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: 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: 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(PETSC_COMM_SELF, 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: 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: 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: 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: 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 = aj + ai[row];
419:     if (!A->structure_only) ap = 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: 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  = 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: [](chapter_matrices), `Mat`, `MatSetOption()`, `MAT_SORTED_FULL`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`
531: @*/

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

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

548:   /* allocate the matrix space */
549:   if (A->structure_only) {
550:     PetscCall(PetscMalloc1(nztotal, &a->j));
551:     PetscCall(PetscMalloc1(A->rmap->n + 1, &a->i));
552:   } else {
553:     PetscCall(PetscMalloc3(nztotal, &a->a, nztotal, &a->j, A->rmap->n + 1, &a->i));
554:   }
555:   a->i[0] = 0;
556:   if (A->structure_only) {
557:     a->singlemalloc = PETSC_FALSE;
558:     a->free_a       = PETSC_FALSE;
559:   } else {
560:     a->singlemalloc = PETSC_TRUE;
561:     a->free_a       = PETSC_TRUE;
562:   }
563:   a->free_ij        = PETSC_TRUE;
564:   A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
565:   A->preallocated   = PETSC_TRUE;
566:   PetscFunctionReturn(PETSC_SUCCESS);
567: }

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

577:   PetscFunctionBegin;
578:   PetscCall(MatSeqAIJGetArray(A, &aa));
579:   for (k = 0; k < m; k++) { /* loop over added rows */
580:     row = im[k];
581:     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);
582:     rp = aj + ai[row];
583:     ap = aa + ai[row];
584:     if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
585:     if (!A->structure_only) {
586:       if (v) {
587:         PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
588:         v += n;
589:       } else {
590:         PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
591:       }
592:     }
593:     ailen[row] = n;
594:     a->nz += n;
595:   }
596:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
597:   PetscFunctionReturn(PETSC_SUCCESS);
598: }

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

607:   PetscFunctionBegin;
608:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
609:   for (k = 0; k < m; k++) { /* loop over rows */
610:     row = im[k];
611:     if (row < 0) {
612:       v += n;
613:       continue;
614:     } /* negative row */
615:     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);
616:     rp   = aj + ai[row];
617:     ap   = aa + ai[row];
618:     nrow = ailen[row];
619:     for (l = 0; l < n; l++) { /* loop over columns */
620:       if (in[l] < 0) {
621:         v++;
622:         continue;
623:       } /* negative column */
624:       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);
625:       col  = in[l];
626:       high = nrow;
627:       low  = 0; /* assume unsorted */
628:       while (high - low > 5) {
629:         t = (low + high) / 2;
630:         if (rp[t] > col) high = t;
631:         else low = t;
632:       }
633:       for (i = low; i < high; i++) {
634:         if (rp[i] > col) break;
635:         if (rp[i] == col) {
636:           *v++ = ap[i];
637:           goto finished;
638:         }
639:       }
640:       *v++ = 0.0;
641:     finished:;
642:     }
643:   }
644:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
645:   PetscFunctionReturn(PETSC_SUCCESS);
646: }

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

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

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

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

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

682:   /* write block size option to the viewer's .info file */
683:   PetscCall(MatView_Binary_BlockSizes(mat, viewer));
684:   PetscFunctionReturn(PETSC_SUCCESS);
685: }

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

692:   PetscFunctionBegin;
693:   PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
694:   for (i = 0; i < m; i++) {
695:     PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
696:     for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ") ", a->j[k]));
697:     PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
698:   }
699:   PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
700:   PetscFunctionReturn(PETSC_SUCCESS);
701: }

703: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat, PetscViewer);

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

713:   PetscFunctionBegin;
714:   if (A->structure_only) {
715:     PetscCall(MatView_SeqAIJ_ASCII_structonly(A, viewer));
716:     PetscFunctionReturn(PETSC_SUCCESS);
717:   }

719:   PetscCall(PetscViewerGetFormat(viewer, &format));
720:   if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(PETSC_SUCCESS);

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

1113:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1114:   for (i = 1; i < m; i++) {
1115:     /* move each row back by the amount of empty slots (fshift) before it*/
1116:     fshift += imax[i - 1] - ailen[i - 1];
1117:     rmax = PetscMax(rmax, ailen[i]);
1118:     if (fshift) {
1119:       ip = aj + ai[i];
1120:       ap = aa + ai[i];
1121:       N  = ailen[i];
1122:       PetscCall(PetscArraymove(ip - fshift, ip, N));
1123:       if (!A->structure_only) PetscCall(PetscArraymove(ap - fshift, ap, N));
1124:     }
1125:     ai[i] = ai[i - 1] + ailen[i - 1];
1126:   }
1127:   if (m) {
1128:     fshift += imax[m - 1] - ailen[m - 1];
1129:     ai[m] = ai[m - 1] + ailen[m - 1];
1130:   }
1131:   /* reset ilen and imax for each row */
1132:   a->nonzerorowcnt = 0;
1133:   if (A->structure_only) {
1134:     PetscCall(PetscFree(a->imax));
1135:     PetscCall(PetscFree(a->ilen));
1136:   } else { /* !A->structure_only */
1137:     for (i = 0; i < m; i++) {
1138:       ailen[i] = imax[i] = ai[i + 1] - ai[i];
1139:       a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
1140:     }
1141:   }
1142:   a->nz = ai[m];
1143:   PetscCheck(!fshift || a->nounused != -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unused space detected in matrix: %" PetscInt_FMT " X %" PetscInt_FMT ", %" PetscInt_FMT " unneeded", m, A->cmap->n, fshift);

1145:   PetscCall(MatMarkDiagonal_SeqAIJ(A));
1146:   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));
1147:   PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs));
1148:   PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax));

1150:   A->info.mallocs += a->reallocs;
1151:   a->reallocs         = 0;
1152:   A->info.nz_unneeded = (PetscReal)fshift;
1153:   a->rmax             = rmax;

1155:   if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio));
1156:   PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1157:   PetscFunctionReturn(PETSC_SUCCESS);
1158: }

1160: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1161: {
1162:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1163:   PetscInt    i, nz = a->nz;
1164:   MatScalar  *aa;

1166:   PetscFunctionBegin;
1167:   PetscCall(MatSeqAIJGetArray(A, &aa));
1168:   for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
1169:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
1170:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
1171:   PetscFunctionReturn(PETSC_SUCCESS);
1172: }

1174: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1175: {
1176:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1177:   PetscInt    i, nz = a->nz;
1178:   MatScalar  *aa;

1180:   PetscFunctionBegin;
1181:   PetscCall(MatSeqAIJGetArray(A, &aa));
1182:   for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1183:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
1184:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
1185:   PetscFunctionReturn(PETSC_SUCCESS);
1186: }

1188: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1189: {
1190:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1191:   MatScalar  *aa;

1193:   PetscFunctionBegin;
1194:   PetscCall(MatSeqAIJGetArrayWrite(A, &aa));
1195:   PetscCall(PetscArrayzero(aa, a->i[A->rmap->n]));
1196:   PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa));
1197:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
1198:   PetscFunctionReturn(PETSC_SUCCESS);
1199: }

1201: PETSC_INTERN PetscErrorCode MatResetPreallocationCOO_SeqAIJ(Mat A)
1202: {
1203:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

1205:   PetscFunctionBegin;
1206:   PetscCall(PetscFree(a->perm));
1207:   PetscCall(PetscFree(a->jmap));
1208:   PetscFunctionReturn(PETSC_SUCCESS);
1209: }

1211: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1212: {
1213:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

1215:   PetscFunctionBegin;
1216: #if defined(PETSC_USE_LOG)
1217:   PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
1218: #endif
1219:   PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1220:   PetscCall(MatResetPreallocationCOO_SeqAIJ(A));
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: 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: PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1526: {
1527:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1528:   PetscScalar       *y;
1529:   const PetscScalar *x;
1530:   const MatScalar   *aa, *a_a;
1531:   PetscInt           m = A->rmap->n;
1532:   const PetscInt    *aj, *ii, *ridx   = NULL;
1533:   PetscInt           n, i, nonzerorow = 0;
1534:   PetscScalar        sum;
1535:   PetscBool          usecprow = a->compressedrow.use;

1537: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1538:   #pragma disjoint(*x, *y, *aa)
1539: #endif

1541:   PetscFunctionBegin;
1542:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1543:   PetscCall(VecGetArrayRead(xx, &x));
1544:   PetscCall(VecGetArray(yy, &y));
1545:   if (usecprow) { /* use compressed row format */
1546:     m    = a->compressedrow.nrows;
1547:     ii   = a->compressedrow.i;
1548:     ridx = a->compressedrow.rindex;
1549:     for (i = 0; i < m; i++) {
1550:       n   = ii[i + 1] - ii[i];
1551:       aj  = a->j + ii[i];
1552:       aa  = a_a + ii[i];
1553:       sum = 0.0;
1554:       nonzerorow += (n > 0);
1555:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1556:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1557:       y[*ridx++] = sum;
1558:     }
1559:   } else { /* do not use compressed row format */
1560:     ii = a->i;
1561:     for (i = 0; i < m; i++) {
1562:       n   = ii[i + 1] - ii[i];
1563:       aj  = a->j + ii[i];
1564:       aa  = a_a + ii[i];
1565:       sum = 0.0;
1566:       nonzerorow += (n > 0);
1567:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1568:       y[i] = sum;
1569:     }
1570:   }
1571:   PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1572:   PetscCall(VecRestoreArrayRead(xx, &x));
1573:   PetscCall(VecRestoreArray(yy, &y));
1574:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1575:   PetscFunctionReturn(PETSC_SUCCESS);
1576: }

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

1589:   PetscFunctionBegin;
1590:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1591:   PetscCall(VecGetArrayRead(xx, &x));
1592:   PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1593:   if (usecprow) { /* use compressed row format */
1594:     if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1595:     m    = a->compressedrow.nrows;
1596:     ii   = a->compressedrow.i;
1597:     ridx = a->compressedrow.rindex;
1598:     for (i = 0; i < m; i++) {
1599:       n   = ii[i + 1] - ii[i];
1600:       aj  = a->j + ii[i];
1601:       aa  = a_a + ii[i];
1602:       sum = y[*ridx];
1603:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1604:       z[*ridx++] = sum;
1605:     }
1606:   } else { /* do not use compressed row format */
1607:     ii = a->i;
1608:     for (i = 0; i < m; i++) {
1609:       n   = ii[i + 1] - ii[i];
1610:       aj  = a->j + ii[i];
1611:       aa  = a_a + ii[i];
1612:       sum = y[i];
1613:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1614:       z[i] = sum;
1615:     }
1616:   }
1617:   PetscCall(PetscLogFlops(2.0 * a->nz));
1618:   PetscCall(VecRestoreArrayRead(xx, &x));
1619:   PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1620:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1621:   PetscFunctionReturn(PETSC_SUCCESS);
1622: }

1624: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1625: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1626: {
1627:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1628:   PetscScalar       *y, *z;
1629:   const PetscScalar *x;
1630:   const MatScalar   *aa, *a_a;
1631:   const PetscInt    *aj, *ii, *ridx = NULL;
1632:   PetscInt           m = A->rmap->n, n, i;
1633:   PetscScalar        sum;
1634:   PetscBool          usecprow = a->compressedrow.use;

1636:   PetscFunctionBegin;
1637:   if (a->inode.use && a->inode.checked) {
1638:     PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
1639:     PetscFunctionReturn(PETSC_SUCCESS);
1640:   }
1641:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1642:   PetscCall(VecGetArrayRead(xx, &x));
1643:   PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1644:   if (usecprow) { /* use compressed row format */
1645:     if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1646:     m    = a->compressedrow.nrows;
1647:     ii   = a->compressedrow.i;
1648:     ridx = a->compressedrow.rindex;
1649:     for (i = 0; i < m; i++) {
1650:       n   = ii[i + 1] - ii[i];
1651:       aj  = a->j + ii[i];
1652:       aa  = a_a + ii[i];
1653:       sum = y[*ridx];
1654:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
1655:       z[*ridx++] = sum;
1656:     }
1657:   } else { /* do not use compressed row format */
1658:     ii = a->i;
1659: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1660:     aj = a->j;
1661:     aa = a_a;
1662:     fortranmultaddaij_(&m, x, ii, aj, aa, y, z);
1663: #else
1664:     for (i = 0; i < m; i++) {
1665:       n   = ii[i + 1] - ii[i];
1666:       aj  = a->j + ii[i];
1667:       aa  = a_a + ii[i];
1668:       sum = y[i];
1669:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
1670:       z[i] = sum;
1671:     }
1672: #endif
1673:   }
1674:   PetscCall(PetscLogFlops(2.0 * a->nz));
1675:   PetscCall(VecRestoreArrayRead(xx, &x));
1676:   PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1677:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1678:   PetscFunctionReturn(PETSC_SUCCESS);
1679: }

1681: /*
1682:      Adds diagonal pointers to sparse matrix structure.
1683: */
1684: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1685: {
1686:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1687:   PetscInt    i, j, m = A->rmap->n;
1688:   PetscBool   alreadySet = PETSC_TRUE;

1690:   PetscFunctionBegin;
1691:   if (!a->diag) {
1692:     PetscCall(PetscMalloc1(m, &a->diag));
1693:     alreadySet = PETSC_FALSE;
1694:   }
1695:   for (i = 0; i < A->rmap->n; i++) {
1696:     /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
1697:     if (alreadySet) {
1698:       PetscInt pos = a->diag[i];
1699:       if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
1700:     }

1702:     a->diag[i] = a->i[i + 1];
1703:     for (j = a->i[i]; j < a->i[i + 1]; j++) {
1704:       if (a->j[j] == i) {
1705:         a->diag[i] = j;
1706:         break;
1707:       }
1708:     }
1709:   }
1710:   PetscFunctionReturn(PETSC_SUCCESS);
1711: }

1713: PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1714: {
1715:   Mat_SeqAIJ     *a    = (Mat_SeqAIJ *)A->data;
1716:   const PetscInt *diag = (const PetscInt *)a->diag;
1717:   const PetscInt *ii   = (const PetscInt *)a->i;
1718:   PetscInt        i, *mdiag = NULL;
1719:   PetscInt        cnt = 0; /* how many diagonals are missing */

1721:   PetscFunctionBegin;
1722:   if (!A->preallocated || !a->nz) {
1723:     PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1724:     PetscCall(MatShift_Basic(A, v));
1725:     PetscFunctionReturn(PETSC_SUCCESS);
1726:   }

1728:   if (a->diagonaldense) {
1729:     cnt = 0;
1730:   } else {
1731:     PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1732:     for (i = 0; i < A->rmap->n; i++) {
1733:       if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1734:         cnt++;
1735:         mdiag[i] = 1;
1736:       }
1737:     }
1738:   }
1739:   if (!cnt) {
1740:     PetscCall(MatShift_Basic(A, v));
1741:   } else {
1742:     PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1743:     PetscInt    *oldj = a->j, *oldi = a->i;
1744:     PetscBool    singlemalloc = a->singlemalloc, free_a = a->free_a, free_ij = a->free_ij;

1746:     a->a = NULL;
1747:     a->j = NULL;
1748:     a->i = NULL;
1749:     /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1750:     for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1751:     PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));

1753:     /* copy old values into new matrix data structure */
1754:     for (i = 0; i < A->rmap->n; i++) {
1755:       PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1756:       if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1757:     }
1758:     PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1759:     PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1760:     if (singlemalloc) {
1761:       PetscCall(PetscFree3(olda, oldj, oldi));
1762:     } else {
1763:       if (free_a) PetscCall(PetscFree(olda));
1764:       if (free_ij) PetscCall(PetscFree(oldj));
1765:       if (free_ij) PetscCall(PetscFree(oldi));
1766:     }
1767:   }
1768:   PetscCall(PetscFree(mdiag));
1769:   a->diagonaldense = PETSC_TRUE;
1770:   PetscFunctionReturn(PETSC_SUCCESS);
1771: }

1773: /*
1774:      Checks for missing diagonals
1775: */
1776: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
1777: {
1778:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1779:   PetscInt   *diag, *ii = a->i, i;

