;
2536:     i_new = c->i;
2537:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2538:     for (i = 0; i < nrows; i++) {
2539:       ii    = starts[i];
2540:       lensi = lens[i];
2541:       if (lensi) {
2542:         for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2543:         PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2544:         a_new += lensi;
2545:       }
2546:       i_new[i + 1] = i_new[i] + lensi;
2547:       c->ilen[i]   = lensi;
2548:     }
2549:     PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2550:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2551:     PetscCall(PetscFree2(lens, starts));
2552:   } else {
2553:     PetscCall(ISGetIndices(iscol, &icol));
2554:     PetscCall(PetscCalloc1(oldcols, &smap));
2555:     PetscCall(PetscMalloc1(1 + nrows, &lens));
2556:     for (i = 0; i < ncols; i++) {
2557:       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);
2558:       smap[icol[i]] = i + 1;
2559:     }

2561:     /* determine lens of each row */
2562:     for (i = 0; i < nrows; i++) {
2563:       kstart  = ai[irow[i]];
2564:       kend    = kstart + a->ilen[irow[i]];
2565:       lens[i] = 0;
2566:       for (k = kstart; k < kend; k++) {
2567:         if (smap[aj[k]]) lens[i]++;
2568:       }
2569:     }
2570:     /* Create and fill new matrix */
2571:     if (scall == MAT_REUSE_MATRIX) {
2572:       PetscBool equal;

2574:       c = (Mat_SeqAIJ *)((*B)->data);
2575:       PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2576:       PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2577:       PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong number of nonzeros");
2578:       PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2579:       C = *B;
2580:     } else {
2581:       PetscInt rbs, cbs;
2582:       PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2583:       PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2584:       PetscCall(ISGetBlockSize(isrow, &rbs));
2585:       PetscCall(ISGetBlockSize(iscol, &cbs));
2586:       if (rbs > 1 || cbs > 1) PetscCall(MatSetBlockSizes(C, rbs, cbs));
2587:       PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2588:       PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2589:     }
2590:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));

2592:     c = (Mat_SeqAIJ *)C->data;
2593:     PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2594:     for (i = 0; i < nrows; i++) {
2595:       row      = irow[i];
2596:       kstart   = ai[row];
2597:       kend     = kstart + a->ilen[row];
2598:       mat_i    = c->i[i];
2599:       mat_j    = PetscSafePointerPlusOffset(c->j, mat_i);
2600:       mat_a    = PetscSafePointerPlusOffset(c_a, mat_i);
2601:       mat_ilen = c->ilen + i;
2602:       for (k = kstart; k < kend; k++) {
2603:         if ((tcol = smap[a->j[k]])) {
2604:           *mat_j++ = tcol - 1;
2605:           *mat_a++ = aa[k];
2606:           (*mat_ilen)++;
2607:         }
2608:       }
2609:     }
2610:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2611:     /* Free work space */
2612:     PetscCall(ISRestoreIndices(iscol, &icol));
2613:     PetscCall(PetscFree(smap));
2614:     PetscCall(PetscFree(lens));
2615:     /* sort */
2616:     for (i = 0; i < nrows; i++) {
2617:       PetscInt ilen;

2619:       mat_i = c->i[i];
2620:       mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2621:       mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2622:       ilen  = c->ilen[i];
2623:       PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2624:     }
2625:     PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2626:   }
2627: #if defined(PETSC_HAVE_DEVICE)
2628:   PetscCall(MatBindToCPU(C, A->boundtocpu));
2629: #endif
2630:   PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2631:   PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));

2633:   PetscCall(ISRestoreIndices(isrow, &irow));
2634:   *B = C;
2635:   PetscFunctionReturn(PETSC_SUCCESS);
2636: }

2638: static PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2639: {
2640:   Mat B;

2642:   PetscFunctionBegin;
2643:   if (scall == MAT_INITIAL_MATRIX) {
2644:     PetscCall(MatCreate(subComm, &B));
2645:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2646:     PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2647:     PetscCall(MatSetType(B, MATSEQAIJ));
2648:     PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2649:     *subMat = B;
2650:   } else {
2651:     PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2652:   }
2653:   PetscFunctionReturn(PETSC_SUCCESS);
2654: }

2656: static PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2657: {
2658:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2659:   Mat         outA;
2660:   PetscBool   row_identity, col_identity;

2662:   PetscFunctionBegin;
2663:   PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");

2665:   PetscCall(ISIdentity(row, &row_identity));
2666:   PetscCall(ISIdentity(col, &col_identity));

2668:   outA             = inA;
2669:   outA->factortype = MAT_FACTOR_LU;
2670:   PetscCall(PetscFree(inA->solvertype));
2671:   PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));

2673:   PetscCall(PetscObjectReference((PetscObject)row));
2674:   PetscCall(ISDestroy(&a->row));

2676:   a->row = row;

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

2681:   a->col = col;

2683:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2684:   PetscCall(ISDestroy(&a->icol));
2685:   PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));

2687:   if (!a->solve_work) { /* this matrix may have been factored before */
2688:     PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
2689:   }

2691:   PetscCall(MatMarkDiagonal_SeqAIJ(inA));
2692:   if (row_identity && col_identity) {
2693:     PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2694:   } else {
2695:     PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2696:   }
2697:   PetscFunctionReturn(PETSC_SUCCESS);
2698: }

2700: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2701: {
2702:   Mat_SeqAIJ  *a = (Mat_SeqAIJ *)inA->data;
2703:   PetscScalar *v;
2704:   PetscBLASInt one = 1, bnz;

2706:   PetscFunctionBegin;
2707:   PetscCall(MatSeqAIJGetArray(inA, &v));
2708:   PetscCall(PetscBLASIntCast(a->nz, &bnz));
2709:   PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2710:   PetscCall(PetscLogFlops(a->nz));
2711:   PetscCall(MatSeqAIJRestoreArray(inA, &v));
2712:   PetscCall(MatSeqAIJInvalidateDiagonal(inA));
2713:   PetscFunctionReturn(PETSC_SUCCESS);
2714: }

2716: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2717: {
2718:   PetscInt i;

2720:   PetscFunctionBegin;
2721:   if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2722:     PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));

2724:     for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2725:     PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));

2727:     if (submatj->rbuf1) {
2728:       PetscCall(PetscFree(submatj->rbuf1[0]));
2729:       PetscCall(PetscFree(submatj->rbuf1));
2730:     }

2732:     for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2733:     PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2734:     PetscCall(PetscFree(submatj->pa));
2735:   }

2737: #if defined(PETSC_USE_CTABLE)
2738:   PetscCall(PetscHMapIDestroy(&submatj->rmap));
2739:   if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2740:   PetscCall(PetscFree(submatj->rmap_loc));
2741: #else
2742:   PetscCall(PetscFree(submatj->rmap));
2743: #endif

2745:   if (!submatj->allcolumns) {
2746: #if defined(PETSC_USE_CTABLE)
2747:     PetscCall(PetscHMapIDestroy((PetscHMapI *)&submatj->cmap));
2748: #else
2749:     PetscCall(PetscFree(submatj->cmap));
2750: #endif
2751:   }
2752:   PetscCall(PetscFree(submatj->row2proc));

2754:   PetscCall(PetscFree(submatj));
2755:   PetscFunctionReturn(PETSC_SUCCESS);
2756: }

2758: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2759: {
2760:   Mat_SeqAIJ  *c       = (Mat_SeqAIJ *)C->data;
2761:   Mat_SubSppt *submatj = c->submatis1;

2763:   PetscFunctionBegin;
2764:   PetscCall((*submatj->destroy)(C));
2765:   PetscCall(MatDestroySubMatrix_Private(submatj));
2766:   PetscFunctionReturn(PETSC_SUCCESS);
2767: }

2769: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2770: static PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2771: {
2772:   PetscInt     i;
2773:   Mat          C;
2774:   Mat_SeqAIJ  *c;
2775:   Mat_SubSppt *submatj;

2777:   PetscFunctionBegin;
2778:   for (i = 0; i < n; i++) {
2779:     C       = (*mat)[i];
2780:     c       = (Mat_SeqAIJ *)C->data;
2781:     submatj = c->submatis1;
2782:     if (submatj) {
2783:       if (--((PetscObject)C)->refct <= 0) {
2784:         PetscCall(PetscFree(C->factorprefix));
2785:         PetscCall((*submatj->destroy)(C));
2786:         PetscCall(MatDestroySubMatrix_Private(submatj));
2787:         PetscCall(PetscFree(C->defaultvectype));
2788:         PetscCall(PetscFree(C->defaultrandtype));
2789:         PetscCall(PetscLayoutDestroy(&C->rmap));
2790:         PetscCall(PetscLayoutDestroy(&C->cmap));
2791:         PetscCall(PetscHeaderDestroy(&C));
2792:       }
2793:     } else {
2794:       PetscCall(MatDestroy(&C));
2795:     }
2796:   }

2798:   /* Destroy Dummy submatrices created for reuse */
2799:   PetscCall(MatDestroySubMatrices_Dummy(n, mat));

2801:   PetscCall(PetscFree(*mat));
2802:   PetscFunctionReturn(PETSC_SUCCESS);
2803: }

2805: static PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2806: {
2807:   PetscInt i;

2809:   PetscFunctionBegin;
2810:   if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));

2812:   for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2813:   PetscFunctionReturn(PETSC_SUCCESS);
2814: }

2816: static PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2817: {
2818:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
2819:   PetscInt        row, i, j, k, l, ll, m, n, *nidx, isz, val;
2820:   const PetscInt *idx;
2821:   PetscInt        start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1;
2822:   PetscBT         table;

2824:   PetscFunctionBegin;
2825:   m  = A->rmap->n / bs;
2826:   ai = a->i;
2827:   aj = a->j;

2829:   PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");

2831:   PetscCall(PetscMalloc1(m + 1, &nidx));
2832:   PetscCall(PetscBTCreate(m, &table));

2834:   for (i = 0; i < is_max; i++) {
2835:     /* Initialize the two local arrays */
2836:     isz = 0;
2837:     PetscCall(PetscBTMemzero(m, table));

2839:     /* Extract the indices, assume there can be duplicate entries */
2840:     PetscCall(ISGetIndices(is[i], &idx));
2841:     PetscCall(ISGetLocalSize(is[i], &n));

2843:     if (bs > 1) {
2844:       /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2845:       for (j = 0; j < n; ++j) {
2846:         if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2847:       }
2848:       PetscCall(ISRestoreIndices(is[i], &idx));
2849:       PetscCall(ISDestroy(&is[i]));

2851:       k = 0;
2852:       for (j = 0; j < ov; j++) { /* for each overlap */
2853:         n = isz;
2854:         for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2855:           for (ll = 0; ll < bs; ll++) {
2856:             row   = bs * nidx[k] + ll;
2857:             start = ai[row];
2858:             end   = ai[row + 1];
2859:             for (l = start; l < end; l++) {
2860:               val = aj[l] / bs;
2861:               if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2862:             }
2863:           }
2864:         }
2865:       }
2866:       PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2867:     } else {
2868:       /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2869:       for (j = 0; j < n; ++j) {
2870:         if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2871:       }
2872:       PetscCall(ISRestoreIndices(is[i], &idx));
2873:       PetscCall(ISDestroy(&is[i]));

2875:       k = 0;
2876:       for (j = 0; j < ov; j++) { /* for each overlap */
2877:         n = isz;
2878:         for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2879:           row   = nidx[k];
2880:           start = ai[row];
2881:           end   = ai[row + 1];
2882:           for (l = start; l < end; l++) {
2883:             val = aj[l];
2884:             if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2885:           }
2886:         }
2887:       }
2888:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2889:     }
2890:   }
2891:   PetscCall(PetscBTDestroy(&table));
2892:   PetscCall(PetscFree(nidx));
2893:   PetscFunctionReturn(PETSC_SUCCESS);
2894: }

