Actual source code: matrix.c

  1: /*
  2:    This is where the abstract matrix operations are defined
  3:    Portions of this code are under:
  4:    Copyright (c) 2022 Advanced Micro Devices, Inc. All rights reserved.
  5: */

  7: #include <petsc/private/matimpl.h>
  8: #include <petsc/private/isimpl.h>
  9: #include <petsc/private/vecimpl.h>

 11: /* Logging support */
 12: PetscClassId MAT_CLASSID;
 13: PetscClassId MAT_COLORING_CLASSID;
 14: PetscClassId MAT_FDCOLORING_CLASSID;
 15: PetscClassId MAT_TRANSPOSECOLORING_CLASSID;

 17: PetscLogEvent MAT_Mult, MAT_MultAdd, MAT_MultTranspose;
 18: PetscLogEvent MAT_MultTransposeAdd, MAT_Solve, MAT_Solves, MAT_SolveAdd, MAT_SolveTranspose, MAT_MatSolve, MAT_MatTrSolve;
 19: PetscLogEvent MAT_SolveTransposeAdd, MAT_SOR, MAT_ForwardSolve, MAT_BackwardSolve, MAT_LUFactor, MAT_LUFactorSymbolic;
 20: PetscLogEvent MAT_LUFactorNumeric, MAT_CholeskyFactor, MAT_CholeskyFactorSymbolic, MAT_CholeskyFactorNumeric, MAT_ILUFactor;
 21: PetscLogEvent MAT_ILUFactorSymbolic, MAT_ICCFactorSymbolic, MAT_Copy, MAT_Convert, MAT_Scale, MAT_AssemblyBegin;
 22: PetscLogEvent MAT_QRFactorNumeric, MAT_QRFactorSymbolic, MAT_QRFactor;
 23: PetscLogEvent MAT_AssemblyEnd, MAT_SetValues, MAT_GetValues, MAT_GetRow, MAT_GetRowIJ, MAT_CreateSubMats, MAT_GetOrdering, MAT_RedundantMat, MAT_GetSeqNonzeroStructure;
 24: PetscLogEvent MAT_IncreaseOverlap, MAT_Partitioning, MAT_PartitioningND, MAT_Coarsen, MAT_ZeroEntries, MAT_Load, MAT_View, MAT_AXPY, MAT_FDColoringCreate;
 25: PetscLogEvent MAT_FDColoringSetUp, MAT_FDColoringApply, MAT_Transpose, MAT_FDColoringFunction, MAT_CreateSubMat;
 26: PetscLogEvent MAT_TransposeColoringCreate;
 27: PetscLogEvent MAT_MatMult, MAT_MatMultSymbolic, MAT_MatMultNumeric;
 28: PetscLogEvent MAT_PtAP, MAT_PtAPSymbolic, MAT_PtAPNumeric, MAT_RARt, MAT_RARtSymbolic, MAT_RARtNumeric;
 29: PetscLogEvent MAT_MatTransposeMult, MAT_MatTransposeMultSymbolic, MAT_MatTransposeMultNumeric;
 30: PetscLogEvent MAT_TransposeMatMult, MAT_TransposeMatMultSymbolic, MAT_TransposeMatMultNumeric;
 31: PetscLogEvent MAT_MatMatMult, MAT_MatMatMultSymbolic, MAT_MatMatMultNumeric;
 32: PetscLogEvent MAT_MultHermitianTranspose, MAT_MultHermitianTransposeAdd;
 33: PetscLogEvent MAT_Getsymtransreduced, MAT_GetBrowsOfAcols;
 34: PetscLogEvent MAT_GetBrowsOfAocols, MAT_Getlocalmat, MAT_Getlocalmatcondensed, MAT_Seqstompi, MAT_Seqstompinum, MAT_Seqstompisym;
 35: PetscLogEvent MAT_GetMultiProcBlock;
 36: PetscLogEvent MAT_CUSPARSECopyToGPU, MAT_CUSPARSECopyFromGPU, MAT_CUSPARSEGenerateTranspose, MAT_CUSPARSESolveAnalysis;
 37: PetscLogEvent MAT_HIPSPARSECopyToGPU, MAT_HIPSPARSECopyFromGPU, MAT_HIPSPARSEGenerateTranspose, MAT_HIPSPARSESolveAnalysis;
 38: PetscLogEvent MAT_PreallCOO, MAT_SetVCOO;
 39: PetscLogEvent MAT_CreateGraph;
 40: PetscLogEvent MAT_SetValuesBatch;
 41: PetscLogEvent MAT_ViennaCLCopyToGPU;
 42: PetscLogEvent MAT_CUDACopyToGPU, MAT_HIPCopyToGPU;
 43: PetscLogEvent MAT_DenseCopyToGPU, MAT_DenseCopyFromGPU;
 44: PetscLogEvent MAT_Merge, MAT_Residual, MAT_SetRandom;
 45: PetscLogEvent MAT_FactorFactS, MAT_FactorInvS;
 46: PetscLogEvent MATCOLORING_Apply, MATCOLORING_Comm, MATCOLORING_Local, MATCOLORING_ISCreate, MATCOLORING_SetUp, MATCOLORING_Weights;
 47: PetscLogEvent MAT_H2Opus_Build, MAT_H2Opus_Compress, MAT_H2Opus_Orthog, MAT_H2Opus_LR;

 49: const char *const MatFactorTypes[] = {"NONE", "LU", "CHOLESKY", "ILU", "ICC", "ILUDT", "QR", "MatFactorType", "MAT_FACTOR_", NULL};

 51: /*@
 52:   MatSetRandom - Sets all components of a matrix to random numbers.

 54:   Logically Collective

 56:   Input Parameters:
 57: + x    - the matrix
 58: - rctx - the `PetscRandom` object, formed by `PetscRandomCreate()`, or `NULL` and
 59:           it will create one internally.

 61:   Example:
 62: .vb
 63:      PetscRandomCreate(PETSC_COMM_WORLD,&rctx);
 64:      MatSetRandom(x,rctx);
 65:      PetscRandomDestroy(rctx);
 66: .ve

 68:   Level: intermediate

 70:   Notes:
 71:   For sparse matrices that have been preallocated but not been assembled, it randomly selects appropriate locations,

 73:   for sparse matrices that already have nonzero locations, it fills the locations with random numbers.

 75:   It generates an error if used on unassembled sparse matrices that have not been preallocated.

 77: .seealso: [](ch_matrices), `Mat`, `PetscRandom`, `PetscRandomCreate()`, `MatZeroEntries()`, `MatSetValues()`, `PetscRandomDestroy()`
 78: @*/
 79: PetscErrorCode MatSetRandom(Mat x, PetscRandom rctx)
 80: {
 81:   PetscRandom randObj = NULL;

 83:   PetscFunctionBegin;
 87:   MatCheckPreallocated(x, 1);

 89:   if (!rctx) {
 90:     MPI_Comm comm;
 91:     PetscCall(PetscObjectGetComm((PetscObject)x, &comm));
 92:     PetscCall(PetscRandomCreate(comm, &randObj));
 93:     PetscCall(PetscRandomSetType(randObj, x->defaultrandtype));
 94:     PetscCall(PetscRandomSetFromOptions(randObj));
 95:     rctx = randObj;
 96:   }
 97:   PetscCall(PetscLogEventBegin(MAT_SetRandom, x, rctx, 0, 0));
 98:   PetscUseTypeMethod(x, setrandom, rctx);
 99:   PetscCall(PetscLogEventEnd(MAT_SetRandom, x, rctx, 0, 0));

101:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
102:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
103:   PetscCall(PetscRandomDestroy(&randObj));
104:   PetscFunctionReturn(PETSC_SUCCESS);
105: }

107: /*@
108:   MatCopyHashToXAIJ - copy hash table entries into an XAIJ matrix type

110:   Logically Collective

112:   Input Parameter:
113: . A - A matrix in unassembled, hash table form

115:   Output Parameter:
116: . B - The XAIJ matrix. This can either be `A` or some matrix of equivalent size, e.g. obtained from `A` via `MatDuplicate()`

118:   Example:
119: .vb
120:      PetscCall(MatDuplicate(A, MAT_DO_NOT_COPY_VALUES, &B));
121:      PetscCall(MatCopyHashToXAIJ(A, B));
122: .ve

124:   Level: advanced

126:   Notes:
127:   If `B` is `A`, then the hash table data structure will be destroyed. `B` is assembled

129: .seealso: [](ch_matrices), `Mat`, `MAT_USE_HASH_TABLE`
130: @*/
131: PetscErrorCode MatCopyHashToXAIJ(Mat A, Mat B)
132: {
133:   PetscFunctionBegin;
135:   PetscUseTypeMethod(A, copyhashtoxaij, B);
136:   PetscFunctionReturn(PETSC_SUCCESS);
137: }

139: /*@
140:   MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in

142:   Logically Collective

144:   Input Parameter:
145: . mat - the factored matrix

147:   Output Parameters:
148: + pivot - the pivot value computed
149: - row   - the row that the zero pivot occurred. This row value must be interpreted carefully due to row reorderings and which processes
150:          the share the matrix

152:   Level: advanced

154:   Notes:
155:   This routine does not work for factorizations done with external packages.

157:   This routine should only be called if `MatGetFactorError()` returns a value of `MAT_FACTOR_NUMERIC_ZEROPIVOT`

159:   This can also be called on non-factored matrices that come from, for example, matrices used in SOR.

161: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`,
162: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorClearError()`,
163: `MAT_FACTOR_NUMERIC_ZEROPIVOT`
164: @*/
165: PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat, PetscReal *pivot, PetscInt *row)
166: {
167:   PetscFunctionBegin;
169:   PetscAssertPointer(pivot, 2);
170:   PetscAssertPointer(row, 3);
171:   *pivot = mat->factorerror_zeropivot_value;
172:   *row   = mat->factorerror_zeropivot_row;
173:   PetscFunctionReturn(PETSC_SUCCESS);
174: }

176: /*@
177:   MatFactorGetError - gets the error code from a factorization

179:   Logically Collective

181:   Input Parameter:
182: . mat - the factored matrix

184:   Output Parameter:
185: . err - the error code

187:   Level: advanced

189:   Note:
190:   This can also be called on non-factored matrices that come from, for example, matrices used in SOR.

192: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`,
193:           `MatFactorClearError()`, `MatFactorGetErrorZeroPivot()`, `MatFactorError`
194: @*/
195: PetscErrorCode MatFactorGetError(Mat mat, MatFactorError *err)
196: {
197:   PetscFunctionBegin;
199:   PetscAssertPointer(err, 2);
200:   *err = mat->factorerrortype;
201:   PetscFunctionReturn(PETSC_SUCCESS);
202: }

204: /*@
205:   MatFactorClearError - clears the error code in a factorization

207:   Logically Collective

209:   Input Parameter:
210: . mat - the factored matrix

212:   Level: developer

214:   Note:
215:   This can also be called on non-factored matrices that come from, for example, matrices used in SOR.

217: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorGetError()`, `MatFactorGetErrorZeroPivot()`,
218:           `MatGetErrorCode()`, `MatFactorError`
219: @*/
220: PetscErrorCode MatFactorClearError(Mat mat)
221: {
222:   PetscFunctionBegin;
224:   mat->factorerrortype             = MAT_FACTOR_NOERROR;
225:   mat->factorerror_zeropivot_value = 0.0;
226:   mat->factorerror_zeropivot_row   = 0;
227:   PetscFunctionReturn(PETSC_SUCCESS);
228: }

230: PetscErrorCode MatFindNonzeroRowsOrCols_Basic(Mat mat, PetscBool cols, PetscReal tol, IS *nonzero)
231: {
232:   Vec                r, l;
233:   const PetscScalar *al;
234:   PetscInt           i, nz, gnz, N, n, st;

236:   PetscFunctionBegin;
237:   PetscCall(MatCreateVecs(mat, &r, &l));
238:   if (!cols) { /* nonzero rows */
239:     PetscCall(MatGetOwnershipRange(mat, &st, NULL));
240:     PetscCall(MatGetSize(mat, &N, NULL));
241:     PetscCall(MatGetLocalSize(mat, &n, NULL));
242:     PetscCall(VecSet(l, 0.0));
243:     PetscCall(VecSetRandom(r, NULL));
244:     PetscCall(MatMult(mat, r, l));
245:     PetscCall(VecGetArrayRead(l, &al));
246:   } else { /* nonzero columns */
247:     PetscCall(MatGetOwnershipRangeColumn(mat, &st, NULL));
248:     PetscCall(MatGetSize(mat, NULL, &N));
249:     PetscCall(MatGetLocalSize(mat, NULL, &n));
250:     PetscCall(VecSet(r, 0.0));
251:     PetscCall(VecSetRandom(l, NULL));
252:     PetscCall(MatMultTranspose(mat, l, r));
253:     PetscCall(VecGetArrayRead(r, &al));
254:   }
255:   if (tol <= 0.0) {
256:     for (i = 0, nz = 0; i < n; i++)
257:       if (al[i] != 0.0) nz++;
258:   } else {
259:     for (i = 0, nz = 0; i < n; i++)
260:       if (PetscAbsScalar(al[i]) > tol) nz++;
261:   }
262:   PetscCallMPI(MPIU_Allreduce(&nz, &gnz, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
263:   if (gnz != N) {
264:     PetscInt *nzr;
265:     PetscCall(PetscMalloc1(nz, &nzr));
266:     if (nz) {
267:       if (tol < 0) {
268:         for (i = 0, nz = 0; i < n; i++)
269:           if (al[i] != 0.0) nzr[nz++] = i + st;
270:       } else {
271:         for (i = 0, nz = 0; i < n; i++)
272:           if (PetscAbsScalar(al[i]) > tol) nzr[nz++] = i + st;
273:       }
274:     }
275:     PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nz, nzr, PETSC_OWN_POINTER, nonzero));
276:   } else *nonzero = NULL;
277:   if (!cols) { /* nonzero rows */
278:     PetscCall(VecRestoreArrayRead(l, &al));
279:   } else {
280:     PetscCall(VecRestoreArrayRead(r, &al));
281:   }
282:   PetscCall(VecDestroy(&l));
283:   PetscCall(VecDestroy(&r));
284:   PetscFunctionReturn(PETSC_SUCCESS);
285: }

287: /*@
288:   MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix

290:   Input Parameter:
291: . mat - the matrix

293:   Output Parameter:
294: . keptrows - the rows that are not completely zero

296:   Level: intermediate

298:   Note:
299:   `keptrows` is set to `NULL` if all rows are nonzero.

301:   Developer Note:
302:   If `keptrows` is not `NULL`, it must be sorted.

304: .seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
305:  @*/
306: PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
307: {
308:   PetscFunctionBegin;
311:   PetscAssertPointer(keptrows, 2);
312:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
313:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
314:   if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
315:   else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
316:   if (keptrows && *keptrows) PetscCall(ISSetInfo(*keptrows, IS_SORTED, IS_GLOBAL, PETSC_FALSE, PETSC_TRUE));
317:   PetscFunctionReturn(PETSC_SUCCESS);
318: }

320: /*@
321:   MatFindZeroRows - Locate all rows that are completely zero in the matrix

323:   Input Parameter:
324: . mat - the matrix

326:   Output Parameter:
327: . zerorows - the rows that are completely zero

329:   Level: intermediate

331:   Note:
332:   `zerorows` is set to `NULL` if no rows are zero.

334: .seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
335:  @*/
336: PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
337: {
338:   IS       keptrows;
339:   PetscInt m, n;

341:   PetscFunctionBegin;
344:   PetscAssertPointer(zerorows, 2);
345:   PetscCall(MatFindNonzeroRows(mat, &keptrows));
346:   /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
347:      In keeping with this convention, we set zerorows to NULL if there are no zero
348:      rows. */
349:   if (keptrows == NULL) {
350:     *zerorows = NULL;
351:   } else {
352:     PetscCall(MatGetOwnershipRange(mat, &m, &n));
353:     PetscCall(ISComplement(keptrows, m, n, zerorows));
354:     PetscCall(ISDestroy(&keptrows));
355:   }
356:   PetscFunctionReturn(PETSC_SUCCESS);
357: }

359: /*@
360:   MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling

362:   Not Collective

364:   Input Parameter:
365: . A - the matrix

367:   Output Parameter:
368: . a - the diagonal part (which is a SEQUENTIAL matrix)

370:   Level: advanced

372:   Notes:
373:   See `MatCreateAIJ()` for more information on the "diagonal part" of the matrix.

375:   Use caution, as the reference count on the returned matrix is not incremented and it is used as part of `A`'s normal operation.

377: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
378: @*/
379: PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
380: {
381:   PetscFunctionBegin;
384:   PetscAssertPointer(a, 2);
385:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
386:   if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
387:   else {
388:     PetscMPIInt size;

390:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
391:     PetscCheck(size == 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for parallel matrix type %s", ((PetscObject)A)->type_name);
392:     *a = A;
393:   }
394:   PetscFunctionReturn(PETSC_SUCCESS);
395: }

397: /*@
398:   MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.

400:   Collective

402:   Input Parameter:
403: . mat - the matrix

405:   Output Parameter:
406: . trace - the sum of the diagonal entries

408:   Level: advanced

410: .seealso: [](ch_matrices), `Mat`
411: @*/
412: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
413: {
414:   Vec diag;

416:   PetscFunctionBegin;
418:   PetscAssertPointer(trace, 2);
419:   PetscCall(MatCreateVecs(mat, &diag, NULL));
420:   PetscCall(MatGetDiagonal(mat, diag));
421:   PetscCall(VecSum(diag, trace));
422:   PetscCall(VecDestroy(&diag));
423:   PetscFunctionReturn(PETSC_SUCCESS);
424: }

426: /*@
427:   MatRealPart - Zeros out the imaginary part of the matrix

429:   Logically Collective

431:   Input Parameter:
432: . mat - the matrix

434:   Level: advanced

436: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
437: @*/
438: PetscErrorCode MatRealPart(Mat mat)
439: {
440:   PetscFunctionBegin;
443:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
444:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
445:   MatCheckPreallocated(mat, 1);
446:   PetscUseTypeMethod(mat, realpart);
447:   PetscFunctionReturn(PETSC_SUCCESS);
448: }

450: /*@C
451:   MatGetGhosts - Get the global indices of all ghost nodes defined by the sparse matrix

453:   Collective

455:   Input Parameter:
456: . mat - the matrix

458:   Output Parameters:
459: + nghosts - number of ghosts (for `MATBAIJ` and `MATSBAIJ` matrices there is one ghost for each matrix block)
460: - ghosts  - the global indices of the ghost points

462:   Level: advanced

464:   Note:
465:   `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()` or `VecCreateGhostBlock()`

467: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
468: @*/
469: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
470: {
471:   PetscFunctionBegin;
474:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
475:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
476:   if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
477:   else {
478:     if (nghosts) *nghosts = 0;
479:     if (ghosts) *ghosts = NULL;
480:   }
481:   PetscFunctionReturn(PETSC_SUCCESS);
482: }

484: /*@
485:   MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part

487:   Logically Collective

489:   Input Parameter:
490: . mat - the matrix

492:   Level: advanced

494: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
495: @*/
496: PetscErrorCode MatImaginaryPart(Mat mat)
497: {
498:   PetscFunctionBegin;
501:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
502:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
503:   MatCheckPreallocated(mat, 1);
504:   PetscUseTypeMethod(mat, imaginarypart);
505:   PetscFunctionReturn(PETSC_SUCCESS);
506: }

508: /*@
509:   MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for `MATBAIJ` and `MATSBAIJ` matrices) in the nonzero structure

511:   Not Collective

513:   Input Parameter:
514: . mat - the matrix

516:   Output Parameters:
517: + missing - is any diagonal entry missing
518: - dd      - first diagonal entry that is missing (optional) on this process

520:   Level: advanced

522:   Note:
523:   This does not return diagonal entries that are in the nonzero structure but happen to have a zero numerical value

525: .seealso: [](ch_matrices), `Mat`
526: @*/
527: PetscErrorCode MatMissingDiagonal(Mat mat, PetscBool *missing, PetscInt *dd)
528: {
529:   PetscFunctionBegin;
532:   PetscAssertPointer(missing, 2);
533:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix %s", ((PetscObject)mat)->type_name);
534:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
535:   PetscUseTypeMethod(mat, missingdiagonal, missing, dd);
536:   PetscFunctionReturn(PETSC_SUCCESS);
537: }

539: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
540: /*@C
541:   MatGetRow - Gets a row of a matrix.  You MUST call `MatRestoreRow()`
542:   for each row that you get to ensure that your application does
543:   not bleed memory.

545:   Not Collective

547:   Input Parameters:
548: + mat - the matrix
549: - row - the row to get

551:   Output Parameters:
552: + ncols - if not `NULL`, the number of nonzeros in `row`
553: . cols  - if not `NULL`, the column numbers
554: - vals  - if not `NULL`, the numerical values

556:   Level: advanced

558:   Notes:
559:   This routine is provided for people who need to have direct access
560:   to the structure of a matrix.  We hope that we provide enough
561:   high-level matrix routines that few users will need it.

563:   `MatGetRow()` always returns 0-based column indices, regardless of
564:   whether the internal representation is 0-based (default) or 1-based.

566:   For better efficiency, set `cols` and/or `vals` to `NULL` if you do
567:   not wish to extract these quantities.

569:   The user can only examine the values extracted with `MatGetRow()`;
570:   the values CANNOT be altered.  To change the matrix entries, one
571:   must use `MatSetValues()`.

573:   You can only have one call to `MatGetRow()` outstanding for a particular
574:   matrix at a time, per processor. `MatGetRow()` can only obtain rows
575:   associated with the given processor, it cannot get rows from the
576:   other processors; for that we suggest using `MatCreateSubMatrices()`, then
577:   `MatGetRow()` on the submatrix. The row index passed to `MatGetRow()`
578:   is in the global number of rows.

580:   Use `MatGetRowIJ()` and `MatRestoreRowIJ()` to access all the local indices of the sparse matrix.

582:   Use `MatSeqAIJGetArray()` and similar functions to access the numerical values for certain matrix types directly.

584:   Fortran Note:
585: .vb
586:   PetscInt, pointer :: cols(:)
587:   PetscScalar, pointer :: vals(:)
588: .ve

590: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
591: @*/
592: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
593: {
594:   PetscInt incols;

596:   PetscFunctionBegin;
599:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
600:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
601:   MatCheckPreallocated(mat, 1);
602:   PetscCheck(row >= mat->rmap->rstart && row < mat->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Only for local rows, %" PetscInt_FMT " not in [%" PetscInt_FMT ",%" PetscInt_FMT ")", row, mat->rmap->rstart, mat->rmap->rend);
603:   PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
604:   PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
605:   if (ncols) *ncols = incols;
606:   PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
607:   PetscFunctionReturn(PETSC_SUCCESS);
608: }

610: /*@
611:   MatConjugate - replaces the matrix values with their complex conjugates

613:   Logically Collective

615:   Input Parameter:
616: . mat - the matrix

618:   Level: advanced

620: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
621: @*/
622: PetscErrorCode MatConjugate(Mat mat)
623: {
624:   PetscFunctionBegin;
626:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
627:   if (PetscDefined(USE_COMPLEX) && mat->hermitian != PETSC_BOOL3_TRUE) {
628:     PetscUseTypeMethod(mat, conjugate);
629:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
630:   }
631:   PetscFunctionReturn(PETSC_SUCCESS);
632: }

634: /*@C
635:   MatRestoreRow - Frees any temporary space allocated by `MatGetRow()`.

637:   Not Collective

639:   Input Parameters:
640: + mat   - the matrix
641: . row   - the row to get
642: . ncols - the number of nonzeros
643: . cols  - the columns of the nonzeros
644: - vals  - if nonzero the column values

646:   Level: advanced

648:   Notes:
649:   This routine should be called after you have finished examining the entries.

651:   This routine zeros out `ncols`, `cols`, and `vals`. This is to prevent accidental
652:   us of the array after it has been restored. If you pass `NULL`, it will
653:   not zero the pointers.  Use of `cols` or `vals` after `MatRestoreRow()` is invalid.

655:   Fortran Note:
656: .vb
657:   PetscInt, pointer :: cols(:)
658:   PetscScalar, pointer :: vals(:)
659: .ve

661: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
662: @*/
663: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
664: {
665:   PetscFunctionBegin;
667:   if (ncols) PetscAssertPointer(ncols, 3);
668:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
669:   PetscTryTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
670:   if (ncols) *ncols = 0;
671:   if (cols) *cols = NULL;
672:   if (vals) *vals = NULL;
673:   PetscFunctionReturn(PETSC_SUCCESS);
674: }

676: /*@
677:   MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
678:   You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.

680:   Not Collective

682:   Input Parameter:
683: . mat - the matrix

685:   Level: advanced

687:   Note:
688:   The flag is to ensure that users are aware that `MatGetRow()` only provides the upper triangular part of the row for the matrices in `MATSBAIJ` format.

690: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
691: @*/
692: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
693: {
694:   PetscFunctionBegin;
697:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
698:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
699:   MatCheckPreallocated(mat, 1);
700:   PetscTryTypeMethod(mat, getrowuppertriangular);
701:   PetscFunctionReturn(PETSC_SUCCESS);
702: }

704: /*@
705:   MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.

707:   Not Collective

709:   Input Parameter:
710: . mat - the matrix

712:   Level: advanced

714:   Note:
715:   This routine should be called after you have finished calls to `MatGetRow()` and `MatRestoreRow()`.

717: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
718: @*/
719: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
720: {
721:   PetscFunctionBegin;
724:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
725:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
726:   MatCheckPreallocated(mat, 1);
727:   PetscTryTypeMethod(mat, restorerowuppertriangular);
728:   PetscFunctionReturn(PETSC_SUCCESS);
729: }

731: /*@
732:   MatSetOptionsPrefix - Sets the prefix used for searching for all
733:   `Mat` options in the database.

735:   Logically Collective

737:   Input Parameters:
738: + A      - the matrix
739: - prefix - the prefix to prepend to all option names

741:   Level: advanced

743:   Notes:
744:   A hyphen (-) must NOT be given at the beginning of the prefix name.
745:   The first character of all runtime options is AUTOMATICALLY the hyphen.

747:   This is NOT used for options for the factorization of the matrix. Normally the
748:   prefix is automatically passed in from the PC calling the factorization. To set
749:   it directly use  `MatSetOptionsPrefixFactor()`

751: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
752: @*/
753: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
754: {
755:   PetscFunctionBegin;
757:   PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
758:   PetscTryMethod(A, "MatSetOptionsPrefix_C", (Mat, const char[]), (A, prefix));
759:   PetscFunctionReturn(PETSC_SUCCESS);
760: }

762: /*@
763:   MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
764:   for matrices created with `MatGetFactor()`

766:   Logically Collective

768:   Input Parameters:
769: + A      - the matrix
770: - prefix - the prefix to prepend to all option names for the factored matrix

772:   Level: developer

774:   Notes:
775:   A hyphen (-) must NOT be given at the beginning of the prefix name.
776:   The first character of all runtime options is AUTOMATICALLY the hyphen.

778:   Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
779:   it directly when not using `KSP`/`PC` use  `MatSetOptionsPrefixFactor()`

781: .seealso: [](ch_matrices), `Mat`,   [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
782: @*/
783: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
784: {
785:   PetscFunctionBegin;
787:   if (prefix) {
788:     PetscAssertPointer(prefix, 2);
789:     PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
790:     if (prefix != A->factorprefix) {
791:       PetscCall(PetscFree(A->factorprefix));
792:       PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
793:     }
794:   } else PetscCall(PetscFree(A->factorprefix));
795:   PetscFunctionReturn(PETSC_SUCCESS);
796: }

798: /*@
799:   MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
800:   for matrices created with `MatGetFactor()`

802:   Logically Collective

804:   Input Parameters:
805: + A      - the matrix
806: - prefix - the prefix to prepend to all option names for the factored matrix

808:   Level: developer

810:   Notes:
811:   A hyphen (-) must NOT be given at the beginning of the prefix name.
812:   The first character of all runtime options is AUTOMATICALLY the hyphen.

814:   Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
815:   it directly when not using `KSP`/`PC` use  `MatAppendOptionsPrefixFactor()`

817: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
818:           `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
819:           `MatSetOptionsPrefix()`
820: @*/
821: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
822: {
823:   size_t len1, len2, new_len;

825:   PetscFunctionBegin;
827:   if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
828:   if (!A->factorprefix) {
829:     PetscCall(MatSetOptionsPrefixFactor(A, prefix));
830:     PetscFunctionReturn(PETSC_SUCCESS);
831:   }
832:   PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");

834:   PetscCall(PetscStrlen(A->factorprefix, &len1));
835:   PetscCall(PetscStrlen(prefix, &len2));
836:   new_len = len1 + len2 + 1;
837:   PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
838:   PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
839:   PetscFunctionReturn(PETSC_SUCCESS);
840: }

842: /*@
843:   MatAppendOptionsPrefix - Appends to the prefix used for searching for all
844:   matrix options in the database.

846:   Logically Collective

848:   Input Parameters:
849: + A      - the matrix
850: - prefix - the prefix to prepend to all option names

852:   Level: advanced

854:   Note:
855:   A hyphen (-) must NOT be given at the beginning of the prefix name.
856:   The first character of all runtime options is AUTOMATICALLY the hyphen.

858: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
859: @*/
860: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
861: {
862:   PetscFunctionBegin;
864:   PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
865:   PetscTryMethod(A, "MatAppendOptionsPrefix_C", (Mat, const char[]), (A, prefix));
866:   PetscFunctionReturn(PETSC_SUCCESS);
867: }

869: /*@
870:   MatGetOptionsPrefix - Gets the prefix used for searching for all
871:   matrix options in the database.

873:   Not Collective

875:   Input Parameter:
876: . A - the matrix

878:   Output Parameter:
879: . prefix - pointer to the prefix string used

881:   Level: advanced

883: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
884: @*/
885: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
886: {
887:   PetscFunctionBegin;
889:   PetscAssertPointer(prefix, 2);
890:   PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
891:   PetscFunctionReturn(PETSC_SUCCESS);
892: }

894: /*@
895:   MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`

897:   Not Collective

899:   Input Parameter:
900: . A - the matrix

902:   Output Parameter:
903: . state - the object state

905:   Level: advanced

907:   Note:
908:   Object state is an integer which gets increased every time
909:   the object is changed. By saving and later querying the object state
910:   one can determine whether information about the object is still current.

912:   See `MatGetNonzeroState()` to determine if the nonzero structure of the matrix has changed.

914: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
915: @*/
916: PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
917: {
918:   PetscFunctionBegin;
920:   PetscAssertPointer(state, 2);
921:   PetscCall(PetscObjectStateGet((PetscObject)A, state));
922:   PetscFunctionReturn(PETSC_SUCCESS);
923: }

925: /*@
926:   MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`

928:   Collective

930:   Input Parameter:
931: . A - the matrix

933:   Level: beginner

935:   Notes:
936:   After calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY` the matrix data structures represent the nonzeros assigned to the
937:   matrix. If that space is less than the preallocated space that extra preallocated space is no longer available to take on new values. `MatResetPreallocation()`
938:   makes all of the preallocation space available

940:   Current values in the matrix are lost in this call

942:   Currently only supported for  `MATAIJ` matrices.

944: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
945: @*/
946: PetscErrorCode MatResetPreallocation(Mat A)
947: {
948:   PetscFunctionBegin;
951:   PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
952:   PetscFunctionReturn(PETSC_SUCCESS);
953: }

955: /*@
956:   MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.

958:   Collective

960:   Input Parameter:
961: . A - the matrix

963:   Level: intermediate

965:   Notes:
966:   The matrix will again delete the hash table data structures after following calls to `MatAssemblyBegin()`/`MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.

968:   Currently only supported for `MATAIJ` matrices.

970: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
971: @*/
972: PetscErrorCode MatResetHash(Mat A)
973: {
974:   PetscFunctionBegin;
977:   PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset to hash state after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
978:   if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
979:   PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
980:   /* These flags are used to determine whether certain setups occur */
981:   A->was_assembled = PETSC_FALSE;
982:   A->assembled     = PETSC_FALSE;
983:   /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
984:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
985:   PetscFunctionReturn(PETSC_SUCCESS);
986: }

988: /*@
989:   MatSetUp - Sets up the internal matrix data structures for later use by the matrix

991:   Collective

993:   Input Parameter:
994: . A - the matrix

996:   Level: advanced

998:   Notes:
999:   If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
1000:   setting values in the matrix.

1002:   This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users

1004: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
1005: @*/
1006: PetscErrorCode MatSetUp(Mat A)
1007: {
1008:   PetscFunctionBegin;
1010:   if (!((PetscObject)A)->type_name) {
1011:     PetscMPIInt size;

1013:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
1014:     PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
1015:   }
1016:   if (!A->preallocated) PetscTryTypeMethod(A, setup);
1017:   PetscCall(PetscLayoutSetUp(A->rmap));
1018:   PetscCall(PetscLayoutSetUp(A->cmap));
1019:   A->preallocated = PETSC_TRUE;
1020:   PetscFunctionReturn(PETSC_SUCCESS);
1021: }

1023: #if defined(PETSC_HAVE_SAWS)
1024: #include <petscviewersaws.h>
1025: #endif

1027: /*
1028:    If threadsafety is on extraneous matrices may be printed

1030:    This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1031: */
1032: #if !defined(PETSC_HAVE_THREADSAFETY)
1033: static PetscInt insidematview = 0;
1034: #endif

1036: /*@
1037:   MatViewFromOptions - View properties of the matrix based on options set in the options database

1039:   Collective

1041:   Input Parameters:
1042: + A    - the matrix
1043: . obj  - optional additional object that provides the options prefix to use
1044: - name - command line option

1046:   Options Database Key:
1047: . -mat_view [viewertype]:... - the viewer and its options

1049:   Level: intermediate

1051:   Note:
1052: .vb
1053:     If no value is provided ascii:stdout is used
1054:        ascii[:[filename][:[format][:append]]]    defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
1055:                                                   for example ascii::ascii_info prints just the information about the object not all details
1056:                                                   unless :append is given filename opens in write mode, overwriting what was already there
1057:        binary[:[filename][:[format][:append]]]   defaults to the file binaryoutput
1058:        draw[:drawtype[:filename]]                for example, draw:tikz, draw:tikz:figure.tex  or draw:x
1059:        socket[:port]                             defaults to the standard output port
1060:        saws[:communicatorname]                    publishes object to the Scientific Application Webserver (SAWs)
1061: .ve

1063: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1064: @*/
1065: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1066: {
1067:   PetscFunctionBegin;
1069: #if !defined(PETSC_HAVE_THREADSAFETY)
1070:   if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1071: #endif
1072:   PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1073:   PetscFunctionReturn(PETSC_SUCCESS);
1074: }

1076: /*@
1077:   MatView - display information about a matrix in a variety ways

1079:   Collective on viewer

1081:   Input Parameters:
1082: + mat    - the matrix
1083: - viewer - visualization context

1085:   Options Database Keys:
1086: + -mat_view ::ascii_info           - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1087: . -mat_view ::ascii_info_detail    - Prints more detailed info
1088: . -mat_view                        - Prints matrix in ASCII format
1089: . -mat_view ::ascii_matlab         - Prints matrix in MATLAB format
1090: . -mat_view draw                   - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1091: . -display <name>                  - Sets display name (default is host)
1092: . -draw_pause <sec>                - Sets number of seconds to pause after display
1093: . -mat_view socket                 - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1094: . -viewer_socket_machine <machine> - -
1095: . -viewer_socket_port <port>       - -
1096: . -mat_view binary                 - save matrix to file in binary format
1097: - -viewer_binary_filename <name>   - -

1099:   Level: beginner

1101:   Notes:
1102:   The available visualization contexts include
1103: +    `PETSC_VIEWER_STDOUT_SELF`   - for sequential matrices
1104: .    `PETSC_VIEWER_STDOUT_WORLD`  - for parallel matrices created on `PETSC_COMM_WORLD`
1105: .    `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1106: -     `PETSC_VIEWER_DRAW_WORLD`   - graphical display of nonzero structure

1108:   The user can open alternative visualization contexts with
1109: +    `PetscViewerASCIIOpen()`  - Outputs matrix to a specified file
1110: .    `PetscViewerBinaryOpen()` - Outputs matrix in binary to a  specified file; corresponding input uses `MatLoad()`
1111: .    `PetscViewerDrawOpen()`   - Outputs nonzero matrix nonzero structure to an X window display
1112: -    `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.

1114:   The user can call `PetscViewerPushFormat()` to specify the output
1115:   format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1116:   `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`).  Available formats include
1117: +    `PETSC_VIEWER_DEFAULT`           - default, prints matrix contents
1118: .    `PETSC_VIEWER_ASCII_MATLAB`      - prints matrix contents in MATLAB format
1119: .    `PETSC_VIEWER_ASCII_DENSE`       - prints entire matrix including zeros
1120: .    `PETSC_VIEWER_ASCII_COMMON`      - prints matrix contents, using a sparse  format common among all matrix types
1121: .    `PETSC_VIEWER_ASCII_IMPL`        - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1122: .    `PETSC_VIEWER_ASCII_INFO`        - prints basic information about the matrix size and structure (not the matrix entries)
1123: -    `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)

1125:   The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1126:   the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.

1128:   In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).

1130:   See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1131:   viewer is used.

1133:   See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1134:   viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.