1781:   PetscFunctionBegin;
1782:   *missing = PETSC_FALSE;
1783:   if (A->rmap->n > 0 && !ii) {
1784:     *missing = PETSC_TRUE;
1785:     if (d) *d = 0;
1786:     PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1787:   } else {
1788:     PetscInt n;
1789:     n    = PetscMin(A->rmap->n, A->cmap->n);
1790:     diag = a->diag;
1791:     for (i = 0; i < n; i++) {
1792:       if (diag[i] >= ii[i + 1]) {
1793:         *missing = PETSC_TRUE;
1794:         if (d) *d = i;
1795:         PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
1796:         break;
1797:       }
1798:     }
1799:   }
1800:   PetscFunctionReturn(PETSC_SUCCESS);
1801: }

1803: #include <petscblaslapack.h>
1804: #include <petsc/private/kernels/blockinvert.h>

1806: /*
1807:     Note that values is allocated externally by the PC and then passed into this routine
1808: */
1809: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1810: {
1811:   PetscInt        n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1812:   PetscBool       allowzeropivot, zeropivotdetected = PETSC_FALSE;
1813:   const PetscReal shift = 0.0;
1814:   PetscInt        ipvt[5];
1815:   PetscCount      flops = 0;
1816:   PetscScalar     work[25], *v_work;

1818:   PetscFunctionBegin;
1819:   allowzeropivot = PetscNot(A->erroriffailure);
1820:   for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1821:   PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1822:   for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1823:   PetscCall(PetscMalloc1(bsizemax, &indx));
1824:   if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1825:   ncnt = 0;
1826:   for (i = 0; i < nblocks; i++) {
1827:     for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1828:     PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1829:     switch (bsizes[i]) {
1830:     case 1:
1831:       *diag = 1.0 / (*diag);
1832:       break;
1833:     case 2:
1834:       PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1835:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1836:       PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1837:       break;
1838:     case 3:
1839:       PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1840:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1841:       PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1842:       break;
1843:     case 4:
1844:       PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1845:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1846:       PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1847:       break;
1848:     case 5:
1849:       PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1850:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1851:       PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1852:       break;
1853:     case 6:
1854:       PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1855:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1856:       PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1857:       break;
1858:     case 7:
1859:       PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1860:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1861:       PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1862:       break;
1863:     default:
1864:       PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1865:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1866:       PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1867:     }
1868:     ncnt += bsizes[i];
1869:     diag += bsizes[i] * bsizes[i];
1870:     flops += 2 * PetscPowInt(bsizes[i], 3) / 3;
1871:   }
1872:   PetscCall(PetscLogFlops(flops));
1873:   if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1874:   PetscCall(PetscFree(indx));
1875:   PetscFunctionReturn(PETSC_SUCCESS);
1876: }

1878: /*
1879:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1880: */
1881: PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1882: {
1883:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
1884:   PetscInt         i, *diag, m = A->rmap->n;
1885:   const MatScalar *v;
1886:   PetscScalar     *idiag, *mdiag;

1888:   PetscFunctionBegin;
1889:   if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
1890:   PetscCall(MatMarkDiagonal_SeqAIJ(A));
1891:   diag = a->diag;
1892:   if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); }

1894:   mdiag = a->mdiag;
1895:   idiag = a->idiag;
1896:   PetscCall(MatSeqAIJGetArrayRead(A, &v));
1897:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1898:     for (i = 0; i < m; i++) {
1899:       mdiag[i] = v[diag[i]];
1900:       if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1901:         if (PetscRealPart(fshift)) {
1902:           PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1903:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1904:           A->factorerror_zeropivot_value = 0.0;
1905:           A->factorerror_zeropivot_row   = i;
1906:         } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1907:       }
1908:       idiag[i] = 1.0 / v[diag[i]];
1909:     }
1910:     PetscCall(PetscLogFlops(m));
1911:   } else {
1912:     for (i = 0; i < m; i++) {
1913:       mdiag[i] = v[diag[i]];
1914:       idiag[i] = omega / (fshift + v[diag[i]]);
1915:     }
1916:     PetscCall(PetscLogFlops(2.0 * m));
1917:   }
1918:   a->idiagvalid = PETSC_TRUE;
1919:   PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1920:   PetscFunctionReturn(PETSC_SUCCESS);
1921: }

1923: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1924: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1925: {
1926:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1927:   PetscScalar       *x, d, sum, *t, scale;
1928:   const MatScalar   *v, *idiag = NULL, *mdiag, *aa;
1929:   const PetscScalar *b, *bs, *xb, *ts;
1930:   PetscInt           n, m = A->rmap->n, i;
1931:   const PetscInt    *idx, *diag;

1933:   PetscFunctionBegin;
1934:   if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1935:     PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1936:     PetscFunctionReturn(PETSC_SUCCESS);
1937:   }
1938:   its = its * lits;

1940:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1941:   if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
1942:   a->fshift = fshift;
1943:   a->omega  = omega;

1945:   diag  = a->diag;
1946:   t     = a->ssor_work;
1947:   idiag = a->idiag;
1948:   mdiag = a->mdiag;

1950:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1951:   PetscCall(VecGetArray(xx, &x));
1952:   PetscCall(VecGetArrayRead(bb, &b));
1953:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1954:   if (flag == SOR_APPLY_UPPER) {
1955:     /* apply (U + D/omega) to the vector */
1956:     bs = b;
1957:     for (i = 0; i < m; i++) {
1958:       d   = fshift + mdiag[i];
1959:       n   = a->i[i + 1] - diag[i] - 1;
1960:       idx = a->j + diag[i] + 1;
1961:       v   = aa + diag[i] + 1;
1962:       sum = b[i] * d / omega;
1963:       PetscSparseDensePlusDot(sum, bs, v, idx, n);
1964:       x[i] = sum;
1965:     }
1966:     PetscCall(VecRestoreArray(xx, &x));
1967:     PetscCall(VecRestoreArrayRead(bb, &b));
1968:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1969:     PetscCall(PetscLogFlops(a->nz));
1970:     PetscFunctionReturn(PETSC_SUCCESS);
1971:   }

1973:   PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1974:   if (flag & SOR_EISENSTAT) {
1975:     /* Let  A = L + U + D; where L is lower triangular,
1976:     U is upper triangular, E = D/omega; This routine applies

1978:             (L + E)^{-1} A (U + E)^{-1}

1980:     to a vector efficiently using Eisenstat's trick.
1981:     */
1982:     scale = (2.0 / omega) - 1.0;

1984:     /*  x = (E + U)^{-1} b */
1985:     for (i = m - 1; i >= 0; i--) {
1986:       n   = a->i[i + 1] - diag[i] - 1;
1987:       idx = a->j + diag[i] + 1;
1988:       v   = aa + diag[i] + 1;
1989:       sum = b[i];
1990:       PetscSparseDenseMinusDot(sum, x, v, idx, n);
1991:       x[i] = sum * idiag[i];
1992:     }

1994:     /*  t = b - (2*E - D)x */
1995:     v = aa;
1996:     for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];

1998:     /*  t = (E + L)^{-1}t */
1999:     ts   = t;
2000:     diag = a->diag;
2001:     for (i = 0; i < m; i++) {
2002:       n   = diag[i] - a->i[i];
2003:       idx = a->j + a->i[i];
2004:       v   = aa + a->i[i];
2005:       sum = t[i];
2006:       PetscSparseDenseMinusDot(sum, ts, v, idx, n);
2007:       t[i] = sum * idiag[i];
2008:       /*  x = x + t */
2009:       x[i] += t[i];
2010:     }

2012:     PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
2013:     PetscCall(VecRestoreArray(xx, &x));
2014:     PetscCall(VecRestoreArrayRead(bb, &b));
2015:     PetscFunctionReturn(PETSC_SUCCESS);
2016:   }
2017:   if (flag & SOR_ZERO_INITIAL_GUESS) {
2018:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2019:       for (i = 0; i < m; i++) {
2020:         n   = diag[i] - a->i[i];
2021:         idx = a->j + a->i[i];
2022:         v   = aa + a->i[i];
2023:         sum = b[i];
2024:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2025:         t[i] = sum;
2026:         x[i] = sum * idiag[i];
2027:       }
2028:       xb = t;
2029:       PetscCall(PetscLogFlops(a->nz));
2030:     } else xb = b;
2031:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2032:       for (i = m - 1; i >= 0; i--) {
2033:         n   = a->i[i + 1] - diag[i] - 1;
2034:         idx = a->j + diag[i] + 1;
2035:         v   = aa + diag[i] + 1;
2036:         sum = xb[i];
2037:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2038:         if (xb == b) {
2039:           x[i] = sum * idiag[i];
2040:         } else {
2041:           x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2042:         }
2043:       }
2044:       PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2045:     }
2046:     its--;
2047:   }
2048:   while (its--) {
2049:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2050:       for (i = 0; i < m; i++) {
2051:         /* lower */
2052:         n   = diag[i] - a->i[i];
2053:         idx = a->j + a->i[i];
2054:         v   = aa + a->i[i];
2055:         sum = b[i];
2056:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2057:         t[i] = sum; /* save application of the lower-triangular part */
2058:         /* upper */
2059:         n   = a->i[i + 1] - diag[i] - 1;
2060:         idx = a->j + diag[i] + 1;
2061:         v   = aa + diag[i] + 1;
2062:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2063:         x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2064:       }
2065:       xb = t;
2066:       PetscCall(PetscLogFlops(2.0 * a->nz));
2067:     } else xb = b;
2068:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2069:       for (i = m - 1; i >= 0; i--) {
2070:         sum = xb[i];
2071:         if (xb == b) {
2072:           /* whole matrix (no checkpointing available) */
2073:           n   = a->i[i + 1] - a->i[i];
2074:           idx = a->j + a->i[i];
2075:           v   = aa + a->i[i];
2076:           PetscSparseDenseMinusDot(sum, x, v, idx, n);
2077:           x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2078:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2079:           n   = a->i[i + 1] - diag[i] - 1;
2080:           idx = a->j + diag[i] + 1;
2081:           v   = aa + diag[i] + 1;
2082:           PetscSparseDenseMinusDot(sum, x, v, idx, n);
2083:           x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2084:         }
2085:       }
2086:       if (xb == b) {
2087:         PetscCall(PetscLogFlops(2.0 * a->nz));
2088:       } else {
2089:         PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2090:       }
2091:     }
2092:   }
2093:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2094:   PetscCall(VecRestoreArray(xx, &x));
2095:   PetscCall(VecRestoreArrayRead(bb, &b));
2096:   PetscFunctionReturn(PETSC_SUCCESS);
2097: }

2099: PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2100: {
2101:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

2103:   PetscFunctionBegin;
2104:   info->block_size   = 1.0;
2105:   info->nz_allocated = a->maxnz;
2106:   info->nz_used      = a->nz;
2107:   info->nz_unneeded  = (a->maxnz - a->nz);
2108:   info->assemblies   = A->num_ass;
2109:   info->mallocs      = A->info.mallocs;
2110:   info->memory       = 0; /* REVIEW ME */
2111:   if (A->factortype) {
2112:     info->fill_ratio_given  = A->info.fill_ratio_given;
2113:     info->fill_ratio_needed = A->info.fill_ratio_needed;
2114:     info->factor_mallocs    = A->info.factor_mallocs;
2115:   } else {
2116:     info->fill_ratio_given  = 0;
2117:     info->fill_ratio_needed = 0;
2118:     info->factor_mallocs    = 0;
2119:   }
2120:   PetscFunctionReturn(PETSC_SUCCESS);
2121: }

2123: PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2124: {
2125:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2126:   PetscInt           i, m = A->rmap->n - 1;
2127:   const PetscScalar *xx;
2128:   PetscScalar       *bb, *aa;
2129:   PetscInt           d = 0;

2131:   PetscFunctionBegin;
2132:   if (x && b) {
2133:     PetscCall(VecGetArrayRead(x, &xx));
2134:     PetscCall(VecGetArray(b, &bb));
2135:     for (i = 0; i < N; i++) {
2136:       PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2137:       if (rows[i] >= A->cmap->n) continue;
2138:       bb[rows[i]] = diag * xx[rows[i]];
2139:     }
2140:     PetscCall(VecRestoreArrayRead(x, &xx));
2141:     PetscCall(VecRestoreArray(b, &bb));
2142:   }

2144:   PetscCall(MatSeqAIJGetArray(A, &aa));
2145:   if (a->keepnonzeropattern) {
2146:     for (i = 0; i < N; i++) {
2147:       PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2148:       PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2149:     }
2150:     if (diag != 0.0) {
2151:       for (i = 0; i < N; i++) {
2152:         d = rows[i];
2153:         if (rows[i] >= A->cmap->n) continue;
2154:         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);
2155:       }
2156:       for (i = 0; i < N; i++) {
2157:         if (rows[i] >= A->cmap->n) continue;
2158:         aa[a->diag[rows[i]]] = diag;
2159:       }
2160:     }
2161:   } else {
2162:     if (diag != 0.0) {
2163:       for (i = 0; i < N; i++) {
2164:         PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2165:         if (a->ilen[rows[i]] > 0) {
2166:           if (rows[i] >= A->cmap->n) {
2167:             a->ilen[rows[i]] = 0;
2168:           } else {
2169:             a->ilen[rows[i]]    = 1;
2170:             aa[a->i[rows[i]]]   = diag;
2171:             a->j[a->i[rows[i]]] = rows[i];
2172:           }
2173:         } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2174:           PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2175:         }
2176:       }
2177:     } else {
2178:       for (i = 0; i < N; i++) {
2179:         PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2180:         a->ilen[rows[i]] = 0;
2181:       }
2182:     }
2183:     A->nonzerostate++;
2184:   }
2185:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
2186:   PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2187:   PetscFunctionReturn(PETSC_SUCCESS);
2188: }

2190: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2191: {
2192:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2193:   PetscInt           i, j, m = A->rmap->n - 1, d = 0;
2194:   PetscBool          missing, *zeroed, vecs = PETSC_FALSE;
2195:   const PetscScalar *xx;
2196:   PetscScalar       *bb, *aa;

2198:   PetscFunctionBegin;
2199:   if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2200:   PetscCall(MatSeqAIJGetArray(A, &aa));
2201:   if (x && b) {
2202:     PetscCall(VecGetArrayRead(x, &xx));
2203:     PetscCall(VecGetArray(b, &bb));
2204:     vecs = PETSC_TRUE;
2205:   }
2206:   PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2207:   for (i = 0; i < N; i++) {
2208:     PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2209:     PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));

2211:     zeroed[rows[i]] = PETSC_TRUE;
2212:   }
2213:   for (i = 0; i < A->rmap->n; i++) {
2214:     if (!zeroed[i]) {
2215:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
2216:         if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2217:           if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2218:           aa[j] = 0.0;
2219:         }
2220:       }
2221:     } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
2222:   }
2223:   if (x && b) {
2224:     PetscCall(VecRestoreArrayRead(x, &xx));
2225:     PetscCall(VecRestoreArray(b, &bb));
2226:   }
2227:   PetscCall(PetscFree(zeroed));
2228:   if (diag != 0.0) {
2229:     PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
2230:     if (missing) {
2231:       for (i = 0; i < N; i++) {
2232:         if (rows[i] >= A->cmap->N) continue;
2233:         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]);
2234:         PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2235:       }
2236:     } else {
2237:       for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
2238:     }
2239:   }
2240:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
2241:   PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2242:   PetscFunctionReturn(PETSC_SUCCESS);
2243: }

2245: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2246: {
2247:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2248:   const PetscScalar *aa;
2249:   PetscInt          *itmp;

2251:   PetscFunctionBegin;
2252:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2253:   *nz = a->i[row + 1] - a->i[row];
2254:   if (v) *v = (PetscScalar *)(aa + a->i[row]);
2255:   if (idx) {
2256:     itmp = a->j + a->i[row];
2257:     if (*nz) *idx = itmp;
2258:     else *idx = NULL;
2259:   }
2260:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2261:   PetscFunctionReturn(PETSC_SUCCESS);
2262: }

2264: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2265: {
2266:   PetscFunctionBegin;
2267:   if (nz) *nz = 0;
2268:   if (idx) *idx = NULL;
2269:   if (v) *v = NULL;
2270:   PetscFunctionReturn(PETSC_SUCCESS);
2271: }