2896: static PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2897: {
2898:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
2899:   PetscInt        i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2900:   const PetscInt *row, *col;
2901:   PetscInt       *cnew, j, *lens;
2902:   IS              icolp, irowp;
2903:   PetscInt       *cwork = NULL;
2904:   PetscScalar    *vwork = NULL;

2906:   PetscFunctionBegin;
2907:   PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2908:   PetscCall(ISGetIndices(irowp, &row));
2909:   PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2910:   PetscCall(ISGetIndices(icolp, &col));

2912:   /* determine lengths of permuted rows */
2913:   PetscCall(PetscMalloc1(m + 1, &lens));
2914:   for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2915:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2916:   PetscCall(MatSetSizes(*B, m, n, m, n));
2917:   PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2918:   PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2919:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2920:   PetscCall(PetscFree(lens));

2922:   PetscCall(PetscMalloc1(n, &cnew));
2923:   for (i = 0; i < m; i++) {
2924:     PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2925:     for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2926:     PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2927:     PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2928:   }
2929:   PetscCall(PetscFree(cnew));

2931:   (*B)->assembled = PETSC_FALSE;

2933: #if defined(PETSC_HAVE_DEVICE)
2934:   PetscCall(MatBindToCPU(*B, A->boundtocpu));
2935: #endif
2936:   PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2937:   PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2938:   PetscCall(ISRestoreIndices(irowp, &row));
2939:   PetscCall(ISRestoreIndices(icolp, &col));
2940:   PetscCall(ISDestroy(&irowp));
2941:   PetscCall(ISDestroy(&icolp));
2942:   if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2943:   PetscFunctionReturn(PETSC_SUCCESS);
2944: }

2946: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2947: {
2948:   PetscFunctionBegin;
2949:   /* If the two matrices have the same copy implementation, use fast copy. */
2950:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2951:     Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2952:     Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;
2953:     const PetscScalar *aa;

2955:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2956:     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]);
2957:     PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n]));
2958:     PetscCall(PetscObjectStateIncrease((PetscObject)B));
2959:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2960:   } else {
2961:     PetscCall(MatCopy_Basic(A, B, str));
2962:   }
2963:   PetscFunctionReturn(PETSC_SUCCESS);
2964: }

2966: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2967: {
2968:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

2970:   PetscFunctionBegin;
2971:   *array = a->a;
2972:   PetscFunctionReturn(PETSC_SUCCESS);
2973: }

2975: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2976: {
2977:   PetscFunctionBegin;
2978:   *array = NULL;
2979:   PetscFunctionReturn(PETSC_SUCCESS);
2980: }

2982: /*
2983:    Computes the number of nonzeros per row needed for preallocation when X and Y
2984:    have different nonzero structure.
2985: */
2986: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
2987: {
2988:   PetscInt i, j, k, nzx, nzy;

2990:   PetscFunctionBegin;
2991:   /* Set the number of nonzeros in the new matrix */
2992:   for (i = 0; i < m; i++) {
2993:     const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2994:     nzx    = xi[i + 1] - xi[i];
2995:     nzy    = yi[i + 1] - yi[i];
2996:     nnz[i] = 0;
2997:     for (j = 0, k = 0; j < nzx; j++) {                  /* Point in X */
2998:       for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
2999:       if (k < nzy && yjj[k] == xjj[j]) k++;             /* Skip duplicate */
3000:       nnz[i]++;
3001:     }
3002:     for (; k < nzy; k++) nnz[i]++;
3003:   }
3004:   PetscFunctionReturn(PETSC_SUCCESS);
3005: }

3007: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
3008: {
3009:   PetscInt    m = Y->rmap->N;
3010:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
3011:   Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;

3013:   PetscFunctionBegin;
3014:   /* Set the number of nonzeros in the new matrix */
3015:   PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
3016:   PetscFunctionReturn(PETSC_SUCCESS);
3017: }

3019: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
3020: {
3021:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;

3023:   PetscFunctionBegin;
3024:   if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
3025:     PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
3026:     if (e) {
3027:       PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
3028:       if (e) {
3029:         PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
3030:         if (e) str = SAME_NONZERO_PATTERN;
3031:       }
3032:     }
3033:     if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
3034:   }
3035:   if (str == SAME_NONZERO_PATTERN) {
3036:     const PetscScalar *xa;
3037:     PetscScalar       *ya, alpha = a;
3038:     PetscBLASInt       one = 1, bnz;

3040:     PetscCall(PetscBLASIntCast(x->nz, &bnz));
3041:     PetscCall(MatSeqAIJGetArray(Y, &ya));
3042:     PetscCall(MatSeqAIJGetArrayRead(X, &xa));
3043:     PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
3044:     PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
3045:     PetscCall(MatSeqAIJRestoreArray(Y, &ya));
3046:     PetscCall(PetscLogFlops(2.0 * bnz));
3047:     PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3048:     PetscCall(PetscObjectStateIncrease((PetscObject)Y));
3049:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3050:     PetscCall(MatAXPY_Basic(Y, a, X, str));
3051:   } else {
3052:     Mat       B;
3053:     PetscInt *nnz;
3054:     PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
3055:     PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
3056:     PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
3057:     PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
3058:     PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
3059:     PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
3060:     PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
3061:     PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
3062:     PetscCall(MatHeaderMerge(Y, &B));
3063:     PetscCall(MatSeqAIJCheckInode(Y));
3064:     PetscCall(PetscFree(nnz));
3065:   }
3066:   PetscFunctionReturn(PETSC_SUCCESS);
3067: }

3069: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3070: {
3071: #if defined(PETSC_USE_COMPLEX)
3072:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ *)mat->data;
3073:   PetscInt     i, nz;
3074:   PetscScalar *a;

3076:   PetscFunctionBegin;
3077:   nz = aij->nz;
3078:   PetscCall(MatSeqAIJGetArray(mat, &a));
3079:   for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
3080:   PetscCall(MatSeqAIJRestoreArray(mat, &a));
3081: #else
3082:   PetscFunctionBegin;
3083: #endif
3084:   PetscFunctionReturn(PETSC_SUCCESS);
3085: }

3087: static PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3088: {
3089:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3090:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3091:   PetscReal        atmp;
3092:   PetscScalar     *x;
3093:   const MatScalar *aa, *av;

3095:   PetscFunctionBegin;
3096:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3097:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3098:   aa = av;
3099:   ai = a->i;
3100:   aj = a->j;

3102:   PetscCall(VecSet(v, 0.0));
3103:   PetscCall(VecGetArrayWrite(v, &x));
3104:   PetscCall(VecGetLocalSize(v, &n));
3105:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3106:   for (i = 0; i < m; i++) {
3107:     ncols = ai[1] - ai[0];
3108:     ai++;
3109:     for (j = 0; j < ncols; j++) {
3110:       atmp = PetscAbsScalar(*aa);
3111:       if (PetscAbsScalar(x[i]) < atmp) {
3112:         x[i] = atmp;
3113:         if (idx) idx[i] = *aj;
3114:       }
3115:       aa++;
3116:       aj++;
3117:     }
3118:   }
3119:   PetscCall(VecRestoreArrayWrite(v, &x));
3120:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3121:   PetscFunctionReturn(PETSC_SUCCESS);
3122: }

3124: static PetscErrorCode MatGetRowSumAbs_SeqAIJ(Mat A, Vec v)
3125: {
3126:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3127:   PetscInt         i, j, m = A->rmap->n, *ai, ncols, n;
3128:   PetscScalar     *x;
3129:   const MatScalar *aa, *av;

3131:   PetscFunctionBegin;
3132:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3133:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3134:   aa = av;
3135:   ai = a->i;

3137:   PetscCall(VecSet(v, 0.0));
3138:   PetscCall(VecGetArrayWrite(v, &x));
3139:   PetscCall(VecGetLocalSize(v, &n));
3140:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3141:   for (i = 0; i < m; i++) {
3142:     ncols = ai[1] - ai[0];
3143:     ai++;
3144:     for (j = 0; j < ncols; j++) {
3145:       x[i] += PetscAbsScalar(*aa);
3146:       aa++;
3147:     }
3148:   }
3149:   PetscCall(VecRestoreArrayWrite(v, &x));
3150:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3151:   PetscFunctionReturn(PETSC_SUCCESS);
3152: }

3154: static PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3155: {
3156:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3157:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3158:   PetscScalar     *x;
3159:   const MatScalar *aa, *av;

3161:   PetscFunctionBegin;
3162:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3163:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3164:   aa = av;
3165:   ai = a->i;
3166:   aj = a->j;

3168:   PetscCall(VecSet(v, 0.0));
3169:   PetscCall(VecGetArrayWrite(v, &x));
3170:   PetscCall(VecGetLocalSize(v, &n));
3171:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3172:   for (i = 0; i < m; i++) {
3173:     ncols = ai[1] - ai[0];
3174:     ai++;
3175:     if (ncols == A->cmap->n) { /* row is dense */
3176:       x[i] = *aa;
3177:       if (idx) idx[i] = 0;
3178:     } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3179:       x[i] = 0.0;
3180:       if (idx) {
3181:         for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3182:           if (aj[j] > j) {
3183:             idx[i] = j;
3184:             break;
3185:           }
3186:         }
3187:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3188:         if (j == ncols && j < A->cmap->n) idx[i] = j;
3189:       }
3190:     }
3191:     for (j = 0; j < ncols; j++) {
3192:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3193:         x[i] = *aa;
3194:         if (idx) idx[i] = *aj;
3195:       }
3196:       aa++;
3197:       aj++;
3198:     }
3199:   }
3200:   PetscCall(VecRestoreArrayWrite(v, &x));
3201:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3202:   PetscFunctionReturn(PETSC_SUCCESS);
3203: }

3205: static PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3206: {
3207:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3208:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3209:   PetscScalar     *x;
3210:   const MatScalar *aa, *av;

3212:   PetscFunctionBegin;
3213:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3214:   aa = av;
3215:   ai = a->i;
3216:   aj = a->j;

3218:   PetscCall(VecSet(v, 0.0));
3219:   PetscCall(VecGetArrayWrite(v, &x));
3220:   PetscCall(VecGetLocalSize(v, &n));
3221:   PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3222:   for (i = 0; i < m; i++) {
3223:     ncols = ai[1] - ai[0];
3224:     ai++;
3225:     if (ncols == A->cmap->n) { /* row is dense */
3226:       x[i] = *aa;
3227:       if (idx) idx[i] = 0;
3228:     } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3229:       x[i] = 0.0;
3230:       if (idx) { /* find first implicit 0.0 in the row */
3231:         for (j = 0; j < ncols; j++) {
3232:           if (aj[j] > j) {
3233:             idx[i] = j;
3234:             break;
3235:           }
3236:         }
3237:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3238:         if (j == ncols && j < A->cmap->n) idx[i] = j;
3239:       }
3240:     }
3241:     for (j = 0; j < ncols; j++) {
3242:       if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3243:         x[i] = *aa;
3244:         if (idx) idx[i] = *aj;
3245:       }
3246:       aa++;
3247:       aj++;
3248:     }
3249:   }
3250:   PetscCall(VecRestoreArrayWrite(v, &x));
3251:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3252:   PetscFunctionReturn(PETSC_SUCCESS);
3253: }

3255: static PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3256: {
3257:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3258:   PetscInt         i, j, m = A->rmap->n, ncols, n;
3259:   const PetscInt  *ai, *aj;
3260:   PetscScalar     *x;
3261:   const MatScalar *aa, *av;