1136:   One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1137:   and then use the following mouse functions.
1138: .vb
1139:   left mouse: zoom in
1140:   middle mouse: zoom out
1141:   right mouse: continue with the simulation
1142: .ve

1144: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1145:           `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1146: @*/
1147: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1148: {
1149:   PetscInt          rows, cols, rbs, cbs;
1150:   PetscBool         isascii, isstring, issaws;
1151:   PetscViewerFormat format;
1152:   PetscMPIInt       size;

1154:   PetscFunctionBegin;
1157:   if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));

1160:   PetscCall(PetscViewerGetFormat(viewer, &format));
1161:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1162:   if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);

1164: #if !defined(PETSC_HAVE_THREADSAFETY)
1165:   insidematview++;
1166: #endif
1167:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1168:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1169:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1170:   PetscCheck((isascii && (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL)) || !mat->factortype, PetscObjectComm((PetscObject)viewer), PETSC_ERR_ARG_WRONGSTATE, "No viewers for factored matrix except ASCII, info, or info_detail");

1172:   PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1173:   if (isascii) {
1174:     if (!mat->preallocated) {
1175:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1176: #if !defined(PETSC_HAVE_THREADSAFETY)
1177:       insidematview--;
1178: #endif
1179:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1180:       PetscFunctionReturn(PETSC_SUCCESS);
1181:     }
1182:     if (!mat->assembled) {
1183:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1184: #if !defined(PETSC_HAVE_THREADSAFETY)
1185:       insidematview--;
1186: #endif
1187:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1188:       PetscFunctionReturn(PETSC_SUCCESS);
1189:     }
1190:     PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1191:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1192:       MatNullSpace nullsp, transnullsp;

1194:       PetscCall(PetscViewerASCIIPushTab(viewer));
1195:       PetscCall(MatGetSize(mat, &rows, &cols));
1196:       PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1197:       if (rbs != 1 || cbs != 1) {
1198:         if (rbs != cbs) PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", rbs=%" PetscInt_FMT ", cbs=%" PetscInt_FMT "%s\n", rows, cols, rbs, cbs, mat->bsizes ? " variable blocks set" : ""));
1199:         else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1200:       } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1201:       if (mat->factortype) {
1202:         MatSolverType solver;
1203:         PetscCall(MatFactorGetSolverType(mat, &solver));
1204:         PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1205:       }
1206:       if (mat->ops->getinfo) {
1207:         PetscBool is_constant_or_diagonal;

1209:         // Don't print nonzero information for constant or diagonal matrices, it just adds noise to the output
1210:         PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &is_constant_or_diagonal, MATCONSTANTDIAGONAL, MATDIAGONAL, ""));
1211:         if (!is_constant_or_diagonal) {
1212:           MatInfo info;

1214:           PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1215:           PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1216:           if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1217:         }
1218:       }
1219:       PetscCall(MatGetNullSpace(mat, &nullsp));
1220:       PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1221:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached null space\n"));
1222:       if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached transposed null space\n"));
1223:       PetscCall(MatGetNearNullSpace(mat, &nullsp));
1224:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached near null space\n"));
1225:       PetscCall(PetscViewerASCIIPushTab(viewer));
1226:       PetscCall(MatProductView(mat, viewer));
1227:       PetscCall(PetscViewerASCIIPopTab(viewer));
1228:       if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1229:         IS tmp;

1231:         PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1232:         PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1233:         PetscCall(PetscViewerASCIIPushTab(viewer));
1234:         PetscCall(ISView(tmp, viewer));
1235:         PetscCall(PetscViewerASCIIPopTab(viewer));
1236:         PetscCall(ISDestroy(&tmp));
1237:       }
1238:     }
1239:   } else if (issaws) {
1240: #if defined(PETSC_HAVE_SAWS)
1241:     PetscMPIInt rank;

1243:     PetscCall(PetscObjectName((PetscObject)mat));
1244:     PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1245:     if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1246: #endif
1247:   } else if (isstring) {
1248:     const char *type;
1249:     PetscCall(MatGetType(mat, &type));
1250:     PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1251:     PetscTryTypeMethod(mat, view, viewer);
1252:   }
1253:   if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1254:     PetscCall(PetscViewerASCIIPushTab(viewer));
1255:     PetscUseTypeMethod(mat, viewnative, viewer);
1256:     PetscCall(PetscViewerASCIIPopTab(viewer));
1257:   } else if (mat->ops->view) {
1258:     PetscCall(PetscViewerASCIIPushTab(viewer));
1259:     PetscUseTypeMethod(mat, view, viewer);
1260:     PetscCall(PetscViewerASCIIPopTab(viewer));
1261:   }
1262:   if (isascii) {
1263:     PetscCall(PetscViewerGetFormat(viewer, &format));
1264:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1265:   }
1266:   PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1267: #if !defined(PETSC_HAVE_THREADSAFETY)
1268:   insidematview--;
1269: #endif
1270:   PetscFunctionReturn(PETSC_SUCCESS);
1271: }

1273: #if defined(PETSC_USE_DEBUG)
1274: #include <../src/sys/totalview/tv_data_display.h>
1275: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1276: {
1277:   TV_add_row("Local rows", "int", &mat->rmap->n);
1278:   TV_add_row("Local columns", "int", &mat->cmap->n);
1279:   TV_add_row("Global rows", "int", &mat->rmap->N);
1280:   TV_add_row("Global columns", "int", &mat->cmap->N);
1281:   TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1282:   return TV_format_OK;
1283: }
1284: #endif

1286: /*@
1287:   MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1288:   with `MatView()`.  The matrix format is determined from the options database.
1289:   Generates a parallel MPI matrix if the communicator has more than one
1290:   processor.  The default matrix type is `MATAIJ`.

1292:   Collective

1294:   Input Parameters:
1295: + mat    - the newly loaded matrix, this needs to have been created with `MatCreate()`
1296:             or some related function before a call to `MatLoad()`
1297: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer

1299:   Options Database Key:
1300: . -matload_block_size <bs> - set block size

1302:   Level: beginner

1304:   Notes:
1305:   If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1306:   `Mat` before calling this routine if you wish to set it from the options database.

1308:   `MatLoad()` automatically loads into the options database any options
1309:   given in the file filename.info where filename is the name of the file
1310:   that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1311:   file will be ignored if you use the -viewer_binary_skip_info option.

1313:   If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1314:   sets the default matrix type AIJ and sets the local and global sizes.
1315:   If type and/or size is already set, then the same are used.

1317:   In parallel, each processor can load a subset of rows (or the
1318:   entire matrix).  This routine is especially useful when a large
1319:   matrix is stored on disk and only part of it is desired on each
1320:   processor.  For example, a parallel solver may access only some of
1321:   the rows from each processor.  The algorithm used here reads
1322:   relatively small blocks of data rather than reading the entire
1323:   matrix and then subsetting it.

1325:   Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1326:   Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1327:   or the sequence like
1328: .vb
1329:     `PetscViewer` v;
1330:     `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1331:     `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1332:     `PetscViewerSetFromOptions`(v);
1333:     `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1334:     `PetscViewerFileSetName`(v,"datafile");
1335: .ve
1336:   The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1337: .vb
1338:   -viewer_type {binary, hdf5}
1339: .ve

1341:   See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1342:   and src/mat/tutorials/ex10.c with the second approach.

1344:   In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1345:   is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1346:   Multiple objects, both matrices and vectors, can be stored within the same file.
1347:   Their `PetscObject` name is ignored; they are loaded in the order of their storage.

1349:   Most users should not need to know the details of the binary storage
1350:   format, since `MatLoad()` and `MatView()` completely hide these details.
1351:   But for anyone who is interested, the standard binary matrix storage
1352:   format is

1354: .vb
1355:     PetscInt    MAT_FILE_CLASSID
1356:     PetscInt    number of rows
1357:     PetscInt    number of columns
1358:     PetscInt    total number of nonzeros
1359:     PetscInt    *number nonzeros in each row
1360:     PetscInt    *column indices of all nonzeros (starting index is zero)
1361:     PetscScalar *values of all nonzeros
1362: .ve
1363:   If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1364:   stored or loaded (each MPI process part of the matrix must have less than `PETSC_INT_MAX` nonzeros). Since the total nonzero count in this
1365:   case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.

1367:   PETSc automatically does the byte swapping for
1368:   machines that store the bytes reversed. Thus if you write your own binary
1369:   read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1370:   and `PetscBinaryWrite()` to see how this may be done.

1372:   In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1373:   Each processor's chunk is loaded independently by its owning MPI process.
1374:   Multiple objects, both matrices and vectors, can be stored within the same file.
1375:   They are looked up by their PetscObject name.

1377:   As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1378:   by default the same structure and naming of the AIJ arrays and column count
1379:   within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1380: .vb
1381:   save example.mat A b -v7.3
1382: .ve
1383:   can be directly read by this routine (see Reference 1 for details).

1385:   Depending on your MATLAB version, this format might be a default,
1386:   otherwise you can set it as default in Preferences.

1388:   Unless -nocompression flag is used to save the file in MATLAB,
1389:   PETSc must be configured with ZLIB package.

1391:   See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c

1393:   This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`

1395:   Corresponding `MatView()` is not yet implemented.

1397:   The loaded matrix is actually a transpose of the original one in MATLAB,
1398:   unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1399:   With this format, matrix is automatically transposed by PETSc,
1400:   unless the matrix is marked as SPD or symmetric
1401:   (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).

1403:   See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>

1405: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1406:  @*/
1407: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1408: {
1409:   PetscBool flg;

1411:   PetscFunctionBegin;

1415:   if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));

1417:   flg = PETSC_FALSE;
1418:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1419:   if (flg) {
1420:     PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1421:     PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1422:   }
1423:   flg = PETSC_FALSE;
1424:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1425:   if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));

1427:   PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1428:   PetscUseTypeMethod(mat, load, viewer);
1429:   PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1430:   PetscFunctionReturn(PETSC_SUCCESS);
1431: }

1433: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1434: {
1435:   Mat_Redundant *redund = *redundant;

1437:   PetscFunctionBegin;
1438:   if (redund) {
1439:     if (redund->matseq) { /* via MatCreateSubMatrices()  */
1440:       PetscCall(ISDestroy(&redund->isrow));
1441:       PetscCall(ISDestroy(&redund->iscol));
1442:       PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1443:     } else {
1444:       PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1445:       PetscCall(PetscFree(redund->sbuf_j));
1446:       PetscCall(PetscFree(redund->sbuf_a));
1447:       for (PetscInt i = 0; i < redund->nrecvs; i++) {
1448:         PetscCall(PetscFree(redund->rbuf_j[i]));
1449:         PetscCall(PetscFree(redund->rbuf_a[i]));
1450:       }
1451:       PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1452:     }

1454:     if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1455:     PetscCall(PetscFree(redund));
1456:   }
1457:   PetscFunctionReturn(PETSC_SUCCESS);
1458: }

1460: /*@
1461:   MatDestroy - Frees space taken by a matrix.

1463:   Collective

1465:   Input Parameter:
1466: . A - the matrix

1468:   Level: beginner

1470:   Developer Note:
1471:   Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1472:   `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1473:   `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1474:   if changes are needed here.

1476: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1477: @*/
1478: PetscErrorCode MatDestroy(Mat *A)
1479: {
1480:   PetscFunctionBegin;
1481:   if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1483:   if (--((PetscObject)*A)->refct > 0) {
1484:     *A = NULL;
1485:     PetscFunctionReturn(PETSC_SUCCESS);
1486:   }

1488:   /* if memory was published with SAWs then destroy it */
1489:   PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1490:   PetscTryTypeMethod(*A, destroy);

1492:   PetscCall(PetscFree((*A)->factorprefix));
1493:   PetscCall(PetscFree((*A)->defaultvectype));
1494:   PetscCall(PetscFree((*A)->defaultrandtype));
1495:   PetscCall(PetscFree((*A)->bsizes));
1496:   PetscCall(PetscFree((*A)->solvertype));
1497:   for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1498:   if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1499:   PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1500:   PetscCall(MatProductClear(*A));
1501:   PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1502:   PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1503:   PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1504:   PetscCall(MatDestroy(&(*A)->schur));
1505:   PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1506:   PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1507:   PetscCall(PetscHeaderDestroy(A));
1508:   PetscFunctionReturn(PETSC_SUCCESS);
1509: }

1511: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1512: /*@
1513:   MatSetValues - Inserts or adds a block of values into a matrix.
1514:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1515:   MUST be called after all calls to `MatSetValues()` have been completed.

1517:   Not Collective

1519:   Input Parameters:
1520: + mat  - the matrix
1521: . m    - the number of rows
1522: . idxm - the global indices of the rows
1523: . n    - the number of columns
1524: . idxn - the global indices of the columns
1525: . v    - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1526:          See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1527: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1529:   Level: beginner

1531:   Notes:
1532:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1533:   options cannot be mixed without intervening calls to the assembly
1534:   routines.

1536:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1537:   as well as in C.

1539:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1540:   simply ignored. This allows easily inserting element stiffness matrices
1541:   with homogeneous Dirichlet boundary conditions that you don't want represented
1542:   in the matrix.

1544:   Efficiency Alert:
1545:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1546:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1548:   Fortran Notes:
1549:   If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1550: .vb
1551:   call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1552: .ve

1554:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

1556:   Developer Note:
1557:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1558:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1560: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1561:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1562: @*/
1563: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1564: {
1565:   PetscFunctionBeginHot;
1568:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1569:   PetscAssertPointer(idxm, 3);
1570:   PetscAssertPointer(idxn, 5);
1571:   MatCheckPreallocated(mat, 1);

1573:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1574:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");

1576:   if (PetscDefined(USE_DEBUG)) {
1577:     PetscInt i, j;

1579:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1580:     if (v) {
1581:       for (i = 0; i < m; i++) {
1582:         for (j = 0; j < n; j++) {
1583:           if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1584: #if defined(PETSC_USE_COMPLEX)
1585:             SETERRQ(PETSC_COMM_SELF, PETSC_ERR_FP, "Inserting %g+i%g at matrix entry (%" PetscInt_FMT ",%" PetscInt_FMT ")", (double)PetscRealPart(v[i * n + j]), (double)PetscImaginaryPart(v[i * n + j]), idxm[i], idxn[j]);
1586: #else
1587:             SETERRQ(PETSC_COMM_SELF, PETSC_ERR_FP, "Inserting %g at matrix entry (%" PetscInt_FMT ",%" PetscInt_FMT ")", (double)v[i * n + j], idxm[i], idxn[j]);
1588: #endif
1589:         }
1590:       }
1591:     }
1592:     for (i = 0; i < m; i++) PetscCheck(idxm[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot insert in row %" PetscInt_FMT ", maximum is %" PetscInt_FMT, idxm[i], mat->rmap->N - 1);
1593:     for (i = 0; i < n; i++) PetscCheck(idxn[i] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot insert in column %" PetscInt_FMT ", maximum is %" PetscInt_FMT, idxn[i], mat->cmap->N - 1);
1594:   }

1596:   if (mat->assembled) {
1597:     mat->was_assembled = PETSC_TRUE;
1598:     mat->assembled     = PETSC_FALSE;
1599:   }
1600:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1601:   PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1602:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1603:   PetscFunctionReturn(PETSC_SUCCESS);
1604: }

1606: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1607: /*@
1608:   MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1609:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1610:   MUST be called after all calls to `MatSetValues()` have been completed.

1612:   Not Collective

1614:   Input Parameters:
1615: + mat  - the matrix
1616: . ism  - the rows to provide
1617: . isn  - the columns to provide
1618: . v    - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1619:          See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1620: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1622:   Level: beginner

1624:   Notes:
1625:   By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.

1627:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1628:   options cannot be mixed without intervening calls to the assembly
1629:   routines.

1631:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1632:   as well as in C.

1634:   Negative indices may be passed in `ism` and `isn`, these rows and columns are
1635:   simply ignored. This allows easily inserting element stiffness matrices
1636:   with homogeneous Dirichlet boundary conditions that you don't want represented
1637:   in the matrix.

1639:   Fortran Note:
1640:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

1642:   Efficiency Alert:
1643:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1644:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1646:   This is currently not optimized for any particular `ISType`

1648: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1649:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1650: @*/
1651: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1652: {
1653:   PetscInt        m, n;
1654:   const PetscInt *rows, *cols;

1656:   PetscFunctionBeginHot;
1658:   PetscCall(ISGetIndices(ism, &rows));
1659:   PetscCall(ISGetIndices(isn, &cols));
1660:   PetscCall(ISGetLocalSize(ism, &m));
1661:   PetscCall(ISGetLocalSize(isn, &n));
1662:   PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1663:   PetscCall(ISRestoreIndices(ism, &rows));
1664:   PetscCall(ISRestoreIndices(isn, &cols));
1665:   PetscFunctionReturn(PETSC_SUCCESS);
1666: }

1668: /*@
1669:   MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1670:   values into a matrix

1672:   Not Collective

1674:   Input Parameters:
1675: + mat - the matrix
1676: . row - the (block) row to set
1677: - v   - a one-dimensional array that contains the values. For `MATBAIJ` they are implicitly stored as a two-dimensional array, by default in row-major order.
1678:         See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.

1680:   Level: intermediate

1682:   Notes:
1683:   The values, `v`, are column-oriented (for the block version) and sorted

1685:   All the nonzero values in `row` must be provided

1687:   The matrix must have previously had its column indices set, likely by having been assembled.

1689:   `row` must belong to this MPI process

1691:   Fortran Note:
1692:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

1694: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1695:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1696: @*/
1697: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1698: {
1699:   PetscInt globalrow;

1701:   PetscFunctionBegin;
1704:   PetscAssertPointer(v, 3);
1705:   PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1706:   PetscCall(MatSetValuesRow(mat, globalrow, v));
1707:   PetscFunctionReturn(PETSC_SUCCESS);
1708: }

1710: /*@
1711:   MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1712:   values into a matrix

1714:   Not Collective

1716:   Input Parameters:
1717: + mat - the matrix
1718: . row - the (block) row to set
1719: - v   - a logically two-dimensional (column major) array of values for  block matrices with blocksize larger than one, otherwise a one dimensional array of values

1721:   Level: advanced

1723:   Notes:
1724:   The values, `v`, are column-oriented for the block version.

1726:   All the nonzeros in `row` must be provided

1728:   THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.

1730:   `row` must belong to this process

1732: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1733:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1734: @*/
1735: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1736: {
1737:   PetscFunctionBeginHot;
1740:   MatCheckPreallocated(mat, 1);
1741:   PetscAssertPointer(v, 3);
1742:   PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1743:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1744:   mat->insertmode = INSERT_VALUES;

1746:   if (mat->assembled) {
1747:     mat->was_assembled = PETSC_TRUE;
1748:     mat->assembled     = PETSC_FALSE;
1749:   }
1750:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1751:   PetscUseTypeMethod(mat, setvaluesrow, row, v);
1752:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1753:   PetscFunctionReturn(PETSC_SUCCESS);
1754: }

1756: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1757: /*@
1758:   MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1759:   Using structured grid indexing

1761:   Not Collective

1763:   Input Parameters:
1764: + mat  - the matrix
1765: . m    - number of rows being entered
1766: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1767: . n    - number of columns being entered
1768: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1769: . v    - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1770:          See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1771: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values

1773:   Level: beginner

1775:   Notes:
1776:   By default the values, `v`, are row-oriented.  See `MatSetOption()` for other options.

1778:   Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1779:   options cannot be mixed without intervening calls to the assembly
1780:   routines.

1782:   The grid coordinates are across the entire grid, not just the local portion

1784:   `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1785:   as well as in C.

1787:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1789:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1790:   or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1792:   The columns and rows in the stencil passed in MUST be contained within the
1793:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1794:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1795:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1796:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1798:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1799:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1800:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1801:   `DM_BOUNDARY_PERIODIC` boundary type.

1803:   For indices that don't mean anything for your case (like the k index when working in 2d) or the c index when you have
1804:   a single value per point) you can skip filling those indices.

1806:   Inspired by the structured grid interface to the HYPRE package
1807:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1809:   Fortran Note:
1810:   If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

1812:   Efficiency Alert:
1813:   The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1814:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1816: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1817:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1818: @*/
1819: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1820: {
1821:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1822:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1823:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1825:   PetscFunctionBegin;
1826:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1829:   PetscAssertPointer(idxm, 3);
1830:   PetscAssertPointer(idxn, 5);

1832:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1833:     jdxm = buf;
1834:     jdxn = buf + m;
1835:   } else {
1836:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1837:     jdxm = bufm;
1838:     jdxn = bufn;
1839:   }
1840:   for (i = 0; i < m; i++) {
1841:     for (j = 0; j < 3 - sdim; j++) dxm++;
1842:     tmp = *dxm++ - starts[0];
1843:     for (j = 0; j < dim - 1; j++) {
1844:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1845:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1846:     }
1847:     if (mat->stencil.noc) dxm++;
1848:     jdxm[i] = tmp;
1849:   }
1850:   for (i = 0; i < n; i++) {
1851:     for (j = 0; j < 3 - sdim; j++) dxn++;
1852:     tmp = *dxn++ - starts[0];
1853:     for (j = 0; j < dim - 1; j++) {
1854:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1855:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1856:     }
1857:     if (mat->stencil.noc) dxn++;
1858:     jdxn[i] = tmp;
1859:   }
1860:   PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1861:   PetscCall(PetscFree2(bufm, bufn));
1862:   PetscFunctionReturn(PETSC_SUCCESS);
1863: }

1865: /*@
1866:   MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1867:   Using structured grid indexing

1869:   Not Collective

1871:   Input Parameters:
1872: + mat  - the matrix
1873: . m    - number of rows being entered
1874: . idxm - grid coordinates for matrix rows being entered
1875: . n    - number of columns being entered
1876: . idxn - grid coordinates for matrix columns being entered
1877: . v    - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1878:          See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1879: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values

1881:   Level: beginner

1883:   Notes:
1884:   By default the values, `v`, are row-oriented and unsorted.
1885:   See `MatSetOption()` for other options.

1887:   Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1888:   options cannot be mixed without intervening calls to the assembly
1889:   routines.

1891:   The grid coordinates are across the entire grid, not just the local portion

1893:   `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1894:   as well as in C.

1896:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1898:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1899:   or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1901:   The columns and rows in the stencil passed in MUST be contained within the
1902:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1903:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1904:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1905:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1907:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1908:   simply ignored. This allows easily inserting element stiffness matrices
1909:   with homogeneous Dirichlet boundary conditions that you don't want represented
1910:   in the matrix.

1912:   Inspired by the structured grid interface to the HYPRE package
1913:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1915:   Fortran Notes:
1916:   `idxm` and `idxn` should be declared as
1917: .vb
1918:     MatStencil idxm(4,m),idxn(4,n)
1919: .ve
1920:   and the values inserted using
1921: .vb
1922:     idxm(MatStencil_i,1) = i
1923:     idxm(MatStencil_j,1) = j
1924:     idxm(MatStencil_k,1) = k
1925:    etc
1926: .ve

1928:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

1930: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1931:           `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1932:           `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1933: @*/
1934: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1935: {
1936:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1937:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1938:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1940:   PetscFunctionBegin;
1941:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1944:   PetscAssertPointer(idxm, 3);
1945:   PetscAssertPointer(idxn, 5);
1946:   PetscAssertPointer(v, 6);

1948:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1949:     jdxm = buf;
1950:     jdxn = buf + m;
1951:   } else {
1952:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1953:     jdxm = bufm;
1954:     jdxn = bufn;
1955:   }
1956:   for (i = 0; i < m; i++) {
1957:     for (j = 0; j < 3 - sdim; j++) dxm++;
1958:     tmp = *dxm++ - starts[0];
1959:     for (j = 0; j < sdim - 1; j++) {
1960:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1961:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1962:     }
1963:     dxm++;
1964:     jdxm[i] = tmp;
1965:   }
1966:   for (i = 0; i < n; i++) {
1967:     for (j = 0; j < 3 - sdim; j++) dxn++;
1968:     tmp = *dxn++ - starts[0];
1969:     for (j = 0; j < sdim - 1; j++) {
1970:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1971:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1972:     }
1973:     dxn++;
1974:     jdxn[i] = tmp;
1975:   }
1976:   PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1977:   PetscCall(PetscFree2(bufm, bufn));
1978:   PetscFunctionReturn(PETSC_SUCCESS);
1979: }

1981: /*@
1982:   MatSetStencil - Sets the grid information for setting values into a matrix via
1983:   `MatSetValuesStencil()`

1985:   Not Collective

1987:   Input Parameters:
1988: + mat    - the matrix
1989: . dim    - dimension of the grid 1, 2, or 3
1990: . dims   - number of grid points in x, y, and z direction, including ghost points on your processor
1991: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1992: - dof    - number of degrees of freedom per node

1994:   Level: beginner

1996:   Notes:
1997:   Inspired by the structured grid interface to the HYPRE package
1998:   (www.llnl.gov/CASC/hyper)

2000:   For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
2001:   user.

2003: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
2004:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
2005: @*/
2006: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
2007: {
2008:   PetscFunctionBegin;
2010:   PetscAssertPointer(dims, 3);
2011:   PetscAssertPointer(starts, 4);

2013:   mat->stencil.dim = dim + (dof > 1);
2014:   for (PetscInt i = 0; i < dim; i++) {
2015:     mat->stencil.dims[i]   = dims[dim - i - 1]; /* copy the values in backwards */
2016:     mat->stencil.starts[i] = starts[dim - i - 1];
2017:   }
2018:   mat->stencil.dims[dim]   = dof;
2019:   mat->stencil.starts[dim] = 0;
2020:   mat->stencil.noc         = (PetscBool)(dof == 1);
2021:   PetscFunctionReturn(PETSC_SUCCESS);
2022: }

2024: /*@
2025:   MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

2027:   Not Collective

2029:   Input Parameters:
2030: + mat  - the matrix
2031: . m    - the number of block rows
2032: . idxm - the global block indices
2033: . n    - the number of block columns
2034: . idxn - the global block indices
2035: . v    - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2036:          See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2037: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values

2039:   Level: intermediate

2041:   Notes:
2042:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2043:   MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.

2045:   The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2046:   NOT the total number of rows/columns; for example, if the block size is 2 and
2047:   you are passing in values for rows 2,3,4,5  then `m` would be 2 (not 4).
2048:   The values in `idxm` would be 1 2; that is the first index for each block divided by
2049:   the block size.

2051:   You must call `MatSetBlockSize()` when constructing this matrix (before
2052:   preallocating it).

2054:   By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.

2056:   Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2057:   options cannot be mixed without intervening calls to the assembly
2058:   routines.

2060:   `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2061:   as well as in C.

2063:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2064:   simply ignored. This allows easily inserting element stiffness matrices
2065:   with homogeneous Dirichlet boundary conditions that you don't want represented
2066:   in the matrix.

2068:   Each time an entry is set within a sparse matrix via `MatSetValues()`,
2069:   internal searching must be done to determine where to place the
2070:   data in the matrix storage space.  By instead inserting blocks of
2071:   entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2072:   reduced.

2074:   Example:
2075: .vb
2076:    Suppose m=n=2 and block size(bs) = 2 The array is

2078:    1  2  | 3  4
2079:    5  6  | 7  8
2080:    - - - | - - -
2081:    9  10 | 11 12
2082:    13 14 | 15 16

2084:    v[] should be passed in like
2085:    v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]

2087:   If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2088:    v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2089: .ve

2091:   Fortran Notes:
2092:   If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2093: .vb
2094:   call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2095: .ve

2097:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2099: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2100: @*/
2101: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2102: {
2103:   PetscFunctionBeginHot;
2106:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2107:   PetscAssertPointer(idxm, 3);
2108:   PetscAssertPointer(idxn, 5);
2109:   MatCheckPreallocated(mat, 1);
2110:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2111:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2112:   if (PetscDefined(USE_DEBUG)) {
2113:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2114:     PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2115:   }
2116:   if (PetscDefined(USE_DEBUG)) {
2117:     PetscInt rbs, cbs, M, N, i;
2118:     PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2119:     PetscCall(MatGetSize(mat, &M, &N));
2120:     for (i = 0; i < m; i++) PetscCheck(idxm[i] * rbs < M, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row block %" PetscInt_FMT " contains an index %" PetscInt_FMT "*%" PetscInt_FMT " greater than row length %" PetscInt_FMT, i, idxm[i], rbs, M);
2121:     for (i = 0; i < n; i++)
2122:       PetscCheck(idxn[i] * cbs < N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column block %" PetscInt_FMT " contains an index %" PetscInt_FMT "*%" PetscInt_FMT " greater than column length %" PetscInt_FMT, i, idxn[i], cbs, N);
2123:   }
2124:   if (mat->assembled) {
2125:     mat->was_assembled = PETSC_TRUE;
2126:     mat->assembled     = PETSC_FALSE;
2127:   }
2128:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2129:   if (mat->ops->setvaluesblocked) {
2130:     PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2131:   } else {
2132:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2133:     PetscInt i, j, bs, cbs;

2135:     PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2136:     if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2137:       iidxm = buf;
2138:       iidxn = buf + m * bs;
2139:     } else {
2140:       PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2141:       iidxm = bufr;
2142:       iidxn = bufc;
2143:     }
2144:     for (i = 0; i < m; i++) {
2145:       for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2146:     }
2147:     if (m != n || bs != cbs || idxm != idxn) {
2148:       for (i = 0; i < n; i++) {
2149:         for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2150:       }
2151:     } else iidxn = iidxm;
2152:     PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2153:     PetscCall(PetscFree2(bufr, bufc));
2154:   }
2155:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2156:   PetscFunctionReturn(PETSC_SUCCESS);
2157: }

2159: /*@
2160:   MatGetValues - Gets a block of local values from a matrix.

2162:   Not Collective; can only return values that are owned by the give process

2164:   Input Parameters:
2165: + mat  - the matrix
2166: . v    - a logically two-dimensional array for storing the values
2167: . m    - the number of rows
2168: . idxm - the  global indices of the rows
2169: . n    - the number of columns
2170: - idxn - the global indices of the columns

2172:   Level: advanced

2174:   Notes:
2175:   The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2176:   The values, `v`, are then returned in a row-oriented format,
2177:   analogous to that used by default in `MatSetValues()`.

2179:   `MatGetValues()` uses 0-based row and column numbers in
2180:   Fortran as well as in C.

2182:   `MatGetValues()` requires that the matrix has been assembled
2183:   with `MatAssemblyBegin()`/`MatAssemblyEnd()`.  Thus, calls to
2184:   `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2185:   without intermediate matrix assembly.

2187:   Negative row or column indices will be ignored and those locations in `v` will be
2188:   left unchanged.

2190:   For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2191:   That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2192:   from `MatGetOwnershipRange`(mat,&rstart,&rend).

2194: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2195: @*/
2196: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2197: {
2198:   PetscFunctionBegin;
2201:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2202:   PetscAssertPointer(idxm, 3);
2203:   PetscAssertPointer(idxn, 5);
2204:   PetscAssertPointer(v, 6);
2205:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2206:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2207:   MatCheckPreallocated(mat, 1);

2209:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2210:   PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2211:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2212:   PetscFunctionReturn(PETSC_SUCCESS);
2213: }

2215: /*@
2216:   MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2217:   defined previously by `MatSetLocalToGlobalMapping()`

2219:   Not Collective

2221:   Input Parameters:
2222: + mat  - the matrix
2223: . nrow - number of rows
2224: . irow - the row local indices
2225: . ncol - number of columns
2226: - icol - the column local indices

2228:   Output Parameter:
2229: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2230:       See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.

2232:   Level: advanced

2234:   Notes:
2235:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.

2237:   This routine can only return values that are owned by the requesting MPI process. That is, for standard matrix formats, rows that, in the global numbering,
2238:   are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2239:   determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2240:   with `MatSetLocalToGlobalMapping()`.

2242: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2243:           `MatSetValuesLocal()`, `MatGetValues()`
2244: @*/
2245: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2246: {
2247:   PetscFunctionBeginHot;
2250:   MatCheckPreallocated(mat, 1);
2251:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2252:   PetscAssertPointer(irow, 3);
2253:   PetscAssertPointer(icol, 5);
2254:   if (PetscDefined(USE_DEBUG)) {
2255:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2256:     PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2257:   }
2258:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2259:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2260:   if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2261:   else {
2262:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2263:     if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2264:       irowm = buf;
2265:       icolm = buf + nrow;
2266:     } else {
2267:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2268:       irowm = bufr;
2269:       icolm = bufc;
2270:     }
2271:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2272:     PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2273:     PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2274:     PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2275:     PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2276:     PetscCall(PetscFree2(bufr, bufc));
2277:   }
2278:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2279:   PetscFunctionReturn(PETSC_SUCCESS);
2280: }

2282: /*@
2283:   MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2284:   the same size. Currently, this can only be called once and creates the given matrix.

2286:   Not Collective

2288:   Input Parameters:
2289: + mat  - the matrix
2290: . nb   - the number of blocks
2291: . bs   - the number of rows (and columns) in each block
2292: . rows - a concatenation of the rows for each block
2293: - v    - a concatenation of logically two-dimensional arrays of values

2295:   Level: advanced

2297:   Notes:
2298:   `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values

2300:   In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.

2302: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2303:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2304: @*/
2305: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2306: {
2307:   PetscFunctionBegin;
2310:   PetscAssertPointer(rows, 4);
2311:   PetscAssertPointer(v, 5);
2312:   PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

2314:   PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2315:   if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2316:   else {
2317:     for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2318:   }
2319:   PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2320:   PetscFunctionReturn(PETSC_SUCCESS);
2321: }

2323: /*@
2324:   MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2325:   the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2326:   using a local (per-processor) numbering.

2328:   Not Collective

2330:   Input Parameters:
2331: + x        - the matrix
2332: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2333: - cmapping - column mapping

2335:   Level: intermediate

2337:   Note:
2338:   If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix

2340: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2341: @*/
2342: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2343: {
2344:   PetscFunctionBegin;
2349:   if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2350:   else {
2351:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2352:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2353:   }
2354:   PetscFunctionReturn(PETSC_SUCCESS);
2355: }

2357: /*@
2358:   MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`

2360:   Not Collective

2362:   Input Parameter:
2363: . A - the matrix

2365:   Output Parameters:
2366: + rmapping - row mapping
2367: - cmapping - column mapping

2369:   Level: advanced

2371: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2372: @*/
2373: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2374: {
2375:   PetscFunctionBegin;
2378:   if (rmapping) {
2379:     PetscAssertPointer(rmapping, 2);
2380:     *rmapping = A->rmap->mapping;
2381:   }
2382:   if (cmapping) {
2383:     PetscAssertPointer(cmapping, 3);
2384:     *cmapping = A->cmap->mapping;
2385:   }
2386:   PetscFunctionReturn(PETSC_SUCCESS);
2387: }

2389: /*@
2390:   MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix

2392:   Logically Collective

2394:   Input Parameters:
2395: + A    - the matrix
2396: . rmap - row layout
2397: - cmap - column layout

2399:   Level: advanced

2401:   Note:
2402:   The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.

2404: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2405: @*/
2406: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2407: {
2408:   PetscFunctionBegin;
2410:   PetscCall(PetscLayoutReference(rmap, &A->rmap));
2411:   PetscCall(PetscLayoutReference(cmap, &A->cmap));
2412:   PetscFunctionReturn(PETSC_SUCCESS);
2413: }

2415: /*@
2416:   MatGetLayouts - Gets the `PetscLayout` objects for rows and columns

2418:   Not Collective

2420:   Input Parameter:
2421: . A - the matrix

2423:   Output Parameters:
2424: + rmap - row layout
2425: - cmap - column layout

2427:   Level: advanced

2429: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2430: @*/
2431: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2432: {
2433:   PetscFunctionBegin;
2436:   if (rmap) {
2437:     PetscAssertPointer(rmap, 2);
2438:     *rmap = A->rmap;
2439:   }
2440:   if (cmap) {
2441:     PetscAssertPointer(cmap, 3);
2442:     *cmap = A->cmap;
2443:   }
2444:   PetscFunctionReturn(PETSC_SUCCESS);
2445: }

2447: /*@
2448:   MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2449:   using a local numbering of the rows and columns.

2451:   Not Collective

2453:   Input Parameters:
2454: + mat  - the matrix
2455: . nrow - number of rows
2456: . irow - the row local indices
2457: . ncol - number of columns
2458: . icol - the column local indices
2459: . y    - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2460:          See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2461: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2463:   Level: intermediate

2465:   Notes:
2466:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine

2468:   Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2469:   options cannot be mixed without intervening calls to the assembly
2470:   routines.

2472:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2473:   MUST be called after all calls to `MatSetValuesLocal()` have been completed.

2475:   Fortran Notes:
2476:   If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2477: .vb
2478:   call MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2479: .ve

2481:   If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2483: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2484:           `MatGetValuesLocal()`
2485: @*/
2486: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2487: {
2488:   PetscFunctionBeginHot;
2491:   MatCheckPreallocated(mat, 1);
2492:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2493:   PetscAssertPointer(irow, 3);
2494:   PetscAssertPointer(icol, 5);
2495:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2496:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2497:   if (PetscDefined(USE_DEBUG)) {
2498:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2499:     PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2500:   }

2502:   if (mat->assembled) {
2503:     mat->was_assembled = PETSC_TRUE;
2504:     mat->assembled     = PETSC_FALSE;
2505:   }
2506:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2507:   if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2508:   else {
2509:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2510:     const PetscInt *irowm, *icolm;

2512:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2513:       bufr  = buf;
2514:       bufc  = buf + nrow;
2515:       irowm = bufr;
2516:       icolm = bufc;
2517:     } else {
2518:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2519:       irowm = bufr;
2520:       icolm = bufc;
2521:     }
2522:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2523:     else irowm = irow;
2524:     if (mat->cmap->mapping) {
2525:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2526:       else icolm = irowm;
2527:     } else icolm = icol;
2528:     PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2529:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2530:   }
2531:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2532:   PetscFunctionReturn(PETSC_SUCCESS);
2533: }

2535: /*@
2536:   MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2537:   using a local ordering of the nodes a block at a time.

2539:   Not Collective

2541:   Input Parameters:
2542: + mat  - the matrix
2543: . nrow - number of rows
2544: . irow - the row local indices
2545: . ncol - number of columns
2546: . icol - the column local indices
2547: . y    - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2548:          See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2549: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2551:   Level: intermediate

2553:   Notes:
2554:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2555:   before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the

2557:   Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2558:   options cannot be mixed without intervening calls to the assembly
2559:   routines.

2561:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2562:   MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.