2273: 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 = 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: 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: 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 MatIsSymmetric_SeqAIJ(Mat A, PetscReal tol, PetscBool *f)
2428: {
2429:   PetscFunctionBegin;
2430:   PetscCall(MatIsTranspose_SeqAIJ(A, A, tol, f));
2431:   PetscFunctionReturn(PETSC_SUCCESS);
2432: }

2434: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A, PetscReal tol, PetscBool *f)
2435: {
2436:   PetscFunctionBegin;
2437:   PetscCall(MatIsHermitianTranspose_SeqAIJ(A, A, tol, f));
2438:   PetscFunctionReturn(PETSC_SUCCESS);
2439: }

2441: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2442: {
2443:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2444:   const PetscScalar *l, *r;
2445:   PetscScalar        x;
2446:   MatScalar         *v;
2447:   PetscInt           i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2448:   const PetscInt    *jj;

2450:   PetscFunctionBegin;
2451:   if (ll) {
2452:     /* The local size is used so that VecMPI can be passed to this routine
2453:        by MatDiagonalScale_MPIAIJ */
2454:     PetscCall(VecGetLocalSize(ll, &m));
2455:     PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2456:     PetscCall(VecGetArrayRead(ll, &l));
2457:     PetscCall(MatSeqAIJGetArray(A, &v));
2458:     for (i = 0; i < m; i++) {
2459:       x = l[i];
2460:       M = a->i[i + 1] - a->i[i];
2461:       for (j = 0; j < M; j++) (*v++) *= x;
2462:     }
2463:     PetscCall(VecRestoreArrayRead(ll, &l));
2464:     PetscCall(PetscLogFlops(nz));
2465:     PetscCall(MatSeqAIJRestoreArray(A, &v));
2466:   }
2467:   if (rr) {
2468:     PetscCall(VecGetLocalSize(rr, &n));
2469:     PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2470:     PetscCall(VecGetArrayRead(rr, &r));
2471:     PetscCall(MatSeqAIJGetArray(A, &v));
2472:     jj = a->j;
2473:     for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2474:     PetscCall(MatSeqAIJRestoreArray(A, &v));
2475:     PetscCall(VecRestoreArrayRead(rr, &r));
2476:     PetscCall(PetscLogFlops(nz));
2477:   }
2478:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
2479:   PetscFunctionReturn(PETSC_SUCCESS);
2480: }

2482: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2483: {
2484:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data, *c;
2485:   PetscInt          *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2486:   PetscInt           row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2487:   const PetscInt    *irow, *icol;
2488:   const PetscScalar *aa;
2489:   PetscInt           nrows, ncols;
2490:   PetscInt          *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2491:   MatScalar         *a_new, *mat_a, *c_a;
2492:   Mat                C;
2493:   PetscBool          stride;

2495:   PetscFunctionBegin;
2496:   PetscCall(ISGetIndices(isrow, &irow));
2497:   PetscCall(ISGetLocalSize(isrow, &nrows));
2498:   PetscCall(ISGetLocalSize(iscol, &ncols));

2500:   PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2501:   if (stride) {
2502:     PetscCall(ISStrideGetInfo(iscol, &first, &step));
2503:   } else {
2504:     first = 0;
2505:     step  = 0;
2506:   }
2507:   if (stride && step == 1) {
2508:     /* special case of contiguous rows */
2509:     PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2510:     /* loop over new rows determining lens and starting points */
2511:     for (i = 0; i < nrows; i++) {
2512:       kstart    = ai[irow[i]];
2513:       kend      = kstart + ailen[irow[i]];
2514:       starts[i] = kstart;
2515:       for (k = kstart; k < kend; k++) {
2516:         if (aj[k] >= first) {
2517:           starts[i] = k;
2518:           break;
2519:         }
2520:       }
2521:       sum = 0;
2522:       while (k < kend) {
2523:         if (aj[k++] >= first + ncols) break;
2524:         sum++;
2525:       }
2526:       lens[i] = sum;
2527:     }
2528:     /* create submatrix */
2529:     if (scall == MAT_REUSE_MATRIX) {
2530:       PetscInt n_cols, n_rows;
2531:       PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2532:       PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2533:       PetscCall(MatZeroEntries(*B));
2534:       C = *B;
2535:     } else {
2536:       PetscInt rbs, cbs;
2537:       PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2538:       PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2539:       PetscCall(ISGetBlockSize(isrow, &rbs));
2540:       PetscCall(ISGetBlockSize(iscol, &cbs));
2541:       PetscCall(MatSetBlockSizes(C, rbs, cbs));
2542:       PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2543:       PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2544:     }
2545:     c = (Mat_SeqAIJ *)C->data;

2547:     /* loop over rows inserting into submatrix */
2548:     PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2549:     j_new = c->j;
2550:     i_new = c->i;
2551:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2552:     for (i = 0; i < nrows; i++) {
2553:       ii    = starts[i];
2554:       lensi = lens[i];
2555:       for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2556:       PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2557:       a_new += lensi;
2558:       i_new[i + 1] = i_new[i] + lensi;
2559:       c->ilen[i]   = lensi;
2560:     }
2561:     PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2562:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2563:     PetscCall(PetscFree2(lens, starts));
2564:   } else {
2565:     PetscCall(ISGetIndices(iscol, &icol));
2566:     PetscCall(PetscCalloc1(oldcols, &smap));
2567:     PetscCall(PetscMalloc1(1 + nrows, &lens));
2568:     for (i = 0; i < ncols; i++) {
2569:       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);
2570:       smap[icol[i]] = i + 1;
2571:     }

2573:     /* determine lens of each row */
2574:     for (i = 0; i < nrows; i++) {
2575:       kstart  = ai[irow[i]];
2576:       kend    = kstart + a->ilen[irow[i]];
2577:       lens[i] = 0;
2578:       for (k = kstart; k < kend; k++) {
2579:         if (smap[aj[k]]) lens[i]++;
2580:       }
2581:     }
2582:     /* Create and fill new matrix */
2583:     if (scall == MAT_REUSE_MATRIX) {
2584:       PetscBool equal;

2586:       c = (Mat_SeqAIJ *)((*B)->data);
2587:       PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2588:       PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2589:       PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong no of nonzeros");
2590:       PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2591:       C = *B;
2592:     } else {
2593:       PetscInt rbs, cbs;
2594:       PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2595:       PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2596:       PetscCall(ISGetBlockSize(isrow, &rbs));
2597:       PetscCall(ISGetBlockSize(iscol, &cbs));
2598:       PetscCall(MatSetBlockSizes(C, rbs, cbs));
2599:       PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2600:       PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2601:     }
2602:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));

2604:     c = (Mat_SeqAIJ *)(C->data);
2605:     PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2606:     for (i = 0; i < nrows; i++) {
2607:       row      = irow[i];
2608:       kstart   = ai[row];
2609:       kend     = kstart + a->ilen[row];
2610:       mat_i    = c->i[i];
2611:       mat_j    = c->j + mat_i;
2612:       mat_a    = c_a + mat_i;
2613:       mat_ilen = c->ilen + i;
2614:       for (k = kstart; k < kend; k++) {
2615:         if ((tcol = smap[a->j[k]])) {
2616:           *mat_j++ = tcol - 1;
2617:           *mat_a++ = aa[k];
2618:           (*mat_ilen)++;
2619:         }
2620:       }
2621:     }
2622:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2623:     /* Free work space */
2624:     PetscCall(ISRestoreIndices(iscol, &icol));
2625:     PetscCall(PetscFree(smap));
2626:     PetscCall(PetscFree(lens));
2627:     /* sort */
2628:     for (i = 0; i < nrows; i++) {
2629:       PetscInt ilen;

2631:       mat_i = c->i[i];
2632:       mat_j = c->j + mat_i;
2633:       mat_a = c_a + mat_i;
2634:       ilen  = c->ilen[i];
2635:       PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2636:     }
2637:     PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2638:   }
2639: #if defined(PETSC_HAVE_DEVICE)
2640:   PetscCall(MatBindToCPU(C, A->boundtocpu));
2641: #endif
2642:   PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2643:   PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));

2645:   PetscCall(ISRestoreIndices(isrow, &irow));
2646:   *B = C;
2647:   PetscFunctionReturn(PETSC_SUCCESS);
2648: }

2650: PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2651: {
2652:   Mat B;

2654:   PetscFunctionBegin;
2655:   if (scall == MAT_INITIAL_MATRIX) {
2656:     PetscCall(MatCreate(subComm, &B));
2657:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2658:     PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2659:     PetscCall(MatSetType(B, MATSEQAIJ));
2660:     PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2661:     *subMat = B;
2662:   } else {
2663:     PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2664:   }
2665:   PetscFunctionReturn(PETSC_SUCCESS);
2666: }

2668: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2669: {
2670:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2671:   Mat         outA;
2672:   PetscBool   row_identity, col_identity;

2674:   PetscFunctionBegin;
2675:   PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");

2677:   PetscCall(ISIdentity(row, &row_identity));
2678:   PetscCall(ISIdentity(col, &col_identity));

2680:   outA             = inA;
2681:   outA->factortype = MAT_FACTOR_LU;
2682:   PetscCall(PetscFree(inA->solvertype));
2683:   PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));

2685:   PetscCall(PetscObjectReference((PetscObject)row));
2686:   PetscCall(ISDestroy(&a->row));

2688:   a->row = row;

2690:   PetscCall(PetscObjectReference((PetscObject)col));
2691:   PetscCall(ISDestroy(&a->col));

2693:   a->col = col;

2695:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2696:   PetscCall(ISDestroy(&a->icol));
2697:   PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));

2699:   if (!a->solve_work) { /* this matrix may have been factored before */
2700:     PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
2701:   }

2703:   PetscCall(MatMarkDiagonal_SeqAIJ(inA));
2704:   if (row_identity && col_identity) {
2705:     PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2706:   } else {
2707:     PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2708:   }
2709:   PetscFunctionReturn(PETSC_SUCCESS);
2710: }

2712: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2713: {
2714:   Mat_SeqAIJ  *a = (Mat_SeqAIJ *)inA->data;
2715:   PetscScalar *v;
2716:   PetscBLASInt one = 1, bnz;

2718:   PetscFunctionBegin;
2719:   PetscCall(MatSeqAIJGetArray(inA, &v));
2720:   PetscCall(PetscBLASIntCast(a->nz, &bnz));
2721:   PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2722:   PetscCall(PetscLogFlops(a->nz));
2723:   PetscCall(MatSeqAIJRestoreArray(inA, &v));
2724:   PetscCall(MatSeqAIJInvalidateDiagonal(inA));
2725:   PetscFunctionReturn(PETSC_SUCCESS);
2726: }

2728: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2729: {
2730:   PetscInt i;

2732:   PetscFunctionBegin;
2733:   if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2734:     PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));

2736:     for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2737:     PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));

2739:     if (submatj->rbuf1) {
2740:       PetscCall(PetscFree(submatj->rbuf1[0]));
2741:       PetscCall(PetscFree(submatj->rbuf1));
2742:     }

2744:     for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2745:     PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2746:     PetscCall(PetscFree(submatj->pa));
2747:   }

2749: #if defined(PETSC_USE_CTABLE)
2750:   PetscCall(PetscHMapIDestroy(&submatj->rmap));
2751:   if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2752:   PetscCall(PetscFree(submatj->rmap_loc));
2753: #else
2754:   PetscCall(PetscFree(submatj->rmap));
2755: #endif

2757:   if (!submatj->allcolumns) {
2758: #if defined(PETSC_USE_CTABLE)
2759:     PetscCall(PetscHMapIDestroy((PetscHMapI *)&submatj->cmap));
2760: #else
2761:     PetscCall(PetscFree(submatj->cmap));
2762: #endif
2763:   }
2764:   PetscCall(PetscFree(submatj->row2proc));

2766:   PetscCall(PetscFree(submatj));
2767:   PetscFunctionReturn(PETSC_SUCCESS);
2768: }

2770: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2771: {
2772:   Mat_SeqAIJ  *c       = (Mat_SeqAIJ *)C->data;
2773:   Mat_SubSppt *submatj = c->submatis1;

2775:   PetscFunctionBegin;
2776:   PetscCall((*submatj->destroy)(C));
2777:   PetscCall(MatDestroySubMatrix_Private(submatj));
2778:   PetscFunctionReturn(PETSC_SUCCESS);
2779: }

2781: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2782: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2783: {
2784:   PetscInt     i;
2785:   Mat          C;
2786:   Mat_SeqAIJ  *c;
2787:   Mat_SubSppt *submatj;

2789:   PetscFunctionBegin;
2790:   for (i = 0; i < n; i++) {
2791:     C       = (*mat)[i];
2792:     c       = (Mat_SeqAIJ *)C->data;
2793:     submatj = c->submatis1;
2794:     if (submatj) {
2795:       if (--((PetscObject)C)->refct <= 0) {
2796:         PetscCall(PetscFree(C->factorprefix));
2797:         PetscCall((*submatj->destroy)(C));
2798:         PetscCall(MatDestroySubMatrix_Private(submatj));
2799:         PetscCall(PetscFree(C->defaultvectype));
2800:         PetscCall(PetscFree(C->defaultrandtype));
2801:         PetscCall(PetscLayoutDestroy(&C->rmap));
2802:         PetscCall(PetscLayoutDestroy(&C->cmap));
2803:         PetscCall(PetscHeaderDestroy(&C));
2804:       }
2805:     } else {
2806:       PetscCall(MatDestroy(&C));
2807:     }
2808:   }

2810:   /* Destroy Dummy submatrices created for reuse */
2811:   PetscCall(MatDestroySubMatrices_Dummy(n, mat));

2813:   PetscCall(PetscFree(*mat));
2814:   PetscFunctionReturn(PETSC_SUCCESS);
2815: }

2817: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2818: {
2819:   PetscInt i;

2821:   PetscFunctionBegin;
2822:   if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));

2824:   for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2825:   PetscFunctionReturn(PETSC_SUCCESS);
2826: }

2828: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2829: {
2830:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
2831:   PetscInt        row, i, j, k, l, ll, m, n, *nidx, isz, val;
2832:   const PetscInt *idx;
2833:   PetscInt        start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1;
2834:   PetscBT         table;

2836:   PetscFunctionBegin;
2837:   m  = A->rmap->n / bs;
2838:   ai = a->i;
2839:   aj = a->j;

2841:   PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");

2843:   PetscCall(PetscMalloc1(m + 1, &nidx));
2844:   PetscCall(PetscBTCreate(m, &table));

2846:   for (i = 0; i < is_max; i++) {
2847:     /* Initialize the two local arrays */
2848:     isz = 0;
2849:     PetscCall(PetscBTMemzero(m, table));

2851:     /* Extract the indices, assume there can be duplicate entries */
2852:     PetscCall(ISGetIndices(is[i], &idx));
2853:     PetscCall(ISGetLocalSize(is[i], &n));

2855:     if (bs > 1) {
2856:       /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2857:       for (j = 0; j < n; ++j) {
2858:         if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2859:       }
2860:       PetscCall(ISRestoreIndices(is[i], &idx));
2861:       PetscCall(ISDestroy(&is[i]));

2863:       k = 0;
2864:       for (j = 0; j < ov; j++) { /* for each overlap */
2865:         n = isz;
2866:         for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2867:           for (ll = 0; ll < bs; ll++) {
2868:             row   = bs * nidx[k] + ll;
2869:             start = ai[row];
2870:             end   = ai[row + 1];
2871:             for (l = start; l < end; l++) {
2872:               val = aj[l] / bs;
2873:               if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2874:             }
2875:           }
2876:         }
2877:       }
2878:       PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2879:     } else {
2880:       /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2881:       for (j = 0; j < n; ++j) {
2882:         if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2883:       }
2884:       PetscCall(ISRestoreIndices(is[i], &idx));
2885:       PetscCall(ISDestroy(&is[i]));

2887:       k = 0;
2888:       for (j = 0; j < ov; j++) { /* for each overlap */
2889:         n = isz;
2890:         for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2891:           row   = nidx[k];
2892:           start = ai[row];
2893:           end   = ai[row + 1];
2894:           for (l = start; l < end; l++) {
2895:             val = aj[l];
2896:             if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2897:           }
2898:         }
2899:       }
2900:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2901:     }
2902:   }
2903:   PetscCall(PetscBTDestroy(&table));
2904:   PetscCall(PetscFree(nidx));
2905:   PetscFunctionReturn(PETSC_SUCCESS);
2906: }