3263:   PetscFunctionBegin;
3264:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3265:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3266:   aa = av;
3267:   ai = a->i;
3268:   aj = a->j;

3270:   PetscCall(VecSet(v, 0.0));
3271:   PetscCall(VecGetArrayWrite(v, &x));
3272:   PetscCall(VecGetLocalSize(v, &n));
3273:   PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3274:   for (i = 0; i < m; i++) {
3275:     ncols = ai[1] - ai[0];
3276:     ai++;
3277:     if (ncols == A->cmap->n) { /* row is dense */
3278:       x[i] = *aa;
3279:       if (idx) idx[i] = 0;
3280:     } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3281:       x[i] = 0.0;
3282:       if (idx) { /* find first implicit 0.0 in the row */
3283:         for (j = 0; j < ncols; j++) {
3284:           if (aj[j] > j) {
3285:             idx[i] = j;
3286:             break;
3287:           }
3288:         }
3289:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3290:         if (j == ncols && j < A->cmap->n) idx[i] = j;
3291:       }
3292:     }
3293:     for (j = 0; j < ncols; j++) {
3294:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
3295:         x[i] = *aa;
3296:         if (idx) idx[i] = *aj;
3297:       }
3298:       aa++;
3299:       aj++;
3300:     }
3301:   }
3302:   PetscCall(VecRestoreArrayWrite(v, &x));
3303:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3304:   PetscFunctionReturn(PETSC_SUCCESS);
3305: }

3307: static PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
3308: {
3309:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
3310:   PetscInt        i, bs = PetscAbs(A->rmap->bs), mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
3311:   MatScalar      *diag, work[25], *v_work;
3312:   const PetscReal shift = 0.0;
3313:   PetscBool       allowzeropivot, zeropivotdetected = PETSC_FALSE;

3315:   PetscFunctionBegin;
3316:   allowzeropivot = PetscNot(A->erroriffailure);
3317:   if (a->ibdiagvalid) {
3318:     if (values) *values = a->ibdiag;
3319:     PetscFunctionReturn(PETSC_SUCCESS);
3320:   }
3321:   PetscCall(MatMarkDiagonal_SeqAIJ(A));
3322:   if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); }
3323:   diag = a->ibdiag;
3324:   if (values) *values = a->ibdiag;
3325:   /* factor and invert each block */
3326:   switch (bs) {
3327:   case 1:
3328:     for (i = 0; i < mbs; i++) {
3329:       PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
3330:       if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3331:         if (allowzeropivot) {
3332:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3333:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3334:           A->factorerror_zeropivot_row   = i;
3335:           PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
3336:         } 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);
3337:       }
3338:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3339:     }
3340:     break;
3341:   case 2:
3342:     for (i = 0; i < mbs; i++) {
3343:       ij[0] = 2 * i;
3344:       ij[1] = 2 * i + 1;
3345:       PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
3346:       PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
3347:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3348:       PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
3349:       diag += 4;
3350:     }
3351:     break;
3352:   case 3:
3353:     for (i = 0; i < mbs; i++) {
3354:       ij[0] = 3 * i;
3355:       ij[1] = 3 * i + 1;
3356:       ij[2] = 3 * i + 2;
3357:       PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
3358:       PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
3359:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3360:       PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
3361:       diag += 9;
3362:     }
3363:     break;
3364:   case 4:
3365:     for (i = 0; i < mbs; i++) {
3366:       ij[0] = 4 * i;
3367:       ij[1] = 4 * i + 1;
3368:       ij[2] = 4 * i + 2;
3369:       ij[3] = 4 * i + 3;
3370:       PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
3371:       PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
3372:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3373:       PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
3374:       diag += 16;
3375:     }
3376:     break;
3377:   case 5:
3378:     for (i = 0; i < mbs; i++) {
3379:       ij[0] = 5 * i;
3380:       ij[1] = 5 * i + 1;
3381:       ij[2] = 5 * i + 2;
3382:       ij[3] = 5 * i + 3;
3383:       ij[4] = 5 * i + 4;
3384:       PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
3385:       PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
3386:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3387:       PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
3388:       diag += 25;
3389:     }
3390:     break;
3391:   case 6:
3392:     for (i = 0; i < mbs; i++) {
3393:       ij[0] = 6 * i;
3394:       ij[1] = 6 * i + 1;
3395:       ij[2] = 6 * i + 2;
3396:       ij[3] = 6 * i + 3;
3397:       ij[4] = 6 * i + 4;
3398:       ij[5] = 6 * i + 5;
3399:       PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
3400:       PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
3401:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3402:       PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
3403:       diag += 36;
3404:     }
3405:     break;
3406:   case 7:
3407:     for (i = 0; i < mbs; i++) {
3408:       ij[0] = 7 * i;
3409:       ij[1] = 7 * i + 1;
3410:       ij[2] = 7 * i + 2;
3411:       ij[3] = 7 * i + 3;
3412:       ij[4] = 7 * i + 4;
3413:       ij[5] = 7 * i + 5;
3414:       ij[6] = 7 * i + 6;
3415:       PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
3416:       PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
3417:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3418:       PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
3419:       diag += 49;
3420:     }
3421:     break;
3422:   default:
3423:     PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
3424:     for (i = 0; i < mbs; i++) {
3425:       for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
3426:       PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
3427:       PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
3428:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3429:       PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
3430:       diag += bs2;
3431:     }
3432:     PetscCall(PetscFree3(v_work, v_pivots, IJ));
3433:   }
3434:   a->ibdiagvalid = PETSC_TRUE;
3435:   PetscFunctionReturn(PETSC_SUCCESS);
3436: }

3438: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
3439: {
3440:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3441:   PetscScalar a, *aa;
3442:   PetscInt    m, n, i, j, col;

3444:   PetscFunctionBegin;
3445:   if (!x->assembled) {
3446:     PetscCall(MatGetSize(x, &m, &n));
3447:     for (i = 0; i < m; i++) {
3448:       for (j = 0; j < aij->imax[i]; j++) {
3449:         PetscCall(PetscRandomGetValue(rctx, &a));
3450:         col = (PetscInt)(n * PetscRealPart(a));
3451:         PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3452:       }
3453:     }
3454:   } else {
3455:     PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
3456:     for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
3457:     PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
3458:   }
3459:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3460:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3461:   PetscFunctionReturn(PETSC_SUCCESS);
3462: }

3464: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3465: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
3466: {
3467:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3468:   PetscScalar a;
3469:   PetscInt    m, n, i, j, col, nskip;

3471:   PetscFunctionBegin;
3472:   nskip = high - low;
3473:   PetscCall(MatGetSize(x, &m, &n));
3474:   n -= nskip; /* shrink number of columns where nonzeros can be set */
3475:   for (i = 0; i < m; i++) {
3476:     for (j = 0; j < aij->imax[i]; j++) {
3477:       PetscCall(PetscRandomGetValue(rctx, &a));
3478:       col = (PetscInt)(n * PetscRealPart(a));
3479:       if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3480:       PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3481:     }
3482:   }
3483:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3484:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3485:   PetscFunctionReturn(PETSC_SUCCESS);
3486: }

3488: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3489:                                        MatGetRow_SeqAIJ,
3490:                                        MatRestoreRow_SeqAIJ,
3491:                                        MatMult_SeqAIJ,
3492:                                        /*  4*/ MatMultAdd_SeqAIJ,
3493:                                        MatMultTranspose_SeqAIJ,
3494:                                        MatMultTransposeAdd_SeqAIJ,
3495:                                        NULL,
3496:                                        NULL,
3497:                                        NULL,
3498:                                        /* 10*/ NULL,
3499:                                        MatLUFactor_SeqAIJ,
3500:                                        NULL,
3501:                                        MatSOR_SeqAIJ,
3502:                                        MatTranspose_SeqAIJ,
3503:                                        /*1 5*/ MatGetInfo_SeqAIJ,
3504:                                        MatEqual_SeqAIJ,
3505:                                        MatGetDiagonal_SeqAIJ,
3506:                                        MatDiagonalScale_SeqAIJ,
3507:                                        MatNorm_SeqAIJ,
3508:                                        /* 20*/ NULL,
3509:                                        MatAssemblyEnd_SeqAIJ,
3510:                                        MatSetOption_SeqAIJ,
3511:                                        MatZeroEntries_SeqAIJ,
3512:                                        /* 24*/ MatZeroRows_SeqAIJ,
3513:                                        NULL,
3514:                                        NULL,
3515:                                        NULL,
3516:                                        NULL,
3517:                                        /* 29*/ MatSetUp_Seq_Hash,
3518:                                        NULL,
3519:                                        NULL,
3520:                                        NULL,
3521:                                        NULL,
3522:                                        /* 34*/ MatDuplicate_SeqAIJ,
3523:                                        NULL,
3524:                                        NULL,
3525:                                        MatILUFactor_SeqAIJ,
3526:                                        NULL,
3527:                                        /* 39*/ MatAXPY_SeqAIJ,
3528:                                        MatCreateSubMatrices_SeqAIJ,
3529:                                        MatIncreaseOverlap_SeqAIJ,
3530:                                        MatGetValues_SeqAIJ,
3531:                                        MatCopy_SeqAIJ,
3532:                                        /* 44*/ MatGetRowMax_SeqAIJ,
3533:                                        MatScale_SeqAIJ,
3534:                                        MatShift_SeqAIJ,
3535:                                        MatDiagonalSet_SeqAIJ,
3536:                                        MatZeroRowsColumns_SeqAIJ,
3537:                                        /* 49*/ MatSetRandom_SeqAIJ,
3538:                                        MatGetRowIJ_SeqAIJ,
3539:                                        MatRestoreRowIJ_SeqAIJ,
3540:                                        MatGetColumnIJ_SeqAIJ,
3541:                                        MatRestoreColumnIJ_SeqAIJ,
3542:                                        /* 54*/ MatFDColoringCreate_SeqXAIJ,
3543:                                        NULL,
3544:                                        NULL,
3545:                                        MatPermute_SeqAIJ,
3546:                                        NULL,
3547:                                        /* 59*/ NULL,
3548:                                        MatDestroy_SeqAIJ,
3549:                                        MatView_SeqAIJ,
3550:                                        NULL,
3551:                                        NULL,
3552:                                        /* 64*/ NULL,
3553:                                        MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3554:                                        NULL,
3555:                                        NULL,
3556:                                        NULL,
3557:                                        /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3558:                                        MatGetRowMinAbs_SeqAIJ,
3559:                                        NULL,
3560:                                        NULL,
3561:                                        NULL,
3562:                                        /* 74*/ NULL,
3563:                                        MatFDColoringApply_AIJ,
3564:                                        NULL,
3565:                                        NULL,
3566:                                        NULL,
3567:                                        /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3568:                                        NULL,
3569:                                        NULL,
3570:                                        NULL,
3571:                                        MatLoad_SeqAIJ,
3572:                                        /* 84*/ NULL,
3573:                                        NULL,
3574:                                        NULL,
3575:                                        NULL,
3576:                                        NULL,
3577:                                        /* 89*/ NULL,
3578:                                        NULL,
3579:                                        MatMatMultNumeric_SeqAIJ_SeqAIJ,
3580:                                        NULL,
3581:                                        NULL,
3582:                                        /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3583:                                        NULL,
3584:                                        NULL,
3585:                                        MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3586:                                        NULL,
3587:                                        /* 99*/ MatProductSetFromOptions_SeqAIJ,
3588:                                        NULL,
3589:                                        NULL,
3590:                                        MatConjugate_SeqAIJ,
3591:                                        NULL,
3592:                                        /*104*/ MatSetValuesRow_SeqAIJ,
3593:                                        MatRealPart_SeqAIJ,
3594:                                        MatImaginaryPart_SeqAIJ,
3595:                                        NULL,
3596:                                        NULL,
3597:                                        /*109*/ MatMatSolve_SeqAIJ,
3598:                                        NULL,
3599:                                        MatGetRowMin_SeqAIJ,
3600:                                        NULL,
3601:                                        MatMissingDiagonal_SeqAIJ,
3602:                                        /*114*/ NULL,
3603:                                        NULL,
3604:                                        NULL,
3605:                                        NULL,
3606:                                        NULL,
3607:                                        /*119*/ NULL,
3608:                                        NULL,
3609:                                        NULL,
3610:                                        NULL,
3611:                                        MatGetMultiProcBlock_SeqAIJ,
3612:                                        /*124*/ MatFindNonzeroRows_SeqAIJ,
3613:                                        MatGetColumnReductions_SeqAIJ,
3614:                                        MatInvertBlockDiagonal_SeqAIJ,
3615:                                        MatInvertVariableBlockDiagonal_SeqAIJ,
3616:                                        NULL,
3617:                                        /*129*/ NULL,
3618:                                        NULL,
3619:                                        NULL,
3620:                                        MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3621:                                        MatTransposeColoringCreate_SeqAIJ,
3622:                                        /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3623:                                        MatTransColoringApplyDenToSp_SeqAIJ,
3624:                                        NULL,
3625:                                        NULL,
3626:                                        MatRARtNumeric_SeqAIJ_SeqAIJ,
3627:                                        /*139*/ NULL,
3628:                                        NULL,
3629:                                        NULL,
3630:                                        MatFDColoringSetUp_SeqXAIJ,
3631:                                        MatFindOffBlockDiagonalEntries_SeqAIJ,
3632:                                        MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3633:                                        /*145*/ MatDestroySubMatrices_SeqAIJ,
3634:                                        NULL,
3635:                                        NULL,
3636:                                        MatCreateGraph_Simple_AIJ,
3637:                                        NULL,
3638:                                        /*150*/ MatTransposeSymbolic_SeqAIJ,
3639:                                        MatEliminateZeros_SeqAIJ,
3640:                                        MatGetRowSumAbs_SeqAIJ};