2564:   Fortran Notes:
2565:   If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2566: .vb
2567:   call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2568: .ve

2570:   If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2572: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2573:           `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2574: @*/
2575: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2576: {
2577:   PetscFunctionBeginHot;
2580:   MatCheckPreallocated(mat, 1);
2581:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2582:   PetscAssertPointer(irow, 3);
2583:   PetscAssertPointer(icol, 5);
2584:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2585:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2586:   if (PetscDefined(USE_DEBUG)) {
2587:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2588:     PetscCheck(mat->ops->setvaluesblockedlocal || mat->ops->setvaluesblocked || mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2589:   }

2591:   if (mat->assembled) {
2592:     mat->was_assembled = PETSC_TRUE;
2593:     mat->assembled     = PETSC_FALSE;
2594:   }
2595:   if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2596:     PetscInt irbs, rbs;
2597:     PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2598:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2599:     PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2600:   }
2601:   if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2602:     PetscInt icbs, cbs;
2603:     PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2604:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2605:     PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2606:   }
2607:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2608:   if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2609:   else {
2610:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2611:     const PetscInt *irowm, *icolm;

2613:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2614:       bufr  = buf;
2615:       bufc  = buf + nrow;
2616:       irowm = bufr;
2617:       icolm = bufc;
2618:     } else {
2619:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2620:       irowm = bufr;
2621:       icolm = bufc;
2622:     }
2623:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2624:     else irowm = irow;
2625:     if (mat->cmap->mapping) {
2626:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2627:       else icolm = irowm;
2628:     } else icolm = icol;
2629:     PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2630:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2631:   }
2632:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2633:   PetscFunctionReturn(PETSC_SUCCESS);
2634: }

2636: /*@
2637:   MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal

2639:   Collective

2641:   Input Parameters:
2642: + mat - the matrix
2643: - x   - the vector to be multiplied

2645:   Output Parameter:
2646: . y - the result

2648:   Level: developer

2650:   Note:
2651:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2652:   call `MatMultDiagonalBlock`(A,y,y).

2654: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2655: @*/
2656: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2657: {
2658:   PetscFunctionBegin;

2664:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2665:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2666:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2667:   MatCheckPreallocated(mat, 1);

2669:   PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2670:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2671:   PetscFunctionReturn(PETSC_SUCCESS);
2672: }

2674: /*@
2675:   MatMult - Computes the matrix-vector product, $y = Ax$.

2677:   Neighbor-wise Collective

2679:   Input Parameters:
2680: + mat - the matrix
2681: - x   - the vector to be multiplied

2683:   Output Parameter:
2684: . y - the result

2686:   Level: beginner

2688:   Note:
2689:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2690:   call `MatMult`(A,y,y).

2692: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2693: @*/
2694: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2695: {
2696:   PetscFunctionBegin;
2700:   VecCheckAssembled(x);
2702:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2703:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2704:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2705:   PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
2706:   PetscCheck(mat->rmap->N == y->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, y->map->N);
2707:   PetscCheck(mat->cmap->n == x->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->n, x->map->n);
2708:   PetscCheck(mat->rmap->n == y->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, y->map->n);
2709:   PetscCall(VecSetErrorIfLocked(y, 3));
2710:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2711:   MatCheckPreallocated(mat, 1);

2713:   PetscCall(VecLockReadPush(x));
2714:   PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2715:   PetscUseTypeMethod(mat, mult, x, y);
2716:   PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2717:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2718:   PetscCall(VecLockReadPop(x));
2719:   PetscFunctionReturn(PETSC_SUCCESS);
2720: }

2722: /*@
2723:   MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.

2725:   Neighbor-wise Collective

2727:   Input Parameters:
2728: + mat - the matrix
2729: - x   - the vector to be multiplied

2731:   Output Parameter:
2732: . y - the result

2734:   Level: beginner

2736:   Notes:
2737:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2738:   call `MatMultTranspose`(A,y,y).

2740:   For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2741:   use `MatMultHermitianTranspose()`

2743: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2744: @*/
2745: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2746: {
2747:   PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;

2749:   PetscFunctionBegin;
2753:   VecCheckAssembled(x);

2756:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2757:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2758:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2759:   PetscCheck(mat->cmap->N == y->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, y->map->N);
2760:   PetscCheck(mat->rmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, x->map->N);
2761:   PetscCheck(mat->cmap->n == y->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->n, y->map->n);
2762:   PetscCheck(mat->rmap->n == x->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, x->map->n);
2763:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2764:   MatCheckPreallocated(mat, 1);

2766:   if (!mat->ops->multtranspose) {
2767:     if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2768:     PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s does not have a multiply transpose defined or is symmetric and does not have a multiply defined", ((PetscObject)mat)->type_name);
2769:   } else op = mat->ops->multtranspose;
2770:   PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2771:   PetscCall(VecLockReadPush(x));
2772:   PetscCall((*op)(mat, x, y));
2773:   PetscCall(VecLockReadPop(x));
2774:   PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2775:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2776:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2777:   PetscFunctionReturn(PETSC_SUCCESS);
2778: }

2780: /*@
2781:   MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.

2783:   Neighbor-wise Collective

2785:   Input Parameters:
2786: + mat - the matrix
2787: - x   - the vector to be multiplied

2789:   Output Parameter:
2790: . y - the result

2792:   Level: beginner

2794:   Notes:
2795:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2796:   call `MatMultHermitianTranspose`(A,y,y).

2798:   Also called the conjugate transpose, complex conjugate transpose, or adjoint.

2800:   For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.

2802: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2803: @*/
2804: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2805: {
2806:   PetscFunctionBegin;

2812:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2813:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2814:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2815:   PetscCheck(mat->cmap->N == y->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, y->map->N);
2816:   PetscCheck(mat->rmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, x->map->N);
2817:   PetscCheck(mat->cmap->n == y->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->n, y->map->n);
2818:   PetscCheck(mat->rmap->n == x->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, x->map->n);
2819:   MatCheckPreallocated(mat, 1);

2821:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2822: #if defined(PETSC_USE_COMPLEX)
2823:   if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2824:     PetscCall(VecLockReadPush(x));
2825:     if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2826:     else PetscUseTypeMethod(mat, mult, x, y);
2827:     PetscCall(VecLockReadPop(x));
2828:   } else {
2829:     Vec w;
2830:     PetscCall(VecDuplicate(x, &w));
2831:     PetscCall(VecCopy(x, w));
2832:     PetscCall(VecConjugate(w));
2833:     PetscCall(MatMultTranspose(mat, w, y));
2834:     PetscCall(VecDestroy(&w));
2835:     PetscCall(VecConjugate(y));
2836:   }
2837:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2838: #else
2839:   PetscCall(MatMultTranspose(mat, x, y));
2840: #endif
2841:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2842:   PetscFunctionReturn(PETSC_SUCCESS);
2843: }

2845: /*@
2846:   MatMultAdd -  Computes $v3 = v2 + A * v1$.

2848:   Neighbor-wise Collective

2850:   Input Parameters:
2851: + mat - the matrix
2852: . v1  - the vector to be multiplied by `mat`
2853: - v2  - the vector to be added to the result

2855:   Output Parameter:
2856: . v3 - the result

2858:   Level: beginner

2860:   Note:
2861:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2862:   call `MatMultAdd`(A,v1,v2,v1).

2864: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2865: @*/
2866: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2867: {
2868:   PetscFunctionBegin;

2875:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2876:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2877:   PetscCheck(mat->cmap->N == v1->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v1: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v1->map->N);
2878:   /* PetscCheck(mat->rmap->N == v2->map->N,PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %" PetscInt_FMT " %" PetscInt_FMT,mat->rmap->N,v2->map->N);
2879:      PetscCheck(mat->rmap->N == v3->map->N,PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %" PetscInt_FMT " %" PetscInt_FMT,mat->rmap->N,v3->map->N); */
2880:   PetscCheck(mat->rmap->n == v3->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec v3: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, v3->map->n);
2881:   PetscCheck(mat->rmap->n == v2->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec v2: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, v2->map->n);
2882:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2883:   MatCheckPreallocated(mat, 1);

2885:   PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2886:   PetscCall(VecLockReadPush(v1));
2887:   PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2888:   PetscCall(VecLockReadPop(v1));
2889:   PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2890:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2891:   PetscFunctionReturn(PETSC_SUCCESS);
2892: }

2894: /*@
2895:   MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.

2897:   Neighbor-wise Collective

2899:   Input Parameters:
2900: + mat - the matrix
2901: . v1  - the vector to be multiplied by the transpose of the matrix
2902: - v2  - the vector to be added to the result

2904:   Output Parameter:
2905: . v3 - the result

2907:   Level: beginner

2909:   Note:
2910:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2911:   call `MatMultTransposeAdd`(A,v1,v2,v1).

2913: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2914: @*/
2915: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2916: {
2917:   PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;

2919:   PetscFunctionBegin;

2926:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2927:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2928:   PetscCheck(mat->rmap->N == v1->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v1: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, v1->map->N);
2929:   PetscCheck(mat->cmap->N == v2->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v2: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v2->map->N);
2930:   PetscCheck(mat->cmap->N == v3->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v3: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v3->map->N);
2931:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2932:   PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2933:   MatCheckPreallocated(mat, 1);

2935:   PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2936:   PetscCall(VecLockReadPush(v1));
2937:   PetscCall((*op)(mat, v1, v2, v3));
2938:   PetscCall(VecLockReadPop(v1));
2939:   PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2940:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2941:   PetscFunctionReturn(PETSC_SUCCESS);
2942: }

2944: /*@
2945:   MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.

2947:   Neighbor-wise Collective

2949:   Input Parameters:
2950: + mat - the matrix
2951: . v1  - the vector to be multiplied by the Hermitian transpose
2952: - v2  - the vector to be added to the result

2954:   Output Parameter:
2955: . v3 - the result

2957:   Level: beginner

2959:   Note:
2960:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2961:   call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).

2963: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2964: @*/
2965: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2966: {
2967:   PetscFunctionBegin;

2974:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2975:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2976:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2977:   PetscCheck(mat->rmap->N == v1->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v1: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, v1->map->N);
2978:   PetscCheck(mat->cmap->N == v2->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v2: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v2->map->N);
2979:   PetscCheck(mat->cmap->N == v3->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v3: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v3->map->N);
2980:   MatCheckPreallocated(mat, 1);

2982:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2983:   PetscCall(VecLockReadPush(v1));
2984:   if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2985:   else {
2986:     Vec w, z;
2987:     PetscCall(VecDuplicate(v1, &w));
2988:     PetscCall(VecCopy(v1, w));
2989:     PetscCall(VecConjugate(w));
2990:     PetscCall(VecDuplicate(v3, &z));
2991:     PetscCall(MatMultTranspose(mat, w, z));
2992:     PetscCall(VecDestroy(&w));
2993:     PetscCall(VecConjugate(z));
2994:     if (v2 != v3) {
2995:       PetscCall(VecWAXPY(v3, 1.0, v2, z));
2996:     } else {
2997:       PetscCall(VecAXPY(v3, 1.0, z));
2998:     }
2999:     PetscCall(VecDestroy(&z));
3000:   }
3001:   PetscCall(VecLockReadPop(v1));
3002:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
3003:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
3004:   PetscFunctionReturn(PETSC_SUCCESS);
3005: }

3007: /*@
3008:   MatGetFactorType - gets the type of factorization a matrix is

3010:   Not Collective

3012:   Input Parameter:
3013: . mat - the matrix

3015:   Output Parameter:
3016: . t - the type, one of `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`, `MAT_FACTOR_ICC,MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`

3018:   Level: intermediate

3020: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3021:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3022: @*/
3023: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3024: {
3025:   PetscFunctionBegin;
3028:   PetscAssertPointer(t, 2);
3029:   *t = mat->factortype;
3030:   PetscFunctionReturn(PETSC_SUCCESS);
3031: }

3033: /*@
3034:   MatSetFactorType - sets the type of factorization a matrix is

3036:   Logically Collective

3038:   Input Parameters:
3039: + mat - the matrix
3040: - t   - the type, one of `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`, `MAT_FACTOR_ICC,MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`

3042:   Level: intermediate

3044: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3045:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3046: @*/
3047: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3048: {
3049:   PetscFunctionBegin;
3052:   mat->factortype = t;
3053:   PetscFunctionReturn(PETSC_SUCCESS);
3054: }

3056: /*@
3057:   MatGetInfo - Returns information about matrix storage (number of
3058:   nonzeros, memory, etc.).

3060:   Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag

3062:   Input Parameters:
3063: + mat  - the matrix
3064: - flag - flag indicating the type of parameters to be returned (`MAT_LOCAL` - local matrix, `MAT_GLOBAL_MAX` - maximum over all processors, `MAT_GLOBAL_SUM` - sum over all processors)

3066:   Output Parameter:
3067: . info - matrix information context

3069:   Options Database Key:
3070: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`

3072:   Level: intermediate

3074:   Notes:
3075:   The `MatInfo` context contains a variety of matrix data, including
3076:   number of nonzeros allocated and used, number of mallocs during
3077:   matrix assembly, etc.  Additional information for factored matrices
3078:   is provided (such as the fill ratio, number of mallocs during
3079:   factorization, etc.).

3081:   Example:
3082:   See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3083:   data within the `MatInfo` context.  For example,
3084: .vb
3085:       MatInfo info;
3086:       Mat     A;
3087:       double  mal, nz_a, nz_u;

3089:       MatGetInfo(A, MAT_LOCAL, &info);
3090:       mal  = info.mallocs;
3091:       nz_a = info.nz_allocated;
3092: .ve

3094: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3095: @*/
3096: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3097: {
3098:   PetscFunctionBegin;
3101:   PetscAssertPointer(info, 3);
3102:   MatCheckPreallocated(mat, 1);
3103:   PetscUseTypeMethod(mat, getinfo, flag, info);
3104:   PetscFunctionReturn(PETSC_SUCCESS);
3105: }

3107: /*
3108:    This is used by external packages where it is not easy to get the info from the actual
3109:    matrix factorization.
3110: */
3111: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3112: {
3113:   PetscFunctionBegin;
3114:   PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3115:   PetscFunctionReturn(PETSC_SUCCESS);
3116: }

3118: /*@
3119:   MatLUFactor - Performs in-place LU factorization of matrix.

3121:   Collective

3123:   Input Parameters:
3124: + mat  - the matrix
3125: . row  - row permutation
3126: . col  - column permutation
3127: - info - options for factorization, includes
3128: .vb
3129:           fill - expected fill as ratio of original fill.
3130:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3131:                    Run with the option -info to determine an optimal value to use
3132: .ve

3134:   Level: developer

3136:   Notes:
3137:   Most users should employ the `KSP` interface for linear solvers
3138:   instead of working directly with matrix algebra routines such as this.
3139:   See, e.g., `KSPCreate()`.

3141:   This changes the state of the matrix to a factored matrix; it cannot be used
3142:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3144:   This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3145:   when not using `KSP`.

3147:   Fortran Note:
3148:   A valid (non-null) `info` argument must be provided

3150: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3151:           `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3152: @*/
3153: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3154: {
3155:   MatFactorInfo tinfo;

3157:   PetscFunctionBegin;
3161:   if (info) PetscAssertPointer(info, 4);
3163:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3164:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3165:   MatCheckPreallocated(mat, 1);
3166:   if (!info) {
3167:     PetscCall(MatFactorInfoInitialize(&tinfo));
3168:     info = &tinfo;
3169:   }

3171:   PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3172:   PetscUseTypeMethod(mat, lufactor, row, col, info);
3173:   PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3174:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3175:   PetscFunctionReturn(PETSC_SUCCESS);
3176: }

3178: /*@
3179:   MatILUFactor - Performs in-place ILU factorization of matrix.

3181:   Collective

3183:   Input Parameters:
3184: + mat  - the matrix
3185: . row  - row permutation
3186: . col  - column permutation
3187: - info - structure containing
3188: .vb
3189:       levels - number of levels of fill.
3190:       expected fill - as ratio of original fill.
3191:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3192:                 missing diagonal entries)
3193: .ve

3195:   Level: developer

3197:   Notes:
3198:   Most users should employ the `KSP` interface for linear solvers
3199:   instead of working directly with matrix algebra routines such as this.
3200:   See, e.g., `KSPCreate()`.

3202:   Probably really in-place only when level of fill is zero, otherwise allocates
3203:   new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3204:   when not using `KSP`.

3206:   Fortran Note:
3207:   A valid (non-null) `info` argument must be provided

3209: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3210: @*/
3211: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3212: {
3213:   PetscFunctionBegin;
3217:   PetscAssertPointer(info, 4);
3219:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3220:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3221:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3222:   MatCheckPreallocated(mat, 1);

3224:   PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3225:   PetscUseTypeMethod(mat, ilufactor, row, col, info);
3226:   PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3227:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3228:   PetscFunctionReturn(PETSC_SUCCESS);
3229: }

3231: /*@
3232:   MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3233:   Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.

3235:   Collective

3237:   Input Parameters:
3238: + fact - the factor matrix obtained with `MatGetFactor()`
3239: . mat  - the matrix
3240: . row  - the row permutation
3241: . col  - the column permutation
3242: - info - options for factorization, includes
3243: .vb
3244:           fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3245:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3246: .ve

3248:   Level: developer

3250:   Notes:
3251:   See [Matrix Factorization](sec_matfactor) for additional information about factorizations

3253:   Most users should employ the simplified `KSP` interface for linear solvers
3254:   instead of working directly with matrix algebra routines such as this.
3255:   See, e.g., `KSPCreate()`.

3257:   Fortran Note:
3258:   A valid (non-null) `info` argument must be provided

3260: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3261: @*/
3262: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3263: {
3264:   MatFactorInfo tinfo;

3266:   PetscFunctionBegin;
3271:   if (info) PetscAssertPointer(info, 5);
3274:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3275:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3276:   MatCheckPreallocated(mat, 2);
3277:   if (!info) {
3278:     PetscCall(MatFactorInfoInitialize(&tinfo));
3279:     info = &tinfo;
3280:   }

3282:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3283:   PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3284:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3285:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3286:   PetscFunctionReturn(PETSC_SUCCESS);
3287: }

3289: /*@
3290:   MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3291:   Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.

3293:   Collective

3295:   Input Parameters:
3296: + fact - the factor matrix obtained with `MatGetFactor()`
3297: . mat  - the matrix
3298: - info - options for factorization

3300:   Level: developer

3302:   Notes:
3303:   See `MatLUFactor()` for in-place factorization.  See
3304:   `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.

3306:   Most users should employ the `KSP` interface for linear solvers
3307:   instead of working directly with matrix algebra routines such as this.
3308:   See, e.g., `KSPCreate()`.

3310:   Fortran Note:
3311:   A valid (non-null) `info` argument must be provided

3313: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3314: @*/
3315: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3316: {
3317:   MatFactorInfo tinfo;

3319:   PetscFunctionBegin;
3324:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3325:   PetscCheck(mat->rmap->N == (fact)->rmap->N && mat->cmap->N == (fact)->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Mat fact: global dimensions are different %" PetscInt_FMT " should = %" PetscInt_FMT " %" PetscInt_FMT " should = %" PetscInt_FMT,
3326:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3328:   MatCheckPreallocated(mat, 2);
3329:   if (!info) {
3330:     PetscCall(MatFactorInfoInitialize(&tinfo));
3331:     info = &tinfo;
3332:   }

3334:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3335:   else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3336:   PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3337:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3338:   else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3339:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3340:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3341:   PetscFunctionReturn(PETSC_SUCCESS);
3342: }

3344: /*@
3345:   MatCholeskyFactor - Performs in-place Cholesky factorization of a
3346:   symmetric matrix.

3348:   Collective

3350:   Input Parameters:
3351: + mat  - the matrix
3352: . perm - row and column permutations
3353: - info - expected fill as ratio of original fill

3355:   Level: developer

3357:   Notes:
3358:   See `MatLUFactor()` for the nonsymmetric case.  See also `MatGetFactor()`,
3359:   `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.

3361:   Most users should employ the `KSP` interface for linear solvers
3362:   instead of working directly with matrix algebra routines such as this.
3363:   See, e.g., `KSPCreate()`.

3365:   Fortran Note:
3366:   A valid (non-null) `info` argument must be provided

3368: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3369:           `MatGetOrdering()`
3370: @*/
3371: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3372: {
3373:   MatFactorInfo tinfo;

3375:   PetscFunctionBegin;
3378:   if (info) PetscAssertPointer(info, 3);
3380:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3381:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3382:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3383:   MatCheckPreallocated(mat, 1);
3384:   if (!info) {
3385:     PetscCall(MatFactorInfoInitialize(&tinfo));
3386:     info = &tinfo;
3387:   }

3389:   PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3390:   PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3391:   PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3392:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3393:   PetscFunctionReturn(PETSC_SUCCESS);
3394: }

3396: /*@
3397:   MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3398:   of a symmetric matrix.

3400:   Collective

3402:   Input Parameters:
3403: + fact - the factor matrix obtained with `MatGetFactor()`
3404: . mat  - the matrix
3405: . perm - row and column permutations
3406: - info - options for factorization, includes
3407: .vb
3408:           fill - expected fill as ratio of original fill.
3409:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3410:                    Run with the option -info to determine an optimal value to use
3411: .ve

3413:   Level: developer

3415:   Notes:
3416:   See `MatLUFactorSymbolic()` for the nonsymmetric case.  See also
3417:   `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.

3419:   Most users should employ the `KSP` interface for linear solvers
3420:   instead of working directly with matrix algebra routines such as this.
3421:   See, e.g., `KSPCreate()`.

3423:   Fortran Note:
3424:   A valid (non-null) `info` argument must be provided

3426: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3427:           `MatGetOrdering()`
3428: @*/
3429: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3430: {
3431:   MatFactorInfo tinfo;

3433:   PetscFunctionBegin;
3437:   if (info) PetscAssertPointer(info, 4);
3440:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3441:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3442:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3443:   MatCheckPreallocated(mat, 2);
3444:   if (!info) {
3445:     PetscCall(MatFactorInfoInitialize(&tinfo));
3446:     info = &tinfo;
3447:   }

3449:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3450:   PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3451:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3452:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3453:   PetscFunctionReturn(PETSC_SUCCESS);
3454: }

3456: /*@
3457:   MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3458:   of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3459:   `MatCholeskyFactorSymbolic()`.

3461:   Collective

3463:   Input Parameters:
3464: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3465: . mat  - the initial matrix that is to be factored
3466: - info - options for factorization

3468:   Level: developer

3470:   Note:
3471:   Most users should employ the `KSP` interface for linear solvers
3472:   instead of working directly with matrix algebra routines such as this.
3473:   See, e.g., `KSPCreate()`.

3475:   Fortran Note:
3476:   A valid (non-null) `info` argument must be provided

3478: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3479: @*/
3480: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3481: {
3482:   MatFactorInfo tinfo;

3484:   PetscFunctionBegin;
3489:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3490:   PetscCheck(mat->rmap->N == (fact)->rmap->N && mat->cmap->N == (fact)->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Mat fact: global dim %" PetscInt_FMT " should = %" PetscInt_FMT " %" PetscInt_FMT " should = %" PetscInt_FMT,
3491:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3492:   MatCheckPreallocated(mat, 2);
3493:   if (!info) {
3494:     PetscCall(MatFactorInfoInitialize(&tinfo));
3495:     info = &tinfo;
3496:   }

3498:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3499:   else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3500:   PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3501:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3502:   else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3503:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3504:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3505:   PetscFunctionReturn(PETSC_SUCCESS);
3506: }

3508: /*@
3509:   MatQRFactor - Performs in-place QR factorization of matrix.

3511:   Collective

3513:   Input Parameters:
3514: + mat  - the matrix
3515: . col  - column permutation
3516: - info - options for factorization, includes
3517: .vb
3518:           fill - expected fill as ratio of original fill.
3519:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3520:                    Run with the option -info to determine an optimal value to use
3521: .ve

3523:   Level: developer

3525:   Notes:
3526:   Most users should employ the `KSP` interface for linear solvers
3527:   instead of working directly with matrix algebra routines such as this.
3528:   See, e.g., `KSPCreate()`.

3530:   This changes the state of the matrix to a factored matrix; it cannot be used
3531:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3533:   Fortran Note:
3534:   A valid (non-null) `info` argument must be provided

3536: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3537:           `MatSetUnfactored()`
3538: @*/
3539: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3540: {
3541:   PetscFunctionBegin;
3544:   if (info) PetscAssertPointer(info, 3);
3546:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3547:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3548:   MatCheckPreallocated(mat, 1);
3549:   PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3550:   PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3551:   PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3552:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3553:   PetscFunctionReturn(PETSC_SUCCESS);
3554: }

3556: /*@
3557:   MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3558:   Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.

3560:   Collective

3562:   Input Parameters:
3563: + fact - the factor matrix obtained with `MatGetFactor()`
3564: . mat  - the matrix
3565: . col  - column permutation
3566: - info - options for factorization, includes
3567: .vb
3568:           fill - expected fill as ratio of original fill.
3569:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3570:                    Run with the option -info to determine an optimal value to use
3571: .ve

3573:   Level: developer

3575:   Note:
3576:   Most users should employ the `KSP` interface for linear solvers
3577:   instead of working directly with matrix algebra routines such as this.
3578:   See, e.g., `KSPCreate()`.

3580:   Fortran Note:
3581:   A valid (non-null) `info` argument must be provided

3583: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3584: @*/
3585: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3586: {
3587:   MatFactorInfo tinfo;

3589:   PetscFunctionBegin;
3593:   if (info) PetscAssertPointer(info, 4);
3596:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3597:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3598:   MatCheckPreallocated(mat, 2);
3599:   if (!info) {
3600:     PetscCall(MatFactorInfoInitialize(&tinfo));
3601:     info = &tinfo;
3602:   }

3604:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3605:   PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3606:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3607:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3608:   PetscFunctionReturn(PETSC_SUCCESS);
3609: }

3611: /*@
3612:   MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3613:   Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.

3615:   Collective

3617:   Input Parameters:
3618: + fact - the factor matrix obtained with `MatGetFactor()`
3619: . mat  - the matrix
3620: - info - options for factorization

3622:   Level: developer

3624:   Notes:
3625:   See `MatQRFactor()` for in-place factorization.

3627:   Most users should employ the `KSP` interface for linear solvers
3628:   instead of working directly with matrix algebra routines such as this.
3629:   See, e.g., `KSPCreate()`.

3631:   Fortran Note:
3632:   A valid (non-null) `info` argument must be provided

3634: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3635: @*/
3636: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3637: {
3638:   MatFactorInfo tinfo;

3640:   PetscFunctionBegin;
3645:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3646:   PetscCheck(mat->rmap->N == fact->rmap->N && mat->cmap->N == fact->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Mat fact: global dimensions are different %" PetscInt_FMT " should = %" PetscInt_FMT " %" PetscInt_FMT " should = %" PetscInt_FMT,
3647:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3649:   MatCheckPreallocated(mat, 2);
3650:   if (!info) {
3651:     PetscCall(MatFactorInfoInitialize(&tinfo));
3652:     info = &tinfo;
3653:   }

3655:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3656:   else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3657:   PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3658:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3659:   else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3660:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3661:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3662:   PetscFunctionReturn(PETSC_SUCCESS);
3663: }

3665: /*@
3666:   MatSolve - Solves $A x = b$, given a factored matrix.

3668:   Neighbor-wise Collective

3670:   Input Parameters:
3671: + mat - the factored matrix
3672: - b   - the right-hand-side vector

3674:   Output Parameter:
3675: . x - the result vector

3677:   Level: developer

3679:   Notes:
3680:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3681:   call `MatSolve`(A,x,x).

3683:   Most users should employ the `KSP` interface for linear solvers
3684:   instead of working directly with matrix algebra routines such as this.
3685:   See, e.g., `KSPCreate()`.

3687: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3688: @*/
3689: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3690: {
3691:   PetscFunctionBegin;
3696:   PetscCheckSameComm(mat, 1, b, 2);
3697:   PetscCheckSameComm(mat, 1, x, 3);
3698:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3699:   PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
3700:   PetscCheck(mat->rmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, b->map->N);
3701:   PetscCheck(mat->rmap->n == b->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, b->map->n);
3702:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3703:   MatCheckPreallocated(mat, 1);

3705:   PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3706:   PetscCall(VecFlag(x, mat->factorerrortype));
3707:   if (mat->factorerrortype) PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3708:   else PetscUseTypeMethod(mat, solve, b, x);
3709:   PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3710:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3711:   PetscFunctionReturn(PETSC_SUCCESS);
3712: }

3714: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3715: {
3716:   Vec      b, x;
3717:   PetscInt N, i;
3718:   PetscErrorCode (*f)(Mat, Vec, Vec);
3719:   PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;

3721:   PetscFunctionBegin;
3722:   if (A->factorerrortype) {
3723:     PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3724:     PetscCall(MatSetInf(X));
3725:     PetscFunctionReturn(PETSC_SUCCESS);
3726:   }
3727:   f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3728:   PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3729:   PetscCall(MatBoundToCPU(A, &Abound));
3730:   if (!Abound) {
3731:     PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3732:     PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3733:   }
3734: #if PetscDefined(HAVE_CUDA)
3735:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3736:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3737: #elif PetscDefined(HAVE_HIP)
3738:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3739:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3740: #endif
3741:   PetscCall(MatGetSize(B, NULL, &N));
3742:   for (i = 0; i < N; i++) {
3743:     PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3744:     PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3745:     PetscCall((*f)(A, b, x));
3746:     PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3747:     PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3748:   }
3749:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3750:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3751:   PetscFunctionReturn(PETSC_SUCCESS);
3752: }

3754: /*@
3755:   MatMatSolve - Solves $A X = B$, given a factored matrix.

3757:   Neighbor-wise Collective

3759:   Input Parameters:
3760: + A - the factored matrix
3761: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)

3763:   Output Parameter:
3764: . X - the result matrix (dense matrix)

3766:   Level: developer

3768:   Note:
3769:   If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3770:   otherwise, `B` and `X` cannot be the same.

3772: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3773: @*/
3774: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3775: {
3776:   PetscFunctionBegin;
3781:   PetscCheckSameComm(A, 1, B, 2);
3782:   PetscCheckSameComm(A, 1, X, 3);
3783:   PetscCheck(A->cmap->N == X->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat X: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->cmap->N, X->rmap->N);
3784:   PetscCheck(A->rmap->N == B->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat B: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->N, B->rmap->N);
3785:   PetscCheck(X->cmap->N == B->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Solution matrix must have same number of columns as rhs matrix");
3786:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3787:   MatCheckPreallocated(A, 1);

3789:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3790:   if (!A->ops->matsolve) {
3791:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3792:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3793:   } else PetscUseTypeMethod(A, matsolve, B, X);
3794:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3795:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3796:   PetscFunctionReturn(PETSC_SUCCESS);
3797: }

3799: /*@
3800:   MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.

3802:   Neighbor-wise Collective

3804:   Input Parameters:
3805: + A - the factored matrix
3806: - B - the right-hand-side matrix  (`MATDENSE` matrix)

3808:   Output Parameter:
3809: . X - the result matrix (dense matrix)

3811:   Level: developer

3813:   Note:
3814:   The matrices `B` and `X` cannot be the same.  I.e., one cannot
3815:   call `MatMatSolveTranspose`(A,X,X).

3817: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3818: @*/
3819: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3820: {
3821:   PetscFunctionBegin;
3826:   PetscCheckSameComm(A, 1, B, 2);
3827:   PetscCheckSameComm(A, 1, X, 3);
3828:   PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3829:   PetscCheck(A->cmap->N == X->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat X: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->cmap->N, X->rmap->N);
3830:   PetscCheck(A->rmap->N == B->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat B: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->N, B->rmap->N);
3831:   PetscCheck(A->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat A,Mat B: local dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->n, B->rmap->n);
3832:   PetscCheck(X->cmap->N >= B->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Solution matrix must have same number of columns as rhs matrix");
3833:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3834:   MatCheckPreallocated(A, 1);

3836:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3837:   if (!A->ops->matsolvetranspose) {
3838:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3839:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3840:   } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3841:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3842:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3843:   PetscFunctionReturn(PETSC_SUCCESS);
3844: }

3846: /*@
3847:   MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.

3849:   Neighbor-wise Collective

3851:   Input Parameters:
3852: + A  - the factored matrix
3853: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`

3855:   Output Parameter:
3856: . X - the result matrix (dense matrix)

3858:   Level: developer

3860:   Note:
3861:   For MUMPS, it only supports centralized sparse compressed column format on the host processor for right-hand side matrix. User must create `Bt` in sparse compressed row
3862:   format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.

3864: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3865: @*/
3866: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3867: {
3868:   PetscFunctionBegin;
3873:   PetscCheckSameComm(A, 1, Bt, 2);
3874:   PetscCheckSameComm(A, 1, X, 3);

3876:   PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3877:   PetscCheck(A->cmap->N == X->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat X: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->cmap->N, X->rmap->N);
3878:   PetscCheck(A->rmap->N == Bt->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat Bt: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->N, Bt->cmap->N);
3879:   PetscCheck(X->cmap->N >= Bt->rmap->N, PetscObjectComm((PetscObject)X), PETSC_ERR_ARG_SIZ, "Solution matrix must have same number of columns as row number of the rhs matrix");
3880:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3881:   PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3882:   MatCheckPreallocated(A, 1);

3884:   PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3885:   PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3886:   PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3887:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3888:   PetscFunctionReturn(PETSC_SUCCESS);
3889: }

3891: /*@
3892:   MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3893:   $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

3895:   Neighbor-wise Collective

3897:   Input Parameters:
3898: + mat - the factored matrix
3899: - b   - the right-hand-side vector

3901:   Output Parameter:
3902: . x - the result vector

3904:   Level: developer

3906:   Notes:
3907:   `MatSolve()` should be used for most applications, as it performs
3908:   a forward solve followed by a backward solve.

3910:   The vectors `b` and `x` cannot be the same,  i.e., one cannot
3911:   call `MatForwardSolve`(A,x,x).

3913:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3914:   the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3915:   `MatForwardSolve()` solves $U^T*D y = b$, and
3916:   `MatBackwardSolve()` solves $U x = y$.
3917:   Thus they do not provide a symmetric preconditioner.

3919: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3920: @*/
3921: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3922: {
3923:   PetscFunctionBegin;
3928:   PetscCheckSameComm(mat, 1, b, 2);
3929:   PetscCheckSameComm(mat, 1, x, 3);
3930:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3931:   PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
3932:   PetscCheck(mat->rmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, b->map->N);
3933:   PetscCheck(mat->rmap->n == b->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, b->map->n);
3934:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3935:   MatCheckPreallocated(mat, 1);

3937:   PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3938:   PetscUseTypeMethod(mat, forwardsolve, b, x);
3939:   PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3940:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3941:   PetscFunctionReturn(PETSC_SUCCESS);
3942: }

3944: /*@
3945:   MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3946:   $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

3948:   Neighbor-wise Collective

3950:   Input Parameters:
3951: + mat - the factored matrix
3952: - b   - the right-hand-side vector

3954:   Output Parameter:
3955: . x - the result vector

3957:   Level: developer

3959:   Notes:
3960:   `MatSolve()` should be used for most applications, as it performs
3961:   a forward solve followed by a backward solve.

3963:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3964:   call `MatBackwardSolve`(A,x,x).

3966:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3967:   the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3968:   `MatForwardSolve()` solves $U^T*D y = b$, and
3969:   `MatBackwardSolve()` solves $U x = y$.
3970:   Thus they do not provide a symmetric preconditioner.

3972: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3973: @*/
3974: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3975: {
3976:   PetscFunctionBegin;
3981:   PetscCheckSameComm(mat, 1, b, 2);
3982:   PetscCheckSameComm(mat, 1, x, 3);
3983:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3984:   PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
3985:   PetscCheck(mat->rmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, b->map->N);
3986:   PetscCheck(mat->rmap->n == b->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, b->map->n);
3987:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3988:   MatCheckPreallocated(mat, 1);

3990:   PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3991:   PetscUseTypeMethod(mat, backwardsolve, b, x);
3992:   PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3993:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3994:   PetscFunctionReturn(PETSC_SUCCESS);
3995: }

3997: /*@
3998:   MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.

4000:   Neighbor-wise Collective

4002:   Input Parameters:
4003: + mat - the factored matrix
4004: . b   - the right-hand-side vector
4005: - y   - the vector to be added to

4007:   Output Parameter:
4008: . x - the result vector

4010:   Level: developer

4012:   Note:
4013:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4014:   call `MatSolveAdd`(A,x,y,x).

4016: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4017: @*/
4018: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4019: {
4020:   PetscScalar one = 1.0;
4021:   Vec         tmp;

4023:   PetscFunctionBegin;
4029:   PetscCheckSameComm(mat, 1, b, 2);
4030:   PetscCheckSameComm(mat, 1, y, 3);
4031:   PetscCheckSameComm(mat, 1, x, 4);
4032:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4033:   PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
4034:   PetscCheck(mat->rmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, b->map->N);
4035:   PetscCheck(mat->rmap->N == y->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, y->map->N);
4036:   PetscCheck(mat->rmap->n == b->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, b->map->n);
4037:   PetscCheck(x->map->n == y->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Vec x,Vec y: local dim %" PetscInt_FMT " %" PetscInt_FMT, x->map->n, y->map->n);
4038:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4039:   MatCheckPreallocated(mat, 1);

4041:   PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4042:   PetscCall(VecFlag(x, mat->factorerrortype));
4043:   if (mat->factorerrortype) {
4044:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4045:   } else if (mat->ops->solveadd) {
4046:     PetscUseTypeMethod(mat, solveadd, b, y, x);
4047:   } else {
4048:     /* do the solve then the add manually */
4049:     if (x != y) {
4050:       PetscCall(MatSolve(mat, b, x));
4051:       PetscCall(VecAXPY(x, one, y));
4052:     } else {
4053:       PetscCall(VecDuplicate(x, &tmp));
4054:       PetscCall(VecCopy(x, tmp));
4055:       PetscCall(MatSolve(mat, b, x));
4056:       PetscCall(VecAXPY(x, one, tmp));
4057:       PetscCall(VecDestroy(&tmp));
4058:     }
4059:   }
4060:   PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4061:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4062:   PetscFunctionReturn(PETSC_SUCCESS);
4063: }

4065: /*@
4066:   MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.

4068:   Neighbor-wise Collective

4070:   Input Parameters:
4071: + mat - the factored matrix
4072: - b   - the right-hand-side vector

4074:   Output Parameter:
4075: . x - the result vector

4077:   Level: developer

4079:   Notes:
4080:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4081:   call `MatSolveTranspose`(A,x,x).

4083:   Most users should employ the `KSP` interface for linear solvers
4084:   instead of working directly with matrix algebra routines such as this.
4085:   See, e.g., `KSPCreate()`.