2908: PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2909: {
2910:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
2911:   PetscInt        i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2912:   const PetscInt *row, *col;
2913:   PetscInt       *cnew, j, *lens;
2914:   IS              icolp, irowp;
2915:   PetscInt       *cwork = NULL;
2916:   PetscScalar    *vwork = NULL;

2918:   PetscFunctionBegin;
2919:   PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2920:   PetscCall(ISGetIndices(irowp, &row));
2921:   PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2922:   PetscCall(ISGetIndices(icolp, &col));

2924:   /* determine lengths of permuted rows */
2925:   PetscCall(PetscMalloc1(m + 1, &lens));
2926:   for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2927:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2928:   PetscCall(MatSetSizes(*B, m, n, m, n));
2929:   PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2930:   PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2931:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2932:   PetscCall(PetscFree(lens));

2934:   PetscCall(PetscMalloc1(n, &cnew));
2935:   for (i = 0; i < m; i++) {
2936:     PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2937:     for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2938:     PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2939:     PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2940:   }
2941:   PetscCall(PetscFree(cnew));

2943:   (*B)->assembled = PETSC_FALSE;

2945: #if defined(PETSC_HAVE_DEVICE)
2946:   PetscCall(MatBindToCPU(*B, A->boundtocpu));
2947: #endif
2948:   PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2949:   PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2950:   PetscCall(ISRestoreIndices(irowp, &row));
2951:   PetscCall(ISRestoreIndices(icolp, &col));
2952:   PetscCall(ISDestroy(&irowp));
2953:   PetscCall(ISDestroy(&icolp));
2954:   if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2955:   PetscFunctionReturn(PETSC_SUCCESS);
2956: }

2958: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2959: {
2960:   PetscFunctionBegin;
2961:   /* If the two matrices have the same copy implementation, use fast copy. */
2962:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2963:     Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2964:     Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;
2965:     const PetscScalar *aa;

2967:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2968:     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]);
2969:     PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n]));
2970:     PetscCall(PetscObjectStateIncrease((PetscObject)B));
2971:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2972:   } else {
2973:     PetscCall(MatCopy_Basic(A, B, str));
2974:   }
2975:   PetscFunctionReturn(PETSC_SUCCESS);
2976: }

2978: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2979: {
2980:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

2982:   PetscFunctionBegin;
2983:   *array = a->a;
2984:   PetscFunctionReturn(PETSC_SUCCESS);
2985: }

2987: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2988: {
2989:   PetscFunctionBegin;
2990:   *array = NULL;
2991:   PetscFunctionReturn(PETSC_SUCCESS);
2992: }

2994: /*
2995:    Computes the number of nonzeros per row needed for preallocation when X and Y
2996:    have different nonzero structure.
2997: */
2998: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
2999: {
3000:   PetscInt i, j, k, nzx, nzy;

3002:   PetscFunctionBegin;
3003:   /* Set the number of nonzeros in the new matrix */
3004:   for (i = 0; i < m; i++) {
3005:     const PetscInt *xjj = xj + xi[i], *yjj = yj + yi[i];
3006:     nzx    = xi[i + 1] - xi[i];
3007:     nzy    = yi[i + 1] - yi[i];
3008:     nnz[i] = 0;
3009:     for (j = 0, k = 0; j < nzx; j++) {                  /* Point in X */
3010:       for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
3011:       if (k < nzy && yjj[k] == xjj[j]) k++;             /* Skip duplicate */
3012:       nnz[i]++;
3013:     }
3014:     for (; k < nzy; k++) nnz[i]++;
3015:   }
3016:   PetscFunctionReturn(PETSC_SUCCESS);
3017: }

3019: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
3020: {
3021:   PetscInt    m = Y->rmap->N;
3022:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
3023:   Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;

3025:   PetscFunctionBegin;
3026:   /* Set the number of nonzeros in the new matrix */
3027:   PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
3028:   PetscFunctionReturn(PETSC_SUCCESS);
3029: }

3031: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
3032: {
3033:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;

3035:   PetscFunctionBegin;
3036:   if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
3037:     PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
3038:     if (e) {
3039:       PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
3040:       if (e) {
3041:         PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
3042:         if (e) str = SAME_NONZERO_PATTERN;
3043:       }
3044:     }
3045:     if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
3046:   }
3047:   if (str == SAME_NONZERO_PATTERN) {
3048:     const PetscScalar *xa;
3049:     PetscScalar       *ya, alpha = a;
3050:     PetscBLASInt       one = 1, bnz;

3052:     PetscCall(PetscBLASIntCast(x->nz, &bnz));
3053:     PetscCall(MatSeqAIJGetArray(Y, &ya));
3054:     PetscCall(MatSeqAIJGetArrayRead(X, &xa));
3055:     PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
3056:     PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
3057:     PetscCall(MatSeqAIJRestoreArray(Y, &ya));
3058:     PetscCall(PetscLogFlops(2.0 * bnz));
3059:     PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3060:     PetscCall(PetscObjectStateIncrease((PetscObject)Y));
3061:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3062:     PetscCall(MatAXPY_Basic(Y, a, X, str));
3063:   } else {
3064:     Mat       B;
3065:     PetscInt *nnz;
3066:     PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
3067:     PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
3068:     PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
3069:     PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
3070:     PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
3071:     PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
3072:     PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
3073:     PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
3074:     PetscCall(MatHeaderMerge(Y, &B));
3075:     PetscCall(MatSeqAIJCheckInode(Y));
3076:     PetscCall(PetscFree(nnz));
3077:   }
3078:   PetscFunctionReturn(PETSC_SUCCESS);
3079: }

3081: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3082: {
3083: #if defined(PETSC_USE_COMPLEX)
3084:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ *)mat->data;
3085:   PetscInt     i, nz;
3086:   PetscScalar *a;

3088:   PetscFunctionBegin;
3089:   nz = aij->nz;
3090:   PetscCall(MatSeqAIJGetArray(mat, &a));
3091:   for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
3092:   PetscCall(MatSeqAIJRestoreArray(mat, &a));
3093: #else
3094:   PetscFunctionBegin;
3095: #endif
3096:   PetscFunctionReturn(PETSC_SUCCESS);
3097: }

3099: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3100: {
3101:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3102:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3103:   PetscReal        atmp;
3104:   PetscScalar     *x;
3105:   const MatScalar *aa, *av;

3107:   PetscFunctionBegin;
3108:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3109:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3110:   aa = av;
3111:   ai = a->i;
3112:   aj = a->j;

3114:   PetscCall(VecSet(v, 0.0));
3115:   PetscCall(VecGetArrayWrite(v, &x));
3116:   PetscCall(VecGetLocalSize(v, &n));
3117:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3118:   for (i = 0; i < m; i++) {
3119:     ncols = ai[1] - ai[0];
3120:     ai++;
3121:     for (j = 0; j < ncols; j++) {
3122:       atmp = PetscAbsScalar(*aa);
3123:       if (PetscAbsScalar(x[i]) < atmp) {
3124:         x[i] = atmp;
3125:         if (idx) idx[i] = *aj;
3126:       }
3127:       aa++;
3128:       aj++;
3129:     }
3130:   }
3131:   PetscCall(VecRestoreArrayWrite(v, &x));
3132:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3133:   PetscFunctionReturn(PETSC_SUCCESS);
3134: }

3136: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3137: {
3138:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3139:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3140:   PetscScalar     *x;
3141:   const MatScalar *aa, *av;

3143:   PetscFunctionBegin;
3144:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3145:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3146:   aa = av;
3147:   ai = a->i;
3148:   aj = a->j;

3150:   PetscCall(VecSet(v, 0.0));
3151:   PetscCall(VecGetArrayWrite(v, &x));
3152:   PetscCall(VecGetLocalSize(v, &n));
3153:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3154:   for (i = 0; i < m; i++) {
3155:     ncols = ai[1] - ai[0];
3156:     ai++;
3157:     if (ncols == A->cmap->n) { /* row is dense */
3158:       x[i] = *aa;
3159:       if (idx) idx[i] = 0;
3160:     } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3161:       x[i] = 0.0;
3162:       if (idx) {
3163:         for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3164:           if (aj[j] > j) {
3165:             idx[i] = j;
3166:             break;
3167:           }
3168:         }
3169:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3170:         if (j == ncols && j < A->cmap->n) idx[i] = j;
3171:       }
3172:     }
3173:     for (j = 0; j < ncols; j++) {
3174:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3175:         x[i] = *aa;
3176:         if (idx) idx[i] = *aj;
3177:       }
3178:       aa++;
3179:       aj++;
3180:     }
3181:   }
3182:   PetscCall(VecRestoreArrayWrite(v, &x));
3183:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3184:   PetscFunctionReturn(PETSC_SUCCESS);
3185: }

3187: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3188: {
3189:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3190:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3191:   PetscScalar     *x;
3192:   const MatScalar *aa, *av;

3194:   PetscFunctionBegin;
3195:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3196:   aa = av;
3197:   ai = a->i;
3198:   aj = a->j;

3200:   PetscCall(VecSet(v, 0.0));
3201:   PetscCall(VecGetArrayWrite(v, &x));
3202:   PetscCall(VecGetLocalSize(v, &n));
3203:   PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3204:   for (i = 0; i < m; i++) {
3205:     ncols = ai[1] - ai[0];
3206:     ai++;
3207:     if (ncols == A->cmap->n) { /* row is dense */
3208:       x[i] = *aa;
3209:       if (idx) idx[i] = 0;
3210:     } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3211:       x[i] = 0.0;
3212:       if (idx) { /* find first implicit 0.0 in the row */
3213:         for (j = 0; j < ncols; j++) {
3214:           if (aj[j] > j) {
3215:             idx[i] = j;
3216:             break;
3217:           }
3218:         }
3219:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3220:         if (j == ncols && j < A->cmap->n) idx[i] = j;
3221:       }
3222:     }
3223:     for (j = 0; j < ncols; j++) {
3224:       if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3225:         x[i] = *aa;
3226:         if (idx) idx[i] = *aj;
3227:       }
3228:       aa++;
3229:       aj++;
3230:     }
3231:   }
3232:   PetscCall(VecRestoreArrayWrite(v, &x));
3233:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3234:   PetscFunctionReturn(PETSC_SUCCESS);
3235: }

3237: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3238: {
3239:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3240:   PetscInt         i, j, m = A->rmap->n, ncols, n;
3241:   const PetscInt  *ai, *aj;
3242:   PetscScalar     *x;
3243:   const MatScalar *aa, *av;

3245:   PetscFunctionBegin;
3246:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3247:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3248:   aa = av;
3249:   ai = a->i;
3250:   aj = a->j;

3252:   PetscCall(VecSet(v, 0.0));
3253:   PetscCall(VecGetArrayWrite(v, &x));
3254:   PetscCall(VecGetLocalSize(v, &n));
3255:   PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3256:   for (i = 0; i < m; i++) {
3257:     ncols = ai[1] - ai[0];
3258:     ai++;
3259:     if (ncols == A->cmap->n) { /* row is dense */
3260:       x[i] = *aa;
3261:       if (idx) idx[i] = 0;
3262:     } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3263:       x[i] = 0.0;
3264:       if (idx) { /* find first implicit 0.0 in the row */
3265:         for (j = 0; j < ncols; j++) {
3266:           if (aj[j] > j) {
3267:             idx[i] = j;
3268:             break;
3269:           }
3270:         }
3271:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3272:         if (j == ncols && j < A->cmap->n) idx[i] = j;
3273:       }
3274:     }
3275:     for (j = 0; j < ncols; j++) {
3276:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
3277:         x[i] = *aa;
3278:         if (idx) idx[i] = *aj;
3279:       }
3280:       aa++;
3281:       aj++;
3282:     }
3283:   }
3284:   PetscCall(VecRestoreArrayWrite(v, &x));
3285:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3286:   PetscFunctionReturn(PETSC_SUCCESS);
3287: }

3289: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
3290: {
3291:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
3292:   PetscInt        i, bs = PetscAbs(A->rmap->bs), mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
3293:   MatScalar      *diag, work[25], *v_work;
3294:   const PetscReal shift = 0.0;
3295:   PetscBool       allowzeropivot, zeropivotdetected = PETSC_FALSE;

3297:   PetscFunctionBegin;
3298:   allowzeropivot = PetscNot(A->erroriffailure);
3299:   if (a->ibdiagvalid) {
3300:     if (values) *values = a->ibdiag;
3301:     PetscFunctionReturn(PETSC_SUCCESS);
3302:   }
3303:   PetscCall(MatMarkDiagonal_SeqAIJ(A));
3304:   if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); }
3305:   diag = a->ibdiag;
3306:   if (values) *values = a->ibdiag;
3307:   /* factor and invert each block */
3308:   switch (bs) {
3309:   case 1:
3310:     for (i = 0; i < mbs; i++) {
3311:       PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
3312:       if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3313:         if (allowzeropivot) {
3314:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3315:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3316:           A->factorerror_zeropivot_row   = i;
3317:           PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
3318:         } 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);
3319:       }
3320:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3321:     }
3322:     break;
3323:   case 2:
3324:     for (i = 0; i < mbs; i++) {
3325:       ij[0] = 2 * i;
3326:       ij[1] = 2 * i + 1;
3327:       PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
3328:       PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
3329:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3330:       PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
3331:       diag += 4;
3332:     }
3333:     break;
3334:   case 3:
3335:     for (i = 0; i < mbs; i++) {
3336:       ij[0] = 3 * i;
3337:       ij[1] = 3 * i + 1;
3338:       ij[2] = 3 * i + 2;
3339:       PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
3340:       PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
3341:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3342:       PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
3343:       diag += 9;
3344:     }
3345:     break;
3346:   case 4:
3347:     for (i = 0; i < mbs; i++) {
3348:       ij[0] = 4 * i;
3349:       ij[1] = 4 * i + 1;
3350:       ij[2] = 4 * i + 2;
3351:       ij[3] = 4 * i + 3;
3352:       PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
3353:       PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
3354:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3355:       PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
3356:       diag += 16;
3357:     }
3358:     break;
3359:   case 5:
3360:     for (i = 0; i < mbs; i++) {
3361:       ij[0] = 5 * i;
3362:       ij[1] = 5 * i + 1;
3363:       ij[2] = 5 * i + 2;
3364:       ij[3] = 5 * i + 3;
3365:       ij[4] = 5 * i + 4;
3366:       PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
3367:       PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
3368:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3369:       PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
3370:       diag += 25;
3371:     }
3372:     break;
3373:   case 6:
3374:     for (i = 0; i < mbs; i++) {
3375:       ij[0] = 6 * i;
3376:       ij[1] = 6 * i + 1;
3377:       ij[2] = 6 * i + 2;
3378:       ij[3] = 6 * i + 3;
3379:       ij[4] = 6 * i + 4;
3380:       ij[5] = 6 * i + 5;
3381:       PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
3382:       PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
3383:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3384:       PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
3385:       diag += 36;
3386:     }
3387:     break;
3388:   case 7:
3389:     for (i = 0; i < mbs; i++) {
3390:       ij[0] = 7 * i;
3391:       ij[1] = 7 * i + 1;
3392:       ij[2] = 7 * i + 2;
3393:       ij[3] = 7 * i + 3;
3394:       ij[4] = 7 * i + 4;
3395:       ij[5] = 7 * i + 5;
3396:       ij[6] = 7 * i + 6;
3397:       PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
3398:       PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
3399:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3400:       PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
3401:       diag += 49;
3402:     }
3403:     break;
3404:   default:
3405:     PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
3406:     for (i = 0; i < mbs; i++) {
3407:       for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
3408:       PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
3409:       PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
3410:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3411:       PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
3412:       diag += bs2;
3413:     }
3414:     PetscCall(PetscFree3(v_work, v_pivots, IJ));
3415:   }
3416:   a->ibdiagvalid = PETSC_TRUE;
3417:   PetscFunctionReturn(PETSC_SUCCESS);
3418: }

3420: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
3421: {
3422:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3423:   PetscScalar a, *aa;
3424:   PetscInt    m, n, i, j, col;