3642: static PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3643: {
3644:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3645:   PetscInt    i, nz, n;

3647:   PetscFunctionBegin;
3648:   nz = aij->maxnz;
3649:   n  = mat->rmap->n;
3650:   for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3651:   aij->nz = nz;
3652:   for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3653:   PetscFunctionReturn(PETSC_SUCCESS);
3654: }

3656: /*
3657:  * Given a sparse matrix with global column indices, compact it by using a local column space.
3658:  * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3659:  */
3660: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3661: {
3662:   Mat_SeqAIJ   *aij = (Mat_SeqAIJ *)mat->data;
3663:   PetscHMapI    gid1_lid1;
3664:   PetscHashIter tpos;
3665:   PetscInt      gid, lid, i, ec, nz = aij->nz;
3666:   PetscInt     *garray, *jj = aij->j;

3668:   PetscFunctionBegin;
3670:   PetscAssertPointer(mapping, 2);
3671:   /* use a table */
3672:   PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3673:   ec = 0;
3674:   for (i = 0; i < nz; i++) {
3675:     PetscInt data, gid1 = jj[i] + 1;
3676:     PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3677:     if (!data) {
3678:       /* one based table */
3679:       PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3680:     }
3681:   }
3682:   /* form array of columns we need */
3683:   PetscCall(PetscMalloc1(ec, &garray));
3684:   PetscHashIterBegin(gid1_lid1, tpos);
3685:   while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3686:     PetscHashIterGetKey(gid1_lid1, tpos, gid);
3687:     PetscHashIterGetVal(gid1_lid1, tpos, lid);
3688:     PetscHashIterNext(gid1_lid1, tpos);
3689:     gid--;
3690:     lid--;
3691:     garray[lid] = gid;
3692:   }
3693:   PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3694:   PetscCall(PetscHMapIClear(gid1_lid1));
3695:   for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3696:   /* compact out the extra columns in B */
3697:   for (i = 0; i < nz; i++) {
3698:     PetscInt gid1 = jj[i] + 1;
3699:     PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3700:     lid--;
3701:     jj[i] = lid;
3702:   }
3703:   PetscCall(PetscLayoutDestroy(&mat->cmap));
3704:   PetscCall(PetscHMapIDestroy(&gid1_lid1));
3705:   PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3706:   PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3707:   PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3708:   PetscFunctionReturn(PETSC_SUCCESS);
3709: }

3711: /*@
3712:   MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3713:   in the matrix.

3715:   Input Parameters:
3716: + mat     - the `MATSEQAIJ` matrix
3717: - indices - the column indices

3719:   Level: advanced

3721:   Notes:
3722:   This can be called if you have precomputed the nonzero structure of the
3723:   matrix and want to provide it to the matrix object to improve the performance
3724:   of the `MatSetValues()` operation.

3726:   You MUST have set the correct numbers of nonzeros per row in the call to
3727:   `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.

3729:   MUST be called before any calls to `MatSetValues()`

3731:   The indices should start with zero, not one.

3733: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`
3734: @*/
3735: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3736: {
3737:   PetscFunctionBegin;
3739:   PetscAssertPointer(indices, 2);
3740:   PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3741:   PetscFunctionReturn(PETSC_SUCCESS);
3742: }

3744: static PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3745: {
3746:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3747:   size_t      nz  = aij->i[mat->rmap->n];

3749:   PetscFunctionBegin;
3750:   PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

3752:   /* allocate space for values if not already there */
3753:   if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); }

3755:   /* copy values over */
3756:   PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3757:   PetscFunctionReturn(PETSC_SUCCESS);
3758: }

3760: /*@
3761:   MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
3762:   nonlinear portion.

3764:   Logically Collect

3766:   Input Parameter:
3767: . mat - the matrix (currently only `MATAIJ` matrices support this option)

3769:   Level: advanced

3771:   Example Usage:
3772: .vb
3773:     Using SNES
3774:     Create Jacobian matrix
3775:     Set linear terms into matrix
3776:     Apply boundary conditions to matrix, at this time matrix must have
3777:       final nonzero structure (i.e. setting the nonlinear terms and applying
3778:       boundary conditions again will not change the nonzero structure
3779:     MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3780:     MatStoreValues(mat);
3781:     Call SNESSetJacobian() with matrix
3782:     In your Jacobian routine
3783:       MatRetrieveValues(mat);
3784:       Set nonlinear terms in matrix

3786:     Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3787:     // build linear portion of Jacobian
3788:     MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3789:     MatStoreValues(mat);
3790:     loop over nonlinear iterations
3791:        MatRetrieveValues(mat);
3792:        // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3793:        // call MatAssemblyBegin/End() on matrix
3794:        Solve linear system with Jacobian
3795:     endloop
3796: .ve

3798:   Notes:
3799:   Matrix must already be assembled before calling this routine
3800:   Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3801:   calling this routine.

3803:   When this is called multiple times it overwrites the previous set of stored values
3804:   and does not allocated additional space.

3806: .seealso: [](ch_matrices), `Mat`, `MatRetrieveValues()`
3807: @*/
3808: PetscErrorCode MatStoreValues(Mat mat)
3809: {
3810:   PetscFunctionBegin;
3812:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3813:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3814:   PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3815:   PetscFunctionReturn(PETSC_SUCCESS);
3816: }

3818: static PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3819: {
3820:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3821:   PetscInt    nz  = aij->i[mat->rmap->n];

3823:   PetscFunctionBegin;
3824:   PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3825:   PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3826:   /* copy values over */
3827:   PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3828:   PetscFunctionReturn(PETSC_SUCCESS);
3829: }

3831: /*@
3832:   MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`

3834:   Logically Collect

3836:   Input Parameter:
3837: . mat - the matrix (currently only `MATAIJ` matrices support this option)

3839:   Level: advanced

3841: .seealso: [](ch_matrices), `Mat`, `MatStoreValues()`
3842: @*/
3843: PetscErrorCode MatRetrieveValues(Mat mat)
3844: {
3845:   PetscFunctionBegin;
3847:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3848:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3849:   PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3850:   PetscFunctionReturn(PETSC_SUCCESS);
3851: }

3853: /*@C
3854:   MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3855:   (the default parallel PETSc format).  For good matrix assembly performance
3856:   the user should preallocate the matrix storage by setting the parameter `nz`
3857:   (or the array `nnz`).

3859:   Collective

3861:   Input Parameters:
3862: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3863: . m    - number of rows
3864: . n    - number of columns
3865: . nz   - number of nonzeros per row (same for all rows)
3866: - nnz  - array containing the number of nonzeros in the various rows
3867:          (possibly different for each row) or NULL

3869:   Output Parameter:
3870: . A - the matrix

3872:   Options Database Keys:
3873: + -mat_no_inode            - Do not use inodes
3874: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3876:   Level: intermediate

3878:   Notes:
3879:   It is recommend to use `MatCreateFromOptions()` instead of this routine

3881:   If `nnz` is given then `nz` is ignored

3883:   The `MATSEQAIJ` format, also called
3884:   compressed row storage, is fully compatible with standard Fortran
3885:   storage.  That is, the stored row and column indices can begin at
3886:   either one (as in Fortran) or zero.

3888:   Specify the preallocated storage with either `nz` or `nnz` (not both).
3889:   Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3890:   allocation.

3892:   By default, this format uses inodes (identical nodes) when possible, to
3893:   improve numerical efficiency of matrix-vector products and solves. We
3894:   search for consecutive rows with the same nonzero structure, thereby
3895:   reusing matrix information to achieve increased efficiency.

3897: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3898: @*/
3899: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3900: {
3901:   PetscFunctionBegin;
3902:   PetscCall(MatCreate(comm, A));
3903:   PetscCall(MatSetSizes(*A, m, n, m, n));
3904:   PetscCall(MatSetType(*A, MATSEQAIJ));
3905:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3906:   PetscFunctionReturn(PETSC_SUCCESS);
3907: }

3909: /*@C
3910:   MatSeqAIJSetPreallocation - For good matrix assembly performance
3911:   the user should preallocate the matrix storage by setting the parameter nz
3912:   (or the array nnz).  By setting these parameters accurately, performance
3913:   during matrix assembly can be increased by more than a factor of 50.

3915:   Collective

3917:   Input Parameters:
3918: + B   - The matrix
3919: . nz  - number of nonzeros per row (same for all rows)
3920: - nnz - array containing the number of nonzeros in the various rows
3921:          (possibly different for each row) or NULL

3923:   Options Database Keys:
3924: + -mat_no_inode            - Do not use inodes
3925: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3927:   Level: intermediate

3929:   Notes:
3930:   If `nnz` is given then `nz` is ignored

3932:   The `MATSEQAIJ` format also called
3933:   compressed row storage, is fully compatible with standard Fortran
3934:   storage.  That is, the stored row and column indices can begin at
3935:   either one (as in Fortran) or zero.  See the users' manual for details.

3937:   Specify the preallocated storage with either `nz` or `nnz` (not both).
3938:   Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3939:   allocation.

3941:   You can call `MatGetInfo()` to get information on how effective the preallocation was;
3942:   for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3943:   You can also run with the option -info and look for messages with the string
3944:   malloc in them to see if additional memory allocation was needed.

3946:   Developer Notes:
3947:   Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3948:   entries or columns indices

3950:   By default, this format uses inodes (identical nodes) when possible, to
3951:   improve numerical efficiency of matrix-vector products and solves. We
3952:   search for consecutive rows with the same nonzero structure, thereby
3953:   reusing matrix information to achieve increased efficiency.