4087: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4088: @*/
4089: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4090: {
4091:   PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;

4093:   PetscFunctionBegin;
4098:   PetscCheckSameComm(mat, 1, b, 2);
4099:   PetscCheckSameComm(mat, 1, x, 3);
4100:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4101:   PetscCheck(mat->rmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, x->map->N);
4102:   PetscCheck(mat->cmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, b->map->N);
4103:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4104:   MatCheckPreallocated(mat, 1);
4105:   PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4106:   PetscCall(VecFlag(x, mat->factorerrortype));
4107:   if (mat->factorerrortype) {
4108:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4109:   } else {
4110:     PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4111:     PetscCall((*f)(mat, b, x));
4112:   }
4113:   PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4114:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4115:   PetscFunctionReturn(PETSC_SUCCESS);
4116: }

4118: /*@
4119:   MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4120:   factored matrix.

4122:   Neighbor-wise Collective

4124:   Input Parameters:
4125: + mat - the factored matrix
4126: . b   - the right-hand-side vector
4127: - y   - the vector to be added to

4129:   Output Parameter:
4130: . x - the result vector

4132:   Level: developer

4134:   Note:
4135:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4136:   call `MatSolveTransposeAdd`(A,x,y,x).

4138: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4139: @*/
4140: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4141: {
4142:   PetscScalar one = 1.0;
4143:   Vec         tmp;
4144:   PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;

4146:   PetscFunctionBegin;
4152:   PetscCheckSameComm(mat, 1, b, 2);
4153:   PetscCheckSameComm(mat, 1, y, 3);
4154:   PetscCheckSameComm(mat, 1, x, 4);
4155:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4156:   PetscCheck(mat->rmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, x->map->N);
4157:   PetscCheck(mat->cmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, b->map->N);
4158:   PetscCheck(mat->cmap->N == y->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, y->map->N);
4159:   PetscCheck(x->map->n == y->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Vec x,Vec y: local dim %" PetscInt_FMT " %" PetscInt_FMT, x->map->n, y->map->n);
4160:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4161:   MatCheckPreallocated(mat, 1);

4163:   PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4164:   PetscCall(VecFlag(x, mat->factorerrortype));
4165:   if (mat->factorerrortype) {
4166:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4167:   } else if (f) {
4168:     PetscCall((*f)(mat, b, y, x));
4169:   } else {
4170:     /* do the solve then the add manually */
4171:     if (x != y) {
4172:       PetscCall(MatSolveTranspose(mat, b, x));
4173:       PetscCall(VecAXPY(x, one, y));
4174:     } else {
4175:       PetscCall(VecDuplicate(x, &tmp));
4176:       PetscCall(VecCopy(x, tmp));
4177:       PetscCall(MatSolveTranspose(mat, b, x));
4178:       PetscCall(VecAXPY(x, one, tmp));
4179:       PetscCall(VecDestroy(&tmp));
4180:     }
4181:   }
4182:   PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4183:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4184:   PetscFunctionReturn(PETSC_SUCCESS);
4185: }

4187: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4188: /*@
4189:   MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

4191:   Neighbor-wise Collective

4193:   Input Parameters:
4194: + mat   - the matrix
4195: . b     - the right-hand side
4196: . omega - the relaxation factor
4197: . flag  - flag indicating the type of SOR (see below)
4198: . shift - diagonal shift
4199: . its   - the number of iterations
4200: - lits  - the number of local iterations

4202:   Output Parameter:
4203: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)

4205:   SOR Flags:
4206: +     `SOR_FORWARD_SWEEP` - forward SOR
4207: .     `SOR_BACKWARD_SWEEP` - backward SOR
4208: .     `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4209: .     `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4210: .     `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4211: .     `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4212: .     `SOR_EISENSTAT` - SOR with Eisenstat trick
4213: .     `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4214:   upper/lower triangular part of matrix to
4215:   vector (with omega)
4216: -     `SOR_ZERO_INITIAL_GUESS` - zero initial guess

4218:   Level: developer

4220:   Notes:
4221:   `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4222:   `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4223:   on each processor.

4225:   Application programmers will not generally use `MatSOR()` directly,
4226:   but instead will employ the `KSP`/`PC` interface.

4228:   For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

4230:   Most users should employ the `KSP` interface for linear solvers
4231:   instead of working directly with matrix algebra routines such as this.
4232:   See, e.g., `KSPCreate()`.

4234:   Vectors `x` and `b` CANNOT be the same

4236:   The flags are implemented as bitwise inclusive or operations.
4237:   For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4238:   to specify a zero initial guess for SSOR.

4240:   Developer Note:
4241:   We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes

4243: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4244: @*/
4245: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4246: {
4247:   PetscFunctionBegin;
4252:   PetscCheckSameComm(mat, 1, b, 2);
4253:   PetscCheckSameComm(mat, 1, x, 8);
4254:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4255:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4256:   PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
4257:   PetscCheck(mat->rmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, b->map->N);
4258:   PetscCheck(mat->rmap->n == b->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, b->map->n);
4259:   PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4260:   PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4261:   PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");

4263:   MatCheckPreallocated(mat, 1);
4264:   PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4265:   PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4266:   PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4267:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4268:   PetscFunctionReturn(PETSC_SUCCESS);
4269: }

4271: /*
4272:       Default matrix copy routine.
4273: */
4274: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4275: {
4276:   PetscInt           i, rstart = 0, rend = 0, nz;
4277:   const PetscInt    *cwork;
4278:   const PetscScalar *vwork;

4280:   PetscFunctionBegin;
4281:   if (B->assembled) PetscCall(MatZeroEntries(B));
4282:   if (str == SAME_NONZERO_PATTERN) {
4283:     PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4284:     for (i = rstart; i < rend; i++) {
4285:       PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4286:       PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4287:       PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4288:     }
4289:   } else {
4290:     PetscCall(MatAYPX(B, 0.0, A, str));
4291:   }
4292:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4293:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4294:   PetscFunctionReturn(PETSC_SUCCESS);
4295: }

4297: /*@
4298:   MatCopy - Copies a matrix to another matrix.

4300:   Collective

4302:   Input Parameters:
4303: + A   - the matrix
4304: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`

4306:   Output Parameter:
4307: . B - where the copy is put

4309:   Level: intermediate

4311:   Notes:
4312:   If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.

4314:   `MatCopy()` copies the matrix entries of a matrix to another existing
4315:   matrix (after first zeroing the second matrix).  A related routine is
4316:   `MatConvert()`, which first creates a new matrix and then copies the data.

4318: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4319: @*/
4320: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4321: {
4322:   PetscInt i;

4324:   PetscFunctionBegin;
4329:   PetscCheckSameComm(A, 1, B, 2);
4330:   MatCheckPreallocated(B, 2);
4331:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4332:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4333:   PetscCheck(A->rmap->N == B->rmap->N && A->cmap->N == B->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat B: global dim (%" PetscInt_FMT ",%" PetscInt_FMT ") (%" PetscInt_FMT ",%" PetscInt_FMT ")", A->rmap->N, B->rmap->N,
4334:              A->cmap->N, B->cmap->N);
4335:   MatCheckPreallocated(A, 1);
4336:   if (A == B) PetscFunctionReturn(PETSC_SUCCESS);

4338:   PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4339:   if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4340:   else PetscCall(MatCopy_Basic(A, B, str));

4342:   B->stencil.dim = A->stencil.dim;
4343:   B->stencil.noc = A->stencil.noc;
4344:   for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4345:     B->stencil.dims[i]   = A->stencil.dims[i];
4346:     B->stencil.starts[i] = A->stencil.starts[i];
4347:   }

4349:   PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4350:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4351:   PetscFunctionReturn(PETSC_SUCCESS);
4352: }

4354: /*@
4355:   MatConvert - Converts a matrix to another matrix, either of the same
4356:   or different type.

4358:   Collective

4360:   Input Parameters:
4361: + mat     - the matrix
4362: . newtype - new matrix type.  Use `MATSAME` to create a new matrix of the
4363:             same type as the original matrix.
4364: - reuse   - denotes if the destination matrix is to be created or reused.
4365:             Use `MAT_INPLACE_MATRIX` for inplace conversion (that is when you want the input `Mat` to be changed to contain the matrix in the new format), otherwise use
4366:             `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX` (can only be used after the first call was made with `MAT_INITIAL_MATRIX`, causes the matrix space in M to be reused).

4368:   Output Parameter:
4369: . M - pointer to place new matrix

4371:   Level: intermediate

4373:   Notes:
4374:   `MatConvert()` first creates a new matrix and then copies the data from
4375:   the first matrix.  A related routine is `MatCopy()`, which copies the matrix
4376:   entries of one matrix to another already existing matrix context.

4378:   Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4379:   the MPI communicator of the generated matrix is always the same as the communicator
4380:   of the input matrix.

4382: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4383: @*/
4384: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4385: {
4386:   PetscBool  sametype, issame, flg;
4387:   PetscBool3 issymmetric, ishermitian;
4388:   char       convname[256], mtype[256];
4389:   Mat        B;

4391:   PetscFunctionBegin;
4394:   PetscAssertPointer(M, 4);
4395:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4396:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4397:   MatCheckPreallocated(mat, 1);

4399:   PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4400:   if (flg) newtype = mtype;

4402:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4403:   PetscCall(PetscStrcmp(newtype, "same", &issame));
4404:   PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4405:   if (reuse == MAT_REUSE_MATRIX) {
4407:     PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4408:   }

4410:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4411:     PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4412:     PetscFunctionReturn(PETSC_SUCCESS);
4413:   }

4415:   /* Cache Mat options because some converters use MatHeaderReplace  */
4416:   issymmetric = mat->symmetric;
4417:   ishermitian = mat->hermitian;

4419:   if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4420:     PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4421:     PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4422:   } else {
4423:     PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4424:     const char *prefix[3]                                 = {"seq", "mpi", ""};
4425:     PetscInt    i;
4426:     /*
4427:        Order of precedence:
4428:        0) See if newtype is a superclass of the current matrix.
4429:        1) See if a specialized converter is known to the current matrix.
4430:        2) See if a specialized converter is known to the desired matrix class.
4431:        3) See if a good general converter is registered for the desired class
4432:           (as of 6/27/03 only MATMPIADJ falls into this category).
4433:        4) See if a good general converter is known for the current matrix.
4434:        5) Use a really basic converter.
4435:     */

4437:     /* 0) See if newtype is a superclass of the current matrix.
4438:           i.e mat is mpiaij and newtype is aij */
4439:     for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4440:       PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4441:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4442:       PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4443:       PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4444:       if (flg) {
4445:         if (reuse == MAT_INPLACE_MATRIX) {
4446:           PetscCall(PetscInfo(mat, "Early return\n"));
4447:           PetscFunctionReturn(PETSC_SUCCESS);
4448:         } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4449:           PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4450:           PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4451:           PetscFunctionReturn(PETSC_SUCCESS);
4452:         } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4453:           PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4454:           PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4455:           PetscFunctionReturn(PETSC_SUCCESS);
4456:         }
4457:       }
4458:     }
4459:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
4460:     for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4461:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4462:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4463:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4464:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4465:       PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4466:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4467:       PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4468:       PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4469:       if (conv) goto foundconv;
4470:     }

4472:     /* 2)  See if a specialized converter is known to the desired matrix class. */
4473:     PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4474:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4475:     PetscCall(MatSetType(B, newtype));
4476:     for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4477:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4478:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4479:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4480:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4481:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4482:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4483:       PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4484:       PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4485:       if (conv) {
4486:         PetscCall(MatDestroy(&B));
4487:         goto foundconv;
4488:       }
4489:     }

4491:     /* 3) See if a good general converter is registered for the desired class */
4492:     conv = B->ops->convertfrom;
4493:     PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4494:     PetscCall(MatDestroy(&B));
4495:     if (conv) goto foundconv;

4497:     /* 4) See if a good general converter is known for the current matrix */
4498:     if (mat->ops->convert) conv = mat->ops->convert;
4499:     PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4500:     if (conv) goto foundconv;

4502:     /* 5) Use a really basic converter. */
4503:     PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4504:     conv = MatConvert_Basic;

4506:   foundconv:
4507:     PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4508:     PetscCall((*conv)(mat, newtype, reuse, M));
4509:     if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4510:       /* the block sizes must be same if the mappings are copied over */
4511:       (*M)->rmap->bs = mat->rmap->bs;
4512:       (*M)->cmap->bs = mat->cmap->bs;
4513:       PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4514:       PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4515:       (*M)->rmap->mapping = mat->rmap->mapping;
4516:       (*M)->cmap->mapping = mat->cmap->mapping;
4517:     }
4518:     (*M)->stencil.dim = mat->stencil.dim;
4519:     (*M)->stencil.noc = mat->stencil.noc;
4520:     for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4521:       (*M)->stencil.dims[i]   = mat->stencil.dims[i];
4522:       (*M)->stencil.starts[i] = mat->stencil.starts[i];
4523:     }
4524:     PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4525:   }
4526:   PetscCall(PetscObjectStateIncrease((PetscObject)*M));

4528:   /* Copy Mat options */
4529:   if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4530:   else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4531:   if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4532:   else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4533:   PetscFunctionReturn(PETSC_SUCCESS);
4534: }

4536: /*@
4537:   MatFactorGetSolverType - Returns name of the package providing the factorization routines

4539:   Not Collective

4541:   Input Parameter:
4542: . mat - the matrix, must be a factored matrix

4544:   Output Parameter:
4545: . type - the string name of the package (do not free this string)

4547:   Level: intermediate

4549: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4550: @*/
4551: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4552: {
4553:   PetscErrorCode (*conv)(Mat, MatSolverType *);

4555:   PetscFunctionBegin;
4558:   PetscAssertPointer(type, 2);
4559:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4560:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4561:   if (conv) PetscCall((*conv)(mat, type));
4562:   else *type = MATSOLVERPETSC;
4563:   PetscFunctionReturn(PETSC_SUCCESS);
4564: }

4566: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4567: struct _MatSolverTypeForSpecifcType {
4568:   MatType mtype;
4569:   /* no entry for MAT_FACTOR_NONE */
4570:   PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4571:   MatSolverTypeForSpecifcType next;
4572: };

4574: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4575: struct _MatSolverTypeHolder {
4576:   char                       *name;
4577:   MatSolverTypeForSpecifcType handlers;
4578:   MatSolverTypeHolder         next;
4579: };

4581: static MatSolverTypeHolder MatSolverTypeHolders = NULL;

4583: /*@C
4584:   MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type

4586:   Logically Collective, No Fortran Support

4588:   Input Parameters:
4589: + package      - name of the package, for example `petsc` or `superlu`
4590: . mtype        - the matrix type that works with this package
4591: . ftype        - the type of factorization supported by the package
4592: - createfactor - routine that will create the factored matrix ready to be used

4594:   Level: developer

4596: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4597:   `MatGetFactor()`
4598: @*/
4599: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4600: {
4601:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev = NULL;
4602:   PetscBool                   flg;
4603:   MatSolverTypeForSpecifcType inext, iprev = NULL;

4605:   PetscFunctionBegin;
4606:   PetscCall(MatInitializePackage());
4607:   if (!next) {
4608:     PetscCall(PetscNew(&MatSolverTypeHolders));
4609:     PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4610:     PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4611:     PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4612:     MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4613:     PetscFunctionReturn(PETSC_SUCCESS);
4614:   }
4615:   while (next) {
4616:     PetscCall(PetscStrcasecmp(package, next->name, &flg));
4617:     if (flg) {
4618:       PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4619:       inext = next->handlers;
4620:       while (inext) {
4621:         PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4622:         if (flg) {
4623:           inext->createfactor[(int)ftype - 1] = createfactor;
4624:           PetscFunctionReturn(PETSC_SUCCESS);
4625:         }
4626:         iprev = inext;
4627:         inext = inext->next;
4628:       }
4629:       PetscCall(PetscNew(&iprev->next));
4630:       PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4631:       iprev->next->createfactor[(int)ftype - 1] = createfactor;
4632:       PetscFunctionReturn(PETSC_SUCCESS);
4633:     }
4634:     prev = next;
4635:     next = next->next;
4636:   }
4637:   PetscCall(PetscNew(&prev->next));
4638:   PetscCall(PetscStrallocpy(package, &prev->next->name));
4639:   PetscCall(PetscNew(&prev->next->handlers));
4640:   PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4641:   prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4642:   PetscFunctionReturn(PETSC_SUCCESS);
4643: }

4645: /*@C
4646:   MatSolverTypeGet - Gets the function that creates the factor matrix if it exist

4648:   Input Parameters:
4649: + type  - name of the package, for example `petsc` or `superlu`, if this is 'NULL', then the first result that satisfies the other criteria is returned
4650: . ftype - the type of factorization supported by the type
4651: - mtype - the matrix type that works with this type

4653:   Output Parameters:
4654: + foundtype    - `PETSC_TRUE` if the type was registered
4655: . foundmtype   - `PETSC_TRUE` if the type supports the requested mtype
4656: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found

4658:   Calling sequence of `createfactor`:
4659: + A     - the matrix providing the factor matrix
4660: . ftype - the `MatFactorType` of the factor requested
4661: - B     - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`

4663:   Level: developer

4665:   Note:
4666:   When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4667:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4668:   For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.

4670: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4671:           `MatInitializePackage()`
4672: @*/
4673: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4674: {
4675:   MatSolverTypeHolder         next = MatSolverTypeHolders;
4676:   PetscBool                   flg;
4677:   MatSolverTypeForSpecifcType inext;

4679:   PetscFunctionBegin;
4680:   if (foundtype) *foundtype = PETSC_FALSE;
4681:   if (foundmtype) *foundmtype = PETSC_FALSE;
4682:   if (createfactor) *createfactor = NULL;

4684:   if (type) {
4685:     while (next) {
4686:       PetscCall(PetscStrcasecmp(type, next->name, &flg));
4687:       if (flg) {
4688:         if (foundtype) *foundtype = PETSC_TRUE;
4689:         inext = next->handlers;
4690:         while (inext) {
4691:           PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4692:           if (flg) {
4693:             if (foundmtype) *foundmtype = PETSC_TRUE;
4694:             if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4695:             PetscFunctionReturn(PETSC_SUCCESS);
4696:           }
4697:           inext = inext->next;
4698:         }
4699:       }
4700:       next = next->next;
4701:     }
4702:   } else {
4703:     while (next) {
4704:       inext = next->handlers;
4705:       while (inext) {
4706:         PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4707:         if (flg && inext->createfactor[(int)ftype - 1]) {
4708:           if (foundtype) *foundtype = PETSC_TRUE;
4709:           if (foundmtype) *foundmtype = PETSC_TRUE;
4710:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4711:           PetscFunctionReturn(PETSC_SUCCESS);
4712:         }
4713:         inext = inext->next;
4714:       }
4715:       next = next->next;
4716:     }
4717:     /* try with base classes inext->mtype */
4718:     next = MatSolverTypeHolders;
4719:     while (next) {
4720:       inext = next->handlers;
4721:       while (inext) {
4722:         PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4723:         if (flg && inext->createfactor[(int)ftype - 1]) {
4724:           if (foundtype) *foundtype = PETSC_TRUE;
4725:           if (foundmtype) *foundmtype = PETSC_TRUE;
4726:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4727:           PetscFunctionReturn(PETSC_SUCCESS);
4728:         }
4729:         inext = inext->next;
4730:       }
4731:       next = next->next;
4732:     }
4733:   }
4734:   PetscFunctionReturn(PETSC_SUCCESS);
4735: }

4737: PetscErrorCode MatSolverTypeDestroy(void)
4738: {
4739:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev;
4740:   MatSolverTypeForSpecifcType inext, iprev;

4742:   PetscFunctionBegin;
4743:   while (next) {
4744:     PetscCall(PetscFree(next->name));
4745:     inext = next->handlers;
4746:     while (inext) {
4747:       PetscCall(PetscFree(inext->mtype));
4748:       iprev = inext;
4749:       inext = inext->next;
4750:       PetscCall(PetscFree(iprev));
4751:     }
4752:     prev = next;
4753:     next = next->next;
4754:     PetscCall(PetscFree(prev));
4755:   }
4756:   MatSolverTypeHolders = NULL;
4757:   PetscFunctionReturn(PETSC_SUCCESS);
4758: }

4760: /*@
4761:   MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`

4763:   Logically Collective

4765:   Input Parameter:
4766: . mat - the matrix

4768:   Output Parameter:
4769: . flg - `PETSC_TRUE` if uses the ordering

4771:   Level: developer

4773:   Note:
4774:   Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4775:   packages do not, thus we want to skip generating the ordering when it is not needed or used.

4777: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4778: @*/
4779: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4780: {
4781:   PetscFunctionBegin;
4782:   *flg = mat->canuseordering;
4783:   PetscFunctionReturn(PETSC_SUCCESS);
4784: }

4786: /*@
4787:   MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object

4789:   Logically Collective

4791:   Input Parameters:
4792: + mat   - the matrix obtained with `MatGetFactor()`
4793: - ftype - the factorization type to be used

4795:   Output Parameter:
4796: . otype - the preferred ordering type

4798:   Level: developer

4800: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4801: @*/
4802: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4803: {
4804:   PetscFunctionBegin;
4805:   *otype = mat->preferredordering[ftype];
4806:   PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4807:   PetscFunctionReturn(PETSC_SUCCESS);
4808: }

4810: /*@
4811:   MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()

4813:   Collective

4815:   Input Parameters:
4816: + mat   - the matrix
4817: . type  - name of solver type, for example, `superlu`, `petsc` (to use PETSc's solver if it is available), if this is 'NULL', then the first result that satisfies
4818:           the other criteria is returned
4819: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4821:   Output Parameter:
4822: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.

4824:   Options Database Keys:
4825: + -pc_factor_mat_solver_type <type>    - choose the type at run time. When using `KSP` solvers
4826: . -pc_factor_mat_factor_on_host <bool> - do mat factorization on host (with device matrices). Default is doing it on device
4827: - -pc_factor_mat_solve_on_host <bool>  - do mat solve on host (with device matrices). Default is doing it on device

4829:   Level: intermediate

4831:   Notes:
4832:   The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4833:   types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.

4835:   Users usually access the factorization solvers via `KSP`

4837:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4838:   such as pastix, superlu, mumps etc. PETSc must have been ./configure to use the external solver, using the option --download-package or --with-package-dir

4840:   When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4841:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4842:   For example if one configuration had --download-mumps while a different one had --download-superlu_dist.

4844:   Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4845:   where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4846:   call `MatSetOptionsPrefixFactor()` on the originating matrix or  `MatSetOptionsPrefix()` on the resulting factor matrix.

4848:   Developer Note:
4849:   This should actually be called `MatCreateFactor()` since it creates a new factor object

4851: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4852:           `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4853:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4854: @*/
4855: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4856: {
4857:   PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4858:   PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);

4860:   PetscFunctionBegin;

4864:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4865:   MatCheckPreallocated(mat, 1);

4867:   PetscCall(MatIsShell(mat, &shell));
4868:   if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4869:   if (hasop) {
4870:     PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4871:     PetscFunctionReturn(PETSC_SUCCESS);
4872:   }

4874:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4875:   if (!foundtype) {
4876:     if (type) {
4877:       SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "Could not locate solver type %s for factorization type %s and matrix type %s. Perhaps you must ./configure with --download-%s", type, MatFactorTypes[ftype],
4878:               ((PetscObject)mat)->type_name, type);
4879:     } else {
4880:       SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "Could not locate a solver type for factorization type %s and matrix type %s.", MatFactorTypes[ftype], ((PetscObject)mat)->type_name);
4881:     }
4882:   }
4883:   PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4884:   PetscCheck(conv, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support factorization type %s for matrix type %s", type, MatFactorTypes[ftype], ((PetscObject)mat)->type_name);

4886:   PetscCall((*conv)(mat, ftype, f));
4887:   if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4888:   PetscFunctionReturn(PETSC_SUCCESS);
4889: }

4891: /*@
4892:   MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type

4894:   Not Collective

4896:   Input Parameters:
4897: + mat   - the matrix
4898: . type  - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
4899: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4901:   Output Parameter:
4902: . flg - PETSC_TRUE if the factorization is available

4904:   Level: intermediate

4906:   Notes:
4907:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4908:   such as pastix, superlu, mumps etc.

4910:   PETSc must have been ./configure to use the external solver, using the option --download-package

4912:   Developer Note:
4913:   This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object

4915: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4916:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4917: @*/
4918: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4919: {
4920:   PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);

4922:   PetscFunctionBegin;
4924:   PetscAssertPointer(flg, 4);

4926:   *flg = PETSC_FALSE;
4927:   if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);

4929:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4930:   MatCheckPreallocated(mat, 1);

4932:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4933:   *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4934:   PetscFunctionReturn(PETSC_SUCCESS);
4935: }

4937: /*@
4938:   MatDuplicate - Duplicates a matrix including the non-zero structure.

4940:   Collective

4942:   Input Parameters:
4943: + mat - the matrix
4944: - op  - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4945:         See the manual page for `MatDuplicateOption()` for an explanation of these options.

4947:   Output Parameter:
4948: . M - pointer to place new matrix

4950:   Level: intermediate

4952:   Notes:
4953:   You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.

4955:   If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.

4957:   May be called with an unassembled input `Mat` if `MAT_DO_NOT_COPY_VALUES` is used, in which case the output `Mat` is unassembled as well.

4959:   When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4960:   is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4961:   User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.

4963: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4964: @*/
4965: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4966: {
4967:   Mat         B;
4968:   VecType     vtype;
4969:   PetscInt    i;
4970:   PetscObject dm, container_h, container_d;
4971:   void (*viewf)(void);

4973:   PetscFunctionBegin;
4976:   PetscAssertPointer(M, 3);
4977:   PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4978:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4979:   MatCheckPreallocated(mat, 1);

4981:   PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4982:   PetscUseTypeMethod(mat, duplicate, op, M);
4983:   PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4984:   B = *M;

4986:   PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4987:   if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4988:   PetscCall(MatGetVecType(mat, &vtype));
4989:   PetscCall(MatSetVecType(B, vtype));

4991:   B->stencil.dim = mat->stencil.dim;
4992:   B->stencil.noc = mat->stencil.noc;
4993:   for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4994:     B->stencil.dims[i]   = mat->stencil.dims[i];
4995:     B->stencil.starts[i] = mat->stencil.starts[i];
4996:   }

4998:   B->nooffproczerorows = mat->nooffproczerorows;
4999:   B->nooffprocentries  = mat->nooffprocentries;

5001:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
5002:   if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
5003:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
5004:   if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
5005:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
5006:   if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
5007:   if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
5008:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
5009:   PetscFunctionReturn(PETSC_SUCCESS);
5010: }

5012: /*@
5013:   MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`

5015:   Logically Collective

5017:   Input Parameter:
5018: . mat - the matrix

5020:   Output Parameter:
5021: . v - the diagonal of the matrix

5023:   Level: intermediate

5025:   Note:
5026:   If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5027:   of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5028:   is larger than `ndiag`, the values of the remaining entries are unspecified.

5030:   Currently only correct in parallel for square matrices.

5032: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5033: @*/
5034: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5035: {
5036:   PetscFunctionBegin;
5040:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5041:   MatCheckPreallocated(mat, 1);
5042:   if (PetscDefined(USE_DEBUG)) {
5043:     PetscInt nv, row, col, ndiag;

5045:     PetscCall(VecGetLocalSize(v, &nv));
5046:     PetscCall(MatGetLocalSize(mat, &row, &col));
5047:     ndiag = PetscMin(row, col);
5048:     PetscCheck(nv >= ndiag, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming Mat and Vec. Vec local size %" PetscInt_FMT " < Mat local diagonal length %" PetscInt_FMT, nv, ndiag);
5049:   }

5051:   PetscUseTypeMethod(mat, getdiagonal, v);
5052:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5053:   PetscFunctionReturn(PETSC_SUCCESS);
5054: }

5056: /*@
5057:   MatGetRowMin - Gets the minimum value (of the real part) of each
5058:   row of the matrix

5060:   Logically Collective

5062:   Input Parameter:
5063: . mat - the matrix

5065:   Output Parameters:
5066: + v   - the vector for storing the maximums
5067: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)

5069:   Level: intermediate

5071:   Note:
5072:   The result of this call are the same as if one converted the matrix to dense format
5073:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

5075:   This code is only implemented for a couple of matrix formats.

5077: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5078:           `MatGetRowMax()`
5079: @*/
5080: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5081: {
5082:   PetscFunctionBegin;
5086:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5088:   if (!mat->cmap->N) {
5089:     PetscCall(VecSet(v, PETSC_MAX_REAL));
5090:     if (idx) {
5091:       PetscInt i, m = mat->rmap->n;
5092:       for (i = 0; i < m; i++) idx[i] = -1;
5093:     }
5094:   } else {
5095:     MatCheckPreallocated(mat, 1);
5096:   }
5097:   PetscUseTypeMethod(mat, getrowmin, v, idx);
5098:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5099:   PetscFunctionReturn(PETSC_SUCCESS);
5100: }

5102: /*@
5103:   MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5104:   row of the matrix

5106:   Logically Collective

5108:   Input Parameter:
5109: . mat - the matrix

5111:   Output Parameters:
5112: + v   - the vector for storing the minimums
5113: - idx - the indices of the column found for each row (or `NULL` if not needed)

5115:   Level: intermediate

5117:   Notes:
5118:   if a row is completely empty or has only 0.0 values, then the `idx` value for that
5119:   row is 0 (the first column).

5121:   This code is only implemented for a couple of matrix formats.

5123: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5124: @*/
5125: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5126: {
5127:   PetscFunctionBegin;
5131:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5132:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

5134:   if (!mat->cmap->N) {
5135:     PetscCall(VecSet(v, 0.0));
5136:     if (idx) {
5137:       PetscInt i, m = mat->rmap->n;
5138:       for (i = 0; i < m; i++) idx[i] = -1;
5139:     }
5140:   } else {
5141:     MatCheckPreallocated(mat, 1);
5142:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5143:     PetscUseTypeMethod(mat, getrowminabs, v, idx);
5144:   }
5145:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5146:   PetscFunctionReturn(PETSC_SUCCESS);
5147: }

5149: /*@
5150:   MatGetRowMax - Gets the maximum value (of the real part) of each
5151:   row of the matrix

5153:   Logically Collective

5155:   Input Parameter:
5156: . mat - the matrix

5158:   Output Parameters:
5159: + v   - the vector for storing the maximums
5160: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)

5162:   Level: intermediate

5164:   Notes:
5165:   The result of this call are the same as if one converted the matrix to dense format
5166:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

5168:   This code is only implemented for a couple of matrix formats.

5170: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5171: @*/
5172: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5173: {
5174:   PetscFunctionBegin;
5178:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5180:   if (!mat->cmap->N) {
5181:     PetscCall(VecSet(v, PETSC_MIN_REAL));
5182:     if (idx) {
5183:       PetscInt i, m = mat->rmap->n;
5184:       for (i = 0; i < m; i++) idx[i] = -1;
5185:     }
5186:   } else {
5187:     MatCheckPreallocated(mat, 1);
5188:     PetscUseTypeMethod(mat, getrowmax, v, idx);
5189:   }
5190:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5191:   PetscFunctionReturn(PETSC_SUCCESS);
5192: }

5194: /*@
5195:   MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5196:   row of the matrix

5198:   Logically Collective

5200:   Input Parameter:
5201: . mat - the matrix

5203:   Output Parameters:
5204: + v   - the vector for storing the maximums
5205: - idx - the indices of the column found for each row (or `NULL` if not needed)

5207:   Level: intermediate

5209:   Notes:
5210:   if a row is completely empty or has only 0.0 values, then the `idx` value for that
5211:   row is 0 (the first column).

5213:   This code is only implemented for a couple of matrix formats.

5215: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5216: @*/
5217: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5218: {
5219:   PetscFunctionBegin;
5223:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5225:   if (!mat->cmap->N) {
5226:     PetscCall(VecSet(v, 0.0));
5227:     if (idx) {
5228:       PetscInt i, m = mat->rmap->n;
5229:       for (i = 0; i < m; i++) idx[i] = -1;
5230:     }
5231:   } else {
5232:     MatCheckPreallocated(mat, 1);
5233:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5234:     PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5235:   }
5236:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5237:   PetscFunctionReturn(PETSC_SUCCESS);
5238: }

5240: /*@
5241:   MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix

5243:   Logically Collective

5245:   Input Parameter:
5246: . mat - the matrix

5248:   Output Parameter:
5249: . v - the vector for storing the sum

5251:   Level: intermediate

5253:   This code is only implemented for a couple of matrix formats.

5255: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5256: @*/
5257: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5258: {
5259:   PetscFunctionBegin;
5263:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5265:   if (!mat->cmap->N) {
5266:     PetscCall(VecSet(v, 0.0));
5267:   } else {
5268:     MatCheckPreallocated(mat, 1);
5269:     PetscUseTypeMethod(mat, getrowsumabs, v);
5270:   }
5271:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5272:   PetscFunctionReturn(PETSC_SUCCESS);
5273: }

5275: /*@
5276:   MatGetRowSum - Gets the sum of each row of the matrix

5278:   Logically or Neighborhood Collective

5280:   Input Parameter:
5281: . mat - the matrix

5283:   Output Parameter:
5284: . v - the vector for storing the sum of rows

5286:   Level: intermediate

5288:   Note:
5289:   This code is slow since it is not currently specialized for different formats

5291: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5292: @*/
5293: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5294: {
5295:   Vec ones;

5297:   PetscFunctionBegin;
5301:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5302:   MatCheckPreallocated(mat, 1);
5303:   PetscCall(MatCreateVecs(mat, &ones, NULL));
5304:   PetscCall(VecSet(ones, 1.));
5305:   PetscCall(MatMult(mat, ones, v));
5306:   PetscCall(VecDestroy(&ones));
5307:   PetscFunctionReturn(PETSC_SUCCESS);
5308: }

5310: /*@
5311:   MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5312:   when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)

5314:   Collective

5316:   Input Parameter:
5317: . mat - the matrix to provide the transpose

5319:   Output Parameter:
5320: . B - the matrix to contain the transpose; it MUST have the nonzero structure of the transpose of A or the code will crash or generate incorrect results

5322:   Level: advanced

5324:   Note:
5325:   Normally the use of `MatTranspose`(A, `MAT_REUSE_MATRIX`, &B) requires that `B` was obtained with a call to `MatTranspose`(A, `MAT_INITIAL_MATRIX`, &B). This
5326:   routine allows bypassing that call.

5328: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5329: @*/
5330: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5331: {
5332:   MatParentState *rb = NULL;

5334:   PetscFunctionBegin;
5335:   PetscCall(PetscNew(&rb));
5336:   rb->id    = ((PetscObject)mat)->id;
5337:   rb->state = 0;
5338:   PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5339:   PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5340:   PetscFunctionReturn(PETSC_SUCCESS);
5341: }

5343: /*@
5344:   MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.

5346:   Collective

5348:   Input Parameters:
5349: + mat   - the matrix to transpose
5350: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5352:   Output Parameter:
5353: . B - the transpose of the matrix

5355:   Level: intermediate

5357:   Notes:
5358:   If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`

5360:   `MAT_REUSE_MATRIX` uses the `B` matrix obtained from a previous call to this function with `MAT_INITIAL_MATRIX` to store the transpose. If you already have a matrix to contain the
5361:   transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.

5363:   If the nonzero structure of `mat` changed from the previous call to this function with the same matrices an error will be generated for some matrix types.

5365:   Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5366:   For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.

5368:   If `mat` is unchanged from the last call this function returns immediately without recomputing the result

5370:   If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`

5372: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5373:           `MatTransposeSymbolic()`, `MatCreateTranspose()`
5374: @*/
5375: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5376: {
5377:   PetscContainer  rB = NULL;
5378:   MatParentState *rb = NULL;

5380:   PetscFunctionBegin;
5383:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5384:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5385:   PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5386:   PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5387:   MatCheckPreallocated(mat, 1);
5388:   if (reuse == MAT_REUSE_MATRIX) {
5389:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5390:     PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5391:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5392:     PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5393:     if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5394:   }

5396:   PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5397:   if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5398:     PetscUseTypeMethod(mat, transpose, reuse, B);
5399:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5400:   }
5401:   PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));

5403:   if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5404:   if (reuse != MAT_INPLACE_MATRIX) {
5405:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5406:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5407:     rb->state        = ((PetscObject)mat)->state;
5408:     rb->nonzerostate = mat->nonzerostate;
5409:   }
5410:   PetscFunctionReturn(PETSC_SUCCESS);
5411: }

5413: /*@
5414:   MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.

5416:   Collective

5418:   Input Parameter:
5419: . A - the matrix to transpose

5421:   Output Parameter:
5422: . B - the transpose. This is a complete matrix but the numerical portion is invalid. One can call `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B) to compute the
5423:       numerical portion.

5425:   Level: intermediate

5427:   Note:
5428:   This is not supported for many matrix types, use `MatTranspose()` in those cases

5430: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5431: @*/
5432: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5433: {
5434:   PetscFunctionBegin;
5437:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5438:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5439:   PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5440:   PetscUseTypeMethod(A, transposesymbolic, B);
5441:   PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));

5443:   PetscCall(MatTransposeSetPrecursor(A, *B));
5444:   PetscFunctionReturn(PETSC_SUCCESS);
5445: }

5447: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5448: {
5449:   PetscContainer  rB;
5450:   MatParentState *rb;

5452:   PetscFunctionBegin;
5455:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5456:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5457:   PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5458:   PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5459:   PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5460:   PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5461:   PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5462:   PetscFunctionReturn(PETSC_SUCCESS);
5463: }

5465: /*@
5466:   MatIsTranspose - Test whether a matrix is another one's transpose,
5467:   or its own, in which case it tests symmetry.

5469:   Collective

5471:   Input Parameters:
5472: + A   - the matrix to test
5473: . B   - the matrix to test against, this can equal the first parameter
5474: - tol - tolerance, differences between entries smaller than this are counted as zero

5476:   Output Parameter:
5477: . flg - the result

5479:   Level: intermediate

5481:   Notes:
5482:   The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5483:   test involves parallel copies of the block off-diagonal parts of the matrix.