3426:   PetscFunctionBegin;
3427:   if (!x->assembled) {
3428:     PetscCall(MatGetSize(x, &m, &n));
3429:     for (i = 0; i < m; i++) {
3430:       for (j = 0; j < aij->imax[i]; j++) {
3431:         PetscCall(PetscRandomGetValue(rctx, &a));
3432:         col = (PetscInt)(n * PetscRealPart(a));
3433:         PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3434:       }
3435:     }
3436:   } else {
3437:     PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
3438:     for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
3439:     PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
3440:   }
3441:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3442:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3443:   PetscFunctionReturn(PETSC_SUCCESS);
3444: }

3446: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3447: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
3448: {
3449:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3450:   PetscScalar a;
3451:   PetscInt    m, n, i, j, col, nskip;

3453:   PetscFunctionBegin;
3454:   nskip = high - low;
3455:   PetscCall(MatGetSize(x, &m, &n));
3456:   n -= nskip; /* shrink number of columns where nonzeros can be set */
3457:   for (i = 0; i < m; i++) {
3458:     for (j = 0; j < aij->imax[i]; j++) {
3459:       PetscCall(PetscRandomGetValue(rctx, &a));
3460:       col = (PetscInt)(n * PetscRealPart(a));
3461:       if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3462:       PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3463:     }
3464:   }
3465:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3466:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3467:   PetscFunctionReturn(PETSC_SUCCESS);
3468: }

3470: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3471:                                        MatGetRow_SeqAIJ,
3472:                                        MatRestoreRow_SeqAIJ,
3473:                                        MatMult_SeqAIJ,
3474:                                        /*  4*/ MatMultAdd_SeqAIJ,
3475:                                        MatMultTranspose_SeqAIJ,
3476:                                        MatMultTransposeAdd_SeqAIJ,
3477:                                        NULL,
3478:                                        NULL,
3479:                                        NULL,
3480:                                        /* 10*/ NULL,
3481:                                        MatLUFactor_SeqAIJ,
3482:                                        NULL,
3483:                                        MatSOR_SeqAIJ,
3484:                                        MatTranspose_SeqAIJ,
3485:                                        /*1 5*/ MatGetInfo_SeqAIJ,
3486:                                        MatEqual_SeqAIJ,
3487:                                        MatGetDiagonal_SeqAIJ,
3488:                                        MatDiagonalScale_SeqAIJ,
3489:                                        MatNorm_SeqAIJ,
3490:                                        /* 20*/ NULL,
3491:                                        MatAssemblyEnd_SeqAIJ,
3492:                                        MatSetOption_SeqAIJ,
3493:                                        MatZeroEntries_SeqAIJ,
3494:                                        /* 24*/ MatZeroRows_SeqAIJ,
3495:                                        NULL,
3496:                                        NULL,
3497:                                        NULL,
3498:                                        NULL,
3499:                                        /* 29*/ MatSetUp_Seq_Hash,
3500:                                        NULL,
3501:                                        NULL,
3502:                                        NULL,
3503:                                        NULL,
3504:                                        /* 34*/ MatDuplicate_SeqAIJ,
3505:                                        NULL,
3506:                                        NULL,
3507:                                        MatILUFactor_SeqAIJ,
3508:                                        NULL,
3509:                                        /* 39*/ MatAXPY_SeqAIJ,
3510:                                        MatCreateSubMatrices_SeqAIJ,
3511:                                        MatIncreaseOverlap_SeqAIJ,
3512:                                        MatGetValues_SeqAIJ,
3513:                                        MatCopy_SeqAIJ,
3514:                                        /* 44*/ MatGetRowMax_SeqAIJ,
3515:                                        MatScale_SeqAIJ,
3516:                                        MatShift_SeqAIJ,
3517:                                        MatDiagonalSet_SeqAIJ,
3518:                                        MatZeroRowsColumns_SeqAIJ,
3519:                                        /* 49*/ MatSetRandom_SeqAIJ,
3520:                                        MatGetRowIJ_SeqAIJ,
3521:                                        MatRestoreRowIJ_SeqAIJ,
3522:                                        MatGetColumnIJ_SeqAIJ,
3523:                                        MatRestoreColumnIJ_SeqAIJ,
3524:                                        /* 54*/ MatFDColoringCreate_SeqXAIJ,
3525:                                        NULL,
3526:                                        NULL,
3527:                                        MatPermute_SeqAIJ,
3528:                                        NULL,
3529:                                        /* 59*/ NULL,
3530:                                        MatDestroy_SeqAIJ,
3531:                                        MatView_SeqAIJ,
3532:                                        NULL,
3533:                                        NULL,
3534:                                        /* 64*/ NULL,
3535:                                        MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3536:                                        NULL,
3537:                                        NULL,
3538:                                        NULL,
3539:                                        /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3540:                                        MatGetRowMinAbs_SeqAIJ,
3541:                                        NULL,
3542:                                        NULL,
3543:                                        NULL,
3544:                                        /* 74*/ NULL,
3545:                                        MatFDColoringApply_AIJ,
3546:                                        NULL,
3547:                                        NULL,
3548:                                        NULL,
3549:                                        /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3550:                                        NULL,
3551:                                        NULL,
3552:                                        NULL,
3553:                                        MatLoad_SeqAIJ,
3554:                                        /* 84*/ MatIsSymmetric_SeqAIJ,
3555:                                        MatIsHermitian_SeqAIJ,
3556:                                        NULL,
3557:                                        NULL,
3558:                                        NULL,
3559:                                        /* 89*/ NULL,
3560:                                        NULL,
3561:                                        MatMatMultNumeric_SeqAIJ_SeqAIJ,
3562:                                        NULL,
3563:                                        NULL,
3564:                                        /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3565:                                        NULL,
3566:                                        NULL,
3567:                                        MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3568:                                        NULL,
3569:                                        /* 99*/ MatProductSetFromOptions_SeqAIJ,
3570:                                        NULL,
3571:                                        NULL,
3572:                                        MatConjugate_SeqAIJ,
3573:                                        NULL,
3574:                                        /*104*/ MatSetValuesRow_SeqAIJ,
3575:                                        MatRealPart_SeqAIJ,
3576:                                        MatImaginaryPart_SeqAIJ,
3577:                                        NULL,
3578:                                        NULL,
3579:                                        /*109*/ MatMatSolve_SeqAIJ,
3580:                                        NULL,
3581:                                        MatGetRowMin_SeqAIJ,
3582:                                        NULL,
3583:                                        MatMissingDiagonal_SeqAIJ,
3584:                                        /*114*/ NULL,
3585:                                        NULL,
3586:                                        NULL,
3587:                                        NULL,
3588:                                        NULL,
3589:                                        /*119*/ NULL,
3590:                                        NULL,
3591:                                        NULL,
3592:                                        NULL,
3593:                                        MatGetMultiProcBlock_SeqAIJ,
3594:                                        /*124*/ MatFindNonzeroRows_SeqAIJ,
3595:                                        MatGetColumnReductions_SeqAIJ,
3596:                                        MatInvertBlockDiagonal_SeqAIJ,
3597:                                        MatInvertVariableBlockDiagonal_SeqAIJ,
3598:                                        NULL,
3599:                                        /*129*/ NULL,
3600:                                        NULL,
3601:                                        NULL,
3602:                                        MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3603:                                        MatTransposeColoringCreate_SeqAIJ,
3604:                                        /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3605:                                        MatTransColoringApplyDenToSp_SeqAIJ,
3606:                                        NULL,
3607:                                        NULL,
3608:                                        MatRARtNumeric_SeqAIJ_SeqAIJ,
3609:                                        /*139*/ NULL,
3610:                                        NULL,
3611:                                        NULL,
3612:                                        MatFDColoringSetUp_SeqXAIJ,
3613:                                        MatFindOffBlockDiagonalEntries_SeqAIJ,
3614:                                        MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3615:                                        /*145*/ MatDestroySubMatrices_SeqAIJ,
3616:                                        NULL,
3617:                                        NULL,
3618:                                        MatCreateGraph_Simple_AIJ,
3619:                                        NULL,
3620:                                        /*150*/ MatTransposeSymbolic_SeqAIJ,
3621:                                        MatEliminateZeros_SeqAIJ};

3623: PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3624: {
3625:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3626:   PetscInt    i, nz, n;

3628:   PetscFunctionBegin;
3629:   nz = aij->maxnz;
3630:   n  = mat->rmap->n;
3631:   for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3632:   aij->nz = nz;
3633:   for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3634:   PetscFunctionReturn(PETSC_SUCCESS);
3635: }

3637: /*
3638:  * Given a sparse matrix with global column indices, compact it by using a local column space.
3639:  * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3640:  */
3641: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3642: {
3643:   Mat_SeqAIJ   *aij = (Mat_SeqAIJ *)mat->data;
3644:   PetscHMapI    gid1_lid1;
3645:   PetscHashIter tpos;
3646:   PetscInt      gid, lid, i, ec, nz = aij->nz;
3647:   PetscInt     *garray, *jj = aij->j;

3649:   PetscFunctionBegin;
3652:   /* use a table */
3653:   PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3654:   ec = 0;
3655:   for (i = 0; i < nz; i++) {
3656:     PetscInt data, gid1 = jj[i] + 1;
3657:     PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3658:     if (!data) {
3659:       /* one based table */
3660:       PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3661:     }
3662:   }
3663:   /* form array of columns we need */
3664:   PetscCall(PetscMalloc1(ec, &garray));
3665:   PetscHashIterBegin(gid1_lid1, tpos);
3666:   while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3667:     PetscHashIterGetKey(gid1_lid1, tpos, gid);
3668:     PetscHashIterGetVal(gid1_lid1, tpos, lid);
3669:     PetscHashIterNext(gid1_lid1, tpos);
3670:     gid--;
3671:     lid--;
3672:     garray[lid] = gid;
3673:   }
3674:   PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3675:   PetscCall(PetscHMapIClear(gid1_lid1));
3676:   for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3677:   /* compact out the extra columns in B */
3678:   for (i = 0; i < nz; i++) {
3679:     PetscInt gid1 = jj[i] + 1;
3680:     PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3681:     lid--;
3682:     jj[i] = lid;
3683:   }
3684:   PetscCall(PetscLayoutDestroy(&mat->cmap));
3685:   PetscCall(PetscHMapIDestroy(&gid1_lid1));
3686:   PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3687:   PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3688:   PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3689:   PetscFunctionReturn(PETSC_SUCCESS);
3690: }

3692: /*@
3693:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3694:        in the matrix.

3696:   Input Parameters:
3697: +  mat - the `MATSEQAIJ` matrix
3698: -  indices - the column indices

3700:   Level: advanced

3702:   Notes:
3703:     This can be called if you have precomputed the nonzero structure of the
3704:   matrix and want to provide it to the matrix object to improve the performance
3705:   of the `MatSetValues()` operation.

3707:     You MUST have set the correct numbers of nonzeros per row in the call to
3708:   `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.

3710:     MUST be called before any calls to `MatSetValues()`

3712:     The indices should start with zero, not one.

3714: .seealso: [](chapter_matrices), `Mat`, `MATSEQAIJ`
3715: @*/
3716: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3717: {
3718:   PetscFunctionBegin;
3721:   PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3722:   PetscFunctionReturn(PETSC_SUCCESS);
3723: }

3725: PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3726: {
3727:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3728:   size_t      nz  = aij->i[mat->rmap->n];

3730:   PetscFunctionBegin;
3731:   PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

3733:   /* allocate space for values if not already there */
3734:   if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); }

3736:   /* copy values over */
3737:   PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3738:   PetscFunctionReturn(PETSC_SUCCESS);
3739: }

3741: /*@
3742:     MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
3743:     nonlinear portion.

3745:    Logically Collect

3747:   Input Parameter:
3748: .  mat - the matrix (currently only `MATAIJ` matrices support this option)

3750:   Level: advanced

3752:   Usage:
3753: .vb
3754:     Using SNES
3755:     Create Jacobian matrix
3756:     Set linear terms into matrix
3757:     Apply boundary conditions to matrix, at this time matrix must have
3758:       final nonzero structure (i.e. setting the nonlinear terms and applying
3759:       boundary conditions again will not change the nonzero structure
3760:     MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3761:     MatStoreValues(mat);
3762:     Call SNESSetJacobian() with matrix
3763:     In your Jacobian routine
3764:       MatRetrieveValues(mat);
3765:       Set nonlinear terms in matrix

3767:     Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3768:     // build linear portion of Jacobian
3769:     MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3770:     MatStoreValues(mat);
3771:     loop over nonlinear iterations
3772:        MatRetrieveValues(mat);
3773:        // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3774:        // call MatAssemblyBegin/End() on matrix
3775:        Solve linear system with Jacobian
3776:     endloop
3777: .ve

3779:   Notes:
3780:     Matrix must already be assembled before calling this routine
3781:     Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3782:     calling this routine.

3784:     When this is called multiple times it overwrites the previous set of stored values
3785:     and does not allocated additional space.

3787: .seealso: [](chapter_matrices), `Mat`, `Mat`, `MatRetrieveValues()`
3788: @*/
3789: PetscErrorCode MatStoreValues(Mat mat)
3790: {
3791:   PetscFunctionBegin;
3793:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3794:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3795:   PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3796:   PetscFunctionReturn(PETSC_SUCCESS);
3797: }

3799: PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3800: {
3801:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3802:   PetscInt    nz  = aij->i[mat->rmap->n];

3804:   PetscFunctionBegin;
3805:   PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3806:   PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3807:   /* copy values over */
3808:   PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3809:   PetscFunctionReturn(PETSC_SUCCESS);
3810: }

3812: /*@
3813:     MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`

3815:    Logically Collect

3817:   Input Parameter:
3818: .  mat - the matrix (currently only `MATAIJ` matrices support this option)

3820:   Level: advanced

3822: .seealso: [](chapter_matrices), `Mat`, `MatStoreValues()`
3823: @*/
3824: PetscErrorCode MatRetrieveValues(Mat mat)
3825: {
3826:   PetscFunctionBegin;
3828:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3829:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3830:   PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3831:   PetscFunctionReturn(PETSC_SUCCESS);
3832: }

3834: /*@C
3835:    MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3836:    (the default parallel PETSc format).  For good matrix assembly performance
3837:    the user should preallocate the matrix storage by setting the parameter `nz`
3838:    (or the array `nnz`).

3840:    Collective

3842:    Input Parameters:
3843: +  comm - MPI communicator, set to `PETSC_COMM_SELF`
3844: .  m - number of rows
3845: .  n - number of columns
3846: .  nz - number of nonzeros per row (same for all rows)
3847: -  nnz - array containing the number of nonzeros in the various rows
3848:          (possibly different for each row) or NULL

3850:    Output Parameter:
3851: .  A - the matrix

3853:    Options Database Keys:
3854: +  -mat_no_inode  - Do not use inodes
3855: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3857:    Level: intermediate

3859:    Notes:
3860:    If `nnz` is given then `nz` is ignored

3862:    The `MATSEQAIJ` format, also called
3863:    compressed row storage, is fully compatible with standard Fortran
3864:    storage.  That is, the stored row and column indices can begin at
3865:    either one (as in Fortran) or zero.

3867:    Specify the preallocated storage with either `nz` or `nnz` (not both).
3868:    Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3869:    allocation.

3871:    By default, this format uses inodes (identical nodes) when possible, to
3872:    improve numerical efficiency of matrix-vector products and solves. We
3873:    search for consecutive rows with the same nonzero structure, thereby
3874:    reusing matrix information to achieve increased efficiency.

3876: .seealso: [](chapter_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3877: @*/
3878: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3879: {
3880:   PetscFunctionBegin;
3881:   PetscCall(MatCreate(comm, A));
3882:   PetscCall(MatSetSizes(*A, m, n, m, n));
3883:   PetscCall(MatSetType(*A, MATSEQAIJ));
3884:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3885:   PetscFunctionReturn(PETSC_SUCCESS);
3886: }

3888: /*@C
3889:    MatSeqAIJSetPreallocation - For good matrix assembly performance
3890:    the user should preallocate the matrix storage by setting the parameter nz
3891:    (or the array nnz).  By setting these parameters accurately, performance
3892:    during matrix assembly can be increased by more than a factor of 50.