3955: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3956:           `MatSeqAIJSetTotalPreallocation()`
3957: @*/
3958: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3959: {
3960:   PetscFunctionBegin;
3963:   PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3964:   PetscFunctionReturn(PETSC_SUCCESS);
3965: }

3967: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3968: {
3969:   Mat_SeqAIJ *b              = (Mat_SeqAIJ *)B->data;
3970:   PetscBool   skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3971:   PetscInt    i;

3973:   PetscFunctionBegin;
3974:   if (B->hash_active) {
3975:     B->ops[0] = b->cops;
3976:     PetscCall(PetscHMapIJVDestroy(&b->ht));
3977:     PetscCall(PetscFree(b->dnz));
3978:     B->hash_active = PETSC_FALSE;
3979:   }
3980:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3981:   if (nz == MAT_SKIP_ALLOCATION) {
3982:     skipallocation = PETSC_TRUE;
3983:     nz             = 0;
3984:   }
3985:   PetscCall(PetscLayoutSetUp(B->rmap));
3986:   PetscCall(PetscLayoutSetUp(B->cmap));

3988:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3989:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3990:   if (PetscUnlikelyDebug(nnz)) {
3991:     for (i = 0; i < B->rmap->n; i++) {
3992:       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]);
3993:       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);
3994:     }
3995:   }

3997:   B->preallocated = PETSC_TRUE;
3998:   if (!skipallocation) {
3999:     if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); }
4000:     if (!b->ilen) {
4001:       /* b->ilen will count nonzeros in each row so far. */
4002:       PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
4003:     } else {
4004:       PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
4005:     }
4006:     if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
4007:     if (!nnz) {
4008:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
4009:       else if (nz < 0) nz = 1;
4010:       nz = PetscMin(nz, B->cmap->n);
4011:       for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
4012:       PetscCall(PetscIntMultError(nz, B->rmap->n, &nz));
4013:     } else {
4014:       PetscInt64 nz64 = 0;
4015:       for (i = 0; i < B->rmap->n; i++) {
4016:         b->imax[i] = nnz[i];
4017:         nz64 += nnz[i];
4018:       }
4019:       PetscCall(PetscIntCast(nz64, &nz));
4020:     }

4022:     /* allocate the matrix space */
4023:     /* FIXME: should B's old memory be unlogged? */
4024:     PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
4025:     if (B->structure_only) {
4026:       PetscCall(PetscMalloc1(nz, &b->j));
4027:       PetscCall(PetscMalloc1(B->rmap->n + 1, &b->i));
4028:     } else {
4029:       PetscCall(PetscMalloc3(nz, &b->a, nz, &b->j, B->rmap->n + 1, &b->i));
4030:     }
4031:     b->i[0] = 0;
4032:     for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
4033:     if (B->structure_only) {
4034:       b->singlemalloc = PETSC_FALSE;
4035:       b->free_a       = PETSC_FALSE;
4036:     } else {
4037:       b->singlemalloc = PETSC_TRUE;
4038:       b->free_a       = PETSC_TRUE;
4039:     }
4040:     b->free_ij = PETSC_TRUE;
4041:   } else {
4042:     b->free_a  = PETSC_FALSE;
4043:     b->free_ij = PETSC_FALSE;
4044:   }

4046:   if (b->ipre && nnz != b->ipre && b->imax) {
4047:     /* reserve user-requested sparsity */
4048:     PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
4049:   }

4051:   b->nz               = 0;
4052:   b->maxnz            = nz;
4053:   B->info.nz_unneeded = (double)b->maxnz;
4054:   if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
4055:   B->was_assembled = PETSC_FALSE;
4056:   B->assembled     = PETSC_FALSE;
4057:   /* We simply deem preallocation has changed nonzero state. Updating the state
4058:      will give clients (like AIJKokkos) a chance to know something has happened.
4059:   */
4060:   B->nonzerostate++;
4061:   PetscFunctionReturn(PETSC_SUCCESS);
4062: }

4064: static PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4065: {
4066:   Mat_SeqAIJ *a;
4067:   PetscInt    i;
4068:   PetscBool   skipreset;

4070:   PetscFunctionBegin;

4073:   /* Check local size. If zero, then return */
4074:   if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);

4076:   a = (Mat_SeqAIJ *)A->data;
4077:   /* if no saved info, we error out */
4078:   PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");

4080:   PetscCheck(a->i && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and can not reset preallocation ");

4082:   PetscCall(PetscArraycmp(a->ipre, a->ilen, A->rmap->n, &skipreset));
4083:   if (!skipreset) {
4084:     PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
4085:     PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
4086:     a->i[0] = 0;
4087:     for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
4088:     A->preallocated     = PETSC_TRUE;
4089:     a->nz               = 0;
4090:     a->maxnz            = a->i[A->rmap->n];
4091:     A->info.nz_unneeded = (double)a->maxnz;
4092:     A->was_assembled    = PETSC_FALSE;
4093:     A->assembled        = PETSC_FALSE;
4094:   }
4095:   PetscFunctionReturn(PETSC_SUCCESS);
4096: }

4098: /*@
4099:   MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.

4101:   Input Parameters:
4102: + B - the matrix
4103: . i - the indices into `j` for the start of each row (indices start with zero)
4104: . j - the column indices for each row (indices start with zero) these must be sorted for each row
4105: - v - optional values in the matrix, use `NULL` if not provided

4107:   Level: developer

4109:   Notes:
4110:   The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`

4112:   This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4113:   structure will be the union of all the previous nonzero structures.

4115:   Developer Notes:
4116:   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
4117:   then just copies the `v` values directly with `PetscMemcpy()`.

4119:   This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.

4121: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MATSEQAIJ`, `MatResetPreallocation()`
4122: @*/
4123: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4124: {
4125:   PetscFunctionBegin;
4128:   PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4129:   PetscFunctionReturn(PETSC_SUCCESS);
4130: }

4132: static PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4133: {
4134:   PetscInt  i;
4135:   PetscInt  m, n;
4136:   PetscInt  nz;
4137:   PetscInt *nnz;

4139:   PetscFunctionBegin;
4140:   PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);

4142:   PetscCall(PetscLayoutSetUp(B->rmap));
4143:   PetscCall(PetscLayoutSetUp(B->cmap));

4145:   PetscCall(MatGetSize(B, &m, &n));
4146:   PetscCall(PetscMalloc1(m + 1, &nnz));
4147:   for (i = 0; i < m; i++) {
4148:     nz = Ii[i + 1] - Ii[i];
4149:     PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4150:     nnz[i] = nz;
4151:   }
4152:   PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4153:   PetscCall(PetscFree(nnz));

4155:   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));

4157:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4158:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));

4160:   PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4161:   PetscFunctionReturn(PETSC_SUCCESS);
4162: }

4164: /*@
4165:   MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.

4167:   Input Parameters:
4168: + A     - left-hand side matrix
4169: . B     - right-hand side matrix
4170: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

4172:   Output Parameter:
4173: . C - Kronecker product of `A` and `B`

4175:   Level: intermediate

4177:   Note:
4178:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.

4180: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4181: @*/
4182: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4183: {
4184:   PetscFunctionBegin;
4189:   PetscAssertPointer(C, 4);
4190:   if (reuse == MAT_REUSE_MATRIX) {
4193:   }
4194:   PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4195:   PetscFunctionReturn(PETSC_SUCCESS);
4196: }

4198: static PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4199: {
4200:   Mat                newmat;
4201:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
4202:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;
4203:   PetscScalar       *v;
4204:   const PetscScalar *aa, *ba;
4205:   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;
4206:   PetscBool          flg;

4208:   PetscFunctionBegin;
4209:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4210:   PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4211:   PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4212:   PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4213:   PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4214:   PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4215:   PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4216:   if (reuse == MAT_INITIAL_MATRIX) {
4217:     PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4218:     PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4219:     PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4220:     PetscCall(MatSetType(newmat, MATAIJ));
4221:     i[0] = 0;
4222:     for (m = 0; m < am; ++m) {
4223:       for (p = 0; p < bm; ++p) {
4224:         i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4225:         for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4226:           for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4227:         }
4228:       }
4229:     }
4230:     PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4231:     *C = newmat;
4232:     PetscCall(PetscFree2(i, j));
4233:     nnz = 0;
4234:   }
4235:   PetscCall(MatSeqAIJGetArray(*C, &v));
4236:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4237:   PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4238:   for (m = 0; m < am; ++m) {
4239:     for (p = 0; p < bm; ++p) {
4240:       for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4241:         for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4242:       }
4243:     }
4244:   }
4245:   PetscCall(MatSeqAIJRestoreArray(*C, &v));
4246:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4247:   PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4248:   PetscFunctionReturn(PETSC_SUCCESS);
4249: }

4251: #include <../src/mat/impls/dense/seq/dense.h>
4252: #include <petsc/private/kernels/petscaxpy.h>

4254: /*
4255:     Computes (B'*A')' since computing B*A directly is untenable

4257:                n                       p                          p
4258:         [             ]       [             ]         [                 ]
4259:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
4260:         [             ]       [             ]         [                 ]

4262: */
4263: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4264: {
4265:   Mat_SeqDense      *sub_a = (Mat_SeqDense *)A->data;
4266:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ *)B->data;
4267:   Mat_SeqDense      *sub_c = (Mat_SeqDense *)C->data;
4268:   PetscInt           i, j, n, m, q, p;
4269:   const PetscInt    *ii, *idx;
4270:   const PetscScalar *b, *a, *a_q;
4271:   PetscScalar       *c, *c_q;
4272:   PetscInt           clda = sub_c->lda;
4273:   PetscInt           alda = sub_a->lda;

4275:   PetscFunctionBegin;
4276:   m = A->rmap->n;
4277:   n = A->cmap->n;
4278:   p = B->cmap->n;
4279:   a = sub_a->v;
4280:   b = sub_b->a;
4281:   c = sub_c->v;
4282:   if (clda == m) {
4283:     PetscCall(PetscArrayzero(c, m * p));
4284:   } else {
4285:     for (j = 0; j < p; j++)
4286:       for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4287:   }
4288:   ii  = sub_b->i;
4289:   idx = sub_b->j;
4290:   for (i = 0; i < n; i++) {
4291:     q = ii[i + 1] - ii[i];
4292:     while (q-- > 0) {
4293:       c_q = c + clda * (*idx);
4294:       a_q = a + alda * i;
4295:       PetscKernelAXPY(c_q, *b, a_q, m);
4296:       idx++;
4297:       b++;
4298:     }
4299:   }
4300:   PetscFunctionReturn(PETSC_SUCCESS);
4301: }

4303: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4304: {
4305:   PetscInt  m = A->rmap->n, n = B->cmap->n;
4306:   PetscBool cisdense;

4308:   PetscFunctionBegin;
4309:   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);
4310:   PetscCall(MatSetSizes(C, m, n, m, n));
4311:   PetscCall(MatSetBlockSizesFromMats(C, A, B));
4312:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4313:   if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4314:   PetscCall(MatSetUp(C));

4316:   C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4317:   PetscFunctionReturn(PETSC_SUCCESS);
4318: }

4320: /*MC
4321:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4322:    based on compressed sparse row format.

4324:    Options Database Key:
4325: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

4327:    Level: beginner

4329:    Notes:
4330:     `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4331:     in this case the values associated with the rows and columns one passes in are set to zero
4332:     in the matrix

4334:     `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4335:     space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored

4337:   Developer Note:
4338:     It would be nice if all matrix formats supported passing `NULL` in for the numerical values

4340: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4341: M*/

4343: /*MC
4344:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

4346:    This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4347:    and `MATMPIAIJ` otherwise.  As a result, for single process communicators,
4348:    `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4349:    for communicators controlling multiple processes.  It is recommended that you call both of
4350:    the above preallocation routines for simplicity.