5485: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5486: @*/
5487: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5488: {
5489:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5491:   PetscFunctionBegin;
5494:   PetscAssertPointer(flg, 4);
5495:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5496:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5497:   *flg = PETSC_FALSE;
5498:   if (f && g) {
5499:     PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5500:     PetscCall((*f)(A, B, tol, flg));
5501:   } else {
5502:     MatType mattype;

5504:     PetscCall(MatGetType(f ? B : A, &mattype));
5505:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5506:   }
5507:   PetscFunctionReturn(PETSC_SUCCESS);
5508: }

5510: /*@
5511:   MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.

5513:   Collective

5515:   Input Parameters:
5516: + mat   - the matrix to transpose and complex conjugate
5517: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5519:   Output Parameter:
5520: . B - the Hermitian transpose

5522:   Level: intermediate

5524: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5525: @*/
5526: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5527: {
5528:   PetscFunctionBegin;
5529:   PetscCall(MatTranspose(mat, reuse, B));
5530: #if defined(PETSC_USE_COMPLEX)
5531:   PetscCall(MatConjugate(*B));
5532: #endif
5533:   PetscFunctionReturn(PETSC_SUCCESS);
5534: }

5536: /*@
5537:   MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

5539:   Collective

5541:   Input Parameters:
5542: + A   - the matrix to test
5543: . B   - the matrix to test against, this can equal the first parameter
5544: - tol - tolerance, differences between entries smaller than this are counted as zero

5546:   Output Parameter:
5547: . flg - the result

5549:   Level: intermediate

5551:   Notes:
5552:   Only available for `MATAIJ` matrices.

5554:   The sequential algorithm
5555:   has a running time of the order of the number of nonzeros; the parallel
5556:   test involves parallel copies of the block off-diagonal parts of the matrix.

5558: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5559: @*/
5560: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5561: {
5562:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5564:   PetscFunctionBegin;
5567:   PetscAssertPointer(flg, 4);
5568:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5569:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5570:   if (f && g) {
5571:     PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5572:     PetscCall((*f)(A, B, tol, flg));
5573:   }
5574:   PetscFunctionReturn(PETSC_SUCCESS);
5575: }

5577: /*@
5578:   MatPermute - Creates a new matrix with rows and columns permuted from the
5579:   original.

5581:   Collective

5583:   Input Parameters:
5584: + mat - the matrix to permute
5585: . row - row permutation, each processor supplies only the permutation for its rows
5586: - col - column permutation, each processor supplies only the permutation for its columns

5588:   Output Parameter:
5589: . B - the permuted matrix

5591:   Level: advanced

5593:   Note:
5594:   The index sets map from row/col of permuted matrix to row/col of original matrix.
5595:   The index sets should be on the same communicator as mat and have the same local sizes.

5597:   Developer Note:
5598:   If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5599:   exploit the fact that row and col are permutations, consider implementing the
5600:   more general `MatCreateSubMatrix()` instead.

5602: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5603: @*/
5604: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5605: {
5606:   PetscFunctionBegin;
5611:   PetscAssertPointer(B, 4);
5612:   PetscCheckSameComm(mat, 1, row, 2);
5613:   if (row != col) PetscCheckSameComm(row, 2, col, 3);
5614:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5615:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5616:   PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5617:   MatCheckPreallocated(mat, 1);

5619:   if (mat->ops->permute) {
5620:     PetscUseTypeMethod(mat, permute, row, col, B);
5621:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5622:   } else {
5623:     PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5624:   }
5625:   PetscFunctionReturn(PETSC_SUCCESS);
5626: }

5628: /*@
5629:   MatEqual - Compares two matrices.

5631:   Collective

5633:   Input Parameters:
5634: + A - the first matrix
5635: - B - the second matrix

5637:   Output Parameter:
5638: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.

5640:   Level: intermediate

5642:   Note:
5643:   If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing
5644:   the results of several matrix-vector product using randomly created vectors, see `MatMultEqual()`.

5646: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5647: @*/
5648: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5649: {
5650:   PetscFunctionBegin;
5655:   PetscAssertPointer(flg, 3);
5656:   PetscCheckSameComm(A, 1, B, 2);
5657:   MatCheckPreallocated(A, 1);
5658:   MatCheckPreallocated(B, 2);
5659:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5660:   PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5661:   PetscCheck(A->rmap->N == B->rmap->N && A->cmap->N == B->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat B: global dim %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->N, B->rmap->N, A->cmap->N,
5662:              B->cmap->N);
5663:   if (A->ops->equal && A->ops->equal == B->ops->equal) {
5664:     PetscUseTypeMethod(A, equal, B, flg);
5665:   } else {
5666:     PetscCall(MatMultEqual(A, B, 10, flg));
5667:   }
5668:   PetscFunctionReturn(PETSC_SUCCESS);
5669: }

5671: /*@
5672:   MatDiagonalScale - Scales a matrix on the left and right by diagonal
5673:   matrices that are stored as vectors.  Either of the two scaling
5674:   matrices can be `NULL`.

5676:   Collective

5678:   Input Parameters:
5679: + mat - the matrix to be scaled
5680: . l   - the left scaling vector (or `NULL`)
5681: - r   - the right scaling vector (or `NULL`)

5683:   Level: intermediate

5685:   Note:
5686:   `MatDiagonalScale()` computes $A = LAR$, where
5687:   L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5688:   The L scales the rows of the matrix, the R scales the columns of the matrix.

5690: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5691: @*/
5692: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5693: {
5694:   PetscFunctionBegin;
5697:   if (l) {
5699:     PetscCheckSameComm(mat, 1, l, 2);
5700:   }
5701:   if (r) {
5703:     PetscCheckSameComm(mat, 1, r, 3);
5704:   }
5705:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5706:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5707:   MatCheckPreallocated(mat, 1);
5708:   if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);

5710:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5711:   PetscUseTypeMethod(mat, diagonalscale, l, r);
5712:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5713:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5714:   if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5715:   PetscFunctionReturn(PETSC_SUCCESS);
5716: }

5718: /*@
5719:   MatScale - Scales all elements of a matrix by a given number.

5721:   Logically Collective

5723:   Input Parameters:
5724: + mat - the matrix to be scaled
5725: - a   - the scaling value

5727:   Level: intermediate

5729: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5730: @*/
5731: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5732: {
5733:   PetscFunctionBegin;
5736:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5737:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5739:   MatCheckPreallocated(mat, 1);

5741:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5742:   if (a != (PetscScalar)1.0) {
5743:     PetscUseTypeMethod(mat, scale, a);
5744:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5745:   }
5746:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5747:   PetscFunctionReturn(PETSC_SUCCESS);
5748: }

5750: /*@
5751:   MatNorm - Calculates various norms of a matrix.

5753:   Collective

5755:   Input Parameters:
5756: + mat  - the matrix
5757: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`

5759:   Output Parameter:
5760: . nrm - the resulting norm

5762:   Level: intermediate

5764: .seealso: [](ch_matrices), `Mat`
5765: @*/
5766: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5767: {
5768:   PetscFunctionBegin;
5771:   PetscAssertPointer(nrm, 3);

5773:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5774:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5775:   MatCheckPreallocated(mat, 1);

5777:   PetscUseTypeMethod(mat, norm, type, nrm);
5778:   PetscFunctionReturn(PETSC_SUCCESS);
5779: }

5781: /*
5782:      This variable is used to prevent counting of MatAssemblyBegin() that
5783:    are called from within a MatAssemblyEnd().
5784: */
5785: static PetscInt MatAssemblyEnd_InUse = 0;
5786: /*@
5787:   MatAssemblyBegin - Begins assembling the matrix.  This routine should
5788:   be called after completing all calls to `MatSetValues()`.

5790:   Collective

5792:   Input Parameters:
5793: + mat  - the matrix
5794: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5796:   Level: beginner

5798:   Notes:
5799:   `MatSetValues()` generally caches the values that belong to other MPI processes.  The matrix is ready to
5800:   use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.

5802:   Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5803:   in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5804:   using the matrix.

5806:   ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5807:   same flag of `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY` for all processes. Thus you CANNOT locally change from `ADD_VALUES` to `INSERT_VALUES`, that is
5808:   a global collective operation requiring all processes that share the matrix.

5810:   Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5811:   out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5812:   before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.

5814: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5815: @*/
5816: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5817: {
5818:   PetscFunctionBegin;
5821:   MatCheckPreallocated(mat, 1);
5822:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5823:   if (mat->assembled) {
5824:     mat->was_assembled = PETSC_TRUE;
5825:     mat->assembled     = PETSC_FALSE;
5826:   }

5828:   if (!MatAssemblyEnd_InUse) {
5829:     PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5830:     PetscTryTypeMethod(mat, assemblybegin, type);
5831:     PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5832:   } else PetscTryTypeMethod(mat, assemblybegin, type);
5833:   PetscFunctionReturn(PETSC_SUCCESS);
5834: }

5836: /*@
5837:   MatAssembled - Indicates if a matrix has been assembled and is ready for
5838:   use; for example, in matrix-vector product.

5840:   Not Collective

5842:   Input Parameter:
5843: . mat - the matrix

5845:   Output Parameter:
5846: . assembled - `PETSC_TRUE` or `PETSC_FALSE`

5848:   Level: advanced

5850: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5851: @*/
5852: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5853: {
5854:   PetscFunctionBegin;
5856:   PetscAssertPointer(assembled, 2);
5857:   *assembled = mat->assembled;
5858:   PetscFunctionReturn(PETSC_SUCCESS);
5859: }

5861: /*@
5862:   MatAssemblyEnd - Completes assembling the matrix.  This routine should
5863:   be called after `MatAssemblyBegin()`.

5865:   Collective

5867:   Input Parameters:
5868: + mat  - the matrix
5869: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5871:   Options Database Keys:
5872: + -mat_view ::ascii_info             - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5873: . -mat_view ::ascii_info_detail      - Prints more detailed info
5874: . -mat_view                          - Prints matrix in ASCII format
5875: . -mat_view ::ascii_matlab           - Prints matrix in MATLAB format
5876: . -mat_view draw                     - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5877: . -display <name>                    - Sets display name (default is host)
5878: . -draw_pause <sec>                  - Sets number of seconds to pause after display
5879: . -mat_view socket                   - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5880: . -viewer_socket_machine <machine>   - Machine to use for socket
5881: . -viewer_socket_port <port>         - Port number to use for socket
5882: - -mat_view binary:filename[:append] - Save matrix to file in binary format

5884:   Level: beginner

5886: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5887: @*/
5888: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5889: {
5890:   static PetscInt inassm = 0;
5891:   PetscBool       flg    = PETSC_FALSE;

5893:   PetscFunctionBegin;

5897:   inassm++;
5898:   MatAssemblyEnd_InUse++;
5899:   if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5900:     PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5901:     PetscTryTypeMethod(mat, assemblyend, type);
5902:     PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5903:   } else PetscTryTypeMethod(mat, assemblyend, type);

5905:   /* Flush assembly is not a true assembly */
5906:   if (type != MAT_FLUSH_ASSEMBLY) {
5907:     if (mat->num_ass) {
5908:       if (!mat->symmetry_eternal) {
5909:         mat->symmetric = PETSC_BOOL3_UNKNOWN;
5910:         mat->hermitian = PETSC_BOOL3_UNKNOWN;
5911:       }
5912:       if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5913:       if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5914:     }
5915:     mat->num_ass++;
5916:     mat->assembled        = PETSC_TRUE;
5917:     mat->ass_nonzerostate = mat->nonzerostate;
5918:   }

5920:   mat->insertmode = NOT_SET_VALUES;
5921:   MatAssemblyEnd_InUse--;
5922:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5923:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5924:     PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));

5926:     if (mat->checksymmetryonassembly) {
5927:       PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5928:       if (flg) {
5929:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5930:       } else {
5931:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5932:       }
5933:     }
5934:     if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5935:   }
5936:   inassm--;
5937:   PetscFunctionReturn(PETSC_SUCCESS);
5938: }

5940: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5941: /*@
5942:   MatSetOption - Sets a parameter option for a matrix. Some options
5943:   may be specific to certain storage formats.  Some options
5944:   determine how values will be inserted (or added). Sorted,
5945:   row-oriented input will generally assemble the fastest. The default
5946:   is row-oriented.

5948:   Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`

5950:   Input Parameters:
5951: + mat - the matrix
5952: . op  - the option, one of those listed below (and possibly others),
5953: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

5955:   Options Describing Matrix Structure:
5956: + `MAT_SPD`                         - symmetric positive definite
5957: . `MAT_SYMMETRIC`                   - symmetric in terms of both structure and value
5958: . `MAT_HERMITIAN`                   - transpose is the complex conjugation
5959: . `MAT_STRUCTURALLY_SYMMETRIC`      - symmetric nonzero structure
5960: . `MAT_SYMMETRY_ETERNAL`            - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5961: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5962: . `MAT_SPD_ETERNAL`                 - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix

5964:    These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5965:    do not need to be computed (usually at a high cost)

5967:    Options For Use with `MatSetValues()`:
5968:    Insert a logically dense subblock, which can be
5969: . `MAT_ROW_ORIENTED`                - row-oriented (default)

5971:    These options reflect the data you pass in with `MatSetValues()`; it has
5972:    nothing to do with how the data is stored internally in the matrix
5973:    data structure.

5975:    When (re)assembling a matrix, we can restrict the input for
5976:    efficiency/debugging purposes.  These options include
5977: . `MAT_NEW_NONZERO_LOCATIONS`       - additional insertions will be allowed if they generate a new nonzero (slow)
5978: . `MAT_FORCE_DIAGONAL_ENTRIES`      - forces diagonal entries to be allocated
5979: . `MAT_IGNORE_OFF_PROC_ENTRIES`     - drops off-processor entries
5980: . `MAT_NEW_NONZERO_LOCATION_ERR`    - generates an error for new matrix entry
5981: . `MAT_USE_HASH_TABLE`              - uses a hash table to speed up matrix assembly
5982: . `MAT_NO_OFF_PROC_ENTRIES`         - you know each process will only set values for its own rows, will generate an error if
5983:         any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5984:         performance for very large process counts.
5985: - `MAT_SUBSET_OFF_PROC_ENTRIES`     - you know that the first assembly after setting this flag will set a superset
5986:         of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5987:         functions, instead sending only neighbor messages.

5989:   Level: intermediate

5991:   Notes:
5992:   Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and  `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!

5994:   Some options are relevant only for particular matrix types and
5995:   are thus ignored by others.  Other options are not supported by
5996:   certain matrix types and will generate an error message if set.

5998:   If using Fortran to compute a matrix, one may need to
5999:   use the column-oriented option (or convert to the row-oriented
6000:   format).

6002:   `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
6003:   that would generate a new entry in the nonzero structure is instead
6004:   ignored.  Thus, if memory has not already been allocated for this particular
6005:   data, then the insertion is ignored. For dense matrices, in which
6006:   the entire array is allocated, no entries are ever ignored.
6007:   Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

6009:   `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
6010:   that would generate a new entry in the nonzero structure instead produces
6011:   an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats only.) If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

6013:   `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6014:   that would generate a new entry that has not been preallocated will
6015:   instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6016:   only.) This is a useful flag when debugging matrix memory preallocation.
6017:   If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

6019:   `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6020:   other processors should be dropped, rather than stashed.
6021:   This is useful if you know that the "owning" processor is also
6022:   always generating the correct matrix entries, so that PETSc need
6023:   not transfer duplicate entries generated on another processor.

6025:   `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6026:   searches during matrix assembly. When this flag is set, the hash table
6027:   is created during the first matrix assembly. This hash table is
6028:   used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6029:   to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6030:   should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6031:   supported by `MATMPIBAIJ` format only.

6033:   `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6034:   are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`

6036:   `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6037:   a zero location in the matrix

6039:   `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types

6041:   `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6042:   zero row routines and thus improves performance for very large process counts.

6044:   `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6045:   part of the matrix (since they should match the upper triangular part).

6047:   `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6048:   single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6049:   with finite difference schemes with non-periodic boundary conditions.

6051:   Developer Note:
6052:   `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6053:   places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6054:   to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6055:   not changed.

6057: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6058: @*/
6059: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6060: {
6061:   PetscFunctionBegin;
6063:   if (op > 0) {
6066:   }

6068:   PetscCheck(((int)op) > MAT_OPTION_MIN && ((int)op) < MAT_OPTION_MAX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Options %d is out of range", (int)op);

6070:   switch (op) {
6071:   case MAT_FORCE_DIAGONAL_ENTRIES:
6072:     mat->force_diagonals = flg;
6073:     PetscFunctionReturn(PETSC_SUCCESS);
6074:   case MAT_NO_OFF_PROC_ENTRIES:
6075:     mat->nooffprocentries = flg;
6076:     PetscFunctionReturn(PETSC_SUCCESS);
6077:   case MAT_SUBSET_OFF_PROC_ENTRIES:
6078:     mat->assembly_subset = flg;
6079:     if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6080: #if !defined(PETSC_HAVE_MPIUNI)
6081:       PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6082: #endif
6083:       mat->stash.first_assembly_done = PETSC_FALSE;
6084:     }
6085:     PetscFunctionReturn(PETSC_SUCCESS);
6086:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6087:     mat->nooffproczerorows = flg;
6088:     PetscFunctionReturn(PETSC_SUCCESS);
6089:   case MAT_SPD:
6090:     if (flg) {
6091:       mat->spd                    = PETSC_BOOL3_TRUE;
6092:       mat->symmetric              = PETSC_BOOL3_TRUE;
6093:       mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6094:     } else {
6095:       mat->spd = PETSC_BOOL3_FALSE;
6096:     }
6097:     break;
6098:   case MAT_SYMMETRIC:
6099:     mat->symmetric = PetscBoolToBool3(flg);
6100:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6101: #if !defined(PETSC_USE_COMPLEX)
6102:     mat->hermitian = PetscBoolToBool3(flg);
6103: #endif
6104:     break;
6105:   case MAT_HERMITIAN:
6106:     mat->hermitian = PetscBoolToBool3(flg);
6107:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6108: #if !defined(PETSC_USE_COMPLEX)
6109:     mat->symmetric = PetscBoolToBool3(flg);
6110: #endif
6111:     break;
6112:   case MAT_STRUCTURALLY_SYMMETRIC:
6113:     mat->structurally_symmetric = PetscBoolToBool3(flg);
6114:     break;
6115:   case MAT_SYMMETRY_ETERNAL:
6116:     PetscCheck(mat->symmetric != PETSC_BOOL3_UNKNOWN, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot set MAT_SYMMETRY_ETERNAL without first setting MAT_SYMMETRIC to true or false");
6117:     mat->symmetry_eternal = flg;
6118:     if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6119:     break;
6120:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6121:     PetscCheck(mat->structurally_symmetric != PETSC_BOOL3_UNKNOWN, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot set MAT_STRUCTURAL_SYMMETRY_ETERNAL without first setting MAT_STRUCTURALLY_SYMMETRIC to true or false");
6122:     mat->structural_symmetry_eternal = flg;
6123:     break;
6124:   case MAT_SPD_ETERNAL:
6125:     PetscCheck(mat->spd != PETSC_BOOL3_UNKNOWN, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot set MAT_SPD_ETERNAL without first setting MAT_SPD to true or false");
6126:     mat->spd_eternal = flg;
6127:     if (flg) {
6128:       mat->structural_symmetry_eternal = PETSC_TRUE;
6129:       mat->symmetry_eternal            = PETSC_TRUE;
6130:     }
6131:     break;
6132:   case MAT_STRUCTURE_ONLY:
6133:     mat->structure_only = flg;
6134:     break;
6135:   case MAT_SORTED_FULL:
6136:     mat->sortedfull = flg;
6137:     break;
6138:   default:
6139:     break;
6140:   }
6141:   PetscTryTypeMethod(mat, setoption, op, flg);
6142:   PetscFunctionReturn(PETSC_SUCCESS);
6143: }

6145: /*@
6146:   MatGetOption - Gets a parameter option that has been set for a matrix.

6148:   Logically Collective

6150:   Input Parameters:
6151: + mat - the matrix
6152: - op  - the option, this only responds to certain options, check the code for which ones

6154:   Output Parameter:
6155: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

6157:   Level: intermediate

6159:   Notes:
6160:   Can only be called after `MatSetSizes()` and `MatSetType()` have been set.

6162:   Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6163:   `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`

6165: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6166:     `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6167: @*/
6168: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6169: {
6170:   PetscFunctionBegin;

6174:   PetscCheck(((int)op) > MAT_OPTION_MIN && ((int)op) < MAT_OPTION_MAX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Options %d is out of range", (int)op);
6175:   PetscCheck(((PetscObject)mat)->type_name, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_TYPENOTSET, "Cannot get options until type and size have been set, see MatSetType() and MatSetSizes()");

6177:   switch (op) {
6178:   case MAT_NO_OFF_PROC_ENTRIES:
6179:     *flg = mat->nooffprocentries;
6180:     break;
6181:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6182:     *flg = mat->nooffproczerorows;
6183:     break;
6184:   case MAT_SYMMETRIC:
6185:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6186:     break;
6187:   case MAT_HERMITIAN:
6188:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6189:     break;
6190:   case MAT_STRUCTURALLY_SYMMETRIC:
6191:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6192:     break;
6193:   case MAT_SPD:
6194:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6195:     break;
6196:   case MAT_SYMMETRY_ETERNAL:
6197:     *flg = mat->symmetry_eternal;
6198:     break;
6199:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6200:     *flg = mat->symmetry_eternal;
6201:     break;
6202:   default:
6203:     break;
6204:   }
6205:   PetscFunctionReturn(PETSC_SUCCESS);
6206: }

6208: /*@
6209:   MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
6210:   this routine retains the old nonzero structure.

6212:   Logically Collective

6214:   Input Parameter:
6215: . mat - the matrix

6217:   Level: intermediate

6219:   Note:
6220:   If the matrix was not preallocated then a default, likely poor preallocation will be set in the matrix, so this should be called after the preallocation phase.
6221:   See the Performance chapter of the users manual for information on preallocating matrices.

6223: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6224: @*/
6225: PetscErrorCode MatZeroEntries(Mat mat)
6226: {
6227:   PetscFunctionBegin;
6230:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6231:   PetscCheck(mat->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for matrices where you have set values but not yet assembled");
6232:   MatCheckPreallocated(mat, 1);

6234:   PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6235:   PetscUseTypeMethod(mat, zeroentries);
6236:   PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6237:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6238:   PetscFunctionReturn(PETSC_SUCCESS);
6239: }

6241: /*@
6242:   MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6243:   of a set of rows and columns of a matrix.

6245:   Collective

6247:   Input Parameters:
6248: + mat     - the matrix
6249: . numRows - the number of rows/columns to zero
6250: . rows    - the global row indices
6251: . diag    - value put in the diagonal of the eliminated rows
6252: . x       - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6253: - b       - optional vector of the right-hand side, that will be adjusted by provided solution entries

6255:   Level: intermediate

6257:   Notes:
6258:   This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6260:   For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6261:   The other entries of `b` will be adjusted by the known values of `x` times the corresponding matrix entries in the columns that are being eliminated

6263:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6264:   Krylov method to take advantage of the known solution on the zeroed rows.

6266:   For the parallel case, all processes that share the matrix (i.e.,
6267:   those in the communicator used for matrix creation) MUST call this
6268:   routine, regardless of whether any rows being zeroed are owned by
6269:   them.

6271:   Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6272:   removes them from the nonzero pattern. The nonzero pattern of the matrix can still change if a nonzero needs to be inserted on a diagonal entry that was previously
6273:   missing.

6275:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6276:   list only rows local to itself).

6278:   The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.

6280: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6281:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6282: @*/
6283: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6284: {
6285:   PetscFunctionBegin;
6288:   if (numRows) PetscAssertPointer(rows, 3);
6289:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6290:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6291:   MatCheckPreallocated(mat, 1);

6293:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6294:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6295:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6296:   PetscFunctionReturn(PETSC_SUCCESS);
6297: }

6299: /*@
6300:   MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6301:   of a set of rows and columns of a matrix.

6303:   Collective

6305:   Input Parameters:
6306: + mat  - the matrix
6307: . is   - the rows to zero
6308: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6309: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6310: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6312:   Level: intermediate

6314:   Note:
6315:   See `MatZeroRowsColumns()` for details on how this routine operates.

6317: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6318:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6319: @*/
6320: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6321: {
6322:   PetscInt        numRows;
6323:   const PetscInt *rows;

6325:   PetscFunctionBegin;
6330:   PetscCall(ISGetLocalSize(is, &numRows));
6331:   PetscCall(ISGetIndices(is, &rows));
6332:   PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6333:   PetscCall(ISRestoreIndices(is, &rows));
6334:   PetscFunctionReturn(PETSC_SUCCESS);
6335: }

6337: /*@
6338:   MatZeroRows - Zeros all entries (except possibly the main diagonal)
6339:   of a set of rows of a matrix.

6341:   Collective

6343:   Input Parameters:
6344: + mat     - the matrix
6345: . numRows - the number of rows to zero
6346: . rows    - the global row indices
6347: . diag    - value put in the diagonal of the zeroed rows
6348: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6349: - b       - optional vector of right-hand side, that will be adjusted by provided solution entries

6351:   Level: intermediate

6353:   Notes:
6354:   This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6356:   For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.

6358:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6359:   Krylov method to take advantage of the known solution on the zeroed rows.

6361:   May be followed by using a `PC` of type `PCREDISTRIBUTE` to solve the reduced problem (`PCDISTRIBUTE` completely eliminates the zeroed rows and their corresponding columns)
6362:   from the matrix.

6364:   Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6365:   but does not release memory.  Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6366:   formats this does not alter the nonzero structure.

6368:   If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6369:   of the matrix is not changed the values are
6370:   merely zeroed.

6372:   The user can set a value in the diagonal entry (or for the `MATAIJ` format
6373:   formats can optionally remove the main diagonal entry from the
6374:   nonzero structure as well, by passing 0.0 as the final argument).

6376:   For the parallel case, all processes that share the matrix (i.e.,
6377:   those in the communicator used for matrix creation) MUST call this
6378:   routine, regardless of whether any rows being zeroed are owned by
6379:   them.

6381:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6382:   list only rows local to itself).

6384:   You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6385:   owns that are to be zeroed. This saves a global synchronization in the implementation.

6387: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6388:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6389: @*/
6390: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6391: {
6392:   PetscFunctionBegin;
6395:   if (numRows) PetscAssertPointer(rows, 3);
6396:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6397:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6398:   MatCheckPreallocated(mat, 1);

6400:   PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6401:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6402:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6403:   PetscFunctionReturn(PETSC_SUCCESS);
6404: }

6406: /*@
6407:   MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6408:   of a set of rows of a matrix indicated by an `IS`

6410:   Collective

6412:   Input Parameters:
6413: + mat  - the matrix
6414: . is   - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6415: . diag - value put in all diagonals of eliminated rows
6416: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6417: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6419:   Level: intermediate

6421:   Note:
6422:   See `MatZeroRows()` for details on how this routine operates.

6424: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6425:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6426: @*/
6427: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6428: {
6429:   PetscInt        numRows = 0;
6430:   const PetscInt *rows    = NULL;

6432:   PetscFunctionBegin;
6435:   if (is) {
6437:     PetscCall(ISGetLocalSize(is, &numRows));
6438:     PetscCall(ISGetIndices(is, &rows));
6439:   }
6440:   PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6441:   if (is) PetscCall(ISRestoreIndices(is, &rows));
6442:   PetscFunctionReturn(PETSC_SUCCESS);
6443: }

6445: /*@
6446:   MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6447:   of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.

6449:   Collective

6451:   Input Parameters:
6452: + mat     - the matrix
6453: . numRows - the number of rows to remove
6454: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6455: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6456: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6457: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6459:   Level: intermediate

6461:   Notes:
6462:   See `MatZeroRows()` for details on how this routine operates.

6464:   The grid coordinates are across the entire grid, not just the local portion

6466:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6467:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6468:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6469:   `DM_BOUNDARY_PERIODIC` boundary type.

6471:   For indices that don't mean anything for your case (like the `k` index when working in 2d) or the `c` index when you have
6472:   a single value per point) you can skip filling those indices.

6474:   Fortran Note:
6475:   `idxm` and `idxn` should be declared as
6476: .vb
6477:     MatStencil idxm(4, m)
6478: .ve
6479:   and the values inserted using
6480: .vb
6481:     idxm(MatStencil_i, 1) = i
6482:     idxm(MatStencil_j, 1) = j
6483:     idxm(MatStencil_k, 1) = k
6484:     idxm(MatStencil_c, 1) = c
6485:    etc
6486: .ve

6488: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6489:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6490: @*/
6491: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6492: {
6493:   PetscInt  dim    = mat->stencil.dim;
6494:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6495:   PetscInt *dims   = mat->stencil.dims + 1;
6496:   PetscInt *starts = mat->stencil.starts;
6497:   PetscInt *dxm    = (PetscInt *)rows;
6498:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6500:   PetscFunctionBegin;
6503:   if (numRows) PetscAssertPointer(rows, 3);

6505:   PetscCall(PetscMalloc1(numRows, &jdxm));
6506:   for (i = 0; i < numRows; ++i) {
6507:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6508:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6509:     /* Local index in X dir */
6510:     tmp = *dxm++ - starts[0];
6511:     /* Loop over remaining dimensions */
6512:     for (j = 0; j < dim - 1; ++j) {
6513:       /* If nonlocal, set index to be negative */
6514:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6515:       /* Update local index */
6516:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6517:     }
6518:     /* Skip component slot if necessary */
6519:     if (mat->stencil.noc) dxm++;
6520:     /* Local row number */
6521:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6522:   }
6523:   PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6524:   PetscCall(PetscFree(jdxm));
6525:   PetscFunctionReturn(PETSC_SUCCESS);
6526: }

6528: /*@
6529:   MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6530:   of a set of rows and columns of a matrix.

6532:   Collective

6534:   Input Parameters:
6535: + mat     - the matrix
6536: . numRows - the number of rows/columns to remove
6537: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows
6538: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6539: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6540: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6542:   Level: intermediate

6544:   Notes:
6545:   See `MatZeroRowsColumns()` for details on how this routine operates.

6547:   The grid coordinates are across the entire grid, not just the local portion

6549:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6550:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6551:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6552:   `DM_BOUNDARY_PERIODIC` boundary type.

6554:   For indices that don't mean anything for your case (like the `k` index when working in 2d) or the `c` index when you have
6555:   a single value per point) you can skip filling those indices.

6557:   Fortran Note:
6558:   `idxm` and `idxn` should be declared as
6559: .vb
6560:     MatStencil idxm(4, m)
6561: .ve
6562:   and the values inserted using
6563: .vb
6564:     idxm(MatStencil_i, 1) = i
6565:     idxm(MatStencil_j, 1) = j
6566:     idxm(MatStencil_k, 1) = k
6567:     idxm(MatStencil_c, 1) = c
6568:     etc
6569: .ve

6571: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6572:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6573: @*/
6574: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6575: {
6576:   PetscInt  dim    = mat->stencil.dim;
6577:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6578:   PetscInt *dims   = mat->stencil.dims + 1;
6579:   PetscInt *starts = mat->stencil.starts;
6580:   PetscInt *dxm    = (PetscInt *)rows;
6581:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6583:   PetscFunctionBegin;
6586:   if (numRows) PetscAssertPointer(rows, 3);

6588:   PetscCall(PetscMalloc1(numRows, &jdxm));
6589:   for (i = 0; i < numRows; ++i) {
6590:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6591:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6592:     /* Local index in X dir */
6593:     tmp = *dxm++ - starts[0];
6594:     /* Loop over remaining dimensions */
6595:     for (j = 0; j < dim - 1; ++j) {
6596:       /* If nonlocal, set index to be negative */
6597:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6598:       /* Update local index */
6599:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6600:     }
6601:     /* Skip component slot if necessary */
6602:     if (mat->stencil.noc) dxm++;
6603:     /* Local row number */
6604:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6605:   }
6606:   PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6607:   PetscCall(PetscFree(jdxm));
6608:   PetscFunctionReturn(PETSC_SUCCESS);
6609: }

6611: /*@
6612:   MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6613:   of a set of rows of a matrix; using local numbering of rows.

6615:   Collective

6617:   Input Parameters:
6618: + mat     - the matrix
6619: . numRows - the number of rows to remove
6620: . rows    - the local row indices
6621: . diag    - value put in all diagonals of eliminated rows
6622: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6623: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6625:   Level: intermediate

6627:   Notes:
6628:   Before calling `MatZeroRowsLocal()`, the user must first set the
6629:   local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.

6631:   See `MatZeroRows()` for details on how this routine operates.

6633: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6634:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6635: @*/
6636: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6637: {
6638:   PetscFunctionBegin;
6641:   if (numRows) PetscAssertPointer(rows, 3);
6642:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6643:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6644:   MatCheckPreallocated(mat, 1);

6646:   if (mat->ops->zerorowslocal) {
6647:     PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6648:   } else {
6649:     IS              is, newis;
6650:     const PetscInt *newRows;

6652:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6653:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6654:     PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6655:     PetscCall(ISGetIndices(newis, &newRows));
6656:     PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6657:     PetscCall(ISRestoreIndices(newis, &newRows));
6658:     PetscCall(ISDestroy(&newis));
6659:     PetscCall(ISDestroy(&is));
6660:   }
6661:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6662:   PetscFunctionReturn(PETSC_SUCCESS);
6663: }

6665: /*@
6666:   MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6667:   of a set of rows of a matrix; using local numbering of rows.

6669:   Collective

6671:   Input Parameters:
6672: + mat  - the matrix
6673: . is   - index set of rows to remove
6674: . diag - value put in all diagonals of eliminated rows
6675: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6676: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6678:   Level: intermediate

6680:   Notes:
6681:   Before calling `MatZeroRowsLocalIS()`, the user must first set the
6682:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6684:   See `MatZeroRows()` for details on how this routine operates.

6686: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6687:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6688: @*/
6689: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6690: {
6691:   PetscInt        numRows;
6692:   const PetscInt *rows;

6694:   PetscFunctionBegin;
6698:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6699:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6700:   MatCheckPreallocated(mat, 1);

6702:   PetscCall(ISGetLocalSize(is, &numRows));
6703:   PetscCall(ISGetIndices(is, &rows));
6704:   PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6705:   PetscCall(ISRestoreIndices(is, &rows));
6706:   PetscFunctionReturn(PETSC_SUCCESS);
6707: }

6709: /*@
6710:   MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6711:   of a set of rows and columns of a matrix; using local numbering of rows.

6713:   Collective

6715:   Input Parameters:
6716: + mat     - the matrix
6717: . numRows - the number of rows to remove
6718: . rows    - the global row indices
6719: . diag    - value put in all diagonals of eliminated rows
6720: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6721: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6723:   Level: intermediate

6725:   Notes:
6726:   Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6727:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6729:   See `MatZeroRowsColumns()` for details on how this routine operates.

6731: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6732:           `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6733: @*/
6734: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6735: {
6736:   IS              is, newis;
6737:   const PetscInt *newRows;

6739:   PetscFunctionBegin;
6742:   if (numRows) PetscAssertPointer(rows, 3);
6743:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6744:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6745:   MatCheckPreallocated(mat, 1);

6747:   PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6748:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6749:   PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6750:   PetscCall(ISGetIndices(newis, &newRows));
6751:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6752:   PetscCall(ISRestoreIndices(newis, &newRows));
6753:   PetscCall(ISDestroy(&newis));
6754:   PetscCall(ISDestroy(&is));
6755:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6756:   PetscFunctionReturn(PETSC_SUCCESS);
6757: }

6759: /*@
6760:   MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6761:   of a set of rows and columns of a matrix; using local numbering of rows.

6763:   Collective

6765:   Input Parameters:
6766: + mat  - the matrix
6767: . is   - index set of rows to remove
6768: . diag - value put in all diagonals of eliminated rows
6769: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6770: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6772:   Level: intermediate

6774:   Notes:
6775:   Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6776:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6778:   See `MatZeroRowsColumns()` for details on how this routine operates.

6780: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6781:           `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6782: @*/
6783: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6784: {
6785:   PetscInt        numRows;
6786:   const PetscInt *rows;

6788:   PetscFunctionBegin;
6792:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6793:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6794:   MatCheckPreallocated(mat, 1);

6796:   PetscCall(ISGetLocalSize(is, &numRows));
6797:   PetscCall(ISGetIndices(is, &rows));
6798:   PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6799:   PetscCall(ISRestoreIndices(is, &rows));
6800:   PetscFunctionReturn(PETSC_SUCCESS);
6801: }

6803: /*@
6804:   MatGetSize - Returns the numbers of rows and columns in a matrix.

6806:   Not Collective

6808:   Input Parameter:
6809: . mat - the matrix

6811:   Output Parameters:
6812: + m - the number of global rows
6813: - n - the number of global columns

6815:   Level: beginner

6817:   Note:
6818:   Both output parameters can be `NULL` on input.

6820: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6821: @*/
6822: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6823: {
6824:   PetscFunctionBegin;
6826:   if (m) *m = mat->rmap->N;
6827:   if (n) *n = mat->cmap->N;
6828:   PetscFunctionReturn(PETSC_SUCCESS);
6829: }

6831: /*@
6832:   MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6833:   of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.

6835:   Not Collective

6837:   Input Parameter:
6838: . mat - the matrix

6840:   Output Parameters:
6841: + m - the number of local rows, use `NULL` to not obtain this value
6842: - n - the number of local columns, use `NULL` to not obtain this value

6844:   Level: beginner

6846: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6847: @*/
6848: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6849: {
6850:   PetscFunctionBegin;
6852:   if (m) PetscAssertPointer(m, 2);
6853:   if (n) PetscAssertPointer(n, 3);
6854:   if (m) *m = mat->rmap->n;
6855:   if (n) *n = mat->cmap->n;
6856:   PetscFunctionReturn(PETSC_SUCCESS);
6857: }

6859: /*@
6860:   MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6861:   vector one multiplies this matrix by that are owned by this processor.

6863:   Not Collective, unless matrix has not been allocated, then collective

6865:   Input Parameter:
6866: . mat - the matrix

6868:   Output Parameters:
6869: + m - the global index of the first local column, use `NULL` to not obtain this value
6870: - n - one more than the global index of the last local column, use `NULL` to not obtain this value

6872:   Level: developer

6874:   Notes:
6875:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6877:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6878:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6880:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6881:   the local values in the matrix.