3894:    Collective

3896:    Input Parameters:
3897: +  B - The matrix
3898: .  nz - number of nonzeros per row (same for all rows)
3899: -  nnz - array containing the number of nonzeros in the various rows
3900:          (possibly different for each row) or NULL

3902:    Options Database Keys:
3903: +  -mat_no_inode  - Do not use inodes
3904: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3906:    Level: intermediate

3908:    Notes:
3909:      If `nnz` is given then `nz` is ignored

3911:     The `MATSEQAIJ` format also called
3912:    compressed row storage, is fully compatible with standard Fortran
3913:    storage.  That is, the stored row and column indices can begin at
3914:    either one (as in Fortran) or zero.  See the users' manual for details.

3916:    Specify the preallocated storage with either `nz` or `nnz` (not both).
3917:    Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3918:    allocation.

3920:    You can call `MatGetInfo()` to get information on how effective the preallocation was;
3921:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3922:    You can also run with the option -info and look for messages with the string
3923:    malloc in them to see if additional memory allocation was needed.

3925:    Developer Notes:
3926:    Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3927:    entries or columns indices

3929:    By default, this format uses inodes (identical nodes) when possible, to
3930:    improve numerical efficiency of matrix-vector products and solves. We
3931:    search for consecutive rows with the same nonzero structure, thereby
3932:    reusing matrix information to achieve increased efficiency.

3934: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3935:           `MatSeqAIJSetTotalPreallocation()`
3936: @*/
3937: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3938: {
3939:   PetscFunctionBegin;
3942:   PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3943:   PetscFunctionReturn(PETSC_SUCCESS);
3944: }

3946: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3947: {
3948:   Mat_SeqAIJ *b              = (Mat_SeqAIJ *)B->data;
3949:   PetscBool   skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3950:   PetscInt    i;

3952:   PetscFunctionBegin;
3953:   if (B->hash_active) {
3954:     PetscCall(PetscMemcpy(&B->ops, &b->cops, sizeof(*(B->ops))));
3955:     PetscCall(PetscHMapIJVDestroy(&b->ht));
3956:     PetscCall(PetscFree(b->dnz));
3957:     B->hash_active = PETSC_FALSE;
3958:   }
3959:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3960:   if (nz == MAT_SKIP_ALLOCATION) {
3961:     skipallocation = PETSC_TRUE;
3962:     nz             = 0;
3963:   }
3964:   PetscCall(PetscLayoutSetUp(B->rmap));
3965:   PetscCall(PetscLayoutSetUp(B->cmap));

3967:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3968:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3969:   if (PetscUnlikelyDebug(nnz)) {
3970:     for (i = 0; i < B->rmap->n; i++) {
3971:       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]);
3972:       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);
3973:     }
3974:   }

3976:   B->preallocated = PETSC_TRUE;
3977:   if (!skipallocation) {
3978:     if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); }
3979:     if (!b->ilen) {
3980:       /* b->ilen will count nonzeros in each row so far. */
3981:       PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
3982:     } else {
3983:       PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
3984:     }
3985:     if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
3986:     if (!nnz) {
3987:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3988:       else if (nz < 0) nz = 1;
3989:       nz = PetscMin(nz, B->cmap->n);
3990:       for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
3991:       nz = nz * B->rmap->n;
3992:     } else {
3993:       PetscInt64 nz64 = 0;
3994:       for (i = 0; i < B->rmap->n; i++) {
3995:         b->imax[i] = nnz[i];
3996:         nz64 += nnz[i];
3997:       }
3998:       PetscCall(PetscIntCast(nz64, &nz));
3999:     }

4001:     /* allocate the matrix space */
4002:     /* FIXME: should B's old memory be unlogged? */
4003:     PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
4004:     if (B->structure_only) {
4005:       PetscCall(PetscMalloc1(nz, &b->j));
4006:       PetscCall(PetscMalloc1(B->rmap->n + 1, &b->i));
4007:     } else {
4008:       PetscCall(PetscMalloc3(nz, &b->a, nz, &b->j, B->rmap->n + 1, &b->i));
4009:     }
4010:     b->i[0] = 0;
4011:     for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
4012:     if (B->structure_only) {
4013:       b->singlemalloc = PETSC_FALSE;
4014:       b->free_a       = PETSC_FALSE;
4015:     } else {
4016:       b->singlemalloc = PETSC_TRUE;
4017:       b->free_a       = PETSC_TRUE;
4018:     }
4019:     b->free_ij = PETSC_TRUE;
4020:   } else {
4021:     b->free_a  = PETSC_FALSE;
4022:     b->free_ij = PETSC_FALSE;
4023:   }

4025:   if (b->ipre && nnz != b->ipre && b->imax) {
4026:     /* reserve user-requested sparsity */
4027:     PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
4028:   }

4030:   b->nz               = 0;
4031:   b->maxnz            = nz;
4032:   B->info.nz_unneeded = (double)b->maxnz;
4033:   if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
4034:   B->was_assembled = PETSC_FALSE;
4035:   B->assembled     = PETSC_FALSE;
4036:   /* We simply deem preallocation has changed nonzero state. Updating the state
4037:      will give clients (like AIJKokkos) a chance to know something has happened.
4038:   */
4039:   B->nonzerostate++;
4040:   PetscFunctionReturn(PETSC_SUCCESS);
4041: }

4043: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4044: {
4045:   Mat_SeqAIJ *a;
4046:   PetscInt    i;

4048:   PetscFunctionBegin;

4051:   /* Check local size. If zero, then return */
4052:   if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);

4054:   a = (Mat_SeqAIJ *)A->data;
4055:   /* if no saved info, we error out */
4056:   PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");

4058:   PetscCheck(a->i && a->j && a->a && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and can not reset preallocation ");

4060:   PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
4061:   PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
4062:   a->i[0] = 0;
4063:   for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
4064:   A->preallocated     = PETSC_TRUE;
4065:   a->nz               = 0;
4066:   a->maxnz            = a->i[A->rmap->n];
4067:   A->info.nz_unneeded = (double)a->maxnz;
4068:   A->was_assembled    = PETSC_FALSE;
4069:   A->assembled        = PETSC_FALSE;
4070:   PetscFunctionReturn(PETSC_SUCCESS);
4071: }

4073: /*@
4074:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.

4076:    Input Parameters:
4077: +  B - the matrix
4078: .  i - the indices into j for the start of each row (starts with zero)
4079: .  j - the column indices for each row (starts with zero) these must be sorted for each row
4080: -  v - optional values in the matrix

4082:    Level: developer

4084:    Notes:
4085:       The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`

4087:       This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4088:       structure will be the union of all the previous nonzero structures.

4090:     Developer Notes:
4091:       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
4092:       then just copies the `v` values directly with `PetscMemcpy()`.

4094:       This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.

4096: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MatResetPreallocation()`
4097: @*/
4098: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4099: {
4100:   PetscFunctionBegin;
4103:   PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4104:   PetscFunctionReturn(PETSC_SUCCESS);
4105: }

4107: PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4108: {
4109:   PetscInt  i;
4110:   PetscInt  m, n;
4111:   PetscInt  nz;
4112:   PetscInt *nnz;

4114:   PetscFunctionBegin;
4115:   PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);

4117:   PetscCall(PetscLayoutSetUp(B->rmap));
4118:   PetscCall(PetscLayoutSetUp(B->cmap));

4120:   PetscCall(MatGetSize(B, &m, &n));
4121:   PetscCall(PetscMalloc1(m + 1, &nnz));
4122:   for (i = 0; i < m; i++) {
4123:     nz = Ii[i + 1] - Ii[i];
4124:     PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4125:     nnz[i] = nz;
4126:   }
4127:   PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4128:   PetscCall(PetscFree(nnz));

4130:   for (i = 0; i < m; i++) PetscCall(MatSetValues_SeqAIJ(B, 1, &i, Ii[i + 1] - Ii[i], J + Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES));

4132:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4133:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));

4135:   PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4136:   PetscFunctionReturn(PETSC_SUCCESS);
4137: }

4139: /*@
4140:    MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.

4142:    Input Parameters:
4143: +  A - left-hand side matrix
4144: .  B - right-hand side matrix
4145: -  reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

4147:    Output Parameter:
4148: .  C - Kronecker product of `A` and `B`

4150:    Level: intermediate

4152:    Note:
4153:       `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.

4155: .seealso: [](chapter_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4156: @*/
4157: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4158: {
4159:   PetscFunctionBegin;
4165:   if (reuse == MAT_REUSE_MATRIX) {
4168:   }
4169:   PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4170:   PetscFunctionReturn(PETSC_SUCCESS);
4171: }

4173: PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4174: {
4175:   Mat                newmat;
4176:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
4177:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;
4178:   PetscScalar       *v;
4179:   const PetscScalar *aa, *ba;
4180:   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;
4181:   PetscBool          flg;

4183:   PetscFunctionBegin;
4184:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4185:   PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4186:   PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4187:   PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4188:   PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4189:   PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4190:   PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4191:   if (reuse == MAT_INITIAL_MATRIX) {
4192:     PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4193:     PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4194:     PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4195:     PetscCall(MatSetType(newmat, MATAIJ));
4196:     i[0] = 0;
4197:     for (m = 0; m < am; ++m) {
4198:       for (p = 0; p < bm; ++p) {
4199:         i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4200:         for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4201:           for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4202:         }
4203:       }
4204:     }
4205:     PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4206:     *C = newmat;
4207:     PetscCall(PetscFree2(i, j));
4208:     nnz = 0;
4209:   }
4210:   PetscCall(MatSeqAIJGetArray(*C, &v));
4211:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4212:   PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4213:   for (m = 0; m < am; ++m) {
4214:     for (p = 0; p < bm; ++p) {
4215:       for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4216:         for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4217:       }
4218:     }
4219:   }
4220:   PetscCall(MatSeqAIJRestoreArray(*C, &v));
4221:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4222:   PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4223:   PetscFunctionReturn(PETSC_SUCCESS);
4224: }

4226: #include <../src/mat/impls/dense/seq/dense.h>
4227: #include <petsc/private/kernels/petscaxpy.h>

4229: /*
4230:     Computes (B'*A')' since computing B*A directly is untenable

4232:                n                       p                          p
4233:         [             ]       [             ]         [                 ]
4234:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
4235:         [             ]       [             ]         [                 ]

4237: */
4238: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4239: {
4240:   Mat_SeqDense      *sub_a = (Mat_SeqDense *)A->data;
4241:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ *)B->data;
4242:   Mat_SeqDense      *sub_c = (Mat_SeqDense *)C->data;
4243:   PetscInt           i, j, n, m, q, p;
4244:   const PetscInt    *ii, *idx;
4245:   const PetscScalar *b, *a, *a_q;
4246:   PetscScalar       *c, *c_q;
4247:   PetscInt           clda = sub_c->lda;
4248:   PetscInt           alda = sub_a->lda;

4250:   PetscFunctionBegin;
4251:   m = A->rmap->n;
4252:   n = A->cmap->n;
4253:   p = B->cmap->n;
4254:   a = sub_a->v;
4255:   b = sub_b->a;
4256:   c = sub_c->v;
4257:   if (clda == m) {
4258:     PetscCall(PetscArrayzero(c, m * p));
4259:   } else {
4260:     for (j = 0; j < p; j++)
4261:       for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4262:   }
4263:   ii  = sub_b->i;
4264:   idx = sub_b->j;
4265:   for (i = 0; i < n; i++) {
4266:     q = ii[i + 1] - ii[i];
4267:     while (q-- > 0) {
4268:       c_q = c + clda * (*idx);
4269:       a_q = a + alda * i;
4270:       PetscKernelAXPY(c_q, *b, a_q, m);
4271:       idx++;
4272:       b++;
4273:     }
4274:   }
4275:   PetscFunctionReturn(PETSC_SUCCESS);
4276: }

4278: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4279: {
4280:   PetscInt  m = A->rmap->n, n = B->cmap->n;
4281:   PetscBool cisdense;

4283:   PetscFunctionBegin;
4284:   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);
4285:   PetscCall(MatSetSizes(C, m, n, m, n));
4286:   PetscCall(MatSetBlockSizesFromMats(C, A, B));
4287:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4288:   if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4289:   PetscCall(MatSetUp(C));

4291:   C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4292:   PetscFunctionReturn(PETSC_SUCCESS);
4293: }

4295: /*MC
4296:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4297:    based on compressed sparse row format.

4299:    Options Database Key:
4300: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

4302:    Level: beginner

4304:    Notes:
4305:     `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4306:     in this case the values associated with the rows and columns one passes in are set to zero
4307:     in the matrix

4309:     `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4310:     space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored

4312:   Developer Note:
4313:     It would be nice if all matrix formats supported passing `NULL` in for the numerical values

4315: .seealso: [](chapter_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4316: M*/

4318: /*MC
4319:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

4321:    This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4322:    and `MATMPIAIJ` otherwise.  As a result, for single process communicators,
4323:    `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4324:    for communicators controlling multiple processes.  It is recommended that you call both of
4325:    the above preallocation routines for simplicity.

4327:    Options Database Key:
4328: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`

4330:   Level: beginner

4332:    Note:
4333:    Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4334:    enough exist.

4336: .seealso: [](chapter_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4337: M*/

4339: /*MC
4340:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

4342:    Options Database Key:
4343: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`

4345:   Level: beginner

4347:    Note:
4348:    This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4349:    and `MATMPIAIJCRL` otherwise.  As a result, for single process communicators,
4350:    `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4351:    for communicators controlling multiple processes.  It is recommended that you call both of
4352:    the above preallocation routines for simplicity.

4354: .seealso: [](chapter_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4355: M*/

4357: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4358: #if defined(PETSC_HAVE_ELEMENTAL)
4359: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4360: #endif
4361: #if defined(PETSC_HAVE_SCALAPACK)
4362: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4363: #endif
4364: #if defined(PETSC_HAVE_HYPRE)
4365: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4366: #endif

4368: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4369: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4370: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

4372: /*@C
4373:    MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored

4375:    Not Collective

4377:    Input Parameter:
4378: .  mat - a `MATSEQAIJ` matrix

4380:    Output Parameter:
4381: .   array - pointer to the data

4383:    Level: intermediate

4385:    Fortran Note:
4386:    `MatSeqAIJGetArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJGetArrayF90()`

4388: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4389: @*/
4390: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar **array)
4391: {
4392:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4394:   PetscFunctionBegin;
4395:   if (aij->ops->getarray) {
4396:     PetscCall((*aij->ops->getarray)(A, array));
4397:   } else {
4398:     *array = aij->a;
4399:   }
4400:   PetscFunctionReturn(PETSC_SUCCESS);
4401: }

4403: /*@C
4404:    MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`

4406:    Not Collective

4408:    Input Parameters:
4409: +  mat - a `MATSEQAIJ` matrix
4410: -  array - pointer to the data

4412:    Level: intermediate

4414:    Fortran Note:
4415:    `MatSeqAIJRestoreArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJRestoreArrayF90()`

4417: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()`
4418: @*/
4419: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar **array)
4420: {
4421:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4423:   PetscFunctionBegin;
4424:   if (aij->ops->restorearray) {
4425:     PetscCall((*aij->ops->restorearray)(A, array));
4426:   } else {
4427:     *array = NULL;
4428:   }
4429:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
4430:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
4431:   PetscFunctionReturn(PETSC_SUCCESS);
4432: }

4434: /*@C
4435:    MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored

4437:    Not Collective; No Fortran Support

4439:    Input Parameter:
4440: .  mat - a `MATSEQAIJ` matrix

4442:    Output Parameter:
4443: .   array - pointer to the data

4445:    Level: intermediate

4447: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4448: @*/
4449: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar **array)
4450: {
4451:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4453:   PetscFunctionBegin;
4454:   if (aij->ops->getarrayread) {
4455:     PetscCall((*aij->ops->getarrayread)(A, array));
4456:   } else {
4457:     *array = aij->a;
4458:   }
4459:   PetscFunctionReturn(PETSC_SUCCESS);
4460: }

4462: /*@C
4463:    MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`

4465:    Not Collective; No Fortran Support

4467:    Input Parameter:
4468: .  mat - a `MATSEQAIJ` matrix

4470:    Output Parameter:
4471: .   array - pointer to the data

4473:    Level: intermediate

4475: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4476: @*/
4477: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar **array)
4478: {
4479:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4481:   PetscFunctionBegin;
4482:   if (aij->ops->restorearrayread) {
4483:     PetscCall((*aij->ops->restorearrayread)(A, array));
4484:   } else {
4485:     *array = NULL;
4486:   }
4487:   PetscFunctionReturn(PETSC_SUCCESS);
4488: }

4490: /*@C
4491:    MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored

4493:    Not Collective; No Fortran Support

4495:    Input Parameter:
4496: .  mat - a `MATSEQAIJ` matrix

4498:    Output Parameter:
4499: .   array - pointer to the data

4501:    Level: intermediate

4503: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4504: @*/
4505: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar **array)
4506: {
4507:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4509:   PetscFunctionBegin;
4510:   if (aij->ops->getarraywrite) {
4511:     PetscCall((*aij->ops->getarraywrite)(A, array));
4512:   } else {
4513:     *array = aij->a;
4514:   }
4515:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
4516:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
4517:   PetscFunctionReturn(PETSC_SUCCESS);
4518: }

4520: /*@C
4521:    MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead

4523:    Not Collective; No Fortran Support

4525:    Input Parameter:
4526: .  mat - a MATSEQAIJ matrix

4528:    Output Parameter:
4529: .   array - pointer to the data

4531:    Level: intermediate

4533: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4534: @*/
4535: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar **array)
4536: {
4537:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4539:   PetscFunctionBegin;
4540:   if (aij->ops->restorearraywrite) {
4541:     PetscCall((*aij->ops->restorearraywrite)(A, array));
4542:   } else {
4543:     *array = NULL;
4544:   }
4545:   PetscFunctionReturn(PETSC_SUCCESS);
4546: }

4548: /*@C
4549:    MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix

4551:    Not Collective; No Fortran Support

4553:    Input Parameter:
4554: .  mat - a matrix of type `MATSEQAIJ` or its subclasses

4556:    Output Parameters:
4557: +  i - row map array of the matrix
4558: .  j - column index array of the matrix
4559: .  a - data array of the matrix
4560: -  memtype - memory type of the arrays

4562:    Level: Developer

4564:   Notes:
4565:    Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4566:    If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.