4352:    Options Database Key:
4353: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`

4355:   Level: beginner

4357:    Note:
4358:    Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4359:    enough exist.

4361: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4362: M*/

4364: /*MC
4365:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

4367:    Options Database Key:
4368: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`

4370:   Level: beginner

4372:    Note:
4373:    This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4374:    and `MATMPIAIJCRL` otherwise.  As a result, for single process communicators,
4375:    `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4376:    for communicators controlling multiple processes.  It is recommended that you call both of
4377:    the above preallocation routines for simplicity.

4379: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4380: M*/

4382: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4383: #if defined(PETSC_HAVE_ELEMENTAL)
4384: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4385: #endif
4386: #if defined(PETSC_HAVE_SCALAPACK)
4387: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4388: #endif
4389: #if defined(PETSC_HAVE_HYPRE)
4390: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4391: #endif

4393: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4394: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4395: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

4397: /*@C
4398:   MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored

4400:   Not Collective

4402:   Input Parameter:
4403: . A - a `MATSEQAIJ` matrix

4405:   Output Parameter:
4406: . array - pointer to the data

4408:   Level: intermediate

4410:   Fortran Notes:
4411:   `MatSeqAIJGetArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJGetArrayF90()`

4413: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4414: @*/
4415: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar **array)
4416: {
4417:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4419:   PetscFunctionBegin;
4420:   if (aij->ops->getarray) {
4421:     PetscCall((*aij->ops->getarray)(A, array));
4422:   } else {
4423:     *array = aij->a;
4424:   }
4425:   PetscFunctionReturn(PETSC_SUCCESS);
4426: }

4428: /*@C
4429:   MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`

4431:   Not Collective

4433:   Input Parameters:
4434: + A     - a `MATSEQAIJ` matrix
4435: - array - pointer to the data

4437:   Level: intermediate

4439:   Fortran Notes:
4440:   `MatSeqAIJRestoreArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJRestoreArrayF90()`

4442: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()`
4443: @*/
4444: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar **array)
4445: {
4446:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4448:   PetscFunctionBegin;
4449:   if (aij->ops->restorearray) {
4450:     PetscCall((*aij->ops->restorearray)(A, array));
4451:   } else {
4452:     *array = NULL;
4453:   }
4454:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
4455:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
4456:   PetscFunctionReturn(PETSC_SUCCESS);
4457: }

4459: /*@C
4460:   MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored

4462:   Not Collective; No Fortran Support

4464:   Input Parameter:
4465: . A - a `MATSEQAIJ` matrix

4467:   Output Parameter:
4468: . array - pointer to the data

4470:   Level: intermediate

4472: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4473: @*/
4474: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar **array)
4475: {
4476:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4478:   PetscFunctionBegin;
4479:   if (aij->ops->getarrayread) {
4480:     PetscCall((*aij->ops->getarrayread)(A, array));
4481:   } else {
4482:     *array = aij->a;
4483:   }
4484:   PetscFunctionReturn(PETSC_SUCCESS);
4485: }

4487: /*@C
4488:   MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`

4490:   Not Collective; No Fortran Support

4492:   Input Parameter:
4493: . A - a `MATSEQAIJ` matrix

4495:   Output Parameter:
4496: . array - pointer to the data

4498:   Level: intermediate

4500: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4501: @*/
4502: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar **array)
4503: {
4504:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4506:   PetscFunctionBegin;
4507:   if (aij->ops->restorearrayread) {
4508:     PetscCall((*aij->ops->restorearrayread)(A, array));
4509:   } else {
4510:     *array = NULL;
4511:   }
4512:   PetscFunctionReturn(PETSC_SUCCESS);
4513: }

4515: /*@C
4516:   MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored

4518:   Not Collective; No Fortran Support

4520:   Input Parameter:
4521: . A - a `MATSEQAIJ` matrix

4523:   Output Parameter:
4524: . array - pointer to the data

4526:   Level: intermediate

4528: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4529: @*/
4530: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar **array)
4531: {
4532:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4534:   PetscFunctionBegin;
4535:   if (aij->ops->getarraywrite) {
4536:     PetscCall((*aij->ops->getarraywrite)(A, array));
4537:   } else {
4538:     *array = aij->a;
4539:   }
4540:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
4541:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
4542:   PetscFunctionReturn(PETSC_SUCCESS);
4543: }

4545: /*@C
4546:   MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead

4548:   Not Collective; No Fortran Support

4550:   Input Parameter:
4551: . A - a MATSEQAIJ matrix

4553:   Output Parameter:
4554: . array - pointer to the data

4556:   Level: intermediate

4558: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4559: @*/
4560: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar **array)
4561: {
4562:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4564:   PetscFunctionBegin;
4565:   if (aij->ops->restorearraywrite) {
4566:     PetscCall((*aij->ops->restorearraywrite)(A, array));
4567:   } else {
4568:     *array = NULL;
4569:   }
4570:   PetscFunctionReturn(PETSC_SUCCESS);
4571: }

4573: /*@C
4574:   MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix

4576:   Not Collective; No Fortran Support

4578:   Input Parameter:
4579: . mat - a matrix of type `MATSEQAIJ` or its subclasses

4581:   Output Parameters:
4582: + i     - row map array of the matrix
4583: . j     - column index array of the matrix
4584: . a     - data array of the matrix
4585: - mtype - memory type of the arrays

4587:   Level: developer

4589:   Notes:
4590:   Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4591:   If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.

4593:   One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4594:   If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.

4596: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4597: @*/
4598: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
4599: {
4600:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;

4602:   PetscFunctionBegin;
4603:   PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4604:   if (aij->ops->getcsrandmemtype) {
4605:     PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4606:   } else {
4607:     if (i) *i = aij->i;
4608:     if (j) *j = aij->j;
4609:     if (a) *a = aij->a;
4610:     if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4611:   }
4612:   PetscFunctionReturn(PETSC_SUCCESS);
4613: }

4615: /*@C
4616:   MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

4618:   Not Collective

4620:   Input Parameter:
4621: . A - a `MATSEQAIJ` matrix

4623:   Output Parameter:
4624: . nz - the maximum number of nonzeros in any row

4626:   Level: intermediate

4628: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4629: @*/
4630: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4631: {
4632:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4634:   PetscFunctionBegin;
4635:   *nz = aij->rmax;
4636:   PetscFunctionReturn(PETSC_SUCCESS);
4637: }

4639: static PetscErrorCode MatCOOStructDestroy_SeqAIJ(void *data)
4640: {
4641:   MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)data;

4643:   PetscFunctionBegin;
4644:   PetscCall(PetscFree(coo->perm));
4645:   PetscCall(PetscFree(coo->jmap));
4646:   PetscCall(PetscFree(coo));
4647:   PetscFunctionReturn(PETSC_SUCCESS);
4648: }

4650: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4651: {
4652:   MPI_Comm             comm;
4653:   PetscInt            *i, *j;
4654:   PetscInt             M, N, row, iprev;
4655:   PetscCount           k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4656:   PetscInt            *Ai;                             /* Change to PetscCount once we use it for row pointers */
4657:   PetscInt            *Aj;
4658:   PetscScalar         *Aa;
4659:   Mat_SeqAIJ          *seqaij = (Mat_SeqAIJ *)mat->data;
4660:   MatType              rtype;
4661:   PetscCount          *perm, *jmap;
4662:   PetscContainer       container;
4663:   MatCOOStruct_SeqAIJ *coo;
4664:   PetscBool            isorted;

4666:   PetscFunctionBegin;
4667:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4668:   PetscCall(MatGetSize(mat, &M, &N));
4669:   i = coo_i;
4670:   j = coo_j;
4671:   PetscCall(PetscMalloc1(coo_n, &perm));

4673:   /* 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) */
4674:   isorted = PETSC_TRUE;
4675:   iprev   = PETSC_INT_MIN;
4676:   for (k = 0; k < coo_n; k++) {
4677:     if (j[k] < 0) i[k] = -1;
4678:     if (isorted) {
4679:       if (i[k] < iprev) isorted = PETSC_FALSE;
4680:       else iprev = i[k];
4681:     }
4682:     perm[k] = k;
4683:   }

4685:   /* Sort by row if not already */
4686:   if (!isorted) PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));

4688:   /* Advance k to the first row with a non-negative index */
4689:   for (k = 0; k < coo_n; k++)
4690:     if (i[k] >= 0) break;
4691:   nneg = k;
4692:   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 */
4693:   nnz = 0;                                          /* Total number of unique nonzeros to be counted */
4694:   jmap++;                                           /* Inc jmap by 1 for convenience */

4696:   PetscCall(PetscCalloc1(M + 1, &Ai));        /* CSR of A */
4697:   PetscCall(PetscMalloc1(coo_n - nneg, &Aj)); /* We have at most coo_n-nneg unique nonzeros */

4699:   /* Support for HYPRE */
4700:   PetscBool   hypre;
4701:   const char *name;
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_MAX_INT;
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:       Ai[row] = end - start;
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) { /* Realloc 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(PetscMalloc1(nnz, &Aj_new));
4805:     PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4806:     PetscCall(PetscFree(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(PetscCalloc1(nnz, &Aa)); /* Zero the matrix */
4821:   PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));

4823:   seqaij->singlemalloc = PETSC_FALSE;            /* Ai, Aj and Aa are not allocated in one big malloc */
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:   coo->nz   = nnz;
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(PetscContainerCreate(PETSC_COMM_SELF, &container));
4834:   PetscCall(PetscContainerSetPointer(container, coo));
4835:   PetscCall(PetscContainerSetUserDestroy(container, MatCOOStructDestroy_SeqAIJ));
4836:   PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
4837:   PetscCall(PetscContainerDestroy(&container));
4838:   PetscFunctionReturn(PETSC_SUCCESS);
4839: }

4841: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4842: {
4843:   Mat_SeqAIJ          *aseq = (Mat_SeqAIJ *)A->data;
4844:   PetscCount           i, j, Annz = aseq->nz;
4845:   PetscCount          *perm, *jmap;
4846:   PetscScalar         *Aa;
4847:   PetscContainer       container;
4848:   MatCOOStruct_SeqAIJ *coo;

4850:   PetscFunctionBegin;
4851:   PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
4852:   PetscCheck(container, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
4853:   PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4854:   perm = coo->perm;
4855:   jmap = coo->jmap;
4856:   PetscCall(MatSeqAIJGetArray(A, &Aa));
4857:   for (i = 0; i < Annz; i++) {
4858:     PetscScalar sum = 0.0;
4859:     for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4860:     Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4861:   }
4862:   PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4863:   PetscFunctionReturn(PETSC_SUCCESS);
4864: }

4866: #if defined(PETSC_HAVE_CUDA)
4867: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4868: #endif
4869: #if defined(PETSC_HAVE_HIP)
4870: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4871: #endif
4872: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4873: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4874: #endif

4876: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4877: {
4878:   Mat_SeqAIJ *b;
4879:   PetscMPIInt size;

4881:   PetscFunctionBegin;
4882:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4883:   PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");

4885:   PetscCall(PetscNew(&b));

4887:   B->data   = (void *)b;
4888:   B->ops[0] = MatOps_Values;
4889:   if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;