6883:   Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6884:   Layouts](sec_matlayout) for details on matrix layouts.

6886: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6887:           `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6888: @*/
6889: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6890: {
6891:   PetscFunctionBegin;
6894:   if (m) PetscAssertPointer(m, 2);
6895:   if (n) PetscAssertPointer(n, 3);
6896:   MatCheckPreallocated(mat, 1);
6897:   if (m) *m = mat->cmap->rstart;
6898:   if (n) *n = mat->cmap->rend;
6899:   PetscFunctionReturn(PETSC_SUCCESS);
6900: }

6902: /*@
6903:   MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6904:   this MPI process.

6906:   Not Collective

6908:   Input Parameter:
6909: . mat - the matrix

6911:   Output Parameters:
6912: + m - the global index of the first local row, use `NULL` to not obtain this value
6913: - n - one more than the global index of the last local row, use `NULL` to not obtain this value

6915:   Level: beginner

6917:   Notes:
6918:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6920:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6921:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6923:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6924:   the local values in the matrix.

6926:   The high argument is one more than the last element stored locally.

6928:   For all matrices  it returns the range of matrix rows associated with rows of a vector that
6929:   would contain the result of a matrix vector product with this matrix. See [Matrix
6930:   Layouts](sec_matlayout) for details on matrix layouts.

6932: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
6933:           `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6934: @*/
6935: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6936: {
6937:   PetscFunctionBegin;
6940:   if (m) PetscAssertPointer(m, 2);
6941:   if (n) PetscAssertPointer(n, 3);
6942:   MatCheckPreallocated(mat, 1);
6943:   if (m) *m = mat->rmap->rstart;
6944:   if (n) *n = mat->rmap->rend;
6945:   PetscFunctionReturn(PETSC_SUCCESS);
6946: }

6948: /*@C
6949:   MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6950:   `MATSCALAPACK`, returns the range of matrix rows owned by each process.

6952:   Not Collective, unless matrix has not been allocated

6954:   Input Parameter:
6955: . mat - the matrix

6957:   Output Parameter:
6958: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
6959:            where `size` is the number of MPI processes used by `mat`

6961:   Level: beginner

6963:   Notes:
6964:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6966:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6967:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6969:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6970:   the local values in the matrix.

6972:   For all matrices  it returns the ranges of matrix rows associated with rows of a vector that
6973:   would contain the result of a matrix vector product with this matrix. See [Matrix
6974:   Layouts](sec_matlayout) for details on matrix layouts.

6976: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6977:           `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
6978:           `DMDAGetGhostCorners()`, `DM`
6979: @*/
6980: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
6981: {
6982:   PetscFunctionBegin;
6985:   MatCheckPreallocated(mat, 1);
6986:   PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6987:   PetscFunctionReturn(PETSC_SUCCESS);
6988: }

6990: /*@C
6991:   MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6992:   vector one multiplies this vector by that are owned by each processor.

6994:   Not Collective, unless matrix has not been allocated

6996:   Input Parameter:
6997: . mat - the matrix

6999:   Output Parameter:
7000: . ranges - start of each processors portion plus one more than the total length at the end

7002:   Level: beginner

7004:   Notes:
7005:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

7007:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7008:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

7010:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7011:   the local values in the matrix.

7013:   Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7014:   Layouts](sec_matlayout) for details on matrix layouts.

7016: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7017:           `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7018:           `DMDAGetGhostCorners()`, `DM`
7019: @*/
7020: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7021: {
7022:   PetscFunctionBegin;
7025:   MatCheckPreallocated(mat, 1);
7026:   PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7027:   PetscFunctionReturn(PETSC_SUCCESS);
7028: }

7030: /*@
7031:   MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.

7033:   Not Collective

7035:   Input Parameter:
7036: . A - matrix

7038:   Output Parameters:
7039: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7040: - cols - columns in which this process owns elements, use `NULL` to not obtain this value

7042:   Level: intermediate

7044:   Note:
7045:   You should call `ISDestroy()` on the returned `IS`

7047:   For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7048:   returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7049:   `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7050:   details on matrix layouts.

7052: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7053: @*/
7054: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7055: {
7056:   PetscErrorCode (*f)(Mat, IS *, IS *);

7058:   PetscFunctionBegin;
7061:   MatCheckPreallocated(A, 1);
7062:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7063:   if (f) {
7064:     PetscCall((*f)(A, rows, cols));
7065:   } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7066:     if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7067:     if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7068:   }
7069:   PetscFunctionReturn(PETSC_SUCCESS);
7070: }

7072: /*@
7073:   MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7074:   Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7075:   to complete the factorization.

7077:   Collective

7079:   Input Parameters:
7080: + fact - the factorized matrix obtained with `MatGetFactor()`
7081: . mat  - the matrix
7082: . row  - row permutation
7083: . col  - column permutation
7084: - info - structure containing
7085: .vb
7086:       levels - number of levels of fill.
7087:       expected fill - as ratio of original fill.
7088:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7089:                 missing diagonal entries)
7090: .ve

7092:   Level: developer

7094:   Notes:
7095:   See [Matrix Factorization](sec_matfactor) for additional information.

7097:   Most users should employ the `KSP` interface for linear solvers
7098:   instead of working directly with matrix algebra routines such as this.
7099:   See, e.g., `KSPCreate()`.

7101:   Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`

7103:   Fortran Note:
7104:   A valid (non-null) `info` argument must be provided

7106: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
7107:           `MatGetOrdering()`, `MatFactorInfo`
7108: @*/
7109: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7110: {
7111:   PetscFunctionBegin;
7116:   PetscAssertPointer(info, 5);
7117:   PetscAssertPointer(fact, 1);
7118:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7119:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7120:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7121:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7122:   MatCheckPreallocated(mat, 2);

7124:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7125:   PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7126:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7127:   PetscFunctionReturn(PETSC_SUCCESS);
7128: }

7130: /*@
7131:   MatICCFactorSymbolic - Performs symbolic incomplete
7132:   Cholesky factorization for a symmetric matrix.  Use
7133:   `MatCholeskyFactorNumeric()` to complete the factorization.

7135:   Collective

7137:   Input Parameters:
7138: + fact - the factorized matrix obtained with `MatGetFactor()`
7139: . mat  - the matrix to be factored
7140: . perm - row and column permutation
7141: - info - structure containing
7142: .vb
7143:       levels - number of levels of fill.
7144:       expected fill - as ratio of original fill.
7145: .ve

7147:   Level: developer

7149:   Notes:
7150:   Most users should employ the `KSP` interface for linear solvers
7151:   instead of working directly with matrix algebra routines such as this.
7152:   See, e.g., `KSPCreate()`.

7154:   This uses the definition of level of fill as in Y. Saad {cite}`saad2003`

7156:   Fortran Note:
7157:   A valid (non-null) `info` argument must be provided

7159: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7160: @*/
7161: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7162: {
7163:   PetscFunctionBegin;
7167:   PetscAssertPointer(info, 4);
7168:   PetscAssertPointer(fact, 1);
7169:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7170:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7171:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7172:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7173:   MatCheckPreallocated(mat, 2);

7175:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7176:   PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7177:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7178:   PetscFunctionReturn(PETSC_SUCCESS);
7179: }

7181: /*@C
7182:   MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7183:   points to an array of valid matrices, they may be reused to store the new
7184:   submatrices.

7186:   Collective

7188:   Input Parameters:
7189: + mat   - the matrix
7190: . n     - the number of submatrixes to be extracted (on this processor, may be zero)
7191: . irow  - index set of rows to extract
7192: . icol  - index set of columns to extract
7193: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7195:   Output Parameter:
7196: . submat - the array of submatrices

7198:   Level: advanced

7200:   Notes:
7201:   `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7202:   (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7203:   to extract a parallel submatrix.

7205:   Some matrix types place restrictions on the row and column
7206:   indices, such as that they be sorted or that they be equal to each other.

7208:   The index sets may not have duplicate entries.

7210:   When extracting submatrices from a parallel matrix, each processor can
7211:   form a different submatrix by setting the rows and columns of its
7212:   individual index sets according to the local submatrix desired.

7214:   When finished using the submatrices, the user should destroy
7215:   them with `MatDestroySubMatrices()`.

7217:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7218:   original matrix has not changed from that last call to `MatCreateSubMatrices()`.

7220:   This routine creates the matrices in submat; you should NOT create them before
7221:   calling it. It also allocates the array of matrix pointers submat.

7223:   For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7224:   request one row/column in a block, they must request all rows/columns that are in
7225:   that block. For example, if the block size is 2 you cannot request just row 0 and
7226:   column 0.

7228:   Fortran Note:
7229: .vb
7230:   Mat, pointer :: submat(:)
7231: .ve

7233: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7234: @*/
7235: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7236: {
7237:   PetscInt  i;
7238:   PetscBool eq;

7240:   PetscFunctionBegin;
7243:   if (n) {
7244:     PetscAssertPointer(irow, 3);
7246:     PetscAssertPointer(icol, 4);
7248:   }
7249:   PetscAssertPointer(submat, 6);
7250:   if (n && scall == MAT_REUSE_MATRIX) {
7251:     PetscAssertPointer(*submat, 6);
7253:   }
7254:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7255:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7256:   MatCheckPreallocated(mat, 1);
7257:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7258:   PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7259:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7260:   for (i = 0; i < n; i++) {
7261:     (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7262:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7263:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7264: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7265:     if (mat->boundtocpu && mat->bindingpropagates) {
7266:       PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7267:       PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7268:     }
7269: #endif
7270:   }
7271:   PetscFunctionReturn(PETSC_SUCCESS);
7272: }

7274: /*@C
7275:   MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).

7277:   Collective

7279:   Input Parameters:
7280: + mat   - the matrix
7281: . n     - the number of submatrixes to be extracted
7282: . irow  - index set of rows to extract
7283: . icol  - index set of columns to extract
7284: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7286:   Output Parameter:
7287: . submat - the array of submatrices

7289:   Level: advanced

7291:   Note:
7292:   This is used by `PCGASM`

7294: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7295: @*/
7296: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7297: {
7298:   PetscInt  i;
7299:   PetscBool eq;

7301:   PetscFunctionBegin;
7304:   if (n) {
7305:     PetscAssertPointer(irow, 3);
7307:     PetscAssertPointer(icol, 4);
7309:   }
7310:   PetscAssertPointer(submat, 6);
7311:   if (n && scall == MAT_REUSE_MATRIX) {
7312:     PetscAssertPointer(*submat, 6);
7314:   }
7315:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7316:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7317:   MatCheckPreallocated(mat, 1);

7319:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7320:   PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7321:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7322:   for (i = 0; i < n; i++) {
7323:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7324:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7325:   }
7326:   PetscFunctionReturn(PETSC_SUCCESS);
7327: }

7329: /*@C
7330:   MatDestroyMatrices - Destroys an array of matrices

7332:   Collective

7334:   Input Parameters:
7335: + n   - the number of local matrices
7336: - mat - the matrices (this is a pointer to the array of matrices)

7338:   Level: advanced

7340:   Notes:
7341:   Frees not only the matrices, but also the array that contains the matrices

7343:   For matrices obtained with  `MatCreateSubMatrices()` use `MatDestroySubMatrices()`

7345: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7346: @*/
7347: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7348: {
7349:   PetscInt i;

7351:   PetscFunctionBegin;
7352:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7353:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7354:   PetscAssertPointer(mat, 2);

7356:   for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));

7358:   /* memory is allocated even if n = 0 */
7359:   PetscCall(PetscFree(*mat));
7360:   PetscFunctionReturn(PETSC_SUCCESS);
7361: }

7363: /*@C
7364:   MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.

7366:   Collective

7368:   Input Parameters:
7369: + n   - the number of local matrices
7370: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)

7372:   Level: advanced

7374:   Note:
7375:   Frees not only the matrices, but also the array that contains the matrices

7377: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7378: @*/
7379: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7380: {
7381:   Mat mat0;

7383:   PetscFunctionBegin;
7384:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7385:   /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7386:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7387:   PetscAssertPointer(mat, 2);

7389:   mat0 = (*mat)[0];
7390:   if (mat0 && mat0->ops->destroysubmatrices) {
7391:     PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7392:   } else {
7393:     PetscCall(MatDestroyMatrices(n, mat));
7394:   }
7395:   PetscFunctionReturn(PETSC_SUCCESS);
7396: }

7398: /*@
7399:   MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process

7401:   Collective

7403:   Input Parameter:
7404: . mat - the matrix

7406:   Output Parameter:
7407: . matstruct - the sequential matrix with the nonzero structure of `mat`

7409:   Level: developer

7411: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7412: @*/
7413: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7414: {
7415:   PetscFunctionBegin;
7417:   PetscAssertPointer(matstruct, 2);

7420:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7421:   MatCheckPreallocated(mat, 1);

7423:   PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7424:   PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7425:   PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7426:   PetscFunctionReturn(PETSC_SUCCESS);
7427: }

7429: /*@C
7430:   MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.

7432:   Collective

7434:   Input Parameter:
7435: . mat - the matrix

7437:   Level: advanced

7439:   Note:
7440:   This is not needed, one can just call `MatDestroy()`

7442: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7443: @*/
7444: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7445: {
7446:   PetscFunctionBegin;
7447:   PetscAssertPointer(mat, 1);
7448:   PetscCall(MatDestroy(mat));
7449:   PetscFunctionReturn(PETSC_SUCCESS);
7450: }

7452: /*@
7453:   MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7454:   replaces the index sets by larger ones that represent submatrices with
7455:   additional overlap.

7457:   Collective

7459:   Input Parameters:
7460: + mat - the matrix
7461: . n   - the number of index sets
7462: . is  - the array of index sets (these index sets will changed during the call)
7463: - ov  - the additional overlap requested

7465:   Options Database Key:
7466: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7468:   Level: developer

7470:   Note:
7471:   The computed overlap preserves the matrix block sizes when the blocks are square.
7472:   That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7473:   that block are included in the overlap regardless of whether each specific column would increase the overlap.

7475: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7476: @*/
7477: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7478: {
7479:   PetscInt i, bs, cbs;

7481:   PetscFunctionBegin;
7485:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7486:   if (n) {
7487:     PetscAssertPointer(is, 3);
7489:   }
7490:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7491:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7492:   MatCheckPreallocated(mat, 1);

7494:   if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7495:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7496:   PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7497:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7498:   PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7499:   if (bs == cbs) {
7500:     for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7501:   }
7502:   PetscFunctionReturn(PETSC_SUCCESS);
7503: }

7505: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);

7507: /*@
7508:   MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7509:   a sub communicator, replaces the index sets by larger ones that represent submatrices with
7510:   additional overlap.

7512:   Collective

7514:   Input Parameters:
7515: + mat - the matrix
7516: . n   - the number of index sets
7517: . is  - the array of index sets (these index sets will changed during the call)
7518: - ov  - the additional overlap requested

7520:   `   Options Database Key:
7521: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7523:   Level: developer

7525: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7526: @*/
7527: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7528: {
7529:   PetscInt i;

7531:   PetscFunctionBegin;
7534:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7535:   if (n) {
7536:     PetscAssertPointer(is, 3);
7538:   }
7539:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7540:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7541:   MatCheckPreallocated(mat, 1);
7542:   if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7543:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7544:   for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7545:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7546:   PetscFunctionReturn(PETSC_SUCCESS);
7547: }

7549: /*@
7550:   MatGetBlockSize - Returns the matrix block size.

7552:   Not Collective

7554:   Input Parameter:
7555: . mat - the matrix

7557:   Output Parameter:
7558: . bs - block size

7560:   Level: intermediate

7562:   Notes:
7563:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.

7565:   If the block size has not been set yet this routine returns 1.

7567: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7568: @*/
7569: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7570: {
7571:   PetscFunctionBegin;
7573:   PetscAssertPointer(bs, 2);
7574:   *bs = mat->rmap->bs;
7575:   PetscFunctionReturn(PETSC_SUCCESS);
7576: }

7578: /*@
7579:   MatGetBlockSizes - Returns the matrix block row and column sizes.

7581:   Not Collective

7583:   Input Parameter:
7584: . mat - the matrix

7586:   Output Parameters:
7587: + rbs - row block size
7588: - cbs - column block size

7590:   Level: intermediate

7592:   Notes:
7593:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7594:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7596:   If a block size has not been set yet this routine returns 1.

7598: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7599: @*/
7600: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7601: {
7602:   PetscFunctionBegin;
7604:   if (rbs) PetscAssertPointer(rbs, 2);
7605:   if (cbs) PetscAssertPointer(cbs, 3);
7606:   if (rbs) *rbs = mat->rmap->bs;
7607:   if (cbs) *cbs = mat->cmap->bs;
7608:   PetscFunctionReturn(PETSC_SUCCESS);
7609: }

7611: /*@
7612:   MatSetBlockSize - Sets the matrix block size.

7614:   Logically Collective

7616:   Input Parameters:
7617: + mat - the matrix
7618: - bs  - block size

7620:   Level: intermediate

7622:   Notes:
7623:   Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7624:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7626:   For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7627:   is compatible with the matrix local sizes.

7629: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7630: @*/
7631: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7632: {
7633:   PetscFunctionBegin;
7636:   PetscCall(MatSetBlockSizes(mat, bs, bs));
7637:   PetscFunctionReturn(PETSC_SUCCESS);
7638: }

7640: typedef struct {
7641:   PetscInt         n;
7642:   IS              *is;
7643:   Mat             *mat;
7644:   PetscObjectState nonzerostate;
7645:   Mat              C;
7646: } EnvelopeData;

7648: static PetscErrorCode EnvelopeDataDestroy(void **ptr)
7649: {
7650:   EnvelopeData *edata = (EnvelopeData *)*ptr;

7652:   PetscFunctionBegin;
7653:   for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7654:   PetscCall(PetscFree(edata->is));
7655:   PetscCall(PetscFree(edata));
7656:   PetscFunctionReturn(PETSC_SUCCESS);
7657: }

7659: /*@
7660:   MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7661:   the sizes of these blocks in the matrix. An individual block may lie over several processes.

7663:   Collective

7665:   Input Parameter:
7666: . mat - the matrix

7668:   Level: intermediate

7670:   Notes:
7671:   There can be zeros within the blocks

7673:   The blocks can overlap between processes, including laying on more than two processes

7675: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7676: @*/
7677: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7678: {
7679:   PetscInt           n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7680:   PetscInt          *diag, *odiag, sc;
7681:   VecScatter         scatter;
7682:   PetscScalar       *seqv;
7683:   const PetscScalar *parv;
7684:   const PetscInt    *ia, *ja;
7685:   PetscBool          set, flag, done;
7686:   Mat                AA = mat, A;
7687:   MPI_Comm           comm;
7688:   PetscMPIInt        rank, size, tag;
7689:   MPI_Status         status;
7690:   PetscContainer     container;
7691:   EnvelopeData      *edata;
7692:   Vec                seq, par;
7693:   IS                 isglobal;

7695:   PetscFunctionBegin;
7697:   PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7698:   if (!set || !flag) {
7699:     /* TODO: only needs nonzero structure of transpose */
7700:     PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7701:     PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7702:   }
7703:   PetscCall(MatAIJGetLocalMat(AA, &A));
7704:   PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7705:   PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");

7707:   PetscCall(MatGetLocalSize(mat, &n, NULL));
7708:   PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7709:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7710:   PetscCallMPI(MPI_Comm_size(comm, &size));
7711:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

7713:   PetscCall(PetscMalloc2(n, &sizes, n, &starts));

7715:   if (rank > 0) {
7716:     PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7717:     PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7718:   }
7719:   PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7720:   for (i = 0; i < n; i++) {
7721:     env = PetscMax(env, ja[ia[i + 1] - 1]);
7722:     II  = rstart + i;
7723:     if (env == II) {
7724:       starts[lblocks]  = tbs;
7725:       sizes[lblocks++] = 1 + II - tbs;
7726:       tbs              = 1 + II;
7727:     }
7728:   }
7729:   if (rank < size - 1) {
7730:     PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7731:     PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7732:   }

7734:   PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7735:   if (!set || !flag) PetscCall(MatDestroy(&AA));
7736:   PetscCall(MatDestroy(&A));

7738:   PetscCall(PetscNew(&edata));
7739:   PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7740:   edata->n = lblocks;
7741:   /* create IS needed for extracting blocks from the original matrix */
7742:   PetscCall(PetscMalloc1(lblocks, &edata->is));
7743:   for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));

7745:   /* Create the resulting inverse matrix nonzero structure with preallocation information */
7746:   PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7747:   PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7748:   PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7749:   PetscCall(MatSetType(edata->C, MATAIJ));

7751:   /* Communicate the start and end of each row, from each block to the correct rank */
7752:   /* TODO: Use PetscSF instead of VecScatter */
7753:   for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7754:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7755:   PetscCall(VecGetArrayWrite(seq, &seqv));
7756:   for (PetscInt i = 0; i < lblocks; i++) {
7757:     for (PetscInt j = 0; j < sizes[i]; j++) {
7758:       seqv[cnt]     = starts[i];
7759:       seqv[cnt + 1] = starts[i] + sizes[i];
7760:       cnt += 2;
7761:     }
7762:   }
7763:   PetscCall(VecRestoreArrayWrite(seq, &seqv));
7764:   PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7765:   sc -= cnt;
7766:   PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7767:   PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7768:   PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7769:   PetscCall(ISDestroy(&isglobal));
7770:   PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7771:   PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7772:   PetscCall(VecScatterDestroy(&scatter));
7773:   PetscCall(VecDestroy(&seq));
7774:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7775:   PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7776:   PetscCall(VecGetArrayRead(par, &parv));
7777:   cnt = 0;
7778:   PetscCall(MatGetSize(mat, NULL, &n));
7779:   for (PetscInt i = 0; i < mat->rmap->n; i++) {
7780:     PetscInt start, end, d = 0, od = 0;

7782:     start = (PetscInt)PetscRealPart(parv[cnt]);
7783:     end   = (PetscInt)PetscRealPart(parv[cnt + 1]);
7784:     cnt += 2;

7786:     if (start < cstart) {
7787:       od += cstart - start + n - cend;
7788:       d += cend - cstart;
7789:     } else if (start < cend) {
7790:       od += n - cend;
7791:       d += cend - start;
7792:     } else od += n - start;
7793:     if (end <= cstart) {
7794:       od -= cstart - end + n - cend;
7795:       d -= cend - cstart;
7796:     } else if (end < cend) {
7797:       od -= n - cend;
7798:       d -= cend - end;
7799:     } else od -= n - end;

7801:     odiag[i] = od;
7802:     diag[i]  = d;
7803:   }
7804:   PetscCall(VecRestoreArrayRead(par, &parv));
7805:   PetscCall(VecDestroy(&par));
7806:   PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7807:   PetscCall(PetscFree2(diag, odiag));
7808:   PetscCall(PetscFree2(sizes, starts));

7810:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7811:   PetscCall(PetscContainerSetPointer(container, edata));
7812:   PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7813:   PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7814:   PetscCall(PetscObjectDereference((PetscObject)container));
7815:   PetscFunctionReturn(PETSC_SUCCESS);
7816: }

7818: /*@
7819:   MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A

7821:   Collective

7823:   Input Parameters:
7824: + A     - the matrix
7825: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine

7827:   Output Parameter:
7828: . C - matrix with inverted block diagonal of `A`

7830:   Level: advanced

7832:   Note:
7833:   For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.

7835: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7836: @*/
7837: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7838: {
7839:   PetscContainer   container;
7840:   EnvelopeData    *edata;
7841:   PetscObjectState nonzerostate;

7843:   PetscFunctionBegin;
7844:   PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7845:   if (!container) {
7846:     PetscCall(MatComputeVariableBlockEnvelope(A));
7847:     PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7848:   }
7849:   PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7850:   PetscCall(MatGetNonzeroState(A, &nonzerostate));
7851:   PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7852:   PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");

7854:   PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7855:   *C = edata->C;

7857:   for (PetscInt i = 0; i < edata->n; i++) {
7858:     Mat          D;
7859:     PetscScalar *dvalues;

7861:     PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7862:     PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7863:     PetscCall(MatSeqDenseInvert(D));
7864:     PetscCall(MatDenseGetArray(D, &dvalues));
7865:     PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7866:     PetscCall(MatDestroy(&D));
7867:   }
7868:   PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7869:   PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7870:   PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7871:   PetscFunctionReturn(PETSC_SUCCESS);
7872: }

7874: /*@
7875:   MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size

7877:   Not Collective

7879:   Input Parameters:
7880: + mat     - the matrix
7881: . nblocks - the number of blocks on this process, each block can only exist on a single process
7882: - bsizes  - the block sizes

7884:   Level: intermediate

7886:   Notes:
7887:   Currently used by `PCVPBJACOBI` for `MATAIJ` matrices

7889:   Each variable point-block set of degrees of freedom must live on a single MPI process. That is a point block cannot straddle two MPI processes.

7891: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7892:           `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7893: @*/
7894: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7895: {
7896:   PetscInt ncnt = 0, nlocal;

7898:   PetscFunctionBegin;
7900:   PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7901:   PetscCheck(nblocks >= 0 && nblocks <= nlocal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number of local blocks %" PetscInt_FMT " is not in [0, %" PetscInt_FMT "]", nblocks, nlocal);
7902:   for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7903:   PetscCheck(ncnt == nlocal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Sum of local block sizes %" PetscInt_FMT " does not equal local size of matrix %" PetscInt_FMT, ncnt, nlocal);
7904:   PetscCall(PetscFree(mat->bsizes));
7905:   mat->nblocks = nblocks;
7906:   PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7907:   PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7908:   PetscFunctionReturn(PETSC_SUCCESS);
7909: }

7911: /*@C
7912:   MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size

7914:   Not Collective; No Fortran Support

7916:   Input Parameter:
7917: . mat - the matrix

7919:   Output Parameters:
7920: + nblocks - the number of blocks on this process
7921: - bsizes  - the block sizes

7923:   Level: intermediate

7925: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7926: @*/
7927: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7928: {
7929:   PetscFunctionBegin;
7931:   if (nblocks) *nblocks = mat->nblocks;
7932:   if (bsizes) *bsizes = mat->bsizes;
7933:   PetscFunctionReturn(PETSC_SUCCESS);
7934: }

7936: /*
7937:   MatSelectVariableBlockSizes - When creating a submatrix, pass on the variable block sizes

7939:   Not Collective

7941:   Input Parameter:
7942: + subA  - the submatrix
7943: . A     - the original matrix
7944: - isrow - The `IS` of selected rows for the submatrix

7946:   Level: developer

7948: .seealso: [](ch_matrices), `Mat`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7949: */
7950: static PetscErrorCode MatSelectVariableBlockSizes(Mat subA, Mat A, IS isrow)
7951: {
7952:   const PetscInt *rows;
7953:   PetscInt        n, rStart, rEnd, Nb = 0;

7955:   PetscFunctionBegin;
7956:   if (!A->bsizes) PetscFunctionReturn(PETSC_SUCCESS);
7957:   // The IS contains global row numbers, we cannot preserve blocks if it contains off-process entries
7958:   PetscCall(MatGetOwnershipRange(A, &rStart, &rEnd));
7959:   PetscCall(ISGetIndices(isrow, &rows));
7960:   PetscCall(ISGetLocalSize(isrow, &n));
7961:   for (PetscInt i = 0; i < n; ++i) {
7962:     if (rows[i] < rStart || rows[i] >= rEnd) {
7963:       PetscCall(ISRestoreIndices(isrow, &rows));
7964:       PetscFunctionReturn(PETSC_SUCCESS);
7965:     }
7966:   }
7967:   for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
7968:     PetscBool occupied = PETSC_FALSE;

7970:     for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
7971:       const PetscInt row = gr + br;

7973:       if (i == n) break;
7974:       if (rows[i] == row) {
7975:         occupied = PETSC_TRUE;
7976:         ++i;
7977:       }
7978:       while (i < n && rows[i] < row) ++i;
7979:     }
7980:     gr += A->bsizes[b];
7981:     if (occupied) ++Nb;
7982:   }
7983:   subA->nblocks = Nb;
7984:   PetscCall(PetscFree(subA->bsizes));
7985:   PetscCall(PetscMalloc1(subA->nblocks, &subA->bsizes));
7986:   PetscInt sb = 0;
7987:   for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
7988:     if (sb < subA->nblocks) subA->bsizes[sb] = 0;
7989:     for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
7990:       const PetscInt row = gr + br;

7992:       if (i == n) break;
7993:       if (rows[i] == row) {
7994:         ++subA->bsizes[sb];
7995:         ++i;
7996:       }
7997:       while (i < n && rows[i] < row) ++i;
7998:     }
7999:     gr += A->bsizes[b];
8000:     if (sb < subA->nblocks && subA->bsizes[sb]) ++sb;
8001:   }
8002:   PetscCheck(sb == subA->nblocks, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Invalid number of blocks %" PetscInt_FMT " != %" PetscInt_FMT, sb, subA->nblocks);
8003:   PetscInt nlocal, ncnt = 0;
8004:   PetscCall(MatGetLocalSize(subA, &nlocal, NULL));
8005:   PetscCheck(subA->nblocks >= 0 && subA->nblocks <= nlocal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number of local blocks %" PetscInt_FMT " is not in [0, %" PetscInt_FMT "]", subA->nblocks, nlocal);
8006:   for (PetscInt i = 0; i < subA->nblocks; i++) ncnt += subA->bsizes[i];
8007:   PetscCheck(ncnt == nlocal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Sum of local block sizes %" PetscInt_FMT " does not equal local size of matrix %" PetscInt_FMT, ncnt, nlocal);
8008:   PetscCall(ISRestoreIndices(isrow, &rows));
8009:   PetscFunctionReturn(PETSC_SUCCESS);
8010: }

8012: /*@
8013:   MatSetBlockSizes - Sets the matrix block row and column sizes.

8015:   Logically Collective

8017:   Input Parameters:
8018: + mat - the matrix
8019: . rbs - row block size
8020: - cbs - column block size

8022:   Level: intermediate

8024:   Notes:
8025:   Block row formats are `MATBAIJ` and  `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
8026:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8027:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

8029:   For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
8030:   are compatible with the matrix local sizes.

8032:   The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.

8034: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8035: @*/
8036: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8037: {
8038:   PetscFunctionBegin;
8042:   PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8043:   if (mat->rmap->refcnt) {
8044:     ISLocalToGlobalMapping l2g  = NULL;
8045:     PetscLayout            nmap = NULL;

8047:     PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8048:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8049:     PetscCall(PetscLayoutDestroy(&mat->rmap));
8050:     mat->rmap          = nmap;
8051:     mat->rmap->mapping = l2g;
8052:   }
8053:   if (mat->cmap->refcnt) {
8054:     ISLocalToGlobalMapping l2g  = NULL;
8055:     PetscLayout            nmap = NULL;

8057:     PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8058:     if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8059:     PetscCall(PetscLayoutDestroy(&mat->cmap));
8060:     mat->cmap          = nmap;
8061:     mat->cmap->mapping = l2g;
8062:   }
8063:   PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8064:   PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8065:   PetscFunctionReturn(PETSC_SUCCESS);
8066: }

8068: /*@
8069:   MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

8071:   Logically Collective

8073:   Input Parameters:
8074: + mat     - the matrix
8075: . fromRow - matrix from which to copy row block size
8076: - fromCol - matrix from which to copy column block size (can be same as fromRow)

8078:   Level: developer

8080: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8081: @*/
8082: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8083: {
8084:   PetscFunctionBegin;
8088:   PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8089:   PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8090:   PetscFunctionReturn(PETSC_SUCCESS);
8091: }

8093: /*@
8094:   MatResidual - Default routine to calculate the residual r = b - Ax

8096:   Collective

8098:   Input Parameters:
8099: + mat - the matrix
8100: . b   - the right-hand-side
8101: - x   - the approximate solution

8103:   Output Parameter:
8104: . r - location to store the residual

8106:   Level: developer

8108: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8109: @*/
8110: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8111: {
8112:   PetscFunctionBegin;
8118:   MatCheckPreallocated(mat, 1);
8119:   PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8120:   if (!mat->ops->residual) {
8121:     PetscCall(MatMult(mat, x, r));
8122:     PetscCall(VecAYPX(r, -1.0, b));
8123:   } else {
8124:     PetscUseTypeMethod(mat, residual, b, x, r);
8125:   }
8126:   PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8127:   PetscFunctionReturn(PETSC_SUCCESS);
8128: }

8130: /*@C
8131:   MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix

8133:   Collective

8135:   Input Parameters:
8136: + mat             - the matrix
8137: . shift           - 0 or 1 indicating we want the indices starting at 0 or 1
8138: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8139: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
8140:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8141:                  always used.

8143:   Output Parameters:
8144: + n    - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8145: . ia   - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix, use `NULL` if not needed
8146: . ja   - the column indices, use `NULL` if not needed
8147: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8148:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

8150:   Level: developer

8152:   Notes:
8153:   You CANNOT change any of the ia[] or ja[] values.

8155:   Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.

8157:   Fortran Notes:
8158:   Use
8159: .vb
8160:     PetscInt, pointer :: ia(:),ja(:)
8161:     call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8162:     ! Access the ith and jth entries via ia(i) and ja(j)
8163: .ve

8165: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8166: @*/
8167: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8168: {
8169:   PetscFunctionBegin;
8172:   if (n) PetscAssertPointer(n, 5);
8173:   if (ia) PetscAssertPointer(ia, 6);
8174:   if (ja) PetscAssertPointer(ja, 7);
8175:   if (done) PetscAssertPointer(done, 8);
8176:   MatCheckPreallocated(mat, 1);
8177:   if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8178:   else {
8179:     if (done) *done = PETSC_TRUE;
8180:     PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8181:     PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8182:     PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8183:   }
8184:   PetscFunctionReturn(PETSC_SUCCESS);
8185: }

8187: /*@C
8188:   MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

8190:   Collective

8192:   Input Parameters:
8193: + mat             - the matrix
8194: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8195: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8196:                 symmetrized
8197: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8198:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8199:                  always used.

8201:   Output Parameters:
8202: + n    - number of columns in the (possibly compressed) matrix
8203: . ia   - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8204: . ja   - the row indices
8205: - done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned

8207:   Level: developer

8209: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8210: @*/
8211: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8212: {
8213:   PetscFunctionBegin;
8216:   PetscAssertPointer(n, 5);
8217:   if (ia) PetscAssertPointer(ia, 6);
8218:   if (ja) PetscAssertPointer(ja, 7);
8219:   PetscAssertPointer(done, 8);
8220:   MatCheckPreallocated(mat, 1);
8221:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8222:   else {
8223:     *done = PETSC_TRUE;
8224:     PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8225:   }
8226:   PetscFunctionReturn(PETSC_SUCCESS);
8227: }

8229: /*@C
8230:   MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.

8232:   Collective

8234:   Input Parameters:
8235: + mat             - the matrix
8236: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8237: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8238: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8239:                     inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8240:                     always used.
8241: . n               - size of (possibly compressed) matrix
8242: . ia              - the row pointers
8243: - ja              - the column indices

8245:   Output Parameter:
8246: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8248:   Level: developer

8250:   Note:
8251:   This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8252:   us of the array after it has been restored. If you pass `NULL`, it will
8253:   not zero the pointers.  Use of ia or ja after `MatRestoreRowIJ()` is invalid.

8255: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8256: @*/
8257: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8258: {
8259:   PetscFunctionBegin;
8262:   if (ia) PetscAssertPointer(ia, 6);
8263:   if (ja) PetscAssertPointer(ja, 7);
8264:   if (done) PetscAssertPointer(done, 8);
8265:   MatCheckPreallocated(mat, 1);

8267:   if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8268:   else {
8269:     if (done) *done = PETSC_TRUE;
8270:     PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8271:     if (n) *n = 0;
8272:     if (ia) *ia = NULL;
8273:     if (ja) *ja = NULL;
8274:   }
8275:   PetscFunctionReturn(PETSC_SUCCESS);
8276: }

8278: /*@C
8279:   MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.

8281:   Collective

8283:   Input Parameters:
8284: + mat             - the matrix
8285: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8286: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8287: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8288:                     inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8289:                     always used.

8291:   Output Parameters:
8292: + n    - size of (possibly compressed) matrix
8293: . ia   - the column pointers
8294: . ja   - the row indices
8295: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8297:   Level: developer

8299: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8300: @*/
8301: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8302: {
8303:   PetscFunctionBegin;
8306:   if (ia) PetscAssertPointer(ia, 6);
8307:   if (ja) PetscAssertPointer(ja, 7);
8308:   PetscAssertPointer(done, 8);
8309:   MatCheckPreallocated(mat, 1);

8311:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8312:   else {
8313:     *done = PETSC_TRUE;
8314:     PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8315:     if (n) *n = 0;
8316:     if (ia) *ia = NULL;
8317:     if (ja) *ja = NULL;
8318:   }
8319:   PetscFunctionReturn(PETSC_SUCCESS);
8320: }

8322: /*@
8323:   MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8324:   `MatGetColumnIJ()`.

8326:   Collective

8328:   Input Parameters:
8329: + mat        - the matrix
8330: . ncolors    - maximum color value
8331: . n          - number of entries in colorarray
8332: - colorarray - array indicating color for each column

8334:   Output Parameter:
8335: . iscoloring - coloring generated using colorarray information

8337:   Level: developer

8339: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8340: @*/
8341: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8342: {
8343:   PetscFunctionBegin;
8346:   PetscAssertPointer(colorarray, 4);
8347:   PetscAssertPointer(iscoloring, 5);
8348:   MatCheckPreallocated(mat, 1);

8350:   if (!mat->ops->coloringpatch) {
8351:     PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8352:   } else {
8353:     PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8354:   }
8355:   PetscFunctionReturn(PETSC_SUCCESS);
8356: }

8358: /*@
8359:   MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

8361:   Logically Collective

8363:   Input Parameter:
8364: . mat - the factored matrix to be reset

8366:   Level: developer

8368:   Notes:
8369:   This routine should be used only with factored matrices formed by in-place
8370:   factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8371:   format).  This option can save memory, for example, when solving nonlinear
8372:   systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8373:   ILU(0) preconditioner.