4568:    One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4569:    If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.

4571: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4572: @*/
4573: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
4574: {
4575:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;

4577:   PetscFunctionBegin;
4578:   PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4579:   if (aij->ops->getcsrandmemtype) {
4580:     PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4581:   } else {
4582:     if (i) *i = aij->i;
4583:     if (j) *j = aij->j;
4584:     if (a) *a = aij->a;
4585:     if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4586:   }
4587:   PetscFunctionReturn(PETSC_SUCCESS);
4588: }

4590: /*@C
4591:    MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

4593:    Not Collective

4595:    Input Parameter:
4596: .  mat - a `MATSEQAIJ` matrix

4598:    Output Parameter:
4599: .   nz - the maximum number of nonzeros in any row

4601:    Level: intermediate

4603: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4604: @*/
4605: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4606: {
4607:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4609:   PetscFunctionBegin;
4610:   *nz = aij->rmax;
4611:   PetscFunctionReturn(PETSC_SUCCESS);
4612: }

4614: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4615: {
4616:   MPI_Comm     comm;
4617:   PetscInt    *i, *j;
4618:   PetscInt     M, N, row;
4619:   PetscCount   k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4620:   PetscInt    *Ai;                             /* Change to PetscCount once we use it for row pointers */
4621:   PetscInt    *Aj;
4622:   PetscScalar *Aa;
4623:   Mat_SeqAIJ  *seqaij = (Mat_SeqAIJ *)(mat->data);
4624:   MatType      rtype;
4625:   PetscCount  *perm, *jmap;

4627:   PetscFunctionBegin;
4628:   PetscCall(MatResetPreallocationCOO_SeqAIJ(mat));
4629:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4630:   PetscCall(MatGetSize(mat, &M, &N));
4631:   i = coo_i;
4632:   j = coo_j;
4633:   PetscCall(PetscMalloc1(coo_n, &perm));
4634:   for (k = 0; k < coo_n; k++) { /* Ignore entries with negative row or col indices */
4635:     if (j[k] < 0) i[k] = -1;
4636:     perm[k] = k;
4637:   }

4639:   /* Sort by row */
4640:   PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));
4641:   for (k = 0; k < coo_n; k++) {
4642:     if (i[k] >= 0) break;
4643:   } /* Advance k to the first row with a non-negative index */
4644:   nneg = k;
4645:   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 */
4646:   nnz = 0;                                          /* Total number of unique nonzeros to be counted */
4647:   jmap++;                                           /* Inc jmap by 1 for convenience */

4649:   PetscCall(PetscCalloc1(M + 1, &Ai));        /* CSR of A */
4650:   PetscCall(PetscMalloc1(coo_n - nneg, &Aj)); /* We have at most coo_n-nneg unique nonzeros */

4652:   /* In each row, sort by column, then unique column indices to get row length */
4653:   Ai++;  /* Inc by 1 for convenience */
4654:   q = 0; /* q-th unique nonzero, with q starting from 0 */
4655:   while (k < coo_n) {
4656:     row   = i[k];
4657:     start = k; /* [start,end) indices for this row */
4658:     while (k < coo_n && i[k] == row) k++;
4659:     end = k;
4660:     PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));
4661:     /* Find number of unique col entries in this row */
4662:     Aj[q]   = j[start]; /* Log the first nonzero in this row */
4663:     jmap[q] = 1;        /* Number of repeats of this nozero entry */
4664:     Ai[row] = 1;
4665:     nnz++;

4667:     for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
4668:       if (j[p] != j[p - 1]) {           /* Meet a new nonzero */
4669:         q++;
4670:         jmap[q] = 1;
4671:         Aj[q]   = j[p];
4672:         Ai[row]++;
4673:         nnz++;
4674:       } else {
4675:         jmap[q]++;
4676:       }
4677:     }
4678:     q++; /* Move to next row and thus next unique nonzero */
4679:   }

4681:   Ai--; /* Back to the beginning of Ai[] */
4682:   for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
4683:   jmap--; /* Back to the beginning of jmap[] */
4684:   jmap[0] = 0;
4685:   for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];
4686:   if (nnz < coo_n - nneg) { /* Realloc with actual number of unique nonzeros */
4687:     PetscCount *jmap_new;
4688:     PetscInt   *Aj_new;

4690:     PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
4691:     PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
4692:     PetscCall(PetscFree(jmap));
4693:     jmap = jmap_new;

4695:     PetscCall(PetscMalloc1(nnz, &Aj_new));
4696:     PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4697:     PetscCall(PetscFree(Aj));
4698:     Aj = Aj_new;
4699:   }

4701:   if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
4702:     PetscCount *perm_new;

4704:     PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
4705:     PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
4706:     PetscCall(PetscFree(perm));
4707:     perm = perm_new;
4708:   }

4710:   PetscCall(MatGetRootType_Private(mat, &rtype));
4711:   PetscCall(PetscCalloc1(nnz, &Aa)); /* Zero the matrix */
4712:   PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));

4714:   seqaij->singlemalloc = PETSC_FALSE;            /* Ai, Aj and Aa are not allocated in one big malloc */
4715:   seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */
4716:   /* Record COO fields */
4717:   seqaij->coo_n = coo_n;
4718:   seqaij->Atot  = coo_n - nneg; /* Annz is seqaij->nz, so no need to record that again */
4719:   seqaij->jmap  = jmap;         /* of length nnz+1 */
4720:   seqaij->perm  = perm;
4721:   PetscFunctionReturn(PETSC_SUCCESS);
4722: }

4724: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4725: {
4726:   Mat_SeqAIJ  *aseq = (Mat_SeqAIJ *)A->data;
4727:   PetscCount   i, j, Annz = aseq->nz;
4728:   PetscCount  *perm = aseq->perm, *jmap = aseq->jmap;
4729:   PetscScalar *Aa;

4731:   PetscFunctionBegin;
4732:   PetscCall(MatSeqAIJGetArray(A, &Aa));
4733:   for (i = 0; i < Annz; i++) {
4734:     PetscScalar sum = 0.0;
4735:     for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4736:     Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4737:   }
4738:   PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4739:   PetscFunctionReturn(PETSC_SUCCESS);
4740: }

4742: #if defined(PETSC_HAVE_CUDA)
4743: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4744: #endif
4745: #if defined(PETSC_HAVE_HIP)
4746: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4747: #endif
4748: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4749: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4750: #endif

4752: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4753: {
4754:   Mat_SeqAIJ *b;
4755:   PetscMPIInt size;

4757:   PetscFunctionBegin;
4758:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4759:   PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");

4761:   PetscCall(PetscNew(&b));

4763:   B->data = (void *)b;

4765:   PetscCall(PetscMemcpy(B->ops, &MatOps_Values, sizeof(struct _MatOps)));
4766:   if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;

4768:   b->row                = NULL;
4769:   b->col                = NULL;
4770:   b->icol               = NULL;
4771:   b->reallocs           = 0;
4772:   b->ignorezeroentries  = PETSC_FALSE;
4773:   b->roworiented        = PETSC_TRUE;
4774:   b->nonew              = 0;
4775:   b->diag               = NULL;
4776:   b->solve_work         = NULL;
4777:   B->spptr              = NULL;
4778:   b->saved_values       = NULL;
4779:   b->idiag              = NULL;
4780:   b->mdiag              = NULL;
4781:   b->ssor_work          = NULL;
4782:   b->omega              = 1.0;
4783:   b->fshift             = 0.0;
4784:   b->idiagvalid         = PETSC_FALSE;
4785:   b->ibdiagvalid        = PETSC_FALSE;
4786:   b->keepnonzeropattern = PETSC_FALSE;

4788:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4789: #if defined(PETSC_HAVE_MATLAB)
4790:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4791:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4792: #endif
4793:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4794:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4795:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4796:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4797:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4798:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4799:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4800: #if defined(PETSC_HAVE_MKL_SPARSE)
4801:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4802: #endif
4803: #if defined(PETSC_HAVE_CUDA)
4804:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4805:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4806:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4807: #endif
4808: #if defined(PETSC_HAVE_HIP)
4809:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4810:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4811:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4812: #endif
4813: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4814:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4815: #endif
4816:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4817: #if defined(PETSC_HAVE_ELEMENTAL)
4818:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4819: #endif
4820: #if defined(PETSC_HAVE_SCALAPACK)
4821:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4822: #endif
4823: #if defined(PETSC_HAVE_HYPRE)
4824:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4825:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4826: #endif
4827:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4828:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4829:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4830:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4831:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsTranspose_SeqAIJ));
4832:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4833:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4834:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4835:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4836:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4837:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4838:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4839:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4840:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4841:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4842:   PetscCall(MatCreate_SeqAIJ_Inode(B));
4843:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4844:   PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4845:   PetscFunctionReturn(PETSC_SUCCESS);
4846: }

4848: /*
4849:     Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4850: */
4851: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4852: {
4853:   Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4854:   PetscInt    m = A->rmap->n, i;

4856:   PetscFunctionBegin;
4857:   PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");

4859:   C->factortype = A->factortype;
4860:   c->row        = NULL;
4861:   c->col        = NULL;
4862:   c->icol       = NULL;
4863:   c->reallocs   = 0;

4865:   C->assembled = A->assembled;

4867:   if (A->preallocated) {
4868:     PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
4869:     PetscCall(PetscLayoutReference(A->cmap, &C->cmap));

4871:     if (!A->hash_active) {
4872:       PetscCall(PetscMalloc1(m, &c->imax));
4873:       PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
4874:       PetscCall(PetscMalloc1(m, &c->ilen));
4875:       PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));

4877:       /* allocate the matrix space */
4878:       if (mallocmatspace) {
4879:         PetscCall(PetscMalloc3(a->i[m], &c->a, a->i[m], &c->j, m + 1, &c->i));

4881:         c->singlemalloc = PETSC_TRUE;

4883:         PetscCall(PetscArraycpy(c->i, a->i, m + 1));
4884:         if (m > 0) {
4885:           PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
4886:           if (cpvalues == MAT_COPY_VALUES) {
4887:             const PetscScalar *aa;

4889:             PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4890:             PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
4891:             PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4892:           } else {
4893:             PetscCall(PetscArrayzero(c->a, a->i[m]));
4894:           }
4895:         }
4896:       }
4897:       C->preallocated = PETSC_TRUE;
4898:     } else {
4899:       PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
4900:       PetscCall(MatSetUp(C));
4901:     }

4903:     c->ignorezeroentries = a->ignorezeroentries;
4904:     c->roworiented       = a->roworiented;
4905:     c->nonew             = a->nonew;
4906:     if (a->diag) {
4907:       PetscCall(PetscMalloc1(m + 1, &c->diag));
4908:       PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
4909:     } else c->diag = NULL;

4911:     c->solve_work         = NULL;
4912:     c->saved_values       = NULL;
4913:     c->idiag              = NULL;
4914:     c->ssor_work          = NULL;
4915:     c->keepnonzeropattern = a->keepnonzeropattern;
4916:     c->free_a             = PETSC_TRUE;
4917:     c->free_ij            = PETSC_TRUE;

4919:     c->rmax  = a->rmax;
4920:     c->nz    = a->nz;
4921:     c->maxnz = a->nz; /* Since we allocate exactly the right amount */

4923:     c->compressedrow.use   = a->compressedrow.use;
4924:     c->compressedrow.nrows = a->compressedrow.nrows;
4925:     if (a->compressedrow.use) {
4926:       i = a->compressedrow.nrows;
4927:       PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
4928:       PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
4929:       PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
4930:     } else {
4931:       c->compressedrow.use    = PETSC_FALSE;
4932:       c->compressedrow.i      = NULL;
4933:       c->compressedrow.rindex = NULL;
4934:     }
4935:     c->nonzerorowcnt = a->nonzerorowcnt;
4936:     C->nonzerostate  = A->nonzerostate;

4938:     PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
4939:   }
4940:   PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
4941:   PetscFunctionReturn(PETSC_SUCCESS);
4942: }

4944: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
4945: {
4946:   PetscFunctionBegin;
4947:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
4948:   PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
4949:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
4950:   PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
4951:   PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
4952:   PetscFunctionReturn(PETSC_SUCCESS);
4953: }

4955: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4956: {
4957:   PetscBool isbinary, ishdf5;

4959:   PetscFunctionBegin;
4962:   /* force binary viewer to load .info file if it has not yet done so */
4963:   PetscCall(PetscViewerSetUp(viewer));
4964:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
4965:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
4966:   if (isbinary) {
4967:     PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
4968:   } else if (ishdf5) {
4969: #if defined(PETSC_HAVE_HDF5)
4970:     PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
4971: #else
4972:     SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
4973: #endif
4974:   } else {
4975:     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);
4976:   }
4977:   PetscFunctionReturn(PETSC_SUCCESS);
4978: }

4980: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
4981: {
4982:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
4983:   PetscInt    header[4], *rowlens, M, N, nz, sum, rows, cols, i;

4985:   PetscFunctionBegin;
4986:   PetscCall(PetscViewerSetUp(viewer));

4988:   /* read in matrix header */
4989:   PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
4990:   PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
4991:   M  = header[1];
4992:   N  = header[2];
4993:   nz = header[3];
4994:   PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
4995:   PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
4996:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");

4998:   /* set block sizes from the viewer's .info file */
4999:   PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5000:   /* set local and global sizes if not set already */
5001:   if (mat->rmap->n < 0) mat->rmap->n = M;
5002:   if (mat->cmap->n < 0) mat->cmap->n = N;
5003:   if (mat->rmap->N < 0) mat->rmap->N = M;
5004:   if (mat->cmap->N < 0) mat->cmap->N = N;
5005:   PetscCall(PetscLayoutSetUp(mat->rmap));
5006:   PetscCall(PetscLayoutSetUp(mat->cmap));

5008:   /* check if the matrix sizes are correct */
5009:   PetscCall(MatGetSize(mat, &rows, &cols));
5010:   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);

5012:   /* read in row lengths */
5013:   PetscCall(PetscMalloc1(M, &rowlens));
5014:   PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5015:   /* check if sum(rowlens) is same as nz */
5016:   sum = 0;
5017:   for (i = 0; i < M; i++) sum += rowlens[i];
5018:   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);
5019:   /* preallocate and check sizes */
5020:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5021:   PetscCall(MatGetSize(mat, &rows, &cols));
5022:   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);
5023:   /* store row lengths */
5024:   PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5025:   PetscCall(PetscFree(rowlens));

5027:   /* fill in "i" row pointers */
5028:   a->i[0] = 0;
5029:   for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5030:   /* read in "j" column indices */
5031:   PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5032:   /* read in "a" nonzero values */
5033:   PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));

5035:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5036:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5037:   PetscFunctionReturn(PETSC_SUCCESS);
5038: }

5040: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5041: {
5042:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5043:   const PetscScalar *aa, *ba;
5044: #if defined(PETSC_USE_COMPLEX)
5045:   PetscInt k;
5046: #endif

5048:   PetscFunctionBegin;
5049:   /* If the  matrix dimensions are not equal,or no of nonzeros */
5050:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5051:     *flg = PETSC_FALSE;
5052:     PetscFunctionReturn(PETSC_SUCCESS);
5053:   }

5055:   /* if the a->i are the same */
5056:   PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5057:   if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);

5059:   /* if a->j are the same */
5060:   PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5061:   if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);

5063:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5064:   PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5065:   /* if a->a are the same */
5066: #if defined(PETSC_USE_COMPLEX)
5067:   for (k = 0; k < a->nz; k++) {
5068:     if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
5069:       *flg = PETSC_FALSE;
5070:       PetscFunctionReturn(PETSC_SUCCESS);
5071:     }
5072:   }
5073: #else
5074:   PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5075: #endif
5076:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5077:   PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5078:   PetscFunctionReturn(PETSC_SUCCESS);
5079: }

5081: /*@
5082:      MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5083:               provided by the user.