4891:   b->row                = NULL;
4892:   b->col                = NULL;
4893:   b->icol               = NULL;
4894:   b->reallocs           = 0;
4895:   b->ignorezeroentries  = PETSC_FALSE;
4896:   b->roworiented        = PETSC_TRUE;
4897:   b->nonew              = 0;
4898:   b->diag               = NULL;
4899:   b->solve_work         = NULL;
4900:   B->spptr              = NULL;
4901:   b->saved_values       = NULL;
4902:   b->idiag              = NULL;
4903:   b->mdiag              = NULL;
4904:   b->ssor_work          = NULL;
4905:   b->omega              = 1.0;
4906:   b->fshift             = 0.0;
4907:   b->idiagvalid         = PETSC_FALSE;
4908:   b->ibdiagvalid        = PETSC_FALSE;
4909:   b->keepnonzeropattern = PETSC_FALSE;

4911:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4912: #if defined(PETSC_HAVE_MATLAB)
4913:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4914:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4915: #endif
4916:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4917:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4918:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4919:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4920:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4921:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4922:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4923: #if defined(PETSC_HAVE_MKL_SPARSE)
4924:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4925: #endif
4926: #if defined(PETSC_HAVE_CUDA)
4927:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4928:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4929:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4930: #endif
4931: #if defined(PETSC_HAVE_HIP)
4932:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4933:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4934:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4935: #endif
4936: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4937:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4938: #endif
4939:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4940: #if defined(PETSC_HAVE_ELEMENTAL)
4941:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4942: #endif
4943: #if defined(PETSC_HAVE_SCALAPACK)
4944:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4945: #endif
4946: #if defined(PETSC_HAVE_HYPRE)
4947:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4948:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4949: #endif
4950:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4951:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4952:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4953:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4954:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsHermitianTranspose_SeqAIJ));
4955:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4956:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4957:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4958:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4959:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4960:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4961:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4962:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4963:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4964:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4965:   PetscCall(MatCreate_SeqAIJ_Inode(B));
4966:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4967:   PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4968:   PetscFunctionReturn(PETSC_SUCCESS);
4969: }

4971: /*
4972:     Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4973: */
4974: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4975: {
4976:   Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4977:   PetscInt    m = A->rmap->n, i;

4979:   PetscFunctionBegin;
4980:   PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");

4982:   C->factortype    = A->factortype;
4983:   c->row           = NULL;
4984:   c->col           = NULL;
4985:   c->icol          = NULL;
4986:   c->reallocs      = 0;
4987:   c->diagonaldense = a->diagonaldense;

4989:   C->assembled = A->assembled;

4991:   if (A->preallocated) {
4992:     PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
4993:     PetscCall(PetscLayoutReference(A->cmap, &C->cmap));

4995:     if (!A->hash_active) {
4996:       PetscCall(PetscMalloc1(m, &c->imax));
4997:       PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
4998:       PetscCall(PetscMalloc1(m, &c->ilen));
4999:       PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));

5001:       /* allocate the matrix space */
5002:       if (mallocmatspace) {
5003:         PetscCall(PetscMalloc3(a->i[m], &c->a, a->i[m], &c->j, m + 1, &c->i));

5005:         c->singlemalloc = PETSC_TRUE;

5007:         PetscCall(PetscArraycpy(c->i, a->i, m + 1));
5008:         if (m > 0) {
5009:           PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
5010:           if (cpvalues == MAT_COPY_VALUES) {
5011:             const PetscScalar *aa;

5013:             PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5014:             PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
5015:             PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5016:           } else {
5017:             PetscCall(PetscArrayzero(c->a, a->i[m]));
5018:           }
5019:         }
5020:       }
5021:       C->preallocated = PETSC_TRUE;
5022:     } else {
5023:       PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
5024:       PetscCall(MatSetUp(C));
5025:     }

5027:     c->ignorezeroentries = a->ignorezeroentries;
5028:     c->roworiented       = a->roworiented;
5029:     c->nonew             = a->nonew;
5030:     if (a->diag) {
5031:       PetscCall(PetscMalloc1(m + 1, &c->diag));
5032:       PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
5033:     } else c->diag = NULL;

5035:     c->solve_work         = NULL;
5036:     c->saved_values       = NULL;
5037:     c->idiag              = NULL;
5038:     c->ssor_work          = NULL;
5039:     c->keepnonzeropattern = a->keepnonzeropattern;
5040:     c->free_a             = PETSC_TRUE;
5041:     c->free_ij            = PETSC_TRUE;

5043:     c->rmax  = a->rmax;
5044:     c->nz    = a->nz;
5045:     c->maxnz = a->nz; /* Since we allocate exactly the right amount */

5047:     c->compressedrow.use   = a->compressedrow.use;
5048:     c->compressedrow.nrows = a->compressedrow.nrows;
5049:     if (a->compressedrow.use) {
5050:       i = a->compressedrow.nrows;
5051:       PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
5052:       PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
5053:       PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
5054:     } else {
5055:       c->compressedrow.use    = PETSC_FALSE;
5056:       c->compressedrow.i      = NULL;
5057:       c->compressedrow.rindex = NULL;
5058:     }
5059:     c->nonzerorowcnt = a->nonzerorowcnt;
5060:     C->nonzerostate  = A->nonzerostate;

5062:     PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
5063:   }
5064:   PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
5065:   PetscFunctionReturn(PETSC_SUCCESS);
5066: }

5068: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
5069: {
5070:   PetscFunctionBegin;
5071:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
5072:   PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
5073:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
5074:   PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
5075:   PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
5076:   PetscFunctionReturn(PETSC_SUCCESS);
5077: }

5079: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
5080: {
5081:   PetscBool isbinary, ishdf5;

5083:   PetscFunctionBegin;
5086:   /* force binary viewer to load .info file if it has not yet done so */
5087:   PetscCall(PetscViewerSetUp(viewer));
5088:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
5089:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
5090:   if (isbinary) {
5091:     PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
5092:   } else if (ishdf5) {
5093: #if defined(PETSC_HAVE_HDF5)
5094:     PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
5095: #else
5096:     SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
5097: #endif
5098:   } else {
5099:     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);
5100:   }
5101:   PetscFunctionReturn(PETSC_SUCCESS);
5102: }

5104: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
5105: {
5106:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
5107:   PetscInt    header[4], *rowlens, M, N, nz, sum, rows, cols, i;

5109:   PetscFunctionBegin;
5110:   PetscCall(PetscViewerSetUp(viewer));

5112:   /* read in matrix header */
5113:   PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
5114:   PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
5115:   M  = header[1];
5116:   N  = header[2];
5117:   nz = header[3];
5118:   PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
5119:   PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
5120:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");

5122:   /* set block sizes from the viewer's .info file */
5123:   PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5124:   /* set local and global sizes if not set already */
5125:   if (mat->rmap->n < 0) mat->rmap->n = M;
5126:   if (mat->cmap->n < 0) mat->cmap->n = N;
5127:   if (mat->rmap->N < 0) mat->rmap->N = M;
5128:   if (mat->cmap->N < 0) mat->cmap->N = N;
5129:   PetscCall(PetscLayoutSetUp(mat->rmap));
5130:   PetscCall(PetscLayoutSetUp(mat->cmap));

5132:   /* check if the matrix sizes are correct */
5133:   PetscCall(MatGetSize(mat, &rows, &cols));
5134:   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);

5136:   /* read in row lengths */
5137:   PetscCall(PetscMalloc1(M, &rowlens));
5138:   PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5139:   /* check if sum(rowlens) is same as nz */
5140:   sum = 0;
5141:   for (i = 0; i < M; i++) sum += rowlens[i];
5142:   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);
5143:   /* preallocate and check sizes */
5144:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5145:   PetscCall(MatGetSize(mat, &rows, &cols));
5146:   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);
5147:   /* store row lengths */
5148:   PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5149:   PetscCall(PetscFree(rowlens));

5151:   /* fill in "i" row pointers */
5152:   a->i[0] = 0;
5153:   for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5154:   /* read in "j" column indices */
5155:   PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5156:   /* read in "a" nonzero values */
5157:   PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));

5159:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5160:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5161:   PetscFunctionReturn(PETSC_SUCCESS);
5162: }

5164: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5165: {
5166:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5167:   const PetscScalar *aa, *ba;
5168: #if defined(PETSC_USE_COMPLEX)
5169:   PetscInt k;
5170: #endif

5172:   PetscFunctionBegin;
5173:   /* If the  matrix dimensions are not equal,or no of nonzeros */
5174:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5175:     *flg = PETSC_FALSE;
5176:     PetscFunctionReturn(PETSC_SUCCESS);
5177:   }

5179:   /* if the a->i are the same */
5180:   PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5181:   if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);

5183:   /* if a->j are the same */
5184:   PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5185:   if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);

5187:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5188:   PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5189:   /* if a->a are the same */
5190: #if defined(PETSC_USE_COMPLEX)
5191:   for (k = 0; k < a->nz; k++) {
5192:     if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
5193:       *flg = PETSC_FALSE;
5194:       PetscFunctionReturn(PETSC_SUCCESS);
5195:     }
5196:   }
5197: #else
5198:   PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5199: #endif
5200:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5201:   PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5202:   PetscFunctionReturn(PETSC_SUCCESS);
5203: }

5205: /*@
5206:   MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5207:   provided by the user.

5209:   Collective

5211:   Input Parameters:
5212: + comm - must be an MPI communicator of size 1
5213: . m    - number of rows
5214: . n    - number of columns
5215: . i    - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5216: . j    - column indices
5217: - a    - matrix values

5219:   Output Parameter:
5220: . mat - the matrix

5222:   Level: intermediate

5224:   Notes:
5225:   The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5226:   once the matrix is destroyed and not before

5228:   You cannot set new nonzero locations into this matrix, that will generate an error.

5230:   The `i` and `j` indices are 0 based

5232:   The format which is used for the sparse matrix input, is equivalent to a
5233:   row-major ordering.. i.e for the following matrix, the input data expected is
5234:   as shown
5235: .vb
5236:         1 0 0
5237:         2 0 3
5238:         4 5 6

5240:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
5241:         j =  {0,0,2,0,1,2}  [size = 6]; values must be sorted for each row
5242:         v =  {1,2,3,4,5,6}  [size = 6]
5243: .ve

5245: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5246: @*/
5247: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5248: {
5249:   PetscInt    ii;
5250:   Mat_SeqAIJ *aij;
5251:   PetscInt    jj;

5253:   PetscFunctionBegin;
5254:   PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5255:   PetscCall(MatCreate(comm, mat));
5256:   PetscCall(MatSetSizes(*mat, m, n, m, n));
5257:   /* PetscCall(MatSetBlockSizes(*mat,,)); */
5258:   PetscCall(MatSetType(*mat, MATSEQAIJ));
5259:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5260:   aij = (Mat_SeqAIJ *)(*mat)->data;
5261:   PetscCall(PetscMalloc1(m, &aij->imax));
5262:   PetscCall(PetscMalloc1(m, &aij->ilen));

5264:   aij->i            = i;
5265:   aij->j            = j;
5266:   aij->a            = a;
5267:   aij->singlemalloc = PETSC_FALSE;
5268:   aij->nonew        = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5269:   aij->free_a       = PETSC_FALSE;
5270:   aij->free_ij      = PETSC_FALSE;

5272:   for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5273:     aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5274:     if (PetscDefined(USE_DEBUG)) {
5275:       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]);
5276:       for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
5277:         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);
5278:         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);
5279:       }
5280:     }
5281:   }
5282:   if (PetscDefined(USE_DEBUG)) {
5283:     for (ii = 0; ii < aij->i[m]; ii++) {
5284:       PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5285:       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);
5286:     }
5287:   }

5289:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5290:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5291:   PetscFunctionReturn(PETSC_SUCCESS);
5292: }

5294: /*@
5295:   MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5296:   provided by the user.