8375:   One can specify in-place ILU(0) factorization by calling
8376: .vb
8377:      PCType(pc,PCILU);
8378:      PCFactorSeUseInPlace(pc);
8379: .ve
8380:   or by using the options -pc_type ilu -pc_factor_in_place

8382:   In-place factorization ILU(0) can also be used as a local
8383:   solver for the blocks within the block Jacobi or additive Schwarz
8384:   methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
8385:   for details on setting local solver options.

8387:   Most users should employ the `KSP` interface for linear solvers
8388:   instead of working directly with matrix algebra routines such as this.
8389:   See, e.g., `KSPCreate()`.

8391: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8392: @*/
8393: PetscErrorCode MatSetUnfactored(Mat mat)
8394: {
8395:   PetscFunctionBegin;
8398:   MatCheckPreallocated(mat, 1);
8399:   mat->factortype = MAT_FACTOR_NONE;
8400:   if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8401:   PetscUseTypeMethod(mat, setunfactored);
8402:   PetscFunctionReturn(PETSC_SUCCESS);
8403: }

8405: /*@
8406:   MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8407:   as the original matrix.

8409:   Collective

8411:   Input Parameters:
8412: + mat   - the original matrix
8413: . isrow - parallel `IS` containing the rows this processor should obtain
8414: . iscol - parallel `IS` containing all columns you wish to keep. Each process should list the columns that will be in IT's "diagonal part" in the new matrix.
8415: - cll   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

8417:   Output Parameter:
8418: . newmat - the new submatrix, of the same type as the original matrix

8420:   Level: advanced

8422:   Notes:
8423:   The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.

8425:   Some matrix types place restrictions on the row and column indices, such
8426:   as that they be sorted or that they be equal to each other. For `MATBAIJ` and `MATSBAIJ` matrices the indices must include all rows/columns of a block;
8427:   for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.

8429:   The index sets may not have duplicate entries.

8431:   The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8432:   the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8433:   to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8434:   will reuse the matrix generated the first time.  You should call `MatDestroy()` on `newmat` when
8435:   you are finished using it.

8437:   The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8438:   the input matrix.

8440:   If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).

8442:   If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8443:   is used by `PCFIELDSPLIT` to allow easy nesting of its use.

8445:   Example usage:
8446:   Consider the following 8x8 matrix with 34 non-zero values, that is
8447:   assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8448:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8449:   as follows
8450: .vb
8451:             1  2  0  |  0  3  0  |  0  4
8452:     Proc0   0  5  6  |  7  0  0  |  8  0
8453:             9  0 10  | 11  0  0  | 12  0
8454:     -------------------------------------
8455:            13  0 14  | 15 16 17  |  0  0
8456:     Proc1   0 18  0  | 19 20 21  |  0  0
8457:             0  0  0  | 22 23  0  | 24  0
8458:     -------------------------------------
8459:     Proc2  25 26 27  |  0  0 28  | 29  0
8460:            30  0  0  | 31 32 33  |  0 34
8461: .ve

8463:   Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6].  The resulting submatrix is

8465: .vb
8466:             2  0  |  0  3  0  |  0
8467:     Proc0   5  6  |  7  0  0  |  8
8468:     -------------------------------
8469:     Proc1  18  0  | 19 20 21  |  0
8470:     -------------------------------
8471:     Proc2  26 27  |  0  0 28  | 29
8472:             0  0  | 31 32 33  |  0
8473: .ve

8475: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8476: @*/
8477: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8478: {
8479:   PetscMPIInt size;
8480:   Mat        *local;
8481:   IS          iscoltmp;
8482:   PetscBool   flg;

8484:   PetscFunctionBegin;
8488:   PetscAssertPointer(newmat, 5);
8491:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8492:   PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");

8494:   MatCheckPreallocated(mat, 1);
8495:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));

8497:   if (!iscol || isrow == iscol) {
8498:     PetscBool   stride;
8499:     PetscMPIInt grabentirematrix = 0, grab;
8500:     PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8501:     if (stride) {
8502:       PetscInt first, step, n, rstart, rend;
8503:       PetscCall(ISStrideGetInfo(isrow, &first, &step));
8504:       if (step == 1) {
8505:         PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8506:         if (rstart == first) {
8507:           PetscCall(ISGetLocalSize(isrow, &n));
8508:           if (n == rend - rstart) grabentirematrix = 1;
8509:         }
8510:       }
8511:     }
8512:     PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8513:     if (grab) {
8514:       PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8515:       if (cll == MAT_INITIAL_MATRIX) {
8516:         *newmat = mat;
8517:         PetscCall(PetscObjectReference((PetscObject)mat));
8518:       }
8519:       PetscFunctionReturn(PETSC_SUCCESS);
8520:     }
8521:   }

8523:   if (!iscol) {
8524:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8525:   } else {
8526:     iscoltmp = iscol;
8527:   }

8529:   /* if original matrix is on just one processor then use submatrix generated */
8530:   if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8531:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8532:     goto setproperties;
8533:   } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8534:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8535:     *newmat = *local;
8536:     PetscCall(PetscFree(local));
8537:     goto setproperties;
8538:   } else if (!mat->ops->createsubmatrix) {
8539:     /* Create a new matrix type that implements the operation using the full matrix */
8540:     PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8541:     switch (cll) {
8542:     case MAT_INITIAL_MATRIX:
8543:       PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8544:       break;
8545:     case MAT_REUSE_MATRIX:
8546:       PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8547:       break;
8548:     default:
8549:       SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8550:     }
8551:     PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8552:     goto setproperties;
8553:   }

8555:   PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8556:   PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8557:   PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));

8559: setproperties:
8560:   if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8561:     PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8562:     if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8563:   }
8564:   if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8565:   if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8566:   if (!iscol || isrow == iscol) PetscCall(MatSelectVariableBlockSizes(*newmat, mat, isrow));
8567:   PetscFunctionReturn(PETSC_SUCCESS);
8568: }

8570: /*@
8571:   MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix

8573:   Not Collective

8575:   Input Parameters:
8576: + A - the matrix we wish to propagate options from
8577: - B - the matrix we wish to propagate options to

8579:   Level: beginner

8581:   Note:
8582:   Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`

8584: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8585: @*/
8586: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8587: {
8588:   PetscFunctionBegin;
8591:   B->symmetry_eternal            = A->symmetry_eternal;
8592:   B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8593:   B->symmetric                   = A->symmetric;
8594:   B->structurally_symmetric      = A->structurally_symmetric;
8595:   B->spd                         = A->spd;
8596:   B->hermitian                   = A->hermitian;
8597:   PetscFunctionReturn(PETSC_SUCCESS);
8598: }

8600: /*@
8601:   MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8602:   used during the assembly process to store values that belong to
8603:   other processors.

8605:   Not Collective

8607:   Input Parameters:
8608: + mat   - the matrix
8609: . size  - the initial size of the stash.
8610: - bsize - the initial size of the block-stash(if used).

8612:   Options Database Keys:
8613: + -matstash_initial_size <size> or <size0,size1,...sizep-1>            - set initial size
8614: - -matstash_block_initial_size <bsize>  or <bsize0,bsize1,...bsizep-1> - set initial block size

8616:   Level: intermediate

8618:   Notes:
8619:   The block-stash is used for values set with `MatSetValuesBlocked()` while
8620:   the stash is used for values set with `MatSetValues()`

8622:   Run with the option -info and look for output of the form
8623:   MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8624:   to determine the appropriate value, MM, to use for size and
8625:   MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8626:   to determine the value, BMM to use for bsize

8628: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8629: @*/
8630: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8631: {
8632:   PetscFunctionBegin;
8635:   PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8636:   PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8637:   PetscFunctionReturn(PETSC_SUCCESS);
8638: }

8640: /*@
8641:   MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8642:   the matrix

8644:   Neighbor-wise Collective

8646:   Input Parameters:
8647: + A - the matrix
8648: . x - the vector to be multiplied by the interpolation operator
8649: - y - the vector to be added to the result

8651:   Output Parameter:
8652: . w - the resulting vector

8654:   Level: intermediate

8656:   Notes:
8657:   `w` may be the same vector as `y`.

8659:   This allows one to use either the restriction or interpolation (its transpose)
8660:   matrix to do the interpolation

8662: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8663: @*/
8664: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8665: {
8666:   PetscInt M, N, Ny;

8668:   PetscFunctionBegin;
8673:   PetscCall(MatGetSize(A, &M, &N));
8674:   PetscCall(VecGetSize(y, &Ny));
8675:   if (M == Ny) {
8676:     PetscCall(MatMultAdd(A, x, y, w));
8677:   } else {
8678:     PetscCall(MatMultTransposeAdd(A, x, y, w));
8679:   }
8680:   PetscFunctionReturn(PETSC_SUCCESS);
8681: }

8683: /*@
8684:   MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8685:   the matrix

8687:   Neighbor-wise Collective

8689:   Input Parameters:
8690: + A - the matrix
8691: - x - the vector to be interpolated

8693:   Output Parameter:
8694: . y - the resulting vector

8696:   Level: intermediate

8698:   Note:
8699:   This allows one to use either the restriction or interpolation (its transpose)
8700:   matrix to do the interpolation

8702: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8703: @*/
8704: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8705: {
8706:   PetscInt M, N, Ny;

8708:   PetscFunctionBegin;
8712:   PetscCall(MatGetSize(A, &M, &N));
8713:   PetscCall(VecGetSize(y, &Ny));
8714:   if (M == Ny) {
8715:     PetscCall(MatMult(A, x, y));
8716:   } else {
8717:     PetscCall(MatMultTranspose(A, x, y));
8718:   }
8719:   PetscFunctionReturn(PETSC_SUCCESS);
8720: }

8722: /*@
8723:   MatRestrict - $y = A*x$ or $A^T*x$

8725:   Neighbor-wise Collective

8727:   Input Parameters:
8728: + A - the matrix
8729: - x - the vector to be restricted

8731:   Output Parameter:
8732: . y - the resulting vector

8734:   Level: intermediate

8736:   Note:
8737:   This allows one to use either the restriction or interpolation (its transpose)
8738:   matrix to do the restriction

8740: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8741: @*/
8742: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8743: {
8744:   PetscInt M, N, Nx;

8746:   PetscFunctionBegin;
8750:   PetscCall(MatGetSize(A, &M, &N));
8751:   PetscCall(VecGetSize(x, &Nx));
8752:   if (M == Nx) {
8753:     PetscCall(MatMultTranspose(A, x, y));
8754:   } else {
8755:     PetscCall(MatMult(A, x, y));
8756:   }
8757:   PetscFunctionReturn(PETSC_SUCCESS);
8758: }

8760: /*@
8761:   MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`

8763:   Neighbor-wise Collective

8765:   Input Parameters:
8766: + A - the matrix
8767: . x - the input dense matrix to be multiplied
8768: - w - the input dense matrix to be added to the result

8770:   Output Parameter:
8771: . y - the output dense matrix

8773:   Level: intermediate

8775:   Note:
8776:   This allows one to use either the restriction or interpolation (its transpose)
8777:   matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8778:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

8780: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8781: @*/
8782: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8783: {
8784:   PetscInt  M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8785:   PetscBool trans = PETSC_TRUE;
8786:   MatReuse  reuse = MAT_INITIAL_MATRIX;

8788:   PetscFunctionBegin;
8794:   PetscCall(MatGetSize(A, &M, &N));
8795:   PetscCall(MatGetSize(x, &Mx, &Nx));
8796:   if (N == Mx) trans = PETSC_FALSE;
8797:   else PetscCheck(M == Mx, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Size mismatch: A %" PetscInt_FMT "x%" PetscInt_FMT ", X %" PetscInt_FMT "x%" PetscInt_FMT, M, N, Mx, Nx);
8798:   Mo = trans ? N : M;
8799:   if (*y) {
8800:     PetscCall(MatGetSize(*y, &My, &Ny));
8801:     if (Mo == My && Nx == Ny) {
8802:       reuse = MAT_REUSE_MATRIX;
8803:     } else {
8804:       PetscCheck(w || *y != w, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot reuse y and w, size mismatch: A %" PetscInt_FMT "x%" PetscInt_FMT ", X %" PetscInt_FMT "x%" PetscInt_FMT ", Y %" PetscInt_FMT "x%" PetscInt_FMT, M, N, Mx, Nx, My, Ny);
8805:       PetscCall(MatDestroy(y));
8806:     }
8807:   }

8809:   if (w && *y == w) { /* this is to minimize changes in PCMG */
8810:     PetscBool flg;

8812:     PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8813:     if (w) {
8814:       PetscInt My, Ny, Mw, Nw;

8816:       PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8817:       PetscCall(MatGetSize(*y, &My, &Ny));
8818:       PetscCall(MatGetSize(w, &Mw, &Nw));
8819:       if (!flg || My != Mw || Ny != Nw) w = NULL;
8820:     }
8821:     if (!w) {
8822:       PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8823:       PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8824:       PetscCall(PetscObjectDereference((PetscObject)w));
8825:     } else {
8826:       PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8827:     }
8828:   }
8829:   if (!trans) {
8830:     PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8831:   } else {
8832:     PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8833:   }
8834:   if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8835:   PetscFunctionReturn(PETSC_SUCCESS);
8836: }

8838: /*@
8839:   MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`

8841:   Neighbor-wise Collective

8843:   Input Parameters:
8844: + A - the matrix
8845: - x - the input dense matrix

8847:   Output Parameter:
8848: . y - the output dense matrix

8850:   Level: intermediate

8852:   Note:
8853:   This allows one to use either the restriction or interpolation (its transpose)
8854:   matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8855:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

8857: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8858: @*/
8859: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8860: {
8861:   PetscFunctionBegin;
8862:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8863:   PetscFunctionReturn(PETSC_SUCCESS);
8864: }

8866: /*@
8867:   MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`

8869:   Neighbor-wise Collective

8871:   Input Parameters:
8872: + A - the matrix
8873: - x - the input dense matrix

8875:   Output Parameter:
8876: . y - the output dense matrix

8878:   Level: intermediate

8880:   Note:
8881:   This allows one to use either the restriction or interpolation (its transpose)
8882:   matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
8883:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

8885: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8886: @*/
8887: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8888: {
8889:   PetscFunctionBegin;
8890:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8891:   PetscFunctionReturn(PETSC_SUCCESS);
8892: }

8894: /*@
8895:   MatGetNullSpace - retrieves the null space of a matrix.

8897:   Logically Collective

8899:   Input Parameters:
8900: + mat    - the matrix
8901: - nullsp - the null space object

8903:   Level: developer

8905: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8906: @*/
8907: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8908: {
8909:   PetscFunctionBegin;
8911:   PetscAssertPointer(nullsp, 2);
8912:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8913:   PetscFunctionReturn(PETSC_SUCCESS);
8914: }

8916: /*@C
8917:   MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices

8919:   Logically Collective

8921:   Input Parameters:
8922: + n   - the number of matrices
8923: - mat - the array of matrices

8925:   Output Parameters:
8926: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`

8928:   Level: developer

8930:   Note:
8931:   Call `MatRestoreNullspaces()` to provide these to another array of matrices

8933: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8934:           `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
8935: @*/
8936: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8937: {
8938:   PetscFunctionBegin;
8939:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8940:   PetscAssertPointer(mat, 2);
8941:   PetscAssertPointer(nullsp, 3);

8943:   PetscCall(PetscCalloc1(3 * n, nullsp));
8944:   for (PetscInt i = 0; i < n; i++) {
8946:     (*nullsp)[i] = mat[i]->nullsp;
8947:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
8948:     (*nullsp)[n + i] = mat[i]->nearnullsp;
8949:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
8950:     (*nullsp)[2 * n + i] = mat[i]->transnullsp;
8951:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
8952:   }
8953:   PetscFunctionReturn(PETSC_SUCCESS);
8954: }

8956: /*@C
8957:   MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices

8959:   Logically Collective

8961:   Input Parameters:
8962: + n      - the number of matrices
8963: . mat    - the array of matrices
8964: - nullsp - an array of null spaces

8966:   Level: developer

8968:   Note:
8969:   Call `MatGetNullSpaces()` to create `nullsp`

8971: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8972:           `MatNullSpaceRemove()`, `MatGetNullSpaces()`
8973: @*/
8974: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8975: {
8976:   PetscFunctionBegin;
8977:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8978:   PetscAssertPointer(mat, 2);
8979:   PetscAssertPointer(nullsp, 3);
8980:   PetscAssertPointer(*nullsp, 3);

8982:   for (PetscInt i = 0; i < n; i++) {
8984:     PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
8985:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
8986:     PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
8987:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
8988:     PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
8989:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
8990:   }
8991:   PetscCall(PetscFree(*nullsp));
8992:   PetscFunctionReturn(PETSC_SUCCESS);
8993: }

8995: /*@
8996:   MatSetNullSpace - attaches a null space to a matrix.

8998:   Logically Collective

9000:   Input Parameters:
9001: + mat    - the matrix
9002: - nullsp - the null space object

9004:   Level: advanced

9006:   Notes:
9007:   This null space is used by the `KSP` linear solvers to solve singular systems.

9009:   Overwrites any previous null space that may have been attached. You can remove the null space from the matrix object by calling this routine with an nullsp of `NULL`

9011:   For inconsistent singular systems (linear systems where the right-hand side is not in the range of the operator) the `KSP` residuals will not converge
9012:   to zero but the linear system will still be solved in a least squares sense.

9014:   The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
9015:   the domain of a matrix $A$ (from $R^n$ to $R^m$ ($m$ rows, $n$ columns) $R^n$ = the direct sum of the null space of $A$, $n(A)$, plus the range of $A^T$, $R(A^T)$.
9016:   Similarly $R^m$ = direct sum $n(A^T) + R(A)$.  Hence the linear system $A x = b$ has a solution only if $b$ in $R(A)$ (or correspondingly $b$ is orthogonal to
9017:   $n(A^T))$ and if $x$ is a solution then $x + \alpha n(A)$ is a solution for any $\alpha$. The minimum norm solution is orthogonal to $n(A)$. For problems without a solution
9018:   the solution that minimizes the norm of the residual (the least squares solution) can be obtained by solving $A x = \hat{b}$ where $\hat{b}$ is $b$ orthogonalized to the $n(A^T)$.
9019:   This  $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.

9021:   If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
9022:   `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9023:   routine also automatically calls `MatSetTransposeNullSpace()`.

9025:   The user should call `MatNullSpaceDestroy()`.

9027: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9028:           `KSPSetPCSide()`
9029: @*/
9030: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9031: {
9032:   PetscFunctionBegin;
9035:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9036:   PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9037:   mat->nullsp = nullsp;
9038:   if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9039:   PetscFunctionReturn(PETSC_SUCCESS);
9040: }

9042: /*@
9043:   MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.

9045:   Logically Collective

9047:   Input Parameters:
9048: + mat    - the matrix
9049: - nullsp - the null space object

9051:   Level: developer

9053: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9054: @*/
9055: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9056: {
9057:   PetscFunctionBegin;
9060:   PetscAssertPointer(nullsp, 2);
9061:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9062:   PetscFunctionReturn(PETSC_SUCCESS);
9063: }

9065: /*@
9066:   MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix

9068:   Logically Collective

9070:   Input Parameters:
9071: + mat    - the matrix
9072: - nullsp - the null space object

9074:   Level: advanced

9076:   Notes:
9077:   This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.

9079:   See `MatSetNullSpace()`

9081: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9082: @*/
9083: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9084: {
9085:   PetscFunctionBegin;
9088:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9089:   PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9090:   mat->transnullsp = nullsp;
9091:   PetscFunctionReturn(PETSC_SUCCESS);
9092: }

9094: /*@
9095:   MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9096:   This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.

9098:   Logically Collective

9100:   Input Parameters:
9101: + mat    - the matrix
9102: - nullsp - the null space object

9104:   Level: advanced

9106:   Notes:
9107:   Overwrites any previous near null space that may have been attached

9109:   You can remove the null space by calling this routine with an `nullsp` of `NULL`

9111: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9112: @*/
9113: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9114: {
9115:   PetscFunctionBegin;
9119:   MatCheckPreallocated(mat, 1);
9120:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9121:   PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9122:   mat->nearnullsp = nullsp;
9123:   PetscFunctionReturn(PETSC_SUCCESS);
9124: }

9126: /*@
9127:   MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`

9129:   Not Collective

9131:   Input Parameter:
9132: . mat - the matrix

9134:   Output Parameter:
9135: . nullsp - the null space object, `NULL` if not set

9137:   Level: advanced

9139: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9140: @*/
9141: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9142: {
9143:   PetscFunctionBegin;
9146:   PetscAssertPointer(nullsp, 2);
9147:   MatCheckPreallocated(mat, 1);
9148:   *nullsp = mat->nearnullsp;
9149:   PetscFunctionReturn(PETSC_SUCCESS);
9150: }

9152: /*@
9153:   MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.

9155:   Collective

9157:   Input Parameters:
9158: + mat  - the matrix
9159: . row  - row/column permutation
9160: - info - information on desired factorization process

9162:   Level: developer

9164:   Notes:
9165:   Probably really in-place only when level of fill is zero, otherwise allocates
9166:   new space to store factored matrix and deletes previous memory.

9168:   Most users should employ the `KSP` interface for linear solvers
9169:   instead of working directly with matrix algebra routines such as this.
9170:   See, e.g., `KSPCreate()`.

9172:   Fortran Note:
9173:   A valid (non-null) `info` argument must be provided

9175: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9176: @*/
9177: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9178: {
9179:   PetscFunctionBegin;
9183:   PetscAssertPointer(info, 3);
9184:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9185:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9186:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9187:   MatCheckPreallocated(mat, 1);
9188:   PetscUseTypeMethod(mat, iccfactor, row, info);
9189:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9190:   PetscFunctionReturn(PETSC_SUCCESS);
9191: }

9193: /*@
9194:   MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9195:   ghosted ones.

9197:   Not Collective

9199:   Input Parameters:
9200: + mat  - the matrix
9201: - diag - the diagonal values, including ghost ones

9203:   Level: developer

9205:   Notes:
9206:   Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices

9208:   This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`

9210: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9211: @*/
9212: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9213: {
9214:   PetscMPIInt size;

9216:   PetscFunctionBegin;

9221:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9222:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9223:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9224:   if (size == 1) {
9225:     PetscInt n, m;
9226:     PetscCall(VecGetSize(diag, &n));
9227:     PetscCall(MatGetSize(mat, NULL, &m));
9228:     PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9229:     PetscCall(MatDiagonalScale(mat, NULL, diag));
9230:   } else {
9231:     PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9232:   }
9233:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9234:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9235:   PetscFunctionReturn(PETSC_SUCCESS);
9236: }

9238: /*@
9239:   MatGetInertia - Gets the inertia from a factored matrix

9241:   Collective

9243:   Input Parameter:
9244: . mat - the matrix

9246:   Output Parameters:
9247: + nneg  - number of negative eigenvalues
9248: . nzero - number of zero eigenvalues
9249: - npos  - number of positive eigenvalues

9251:   Level: advanced

9253:   Note:
9254:   Matrix must have been factored by `MatCholeskyFactor()`

9256: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9257: @*/
9258: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9259: {
9260:   PetscFunctionBegin;
9263:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9264:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9265:   PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9266:   PetscFunctionReturn(PETSC_SUCCESS);
9267: }

9269: /*@C
9270:   MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors

9272:   Neighbor-wise Collective

9274:   Input Parameters:
9275: + mat - the factored matrix obtained with `MatGetFactor()`
9276: - b   - the right-hand-side vectors

9278:   Output Parameter:
9279: . x - the result vectors

9281:   Level: developer

9283:   Note:
9284:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
9285:   call `MatSolves`(A,x,x).

9287: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9288: @*/
9289: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9290: {
9291:   PetscFunctionBegin;
9294:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9295:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9296:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);

9298:   MatCheckPreallocated(mat, 1);
9299:   PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9300:   PetscUseTypeMethod(mat, solves, b, x);
9301:   PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9302:   PetscFunctionReturn(PETSC_SUCCESS);
9303: }

9305: /*@
9306:   MatIsSymmetric - Test whether a matrix is symmetric

9308:   Collective

9310:   Input Parameters:
9311: + A   - the matrix to test
9312: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)

9314:   Output Parameter:
9315: . flg - the result

9317:   Level: intermediate

9319:   Notes:
9320:   For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9322:   If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`

9324:   One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9325:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9327: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9328:           `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9329: @*/
9330: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9331: {
9332:   PetscFunctionBegin;
9334:   PetscAssertPointer(flg, 3);
9335:   if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9336:   else {
9337:     if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9338:     else PetscCall(MatIsTranspose(A, A, tol, flg));
9339:     if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9340:   }
9341:   PetscFunctionReturn(PETSC_SUCCESS);
9342: }

9344: /*@
9345:   MatIsHermitian - Test whether a matrix is Hermitian

9347:   Collective

9349:   Input Parameters:
9350: + A   - the matrix to test
9351: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)

9353:   Output Parameter:
9354: . flg - the result

9356:   Level: intermediate

9358:   Notes:
9359:   For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9361:   If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`

9363:   One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9364:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)

9366: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9367:           `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9368: @*/
9369: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9370: {
9371:   PetscFunctionBegin;
9373:   PetscAssertPointer(flg, 3);
9374:   if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9375:   else {
9376:     if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9377:     else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9378:     if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9379:   }
9380:   PetscFunctionReturn(PETSC_SUCCESS);
9381: }

9383: /*@
9384:   MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state

9386:   Not Collective

9388:   Input Parameter:
9389: . A - the matrix to check

9391:   Output Parameters:
9392: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9393: - flg - the result (only valid if set is `PETSC_TRUE`)

9395:   Level: advanced

9397:   Notes:
9398:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9399:   if you want it explicitly checked

9401:   One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9402:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9404: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9405: @*/
9406: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9407: {
9408:   PetscFunctionBegin;
9410:   PetscAssertPointer(set, 2);
9411:   PetscAssertPointer(flg, 3);
9412:   if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9413:     *set = PETSC_TRUE;
9414:     *flg = PetscBool3ToBool(A->symmetric);
9415:   } else {
9416:     *set = PETSC_FALSE;
9417:   }
9418:   PetscFunctionReturn(PETSC_SUCCESS);
9419: }

9421: /*@
9422:   MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state

9424:   Not Collective

9426:   Input Parameter:
9427: . A - the matrix to check

9429:   Output Parameters:
9430: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9431: - flg - the result (only valid if set is `PETSC_TRUE`)

9433:   Level: advanced

9435:   Notes:
9436:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).

9438:   One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9439:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)

9441: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9442: @*/
9443: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9444: {
9445:   PetscFunctionBegin;
9447:   PetscAssertPointer(set, 2);
9448:   PetscAssertPointer(flg, 3);
9449:   if (A->spd != PETSC_BOOL3_UNKNOWN) {
9450:     *set = PETSC_TRUE;
9451:     *flg = PetscBool3ToBool(A->spd);
9452:   } else {
9453:     *set = PETSC_FALSE;
9454:   }
9455:   PetscFunctionReturn(PETSC_SUCCESS);
9456: }

9458: /*@
9459:   MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state

9461:   Not Collective

9463:   Input Parameter:
9464: . A - the matrix to check

9466:   Output Parameters:
9467: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9468: - flg - the result (only valid if set is `PETSC_TRUE`)

9470:   Level: advanced

9472:   Notes:
9473:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9474:   if you want it explicitly checked

9476:   One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9477:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9479: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9480: @*/
9481: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9482: {
9483:   PetscFunctionBegin;
9485:   PetscAssertPointer(set, 2);
9486:   PetscAssertPointer(flg, 3);
9487:   if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9488:     *set = PETSC_TRUE;
9489:     *flg = PetscBool3ToBool(A->hermitian);
9490:   } else {
9491:     *set = PETSC_FALSE;
9492:   }
9493:   PetscFunctionReturn(PETSC_SUCCESS);
9494: }

9496: /*@
9497:   MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric

9499:   Collective

9501:   Input Parameter:
9502: . A - the matrix to test

9504:   Output Parameter:
9505: . flg - the result

9507:   Level: intermediate

9509:   Notes:
9510:   If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`

9512:   One can declare that a matrix is structurally symmetric with `MatSetOption`(mat,`MAT_STRUCTURALLY_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain structurally
9513:   symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9515: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9516: @*/
9517: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9518: {
9519:   PetscFunctionBegin;
9521:   PetscAssertPointer(flg, 2);
9522:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9523:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9524:   } else {
9525:     PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9526:     PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9527:   }
9528:   PetscFunctionReturn(PETSC_SUCCESS);
9529: }

9531: /*@
9532:   MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state

9534:   Not Collective

9536:   Input Parameter:
9537: . A - the matrix to check

9539:   Output Parameters:
9540: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9541: - flg - the result (only valid if set is PETSC_TRUE)

9543:   Level: advanced

9545:   Notes:
9546:   One can declare that a matrix is structurally symmetric with `MatSetOption`(mat,`MAT_STRUCTURALLY_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain structurally
9547:   symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9549:   Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)

9551: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9552: @*/
9553: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9554: {
9555:   PetscFunctionBegin;
9557:   PetscAssertPointer(set, 2);
9558:   PetscAssertPointer(flg, 3);
9559:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9560:     *set = PETSC_TRUE;
9561:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9562:   } else {
9563:     *set = PETSC_FALSE;
9564:   }
9565:   PetscFunctionReturn(PETSC_SUCCESS);
9566: }

9568: /*@
9569:   MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9570:   to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process

9572:   Not Collective

9574:   Input Parameter:
9575: . mat - the matrix

9577:   Output Parameters:
9578: + nstash    - the size of the stash
9579: . reallocs  - the number of additional mallocs incurred.
9580: . bnstash   - the size of the block stash
9581: - breallocs - the number of additional mallocs incurred.in the block stash

9583:   Level: advanced

9585: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9586: @*/
9587: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9588: {
9589:   PetscFunctionBegin;
9590:   PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9591:   PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9592:   PetscFunctionReturn(PETSC_SUCCESS);
9593: }

9595: /*@
9596:   MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9597:   parallel layout, `PetscLayout` for rows and columns

9599:   Collective

9601:   Input Parameter:
9602: . mat - the matrix

9604:   Output Parameters:
9605: + right - (optional) vector that the matrix can be multiplied against
9606: - left  - (optional) vector that the matrix vector product can be stored in

9608:   Level: advanced

9610:   Notes:
9611:   The blocksize of the returned vectors is determined by the row and column block sizes set with `MatSetBlockSizes()` or the single blocksize (same for both) set by `MatSetBlockSize()`.

9613:   These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed

9615: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9616: @*/
9617: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9618: {
9619:   PetscFunctionBegin;
9622:   if (mat->ops->getvecs) {
9623:     PetscUseTypeMethod(mat, getvecs, right, left);
9624:   } else {
9625:     if (right) {
9626:       PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9627:       PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9628:       PetscCall(VecSetType(*right, mat->defaultvectype));
9629: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9630:       if (mat->boundtocpu && mat->bindingpropagates) {
9631:         PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9632:         PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9633:       }
9634: #endif
9635:     }
9636:     if (left) {
9637:       PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9638:       PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9639:       PetscCall(VecSetType(*left, mat->defaultvectype));
9640: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9641:       if (mat->boundtocpu && mat->bindingpropagates) {
9642:         PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9643:         PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9644:       }
9645: #endif
9646:     }
9647:   }
9648:   PetscFunctionReturn(PETSC_SUCCESS);
9649: }

9651: /*@
9652:   MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9653:   with default values.

9655:   Not Collective

9657:   Input Parameter:
9658: . info - the `MatFactorInfo` data structure

9660:   Level: developer

9662:   Notes:
9663:   The solvers are generally used through the `KSP` and `PC` objects, for example
9664:   `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`

9666:   Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed

9668: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9669: @*/
9670: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9671: {
9672:   PetscFunctionBegin;
9673:   PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9674:   PetscFunctionReturn(PETSC_SUCCESS);
9675: }

9677: /*@
9678:   MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed

9680:   Collective

9682:   Input Parameters:
9683: + mat - the factored matrix
9684: - is  - the index set defining the Schur indices (0-based)

9686:   Level: advanced

9688:   Notes:
9689:   Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.

9691:   You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.

9693:   This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9695: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9696:           `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9697: @*/
9698: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9699: {
9700:   PetscErrorCode (*f)(Mat, IS);

9702:   PetscFunctionBegin;
9707:   PetscCheckSameComm(mat, 1, is, 2);
9708:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9709:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9710:   PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9711:   PetscCall(MatDestroy(&mat->schur));
9712:   PetscCall((*f)(mat, is));
9713:   PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9714:   PetscFunctionReturn(PETSC_SUCCESS);
9715: }

9717: /*@
9718:   MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step

9720:   Logically Collective

9722:   Input Parameters:
9723: + F      - the factored matrix obtained by calling `MatGetFactor()`
9724: . S      - location where to return the Schur complement, can be `NULL`
9725: - status - the status of the Schur complement matrix, can be `NULL`

9727:   Level: advanced

9729:   Notes:
9730:   You must call `MatFactorSetSchurIS()` before calling this routine.

9732:   This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9734:   The routine provides a copy of the Schur matrix stored within the solver data structures.
9735:   The caller must destroy the object when it is no longer needed.
9736:   If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.

9738:   Use `MatFactorGetSchurComplement()` to get access to the Schur complement matrix inside the factored matrix instead of making a copy of it (which this function does)

9740:   See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9742:   Developer Note:
9743:   The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9744:   matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.

9746: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9747: @*/
9748: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9749: {
9750:   PetscFunctionBegin;
9752:   if (S) PetscAssertPointer(S, 2);
9753:   if (status) PetscAssertPointer(status, 3);
9754:   if (S) {
9755:     PetscErrorCode (*f)(Mat, Mat *);

9757:     PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9758:     if (f) {
9759:       PetscCall((*f)(F, S));
9760:     } else {
9761:       PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9762:     }
9763:   }
9764:   if (status) *status = F->schur_status;
9765:   PetscFunctionReturn(PETSC_SUCCESS);
9766: }

9768: /*@
9769:   MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix

9771:   Logically Collective

9773:   Input Parameters:
9774: + F      - the factored matrix obtained by calling `MatGetFactor()`
9775: . S      - location where to return the Schur complement, can be `NULL`
9776: - status - the status of the Schur complement matrix, can be `NULL`

9778:   Level: advanced

9780:   Notes:
9781:   You must call `MatFactorSetSchurIS()` before calling this routine.

9783:   Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`

9785:   The routine returns a the Schur Complement stored within the data structures of the solver.

9787:   If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.

9789:   The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.

9791:   Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix

9793:   See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9795: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9796: @*/
9797: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9798: {
9799:   PetscFunctionBegin;
9801:   if (S) {
9802:     PetscAssertPointer(S, 2);
9803:     *S = F->schur;
9804:   }
9805:   if (status) {
9806:     PetscAssertPointer(status, 3);
9807:     *status = F->schur_status;
9808:   }
9809:   PetscFunctionReturn(PETSC_SUCCESS);
9810: }

9812: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9813: {
9814:   Mat S = F->schur;

9816:   PetscFunctionBegin;
9817:   switch (F->schur_status) {
9818:   case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9819:   case MAT_FACTOR_SCHUR_INVERTED:
9820:     if (S) {
9821:       S->ops->solve             = NULL;
9822:       S->ops->matsolve          = NULL;
9823:       S->ops->solvetranspose    = NULL;
9824:       S->ops->matsolvetranspose = NULL;
9825:       S->ops->solveadd          = NULL;
9826:       S->ops->solvetransposeadd = NULL;
9827:       S->factortype             = MAT_FACTOR_NONE;
9828:       PetscCall(PetscFree(S->solvertype));
9829:     }
9830:   case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9831:     break;
9832:   default:
9833:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9834:   }
9835:   PetscFunctionReturn(PETSC_SUCCESS);
9836: }

9838: /*@
9839:   MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`

9841:   Logically Collective

9843:   Input Parameters:
9844: + F      - the factored matrix obtained by calling `MatGetFactor()`
9845: . S      - location where the Schur complement is stored
9846: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)

9848:   Level: advanced

9850: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9851: @*/
9852: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9853: {
9854:   PetscFunctionBegin;
9856:   if (S) {
9858:     *S = NULL;
9859:   }
9860:   F->schur_status = status;
9861:   PetscCall(MatFactorUpdateSchurStatus_Private(F));
9862:   PetscFunctionReturn(PETSC_SUCCESS);
9863: }

9865: /*@
9866:   MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step

9868:   Logically Collective

9870:   Input Parameters:
9871: + F   - the factored matrix obtained by calling `MatGetFactor()`
9872: . rhs - location where the right-hand side of the Schur complement system is stored
9873: - sol - location where the solution of the Schur complement system has to be returned

9875:   Level: advanced

9877:   Notes:
9878:   The sizes of the vectors should match the size of the Schur complement

9880:   Must be called after `MatFactorSetSchurIS()`

9882: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9883: @*/
9884: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9885: {
9886:   PetscFunctionBegin;
9893:   PetscCheckSameComm(F, 1, rhs, 2);
9894:   PetscCheckSameComm(F, 1, sol, 3);
9895:   PetscCall(MatFactorFactorizeSchurComplement(F));
9896:   switch (F->schur_status) {
9897:   case MAT_FACTOR_SCHUR_FACTORED:
9898:     PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9899:     break;
9900:   case MAT_FACTOR_SCHUR_INVERTED:
9901:     PetscCall(MatMultTranspose(F->schur, rhs, sol));
9902:     break;
9903:   default:
9904:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9905:   }
9906:   PetscFunctionReturn(PETSC_SUCCESS);
9907: }

9909: /*@
9910:   MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step

9912:   Logically Collective

9914:   Input Parameters:
9915: + F   - the factored matrix obtained by calling `MatGetFactor()`
9916: . rhs - location where the right-hand side of the Schur complement system is stored
9917: - sol - location where the solution of the Schur complement system has to be returned

9919:   Level: advanced

9921:   Notes:
9922:   The sizes of the vectors should match the size of the Schur complement

9924:   Must be called after `MatFactorSetSchurIS()`

9926: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9927: @*/
9928: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9929: {
9930:   PetscFunctionBegin;
9937:   PetscCheckSameComm(F, 1, rhs, 2);
9938:   PetscCheckSameComm(F, 1, sol, 3);
9939:   PetscCall(MatFactorFactorizeSchurComplement(F));
9940:   switch (F->schur_status) {
9941:   case MAT_FACTOR_SCHUR_FACTORED:
9942:     PetscCall(MatSolve(F->schur, rhs, sol));
9943:     break;
9944:   case MAT_FACTOR_SCHUR_INVERTED:
9945:     PetscCall(MatMult(F->schur, rhs, sol));
9946:     break;
9947:   default:
9948:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9949:   }
9950:   PetscFunctionReturn(PETSC_SUCCESS);
9951: }

9953: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9954: #if PetscDefined(HAVE_CUDA)
9955: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9956: #endif

9958: /* Schur status updated in the interface */
9959: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9960: {
9961:   Mat S = F->schur;

9963:   PetscFunctionBegin;
9964:   if (S) {
9965:     PetscMPIInt size;
9966:     PetscBool   isdense, isdensecuda;

9968:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9969:     PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9970:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9971:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9972:     PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9973:     PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9974:     if (isdense) {
9975:       PetscCall(MatSeqDenseInvertFactors_Private(S));
9976:     } else if (isdensecuda) {
9977: #if defined(PETSC_HAVE_CUDA)
9978:       PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
9979: #endif
9980:     }
9981:     // HIP??????????????
9982:     PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
9983:   }
9984:   PetscFunctionReturn(PETSC_SUCCESS);
9985: }

9987: /*@
9988:   MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step

9990:   Logically Collective

9992:   Input Parameter:
9993: . F - the factored matrix obtained by calling `MatGetFactor()`

9995:   Level: advanced

9997:   Notes:
9998:   Must be called after `MatFactorSetSchurIS()`.