5085:       Collective

5087:    Input Parameters:
5088: +   comm - must be an MPI communicator of size 1
5089: .   m - number of rows
5090: .   n - number of columns
5091: .   i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5092: .   j - column indices
5093: -   a - matrix values

5095:    Output Parameter:
5096: .   mat - the matrix

5098:    Level: intermediate

5100:    Notes:
5101:        The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5102:     once the matrix is destroyed and not before

5104:        You cannot set new nonzero locations into this matrix, that will generate an error.

5106:        The `i` and `j` indices are 0 based

5108:        The format which is used for the sparse matrix input, is equivalent to a
5109:     row-major ordering.. i.e for the following matrix, the input data expected is
5110:     as shown
5111: .vb
5112:         1 0 0
5113:         2 0 3
5114:         4 5 6

5116:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
5117:         j =  {0,0,2,0,1,2}  [size = 6]; values must be sorted for each row
5118:         v =  {1,2,3,4,5,6}  [size = 6]
5119: .ve

5121: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5122: @*/
5123: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5124: {
5125:   PetscInt    ii;
5126:   Mat_SeqAIJ *aij;
5127:   PetscInt    jj;

5129:   PetscFunctionBegin;
5130:   PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5131:   PetscCall(MatCreate(comm, mat));
5132:   PetscCall(MatSetSizes(*mat, m, n, m, n));
5133:   /* PetscCall(MatSetBlockSizes(*mat,,)); */
5134:   PetscCall(MatSetType(*mat, MATSEQAIJ));
5135:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5136:   aij = (Mat_SeqAIJ *)(*mat)->data;
5137:   PetscCall(PetscMalloc1(m, &aij->imax));
5138:   PetscCall(PetscMalloc1(m, &aij->ilen));

5140:   aij->i            = i;
5141:   aij->j            = j;
5142:   aij->a            = a;
5143:   aij->singlemalloc = PETSC_FALSE;
5144:   aij->nonew        = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5145:   aij->free_a       = PETSC_FALSE;
5146:   aij->free_ij      = PETSC_FALSE;

5148:   for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5149:     aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5150:     if (PetscDefined(USE_DEBUG)) {
5151:       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]);
5152:       for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
5153:         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);
5154:         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);
5155:       }
5156:     }
5157:   }
5158:   if (PetscDefined(USE_DEBUG)) {
5159:     for (ii = 0; ii < aij->i[m]; ii++) {
5160:       PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5161:       PetscCheck(j[ii] <= n - 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index to large at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5162:     }
5163:   }

5165:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5166:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5167:   PetscFunctionReturn(PETSC_SUCCESS);
5168: }

5170: /*@
5171:      MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5172:               provided by the user.

5174:       Collective

5176:    Input Parameters:
5177: +   comm - must be an MPI communicator of size 1
5178: .   m   - number of rows
5179: .   n   - number of columns
5180: .   i   - row indices
5181: .   j   - column indices
5182: .   a   - matrix values
5183: .   nz  - number of nonzeros
5184: -   idx - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`

5186:    Output Parameter:
5187: .   mat - the matrix

5189:    Level: intermediate

5191:    Example:
5192:        For the following matrix, the input data expected is as shown (using 0 based indexing)
5193: .vb
5194:         1 0 0
5195:         2 0 3
5196:         4 5 6

5198:         i =  {0,1,1,2,2,2}
5199:         j =  {0,0,2,0,1,2}
5200:         v =  {1,2,3,4,5,6}
5201: .ve
5202:   Note:
5203:     Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5204:     and are particularly useful in iterative applications.

5206: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5207: @*/
5208: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx)
5209: {
5210:   PetscInt ii, *nnz, one = 1, row, col;

5212:   PetscFunctionBegin;
5213:   PetscCall(PetscCalloc1(m, &nnz));
5214:   for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5215:   PetscCall(MatCreate(comm, mat));
5216:   PetscCall(MatSetSizes(*mat, m, n, m, n));
5217:   PetscCall(MatSetType(*mat, MATSEQAIJ));
5218:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5219:   for (ii = 0; ii < nz; ii++) {
5220:     if (idx) {
5221:       row = i[ii] - 1;
5222:       col = j[ii] - 1;
5223:     } else {
5224:       row = i[ii];
5225:       col = j[ii];
5226:     }
5227:     PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5228:   }
5229:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5230:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5231:   PetscCall(PetscFree(nnz));
5232:   PetscFunctionReturn(PETSC_SUCCESS);
5233: }

5235: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5236: {
5237:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

5239:   PetscFunctionBegin;
5240:   a->idiagvalid  = PETSC_FALSE;
5241:   a->ibdiagvalid = PETSC_FALSE;

5243:   PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
5244:   PetscFunctionReturn(PETSC_SUCCESS);
5245: }

5247: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5248: {
5249:   PetscFunctionBegin;
5250:   PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5251:   PetscFunctionReturn(PETSC_SUCCESS);
5252: }

5254: /*
5255:  Permute A into C's *local* index space using rowemb,colemb.
5256:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5257:  of [0,m), colemb is in [0,n).
5258:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5259:  */
5260: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5261: {
5262:   /* If making this function public, change the error returned in this function away from _PLIB. */
5263:   Mat_SeqAIJ     *Baij;
5264:   PetscBool       seqaij;
5265:   PetscInt        m, n, *nz, i, j, count;
5266:   PetscScalar     v;
5267:   const PetscInt *rowindices, *colindices;

5269:   PetscFunctionBegin;
5270:   if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5271:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5272:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5273:   PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5274:   if (rowemb) {
5275:     PetscCall(ISGetLocalSize(rowemb, &m));
5276:     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);
5277:   } else {
5278:     PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5279:   }
5280:   if (colemb) {
5281:     PetscCall(ISGetLocalSize(colemb, &n));
5282:     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);
5283:   } else {
5284:     PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5285:   }

5287:   Baij = (Mat_SeqAIJ *)(B->data);
5288:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
5289:     PetscCall(PetscMalloc1(B->rmap->n, &nz));
5290:     for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5291:     PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5292:     PetscCall(PetscFree(nz));
5293:   }
5294:   if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5295:   count      = 0;
5296:   rowindices = NULL;
5297:   colindices = NULL;
5298:   if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5299:   if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5300:   for (i = 0; i < B->rmap->n; i++) {
5301:     PetscInt row;
5302:     row = i;
5303:     if (rowindices) row = rowindices[i];
5304:     for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5305:       PetscInt col;
5306:       col = Baij->j[count];
5307:       if (colindices) col = colindices[col];
5308:       v = Baij->a[count];
5309:       PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5310:       ++count;
5311:     }
5312:   }
5313:   /* FIXME: set C's nonzerostate correctly. */
5314:   /* Assembly for C is necessary. */
5315:   C->preallocated  = PETSC_TRUE;
5316:   C->assembled     = PETSC_TRUE;
5317:   C->was_assembled = PETSC_FALSE;
5318:   PetscFunctionReturn(PETSC_SUCCESS);
5319: }

5321: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A)
5322: {
5323:   Mat_SeqAIJ *a  = (Mat_SeqAIJ *)A->data;
5324:   MatScalar  *aa = a->a;
5325:   PetscInt    m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5326:   PetscInt   *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;

5328:   PetscFunctionBegin;
5329:   PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5330:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5331:   for (i = 1; i <= m; i++) {
5332:     /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5333:     for (k = ai[i - 1]; k < ai[i]; k++) {
5334:       if (aa[k] == 0 && aj[k] != i - 1) fshift++;
5335:       else {
5336:         if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5337:         aa[k - fshift] = aa[k];
5338:         aj[k - fshift] = aj[k];
5339:       }
5340:     }
5341:     ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5342:     fshift_prev = fshift;
5343:     /* reset ilen and imax for each row */
5344:     ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5345:     a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5346:     rmax = PetscMax(rmax, ailen[i - 1]);
5347:   }
5348:   if (m) {
5349:     ai[m] -= fshift;
5350:     a->nz = ai[m];
5351:   }
5352:   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));
5353:   A->nonzerostate -= fshift;
5354:   A->info.nz_unneeded += (PetscReal)fshift;
5355:   a->rmax = rmax;
5356:   if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5357:   PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5358:   PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5359:   PetscFunctionReturn(PETSC_SUCCESS);
5360: }

5362: PetscFunctionList MatSeqAIJList = NULL;

5364: /*@C
5365:    MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype

5367:    Collective

5369:    Input Parameters:
5370: +  mat      - the matrix object
5371: -  matype   - matrix type

5373:    Options Database Key:
5374: .  -mat_seqaij_type  <method> - for example seqaijcrl

5376:   Level: intermediate

5378: .seealso: [](chapter_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`, `Mat`
5379: @*/
5380: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5381: {
5382:   PetscBool sametype;
5383:   PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);

5385:   PetscFunctionBegin;
5387:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5388:   if (sametype) PetscFunctionReturn(PETSC_SUCCESS);

5390:   PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5391:   PetscCheck(r, PETSC_COMM_SELF, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5392:   PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5393:   PetscFunctionReturn(PETSC_SUCCESS);
5394: }

5396: /*@C
5397:   MatSeqAIJRegister -  - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices

5399:    Not Collective

5401:    Input Parameters:
5402: +  name - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5403: -  function - routine to convert to subtype

5405:    Level: advanced

5407:    Notes:
5408:    `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.

5410:    Then, your matrix can be chosen with the procedural interface at runtime via the option
5411: $     -mat_seqaij_type my_mat

5413: .seealso: [](chapter_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5414: @*/
5415: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5416: {
5417:   PetscFunctionBegin;
5418:   PetscCall(MatInitializePackage());
5419:   PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5420:   PetscFunctionReturn(PETSC_SUCCESS);
5421: }

5423: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;

5425: /*@C
5426:   MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`

5428:   Not Collective

5430:   Level: advanced

5432:   Note:
5433:   This registers the versions of `MATSEQAIJ` for GPUs

5435: .seealso: [](chapter_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5436: @*/
5437: PetscErrorCode MatSeqAIJRegisterAll(void)
5438: {
5439:   PetscFunctionBegin;
5440:   if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5441:   MatSeqAIJRegisterAllCalled = PETSC_TRUE;

5443:   PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5444:   PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5445:   PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5446: #if defined(PETSC_HAVE_MKL_SPARSE)
5447:   PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5448: #endif
5449: #if defined(PETSC_HAVE_CUDA)
5450:   PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5451: #endif
5452: #if defined(PETSC_HAVE_HIP)
5453:   PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5454: #endif
5455: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5456:   PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5457: #endif
5458: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5459:   PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5460: #endif
5461:   PetscFunctionReturn(PETSC_SUCCESS);
5462: }

5464: /*
5465:     Special version for direct calls from Fortran
5466: */
5467: #include <petsc/private/fortranimpl.h>
5468: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5469:   #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5470: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5471:   #define matsetvaluesseqaij_ matsetvaluesseqaij
5472: #endif

5474: /* Change these macros so can be used in void function */

5476: /* Change these macros so can be used in void function */
5477: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5478: #undef PetscCall
5479: #define PetscCall(...) \
5480:   do { \
5481:     PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5482:     if (PetscUnlikely(ierr_msv_mpiaij)) { \
5483:       *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5484:       return; \
5485:     } \
5486:   } while (0)

5488: #undef SETERRQ
5489: #define SETERRQ(comm, ierr, ...) \
5490:   do { \
5491:     *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5492:     return; \
5493:   } while (0)

5495: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5496: {
5497:   Mat         A = *AA;
5498:   PetscInt    m = *mm, n = *nn;
5499:   InsertMode  is = *isis;
5500:   Mat_SeqAIJ *a  = (Mat_SeqAIJ *)A->data;
5501:   PetscInt   *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5502:   PetscInt   *imax, *ai, *ailen;
5503:   PetscInt   *aj, nonew = a->nonew, lastcol = -1;
5504:   MatScalar  *ap, value, *aa;
5505:   PetscBool   ignorezeroentries = a->ignorezeroentries;
5506:   PetscBool   roworiented       = a->roworiented;

5508:   PetscFunctionBegin;
5509:   MatCheckPreallocated(A, 1);
5510:   imax  = a->imax;
5511:   ai    = a->i;
5512:   ailen = a->ilen;
5513:   aj    = a->j;
5514:   aa    = a->a;

5516:   for (k = 0; k < m; k++) { /* loop over added rows */
5517:     row = im[k];
5518:     if (row < 0) continue;
5519:     PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5520:     rp   = aj + ai[row];
5521:     ap   = aa + ai[row];
5522:     rmax = imax[row];
5523:     nrow = ailen[row];
5524:     low  = 0;
5525:     high = nrow;
5526:     for (l = 0; l < n; l++) { /* loop over added columns */
5527:       if (in[l] < 0) continue;
5528:       PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5529:       col = in[l];
5530:       if (roworiented) value = v[l + k * n];
5531:       else value = v[k + l * m];

5533:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

5535:       if (col <= lastcol) low = 0;
5536:       else high = nrow;
5537:       lastcol = col;
5538:       while (high - low > 5) {
5539:         t = (low + high) / 2;
5540:         if (rp[t] > col) high = t;
5541:         else low = t;
5542:       }
5543:       for (i = low; i < high; i++) {
5544:         if (rp[i] > col) break;
5545:         if (rp[i] == col) {
5546:           if (is == ADD_VALUES) ap[i] += value;
5547:           else ap[i] = value;
5548:           goto noinsert;
5549:         }
5550:       }
5551:       if (value == 0.0 && ignorezeroentries) goto noinsert;
5552:       if (nonew == 1) goto noinsert;
5553:       PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5554:       MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5555:       N = nrow++ - 1;
5556:       a->nz++;
5557:       high++;
5558:       /* shift up all the later entries in this row */
5559:       for (ii = N; ii >= i; ii--) {
5560:         rp[ii + 1] = rp[ii];
5561:         ap[ii + 1] = ap[ii];
5562:       }
5563:       rp[i] = col;
5564:       ap[i] = value;
5565:       A->nonzerostate++;
5566:     noinsert:;
5567:       low = i + 1;
5568:     }
5569:     ailen[row] = nrow;
5570:   }
5571:   PetscFunctionReturnVoid();
5572: }
5573: /* Undefining these here since they were redefined from their original definition above! No
5574:  * other PETSc functions should be defined past this point, as it is impossible to recover the
5575:  * original definitions */
5576: #undef PetscCall
5577: #undef SETERRQ