5298:   Collective

5300:   Input Parameters:
5301: + comm - must be an MPI communicator of size 1
5302: . m    - number of rows
5303: . n    - number of columns
5304: . i    - row indices
5305: . j    - column indices
5306: . a    - matrix values
5307: . nz   - number of nonzeros
5308: - idx  - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`

5310:   Output Parameter:
5311: . mat - the matrix

5313:   Level: intermediate

5315:   Example:
5316:   For the following matrix, the input data expected is as shown (using 0 based indexing)
5317: .vb
5318:         1 0 0
5319:         2 0 3
5320:         4 5 6

5322:         i =  {0,1,1,2,2,2}
5323:         j =  {0,0,2,0,1,2}
5324:         v =  {1,2,3,4,5,6}
5325: .ve

5327:   Note:
5328:   Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5329:   and are particularly useful in iterative applications.

5331: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5332: @*/
5333: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx)
5334: {
5335:   PetscInt ii, *nnz, one = 1, row, col;

5337:   PetscFunctionBegin;
5338:   PetscCall(PetscCalloc1(m, &nnz));
5339:   for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5340:   PetscCall(MatCreate(comm, mat));
5341:   PetscCall(MatSetSizes(*mat, m, n, m, n));
5342:   PetscCall(MatSetType(*mat, MATSEQAIJ));
5343:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5344:   for (ii = 0; ii < nz; ii++) {
5345:     if (idx) {
5346:       row = i[ii] - 1;
5347:       col = j[ii] - 1;
5348:     } else {
5349:       row = i[ii];
5350:       col = j[ii];
5351:     }
5352:     PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5353:   }
5354:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5355:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5356:   PetscCall(PetscFree(nnz));
5357:   PetscFunctionReturn(PETSC_SUCCESS);
5358: }

5360: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5361: {
5362:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

5364:   PetscFunctionBegin;
5365:   a->idiagvalid  = PETSC_FALSE;
5366:   a->ibdiagvalid = PETSC_FALSE;

5368:   PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
5369:   PetscFunctionReturn(PETSC_SUCCESS);
5370: }

5372: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5373: {
5374:   PetscFunctionBegin;
5375:   PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5376:   PetscFunctionReturn(PETSC_SUCCESS);
5377: }

5379: /*
5380:  Permute A into C's *local* index space using rowemb,colemb.
5381:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5382:  of [0,m), colemb is in [0,n).
5383:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5384:  */
5385: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5386: {
5387:   /* If making this function public, change the error returned in this function away from _PLIB. */
5388:   Mat_SeqAIJ     *Baij;
5389:   PetscBool       seqaij;
5390:   PetscInt        m, n, *nz, i, j, count;
5391:   PetscScalar     v;
5392:   const PetscInt *rowindices, *colindices;

5394:   PetscFunctionBegin;
5395:   if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5396:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5397:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5398:   PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5399:   if (rowemb) {
5400:     PetscCall(ISGetLocalSize(rowemb, &m));
5401:     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);
5402:   } else {
5403:     PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5404:   }
5405:   if (colemb) {
5406:     PetscCall(ISGetLocalSize(colemb, &n));
5407:     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);
5408:   } else {
5409:     PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5410:   }

5412:   Baij = (Mat_SeqAIJ *)B->data;
5413:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
5414:     PetscCall(PetscMalloc1(B->rmap->n, &nz));
5415:     for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5416:     PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5417:     PetscCall(PetscFree(nz));
5418:   }
5419:   if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5420:   count      = 0;
5421:   rowindices = NULL;
5422:   colindices = NULL;
5423:   if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5424:   if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5425:   for (i = 0; i < B->rmap->n; i++) {
5426:     PetscInt row;
5427:     row = i;
5428:     if (rowindices) row = rowindices[i];
5429:     for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5430:       PetscInt col;
5431:       col = Baij->j[count];
5432:       if (colindices) col = colindices[col];
5433:       v = Baij->a[count];
5434:       PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5435:       ++count;
5436:     }
5437:   }
5438:   /* FIXME: set C's nonzerostate correctly. */
5439:   /* Assembly for C is necessary. */
5440:   C->preallocated  = PETSC_TRUE;
5441:   C->assembled     = PETSC_TRUE;
5442:   C->was_assembled = PETSC_FALSE;
5443:   PetscFunctionReturn(PETSC_SUCCESS);
5444: }

5446: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A, PetscBool keep)
5447: {
5448:   Mat_SeqAIJ *a  = (Mat_SeqAIJ *)A->data;
5449:   MatScalar  *aa = a->a;
5450:   PetscInt    m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5451:   PetscInt   *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;

5453:   PetscFunctionBegin;
5454:   PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5455:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5456:   for (i = 1; i <= m; i++) {
5457:     /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5458:     for (k = ai[i - 1]; k < ai[i]; k++) {
5459:       if (aa[k] == 0 && (aj[k] != i - 1 || !keep)) fshift++;
5460:       else {
5461:         if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5462:         aa[k - fshift] = aa[k];
5463:         aj[k - fshift] = aj[k];
5464:       }
5465:     }
5466:     ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5467:     fshift_prev = fshift;
5468:     /* reset ilen and imax for each row */
5469:     ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5470:     a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5471:     rmax = PetscMax(rmax, ailen[i - 1]);
5472:   }
5473:   if (fshift) {
5474:     if (m) {
5475:       ai[m] -= fshift;
5476:       a->nz = ai[m];
5477:     }
5478:     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));
5479:     A->nonzerostate++;
5480:     A->info.nz_unneeded += (PetscReal)fshift;
5481:     a->rmax = rmax;
5482:     if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5483:     PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5484:     PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5485:   }
5486:   PetscFunctionReturn(PETSC_SUCCESS);
5487: }

5489: PetscFunctionList MatSeqAIJList = NULL;

5491: /*@C
5492:   MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype

5494:   Collective

5496:   Input Parameters:
5497: + mat    - the matrix object
5498: - matype - matrix type

5500:   Options Database Key:
5501: . -mat_seqaij_type  <method> - for example seqaijcrl

5503:   Level: intermediate

5505: .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`
5506: @*/
5507: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5508: {
5509:   PetscBool sametype;
5510:   PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);

5512:   PetscFunctionBegin;
5514:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5515:   if (sametype) PetscFunctionReturn(PETSC_SUCCESS);

5517:   PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5518:   PetscCheck(r, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5519:   PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5520:   PetscFunctionReturn(PETSC_SUCCESS);
5521: }

5523: /*@C
5524:   MatSeqAIJRegister -  - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices

5526:   Not Collective

5528:   Input Parameters:
5529: + sname    - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5530: - function - routine to convert to subtype

5532:   Level: advanced

5534:   Notes:
5535:   `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.

5537:   Then, your matrix can be chosen with the procedural interface at runtime via the option
5538: $     -mat_seqaij_type my_mat

5540: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5541: @*/
5542: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5543: {
5544:   PetscFunctionBegin;
5545:   PetscCall(MatInitializePackage());
5546:   PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5547:   PetscFunctionReturn(PETSC_SUCCESS);
5548: }

5550: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;

5552: /*@C
5553:   MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`

5555:   Not Collective

5557:   Level: advanced

5559:   Note:
5560:   This registers the versions of `MATSEQAIJ` for GPUs

5562: .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5563: @*/
5564: PetscErrorCode MatSeqAIJRegisterAll(void)
5565: {
5566:   PetscFunctionBegin;
5567:   if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5568:   MatSeqAIJRegisterAllCalled = PETSC_TRUE;

5570:   PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5571:   PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5572:   PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5573: #if defined(PETSC_HAVE_MKL_SPARSE)
5574:   PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5575: #endif
5576: #if defined(PETSC_HAVE_CUDA)
5577:   PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5578: #endif
5579: #if defined(PETSC_HAVE_HIP)
5580:   PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5581: #endif
5582: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5583:   PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5584: #endif
5585: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5586:   PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5587: #endif
5588:   PetscFunctionReturn(PETSC_SUCCESS);
5589: }

5591: /*
5592:     Special version for direct calls from Fortran
5593: */
5594: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5595:   #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5596: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5597:   #define matsetvaluesseqaij_ matsetvaluesseqaij
5598: #endif

5600: /* Change these macros so can be used in void function */

5602: /* Change these macros so can be used in void function */
5603: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5604: #undef PetscCall
5605: #define PetscCall(...) \
5606:   do { \
5607:     PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5608:     if (PetscUnlikely(ierr_msv_mpiaij)) { \
5609:       *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5610:       return; \
5611:     } \
5612:   } while (0)

5614: #undef SETERRQ
5615: #define SETERRQ(comm, ierr, ...) \
5616:   do { \
5617:     *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5618:     return; \
5619:   } while (0)

5621: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5622: {
5623:   Mat         A = *AA;
5624:   PetscInt    m = *mm, n = *nn;
5625:   InsertMode  is = *isis;
5626:   Mat_SeqAIJ *a  = (Mat_SeqAIJ *)A->data;
5627:   PetscInt   *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5628:   PetscInt   *imax, *ai, *ailen;
5629:   PetscInt   *aj, nonew = a->nonew, lastcol = -1;
5630:   MatScalar  *ap, value, *aa;
5631:   PetscBool   ignorezeroentries = a->ignorezeroentries;
5632:   PetscBool   roworiented       = a->roworiented;

5634:   PetscFunctionBegin;
5635:   MatCheckPreallocated(A, 1);
5636:   imax  = a->imax;
5637:   ai    = a->i;
5638:   ailen = a->ilen;
5639:   aj    = a->j;
5640:   aa    = a->a;

5642:   for (k = 0; k < m; k++) { /* loop over added rows */
5643:     row = im[k];
5644:     if (row < 0) continue;
5645:     PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5646:     rp   = aj + ai[row];
5647:     ap   = aa + ai[row];
5648:     rmax = imax[row];
5649:     nrow = ailen[row];
5650:     low  = 0;
5651:     high = nrow;
5652:     for (l = 0; l < n; l++) { /* loop over added columns */
5653:       if (in[l] < 0) continue;
5654:       PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5655:       col = in[l];
5656:       if (roworiented) value = v[l + k * n];
5657:       else value = v[k + l * m];

5659:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

5661:       if (col <= lastcol) low = 0;
5662:       else high = nrow;
5663:       lastcol = col;
5664:       while (high - low > 5) {
5665:         t = (low + high) / 2;
5666:         if (rp[t] > col) high = t;
5667:         else low = t;
5668:       }
5669:       for (i = low; i < high; i++) {
5670:         if (rp[i] > col) break;
5671:         if (rp[i] == col) {
5672:           if (is == ADD_VALUES) ap[i] += value;
5673:           else ap[i] = value;
5674:           goto noinsert;
5675:         }
5676:       }
5677:       if (value == 0.0 && ignorezeroentries) goto noinsert;
5678:       if (nonew == 1) goto noinsert;
5679:       PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5680:       MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5681:       N = nrow++ - 1;
5682:       a->nz++;
5683:       high++;
5684:       /* shift up all the later entries in this row */
5685:       for (ii = N; ii >= i; ii--) {
5686:         rp[ii + 1] = rp[ii];
5687:         ap[ii + 1] = ap[ii];
5688:       }
5689:       rp[i] = col;
5690:       ap[i] = value;
5691:       A->nonzerostate++;
5692:     noinsert:;
5693:       low = i + 1;
5694:     }
5695:     ailen[row] = nrow;
5696:   }
5697:   PetscFunctionReturnVoid();
5698: }
5699: /* Undefining these here since they were redefined from their original definition above! No
5700:  * other PETSc functions should be defined past this point, as it is impossible to recover the
5701:  * original definitions */
5702: #undef PetscCall
5703: #undef SETERRQ