10000:   Call `MatFactorGetSchurComplement()` or  `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.

10002: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
10003: @*/
10004: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
10005: {
10006:   PetscFunctionBegin;
10009:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
10010:   PetscCall(MatFactorFactorizeSchurComplement(F));
10011:   PetscCall(MatFactorInvertSchurComplement_Private(F));
10012:   F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
10013:   PetscFunctionReturn(PETSC_SUCCESS);
10014: }

10016: /*@
10017:   MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step

10019:   Logically Collective

10021:   Input Parameter:
10022: . F - the factored matrix obtained by calling `MatGetFactor()`

10024:   Level: advanced

10026:   Note:
10027:   Must be called after `MatFactorSetSchurIS()`

10029: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10030: @*/
10031: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10032: {
10033:   MatFactorInfo info;

10035:   PetscFunctionBegin;
10038:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10039:   PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10040:   PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10041:   if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10042:     PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10043:   } else {
10044:     PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10045:   }
10046:   PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10047:   F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10048:   PetscFunctionReturn(PETSC_SUCCESS);
10049: }

10051: /*@
10052:   MatPtAP - Creates the matrix product $C = P^T * A * P$

10054:   Neighbor-wise Collective

10056:   Input Parameters:
10057: + A     - the matrix
10058: . P     - the projection matrix
10059: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10060: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10061:           if the result is a dense matrix this is irrelevant

10063:   Output Parameter:
10064: . C - the product matrix

10066:   Level: intermediate

10068:   Notes:
10069:   C will be created and must be destroyed by the user with `MatDestroy()`.

10071:   An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

10073:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10075:   Developer Note:
10076:   For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.

10078: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10079: @*/
10080: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10081: {
10082:   PetscFunctionBegin;
10083:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10084:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10086:   if (scall == MAT_INITIAL_MATRIX) {
10087:     PetscCall(MatProductCreate(A, P, NULL, C));
10088:     PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10089:     PetscCall(MatProductSetAlgorithm(*C, "default"));
10090:     PetscCall(MatProductSetFill(*C, fill));

10092:     (*C)->product->api_user = PETSC_TRUE;
10093:     PetscCall(MatProductSetFromOptions(*C));
10094:     PetscCheck((*C)->ops->productsymbolic, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "MatProduct %s not supported for A %s and P %s", MatProductTypes[MATPRODUCT_PtAP], ((PetscObject)A)->type_name, ((PetscObject)P)->type_name);
10095:     PetscCall(MatProductSymbolic(*C));
10096:   } else { /* scall == MAT_REUSE_MATRIX */
10097:     PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10098:   }

10100:   PetscCall(MatProductNumeric(*C));
10101:   (*C)->symmetric = A->symmetric;
10102:   (*C)->spd       = A->spd;
10103:   PetscFunctionReturn(PETSC_SUCCESS);
10104: }

10106: /*@
10107:   MatRARt - Creates the matrix product $C = R * A * R^T$

10109:   Neighbor-wise Collective

10111:   Input Parameters:
10112: + A     - the matrix
10113: . R     - the projection matrix
10114: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10115: - fill  - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10116:           if the result is a dense matrix this is irrelevant

10118:   Output Parameter:
10119: . C - the product matrix

10121:   Level: intermediate

10123:   Notes:
10124:   `C` will be created and must be destroyed by the user with `MatDestroy()`.

10126:   An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

10128:   This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10129:   which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10130:   the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10131:   We recommend using `MatPtAP()` when possible.

10133:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10135: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10136: @*/
10137: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10138: {
10139:   PetscFunctionBegin;
10140:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10141:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10143:   if (scall == MAT_INITIAL_MATRIX) {
10144:     PetscCall(MatProductCreate(A, R, NULL, C));
10145:     PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10146:     PetscCall(MatProductSetAlgorithm(*C, "default"));
10147:     PetscCall(MatProductSetFill(*C, fill));

10149:     (*C)->product->api_user = PETSC_TRUE;
10150:     PetscCall(MatProductSetFromOptions(*C));
10151:     PetscCheck((*C)->ops->productsymbolic, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "MatProduct %s not supported for A %s and R %s", MatProductTypes[MATPRODUCT_RARt], ((PetscObject)A)->type_name, ((PetscObject)R)->type_name);
10152:     PetscCall(MatProductSymbolic(*C));
10153:   } else { /* scall == MAT_REUSE_MATRIX */
10154:     PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10155:   }

10157:   PetscCall(MatProductNumeric(*C));
10158:   if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10159:   PetscFunctionReturn(PETSC_SUCCESS);
10160: }

10162: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10163: {
10164:   PetscBool flg = PETSC_TRUE;

10166:   PetscFunctionBegin;
10167:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10168:   if (scall == MAT_INITIAL_MATRIX) {
10169:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10170:     PetscCall(MatProductCreate(A, B, NULL, C));
10171:     PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10172:     PetscCall(MatProductSetFill(*C, fill));
10173:   } else { /* scall == MAT_REUSE_MATRIX */
10174:     Mat_Product *product = (*C)->product;

10176:     PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10177:     if (flg && product && product->type != ptype) {
10178:       PetscCall(MatProductClear(*C));
10179:       product = NULL;
10180:     }
10181:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10182:     if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10183:       PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10184:       PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10185:       product        = (*C)->product;
10186:       product->fill  = fill;
10187:       product->clear = PETSC_TRUE;
10188:     } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10189:       flg = PETSC_FALSE;
10190:       PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10191:     }
10192:   }
10193:   if (flg) {
10194:     (*C)->product->api_user = PETSC_TRUE;
10195:     PetscCall(MatProductSetType(*C, ptype));
10196:     PetscCall(MatProductSetFromOptions(*C));
10197:     PetscCall(MatProductSymbolic(*C));
10198:   }
10199:   PetscCall(MatProductNumeric(*C));
10200:   PetscFunctionReturn(PETSC_SUCCESS);
10201: }

10203: /*@
10204:   MatMatMult - Performs matrix-matrix multiplication C=A*B.

10206:   Neighbor-wise Collective

10208:   Input Parameters:
10209: + A     - the left matrix
10210: . B     - the right matrix
10211: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10212: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10213:           if the result is a dense matrix this is irrelevant

10215:   Output Parameter:
10216: . C - the product matrix

10218:   Notes:
10219:   Unless scall is `MAT_REUSE_MATRIX` C will be created.

10221:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call and C was obtained from a previous
10222:   call to this function with `MAT_INITIAL_MATRIX`.

10224:   To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.

10226:   In the special case where matrix `B` (and hence `C`) are dense you can create the correctly sized matrix `C` yourself and then call this routine with `MAT_REUSE_MATRIX`,
10227:   rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.

10229:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10231:   Example of Usage:
10232: .vb
10233:      MatProductCreate(A,B,NULL,&C);
10234:      MatProductSetType(C,MATPRODUCT_AB);
10235:      MatProductSymbolic(C);
10236:      MatProductNumeric(C); // compute C=A * B
10237:      MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10238:      MatProductNumeric(C);
10239:      MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10240:      MatProductNumeric(C);
10241: .ve

10243:   Level: intermediate

10245: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10246: @*/
10247: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10248: {
10249:   PetscFunctionBegin;
10250:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10251:   PetscFunctionReturn(PETSC_SUCCESS);
10252: }

10254: /*@
10255:   MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.

10257:   Neighbor-wise Collective

10259:   Input Parameters:
10260: + A     - the left matrix
10261: . B     - the right matrix
10262: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10263: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known

10265:   Output Parameter:
10266: . C - the product matrix

10268:   Options Database Key:
10269: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10270:               first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10271:               the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.

10273:   Level: intermediate

10275:   Notes:
10276:   C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10278:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call

10280:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10281:   actually needed.

10283:   This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10284:   and for pairs of `MATMPIDENSE` matrices.

10286:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`

10288:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10290: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10291: @*/
10292: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10293: {
10294:   PetscFunctionBegin;
10295:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10296:   if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10297:   PetscFunctionReturn(PETSC_SUCCESS);
10298: }

10300: /*@
10301:   MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.

10303:   Neighbor-wise Collective

10305:   Input Parameters:
10306: + A     - the left matrix
10307: . B     - the right matrix
10308: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10309: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known

10311:   Output Parameter:
10312: . C - the product matrix

10314:   Level: intermediate

10316:   Notes:
10317:   `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10319:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.

10321:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`

10323:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10324:   actually needed.

10326:   This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10327:   which inherit from `MATSEQAIJ`.  `C` will be of the same type as the input matrices.

10329:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10331: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10332: @*/
10333: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10334: {
10335:   PetscFunctionBegin;
10336:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10337:   PetscFunctionReturn(PETSC_SUCCESS);
10338: }

10340: /*@
10341:   MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.

10343:   Neighbor-wise Collective

10345:   Input Parameters:
10346: + A     - the left matrix
10347: . B     - the middle matrix
10348: . C     - the right matrix
10349: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10350: - fill  - expected fill as ratio of nnz(D)/(nnz(A) + nnz(B)+nnz(C)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10351:           if the result is a dense matrix this is irrelevant

10353:   Output Parameter:
10354: . D - the product matrix

10356:   Level: intermediate

10358:   Notes:
10359:   Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.

10361:   `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call

10363:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`

10365:   To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10366:   actually needed.

10368:   If you have many matrices with the same non-zero structure to multiply, you
10369:   should use `MAT_REUSE_MATRIX` in all calls but the first

10371:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10373: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10374: @*/
10375: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10376: {
10377:   PetscFunctionBegin;
10378:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10379:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10381:   if (scall == MAT_INITIAL_MATRIX) {
10382:     PetscCall(MatProductCreate(A, B, C, D));
10383:     PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10384:     PetscCall(MatProductSetAlgorithm(*D, "default"));
10385:     PetscCall(MatProductSetFill(*D, fill));

10387:     (*D)->product->api_user = PETSC_TRUE;
10388:     PetscCall(MatProductSetFromOptions(*D));
10389:     PetscCheck((*D)->ops->productsymbolic, PetscObjectComm((PetscObject)*D), PETSC_ERR_SUP, "MatProduct %s not supported for A %s, B %s and C %s", MatProductTypes[MATPRODUCT_ABC], ((PetscObject)A)->type_name, ((PetscObject)B)->type_name,
10390:                ((PetscObject)C)->type_name);
10391:     PetscCall(MatProductSymbolic(*D));
10392:   } else { /* user may change input matrices when REUSE */
10393:     PetscCall(MatProductReplaceMats(A, B, C, *D));
10394:   }
10395:   PetscCall(MatProductNumeric(*D));
10396:   PetscFunctionReturn(PETSC_SUCCESS);
10397: }

10399: /*@
10400:   MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.

10402:   Collective

10404:   Input Parameters:
10405: + mat      - the matrix
10406: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10407: . subcomm  - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10408: - reuse    - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10410:   Output Parameter:
10411: . matredundant - redundant matrix

10413:   Level: advanced

10415:   Notes:
10416:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10417:   original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.

10419:   This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10420:   calling it.

10422:   `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.

10424: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10425: @*/
10426: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10427: {
10428:   MPI_Comm       comm;
10429:   PetscMPIInt    size;
10430:   PetscInt       mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10431:   Mat_Redundant *redund     = NULL;
10432:   PetscSubcomm   psubcomm   = NULL;
10433:   MPI_Comm       subcomm_in = subcomm;
10434:   Mat           *matseq;
10435:   IS             isrow, iscol;
10436:   PetscBool      newsubcomm = PETSC_FALSE;

10438:   PetscFunctionBegin;
10440:   if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10441:     PetscAssertPointer(*matredundant, 5);
10443:   }

10445:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10446:   if (size == 1 || nsubcomm == 1) {
10447:     if (reuse == MAT_INITIAL_MATRIX) {
10448:       PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10449:     } else {
10450:       PetscCheck(*matredundant != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
10451:       PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10452:     }
10453:     PetscFunctionReturn(PETSC_SUCCESS);
10454:   }

10456:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10457:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10458:   MatCheckPreallocated(mat, 1);

10460:   PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10461:   if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10462:     /* create psubcomm, then get subcomm */
10463:     PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10464:     PetscCallMPI(MPI_Comm_size(comm, &size));
10465:     PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);

10467:     PetscCall(PetscSubcommCreate(comm, &psubcomm));
10468:     PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10469:     PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10470:     PetscCall(PetscSubcommSetFromOptions(psubcomm));
10471:     PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10472:     newsubcomm = PETSC_TRUE;
10473:     PetscCall(PetscSubcommDestroy(&psubcomm));
10474:   }

10476:   /* get isrow, iscol and a local sequential matrix matseq[0] */
10477:   if (reuse == MAT_INITIAL_MATRIX) {
10478:     mloc_sub = PETSC_DECIDE;
10479:     nloc_sub = PETSC_DECIDE;
10480:     if (bs < 1) {
10481:       PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10482:       PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10483:     } else {
10484:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10485:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10486:     }
10487:     PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10488:     rstart = rend - mloc_sub;
10489:     PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10490:     PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10491:     PetscCall(ISSetIdentity(iscol));
10492:   } else { /* reuse == MAT_REUSE_MATRIX */
10493:     PetscCheck(*matredundant != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
10494:     /* retrieve subcomm */
10495:     PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10496:     redund = (*matredundant)->redundant;
10497:     isrow  = redund->isrow;
10498:     iscol  = redund->iscol;
10499:     matseq = redund->matseq;
10500:   }
10501:   PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));

10503:   /* get matredundant over subcomm */
10504:   if (reuse == MAT_INITIAL_MATRIX) {
10505:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));

10507:     /* create a supporting struct and attach it to C for reuse */
10508:     PetscCall(PetscNew(&redund));
10509:     (*matredundant)->redundant = redund;
10510:     redund->isrow              = isrow;
10511:     redund->iscol              = iscol;
10512:     redund->matseq             = matseq;
10513:     if (newsubcomm) {
10514:       redund->subcomm = subcomm;
10515:     } else {
10516:       redund->subcomm = MPI_COMM_NULL;
10517:     }
10518:   } else {
10519:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10520:   }
10521: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10522:   if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10523:     PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10524:     PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10525:   }
10526: #endif
10527:   PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10528:   PetscFunctionReturn(PETSC_SUCCESS);
10529: }

10531: /*@C
10532:   MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10533:   a given `Mat`. Each submatrix can span multiple procs.

10535:   Collective

10537:   Input Parameters:
10538: + mat     - the matrix
10539: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10540: - scall   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10542:   Output Parameter:
10543: . subMat - parallel sub-matrices each spanning a given `subcomm`

10545:   Level: advanced

10547:   Notes:
10548:   The submatrix partition across processors is dictated by `subComm` a
10549:   communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10550:   is not restricted to be grouped with consecutive original MPI processes.

10552:   Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10553:   map directly to the layout of the original matrix [wrt the local
10554:   row,col partitioning]. So the original 'DiagonalMat' naturally maps
10555:   into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10556:   the `subMat`. However the offDiagMat looses some columns - and this is
10557:   reconstructed with `MatSetValues()`

10559:   This is used by `PCBJACOBI` when a single block spans multiple MPI processes.

10561: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10562: @*/
10563: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10564: {
10565:   PetscMPIInt commsize, subCommSize;

10567:   PetscFunctionBegin;
10568:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10569:   PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10570:   PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);

10572:   PetscCheck(scall != MAT_REUSE_MATRIX || *subMat != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
10573:   PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10574:   PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10575:   PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10576:   PetscFunctionReturn(PETSC_SUCCESS);
10577: }

10579: /*@
10580:   MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering

10582:   Not Collective

10584:   Input Parameters:
10585: + mat   - matrix to extract local submatrix from
10586: . isrow - local row indices for submatrix
10587: - iscol - local column indices for submatrix

10589:   Output Parameter:
10590: . submat - the submatrix

10592:   Level: intermediate

10594:   Notes:
10595:   `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.

10597:   Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`.  Its communicator may be
10598:   the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.

10600:   `submat` always implements `MatSetValuesLocal()`.  If `isrow` and `iscol` have the same block size, then
10601:   `MatSetValuesBlockedLocal()` will also be implemented.

10603:   `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10604:   Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.

10606: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10607: @*/
10608: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10609: {
10610:   PetscFunctionBegin;
10614:   PetscCheckSameComm(isrow, 2, iscol, 3);
10615:   PetscAssertPointer(submat, 4);
10616:   PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");

10618:   if (mat->ops->getlocalsubmatrix) {
10619:     PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10620:   } else {
10621:     PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10622:   }
10623:   PetscFunctionReturn(PETSC_SUCCESS);
10624: }

10626: /*@
10627:   MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`

10629:   Not Collective

10631:   Input Parameters:
10632: + mat    - matrix to extract local submatrix from
10633: . isrow  - local row indices for submatrix
10634: . iscol  - local column indices for submatrix
10635: - submat - the submatrix

10637:   Level: intermediate

10639: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10640: @*/
10641: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10642: {
10643:   PetscFunctionBegin;
10647:   PetscCheckSameComm(isrow, 2, iscol, 3);
10648:   PetscAssertPointer(submat, 4);

10651:   if (mat->ops->restorelocalsubmatrix) {
10652:     PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10653:   } else {
10654:     PetscCall(MatDestroy(submat));
10655:   }
10656:   *submat = NULL;
10657:   PetscFunctionReturn(PETSC_SUCCESS);
10658: }

10660: /*@
10661:   MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix

10663:   Collective

10665:   Input Parameter:
10666: . mat - the matrix

10668:   Output Parameter:
10669: . is - if any rows have zero diagonals this contains the list of them

10671:   Level: developer

10673: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10674: @*/
10675: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10676: {
10677:   PetscFunctionBegin;
10680:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10681:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10683:   if (!mat->ops->findzerodiagonals) {
10684:     Vec                diag;
10685:     const PetscScalar *a;
10686:     PetscInt          *rows;
10687:     PetscInt           rStart, rEnd, r, nrow = 0;

10689:     PetscCall(MatCreateVecs(mat, &diag, NULL));
10690:     PetscCall(MatGetDiagonal(mat, diag));
10691:     PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10692:     PetscCall(VecGetArrayRead(diag, &a));
10693:     for (r = 0; r < rEnd - rStart; ++r)
10694:       if (a[r] == 0.0) ++nrow;
10695:     PetscCall(PetscMalloc1(nrow, &rows));
10696:     nrow = 0;
10697:     for (r = 0; r < rEnd - rStart; ++r)
10698:       if (a[r] == 0.0) rows[nrow++] = r + rStart;
10699:     PetscCall(VecRestoreArrayRead(diag, &a));
10700:     PetscCall(VecDestroy(&diag));
10701:     PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10702:   } else {
10703:     PetscUseTypeMethod(mat, findzerodiagonals, is);
10704:   }
10705:   PetscFunctionReturn(PETSC_SUCCESS);
10706: }

10708: /*@
10709:   MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)

10711:   Collective

10713:   Input Parameter:
10714: . mat - the matrix

10716:   Output Parameter:
10717: . is - contains the list of rows with off block diagonal entries

10719:   Level: developer

10721: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10722: @*/
10723: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10724: {
10725:   PetscFunctionBegin;
10728:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10729:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10731:   PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10732:   PetscFunctionReturn(PETSC_SUCCESS);
10733: }

10735: /*@C
10736:   MatInvertBlockDiagonal - Inverts the block diagonal entries.

10738:   Collective; No Fortran Support

10740:   Input Parameter:
10741: . mat - the matrix

10743:   Output Parameter:
10744: . values - the block inverses in column major order (FORTRAN-like)

10746:   Level: advanced

10748:   Notes:
10749:   The size of the blocks is determined by the block size of the matrix.

10751:   The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

10753:   The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size

10755: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10756: @*/
10757: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10758: {
10759:   PetscFunctionBegin;
10761:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10762:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10763:   PetscUseTypeMethod(mat, invertblockdiagonal, values);
10764:   PetscFunctionReturn(PETSC_SUCCESS);
10765: }

10767: /*@
10768:   MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.

10770:   Collective; No Fortran Support

10772:   Input Parameters:
10773: + mat     - the matrix
10774: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10775: - bsizes  - the size of each block on the process, set with `MatSetVariableBlockSizes()`

10777:   Output Parameter:
10778: . values - the block inverses in column major order (FORTRAN-like)

10780:   Level: advanced

10782:   Notes:
10783:   Use `MatInvertBlockDiagonal()` if all blocks have the same size

10785:   The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

10787: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10788: @*/
10789: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10790: {
10791:   PetscFunctionBegin;
10793:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10794:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10795:   PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10796:   PetscFunctionReturn(PETSC_SUCCESS);
10797: }

10799: /*@
10800:   MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A

10802:   Collective

10804:   Input Parameters:
10805: + A - the matrix
10806: - C - matrix with inverted block diagonal of `A`.  This matrix should be created and may have its type set.

10808:   Level: advanced

10810:   Note:
10811:   The blocksize of the matrix is used to determine the blocks on the diagonal of `C`

10813: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10814: @*/
10815: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10816: {
10817:   const PetscScalar *vals;
10818:   PetscInt          *dnnz;
10819:   PetscInt           m, rstart, rend, bs, i, j;

10821:   PetscFunctionBegin;
10822:   PetscCall(MatInvertBlockDiagonal(A, &vals));
10823:   PetscCall(MatGetBlockSize(A, &bs));
10824:   PetscCall(MatGetLocalSize(A, &m, NULL));
10825:   PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10826:   PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
10827:   PetscCall(PetscMalloc1(m / bs, &dnnz));
10828:   for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10829:   PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10830:   PetscCall(PetscFree(dnnz));
10831:   PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10832:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10833:   for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10834:   PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10835:   PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10836:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10837:   PetscFunctionReturn(PETSC_SUCCESS);
10838: }

10840: /*@
10841:   MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10842:   via `MatTransposeColoringCreate()`.

10844:   Collective

10846:   Input Parameter:
10847: . c - coloring context

10849:   Level: intermediate

10851: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10852: @*/
10853: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10854: {
10855:   MatTransposeColoring matcolor = *c;

10857:   PetscFunctionBegin;
10858:   if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10859:   if (--((PetscObject)matcolor)->refct > 0) {
10860:     matcolor = NULL;
10861:     PetscFunctionReturn(PETSC_SUCCESS);
10862:   }

10864:   PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10865:   PetscCall(PetscFree(matcolor->rows));
10866:   PetscCall(PetscFree(matcolor->den2sp));
10867:   PetscCall(PetscFree(matcolor->colorforcol));
10868:   PetscCall(PetscFree(matcolor->columns));
10869:   if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10870:   PetscCall(PetscHeaderDestroy(c));
10871:   PetscFunctionReturn(PETSC_SUCCESS);
10872: }

10874: /*@
10875:   MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
10876:   a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
10877:   `MatTransposeColoring` to sparse `B`.

10879:   Collective

10881:   Input Parameters:
10882: + coloring - coloring context created with `MatTransposeColoringCreate()`
10883: - B        - sparse matrix

10885:   Output Parameter:
10886: . Btdense - dense matrix $B^T$

10888:   Level: developer

10890:   Note:
10891:   These are used internally for some implementations of `MatRARt()`

10893: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10894: @*/
10895: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10896: {
10897:   PetscFunctionBegin;

10902:   PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10903:   PetscFunctionReturn(PETSC_SUCCESS);
10904: }

10906: /*@
10907:   MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
10908:   a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
10909:   in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10910:   $C_{sp}$ from $C_{den}$.

10912:   Collective

10914:   Input Parameters:
10915: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
10916: - Cden        - matrix product of a sparse matrix and a dense matrix Btdense

10918:   Output Parameter:
10919: . Csp - sparse matrix

10921:   Level: developer

10923:   Note:
10924:   These are used internally for some implementations of `MatRARt()`

10926: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10927: @*/
10928: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10929: {
10930:   PetscFunctionBegin;

10935:   PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10936:   PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10937:   PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10938:   PetscFunctionReturn(PETSC_SUCCESS);
10939: }

10941: /*@
10942:   MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.

10944:   Collective

10946:   Input Parameters:
10947: + mat        - the matrix product C
10948: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`

10950:   Output Parameter:
10951: . color - the new coloring context

10953:   Level: intermediate

10955: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10956:           `MatTransColoringApplyDenToSp()`
10957: @*/
10958: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10959: {
10960:   MatTransposeColoring c;
10961:   MPI_Comm             comm;

10963:   PetscFunctionBegin;
10964:   PetscAssertPointer(color, 3);

10966:   PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10967:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10968:   PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
10969:   c->ctype = iscoloring->ctype;
10970:   PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
10971:   *color = c;
10972:   PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10973:   PetscFunctionReturn(PETSC_SUCCESS);
10974: }

10976: /*@
10977:   MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
10978:   matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.

10980:   Not Collective

10982:   Input Parameter:
10983: . mat - the matrix

10985:   Output Parameter:
10986: . state - the current state

10988:   Level: intermediate

10990:   Notes:
10991:   You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
10992:   different matrices

10994:   Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix

10996:   Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.

10998: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
10999: @*/
11000: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
11001: {
11002:   PetscFunctionBegin;
11004:   *state = mat->nonzerostate;
11005:   PetscFunctionReturn(PETSC_SUCCESS);
11006: }

11008: /*@
11009:   MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
11010:   matrices from each processor

11012:   Collective

11014:   Input Parameters:
11015: + comm   - the communicators the parallel matrix will live on
11016: . seqmat - the input sequential matrices
11017: . n      - number of local columns (or `PETSC_DECIDE`)
11018: - reuse  - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

11020:   Output Parameter:
11021: . mpimat - the parallel matrix generated

11023:   Level: developer

11025:   Note:
11026:   The number of columns of the matrix in EACH processor MUST be the same.

11028: .seealso: [](ch_matrices), `Mat`
11029: @*/
11030: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11031: {
11032:   PetscMPIInt size;

11034:   PetscFunctionBegin;
11035:   PetscCallMPI(MPI_Comm_size(comm, &size));
11036:   if (size == 1) {
11037:     if (reuse == MAT_INITIAL_MATRIX) {
11038:       PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11039:     } else {
11040:       PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11041:     }
11042:     PetscFunctionReturn(PETSC_SUCCESS);
11043:   }

11045:   PetscCheck(reuse != MAT_REUSE_MATRIX || seqmat != *mpimat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");

11047:   PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11048:   PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11049:   PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11050:   PetscFunctionReturn(PETSC_SUCCESS);
11051: }

11053: /*@
11054:   MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.

11056:   Collective

11058:   Input Parameters:
11059: + A - the matrix to create subdomains from
11060: - N - requested number of subdomains

11062:   Output Parameters:
11063: + n   - number of subdomains resulting on this MPI process
11064: - iss - `IS` list with indices of subdomains on this MPI process

11066:   Level: advanced

11068:   Note:
11069:   The number of subdomains must be smaller than the communicator size

11071: .seealso: [](ch_matrices), `Mat`, `IS`
11072: @*/
11073: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11074: {
11075:   MPI_Comm    comm, subcomm;
11076:   PetscMPIInt size, rank, color;
11077:   PetscInt    rstart, rend, k;

11079:   PetscFunctionBegin;
11080:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11081:   PetscCallMPI(MPI_Comm_size(comm, &size));
11082:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
11083:   PetscCheck(N >= 1 && N < size, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "number of subdomains must be > 0 and < %d, got N = %" PetscInt_FMT, size, N);
11084:   *n    = 1;
11085:   k     = size / N + (size % N > 0); /* There are up to k ranks to a color */
11086:   color = rank / k;
11087:   PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11088:   PetscCall(PetscMalloc1(1, iss));
11089:   PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11090:   PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11091:   PetscCallMPI(MPI_Comm_free(&subcomm));
11092:   PetscFunctionReturn(PETSC_SUCCESS);
11093: }

11095: /*@
11096:   MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.

11098:   If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11099:   If they are not the same, uses `MatMatMatMult()`.

11101:   Once the coarse grid problem is constructed, correct for interpolation operators
11102:   that are not of full rank, which can legitimately happen in the case of non-nested
11103:   geometric multigrid.

11105:   Input Parameters:
11106: + restrct     - restriction operator
11107: . dA          - fine grid matrix
11108: . interpolate - interpolation operator
11109: . reuse       - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11110: - fill        - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate

11112:   Output Parameter:
11113: . A - the Galerkin coarse matrix

11115:   Options Database Key:
11116: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used

11118:   Level: developer

11120:   Note:
11121:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

11123: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11124: @*/
11125: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11126: {
11127:   IS  zerorows;
11128:   Vec diag;

11130:   PetscFunctionBegin;
11131:   PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11132:   /* Construct the coarse grid matrix */
11133:   if (interpolate == restrct) {
11134:     PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11135:   } else {
11136:     PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11137:   }

11139:   /* If the interpolation matrix is not of full rank, A will have zero rows.
11140:      This can legitimately happen in the case of non-nested geometric multigrid.
11141:      In that event, we set the rows of the matrix to the rows of the identity,
11142:      ignoring the equations (as the RHS will also be zero). */

11144:   PetscCall(MatFindZeroRows(*A, &zerorows));

11146:   if (zerorows != NULL) { /* if there are any zero rows */
11147:     PetscCall(MatCreateVecs(*A, &diag, NULL));
11148:     PetscCall(MatGetDiagonal(*A, diag));
11149:     PetscCall(VecISSet(diag, zerorows, 1.0));
11150:     PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11151:     PetscCall(VecDestroy(&diag));
11152:     PetscCall(ISDestroy(&zerorows));
11153:   }
11154:   PetscFunctionReturn(PETSC_SUCCESS);
11155: }

11157: /*@C
11158:   MatSetOperation - Allows user to set a matrix operation for any matrix type

11160:   Logically Collective

11162:   Input Parameters:
11163: + mat - the matrix
11164: . op  - the name of the operation
11165: - f   - the function that provides the operation

11167:   Level: developer

11169:   Example Usage:
11170: .vb
11171:   extern PetscErrorCode usermult(Mat, Vec, Vec);

11173:   PetscCall(MatCreateXXX(comm, ..., &A));
11174:   PetscCall(MatSetOperation(A, MATOP_MULT, (PetscVoidFn *)usermult));
11175: .ve

11177:   Notes:
11178:   See the file `include/petscmat.h` for a complete list of matrix
11179:   operations, which all have the form MATOP_<OPERATION>, where
11180:   <OPERATION> is the name (in all capital letters) of the
11181:   user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

11183:   All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11184:   sequence as the usual matrix interface routines, since they
11185:   are intended to be accessed via the usual matrix interface
11186:   routines, e.g.,
11187: .vb
11188:   MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11189: .ve

11191:   In particular each function MUST return `PETSC_SUCCESS` on success and
11192:   nonzero on failure.

11194:   This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.

11196: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11197: @*/
11198: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, void (*f)(void))
11199: {
11200:   PetscFunctionBegin;
11202:   if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))mat->ops->view) mat->ops->viewnative = mat->ops->view;
11203:   (((void (**)(void))mat->ops)[op]) = f;
11204:   PetscFunctionReturn(PETSC_SUCCESS);
11205: }

11207: /*@C
11208:   MatGetOperation - Gets a matrix operation for any matrix type.

11210:   Not Collective

11212:   Input Parameters:
11213: + mat - the matrix
11214: - op  - the name of the operation

11216:   Output Parameter:
11217: . f - the function that provides the operation

11219:   Level: developer

11221:   Example Usage:
11222: .vb
11223:   PetscErrorCode (*usermult)(Mat, Vec, Vec);

11225:   MatGetOperation(A, MATOP_MULT, (void (**)(void))&usermult);
11226: .ve

11228:   Notes:
11229:   See the file include/petscmat.h for a complete list of matrix
11230:   operations, which all have the form MATOP_<OPERATION>, where
11231:   <OPERATION> is the name (in all capital letters) of the
11232:   user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

11234:   This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.

11236: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11237: @*/
11238: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, void (**f)(void))
11239: {
11240:   PetscFunctionBegin;
11242:   *f = (((void (**)(void))mat->ops)[op]);
11243:   PetscFunctionReturn(PETSC_SUCCESS);
11244: }

11246: /*@
11247:   MatHasOperation - Determines whether the given matrix supports the particular operation.

11249:   Not Collective

11251:   Input Parameters:
11252: + mat - the matrix
11253: - op  - the operation, for example, `MATOP_GET_DIAGONAL`

11255:   Output Parameter:
11256: . has - either `PETSC_TRUE` or `PETSC_FALSE`

11258:   Level: advanced

11260:   Note:
11261:   See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.

11263: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11264: @*/
11265: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11266: {
11267:   PetscFunctionBegin;
11269:   PetscAssertPointer(has, 3);
11270:   if (mat->ops->hasoperation) {
11271:     PetscUseTypeMethod(mat, hasoperation, op, has);
11272:   } else {
11273:     if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11274:     else {
11275:       *has = PETSC_FALSE;
11276:       if (op == MATOP_CREATE_SUBMATRIX) {
11277:         PetscMPIInt size;

11279:         PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11280:         if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11281:       }
11282:     }
11283:   }
11284:   PetscFunctionReturn(PETSC_SUCCESS);
11285: }

11287: /*@
11288:   MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent

11290:   Collective

11292:   Input Parameter:
11293: . mat - the matrix

11295:   Output Parameter:
11296: . cong - either `PETSC_TRUE` or `PETSC_FALSE`

11298:   Level: beginner

11300: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11301: @*/
11302: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11303: {
11304:   PetscFunctionBegin;
11307:   PetscAssertPointer(cong, 2);
11308:   if (!mat->rmap || !mat->cmap) {
11309:     *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11310:     PetscFunctionReturn(PETSC_SUCCESS);
11311:   }
11312:   if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11313:     PetscCall(PetscLayoutSetUp(mat->rmap));
11314:     PetscCall(PetscLayoutSetUp(mat->cmap));
11315:     PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11316:     if (*cong) mat->congruentlayouts = 1;
11317:     else mat->congruentlayouts = 0;
11318:   } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11319:   PetscFunctionReturn(PETSC_SUCCESS);
11320: }

11322: PetscErrorCode MatSetInf(Mat A)
11323: {
11324:   PetscFunctionBegin;
11325:   PetscUseTypeMethod(A, setinf);
11326:   PetscFunctionReturn(PETSC_SUCCESS);
11327: }

11329: /*@
11330:   MatCreateGraph - create a scalar matrix (that is a matrix with one vertex for each block vertex in the original matrix), for use in graph algorithms
11331:   and possibly removes small values from the graph structure.

11333:   Collective

11335:   Input Parameters:
11336: + A       - the matrix
11337: . sym     - `PETSC_TRUE` indicates that the graph should be symmetrized
11338: . scale   - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11339: . filter  - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11340: . num_idx - size of 'index' array
11341: - index   - array of block indices to use for graph strength of connection weight

11343:   Output Parameter:
11344: . graph - the resulting graph

11346:   Level: advanced

11348: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11349: @*/
11350: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11351: {
11352:   PetscFunctionBegin;
11356:   PetscAssertPointer(graph, 7);
11357:   PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11358:   PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11359:   PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11360:   PetscFunctionReturn(PETSC_SUCCESS);
11361: }

11363: /*@
11364:   MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11365:   meaning the same memory is used for the matrix, and no new memory is allocated.

11367:   Collective

11369:   Input Parameters:
11370: + A    - the matrix
11371: - keep - if for a given row of `A`, the diagonal coefficient is zero, indicates whether it should be left in the structure or eliminated as well

11373:   Level: intermediate

11375:   Developer Note:
11376:   The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11377:   of the arrays in the data structure are unneeded.

11379: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11380: @*/
11381: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11382: {
11383:   PetscFunctionBegin;
11385:   PetscUseTypeMethod(A, eliminatezeros, keep);
11386:   PetscFunctionReturn(PETSC_SUCCESS);
11387: }

11389: /*@C
11390:   MatGetCurrentMemType - Get the memory location of the matrix

11392:   Not Collective, but the result will be the same on all MPI processes

11394:   Input Parameter:
11395: . A - the matrix whose memory type we are checking

11397:   Output Parameter:
11398: . m - the memory type

11400:   Level: intermediate

11402: .seealso: [](ch_matrices), `Mat`, `MatBoundToCPU()`, `PetscMemType`
11403: @*/
11404: PetscErrorCode MatGetCurrentMemType(Mat A, PetscMemType *m)
11405: {
11406:   PetscFunctionBegin;
11408:   PetscAssertPointer(m, 2);
11409:   if (A->ops->getcurrentmemtype) PetscUseTypeMethod(A, getcurrentmemtype, m);
11410:   else *m = PETSC_MEMTYPE_HOST;
11411:   PetscFunctionReturn(PETSC_SUCCESS);
11412: }